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 |
|---|---|---|---|---|
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 _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
@slow
def SCREAMING_SNAKE_CASE_( self ) -> Dict:
lowerCamelCase_ = TFXLMRobertaModel.from_pretrained("jplu/tf-xlm-roberta-base" )
lowerCamelCase_ = {
"input_ids": tf.convert_to_tensor([[0, 2646, 10269, 83, 99942, 2]] , dtype=tf.intaa ), # "My dog is cute"
"attention_mask": tf.convert_to_tensor([[1, 1, 1, 1, 1, 1]] , dtype=tf.intaa ),
}
lowerCamelCase_ = model(lowercase )["last_hidden_state"]
lowerCamelCase_ = tf.TensorShape((1, 6, 768) )
self.assertEqual(output.shape , lowercase )
# compare the actual values for a slice.
lowerCamelCase_ = tf.convert_to_tensor(
[
[
[0.0_6_8_1_7_6_2, 0.1_0_8_9_4_4_5_1, 0.0_6_7_7_2_5_0_4],
[-0.0_6_4_2_3_6_6_8, 0.0_2_3_6_6_6_1_5, 0.0_4_3_2_9_3_4_4],
[-0.0_6_0_5_7_2_9_5, 0.0_9_9_7_4_1_3_5, -0.0_0_0_7_0_5_8_4],
]
] , dtype=tf.floataa , )
self.assertTrue(np.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1e-4 ) )
| 19 |
import requests
_A = "https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="
def lowerCamelCase__ ( __lowerCAmelCase : str ):
"""simple docstring"""
lowerCAmelCase_ = requests.get(_NEWS_API + bbc_news_api_key ).json()
# each article in the list is a dict
for i, article in enumerate(bbc_news_page["articles"] , 1 ):
print(F"""{i}.) {article['title']}""" )
if __name__ == "__main__":
fetch_bbc_news(bbc_news_api_key="<Your BBC News API key goes here>")
| 231 | 0 |
def lowerCAmelCase_ ( snake_case_ ):
return sum(i for i in range(1,number // 2 + 1 ) if number % i == 0 ) == number
if __name__ == "__main__":
print("Program to check whether a number is a Perfect number or not...")
_snake_case = int(input("Enter number: ").strip())
print(f"""{number} is {"" if perfect(number) else "not "}a Perfect Number.""")
| 350 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_snake_case = logging.get_logger(__name__)
_snake_case = {
"microsoft/resnet-50": "https://huggingface.co/microsoft/resnet-50/blob/main/config.json",
}
class lowercase ( UpperCamelCase__,UpperCamelCase__ ):
_a = "resnet"
_a = ["basic", "bottleneck"]
def __init__( self , _a=3 , _a=64 , _a=[256, 512, 1024, 2048] , _a=[3, 4, 6, 3] , _a="bottleneck" , _a="relu" , _a=False , _a=None , _a=None , **_a , ) -> int:
super().__init__(**_a )
if layer_type not in self.layer_types:
raise ValueError(F'''layer_type={layer_type} is not one of {",".join(self.layer_types )}''' )
_A : Optional[Any] = num_channels
_A : List[Any] = embedding_size
_A : int = hidden_sizes
_A : Union[str, Any] = depths
_A : Optional[int] = layer_type
_A : Any = hidden_act
_A : List[Any] = downsample_in_first_stage
_A : int = ["""stem"""] + [F'''stage{idx}''' for idx in range(1 , len(_a ) + 1 )]
_A , _A : str = get_aligned_output_features_output_indices(
out_features=_a , out_indices=_a , stage_names=self.stage_names )
class lowercase ( UpperCamelCase__ ):
_a = version.parse("1.11" )
@property
def a__ ( self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def a__ ( self ) -> float:
return 1e-3
| 343 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase : Optional[Any] = logging.get_logger(__name__)
__lowercase : Tuple = {
'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_ ):
A_ = "markuplm"
def __init__( self , __a=3_0522 , __a=768 , __a=12 , __a=12 , __a=3072 , __a="gelu" , __a=0.1 , __a=0.1 , __a=512 , __a=2 , __a=0.02 , __a=1E-1_2 , __a=0 , __a=0 , __a=2 , __a=256 , __a=1024 , __a=216 , __a=1001 , __a=32 , __a=50 , __a="absolute" , __a=True , __a=None , **__a , ):
'''simple docstring'''
super().__init__(
pad_token_id=__a , bos_token_id=__a , eos_token_id=__a , **__a , )
__a : List[str] = vocab_size
__a : Optional[Any] = hidden_size
__a : Optional[int] = num_hidden_layers
__a : Optional[Any] = num_attention_heads
__a : Optional[int] = hidden_act
__a : Union[str, Any] = intermediate_size
__a : str = hidden_dropout_prob
__a : List[Any] = attention_probs_dropout_prob
__a : Dict = max_position_embeddings
__a : Optional[Any] = type_vocab_size
__a : Union[str, Any] = initializer_range
__a : Optional[Any] = layer_norm_eps
__a : Optional[Any] = position_embedding_type
__a : Optional[int] = use_cache
__a : Optional[Any] = classifier_dropout
# additional properties
__a : int = max_depth
__a : Union[str, Any] = max_xpath_tag_unit_embeddings
__a : str = max_xpath_subs_unit_embeddings
__a : Optional[Any] = tag_pad_id
__a : Dict = subs_pad_id
__a : List[str] = xpath_unit_hidden_size
| 27 |
'''simple docstring'''
import os
def lowerCAmelCase_ ( ):
'''simple docstring'''
A : List[Any] = os.path.join(os.path.dirname(snake_case__ ) , '''num.txt''' )
with open(snake_case__ ) as file_hand:
return str(sum(int(snake_case__ ) for line in file_hand ) )[:10]
if __name__ == "__main__":
print(solution())
| 3 | 0 |
"""simple docstring"""
A : Optional[int] = {
"Pillow": "Pillow<10.0.0",
"accelerate": "accelerate>=0.20.3",
"av": "av==9.2.0",
"beautifulsoup4": "beautifulsoup4",
"black": "black~=23.1",
"codecarbon": "codecarbon==1.2.0",
"cookiecutter": "cookiecutter==1.7.3",
"dataclasses": "dataclasses",
"datasets": "datasets!=2.5.0",
"decord": "decord==0.6.0",
"deepspeed": "deepspeed>=0.9.3",
"diffusers": "diffusers",
"dill": "dill<0.3.5",
"evaluate": "evaluate>=0.2.0",
"fairscale": "fairscale>0.3",
"faiss-cpu": "faiss-cpu",
"fastapi": "fastapi",
"filelock": "filelock",
"flax": "flax>=0.4.1,<=0.7.0",
"ftfy": "ftfy",
"fugashi": "fugashi>=1.0",
"GitPython": "GitPython<3.1.19",
"hf-doc-builder": "hf-doc-builder>=0.3.0",
"huggingface-hub": "huggingface-hub>=0.14.1,<1.0",
"importlib_metadata": "importlib_metadata",
"ipadic": "ipadic>=1.0.0,<2.0",
"isort": "isort>=5.5.4",
"jax": "jax>=0.2.8,!=0.3.2,<=0.4.13",
"jaxlib": "jaxlib>=0.1.65,<=0.4.13",
"jieba": "jieba",
"kenlm": "kenlm",
"keras-nlp": "keras-nlp>=0.3.1",
"librosa": "librosa",
"nltk": "nltk",
"natten": "natten>=0.14.6",
"numpy": "numpy>=1.17",
"onnxconverter-common": "onnxconverter-common",
"onnxruntime-tools": "onnxruntime-tools>=1.4.2",
"onnxruntime": "onnxruntime>=1.4.0",
"opencv-python": "opencv-python",
"optuna": "optuna",
"optax": "optax>=0.0.8,<=0.1.4",
"packaging": "packaging>=20.0",
"parameterized": "parameterized",
"phonemizer": "phonemizer",
"protobuf": "protobuf",
"psutil": "psutil",
"pyyaml": "pyyaml>=5.1",
"pydantic": "pydantic<2",
"pytest": "pytest>=7.2.0",
"pytest-timeout": "pytest-timeout",
"pytest-xdist": "pytest-xdist",
"python": "python>=3.8.0",
"ray[tune]": "ray[tune]",
"regex": "regex!=2019.12.17",
"requests": "requests",
"rhoknp": "rhoknp>=1.1.0,<1.3.1",
"rjieba": "rjieba",
"rouge-score": "rouge-score!=0.0.7,!=0.0.8,!=0.1,!=0.1.1",
"ruff": "ruff>=0.0.241,<=0.0.259",
"sacrebleu": "sacrebleu>=1.4.12,<2.0.0",
"sacremoses": "sacremoses",
"safetensors": "safetensors>=0.3.1",
"sagemaker": "sagemaker>=2.31.0",
"scikit-learn": "scikit-learn",
"sentencepiece": "sentencepiece>=0.1.91,!=0.1.92",
"sigopt": "sigopt",
"starlette": "starlette",
"sudachipy": "sudachipy>=0.6.6",
"sudachidict_core": "sudachidict_core>=20220729",
"tensorflow-cpu": "tensorflow-cpu>=2.6,<2.14",
"tensorflow": "tensorflow>=2.6,<2.14",
"tensorflow-text": "tensorflow-text<2.14",
"tf2onnx": "tf2onnx",
"timeout-decorator": "timeout-decorator",
"timm": "timm",
"tokenizers": "tokenizers>=0.11.1,!=0.11.3,<0.14",
"torch": "torch>=1.9,!=1.12.0",
"torchaudio": "torchaudio",
"torchvision": "torchvision",
"pyctcdecode": "pyctcdecode>=0.4.0",
"tqdm": "tqdm>=4.27",
"unidic": "unidic>=1.0.2",
"unidic_lite": "unidic_lite>=1.0.7",
"urllib3": "urllib3<2.0.0",
"uvicorn": "uvicorn",
}
| 354 |
"""simple docstring"""
from typing import Union
import fire
import torch
from tqdm import tqdm
def _lowerCamelCase ( _UpperCamelCase , _UpperCamelCase = "cpu" , _UpperCamelCase = None ):
'''simple docstring'''
__lowerCAmelCase = torch.load(_UpperCamelCase , map_location=_UpperCamelCase )
for k, v in tqdm(state_dict.items() ):
if not isinstance(_UpperCamelCase , torch.Tensor ):
raise TypeError("FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin" )
__lowerCAmelCase = v.half()
if save_path is None: # overwrite src_path
__lowerCAmelCase = src_path
torch.save(_UpperCamelCase , _UpperCamelCase )
if __name__ == "__main__":
fire.Fire(convert)
| 259 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"uclanlp/visualbert-vqa": "https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json",
"uclanlp/visualbert-vqa-pre": "https://huggingface.co/uclanlp/visualbert-vqa-pre/resolve/main/config.json",
"uclanlp/visualbert-vqa-coco-pre": (
"https://huggingface.co/uclanlp/visualbert-vqa-coco-pre/resolve/main/config.json"
),
"uclanlp/visualbert-vcr": "https://huggingface.co/uclanlp/visualbert-vcr/resolve/main/config.json",
"uclanlp/visualbert-vcr-pre": "https://huggingface.co/uclanlp/visualbert-vcr-pre/resolve/main/config.json",
"uclanlp/visualbert-vcr-coco-pre": (
"https://huggingface.co/uclanlp/visualbert-vcr-coco-pre/resolve/main/config.json"
),
"uclanlp/visualbert-nlvr2": "https://huggingface.co/uclanlp/visualbert-nlvr2/resolve/main/config.json",
"uclanlp/visualbert-nlvr2-pre": "https://huggingface.co/uclanlp/visualbert-nlvr2-pre/resolve/main/config.json",
"uclanlp/visualbert-nlvr2-coco-pre": (
"https://huggingface.co/uclanlp/visualbert-nlvr2-coco-pre/resolve/main/config.json"
)
# See all VisualBERT models at https://huggingface.co/models?filter=visual_bert
}
class lowercase ( UpperCamelCase__ ):
_a = "visual_bert"
def __init__( self , _a=3_0522 , _a=768 , _a=512 , _a=12 , _a=12 , _a=3072 , _a="gelu" , _a=0.1 , _a=0.1 , _a=512 , _a=2 , _a=0.02 , _a=1e-12 , _a=False , _a=True , _a=1 , _a=0 , _a=2 , **_a , ) -> Tuple:
super().__init__(pad_token_id=_a , bos_token_id=_a , eos_token_id=_a , **_a )
_A : int = vocab_size
_A : Dict = max_position_embeddings
_A : Optional[Any] = hidden_size
_A : List[Any] = visual_embedding_dim
_A : Optional[Any] = num_hidden_layers
_A : Tuple = num_attention_heads
_A : str = intermediate_size
_A : Dict = hidden_act
_A : Union[str, Any] = hidden_dropout_prob
_A : Optional[Any] = attention_probs_dropout_prob
_A : Optional[int] = initializer_range
_A : List[Any] = type_vocab_size
_A : int = layer_norm_eps
_A : Optional[int] = bypass_transformer
_A : List[Any] = special_visual_initialize
| 26 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamembertModel
@require_tf
@require_sentencepiece
@require_tokenizers
class lowercase ( unittest.TestCase ):
@slow
def a__ ( self ) -> Any:
_A : Tuple = TFCamembertModel.from_pretrained("""jplu/tf-camembert-base""" )
_A : List[Any] = tf.convert_to_tensor(
[[5, 121, 11, 660, 16, 730, 2_5543, 110, 83, 6]] , dtype=tf.intaa , ) # J'aime le camembert !"
_A : List[str] = model(_a )["""last_hidden_state"""]
_A : Union[str, Any] = tf.TensorShape((1, 10, 768) )
self.assertEqual(output.shape , _a )
# compare the actual values for a slice.
_A : List[Any] = tf.convert_to_tensor(
[[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]] , dtype=tf.floataa , )
# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
# camembert.eval()
# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()
self.assertTrue(np.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1e-4 ) )
| 26 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a = logging.get_logger(__name__)
_a = {
'caidas/swin2sr-classicalsr-x2-64': (
'https://huggingface.co/caidas/swin2sr-classicalsr-x2-64/resolve/main/config.json'
),
}
class _lowerCAmelCase ( lowercase ):
"""simple docstring"""
__UpperCAmelCase : List[Any] = "swin2sr"
__UpperCAmelCase : List[Any] = {
"hidden_size": "embed_dim",
"num_attention_heads": "num_heads",
"num_hidden_layers": "num_layers",
}
def __init__( self : Any, UpperCAmelCase__ : Dict=6_4, UpperCAmelCase__ : List[Any]=1, UpperCAmelCase__ : Dict=3, UpperCAmelCase__ : Optional[Any]=1_8_0, UpperCAmelCase__ : Any=[6, 6, 6, 6, 6, 6], UpperCAmelCase__ : Dict=[6, 6, 6, 6, 6, 6], UpperCAmelCase__ : Tuple=8, UpperCAmelCase__ : Optional[int]=2.0, UpperCAmelCase__ : List[str]=True, UpperCAmelCase__ : Tuple=0.0, UpperCAmelCase__ : Optional[Any]=0.0, UpperCAmelCase__ : List[str]=0.1, UpperCAmelCase__ : Dict="gelu", UpperCAmelCase__ : Dict=False, UpperCAmelCase__ : Dict=0.02, UpperCAmelCase__ : Tuple=1E-5, UpperCAmelCase__ : str=2, UpperCAmelCase__ : str=1.0, UpperCAmelCase__ : Optional[int]="1conv", UpperCAmelCase__ : Dict="pixelshuffle", **UpperCAmelCase__ : List[Any], ):
super().__init__(**UpperCAmelCase__ )
__lowercase = image_size
__lowercase = patch_size
__lowercase = num_channels
__lowercase = embed_dim
__lowercase = depths
__lowercase = len(UpperCAmelCase__ )
__lowercase = num_heads
__lowercase = window_size
__lowercase = mlp_ratio
__lowercase = qkv_bias
__lowercase = hidden_dropout_prob
__lowercase = attention_probs_dropout_prob
__lowercase = drop_path_rate
__lowercase = hidden_act
__lowercase = use_absolute_embeddings
__lowercase = layer_norm_eps
__lowercase = initializer_range
__lowercase = upscale
__lowercase = img_range
__lowercase = resi_connection
__lowercase = upsampler
| 364 |
"""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_funnel import FunnelTokenizer
_a = logging.get_logger(__name__)
_a = {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}
_a = [
'small',
'small-base',
'medium',
'medium-base',
'intermediate',
'intermediate-base',
'large',
'large-base',
'xlarge',
'xlarge-base',
]
_a = {
'vocab_file': {
'funnel-transformer/small': 'https://huggingface.co/funnel-transformer/small/resolve/main/vocab.txt',
'funnel-transformer/small-base': 'https://huggingface.co/funnel-transformer/small-base/resolve/main/vocab.txt',
'funnel-transformer/medium': 'https://huggingface.co/funnel-transformer/medium/resolve/main/vocab.txt',
'funnel-transformer/medium-base': (
'https://huggingface.co/funnel-transformer/medium-base/resolve/main/vocab.txt'
),
'funnel-transformer/intermediate': (
'https://huggingface.co/funnel-transformer/intermediate/resolve/main/vocab.txt'
),
'funnel-transformer/intermediate-base': (
'https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/vocab.txt'
),
'funnel-transformer/large': 'https://huggingface.co/funnel-transformer/large/resolve/main/vocab.txt',
'funnel-transformer/large-base': 'https://huggingface.co/funnel-transformer/large-base/resolve/main/vocab.txt',
'funnel-transformer/xlarge': 'https://huggingface.co/funnel-transformer/xlarge/resolve/main/vocab.txt',
'funnel-transformer/xlarge-base': (
'https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/vocab.txt'
),
},
'tokenizer_file': {
'funnel-transformer/small': 'https://huggingface.co/funnel-transformer/small/resolve/main/tokenizer.json',
'funnel-transformer/small-base': (
'https://huggingface.co/funnel-transformer/small-base/resolve/main/tokenizer.json'
),
'funnel-transformer/medium': 'https://huggingface.co/funnel-transformer/medium/resolve/main/tokenizer.json',
'funnel-transformer/medium-base': (
'https://huggingface.co/funnel-transformer/medium-base/resolve/main/tokenizer.json'
),
'funnel-transformer/intermediate': (
'https://huggingface.co/funnel-transformer/intermediate/resolve/main/tokenizer.json'
),
'funnel-transformer/intermediate-base': (
'https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/tokenizer.json'
),
'funnel-transformer/large': 'https://huggingface.co/funnel-transformer/large/resolve/main/tokenizer.json',
'funnel-transformer/large-base': (
'https://huggingface.co/funnel-transformer/large-base/resolve/main/tokenizer.json'
),
'funnel-transformer/xlarge': 'https://huggingface.co/funnel-transformer/xlarge/resolve/main/tokenizer.json',
'funnel-transformer/xlarge-base': (
'https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/tokenizer.json'
),
},
}
_a = {F"funnel-transformer/{name}": 5_12 for name in _model_names}
_a = {F"funnel-transformer/{name}": {'do_lower_case': True} for name in _model_names}
class _lowerCAmelCase ( lowercase ):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = VOCAB_FILES_NAMES
__UpperCAmelCase : Tuple = PRETRAINED_VOCAB_FILES_MAP
__UpperCAmelCase : Union[str, Any] = PRETRAINED_INIT_CONFIGURATION
__UpperCAmelCase : Optional[Any] = FunnelTokenizer
__UpperCAmelCase : List[str] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__UpperCAmelCase : int = 2
def __init__( self : Dict, UpperCAmelCase__ : Dict=None, UpperCAmelCase__ : Optional[int]=None, UpperCAmelCase__ : Dict=True, UpperCAmelCase__ : List[str]="<unk>", UpperCAmelCase__ : Optional[Any]="<sep>", UpperCAmelCase__ : Optional[Any]="<pad>", UpperCAmelCase__ : Union[str, Any]="<cls>", UpperCAmelCase__ : str="<mask>", UpperCAmelCase__ : Optional[Any]="<s>", UpperCAmelCase__ : Tuple="</s>", UpperCAmelCase__ : Union[str, Any]=True, UpperCAmelCase__ : Optional[int]=True, UpperCAmelCase__ : Optional[Any]=None, UpperCAmelCase__ : Dict="##", **UpperCAmelCase__ : List[str], ):
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__, bos_token=UpperCAmelCase__, eos_token=UpperCAmelCase__, clean_text=UpperCAmelCase__, tokenize_chinese_chars=UpperCAmelCase__, strip_accents=UpperCAmelCase__, wordpieces_prefix=UpperCAmelCase__, **UpperCAmelCase__, )
__lowercase = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get("lowercase", UpperCAmelCase__ ) != do_lower_case
or normalizer_state.get("strip_accents", UpperCAmelCase__ ) != strip_accents
or normalizer_state.get("handle_chinese_chars", UpperCAmelCase__ ) != tokenize_chinese_chars
):
__lowercase = getattr(UpperCAmelCase__, normalizer_state.pop("type" ) )
__lowercase = do_lower_case
__lowercase = strip_accents
__lowercase = tokenize_chinese_chars
__lowercase = normalizer_class(**UpperCAmelCase__ )
__lowercase = do_lower_case
def _lowercase ( self : Tuple, UpperCAmelCase__ : Any, UpperCAmelCase__ : Any=None ):
__lowercase = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def _lowercase ( self : str, UpperCAmelCase__ : List[int], UpperCAmelCase__ : Optional[List[int]] = None ):
__lowercase = [self.sep_token_id]
__lowercase = [self.cls_token_id]
if token_ids_a is None:
return len(cls ) * [self.cls_token_type_id] + len(token_ids_a + sep ) * [0]
return len(cls ) * [self.cls_token_type_id] + len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def _lowercase ( self : Optional[Any], UpperCAmelCase__ : str, UpperCAmelCase__ : Optional[str] = None ):
__lowercase = self._tokenizer.model.save(UpperCAmelCase__, name=UpperCAmelCase__ )
return tuple(UpperCAmelCase__ )
| 144 | 0 |
import json
import os
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
'vocab_file': 'vocab.json',
'tokenizer_config_file': 'tokenizer_config.json',
'merges_file': 'merges.txt',
}
lowerCAmelCase__ = {
'vocab_file': {
'facebook/s2t-wav2vec2-large-en-de': (
'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/vocab.json'
),
},
'tokenizer_config_file': {
'facebook/s2t-wav2vec2-large-en-de': (
'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/tokenizer_config.json'
),
},
'merges_file': {
'facebook/s2t-wav2vec2-large-en-de': (
'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/merges.txt'
),
},
}
lowerCAmelCase__ = '</w>'
lowerCAmelCase__ = '@@ '
def _UpperCAmelCase (UpperCamelCase__ : Optional[Any] ):
_A : Optional[int] = set()
_A : Optional[Any] = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
_A : List[Any] = char
return pairs
# Speech2Text2 has no max input length
lowerCAmelCase__ = {'facebook/s2t-wav2vec2-large-en-de': 10_24}
class lowerCAmelCase__ ( a):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = VOCAB_FILES_NAMES
__SCREAMING_SNAKE_CASE = PRETRAINED_VOCAB_FILES_MAP
__SCREAMING_SNAKE_CASE = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__SCREAMING_SNAKE_CASE = ["input_ids", "attention_mask"]
def __init__( self , __lowerCamelCase , __lowerCamelCase="<s>" , __lowerCamelCase="<pad>" , __lowerCamelCase="</s>" , __lowerCamelCase="<unk>" , __lowerCamelCase=False , __lowerCamelCase=None , **__lowerCamelCase , ) -> Optional[Any]:
super().__init__(
unk_token=__lowerCamelCase , bos_token=__lowerCamelCase , eos_token=__lowerCamelCase , pad_token=__lowerCamelCase , do_lower_case=__lowerCamelCase , **__lowerCamelCase , )
_A : Dict = do_lower_case
with open(__lowerCamelCase , encoding="utf-8") as vocab_handle:
_A : Optional[int] = json.load(__lowerCamelCase)
_A : Optional[Any] = {v: k for k, v in self.encoder.items()}
if merges_file is None:
logger.info(F"No merges files provided. {self.__class__.__name__} can only be used for decoding.")
_A : Optional[Any] = None
_A : Tuple = None
else:
with open(__lowerCamelCase , encoding="utf-8") as merges_handle:
_A : Optional[int] = merges_handle.read().split("\n")[:-1]
_A : Union[str, Any] = [tuple(merge.split()[:2]) for merge in merges]
_A : Optional[int] = dict(zip(__lowerCamelCase , range(len(__lowerCamelCase))))
_A : List[Any] = {}
@property
def _lowerCamelCase ( self) -> int:
return len(self.decoder)
def _lowerCamelCase ( self) -> Dict:
return dict(self.encoder , **self.added_tokens_encoder)
def _lowerCamelCase ( self , __lowerCamelCase) -> Dict:
_A : Tuple = tuple(token[:-1]) + (token[-1] + BPE_TOKEN_MERGES,)
if token in self.cache:
return self.cache[token]
_A : int = get_pairs(__lowerCamelCase)
if not pairs:
return token
while True:
_A : Any = min(__lowerCamelCase , key=lambda __lowerCamelCase: self.bpe_ranks.get(__lowerCamelCase , float("inf")))
if bigram not in self.bpe_ranks:
break
_A , _A : Optional[int] = bigram
_A : int = []
_A : str = 0
while i < len(__lowerCamelCase):
try:
_A : str = word.index(__lowerCamelCase , __lowerCamelCase)
except ValueError:
new_word.extend(word[i:])
break
else:
new_word.extend(word[i:j])
_A : str = j
if word[i] == first and i < len(__lowerCamelCase) - 1 and word[i + 1] == second:
new_word.append(first + second)
i += 2
else:
new_word.append(word[i])
i += 1
_A : List[str] = tuple(__lowerCamelCase)
_A : List[str] = new_word
if len(__lowerCamelCase) == 1:
break
else:
_A : List[Any] = get_pairs(__lowerCamelCase)
_A : Tuple = " ".join(__lowerCamelCase)
if word == "\n " + BPE_TOKEN_MERGES:
_A : List[str] = "\n" + BPE_TOKEN_MERGES
if word.endswith(__lowerCamelCase):
_A : int = word.replace(__lowerCamelCase , "")
_A : int = word.replace(" " , __lowerCamelCase)
_A : Union[str, Any] = word
return word
def _lowerCamelCase ( self , __lowerCamelCase) -> Optional[Any]:
if self.bpe_ranks is None:
raise ValueError(
"This tokenizer was instantiated without a `merges.txt` file, so"
" that it can only be used for decoding, not for encoding."
"Make sure to provide `merges.txt` file at instantiation to enable "
"encoding.")
if self.do_lower_case:
_A : List[Any] = text.lower()
_A : Optional[int] = text.split()
_A : List[str] = []
for token in text:
if token:
split_tokens.extend(list(self.bpe(__lowerCamelCase).split(" ")))
return split_tokens
def _lowerCamelCase ( self , __lowerCamelCase) -> int:
return self.encoder.get(__lowerCamelCase , self.encoder.get(self.unk_token))
def _lowerCamelCase ( self , __lowerCamelCase) -> str:
_A : List[str] = self.decoder.get(__lowerCamelCase , self.unk_token)
return result
def _lowerCamelCase ( self , __lowerCamelCase) -> str:
_A : str = " ".join(__lowerCamelCase)
# make sure @@ tokens are concatenated
_A : int = "".join(string.split(__lowerCamelCase))
return string
def _lowerCamelCase ( self , __lowerCamelCase , __lowerCamelCase = None) -> Tuple[str]:
if not os.path.isdir(__lowerCamelCase):
logger.error(F"Vocabulary path ({save_directory}) should be a directory")
return
_A : Any = os.path.join(
__lowerCamelCase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])
_A : Any = os.path.join(
__lowerCamelCase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"])
with open(__lowerCamelCase , "w" , encoding="utf-8") as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=__lowerCamelCase , ensure_ascii=__lowerCamelCase) + "\n")
_A : Union[str, Any] = 0
if self.bpe_ranks is None:
return (vocab_file,)
with open(__lowerCamelCase , "w" , encoding="utf-8") as writer:
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda __lowerCamelCase: kv[1]):
if index != token_index:
logger.warning(
F"Saving vocabulary to {merges_file}: BPE merge indices are not consecutive."
" Please check that the tokenizer is not corrupted!")
_A : Optional[int] = token_index
writer.write(" ".join(__lowerCamelCase) + "\n")
index += 1
return (vocab_file, merges_file)
| 11 |
"""simple docstring"""
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
UpperCamelCase : Any = logging.get_logger(__name__)
UpperCamelCase : Any = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}
UpperCamelCase : Dict = {
"vocab_file": {
"gpt2": "https://huggingface.co/gpt2/resolve/main/vocab.json",
"gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/vocab.json",
"gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/vocab.json",
"gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/vocab.json",
"distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/vocab.json",
},
"merges_file": {
"gpt2": "https://huggingface.co/gpt2/resolve/main/merges.txt",
"gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/merges.txt",
"gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/merges.txt",
"gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/merges.txt",
"distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/merges.txt",
},
"tokenizer_file": {
"gpt2": "https://huggingface.co/gpt2/resolve/main/tokenizer.json",
"gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json",
"gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/tokenizer.json",
"gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json",
"distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/tokenizer.json",
},
}
UpperCamelCase : Dict = {
"gpt2": 1_0_2_4,
"gpt2-medium": 1_0_2_4,
"gpt2-large": 1_0_2_4,
"gpt2-xl": 1_0_2_4,
"distilgpt2": 1_0_2_4,
}
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
lowercase = VOCAB_FILES_NAMES
lowercase = PRETRAINED_VOCAB_FILES_MAP
lowercase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase = ["input_ids", "attention_mask"]
lowercase = GPTaTokenizer
def __init__( self , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase="<|endoftext|>" , __UpperCAmelCase="<|endoftext|>" , __UpperCAmelCase="<|endoftext|>" , __UpperCAmelCase=False , **__UpperCAmelCase , ):
'''simple docstring'''
super().__init__(
__UpperCAmelCase , __UpperCAmelCase , tokenizer_file=__UpperCAmelCase , unk_token=__UpperCAmelCase , bos_token=__UpperCAmelCase , eos_token=__UpperCAmelCase , add_prefix_space=__UpperCAmelCase , **__UpperCAmelCase , )
__UpperCamelCase = kwargs.pop('add_bos_token' , __UpperCAmelCase )
__UpperCamelCase = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get('add_prefix_space' , __UpperCAmelCase ) != add_prefix_space:
__UpperCamelCase = getattr(__UpperCAmelCase , pre_tok_state.pop('type' ) )
__UpperCamelCase = add_prefix_space
__UpperCamelCase = pre_tok_class(**__UpperCAmelCase )
__UpperCamelCase = add_prefix_space
def UpperCAmelCase ( self , *__UpperCAmelCase , **__UpperCAmelCase ):
'''simple docstring'''
__UpperCamelCase = kwargs.get('is_split_into_words' , __UpperCAmelCase )
assert self.add_prefix_space or not is_split_into_words, (
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 UpperCAmelCase ( self , *__UpperCAmelCase , **__UpperCAmelCase ):
'''simple docstring'''
__UpperCamelCase = kwargs.get('is_split_into_words' , __UpperCAmelCase )
assert self.add_prefix_space or not is_split_into_words, (
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 UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase = None ):
'''simple docstring'''
__UpperCamelCase = self._tokenizer.model.save(__UpperCAmelCase , name=__UpperCAmelCase )
return tuple(__UpperCAmelCase )
def UpperCAmelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
__UpperCamelCase = []
for is_user, text in conversation.iter_texts():
input_ids.extend(self.encode(__UpperCAmelCase , add_special_tokens=__UpperCAmelCase ) + [self.eos_token_id] )
if len(__UpperCAmelCase ) > self.model_max_length:
__UpperCamelCase = input_ids[-self.model_max_length :]
return input_ids
| 316 | 0 |
"""simple docstring"""
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from tensorflow.python.eager import context
from tensorflow.python.framework import ops
from transformers import GradientAccumulator, create_optimizer
@require_tf
class _a ( unittest.TestCase):
"""simple docstring"""
def lowercase__ ( self : List[str] , __UpperCamelCase : str , __UpperCamelCase : Optional[Any] , __UpperCamelCase : Dict )->Tuple:
self.assertEqual(len(__UpperCamelCase ) , len(__UpperCamelCase ) )
for a, b in zip(__UpperCamelCase , __UpperCamelCase ):
self.assertAlmostEqual(__UpperCamelCase , __UpperCamelCase , delta=__UpperCamelCase )
def lowercase__ ( self : List[Any] )->Tuple:
_UpperCAmelCase = GradientAccumulator()
accumulator([tf.constant([1.0, 2.0] )] )
accumulator([tf.constant([-2.0, 1.0] )] )
accumulator([tf.constant([-1.0, 2.0] )] )
with self.assertRaises(__UpperCamelCase ):
accumulator([tf.constant([1.0, 1.0] ), tf.constant([2.0, 2.0] )] )
self.assertEqual(accumulator.step , 3 )
self.assertEqual(len(accumulator.gradients ) , 1 )
self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist() , [-2.0, 5.0] , tol=1e-2 )
accumulator.reset()
self.assertEqual(accumulator.step , 0 )
self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist() , [0.0, 0.0] , tol=1e-2 )
def lowercase__ ( self : Optional[int] )->Tuple:
_UpperCAmelCase = None
ops.enable_eager_execution_internal()
_UpperCAmelCase = tf.config.list_physical_devices('''CPU''' )
if len(__UpperCamelCase ) == 1:
tf.config.set_logical_device_configuration(
physical_devices[0] , [tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()] )
_UpperCAmelCase = tf.config.list_logical_devices(device_type='''CPU''' )
_UpperCAmelCase = tf.distribute.MirroredStrategy(devices=devices[:2] )
with strategy.scope():
_UpperCAmelCase = GradientAccumulator()
_UpperCAmelCase = tf.Variable([4.0, 3.0] )
_UpperCAmelCase , _UpperCAmelCase = create_optimizer(5e-5 , 1_0 , 5 )
_UpperCAmelCase = tf.Variable([0.0, 0.0] , trainable=__UpperCamelCase )
def accumulate_on_replica(__UpperCamelCase : Tuple ):
accumulator([gradient] )
def apply_on_replica():
optimizer.apply_gradients(list(zip(accumulator.gradients , [variable] ) ) )
@tf.function
def accumulate(__UpperCamelCase : Dict , __UpperCamelCase : List[str] ):
with strategy.scope():
_UpperCAmelCase = strategy.experimental_local_results(__UpperCamelCase )
local_variables[0].assign(__UpperCamelCase )
local_variables[1].assign(__UpperCamelCase )
strategy.run(__UpperCamelCase , args=(gradient_placeholder,) )
@tf.function
def apply_grad():
with strategy.scope():
strategy.run(__UpperCamelCase )
def _check_local_values(__UpperCamelCase : Optional[int] , __UpperCamelCase : List[str] ):
_UpperCAmelCase = strategy.experimental_local_results(accumulator._gradients[0] )
self.assertListAlmostEqual(values[0].value() , __UpperCamelCase , tol=1e-2 )
self.assertListAlmostEqual(values[1].value() , __UpperCamelCase , tol=1e-2 )
accumulate([1.0, 2.0] , [-1.0, 1.0] )
accumulate([3.0, -1.0] , [-1.0, -1.0] )
accumulate([-2.0, 2.0] , [3.0, -2.0] )
self.assertEqual(accumulator.step , 3 )
_check_local_values([2.0, 3.0] , [1.0, -2.0] )
apply_grad()
self.assertListAlmostEqual(variable.value() , [4.0, 3.0] , tol=1e-2 )
accumulator.reset()
self.assertEqual(accumulator.step , 0 )
_check_local_values([0.0, 0.0] , [0.0, 0.0] )
| 358 |
"""simple docstring"""
import argparse
from copy import deepcopy
import numpy as np
from datasets import ClassLabel, DatasetDict, load_dataset
from evaluate import load
from transformers import (
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
Trainer,
TrainerCallback,
TrainingArguments,
set_seed,
)
def lowercase ( ):
'''simple docstring'''
_UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument('''--model_ckpt''' , type=_SCREAMING_SNAKE_CASE , default='''microsoft/unixcoder-base-nine''' )
parser.add_argument('''--num_epochs''' , type=_SCREAMING_SNAKE_CASE , default=5 )
parser.add_argument('''--batch_size''' , type=_SCREAMING_SNAKE_CASE , default=6 )
parser.add_argument('''--gradient_accumulation_steps''' , type=_SCREAMING_SNAKE_CASE , default=1 )
parser.add_argument('''--freeze''' , type=_SCREAMING_SNAKE_CASE , default=_SCREAMING_SNAKE_CASE )
parser.add_argument('''--learning_rate''' , type=_SCREAMING_SNAKE_CASE , default=5E-4 )
parser.add_argument('''--seed''' , type=_SCREAMING_SNAKE_CASE , default=0 )
parser.add_argument('''--lr_scheduler_type''' , type=_SCREAMING_SNAKE_CASE , default='''cosine''' )
parser.add_argument('''--num_warmup_steps''' , type=_SCREAMING_SNAKE_CASE , default=10 )
parser.add_argument('''--weight_decay''' , type=_SCREAMING_SNAKE_CASE , default=0.01 )
parser.add_argument('''--output_dir''' , type=_SCREAMING_SNAKE_CASE , default='''./results''' )
return parser.parse_args()
__A : Union[str, Any] = load("accuracy")
def lowercase ( _SCREAMING_SNAKE_CASE : Optional[int] ):
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase = eval_pred
_UpperCAmelCase = np.argmax(_SCREAMING_SNAKE_CASE , axis=1 )
return metric.compute(predictions=_SCREAMING_SNAKE_CASE , references=_SCREAMING_SNAKE_CASE )
class _a ( lowerCAmelCase):
"""simple docstring"""
def __init__( self : str , __UpperCamelCase : Union[str, Any] )->None:
super().__init__()
_UpperCAmelCase = trainer
def lowercase__ ( self : str , __UpperCamelCase : str , __UpperCamelCase : List[str] , __UpperCamelCase : Union[str, Any] , **__UpperCamelCase : List[str] )->Any:
if control.should_evaluate:
_UpperCAmelCase = deepcopy(__UpperCamelCase )
self._trainer.evaluate(eval_dataset=self._trainer.train_dataset , metric_key_prefix='''train''' )
return control_copy
def lowercase ( ):
'''simple docstring'''
_UpperCAmelCase = get_args()
set_seed(args.seed )
_UpperCAmelCase = load_dataset('''codeparrot/codecomplex''' , split='''train''' )
_UpperCAmelCase = dataset.train_test_split(test_size=0.2 )
_UpperCAmelCase = train_test['''test'''].train_test_split(test_size=0.5 )
_UpperCAmelCase = DatasetDict(
{
'''train''': train_test['''train'''],
'''test''': test_validation['''train'''],
'''valid''': test_validation['''test'''],
} )
print('''Loading tokenizer and model''' )
_UpperCAmelCase = AutoTokenizer.from_pretrained(args.model_ckpt )
_UpperCAmelCase = tokenizer.eos_token
_UpperCAmelCase = AutoModelForSequenceClassification.from_pretrained(args.model_ckpt , num_labels=7 )
_UpperCAmelCase = model.config.eos_token_id
if args.freeze:
for param in model.roberta.parameters():
_UpperCAmelCase = False
_UpperCAmelCase = ClassLabel(num_classes=7 , names=list(set(train_test_validation['''train''']['''complexity'''] ) ) )
def tokenize(_SCREAMING_SNAKE_CASE : Any ):
_UpperCAmelCase = tokenizer(example['''src'''] , truncation=_SCREAMING_SNAKE_CASE , max_length=1024 )
_UpperCAmelCase = labels.straint(example['''complexity'''] )
return {
"input_ids": inputs["input_ids"],
"attention_mask": inputs["attention_mask"],
"label": label,
}
_UpperCAmelCase = train_test_validation.map(
_SCREAMING_SNAKE_CASE , batched=_SCREAMING_SNAKE_CASE , remove_columns=train_test_validation['''train'''].column_names , )
_UpperCAmelCase = DataCollatorWithPadding(tokenizer=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = TrainingArguments(
output_dir=args.output_dir , learning_rate=args.learning_rate , lr_scheduler_type=args.lr_scheduler_type , evaluation_strategy='''epoch''' , save_strategy='''epoch''' , logging_strategy='''epoch''' , per_device_train_batch_size=args.batch_size , per_device_eval_batch_size=args.batch_size , num_train_epochs=args.num_epochs , gradient_accumulation_steps=args.gradient_accumulation_steps , weight_decay=0.01 , metric_for_best_model='''accuracy''' , run_name='''complexity-java''' , report_to='''wandb''' , )
_UpperCAmelCase = Trainer(
model=_SCREAMING_SNAKE_CASE , args=_SCREAMING_SNAKE_CASE , train_dataset=tokenized_datasets['''train'''] , eval_dataset=tokenized_datasets['''valid'''] , tokenizer=_SCREAMING_SNAKE_CASE , data_collator=_SCREAMING_SNAKE_CASE , compute_metrics=_SCREAMING_SNAKE_CASE , )
print('''Training...''' )
trainer.add_callback(CustomCallback(_SCREAMING_SNAKE_CASE ) )
trainer.train()
if __name__ == "__main__":
main()
| 326 | 0 |
from __future__ import annotations
def lowerCamelCase_ ( _a : Optional[Any] ): # This function is recursive
'''simple docstring'''
UpperCAmelCase_ : Union[str, Any] = len(SCREAMING_SNAKE_CASE__ )
# If the array contains only one element, we return it (it's the stop condition of
# recursion)
if array_length <= 1:
return array
# Else
UpperCAmelCase_ : Tuple = array[0]
UpperCAmelCase_ : Dict = False
UpperCAmelCase_ : Union[str, Any] = 1
UpperCAmelCase_ : List[Any] = []
while not is_found and i < array_length:
if array[i] < pivot:
UpperCAmelCase_ : Optional[int] = True
UpperCAmelCase_ : List[str] = [element for element in array[i:] if element >= array[i]]
UpperCAmelCase_ : int = longest_subsequence(SCREAMING_SNAKE_CASE__ )
if len(SCREAMING_SNAKE_CASE__ ) > len(SCREAMING_SNAKE_CASE__ ):
UpperCAmelCase_ : Dict = temp_array
else:
i += 1
UpperCAmelCase_ : Any = [element for element in array[1:] if element >= pivot]
UpperCAmelCase_ : Any = [pivot, *longest_subsequence(SCREAMING_SNAKE_CASE__ )]
if len(SCREAMING_SNAKE_CASE__ ) > len(SCREAMING_SNAKE_CASE__ ):
return temp_array
else:
return longest_subseq
if __name__ == "__main__":
import doctest
doctest.testmod()
| 345 |
from __future__ import annotations
from math import pi, sqrt
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
if inductance <= 0:
raise ValueError('''Inductance cannot be 0 or negative''' )
elif capacitance <= 0:
raise ValueError('''Capacitance cannot be 0 or negative''' )
else:
return (
"Resonant frequency",
float(1 / (2 * pi * (sqrt(inductance * capacitance ))) ),
)
if __name__ == "__main__":
import doctest
doctest.testmod() | 8 | 0 |
'''simple docstring'''
import warnings
from contextlib import contextmanager
from ....processing_utils import ProcessorMixin
class _lowerCAmelCase ( __lowercase ):
__SCREAMING_SNAKE_CASE : List[str] = "MCTCTFeatureExtractor"
__SCREAMING_SNAKE_CASE : int = "AutoTokenizer"
def __init__(self , lowercase , lowercase ):
super().__init__(snake_case_ , snake_case_ )
A_ : List[str] = self.feature_extractor
A_ : List[str] = False
def __call__(self , *lowercase , **lowercase ):
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor(*snake_case_ , **snake_case_ )
if "raw_speech" in kwargs:
warnings.warn("""Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.""" )
A_ : List[str] = kwargs.pop("""raw_speech""" )
else:
A_ : Any = kwargs.pop("""audio""" , snake_case_ )
A_ : List[str] = kwargs.pop("""sampling_rate""" , snake_case_ )
A_ : Dict = kwargs.pop("""text""" , snake_case_ )
if len(snake_case_ ) > 0:
A_ : Optional[int] = args[0]
A_ : Optional[int] = args[1:]
if audio is None and text is None:
raise ValueError("""You need to specify either an `audio` or `text` input to process.""" )
if audio is not None:
A_ : int = self.feature_extractor(snake_case_ , *snake_case_ , sampling_rate=snake_case_ , **snake_case_ )
if text is not None:
A_ : Union[str, Any] = self.tokenizer(snake_case_ , **snake_case_ )
if text is None:
return inputs
elif audio is None:
return encodings
else:
A_ : Optional[Any] = encodings['''input_ids''']
return inputs
def _a (self , *lowercase , **lowercase ):
return self.tokenizer.batch_decode(*snake_case_ , **snake_case_ )
def _a (self , *lowercase , **lowercase ):
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor.pad(*snake_case_ , **snake_case_ )
A_ : List[Any] = kwargs.pop("""input_features""" , snake_case_ )
A_ : Dict = kwargs.pop("""labels""" , snake_case_ )
if len(snake_case_ ) > 0:
A_ : str = args[0]
A_ : List[str] = args[1:]
if input_features is not None:
A_ : Union[str, Any] = self.feature_extractor.pad(snake_case_ , *snake_case_ , **snake_case_ )
if labels is not None:
A_ : Optional[int] = self.tokenizer.pad(snake_case_ , **snake_case_ )
if labels is None:
return input_features
elif input_features is None:
return labels
else:
A_ : Dict = labels['''input_ids''']
return input_features
def _a (self , *lowercase , **lowercase ):
return self.tokenizer.decode(*snake_case_ , **snake_case_ )
@contextmanager
def _a (self ):
warnings.warn(
"""`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
"""labels by using the argument `text` of the regular `__call__` method (either in the same call as """
"""your audio inputs, or in a separate call.""" )
A_ : List[Any] = True
A_ : Optional[int] = self.tokenizer
yield
A_ : Dict = self.feature_extractor
A_ : Any = False | 352 |
'''simple docstring'''
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import ViTForImageClassification, ViTForMaskedImageModeling, ViTModel
from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class _lowerCAmelCase :
def __init__(self , lowercase , lowercase=13 , lowercase=30 , lowercase=2 , lowercase=3 , lowercase=True , lowercase=True , lowercase=32 , lowercase=5 , lowercase=4 , lowercase=37 , lowercase="gelu" , lowercase=0.1 , lowercase=0.1 , lowercase=10 , lowercase=0.02 , lowercase=None , lowercase=2 , ):
A_ : List[str] = parent
A_ : str = batch_size
A_ : Optional[Any] = image_size
A_ : List[str] = patch_size
A_ : List[str] = num_channels
A_ : List[str] = is_training
A_ : str = use_labels
A_ : List[str] = hidden_size
A_ : List[Any] = num_hidden_layers
A_ : Any = num_attention_heads
A_ : Any = intermediate_size
A_ : Optional[Any] = hidden_act
A_ : Optional[int] = hidden_dropout_prob
A_ : str = attention_probs_dropout_prob
A_ : Optional[int] = type_sequence_label_size
A_ : Any = initializer_range
A_ : int = scope
A_ : str = encoder_stride
# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
A_ : Dict = (image_size // patch_size) ** 2
A_ : List[str] = num_patches + 1
def _a (self ):
A_ : str = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
A_ : Optional[Any] = None
if self.use_labels:
A_ : str = ids_tensor([self.batch_size] , self.type_sequence_label_size )
A_ : Union[str, Any] = self.get_config()
return config, pixel_values, labels
def _a (self ):
return ViTConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=lowercase , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , )
def _a (self , lowercase , lowercase , lowercase ):
A_ : List[str] = ViTModel(config=lowercase )
model.to(lowercase )
model.eval()
A_ : List[str] = model(lowercase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _a (self , lowercase , lowercase , lowercase ):
A_ : List[str] = ViTForMaskedImageModeling(config=lowercase )
model.to(lowercase )
model.eval()
A_ : Tuple = model(lowercase )
self.parent.assertEqual(
result.reconstruction.shape , (self.batch_size, self.num_channels, self.image_size, self.image_size) )
# test greyscale images
A_ : Union[str, Any] = 1
A_ : Any = ViTForMaskedImageModeling(lowercase )
model.to(lowercase )
model.eval()
A_ : str = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
A_ : Optional[int] = model(lowercase )
self.parent.assertEqual(result.reconstruction.shape , (self.batch_size, 1, self.image_size, self.image_size) )
def _a (self , lowercase , lowercase , lowercase ):
A_ : Dict = self.type_sequence_label_size
A_ : str = ViTForImageClassification(lowercase )
model.to(lowercase )
model.eval()
A_ : List[str] = model(lowercase , labels=lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
A_ : Any = 1
A_ : str = ViTForImageClassification(lowercase )
model.to(lowercase )
model.eval()
A_ : Optional[int] = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
A_ : Union[str, Any] = model(lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def _a (self ):
A_ : str = self.prepare_config_and_inputs()
(
(
A_
), (
A_
), (
A_
),
) : Optional[int] = config_and_inputs
A_ : Tuple = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class _lowerCAmelCase ( __UpperCAmelCase , __UpperCAmelCase , unittest.TestCase ):
__SCREAMING_SNAKE_CASE : Any = (
(
ViTModel,
ViTForImageClassification,
ViTForMaskedImageModeling,
)
if is_torch_available()
else ()
)
__SCREAMING_SNAKE_CASE : Union[str, Any] = (
{'feature-extraction': ViTModel, 'image-classification': ViTForImageClassification}
if is_torch_available()
else {}
)
__SCREAMING_SNAKE_CASE : Union[str, Any] = True
__SCREAMING_SNAKE_CASE : Dict = False
__SCREAMING_SNAKE_CASE : Optional[int] = False
__SCREAMING_SNAKE_CASE : Optional[int] = False
def _a (self ):
A_ : Any = ViTModelTester(self )
A_ : str = ConfigTester(self , config_class=lowercase , has_text_modality=lowercase , hidden_size=37 )
def _a (self ):
self.config_tester.run_common_tests()
@unittest.skip(reason="""ViT does not use inputs_embeds""" )
def _a (self ):
pass
def _a (self ):
A_, A_ : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A_ : Union[str, Any] = model_class(lowercase )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
A_ : Tuple = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(lowercase , nn.Linear ) )
def _a (self ):
A_, A_ : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A_ : Optional[Any] = model_class(lowercase )
A_ : Any = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
A_ : List[str] = [*signature.parameters.keys()]
A_ : Dict = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , lowercase )
def _a (self ):
A_ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowercase )
def _a (self ):
A_ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*lowercase )
def _a (self ):
A_ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*lowercase )
@slow
def _a (self ):
for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
A_ : Dict = ViTModel.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
def a ( ):
'''simple docstring'''
A_ : List[Any] = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_torch
@require_vision
class _lowerCAmelCase ( unittest.TestCase ):
@cached_property
def _a (self ):
return ViTImageProcessor.from_pretrained("""google/vit-base-patch16-224""" ) if is_vision_available() else None
@slow
def _a (self ):
A_ : Optional[int] = ViTForImageClassification.from_pretrained("""google/vit-base-patch16-224""" ).to(lowercase )
A_ : List[str] = self.default_image_processor
A_ : Tuple = prepare_img()
A_ : int = image_processor(images=lowercase , return_tensors="""pt""" ).to(lowercase )
# forward pass
with torch.no_grad():
A_ : str = model(**lowercase )
# verify the logits
A_ : Dict = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , lowercase )
A_ : str = torch.tensor([-0.27_44, 0.82_15, -0.08_36] ).to(lowercase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , lowercase , atol=1E-4 ) )
@slow
def _a (self ):
# ViT models have an `interpolate_pos_encoding` argument in their forward method,
# allowing to interpolate the pre-trained position embeddings in order to use
# the model on higher resolutions. The DINO model by Facebook AI leverages this
# to visualize self-attention on higher resolution images.
A_ : Optional[int] = ViTModel.from_pretrained("""facebook/dino-vits8""" ).to(lowercase )
A_ : List[Any] = ViTImageProcessor.from_pretrained("""facebook/dino-vits8""" , size=480 )
A_ : Dict = prepare_img()
A_ : str = image_processor(images=lowercase , return_tensors="""pt""" )
A_ : int = inputs.pixel_values.to(lowercase )
# forward pass
with torch.no_grad():
A_ : int = model(lowercase , interpolate_pos_encoding=lowercase )
# verify the logits
A_ : int = torch.Size((1, 3601, 384) )
self.assertEqual(outputs.last_hidden_state.shape , lowercase )
A_ : List[Any] = torch.tensor(
[[4.23_40, 4.39_06, -6.66_92], [4.54_63, 1.89_28, -6.72_57], [4.44_29, 0.84_96, -5.85_85]] ).to(lowercase )
self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3] , lowercase , atol=1E-4 ) )
@slow
@require_accelerate
@require_torch_gpu
def _a (self ):
A_ : List[Any] = ViTModel.from_pretrained("""facebook/dino-vits8""" , torch_dtype=torch.floataa , device_map="""auto""" )
A_ : int = self.default_image_processor
A_ : Any = prepare_img()
A_ : List[str] = image_processor(images=lowercase , return_tensors="""pt""" )
A_ : Any = inputs.pixel_values.to(lowercase )
# forward pass to make sure inference works in fp16
with torch.no_grad():
A_ : Optional[Any] = model(lowercase ) | 135 | 0 |
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_flax_cross_test,
require_flax,
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import is_flax_available, is_torch_available, is_vision_available
from ...test_modeling_flax_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_flax_bert import FlaxBertModelTester
from ..clip.test_modeling_flax_clip import FlaxCLIPVisionModelTester
from ..vit.test_modeling_flax_vit import FlaxViTModelTester
if is_flax_available():
from transformers import (
FlaxBertModel,
FlaxCLIPVisionModel,
FlaxVisionTextDualEncoderModel,
FlaxViTModel,
VisionTextDualEncoderConfig,
VisionTextDualEncoderProcessor,
)
from transformers.modeling_flax_pytorch_utils import (
convert_pytorch_state_dict_to_flax,
load_flax_weights_in_pytorch_model,
)
if is_torch_available():
import torch
from transformers import VisionTextDualEncoderModel
if is_vision_available():
from PIL import Image
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> Any:
if isinstance(_UpperCAmelCase , collections.abc.Iterable ):
return x
return (x, x)
@require_flax
class lowerCAmelCase :
def A_ ( self : Any , UpperCAmelCase : Dict , UpperCAmelCase : Optional[Any] ) -> Union[str, Any]:
pass
def A_ ( self : Dict ) -> int:
pass
def A_ ( self : List[Any] ) -> List[str]:
pass
def A_ ( self : Union[str, Any] , UpperCAmelCase : np.ndarray , UpperCAmelCase : np.ndarray , UpperCAmelCase : float ) -> Union[str, Any]:
lowerCamelCase__ : Optional[Any] = np.abs((a - b) ).max()
self.assertLessEqual(UpperCAmelCase , UpperCAmelCase , F"""Difference between torch and flax is {diff} (>= {tol}).""" )
def A_ ( self : Any , UpperCAmelCase : Dict , UpperCAmelCase : int , UpperCAmelCase : Optional[int] , UpperCAmelCase : List[Any] , UpperCAmelCase : Any=None , **UpperCAmelCase : Dict ) -> Tuple:
lowerCamelCase__ : str = VisionTextDualEncoderConfig.from_vision_text_configs(UpperCAmelCase , UpperCAmelCase )
lowerCamelCase__ : str = FlaxVisionTextDualEncoderModel(UpperCAmelCase )
lowerCamelCase__ : int = model(input_ids=UpperCAmelCase , pixel_values=UpperCAmelCase , attention_mask=UpperCAmelCase )
self.assertEqual(output['text_embeds'].shape , (input_ids.shape[0], config.projection_dim) )
self.assertEqual(output['image_embeds'].shape , (pixel_values.shape[0], config.projection_dim) )
def A_ ( self : Tuple , UpperCAmelCase : Any , UpperCAmelCase : List[Any] , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : str , UpperCAmelCase : Dict=None , **UpperCAmelCase : Optional[int] ) -> Dict:
lowerCamelCase__ , lowerCamelCase__ : Optional[Any] = self.get_vision_text_model(UpperCAmelCase , UpperCAmelCase )
lowerCamelCase__ : Union[str, Any] = {'vision_model': vision_model, 'text_model': text_model}
lowerCamelCase__ : str = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**UpperCAmelCase )
lowerCamelCase__ : Any = model(input_ids=UpperCAmelCase , pixel_values=UpperCAmelCase , attention_mask=UpperCAmelCase )
self.assertEqual(output['text_embeds'].shape , (input_ids.shape[0], model.config.projection_dim) )
self.assertEqual(output['image_embeds'].shape , (pixel_values.shape[0], model.config.projection_dim) )
def A_ ( self : Tuple , UpperCAmelCase : List[Any] , UpperCAmelCase : List[Any] , UpperCAmelCase : Optional[int] , UpperCAmelCase : int , UpperCAmelCase : Optional[int]=None , **UpperCAmelCase : List[str] ) -> Dict:
lowerCamelCase__ , lowerCamelCase__ : str = self.get_vision_text_model(UpperCAmelCase , UpperCAmelCase )
lowerCamelCase__ : str = {'vision_model': vision_model, 'text_model': text_model}
lowerCamelCase__ : Union[str, Any] = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**UpperCAmelCase )
lowerCamelCase__ : Any = model(input_ids=UpperCAmelCase , pixel_values=UpperCAmelCase , attention_mask=UpperCAmelCase )
lowerCamelCase__ : Optional[int] = output[0]
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(UpperCAmelCase )
lowerCamelCase__ : Union[str, Any] = FlaxVisionTextDualEncoderModel.from_pretrained(UpperCAmelCase )
lowerCamelCase__ : int = model(input_ids=UpperCAmelCase , pixel_values=UpperCAmelCase , attention_mask=UpperCAmelCase )
lowerCamelCase__ : List[Any] = after_output[0]
lowerCamelCase__ : Optional[Any] = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(UpperCAmelCase , 1e-3 )
def A_ ( self : Optional[int] , UpperCAmelCase : Optional[int] , UpperCAmelCase : Dict , UpperCAmelCase : str , UpperCAmelCase : str , UpperCAmelCase : str=None , **UpperCAmelCase : List[Any] ) -> str:
lowerCamelCase__ , lowerCamelCase__ : Tuple = self.get_vision_text_model(UpperCAmelCase , UpperCAmelCase )
lowerCamelCase__ : Tuple = {'vision_model': vision_model, 'text_model': text_model}
lowerCamelCase__ : Any = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**UpperCAmelCase )
lowerCamelCase__ : int = model(
input_ids=UpperCAmelCase , pixel_values=UpperCAmelCase , attention_mask=UpperCAmelCase , output_attentions=UpperCAmelCase )
lowerCamelCase__ : Optional[Any] = output.vision_model_output.attentions
self.assertEqual(len(UpperCAmelCase ) , vision_config.num_hidden_layers )
# in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
lowerCamelCase__ : str = to_atuple(vision_model.config.image_size )
lowerCamelCase__ : Optional[int] = to_atuple(vision_model.config.patch_size )
lowerCamelCase__ : Tuple = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
lowerCamelCase__ : Optional[Any] = num_patches + 1
self.assertEqual(vision_attentions[0].shape[-3:] , (vision_config.num_attention_heads, seq_len, seq_len) )
lowerCamelCase__ : List[str] = output.text_model_output.attentions
self.assertEqual(len(UpperCAmelCase ) , text_config.num_hidden_layers )
self.assertEqual(
text_attentions[0].shape[-3:] , (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]) , )
def A_ ( self : Any , UpperCAmelCase : int , UpperCAmelCase : Optional[Any] , UpperCAmelCase : List[Any] ) -> str:
pt_model.to(UpperCAmelCase )
pt_model.eval()
# prepare inputs
lowerCamelCase__ : Optional[Any] = inputs_dict
lowerCamelCase__ : Optional[int] = {k: torch.tensor(v.tolist() ) for k, v in flax_inputs.items()}
with torch.no_grad():
lowerCamelCase__ : Optional[int] = pt_model(**UpperCAmelCase ).to_tuple()
lowerCamelCase__ : Tuple = fx_model(**UpperCAmelCase ).to_tuple()
self.assertEqual(len(UpperCAmelCase ) , len(UpperCAmelCase ) , 'Output lengths differ between Flax and PyTorch' )
for fx_output, pt_output in zip(fx_outputs[:4] , pt_outputs[:4] ):
self.assert_almost_equals(UpperCAmelCase , pt_output.numpy() , 4e-2 )
# PT -> Flax
with tempfile.TemporaryDirectory() as tmpdirname:
pt_model.save_pretrained(UpperCAmelCase )
lowerCamelCase__ : Optional[Any] = FlaxVisionTextDualEncoderModel.from_pretrained(UpperCAmelCase , from_pt=UpperCAmelCase )
lowerCamelCase__ : Optional[Any] = fx_model_loaded(**UpperCAmelCase ).to_tuple()
self.assertEqual(len(UpperCAmelCase ) , len(UpperCAmelCase ) , 'Output lengths differ between Flax and PyTorch' )
for fx_output_loaded, pt_output in zip(fx_outputs_loaded[:4] , pt_outputs[:4] ):
self.assert_almost_equals(UpperCAmelCase , pt_output.numpy() , 4e-2 )
# Flax -> PT
with tempfile.TemporaryDirectory() as tmpdirname:
fx_model.save_pretrained(UpperCAmelCase )
lowerCamelCase__ : Optional[int] = VisionTextDualEncoderModel.from_pretrained(UpperCAmelCase , from_flax=UpperCAmelCase )
pt_model_loaded.to(UpperCAmelCase )
pt_model_loaded.eval()
with torch.no_grad():
lowerCamelCase__ : Optional[int] = pt_model_loaded(**UpperCAmelCase ).to_tuple()
self.assertEqual(len(UpperCAmelCase ) , len(UpperCAmelCase ) , 'Output lengths differ between Flax and PyTorch' )
for fx_output, pt_output_loaded in zip(fx_outputs[:4] , pt_outputs_loaded[:4] ):
self.assert_almost_equals(UpperCAmelCase , pt_output_loaded.numpy() , 4e-2 )
def A_ ( self : str , UpperCAmelCase : Tuple , UpperCAmelCase : int , UpperCAmelCase : Optional[int] ) -> Dict:
lowerCamelCase__ : List[Any] = VisionTextDualEncoderConfig.from_vision_text_configs(UpperCAmelCase , UpperCAmelCase )
lowerCamelCase__ : Dict = VisionTextDualEncoderModel(UpperCAmelCase )
lowerCamelCase__ : Tuple = FlaxVisionTextDualEncoderModel(UpperCAmelCase )
lowerCamelCase__ : Optional[int] = convert_pytorch_state_dict_to_flax(pt_model.state_dict() , UpperCAmelCase )
lowerCamelCase__ : Dict = fx_state
self.check_pt_flax_equivalence(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
def A_ ( self : Tuple , UpperCAmelCase : int , UpperCAmelCase : Optional[Any] , UpperCAmelCase : Optional[int] ) -> str:
lowerCamelCase__ : str = VisionTextDualEncoderConfig.from_vision_text_configs(UpperCAmelCase , UpperCAmelCase )
lowerCamelCase__ : Any = VisionTextDualEncoderModel(UpperCAmelCase )
lowerCamelCase__ : Dict = FlaxVisionTextDualEncoderModel(UpperCAmelCase )
lowerCamelCase__ : Dict = load_flax_weights_in_pytorch_model(UpperCAmelCase , fx_model.params )
self.check_pt_flax_equivalence(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
def A_ ( self : List[Any] ) -> int:
lowerCamelCase__ : Optional[int] = self.prepare_config_and_inputs()
self.check_model_from_pretrained_configs(**UpperCAmelCase )
def A_ ( self : Any ) -> Any:
lowerCamelCase__ : List[str] = self.prepare_config_and_inputs()
self.check_vision_text_dual_encoder_from_pretrained(**UpperCAmelCase )
def A_ ( self : Any ) -> Optional[int]:
lowerCamelCase__ : Tuple = self.prepare_config_and_inputs()
self.check_save_load(**UpperCAmelCase )
def A_ ( self : Dict ) -> str:
lowerCamelCase__ : int = self.prepare_config_and_inputs()
self.check_vision_text_output_attention(**UpperCAmelCase )
@is_pt_flax_cross_test
def A_ ( self : int ) -> List[str]:
lowerCamelCase__ : str = self.prepare_config_and_inputs()
lowerCamelCase__ : Dict = config_inputs_dict.pop('vision_config' )
lowerCamelCase__ : List[Any] = config_inputs_dict.pop('text_config' )
lowerCamelCase__ : Optional[Any] = config_inputs_dict
self.check_equivalence_pt_to_flax(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
self.check_equivalence_flax_to_pt(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
@slow
def A_ ( self : Any ) -> Dict:
lowerCamelCase__ , lowerCamelCase__ : Tuple = self.get_pretrained_model_and_inputs()
lowerCamelCase__ : int = model_a(**UpperCAmelCase )
lowerCamelCase__ : List[Any] = outputs[0]
with tempfile.TemporaryDirectory() as tmp_dirname:
model_a.save_pretrained(UpperCAmelCase )
lowerCamelCase__ : Tuple = FlaxVisionTextDualEncoderModel.from_pretrained(UpperCAmelCase )
lowerCamelCase__ : Optional[Any] = model_a(**UpperCAmelCase )
lowerCamelCase__ : int = after_outputs[0]
lowerCamelCase__ : Optional[Any] = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(UpperCAmelCase , 1e-5 )
@require_flax
class lowerCAmelCase ( __UpperCamelCase, unittest.TestCase ):
def A_ ( self : Dict ) -> Tuple:
lowerCamelCase__ : Optional[int] = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
'hf-internal-testing/tiny-random-vit' , 'hf-internal-testing/tiny-bert' , vision_from_pt=UpperCAmelCase , text_from_pt=UpperCAmelCase , )
lowerCamelCase__ : List[str] = 13
lowerCamelCase__ : Optional[int] = floats_tensor(
[
batch_size,
model.config.vision_config.num_channels,
model.config.vision_config.image_size,
model.config.vision_config.image_size,
] )
lowerCamelCase__ : Union[str, Any] = ids_tensor([batch_size, 4] , model.config.text_config.vocab_size )
lowerCamelCase__ : Union[str, Any] = random_attention_mask([batch_size, 4] )
lowerCamelCase__ : Tuple = {'pixel_values': pixel_values, 'input_ids': input_ids, 'attention_mask': attention_mask}
return model, inputs
def A_ ( self : Union[str, Any] , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : str ) -> Optional[int]:
lowerCamelCase__ : str = FlaxViTModel(UpperCAmelCase )
lowerCamelCase__ : int = FlaxBertModel(UpperCAmelCase )
return vision_model, text_model
def A_ ( self : Any ) -> Optional[int]:
lowerCamelCase__ : Any = FlaxViTModelTester(self )
lowerCamelCase__ : Tuple = FlaxBertModelTester(self )
lowerCamelCase__ : Union[str, Any] = vit_model_tester.prepare_config_and_inputs()
lowerCamelCase__ : Tuple = bert_model_tester.prepare_config_and_inputs()
lowerCamelCase__ , lowerCamelCase__ : Dict = vision_config_and_inputs
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ : Optional[Any] = text_config_and_inputs
# make sure that cross attention layers are added
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": attention_mask,
"input_ids": input_ids,
"token_type_ids": token_type_ids,
}
@require_torch
class lowerCAmelCase ( __UpperCamelCase, unittest.TestCase ):
def A_ ( self : Tuple ) -> Union[str, Any]:
lowerCamelCase__ : List[str] = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
'hf-internal-testing/tiny-random-clip' , 'hf-internal-testing/tiny-bert' , vision_from_pt=UpperCAmelCase , text_from_pt=UpperCAmelCase , )
lowerCamelCase__ : List[Any] = 13
lowerCamelCase__ : int = floats_tensor(
[
batch_size,
model.config.vision_config.num_channels,
model.config.vision_config.image_size,
model.config.vision_config.image_size,
] )
lowerCamelCase__ : Optional[Any] = ids_tensor([batch_size, 4] , model.config.text_config.vocab_size )
lowerCamelCase__ : Optional[int] = random_attention_mask([batch_size, 4] )
lowerCamelCase__ : Optional[Any] = {'pixel_values': pixel_values, 'input_ids': input_ids, 'attention_mask': attention_mask}
return model, inputs
def A_ ( self : Any , UpperCAmelCase : Tuple , UpperCAmelCase : Tuple ) -> Any:
lowerCamelCase__ : Any = FlaxCLIPVisionModel(UpperCAmelCase )
lowerCamelCase__ : Any = FlaxBertModel(UpperCAmelCase )
return vision_model, text_model
def A_ ( self : List[Any] ) -> List[Any]:
lowerCamelCase__ : Any = FlaxCLIPVisionModelTester(self )
lowerCamelCase__ : List[str] = FlaxBertModelTester(self )
lowerCamelCase__ : Optional[Any] = clip_model_tester.prepare_config_and_inputs()
lowerCamelCase__ : int = bert_model_tester.prepare_config_and_inputs()
lowerCamelCase__ , lowerCamelCase__ : Tuple = vision_config_and_inputs
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ : int = text_config_and_inputs
# make sure that cross attention layers are added
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": attention_mask,
"input_ids": input_ids,
"token_type_ids": token_type_ids,
}
@require_flax
@require_vision
class lowerCAmelCase ( unittest.TestCase ):
@slow
def A_ ( self : int ) -> str:
lowerCamelCase__ : Tuple = FlaxVisionTextDualEncoderModel.from_pretrained('clip-italian/clip-italian' , logit_scale_init_value=1.0 )
lowerCamelCase__ : int = VisionTextDualEncoderProcessor.from_pretrained('clip-italian/clip-italian' )
lowerCamelCase__ : List[str] = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
lowerCamelCase__ : Optional[Any] = processor(
text=['una foto di un gatto', 'una foto di un cane'] , images=UpperCAmelCase , padding=UpperCAmelCase , return_tensors='np' )
lowerCamelCase__ : int = model(**UpperCAmelCase )
# verify the logits
self.assertEqual(outputs.logits_per_image.shape , (inputs.pixel_values.shape[0], inputs.input_ids.shape[0]) )
self.assertEqual(
outputs.logits_per_text.shape , (inputs.input_ids.shape[0], inputs.pixel_values.shape[0]) , )
lowerCamelCase__ : Optional[int] = np.array([[1.2_2_8_4_7_2_7, 0.3_1_0_4_1_2_2]] )
self.assertTrue(np.allclose(outputs.logits_per_image , UpperCAmelCase , atol=1e-3 ) )
| 50 |
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase ) -> bool:
lowerCamelCase__ : List[str] = len(_UpperCAmelCase )
lowerCamelCase__ : str = [[False] * (required_sum + 1) for _ in range(arr_len + 1 )]
# for each arr value, a sum of zero(0) can be formed by not taking any element
# hence True/1
for i in range(arr_len + 1 ):
lowerCamelCase__ : Tuple = True
# sum is not zero and set is empty then false
for i in range(1 , required_sum + 1 ):
lowerCamelCase__ : Dict = False
for i in range(1 , arr_len + 1 ):
for j in range(1 , required_sum + 1 ):
if arr[i - 1] > j:
lowerCamelCase__ : str = subset[i - 1][j]
if arr[i - 1] <= j:
lowerCamelCase__ : Dict = subset[i - 1][j] or subset[i - 1][j - arr[i - 1]]
return subset[arr_len][required_sum]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 50 | 1 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
torch.set_grad_enabled(False)
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=False ):
'''simple docstring'''
__UpperCamelCase :Dict = []
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((f"""module.blocks.{i}.norm1.weight""", f"""vit.encoder.layer.{i}.layernorm_before.weight""") )
rename_keys.append((f"""module.blocks.{i}.norm1.bias""", f"""vit.encoder.layer.{i}.layernorm_before.bias""") )
rename_keys.append(
(f"""module.blocks.{i}.attn.proj.weight""", f"""vit.encoder.layer.{i}.attention.output.dense.weight""") )
rename_keys.append((f"""module.blocks.{i}.attn.proj.bias""", f"""vit.encoder.layer.{i}.attention.output.dense.bias""") )
rename_keys.append((f"""module.blocks.{i}.norm2.weight""", f"""vit.encoder.layer.{i}.layernorm_after.weight""") )
rename_keys.append((f"""module.blocks.{i}.norm2.bias""", f"""vit.encoder.layer.{i}.layernorm_after.bias""") )
rename_keys.append((f"""module.blocks.{i}.mlp.fc1.weight""", f"""vit.encoder.layer.{i}.intermediate.dense.weight""") )
rename_keys.append((f"""module.blocks.{i}.mlp.fc1.bias""", f"""vit.encoder.layer.{i}.intermediate.dense.bias""") )
rename_keys.append((f"""module.blocks.{i}.mlp.fc2.weight""", f"""vit.encoder.layer.{i}.output.dense.weight""") )
rename_keys.append((f"""module.blocks.{i}.mlp.fc2.bias""", f"""vit.encoder.layer.{i}.output.dense.bias""") )
# projection layer + position embeddings
rename_keys.extend(
[
('''module.cls_token''', '''vit.embeddings.cls_token'''),
('''module.patch_embed.proj.weight''', '''vit.embeddings.patch_embeddings.projection.weight'''),
('''module.patch_embed.proj.bias''', '''vit.embeddings.patch_embeddings.projection.bias'''),
('''module.pos_embed''', '''vit.embeddings.position_embeddings'''),
] )
if base_model:
# layernorm + pooler
rename_keys.extend(
[
('''module.norm.weight''', '''layernorm.weight'''),
('''module.norm.bias''', '''layernorm.bias'''),
] )
# if just the base model, we should remove "vit" from all keys that start with "vit"
__UpperCamelCase :Optional[Any] = [(pair[0], pair[1][4:]) if pair[1].startswith('''vit''' ) else pair for pair in rename_keys]
else:
# layernorm + classification head
rename_keys.extend(
[
('''norm.weight''', '''vit.layernorm.weight'''),
('''norm.bias''', '''vit.layernorm.bias'''),
('''head.weight''', '''classifier.weight'''),
('''head.bias''', '''classifier.bias'''),
] )
return rename_keys
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=False ):
'''simple docstring'''
for i in range(config.num_hidden_layers ):
if base_model:
__UpperCamelCase :Union[str, Any] = ''''''
else:
__UpperCamelCase :int = '''vit.'''
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
__UpperCamelCase :Optional[int] = state_dict.pop(f"""module.blocks.{i}.attn.qkv.weight""" )
__UpperCamelCase :Optional[Any] = state_dict.pop(f"""module.blocks.{i}.attn.qkv.bias""" )
# next, add query, keys and values (in that order) to the state dict
__UpperCamelCase :List[str] = in_proj_weight[
: config.hidden_size, :
]
__UpperCamelCase :Any = in_proj_bias[: config.hidden_size]
__UpperCamelCase :Dict = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
__UpperCamelCase :Union[str, Any] = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
__UpperCamelCase :Any = in_proj_weight[
-config.hidden_size :, :
]
__UpperCamelCase :Optional[Any] = in_proj_bias[-config.hidden_size :]
def lowerCamelCase ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__UpperCamelCase :Optional[int] = ['''head.weight''', '''head.bias''']
for k in ignore_keys:
state_dict.pop(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
def lowerCamelCase ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__UpperCamelCase :Union[str, Any] = [
'''module.fc.fc1.weight''',
'''module.fc.fc1.bias''',
'''module.fc.bn1.weight''',
'''module.fc.bn1.bias''',
'''module.fc.bn1.running_mean''',
'''module.fc.bn1.running_var''',
'''module.fc.bn1.num_batches_tracked''',
'''module.fc.fc2.weight''',
'''module.fc.fc2.bias''',
'''module.fc.bn2.weight''',
'''module.fc.bn2.bias''',
'''module.fc.bn2.running_mean''',
'''module.fc.bn2.running_var''',
'''module.fc.bn2.num_batches_tracked''',
'''module.fc.fc3.weight''',
'''module.fc.fc3.bias''',
]
for k in ignore_keys:
state_dict.pop(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__UpperCamelCase :str = dct.pop(SCREAMING_SNAKE_CASE )
__UpperCamelCase :Union[str, Any] = val
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__UpperCamelCase :Dict = ViTMSNConfig()
__UpperCamelCase :Union[str, Any] = 1_000
__UpperCamelCase :Optional[Any] = '''datasets/huggingface/label-files'''
__UpperCamelCase :Optional[int] = '''imagenet-1k-id2label.json'''
__UpperCamelCase :Optional[int] = json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) , '''r''' ) )
__UpperCamelCase :Tuple = {int(SCREAMING_SNAKE_CASE ): v for k, v in idalabel.items()}
__UpperCamelCase :int = idalabel
__UpperCamelCase :Optional[Any] = {v: k for k, v in idalabel.items()}
if "s16" in checkpoint_url:
__UpperCamelCase :Optional[Any] = 384
__UpperCamelCase :str = 1_536
__UpperCamelCase :Any = 6
elif "l16" in checkpoint_url:
__UpperCamelCase :Union[str, Any] = 1_024
__UpperCamelCase :Union[str, Any] = 4_096
__UpperCamelCase :Optional[int] = 24
__UpperCamelCase :Optional[int] = 16
__UpperCamelCase :Union[str, Any] = 0.1
elif "b4" in checkpoint_url:
__UpperCamelCase :str = 4
elif "l7" in checkpoint_url:
__UpperCamelCase :Dict = 7
__UpperCamelCase :int = 1_024
__UpperCamelCase :List[str] = 4_096
__UpperCamelCase :Optional[Any] = 24
__UpperCamelCase :Any = 16
__UpperCamelCase :Optional[Any] = 0.1
__UpperCamelCase :Optional[Any] = ViTMSNModel(SCREAMING_SNAKE_CASE )
__UpperCamelCase :int = torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE , map_location='''cpu''' )['''target_encoder''']
__UpperCamelCase :Optional[Any] = ViTImageProcessor(size=config.image_size )
remove_projection_head(SCREAMING_SNAKE_CASE )
__UpperCamelCase :Union[str, Any] = create_rename_keys(SCREAMING_SNAKE_CASE , base_model=SCREAMING_SNAKE_CASE )
for src, dest in rename_keys:
rename_key(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
read_in_q_k_v(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , base_model=SCREAMING_SNAKE_CASE )
model.load_state_dict(SCREAMING_SNAKE_CASE )
model.eval()
__UpperCamelCase :Tuple = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
__UpperCamelCase :Tuple = Image.open(requests.get(SCREAMING_SNAKE_CASE , stream=SCREAMING_SNAKE_CASE ).raw )
__UpperCamelCase :Union[str, Any] = ViTImageProcessor(
size=config.image_size , image_mean=SCREAMING_SNAKE_CASE , image_std=SCREAMING_SNAKE_CASE )
__UpperCamelCase :List[str] = image_processor(images=SCREAMING_SNAKE_CASE , return_tensors='''pt''' )
# forward pass
torch.manual_seed(2 )
__UpperCamelCase :Optional[int] = model(**SCREAMING_SNAKE_CASE )
__UpperCamelCase :List[str] = outputs.last_hidden_state
# The following Colab Notebook was used to generate these outputs:
# https://colab.research.google.com/gist/sayakpaul/3672419a04f5997827503fd84079bdd1/scratchpad.ipynb
if "s16" in checkpoint_url:
__UpperCamelCase :Optional[int] = torch.tensor([[-1.0_915, -1.4_876, -1.1_809]] )
elif "b16" in checkpoint_url:
__UpperCamelCase :Optional[int] = torch.tensor([[14.2_889, -18.9_045, 11.7_281]] )
elif "l16" in checkpoint_url:
__UpperCamelCase :List[Any] = torch.tensor([[41.5_028, -22.8_681, 45.6_475]] )
elif "b4" in checkpoint_url:
__UpperCamelCase :int = torch.tensor([[-4.3_868, 5.2_932, -0.4_137]] )
else:
__UpperCamelCase :Optional[Any] = torch.tensor([[-0.1_792, -0.6_465, 2.4_263]] )
# verify logits
assert torch.allclose(last_hidden_state[:, 0, :3] , SCREAMING_SNAKE_CASE , atol=1e-4 )
print(f"""Saving model to {pytorch_dump_folder_path}""" )
model.save_pretrained(SCREAMING_SNAKE_CASE )
print(f"""Saving image processor to {pytorch_dump_folder_path}""" )
image_processor.save_pretrained(SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
__lowercase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--checkpoint_url''',
default='''https://dl.fbaipublicfiles.com/msn/vits16_800ep.pth.tar''',
type=str,
help='''URL of the checkpoint you\'d like to convert.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
)
__lowercase = parser.parse_args()
convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
| 353 | from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, 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 as np
import tensorflow as tf
from transformers import (
TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
FlaubertConfig,
TFFlaubertForMultipleChoice,
TFFlaubertForQuestionAnsweringSimple,
TFFlaubertForSequenceClassification,
TFFlaubertForTokenClassification,
TFFlaubertModel,
TFFlaubertWithLMHeadModel,
)
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self , __lowercase , ) -> Optional[Any]:
__UpperCamelCase :Optional[int] = parent
__UpperCamelCase :Optional[int] = 13
__UpperCamelCase :Dict = 7
__UpperCamelCase :Optional[int] = True
__UpperCamelCase :List[str] = True
__UpperCamelCase :List[Any] = True
__UpperCamelCase :Union[str, Any] = True
__UpperCamelCase :Any = True
__UpperCamelCase :Optional[int] = False
__UpperCamelCase :Any = False
__UpperCamelCase :str = False
__UpperCamelCase :Optional[Any] = 2
__UpperCamelCase :Optional[int] = 99
__UpperCamelCase :Any = 0
__UpperCamelCase :List[Any] = 32
__UpperCamelCase :int = 2
__UpperCamelCase :Optional[Any] = 4
__UpperCamelCase :Dict = 0.1
__UpperCamelCase :Optional[Any] = 0.1
__UpperCamelCase :str = 512
__UpperCamelCase :Any = 16
__UpperCamelCase :str = 2
__UpperCamelCase :Dict = 0.02
__UpperCamelCase :List[Any] = 3
__UpperCamelCase :Optional[int] = 4
__UpperCamelCase :Tuple = '''last'''
__UpperCamelCase :Any = True
__UpperCamelCase :Union[str, Any] = None
__UpperCamelCase :Tuple = 0
def UpperCamelCase__ ( self) -> Dict:
__UpperCamelCase :List[str] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
__UpperCamelCase :Any = random_attention_mask([self.batch_size, self.seq_length] , dtype=tf.floataa)
__UpperCamelCase :Tuple = None
if self.use_input_lengths:
__UpperCamelCase :List[str] = (
ids_tensor([self.batch_size] , vocab_size=2) + self.seq_length - 2
) # small variation of seq_length
__UpperCamelCase :Any = None
if self.use_token_type_ids:
__UpperCamelCase :int = ids_tensor([self.batch_size, self.seq_length] , self.n_langs)
__UpperCamelCase :Dict = None
__UpperCamelCase :List[str] = None
__UpperCamelCase :Union[str, Any] = None
if self.use_labels:
__UpperCamelCase :Dict = ids_tensor([self.batch_size] , self.type_sequence_label_size)
__UpperCamelCase :int = ids_tensor([self.batch_size, self.seq_length] , self.num_labels)
__UpperCamelCase :List[str] = ids_tensor([self.batch_size] , 2 , dtype=tf.floataa)
__UpperCamelCase :Optional[Any] = ids_tensor([self.batch_size] , self.num_choices)
__UpperCamelCase :Optional[int] = FlaubertConfig(
vocab_size=self.vocab_size , n_special=self.n_special , emb_dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , gelu_activation=self.gelu_activation , sinusoidal_embeddings=self.sinusoidal_embeddings , asm=self.asm , causal=self.causal , n_langs=self.n_langs , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , summary_type=self.summary_type , use_proj=self.use_proj , bos_token_id=self.bos_token_id , )
return (
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
)
def UpperCamelCase__ ( self , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , ) -> Optional[Any]:
__UpperCamelCase :str = TFFlaubertModel(config=__lowercase)
__UpperCamelCase :List[str] = {'''input_ids''': input_ids, '''lengths''': input_lengths, '''langs''': token_type_ids}
__UpperCamelCase :Optional[int] = model(__lowercase)
__UpperCamelCase :Optional[int] = [input_ids, input_mask]
__UpperCamelCase :Union[str, Any] = model(__lowercase)
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size))
def UpperCamelCase__ ( self , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , ) -> int:
__UpperCamelCase :str = TFFlaubertWithLMHeadModel(__lowercase)
__UpperCamelCase :List[str] = {'''input_ids''': input_ids, '''lengths''': input_lengths, '''langs''': token_type_ids}
__UpperCamelCase :Optional[int] = model(__lowercase)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size))
def UpperCamelCase__ ( self , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , ) -> Any:
__UpperCamelCase :Any = TFFlaubertForQuestionAnsweringSimple(__lowercase)
__UpperCamelCase :str = {'''input_ids''': input_ids, '''lengths''': input_lengths}
__UpperCamelCase :int = model(__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))
def UpperCamelCase__ ( self , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , ) -> Any:
__UpperCamelCase :int = TFFlaubertForSequenceClassification(__lowercase)
__UpperCamelCase :List[Any] = {'''input_ids''': input_ids, '''lengths''': input_lengths}
__UpperCamelCase :Any = model(__lowercase)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size))
def UpperCamelCase__ ( self , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , ) -> Tuple:
__UpperCamelCase :Optional[int] = self.num_labels
__UpperCamelCase :int = TFFlaubertForTokenClassification(config=__lowercase)
__UpperCamelCase :Union[str, Any] = {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
__UpperCamelCase :Optional[Any] = model(__lowercase)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels))
def UpperCamelCase__ ( self , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , ) -> List[Any]:
__UpperCamelCase :Any = self.num_choices
__UpperCamelCase :Any = TFFlaubertForMultipleChoice(config=__lowercase)
__UpperCamelCase :List[str] = tf.tile(tf.expand_dims(__lowercase , 1) , (1, self.num_choices, 1))
__UpperCamelCase :Optional[Any] = tf.tile(tf.expand_dims(__lowercase , 1) , (1, self.num_choices, 1))
__UpperCamelCase :Tuple = tf.tile(tf.expand_dims(__lowercase , 1) , (1, self.num_choices, 1))
__UpperCamelCase :Any = {
'''input_ids''': multiple_choice_inputs_ids,
'''attention_mask''': multiple_choice_input_mask,
'''token_type_ids''': multiple_choice_token_type_ids,
}
__UpperCamelCase :List[Any] = model(__lowercase)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices))
def UpperCamelCase__ ( self) -> Dict:
__UpperCamelCase :Optional[int] = self.prepare_config_and_inputs()
(
(
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) ,
) :Any = config_and_inputs
__UpperCamelCase :int = {
'''input_ids''': input_ids,
'''token_type_ids''': token_type_ids,
'''langs''': token_type_ids,
'''lengths''': input_lengths,
}
return config, inputs_dict
@require_tf
class lowerCamelCase_ ( UpperCAmelCase_ , UpperCAmelCase_ , unittest.TestCase ):
'''simple docstring'''
a__ : List[Any] = (
(
TFFlaubertModel,
TFFlaubertWithLMHeadModel,
TFFlaubertForSequenceClassification,
TFFlaubertForQuestionAnsweringSimple,
TFFlaubertForTokenClassification,
TFFlaubertForMultipleChoice,
)
if is_tf_available()
else ()
)
a__ : Tuple = (
(TFFlaubertWithLMHeadModel,) if is_tf_available() else ()
) # TODO (PVP): Check other models whether language generation is also applicable
a__ : Tuple = (
{
"""feature-extraction""": TFFlaubertModel,
"""fill-mask""": TFFlaubertWithLMHeadModel,
"""question-answering""": TFFlaubertForQuestionAnsweringSimple,
"""text-classification""": TFFlaubertForSequenceClassification,
"""token-classification""": TFFlaubertForTokenClassification,
"""zero-shot""": TFFlaubertForSequenceClassification,
}
if is_tf_available()
else {}
)
a__ : Tuple = False
a__ : Any = False
def UpperCamelCase__ ( self , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase) -> Any:
if (
pipeline_test_casse_name == "QAPipelineTests"
and tokenizer_name is not None
and not tokenizer_name.endswith('''Fast''')
):
# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
return True
return False
def UpperCamelCase__ ( self) -> Union[str, Any]:
__UpperCamelCase :int = TFFlaubertModelTester(self)
__UpperCamelCase :Dict = ConfigTester(self , config_class=__lowercase , emb_dim=37)
def UpperCamelCase__ ( self) -> str:
self.config_tester.run_common_tests()
def UpperCamelCase__ ( self) -> Any:
__UpperCamelCase :int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_model(*__lowercase)
def UpperCamelCase__ ( self) -> Union[str, Any]:
__UpperCamelCase :Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_lm_head(*__lowercase)
def UpperCamelCase__ ( self) -> Dict:
__UpperCamelCase :Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_qa(*__lowercase)
def UpperCamelCase__ ( self) -> Tuple:
__UpperCamelCase :Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_sequence_classif(*__lowercase)
def UpperCamelCase__ ( self) -> Optional[Any]:
__UpperCamelCase :Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_for_token_classification(*__lowercase)
def UpperCamelCase__ ( self) -> str:
__UpperCamelCase :Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_for_multiple_choice(*__lowercase)
@slow
def UpperCamelCase__ ( self) -> Union[str, Any]:
for model_name in TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__UpperCamelCase :Any = TFFlaubertModel.from_pretrained(__lowercase)
self.assertIsNotNone(__lowercase)
@require_tf
@require_sentencepiece
@require_tokenizers
class lowerCamelCase_ ( unittest.TestCase ):
'''simple docstring'''
@slow
def UpperCamelCase__ ( self) -> Optional[Any]:
__UpperCamelCase :List[Any] = TFFlaubertModel.from_pretrained('''jplu/tf-flaubert-small-cased''')
__UpperCamelCase :Tuple = tf.convert_to_tensor(
[[0, 158, 735, 2_592, 1_424, 6_727, 82, 1]] , dtype=tf.intaa , ) # "J'aime flaubert !"
__UpperCamelCase :Union[str, Any] = model(__lowercase)[0]
__UpperCamelCase :str = tf.TensorShape((1, 8, 512))
self.assertEqual(output.shape , __lowercase)
# compare the actual values for a slice.
__UpperCamelCase :Optional[int] = tf.convert_to_tensor(
[
[
[-1.8_76_87_73, -1.56_65_55, 0.27_07_24_18],
[-1.6_92_00_38, -0.5_87_35_05, 1.9_32_95_99],
[-2.9_56_39_85, -1.6_99_38_35, 1.7_97_20_52],
]
] , dtype=tf.floataa , )
self.assertTrue(np.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1E-4))
| 105 | 0 |
'''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 lowerCAmelCase_ :
def __init__( self , _lowerCAmelCase , _lowerCAmelCase=13 , _lowerCAmelCase=30 , _lowerCAmelCase=2 , _lowerCAmelCase=3 , _lowerCAmelCase=True , _lowerCAmelCase=True , _lowerCAmelCase=32 , _lowerCAmelCase=5 , _lowerCAmelCase=4 , _lowerCAmelCase=37 , _lowerCAmelCase="gelu" , _lowerCAmelCase=0.1 , _lowerCAmelCase=0.1 , _lowerCAmelCase=10 , _lowerCAmelCase=0.02 , _lowerCAmelCase=3 , _lowerCAmelCase=0.6 , _lowerCAmelCase=None , ) -> Optional[Any]:
_lowerCAmelCase = parent
_lowerCAmelCase = batch_size
_lowerCAmelCase = image_size
_lowerCAmelCase = patch_size
_lowerCAmelCase = num_channels
_lowerCAmelCase = is_training
_lowerCAmelCase = use_labels
_lowerCAmelCase = hidden_size
_lowerCAmelCase = num_hidden_layers
_lowerCAmelCase = num_attention_heads
_lowerCAmelCase = intermediate_size
_lowerCAmelCase = hidden_act
_lowerCAmelCase = hidden_dropout_prob
_lowerCAmelCase = attention_probs_dropout_prob
_lowerCAmelCase = type_sequence_label_size
_lowerCAmelCase = initializer_range
_lowerCAmelCase = mask_ratio
_lowerCAmelCase = scope
# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
# (we add 1 for the [CLS] token)
_lowerCAmelCase = (image_size // patch_size) ** 2
_lowerCAmelCase = int(math.ceil((1 - mask_ratio) * (num_patches + 1) ) )
def _snake_case ( self ) -> int:
_lowerCAmelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_lowerCAmelCase = None
if self.use_labels:
_lowerCAmelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_lowerCAmelCase = self.get_config()
return config, pixel_values, labels
def _snake_case ( self ) -> Union[str, 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=_lowerCAmelCase , initializer_range=self.initializer_range , mask_ratio=self.mask_ratio , )
def _snake_case ( self , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> Optional[Any]:
_lowerCAmelCase = ViTMAEModel(config=_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
_lowerCAmelCase = model(_lowerCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _snake_case ( self , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> List[str]:
_lowerCAmelCase = ViTMAEForPreTraining(_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
_lowerCAmelCase = model(_lowerCAmelCase )
_lowerCAmelCase = (self.image_size // self.patch_size) ** 2
_lowerCAmelCase = self.patch_size**2 * self.num_channels
self.parent.assertEqual(result.logits.shape , (self.batch_size, num_patches, expected_num_channels) )
# test greyscale images
_lowerCAmelCase = 1
_lowerCAmelCase = ViTMAEForPreTraining(_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
_lowerCAmelCase = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
_lowerCAmelCase = model(_lowerCAmelCase )
_lowerCAmelCase = self.patch_size**2
self.parent.assertEqual(result.logits.shape , (self.batch_size, num_patches, expected_num_channels) )
def _snake_case ( self ) -> List[str]:
_lowerCAmelCase = self.prepare_config_and_inputs()
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = config_and_inputs
_lowerCAmelCase = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class lowerCAmelCase_ ( __magic_name__ ,__magic_name__ ,unittest.TestCase ):
__lowerCamelCase : Union[str, Any] = (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
__lowerCamelCase : str = {"feature-extraction": ViTMAEModel} if is_torch_available() else {}
__lowerCamelCase : List[Any] = False
__lowerCamelCase : Union[str, Any] = False
__lowerCamelCase : Optional[Any] = False
__lowerCamelCase : List[Any] = False
def _snake_case ( self ) -> Dict:
_lowerCAmelCase = ViTMAEModelTester(self )
_lowerCAmelCase = ConfigTester(self , config_class=_lowerCAmelCase , has_text_modality=_lowerCAmelCase , hidden_size=37 )
def _snake_case ( self ) -> Dict:
self.config_tester.run_common_tests()
@unittest.skip(reason="ViTMAE does not use inputs_embeds" )
def _snake_case ( self ) -> Union[str, Any]:
pass
def _snake_case ( self ) -> Union[str, Any]:
_lowerCAmelCase , _lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_lowerCAmelCase = model_class(_lowerCAmelCase )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
_lowerCAmelCase = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(_lowerCAmelCase , nn.Linear ) )
def _snake_case ( self ) -> Optional[Any]:
_lowerCAmelCase , _lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_lowerCAmelCase = model_class(_lowerCAmelCase )
_lowerCAmelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_lowerCAmelCase = [*signature.parameters.keys()]
_lowerCAmelCase = ["pixel_values"]
self.assertListEqual(arg_names[:1] , _lowerCAmelCase )
def _snake_case ( self ) -> List[str]:
_lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_lowerCAmelCase )
def _snake_case ( self ) -> Dict:
_lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*_lowerCAmelCase )
def _snake_case ( self , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> int:
# make masks reproducible
np.random.seed(2 )
_lowerCAmelCase = int((pt_model.config.image_size // pt_model.config.patch_size) ** 2 )
_lowerCAmelCase = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
_lowerCAmelCase = torch.from_numpy(_lowerCAmelCase )
# Add `noise` argument.
# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
_lowerCAmelCase = pt_noise
super().check_pt_tf_models(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
def _snake_case ( self ) -> Dict:
_lowerCAmelCase , _lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_lowerCAmelCase = model_class(_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
# make random mask reproducible
torch.manual_seed(2 )
with torch.no_grad():
_lowerCAmelCase = model(**self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase ) )
_lowerCAmelCase = outputs[0].cpu().numpy()
_lowerCAmelCase = 0
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(_lowerCAmelCase )
_lowerCAmelCase = model_class.from_pretrained(_lowerCAmelCase )
model.to(_lowerCAmelCase )
# make random mask reproducible
torch.manual_seed(2 )
with torch.no_grad():
_lowerCAmelCase = model(**self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase ) )
# Make sure we don't have nans
_lowerCAmelCase = after_outputs[0].cpu().numpy()
_lowerCAmelCase = 0
_lowerCAmelCase = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(_lowerCAmelCase , 1E-5 )
@unittest.skip(
reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n to get deterministic results." )
def _snake_case ( self ) -> List[Any]:
pass
@unittest.skip(
reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n to get deterministic results." )
def _snake_case ( self ) -> Optional[int]:
pass
@unittest.skip(
reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n to get deterministic results." )
def _snake_case ( self ) -> List[Any]:
pass
@unittest.skip(reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load" )
def _snake_case ( self ) -> int:
pass
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
def _snake_case ( self ) -> Any:
pass
@slow
def _snake_case ( self ) -> Any:
for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowerCAmelCase = ViTMAEModel.from_pretrained(_lowerCAmelCase )
self.assertIsNotNone(_lowerCAmelCase )
def __a():
'''simple docstring'''
_lowerCAmelCase = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
@require_vision
class lowerCAmelCase_ ( unittest.TestCase ):
@cached_property
def _snake_case ( self ) -> str:
return ViTImageProcessor.from_pretrained("facebook/vit-mae-base" ) if is_vision_available() else None
@slow
def _snake_case ( self ) -> Any:
# make random mask reproducible across the PT and TF model
np.random.seed(2 )
_lowerCAmelCase = ViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base" ).to(_lowerCAmelCase )
_lowerCAmelCase = self.default_image_processor
_lowerCAmelCase = prepare_img()
_lowerCAmelCase = image_processor(images=_lowerCAmelCase , return_tensors="pt" ).to(_lowerCAmelCase )
# prepare a noise vector that will be also used for testing the TF model
# (this way we can ensure that the PT and TF models operate on the same inputs)
_lowerCAmelCase = ViTMAEConfig()
_lowerCAmelCase = int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2 )
_lowerCAmelCase = np.random.uniform(size=(1, num_patches) )
# forward pass
with torch.no_grad():
_lowerCAmelCase = model(**_lowerCAmelCase , noise=torch.from_numpy(_lowerCAmelCase ).to(device=_lowerCAmelCase ) )
# verify the logits
_lowerCAmelCase = torch.Size((1, 196, 768) )
self.assertEqual(outputs.logits.shape , _lowerCAmelCase )
_lowerCAmelCase = torch.tensor(
[[-0.0548, -1.7023, -0.9325], [0.3721, -0.5670, -0.2233], [0.8235, -1.3878, -0.3524]] )
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3] , expected_slice.to(_lowerCAmelCase ) , atol=1E-4 ) )
| 158 |
'''simple docstring'''
from unittest.mock import patch
import pyspark
from datasets.packaged_modules.spark.spark import (
Spark,
SparkExamplesIterable,
_generate_iterable_examples,
)
from ..utils import (
require_dill_gt_0_3_2,
require_not_windows,
)
def __a(SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int ):
'''simple docstring'''
_lowerCAmelCase = []
for part_id in partition_order:
_lowerCAmelCase = df.where(F'''SPARK_PARTITION_ID() = {part_id}''' ).collect()
for row_idx, row in enumerate(SCREAMING_SNAKE_CASE_ ):
expected_row_ids_and_row_dicts.append((F'''{part_id}_{row_idx}''', row.asDict()) )
return expected_row_ids_and_row_dicts
@require_not_windows
@require_dill_gt_0_3_2
def __a():
'''simple docstring'''
_lowerCAmelCase = pyspark.sql.SparkSession.builder.master("local[*]" ).appName("pyspark" ).getOrCreate()
_lowerCAmelCase = spark.range(100 ).repartition(1 )
_lowerCAmelCase = Spark(SCREAMING_SNAKE_CASE_ )
# The id ints will be converted to Pyarrow int64s, so each row will be 8 bytes. Setting a max_shard_size of 16 means
# that each partition can hold 2 rows.
spark_builder._repartition_df_if_needed(max_shard_size=16 )
# Given that the dataframe has 100 rows and each partition has 2 rows, we expect 50 partitions.
assert spark_builder.df.rdd.getNumPartitions() == 50
@require_not_windows
@require_dill_gt_0_3_2
def __a():
'''simple docstring'''
_lowerCAmelCase = pyspark.sql.SparkSession.builder.master("local[*]" ).appName("pyspark" ).getOrCreate()
_lowerCAmelCase = spark.range(10 ).repartition(2 )
_lowerCAmelCase = [1, 0]
_lowerCAmelCase = _generate_iterable_examples(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) # Reverse the partitions.
_lowerCAmelCase = _get_expected_row_ids_and_row_dicts_for_partition_order(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
for i, (row_id, row_dict) in enumerate(generate_fn() ):
_lowerCAmelCase , _lowerCAmelCase = expected_row_ids_and_row_dicts[i]
assert row_id == expected_row_id
assert row_dict == expected_row_dict
@require_not_windows
@require_dill_gt_0_3_2
def __a():
'''simple docstring'''
_lowerCAmelCase = pyspark.sql.SparkSession.builder.master("local[*]" ).appName("pyspark" ).getOrCreate()
_lowerCAmelCase = spark.range(10 ).repartition(1 )
_lowerCAmelCase = SparkExamplesIterable(SCREAMING_SNAKE_CASE_ )
assert it.n_shards == 1
for i, (row_id, row_dict) in enumerate(SCREAMING_SNAKE_CASE_ ):
assert row_id == F'''0_{i}'''
assert row_dict == {"id": i}
@require_not_windows
@require_dill_gt_0_3_2
def __a():
'''simple docstring'''
_lowerCAmelCase = pyspark.sql.SparkSession.builder.master("local[*]" ).appName("pyspark" ).getOrCreate()
_lowerCAmelCase = spark.range(30 ).repartition(3 )
# Mock the generator so that shuffle reverses the partition indices.
with patch("numpy.random.Generator" ) as generator_mock:
_lowerCAmelCase = lambda SCREAMING_SNAKE_CASE_ : x.reverse()
_lowerCAmelCase = _get_expected_row_ids_and_row_dicts_for_partition_order(SCREAMING_SNAKE_CASE_ , [2, 1, 0] )
_lowerCAmelCase = SparkExamplesIterable(SCREAMING_SNAKE_CASE_ ).shuffle_data_sources(SCREAMING_SNAKE_CASE_ )
assert shuffled_it.n_shards == 3
for i, (row_id, row_dict) in enumerate(SCREAMING_SNAKE_CASE_ ):
_lowerCAmelCase , _lowerCAmelCase = expected_row_ids_and_row_dicts[i]
assert row_id == expected_row_id
assert row_dict == expected_row_dict
@require_not_windows
@require_dill_gt_0_3_2
def __a():
'''simple docstring'''
_lowerCAmelCase = pyspark.sql.SparkSession.builder.master("local[*]" ).appName("pyspark" ).getOrCreate()
_lowerCAmelCase = spark.range(20 ).repartition(4 )
# Partitions 0 and 2
_lowerCAmelCase = SparkExamplesIterable(SCREAMING_SNAKE_CASE_ ).shard_data_sources(worker_id=0 , num_workers=2 )
assert shard_it_a.n_shards == 2
_lowerCAmelCase = _get_expected_row_ids_and_row_dicts_for_partition_order(SCREAMING_SNAKE_CASE_ , [0, 2] )
for i, (row_id, row_dict) in enumerate(SCREAMING_SNAKE_CASE_ ):
_lowerCAmelCase , _lowerCAmelCase = expected_row_ids_and_row_dicts_a[i]
assert row_id == expected_row_id
assert row_dict == expected_row_dict
# Partitions 1 and 3
_lowerCAmelCase = SparkExamplesIterable(SCREAMING_SNAKE_CASE_ ).shard_data_sources(worker_id=1 , num_workers=2 )
assert shard_it_a.n_shards == 2
_lowerCAmelCase = _get_expected_row_ids_and_row_dicts_for_partition_order(SCREAMING_SNAKE_CASE_ , [1, 3] )
for i, (row_id, row_dict) in enumerate(SCREAMING_SNAKE_CASE_ ):
_lowerCAmelCase , _lowerCAmelCase = expected_row_ids_and_row_dicts_a[i]
assert row_id == expected_row_id
assert row_dict == expected_row_dict
@require_not_windows
@require_dill_gt_0_3_2
def __a():
'''simple docstring'''
_lowerCAmelCase = pyspark.sql.SparkSession.builder.master("local[*]" ).appName("pyspark" ).getOrCreate()
_lowerCAmelCase = spark.range(100 ).repartition(1 )
_lowerCAmelCase = Spark(SCREAMING_SNAKE_CASE_ )
# Choose a small max_shard_size for maximum partitioning.
spark_builder._repartition_df_if_needed(max_shard_size=1 )
# The new number of partitions should not be greater than the number of rows.
assert spark_builder.df.rdd.getNumPartitions() == 100
| 158 | 1 |
'''simple docstring'''
import itertools
import math
def lowercase__ ( __UpperCamelCase )-> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(__UpperCamelCase ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def lowercase__ ( )-> Optional[int]:
UpperCamelCase = 2
while True:
if is_prime(__UpperCamelCase ):
yield num
num += 1
def lowercase__ ( __UpperCamelCase = 10001 )-> int:
return next(itertools.islice(prime_generator() , nth - 1 , __UpperCamelCase ) )
if __name__ == "__main__":
print(f'{solution() = }')
| 351 |
'''simple docstring'''
import unittest
from transformers.utils.backbone_utils import (
BackboneMixin,
get_aligned_output_features_output_indices,
verify_out_features_out_indices,
)
class a_ ( unittest.TestCase ):
def A__ ( self ) -> Tuple:
"""simple docstring"""
UpperCamelCase = ["""a""", """b""", """c"""]
# Defaults to last layer if both are None
UpperCamelCase ,UpperCamelCase = get_aligned_output_features_output_indices(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
self.assertEqual(_SCREAMING_SNAKE_CASE , ["""c"""] )
self.assertEqual(_SCREAMING_SNAKE_CASE , [2] )
# Out indices set to match out features
UpperCamelCase ,UpperCamelCase = get_aligned_output_features_output_indices(["""a""", """c"""] , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
self.assertEqual(_SCREAMING_SNAKE_CASE , ["""a""", """c"""] )
self.assertEqual(_SCREAMING_SNAKE_CASE , [0, 2] )
# Out features set to match out indices
UpperCamelCase ,UpperCamelCase = get_aligned_output_features_output_indices(_SCREAMING_SNAKE_CASE , [0, 2] , _SCREAMING_SNAKE_CASE )
self.assertEqual(_SCREAMING_SNAKE_CASE , ["""a""", """c"""] )
self.assertEqual(_SCREAMING_SNAKE_CASE , [0, 2] )
# Out features selected from negative indices
UpperCamelCase ,UpperCamelCase = get_aligned_output_features_output_indices(_SCREAMING_SNAKE_CASE , [-3, -1] , _SCREAMING_SNAKE_CASE )
self.assertEqual(_SCREAMING_SNAKE_CASE , ["""a""", """c"""] )
self.assertEqual(_SCREAMING_SNAKE_CASE , [-3, -1] )
def A__ ( self ) -> str:
"""simple docstring"""
with self.assertRaises(_SCREAMING_SNAKE_CASE ):
verify_out_features_out_indices(["""a""", """b"""] , (0, 1) , _SCREAMING_SNAKE_CASE )
# Out features must be a list
with self.assertRaises(_SCREAMING_SNAKE_CASE ):
verify_out_features_out_indices(("""a""", """b""") , (0, 1) , ["""a""", """b"""] )
# Out features must be a subset of stage names
with self.assertRaises(_SCREAMING_SNAKE_CASE ):
verify_out_features_out_indices(["""a""", """b"""] , (0, 1) , ["""a"""] )
# Out indices must be a list or tuple
with self.assertRaises(_SCREAMING_SNAKE_CASE ):
verify_out_features_out_indices(_SCREAMING_SNAKE_CASE , 0 , ["""a""", """b"""] )
# Out indices must be a subset of stage names
with self.assertRaises(_SCREAMING_SNAKE_CASE ):
verify_out_features_out_indices(_SCREAMING_SNAKE_CASE , (0, 1) , ["""a"""] )
# Out features and out indices must be the same length
with self.assertRaises(_SCREAMING_SNAKE_CASE ):
verify_out_features_out_indices(["""a""", """b"""] , (0,) , ["""a""", """b""", """c"""] )
# Out features should match out indices
with self.assertRaises(_SCREAMING_SNAKE_CASE ):
verify_out_features_out_indices(["""a""", """b"""] , (0, 2) , ["""a""", """b""", """c"""] )
# Out features and out indices should be in order
with self.assertRaises(_SCREAMING_SNAKE_CASE ):
verify_out_features_out_indices(["""b""", """a"""] , (0, 1) , ["""a""", """b"""] )
# Check passes with valid inputs
verify_out_features_out_indices(["""a""", """b""", """d"""] , (0, 1, -1) , ["""a""", """b""", """c""", """d"""] )
def A__ ( self ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase = BackboneMixin()
UpperCamelCase = ["""a""", """b""", """c"""]
UpperCamelCase = ["""a""", """c"""]
UpperCamelCase = [0, 2]
# Check that the output features and indices are set correctly
self.assertEqual(backbone.out_features , ["""a""", """c"""] )
self.assertEqual(backbone.out_indices , [0, 2] )
# Check out features and indices are updated correctly
UpperCamelCase = ["""a""", """b"""]
self.assertEqual(backbone.out_features , ["""a""", """b"""] )
self.assertEqual(backbone.out_indices , [0, 1] )
UpperCamelCase = [-3, -1]
self.assertEqual(backbone.out_features , ["""a""", """c"""] )
self.assertEqual(backbone.out_indices , [-3, -1] )
| 183 | 0 |
'''simple docstring'''
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
UpperCamelCase__ = argparse.ArgumentParser('''Stable Diffusion script with intel optimization''', add_help=False)
parser.add_argument('''--dpm''', action='''store_true''', help='''Enable DPMSolver or not''')
parser.add_argument('''--steps''', default=None, type=int, help='''Num inference steps''')
UpperCamelCase__ = parser.parse_args()
UpperCamelCase__ = '''cpu'''
UpperCamelCase__ = '''a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings'''
UpperCamelCase__ = '''path-to-your-trained-model'''
UpperCamelCase__ = StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
UpperCamelCase__ = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
UpperCamelCase__ = pipe.to(device)
# to channels last
UpperCamelCase__ = pipe.unet.to(memory_format=torch.channels_last)
UpperCamelCase__ = pipe.vae.to(memory_format=torch.channels_last)
UpperCamelCase__ = pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
UpperCamelCase__ = pipe.safety_checker.to(memory_format=torch.channels_last)
# optimize with ipex
UpperCamelCase__ = torch.randn(2, 4, 6_4, 6_4)
UpperCamelCase__ = torch.rand(1) * 9_9_9
UpperCamelCase__ = torch.randn(2, 7_7, 7_6_8)
UpperCamelCase__ = (sample, timestep, encoder_hidden_status)
try:
UpperCamelCase__ = ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
UpperCamelCase__ = ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
UpperCamelCase__ = ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
UpperCamelCase__ = ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
UpperCamelCase__ = ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)
# compute
UpperCamelCase__ = 6_6_6
UpperCamelCase__ = torch.Generator(device).manual_seed(seed)
UpperCamelCase__ = {'''generator''': generator}
if args.steps is not None:
UpperCamelCase__ = args.steps
with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloataa):
UpperCamelCase__ = pipe(prompt, **generate_kwargs).images[0]
# save image
image.save('''generated.png''')
| 181 |
"""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 = {
"microsoft/beit-base-patch16-224-pt22k": (
"https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json"
),
# See all BEiT models at https://huggingface.co/models?filter=beit
}
class snake_case ( __snake_case ):
SCREAMING_SNAKE_CASE_ : Tuple = """beit"""
def __init__( self : List[Any] , UpperCamelCase__ : List[str]=8_1_9_2 , UpperCamelCase__ : Dict=7_6_8 , UpperCamelCase__ : List[str]=1_2 , UpperCamelCase__ : Union[str, Any]=1_2 , UpperCamelCase__ : Dict=3_0_7_2 , UpperCamelCase__ : Optional[int]="gelu" , UpperCamelCase__ : Tuple=0.0 , UpperCamelCase__ : Tuple=0.0 , UpperCamelCase__ : Union[str, Any]=0.02 , UpperCamelCase__ : Optional[Any]=1e-12 , UpperCamelCase__ : str=2_2_4 , UpperCamelCase__ : str=1_6 , UpperCamelCase__ : Union[str, Any]=3 , UpperCamelCase__ : Optional[int]=False , UpperCamelCase__ : int=False , UpperCamelCase__ : Optional[Any]=False , UpperCamelCase__ : Optional[Any]=False , UpperCamelCase__ : Dict=0.1 , UpperCamelCase__ : List[Any]=0.1 , UpperCamelCase__ : Any=True , UpperCamelCase__ : Optional[Any]=[3, 5, 7, 1_1] , UpperCamelCase__ : Optional[Any]=[1, 2, 3, 6] , UpperCamelCase__ : Optional[Any]=True , UpperCamelCase__ : Tuple=0.4 , UpperCamelCase__ : Optional[Any]=2_5_6 , UpperCamelCase__ : Optional[int]=1 , UpperCamelCase__ : Optional[int]=False , UpperCamelCase__ : Optional[Any]=2_5_5 , **UpperCamelCase__ : Optional[int] , )-> int:
'''simple docstring'''
super().__init__(**UpperCamelCase__)
__lowerCAmelCase: str = vocab_size
__lowerCAmelCase: List[Any] = hidden_size
__lowerCAmelCase: str = num_hidden_layers
__lowerCAmelCase: Tuple = num_attention_heads
__lowerCAmelCase: Union[str, Any] = intermediate_size
__lowerCAmelCase: List[Any] = hidden_act
__lowerCAmelCase: Optional[Any] = hidden_dropout_prob
__lowerCAmelCase: List[Any] = attention_probs_dropout_prob
__lowerCAmelCase: str = initializer_range
__lowerCAmelCase: Optional[Any] = layer_norm_eps
__lowerCAmelCase: Any = image_size
__lowerCAmelCase: Any = patch_size
__lowerCAmelCase: Union[str, Any] = num_channels
__lowerCAmelCase: Tuple = use_mask_token
__lowerCAmelCase: Optional[Any] = use_absolute_position_embeddings
__lowerCAmelCase: List[Any] = use_relative_position_bias
__lowerCAmelCase: Optional[Any] = use_shared_relative_position_bias
__lowerCAmelCase: List[str] = layer_scale_init_value
__lowerCAmelCase: str = drop_path_rate
__lowerCAmelCase: str = use_mean_pooling
# decode head attributes (semantic segmentation)
__lowerCAmelCase: Optional[Any] = out_indices
__lowerCAmelCase: Union[str, Any] = pool_scales
# auxiliary head attributes (semantic segmentation)
__lowerCAmelCase: List[str] = use_auxiliary_head
__lowerCAmelCase: Union[str, Any] = auxiliary_loss_weight
__lowerCAmelCase: Optional[int] = auxiliary_channels
__lowerCAmelCase: Dict = auxiliary_num_convs
__lowerCAmelCase: List[Any] = auxiliary_concat_input
__lowerCAmelCase: str = semantic_loss_ignore_index
class snake_case ( __snake_case ):
SCREAMING_SNAKE_CASE_ : Optional[Any] = version.parse("""1.11""" )
@property
def lowercase_ ( self : str)-> 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
| 217 | 0 |
"""simple docstring"""
def _snake_case ( ) -> int:
'''simple docstring'''
return 1
def _snake_case ( lowercase__ : int ) -> int:
'''simple docstring'''
return 0 if x < 0 else two_pence(x - 2 ) + one_pence()
def _snake_case ( lowercase__ : int ) -> int:
'''simple docstring'''
return 0 if x < 0 else five_pence(x - 5 ) + two_pence(lowercase__ )
def _snake_case ( lowercase__ : int ) -> int:
'''simple docstring'''
return 0 if x < 0 else ten_pence(x - 1_0 ) + five_pence(lowercase__ )
def _snake_case ( lowercase__ : int ) -> int:
'''simple docstring'''
return 0 if x < 0 else twenty_pence(x - 2_0 ) + ten_pence(lowercase__ )
def _snake_case ( lowercase__ : int ) -> int:
'''simple docstring'''
return 0 if x < 0 else fifty_pence(x - 5_0 ) + twenty_pence(lowercase__ )
def _snake_case ( lowercase__ : int ) -> int:
'''simple docstring'''
return 0 if x < 0 else one_pound(x - 1_0_0 ) + fifty_pence(lowercase__ )
def _snake_case ( lowercase__ : int ) -> int:
'''simple docstring'''
return 0 if x < 0 else two_pound(x - 2_0_0 ) + one_pound(lowercase__ )
def _snake_case ( lowercase__ : int = 2_0_0 ) -> int:
'''simple docstring'''
return two_pound(lowercase__ )
if __name__ == "__main__":
print(solution(int(input().strip())))
| 369 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__UpperCAmelCase = {
'configuration_squeezebert': [
'SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'SqueezeBertConfig',
'SqueezeBertOnnxConfig',
],
'tokenization_squeezebert': ['SqueezeBertTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = ['SqueezeBertTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = [
'SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'SqueezeBertForMaskedLM',
'SqueezeBertForMultipleChoice',
'SqueezeBertForQuestionAnswering',
'SqueezeBertForSequenceClassification',
'SqueezeBertForTokenClassification',
'SqueezeBertModel',
'SqueezeBertModule',
'SqueezeBertPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_squeezebert import (
SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
SqueezeBertConfig,
SqueezeBertOnnxConfig,
)
from .tokenization_squeezebert import SqueezeBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_squeezebert import (
SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
SqueezeBertForMaskedLM,
SqueezeBertForMultipleChoice,
SqueezeBertForQuestionAnswering,
SqueezeBertForSequenceClassification,
SqueezeBertForTokenClassification,
SqueezeBertModel,
SqueezeBertModule,
SqueezeBertPreTrainedModel,
)
else:
import sys
__UpperCAmelCase = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 1 | 0 |
import math
import qiskit
def lowerCamelCase_ ( UpperCamelCase__ : int = 1 , UpperCamelCase__ : int = 1 , UpperCamelCase__ : int = 1 ) -> qiskit.result.counts.Counts:
"""simple docstring"""
if (
isinstance(UpperCamelCase__ , UpperCamelCase__ )
or isinstance(UpperCamelCase__ , UpperCamelCase__ )
or isinstance(UpperCamelCase__ , UpperCamelCase__ )
):
raise TypeError('inputs must be integers.' )
if (input_a < 0) or (input_a < 0) or (carry_in < 0):
raise ValueError('inputs must be positive.' )
if (
(math.floor(UpperCamelCase__ ) != input_a)
or (math.floor(UpperCamelCase__ ) != input_a)
or (math.floor(UpperCamelCase__ ) != carry_in)
):
raise ValueError('inputs must be exact integers.' )
if (input_a > 2) or (input_a > 2) or (carry_in > 2):
raise ValueError('inputs must be less or equal to 2.' )
# build registers
__lowerCamelCase = qiskit.QuantumRegister(4 , 'qr' )
__lowerCamelCase = qiskit.ClassicalRegister(2 , 'cr' )
# list the entries
__lowerCamelCase = [input_a, input_a, carry_in]
__lowerCamelCase = qiskit.QuantumCircuit(UpperCamelCase__ , UpperCamelCase__ )
for i in range(0 , 3 ):
if entry[i] == 2:
quantum_circuit.h(UpperCamelCase__ ) # for hadamard entries
elif entry[i] == 1:
quantum_circuit.x(UpperCamelCase__ ) # for 1 entries
elif entry[i] == 0:
quantum_circuit.i(UpperCamelCase__ ) # for 0 entries
# build the circuit
quantum_circuit.ccx(0 , 1 , 3 ) # ccx = toffoli gate
quantum_circuit.cx(0 , 1 )
quantum_circuit.ccx(1 , 2 , 3 )
quantum_circuit.cx(1 , 2 )
quantum_circuit.cx(0 , 1 )
quantum_circuit.measure([2, 3] , UpperCamelCase__ ) # measure the last two qbits
__lowerCamelCase = qiskit.Aer.get_backend('aer_simulator' )
__lowerCamelCase = qiskit.execute(UpperCamelCase__ , UpperCamelCase__ , shots=1000 )
return job.result().get_counts(UpperCamelCase__ )
if __name__ == "__main__":
print(f'''Total sum count for state is: {quantum_full_adder(1, 1, 1)}''')
| 90 |
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import TimesformerConfig
from transformers.models.auto import get_values
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 (
MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
TimesformerForVideoClassification,
TimesformerModel,
)
from transformers.models.timesformer.modeling_timesformer import TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from transformers import VideoMAEImageProcessor
class __lowerCAmelCase :
"""simple docstring"""
def __init__( self , lowerCamelCase__ , lowerCamelCase__=13 , lowerCamelCase__=10 , lowerCamelCase__=3 , lowerCamelCase__=2 , lowerCamelCase__=2 , lowerCamelCase__=True , lowerCamelCase__=True , lowerCamelCase__=32 , lowerCamelCase__=5 , lowerCamelCase__=4 , lowerCamelCase__=37 , lowerCamelCase__="gelu" , lowerCamelCase__=0.1 , lowerCamelCase__=0.1 , lowerCamelCase__=10 , lowerCamelCase__=0.02 , lowerCamelCase__="divided_space_time" , lowerCamelCase__=None , ) -> Any:
'''simple docstring'''
__lowerCamelCase = parent
__lowerCamelCase = batch_size
__lowerCamelCase = image_size
__lowerCamelCase = num_channels
__lowerCamelCase = patch_size
__lowerCamelCase = num_frames
__lowerCamelCase = is_training
__lowerCamelCase = use_labels
__lowerCamelCase = hidden_size
__lowerCamelCase = num_hidden_layers
__lowerCamelCase = num_attention_heads
__lowerCamelCase = intermediate_size
__lowerCamelCase = hidden_act
__lowerCamelCase = hidden_dropout_prob
__lowerCamelCase = attention_probs_dropout_prob
__lowerCamelCase = attention_type
__lowerCamelCase = initializer_range
__lowerCamelCase = scope
__lowerCamelCase = num_labels
# in TimeSformer, the number of spatial tokens equals num_frames * num_patches per frame + 1 CLS token
__lowerCamelCase = (image_size // patch_size) ** 2
__lowerCamelCase = (num_frames) * self.num_patches_per_frame + 1
def lowercase_ ( self ) -> Optional[int]:
'''simple docstring'''
__lowerCamelCase = floats_tensor(
[self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size] )
__lowerCamelCase = None
if self.use_labels:
__lowerCamelCase = ids_tensor([self.batch_size] , self.num_labels )
__lowerCamelCase = self.get_config()
return config, pixel_values, labels
def lowercase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
__lowerCamelCase = TimesformerConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , num_frames=self.num_frames , 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 , initializer_range=self.initializer_range , attention_type=self.attention_type , )
__lowerCamelCase = self.num_labels
return config
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> Tuple:
'''simple docstring'''
__lowerCamelCase = TimesformerModel(config=lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
__lowerCamelCase = model(lowerCamelCase__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> Union[str, Any]:
'''simple docstring'''
__lowerCamelCase = TimesformerForVideoClassification(lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
__lowerCamelCase = model(lowerCamelCase__ )
# verify the logits shape
__lowerCamelCase = torch.Size((self.batch_size, self.num_labels) )
self.parent.assertEqual(result.logits.shape , lowerCamelCase__ )
def lowercase_ ( self ) -> Tuple:
'''simple docstring'''
__lowerCamelCase = self.prepare_config_and_inputs()
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase = config_and_inputs
__lowerCamelCase = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class __lowerCAmelCase ( __magic_name__ , __magic_name__ , unittest.TestCase ):
"""simple docstring"""
snake_case_ = (TimesformerModel, TimesformerForVideoClassification) if is_torch_available() else ()
snake_case_ = (
{'''feature-extraction''': TimesformerModel, '''video-classification''': TimesformerForVideoClassification}
if is_torch_available()
else {}
)
snake_case_ = False
snake_case_ = False
snake_case_ = False
snake_case_ = False
def lowercase_ ( self ) -> List[str]:
'''simple docstring'''
__lowerCamelCase = TimesformerModelTester(self )
__lowerCamelCase = ConfigTester(
self , config_class=lowerCamelCase__ , has_text_modality=lowerCamelCase__ , hidden_size=37 )
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__=False ) -> int:
'''simple docstring'''
__lowerCamelCase = copy.deepcopy(lowerCamelCase__ )
if return_labels:
if model_class in get_values(lowerCamelCase__ ):
__lowerCamelCase = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=lowerCamelCase__ )
return inputs_dict
def lowercase_ ( self ) -> Optional[int]:
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason='TimeSformer does not use inputs_embeds' )
def lowercase_ ( self ) -> List[str]:
'''simple docstring'''
pass
def lowercase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
__lowerCamelCase , __lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowerCamelCase = model_class(lowerCamelCase__ )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
__lowerCamelCase = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(lowerCamelCase__ , nn.Linear ) )
def lowercase_ ( self ) -> List[str]:
'''simple docstring'''
__lowerCamelCase , __lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowerCamelCase = model_class(lowerCamelCase__ )
__lowerCamelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__lowerCamelCase = [*signature.parameters.keys()]
__lowerCamelCase = ['pixel_values']
self.assertListEqual(arg_names[:1] , lowerCamelCase__ )
def lowercase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCamelCase__ )
def lowercase_ ( self ) -> str:
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_video_classification(*lowerCamelCase__ )
@slow
def lowercase_ ( self ) -> Dict:
'''simple docstring'''
for model_name in TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowerCamelCase = TimesformerModel.from_pretrained(lowerCamelCase__ )
self.assertIsNotNone(lowerCamelCase__ )
def lowercase_ ( self ) -> List[Any]:
'''simple docstring'''
if not self.has_attentions:
pass
else:
__lowerCamelCase , __lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
__lowerCamelCase = True
for model_class in self.all_model_classes:
__lowerCamelCase = self.model_tester.seq_length
__lowerCamelCase = self.model_tester.num_frames
__lowerCamelCase = True
__lowerCamelCase = False
__lowerCamelCase = True
__lowerCamelCase = model_class(lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
with torch.no_grad():
__lowerCamelCase = model(**self._prepare_for_class(lowerCamelCase__ , lowerCamelCase__ ) )
__lowerCamelCase = outputs.attentions
self.assertEqual(len(lowerCamelCase__ ) , self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
__lowerCamelCase = True
__lowerCamelCase = model_class(lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
with torch.no_grad():
__lowerCamelCase = model(**self._prepare_for_class(lowerCamelCase__ , lowerCamelCase__ ) )
__lowerCamelCase = outputs.attentions
self.assertEqual(len(lowerCamelCase__ ) , self.model_tester.num_hidden_layers )
# attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1] , )
__lowerCamelCase = len(lowerCamelCase__ )
# Check attention is always last and order is fine
__lowerCamelCase = True
__lowerCamelCase = True
__lowerCamelCase = model_class(lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
with torch.no_grad():
__lowerCamelCase = model(**self._prepare_for_class(lowerCamelCase__ , lowerCamelCase__ ) )
self.assertEqual(out_len + 1 , len(lowerCamelCase__ ) )
__lowerCamelCase = outputs.attentions
self.assertEqual(len(lowerCamelCase__ ) , self.model_tester.num_hidden_layers )
# attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1] , )
def lowercase_ ( self ) -> Tuple:
'''simple docstring'''
def check_hidden_states_output(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
__lowerCamelCase = model_class(lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
with torch.no_grad():
__lowerCamelCase = model(**self._prepare_for_class(lowerCamelCase__ , lowerCamelCase__ ) )
__lowerCamelCase = outputs.hidden_states
__lowerCamelCase = self.model_tester.num_hidden_layers + 1
self.assertEqual(len(lowerCamelCase__ ) , lowerCamelCase__ )
__lowerCamelCase = self.model_tester.seq_length
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [seq_length, self.model_tester.hidden_size] , )
__lowerCamelCase , __lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowerCamelCase = True
check_hidden_states_output(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__lowerCamelCase = True
check_hidden_states_output(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
def lowerCamelCase_ ( ) -> Any:
"""simple docstring"""
__lowerCamelCase = hf_hub_download(
repo_id='hf-internal-testing/spaghetti-video' , filename='eating_spaghetti.npy' , repo_type='dataset' )
__lowerCamelCase = np.load(UpperCamelCase__ )
return list(UpperCamelCase__ )
@require_torch
@require_vision
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def lowercase_ ( self ) -> List[Any]:
'''simple docstring'''
# logits were tested with a different mean and std, so we use the same here
return (
VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5] , image_std=[0.5, 0.5, 0.5] )
if is_vision_available()
else None
)
@slow
def lowercase_ ( self ) -> List[str]:
'''simple docstring'''
__lowerCamelCase = TimesformerForVideoClassification.from_pretrained('facebook/timesformer-base-finetuned-k400' ).to(
lowerCamelCase__ )
__lowerCamelCase = self.default_image_processor
__lowerCamelCase = prepare_video()
__lowerCamelCase = image_processor(video[:8] , return_tensors='pt' ).to(lowerCamelCase__ )
# forward pass
with torch.no_grad():
__lowerCamelCase = model(**lowerCamelCase__ )
# verify the logits
__lowerCamelCase = torch.Size((1, 400) )
self.assertEqual(outputs.logits.shape , lowerCamelCase__ )
__lowerCamelCase = torch.tensor([-0.30_16, -0.77_13, -0.42_05] ).to(lowerCamelCase__ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , lowerCamelCase__ , atol=1e-4 ) )
| 90 | 1 |
"""simple docstring"""
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->str:
a__: int = len(_SCREAMING_SNAKE_CASE )
a__: int = len(_SCREAMING_SNAKE_CASE )
a__: int = (
first_str_length if first_str_length > second_str_length else second_str_length
)
a__: list = []
for char_count in range(_SCREAMING_SNAKE_CASE ):
if char_count < first_str_length:
output_list.append(first_str[char_count] )
if char_count < second_str_length:
output_list.append(second_str[char_count] )
return "".join(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
print(alternative_string_arrange('AB', 'XYZ'), end=' ')
| 203 | """simple docstring"""
import unittest
from knapsack import knapsack as k
class __snake_case ( unittest.TestCase ):
def lowerCamelCase_ ( self) -> Dict:
'''simple docstring'''
a__: List[Any] = 0
a__: Dict = [0]
a__: int = [0]
a__: Optional[Any] = len(lowercase)
self.assertEqual(k.knapsack(lowercase , lowercase , lowercase , lowercase) , 0)
a__: str = [60]
a__: Dict = [10]
a__: List[str] = len(lowercase)
self.assertEqual(k.knapsack(lowercase , lowercase , lowercase , lowercase) , 0)
def lowerCamelCase_ ( self) -> List[Any]:
'''simple docstring'''
a__: int = 3
a__: str = [1, 2, 3]
a__: Dict = [3, 2, 1]
a__: Optional[int] = len(lowercase)
self.assertEqual(k.knapsack(lowercase , lowercase , lowercase , lowercase) , 5)
def lowerCamelCase_ ( self) -> Union[str, Any]:
'''simple docstring'''
a__: Any = 50
a__: Optional[int] = [60, 1_00, 1_20]
a__: str = [10, 20, 30]
a__: int = len(lowercase)
self.assertEqual(k.knapsack(lowercase , lowercase , lowercase , lowercase) , 2_20)
if __name__ == "__main__":
unittest.main()
| 203 | 1 |
import unittest
from dataclasses import dataclass
import pytest
from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict
@dataclass
class SCREAMING_SNAKE_CASE__ ( UpperCamelCase__ ):
__SCREAMING_SNAKE_CASE = ComputeEnvironment.AMAZON_SAGEMAKER
__SCREAMING_SNAKE_CASE = True
__SCREAMING_SNAKE_CASE = '''ml.p3.2xlarge'''
__SCREAMING_SNAKE_CASE = '''accelerate_sagemaker_execution_role'''
__SCREAMING_SNAKE_CASE = '''hf-sm'''
__SCREAMING_SNAKE_CASE = '''us-east-1'''
__SCREAMING_SNAKE_CASE = 1
__SCREAMING_SNAKE_CASE = '''accelerate-sagemaker-1'''
__SCREAMING_SNAKE_CASE = '''1.6'''
__SCREAMING_SNAKE_CASE = '''4.4'''
__SCREAMING_SNAKE_CASE = '''train.py'''
__SCREAMING_SNAKE_CASE = [
'''--model_name_or_path''',
'''bert''',
'''--do_train''',
'''False''',
'''--epochs''',
'''3''',
'''--learning_rate''',
'''5e-5''',
'''--max_steps''',
'''50.5''',
]
__SCREAMING_SNAKE_CASE = [
'''--model_name_or_path''',
'''bert''',
'''--do_train''',
'''--do_test''',
'''False''',
'''--do_predict''',
'''--epochs''',
'''3''',
'''--learning_rate''',
'''5e-5''',
'''--max_steps''',
'''50.5''',
]
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
def UpperCamelCase ( self ):
# If no defaults are changed, `to_kwargs` returns an empty dict.
A__ = _convert_nargs_to_dict(MockLaunchConfig.success_training_script_args )
assert isinstance(converted_args['''model_name_or_path'''],__lowerCamelCase )
assert isinstance(converted_args['''do_train'''],__lowerCamelCase )
assert isinstance(converted_args['''epochs'''],__lowerCamelCase )
assert isinstance(converted_args['''learning_rate'''],__lowerCamelCase )
assert isinstance(converted_args['''max_steps'''],__lowerCamelCase )
with pytest.raises(__lowerCamelCase ):
_convert_nargs_to_dict(MockLaunchConfig.fail_training_script_args )
| 193 |
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 SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
def __init__( self,__lowerCamelCase,__lowerCamelCase=3,__lowerCamelCase=32,__lowerCamelCase=3,__lowerCamelCase=10,__lowerCamelCase=[10, 20, 30, 40],__lowerCamelCase=[1, 1, 2, 1],__lowerCamelCase=True,__lowerCamelCase=True,__lowerCamelCase="relu",__lowerCamelCase=3,__lowerCamelCase=None,):
A__ = parent
A__ = batch_size
A__ = image_size
A__ = num_channels
A__ = embeddings_size
A__ = hidden_sizes
A__ = depths
A__ = is_training
A__ = use_labels
A__ = hidden_act
A__ = num_labels
A__ = scope
A__ = len(__lowerCamelCase )
def UpperCamelCase ( self ):
A__ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
A__ = 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,__lowerCamelCase,__lowerCamelCase ):
A__ = FlaxRegNetModel(config=__lowerCamelCase )
A__ = model(__lowerCamelCase )
# 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,__lowerCamelCase,__lowerCamelCase ):
A__ = self.num_labels
A__ = FlaxRegNetForImageClassification(config=__lowerCamelCase )
A__ = model(__lowerCamelCase )
self.parent.assertEqual(result.logits.shape,(self.batch_size, self.num_labels) )
def UpperCamelCase ( self ):
A__ = self.prepare_config_and_inputs()
A__ , A__ = config_and_inputs
A__ = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_flax
class SCREAMING_SNAKE_CASE__ ( UpperCamelCase__ , unittest.TestCase ):
__SCREAMING_SNAKE_CASE = (FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()
__SCREAMING_SNAKE_CASE = False
__SCREAMING_SNAKE_CASE = False
__SCREAMING_SNAKE_CASE = False
def UpperCamelCase ( self ):
A__ = FlaxRegNetModelTester(self )
A__ = ConfigTester(self,config_class=__lowerCamelCase,has_text_modality=__lowerCamelCase )
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 ):
A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__lowerCamelCase )
def UpperCamelCase ( self ):
A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase )
@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 ):
A__ , A__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A__ = model_class(__lowerCamelCase )
A__ = inspect.signature(model.__call__ )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
A__ = [*signature.parameters.keys()]
A__ = ['''pixel_values''']
self.assertListEqual(arg_names[:1],__lowerCamelCase )
def UpperCamelCase ( self ):
def check_hidden_states_output(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase ):
A__ = model_class(__lowerCamelCase )
A__ = model(**self._prepare_for_class(__lowerCamelCase,__lowerCamelCase ) )
A__ = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
A__ = self.model_tester.num_stages
self.assertEqual(len(__lowerCamelCase ),expected_num_stages + 1 )
A__ , A__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A__ = True
check_hidden_states_output(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
A__ = True
check_hidden_states_output(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase )
def UpperCamelCase ( self ):
A__ , A__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
A__ = self._prepare_for_class(__lowerCamelCase,__lowerCamelCase )
A__ = model_class(__lowerCamelCase )
@jax.jit
def model_jitted(__lowerCamelCase,**__lowerCamelCase ):
return model(pixel_values=__lowerCamelCase,**__lowerCamelCase )
with self.subTest('''JIT Enabled''' ):
A__ = model_jitted(**__lowerCamelCase ).to_tuple()
with self.subTest('''JIT Disabled''' ):
with jax.disable_jit():
A__ = model_jitted(**__lowerCamelCase ).to_tuple()
self.assertEqual(len(__lowerCamelCase ),len(__lowerCamelCase ) )
for jitted_output, output in zip(__lowerCamelCase,__lowerCamelCase ):
self.assertEqual(jitted_output.shape,output.shape )
def UpperCamelCase__( )->Optional[int]:
A__ = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_flax
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
@cached_property
def UpperCamelCase ( self ):
return AutoImageProcessor.from_pretrained('''facebook/regnet-y-040''' ) if is_vision_available() else None
@slow
def UpperCamelCase ( self ):
A__ = FlaxRegNetForImageClassification.from_pretrained('''facebook/regnet-y-040''' )
A__ = self.default_image_processor
A__ = prepare_img()
A__ = image_processor(images=__lowerCamelCase,return_tensors='''np''' )
A__ = model(**__lowerCamelCase )
# verify the logits
A__ = (1, 1000)
self.assertEqual(outputs.logits.shape,__lowerCamelCase )
A__ = jnp.array([-0.4180, -1.5051, -3.4836] )
self.assertTrue(jnp.allclose(outputs.logits[0, :3],__lowerCamelCase,atol=1E-4 ) )
| 193 | 1 |
"""simple docstring"""
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, PLBartTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...test_tokenization_common import TokenizerTesterMixin
__A = get_tests_dir('fixtures/test_sentencepiece.model')
if is_torch_available():
from transformers.models.plbart.modeling_plbart import shift_tokens_right
__A = 5_0003
__A = 5_0002
@require_sentencepiece
@require_tokenizers
class lowerCamelCase__ ( __magic_name__ , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase = PLBartTokenizer
lowerCamelCase = None
lowerCamelCase = False
def _lowerCAmelCase ( self ) -> Any:
super().setUp()
# We have a SentencePiece fixture for testing
_lowerCAmelCase =PLBartTokenizer(__UpperCAmelCase , language_codes="""base""" , keep_accents=__UpperCAmelCase )
tokenizer.save_pretrained(self.tmpdirname )
def _lowerCAmelCase ( self ) -> Optional[Any]:
_lowerCAmelCase =PLBartTokenizer(__UpperCAmelCase , language_codes="""base""" , keep_accents=__UpperCAmelCase )
_lowerCAmelCase =tokenizer.tokenize("""This is a test""" )
self.assertListEqual(__UpperCAmelCase , ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(__UpperCAmelCase ) , [value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]] , )
_lowerCAmelCase =tokenizer.tokenize("""I was born in 92000, and this is falsé.""" )
self.assertListEqual(
__UpperCAmelCase , [
SPIECE_UNDERLINE + """I""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""9""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""s""",
"""é""",
""".""",
] , )
_lowerCAmelCase =tokenizer.convert_tokens_to_ids(__UpperCAmelCase )
self.assertListEqual(
__UpperCAmelCase , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
] , )
_lowerCAmelCase =tokenizer.convert_ids_to_tokens(__UpperCAmelCase )
self.assertListEqual(
__UpperCAmelCase , [
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>""",
""".""",
] , )
_lowerCAmelCase =tokenizer.vocab_size
_lowerCAmelCase =[tokenizer.convert_ids_to_tokens(__UpperCAmelCase ) for x in range(end - 4 , __UpperCAmelCase )]
self.assertListEqual(__UpperCAmelCase , ["""__java__""", """__python__""", """__en_XX__""", """<mask>"""] )
_lowerCAmelCase ="""java.lang.Exception, python.lang.Exception, javascript, php, ruby, go"""
_lowerCAmelCase =tokenizer(__UpperCAmelCase ).input_ids
self.assertEqual(
tokenizer.decode(__UpperCAmelCase , skip_special_tokens=__UpperCAmelCase , clean_up_tokenization_spaces=__UpperCAmelCase ) , __UpperCAmelCase , )
def _lowerCAmelCase ( self ) -> List[Any]:
_lowerCAmelCase =PLBartTokenizer(__UpperCAmelCase , language_codes="""multi""" , keep_accents=__UpperCAmelCase )
_lowerCAmelCase =tokenizer.tokenize("""This is a test""" )
self.assertListEqual(__UpperCAmelCase , ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(__UpperCAmelCase ) , [value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]] , )
_lowerCAmelCase =tokenizer.tokenize("""I was born in 92000, and this is falsé.""" )
self.assertListEqual(
__UpperCAmelCase , [
SPIECE_UNDERLINE + """I""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""9""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""s""",
"""é""",
""".""",
] , )
_lowerCAmelCase =tokenizer.convert_tokens_to_ids(__UpperCAmelCase )
self.assertListEqual(
__UpperCAmelCase , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
] , )
_lowerCAmelCase =tokenizer.convert_ids_to_tokens(__UpperCAmelCase )
self.assertListEqual(
__UpperCAmelCase , [
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>""",
""".""",
] , )
_lowerCAmelCase =tokenizer.vocab_size
_lowerCAmelCase =[tokenizer.convert_ids_to_tokens(__UpperCAmelCase ) for x in range(end - 7 , __UpperCAmelCase )]
self.assertListEqual(
__UpperCAmelCase , ["""__java__""", """__python__""", """__en_XX__""", """__javascript__""", """__php__""", """__ruby__""", """__go__"""] )
_lowerCAmelCase ="""java.lang.Exception, python.lang.Exception, javascript, php, ruby, go"""
_lowerCAmelCase =tokenizer(__UpperCAmelCase ).input_ids
self.assertEqual(
tokenizer.decode(__UpperCAmelCase , skip_special_tokens=__UpperCAmelCase , clean_up_tokenization_spaces=__UpperCAmelCase ) , __UpperCAmelCase , )
@require_torch
@require_sentencepiece
@require_tokenizers
class lowerCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
lowerCamelCase = '''uclanlp/plbart-python-en_XX'''
lowerCamelCase = [
'''def maximum(a,b,c):NEW_LINE_INDENTreturn max([a,b,c])''',
'''def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])''',
]
lowerCamelCase = [
'''Returns the maximum value of a b c.''',
'''Sums the values of a b c.''',
]
lowerCamelCase = [
134,
5_452,
33_460,
33_441,
33_463,
33_465,
33_463,
33_449,
988,
20,
33_456,
19,
33_456,
771,
39,
4_258,
889,
3_318,
33_441,
33_463,
33_465,
33_463,
33_449,
2_471,
2,
PYTHON_CODE,
]
@classmethod
def _lowerCAmelCase ( cls ) -> Union[str, Any]:
_lowerCAmelCase =PLBartTokenizer.from_pretrained(
cls.checkpoint_name , language_codes="""base""" , src_lang="""python""" , tgt_lang="""en_XX""" )
_lowerCAmelCase =1
return cls
def _lowerCAmelCase ( self ) -> Tuple:
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""__java__"""] , 5_00_01 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""__python__"""] , 5_00_02 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""__en_XX__"""] , 5_00_03 )
def _lowerCAmelCase ( self ) -> List[Any]:
_lowerCAmelCase =self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , __UpperCAmelCase )
def _lowerCAmelCase ( self ) -> Any:
self.assertIn(__UpperCAmelCase , self.tokenizer.all_special_ids )
_lowerCAmelCase =[EN_CODE, 90_37, 3_34_42, 57, 7_52, 1_53, 14, 56, 18, 9, 2]
_lowerCAmelCase =self.tokenizer.decode(__UpperCAmelCase , skip_special_tokens=__UpperCAmelCase )
_lowerCAmelCase =self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=__UpperCAmelCase )
self.assertEqual(__UpperCAmelCase , __UpperCAmelCase )
self.assertNotIn(self.tokenizer.eos_token , __UpperCAmelCase )
def _lowerCAmelCase ( self ) -> Tuple:
_lowerCAmelCase =["""def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])""" * 20]
self.assertIsInstance(src_text[0] , __UpperCAmelCase )
_lowerCAmelCase =10
_lowerCAmelCase =self.tokenizer(__UpperCAmelCase , max_length=__UpperCAmelCase , truncation=__UpperCAmelCase ).input_ids[0]
self.assertEqual(ids[-2] , 2 )
self.assertEqual(ids[-1] , __UpperCAmelCase )
self.assertEqual(len(__UpperCAmelCase ) , __UpperCAmelCase )
def _lowerCAmelCase ( self ) -> int:
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["""<mask>""", """__java__"""] ) , [5_00_04, 5_00_01] )
def _lowerCAmelCase ( self ) -> List[Any]:
_lowerCAmelCase =tempfile.mkdtemp()
_lowerCAmelCase =self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(__UpperCAmelCase )
_lowerCAmelCase =PLBartTokenizer.from_pretrained(__UpperCAmelCase )
self.assertDictEqual(new_tok.fairseq_tokens_to_ids , __UpperCAmelCase )
@require_torch
def _lowerCAmelCase ( self ) -> Tuple:
_lowerCAmelCase =self.tokenizer(self.src_text , text_target=self.tgt_text , padding=__UpperCAmelCase , return_tensors="""pt""" )
_lowerCAmelCase =shift_tokens_right(batch["""labels"""] , self.tokenizer.pad_token_id )
# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
self.assertEqual(batch.input_ids[1][-2:].tolist() , [2, PYTHON_CODE] )
self.assertEqual(batch.decoder_input_ids[1][0] , __UpperCAmelCase )
self.assertEqual(batch.decoder_input_ids[1][-1] , 2 )
self.assertEqual(batch.labels[1][-2:].tolist() , [2, EN_CODE] )
@require_torch
def _lowerCAmelCase ( self ) -> Any:
_lowerCAmelCase =self.tokenizer(
self.src_text , text_target=self.tgt_text , padding=__UpperCAmelCase , truncation=__UpperCAmelCase , max_length=len(self.expected_src_tokens ) , return_tensors="""pt""" , )
_lowerCAmelCase =shift_tokens_right(batch["""labels"""] , self.tokenizer.pad_token_id )
self.assertIsInstance(__UpperCAmelCase , __UpperCAmelCase )
self.assertEqual((2, 26) , batch.input_ids.shape )
self.assertEqual((2, 26) , batch.attention_mask.shape )
_lowerCAmelCase =batch.input_ids.tolist()[0]
self.assertListEqual(self.expected_src_tokens , __UpperCAmelCase )
self.assertEqual(2 , batch.decoder_input_ids[0, -1] ) # EOS
# Test that special tokens are reset
self.assertEqual(self.tokenizer.prefix_tokens , [] )
self.assertEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id, PYTHON_CODE] )
def _lowerCAmelCase ( self ) -> Optional[int]:
_lowerCAmelCase =self.tokenizer(self.src_text , padding=__UpperCAmelCase , truncation=__UpperCAmelCase , max_length=3 , return_tensors="""pt""" )
_lowerCAmelCase =self.tokenizer(
text_target=self.tgt_text , padding=__UpperCAmelCase , truncation=__UpperCAmelCase , max_length=10 , return_tensors="""pt""" )
_lowerCAmelCase =targets["""input_ids"""]
_lowerCAmelCase =shift_tokens_right(__UpperCAmelCase , self.tokenizer.pad_token_id )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.decoder_input_ids.shape[1] , 10 )
@require_torch
def _lowerCAmelCase ( self ) -> int:
_lowerCAmelCase =self.tokenizer._build_translation_inputs(
"""A test""" , return_tensors="""pt""" , src_lang="""en_XX""" , tgt_lang="""java""" )
self.assertEqual(
nested_simplify(__UpperCAmelCase ) , {
# A, test, EOS, en_XX
"""input_ids""": [[1_50, 2_42, 2, 5_00_03]],
"""attention_mask""": [[1, 1, 1, 1]],
# java
"""forced_bos_token_id""": 5_00_01,
} , )
| 355 |
"""simple docstring"""
import argparse
import json
import torch
from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def _lowerCamelCase(__UpperCamelCase , __UpperCamelCase=1 ) -> Tuple:
if n_shave_prefix_segments >= 0:
return ".".join(path.split(""".""" )[n_shave_prefix_segments:] )
else:
return ".".join(path.split(""".""" )[:n_shave_prefix_segments] )
def _lowerCamelCase(__UpperCamelCase , __UpperCamelCase=0 ) -> List[str]:
_lowerCAmelCase =[]
for old_item in old_list:
_lowerCAmelCase =old_item.replace("""in_layers.0""" , """norm1""" )
_lowerCAmelCase =new_item.replace("""in_layers.2""" , """conv1""" )
_lowerCAmelCase =new_item.replace("""out_layers.0""" , """norm2""" )
_lowerCAmelCase =new_item.replace("""out_layers.3""" , """conv2""" )
_lowerCAmelCase =new_item.replace("""emb_layers.1""" , """time_emb_proj""" )
_lowerCAmelCase =new_item.replace("""skip_connection""" , """conv_shortcut""" )
_lowerCAmelCase =shave_segments(__UpperCamelCase , n_shave_prefix_segments=__UpperCamelCase )
mapping.append({"""old""": old_item, """new""": new_item} )
return mapping
def _lowerCamelCase(__UpperCamelCase , __UpperCamelCase=0 ) -> Tuple:
_lowerCAmelCase =[]
for old_item in old_list:
_lowerCAmelCase =old_item
_lowerCAmelCase =new_item.replace("""norm.weight""" , """group_norm.weight""" )
_lowerCAmelCase =new_item.replace("""norm.bias""" , """group_norm.bias""" )
_lowerCAmelCase =new_item.replace("""proj_out.weight""" , """proj_attn.weight""" )
_lowerCAmelCase =new_item.replace("""proj_out.bias""" , """proj_attn.bias""" )
_lowerCAmelCase =shave_segments(__UpperCamelCase , n_shave_prefix_segments=__UpperCamelCase )
mapping.append({"""old""": old_item, """new""": new_item} )
return mapping
def _lowerCamelCase(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase=None , __UpperCamelCase=None , __UpperCamelCase=None ) -> Optional[int]:
assert isinstance(__UpperCamelCase , __UpperCamelCase ), "Paths should be a list of dicts containing 'old' and 'new' keys."
# Splits the attention layers into three variables.
if attention_paths_to_split is not None:
for path, path_map in attention_paths_to_split.items():
_lowerCAmelCase =old_checkpoint[path]
_lowerCAmelCase =old_tensor.shape[0] // 3
_lowerCAmelCase =(-1, channels) if len(old_tensor.shape ) == 3 else (-1)
_lowerCAmelCase =old_tensor.shape[0] // config["""num_head_channels"""] // 3
_lowerCAmelCase =old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:] )
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase =old_tensor.split(channels // num_heads , dim=1 )
_lowerCAmelCase =query.reshape(__UpperCamelCase )
_lowerCAmelCase =key.reshape(__UpperCamelCase )
_lowerCAmelCase =value.reshape(__UpperCamelCase )
for path in paths:
_lowerCAmelCase =path["""new"""]
# These have already been assigned
if attention_paths_to_split is not None and new_path in attention_paths_to_split:
continue
# Global renaming happens here
_lowerCAmelCase =new_path.replace("""middle_block.0""" , """mid_block.resnets.0""" )
_lowerCAmelCase =new_path.replace("""middle_block.1""" , """mid_block.attentions.0""" )
_lowerCAmelCase =new_path.replace("""middle_block.2""" , """mid_block.resnets.1""" )
if additional_replacements is not None:
for replacement in additional_replacements:
_lowerCAmelCase =new_path.replace(replacement["""old"""] , replacement["""new"""] )
# proj_attn.weight has to be converted from conv 1D to linear
if "proj_attn.weight" in new_path:
_lowerCAmelCase =old_checkpoint[path["""old"""]][:, :, 0]
else:
_lowerCAmelCase =old_checkpoint[path["""old"""]]
def _lowerCamelCase(__UpperCamelCase , __UpperCamelCase ) -> Optional[Any]:
_lowerCAmelCase ={}
_lowerCAmelCase =checkpoint["""time_embed.0.weight"""]
_lowerCAmelCase =checkpoint["""time_embed.0.bias"""]
_lowerCAmelCase =checkpoint["""time_embed.2.weight"""]
_lowerCAmelCase =checkpoint["""time_embed.2.bias"""]
_lowerCAmelCase =checkpoint["""input_blocks.0.0.weight"""]
_lowerCAmelCase =checkpoint["""input_blocks.0.0.bias"""]
_lowerCAmelCase =checkpoint["""out.0.weight"""]
_lowerCAmelCase =checkpoint["""out.0.bias"""]
_lowerCAmelCase =checkpoint["""out.2.weight"""]
_lowerCAmelCase =checkpoint["""out.2.bias"""]
# Retrieves the keys for the input blocks only
_lowerCAmelCase =len({""".""".join(layer.split(""".""" )[:2] ) for layer in checkpoint if """input_blocks""" in layer} )
_lowerCAmelCase ={
layer_id: [key for key in checkpoint if F'''input_blocks.{layer_id}''' in key]
for layer_id in range(__UpperCamelCase )
}
# Retrieves the keys for the middle blocks only
_lowerCAmelCase =len({""".""".join(layer.split(""".""" )[:2] ) for layer in checkpoint if """middle_block""" in layer} )
_lowerCAmelCase ={
layer_id: [key for key in checkpoint if F'''middle_block.{layer_id}''' in key]
for layer_id in range(__UpperCamelCase )
}
# Retrieves the keys for the output blocks only
_lowerCAmelCase =len({""".""".join(layer.split(""".""" )[:2] ) for layer in checkpoint if """output_blocks""" in layer} )
_lowerCAmelCase ={
layer_id: [key for key in checkpoint if F'''output_blocks.{layer_id}''' in key]
for layer_id in range(__UpperCamelCase )
}
for i in range(1 , __UpperCamelCase ):
_lowerCAmelCase =(i - 1) // (config["""num_res_blocks"""] + 1)
_lowerCAmelCase =(i - 1) % (config["""num_res_blocks"""] + 1)
_lowerCAmelCase =[key for key in input_blocks[i] if F'''input_blocks.{i}.0''' in key]
_lowerCAmelCase =[key for key in input_blocks[i] if F'''input_blocks.{i}.1''' in key]
if F'''input_blocks.{i}.0.op.weight''' in checkpoint:
_lowerCAmelCase =checkpoint[
F'''input_blocks.{i}.0.op.weight'''
]
_lowerCAmelCase =checkpoint[
F'''input_blocks.{i}.0.op.bias'''
]
continue
_lowerCAmelCase =renew_resnet_paths(__UpperCamelCase )
_lowerCAmelCase ={"""old""": F'''input_blocks.{i}.0''', """new""": F'''down_blocks.{block_id}.resnets.{layer_in_block_id}'''}
_lowerCAmelCase ={"""old""": """resnets.2.op""", """new""": """downsamplers.0.op"""}
assign_to_checkpoint(
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , additional_replacements=[meta_path, resnet_op] , config=__UpperCamelCase )
if len(__UpperCamelCase ):
_lowerCAmelCase =renew_attention_paths(__UpperCamelCase )
_lowerCAmelCase ={
"""old""": F'''input_blocks.{i}.1''',
"""new""": F'''down_blocks.{block_id}.attentions.{layer_in_block_id}''',
}
_lowerCAmelCase ={
F'''input_blocks.{i}.1.qkv.bias''': {
"""key""": F'''down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias''',
"""query""": F'''down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias''',
"""value""": F'''down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias''',
},
F'''input_blocks.{i}.1.qkv.weight''': {
"""key""": F'''down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight''',
"""query""": F'''down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight''',
"""value""": F'''down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight''',
},
}
assign_to_checkpoint(
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , additional_replacements=[meta_path] , attention_paths_to_split=__UpperCamelCase , config=__UpperCamelCase , )
_lowerCAmelCase =middle_blocks[0]
_lowerCAmelCase =middle_blocks[1]
_lowerCAmelCase =middle_blocks[2]
_lowerCAmelCase =renew_resnet_paths(__UpperCamelCase )
assign_to_checkpoint(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , config=__UpperCamelCase )
_lowerCAmelCase =renew_resnet_paths(__UpperCamelCase )
assign_to_checkpoint(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , config=__UpperCamelCase )
_lowerCAmelCase =renew_attention_paths(__UpperCamelCase )
_lowerCAmelCase ={
"""middle_block.1.qkv.bias""": {
"""key""": """mid_block.attentions.0.key.bias""",
"""query""": """mid_block.attentions.0.query.bias""",
"""value""": """mid_block.attentions.0.value.bias""",
},
"""middle_block.1.qkv.weight""": {
"""key""": """mid_block.attentions.0.key.weight""",
"""query""": """mid_block.attentions.0.query.weight""",
"""value""": """mid_block.attentions.0.value.weight""",
},
}
assign_to_checkpoint(
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , attention_paths_to_split=__UpperCamelCase , config=__UpperCamelCase )
for i in range(__UpperCamelCase ):
_lowerCAmelCase =i // (config["""num_res_blocks"""] + 1)
_lowerCAmelCase =i % (config["""num_res_blocks"""] + 1)
_lowerCAmelCase =[shave_segments(__UpperCamelCase , 2 ) for name in output_blocks[i]]
_lowerCAmelCase ={}
for layer in output_block_layers:
_lowerCAmelCase , _lowerCAmelCase =layer.split(""".""" )[0], shave_segments(__UpperCamelCase , 1 )
if layer_id in output_block_list:
output_block_list[layer_id].append(__UpperCamelCase )
else:
_lowerCAmelCase =[layer_name]
if len(__UpperCamelCase ) > 1:
_lowerCAmelCase =[key for key in output_blocks[i] if F'''output_blocks.{i}.0''' in key]
_lowerCAmelCase =[key for key in output_blocks[i] if F'''output_blocks.{i}.1''' in key]
_lowerCAmelCase =renew_resnet_paths(__UpperCamelCase )
_lowerCAmelCase =renew_resnet_paths(__UpperCamelCase )
_lowerCAmelCase ={"""old""": F'''output_blocks.{i}.0''', """new""": F'''up_blocks.{block_id}.resnets.{layer_in_block_id}'''}
assign_to_checkpoint(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , additional_replacements=[meta_path] , config=__UpperCamelCase )
if ["conv.weight", "conv.bias"] in output_block_list.values():
_lowerCAmelCase =list(output_block_list.values() ).index(["""conv.weight""", """conv.bias"""] )
_lowerCAmelCase =checkpoint[
F'''output_blocks.{i}.{index}.conv.weight'''
]
_lowerCAmelCase =checkpoint[
F'''output_blocks.{i}.{index}.conv.bias'''
]
# Clear attentions as they have been attributed above.
if len(__UpperCamelCase ) == 2:
_lowerCAmelCase =[]
if len(__UpperCamelCase ):
_lowerCAmelCase =renew_attention_paths(__UpperCamelCase )
_lowerCAmelCase ={
"""old""": F'''output_blocks.{i}.1''',
"""new""": F'''up_blocks.{block_id}.attentions.{layer_in_block_id}''',
}
_lowerCAmelCase ={
F'''output_blocks.{i}.1.qkv.bias''': {
"""key""": F'''up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias''',
"""query""": F'''up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias''',
"""value""": F'''up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias''',
},
F'''output_blocks.{i}.1.qkv.weight''': {
"""key""": F'''up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight''',
"""query""": F'''up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight''',
"""value""": F'''up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight''',
},
}
assign_to_checkpoint(
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , additional_replacements=[meta_path] , attention_paths_to_split=to_split if any("""qkv""" in key for key in attentions ) else None , config=__UpperCamelCase , )
else:
_lowerCAmelCase =renew_resnet_paths(__UpperCamelCase , n_shave_prefix_segments=1 )
for path in resnet_0_paths:
_lowerCAmelCase =""".""".join(["""output_blocks""", str(__UpperCamelCase ), path["""old"""]] )
_lowerCAmelCase =""".""".join(["""up_blocks""", str(__UpperCamelCase ), """resnets""", str(__UpperCamelCase ), path["""new"""]] )
_lowerCAmelCase =checkpoint[old_path]
return new_checkpoint
if __name__ == "__main__":
__A = argparse.ArgumentParser()
parser.add_argument(
'--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
)
parser.add_argument(
'--config_file',
default=None,
type=str,
required=True,
help='The config json file corresponding to the architecture.',
)
parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
__A = parser.parse_args()
__A = torch.load(args.checkpoint_path)
with open(args.config_file) as f:
__A = json.loads(f.read())
__A = convert_ldm_checkpoint(checkpoint, config)
if "ldm" in config:
del config["ldm"]
__A = UNetaDModel(**config)
model.load_state_dict(converted_checkpoint)
try:
__A = DDPMScheduler.from_config('/'.join(args.checkpoint_path.split('/')[:-1]))
__A = VQModel.from_pretrained('/'.join(args.checkpoint_path.split('/')[:-1]))
__A = LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
pipe.save_pretrained(args.dump_path)
except: # noqa: E722
model.save_pretrained(args.dump_path)
| 341 | 0 |
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_DEFAULT_MEAN,
IMAGENET_DEFAULT_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
__snake_case = logging.get_logger(__name__)
class lowercase__ ( _UpperCAmelCase ):
A__ : Optional[Any] =["pixel_values"]
def __init__( self : Optional[int] , UpperCAmelCase_ : Dict = True , UpperCAmelCase_ : Dict = None , UpperCAmelCase_ : int = 0.9 , UpperCAmelCase_ : str = PILImageResampling.BICUBIC , UpperCAmelCase_ : Optional[Any] = True , UpperCAmelCase_ : int = None , UpperCAmelCase_ : Optional[Any] = 1 / 255 , UpperCAmelCase_ : Dict = True , UpperCAmelCase_ : Dict = True , UpperCAmelCase_ : Union[str, Any] = None , UpperCAmelCase_ : Any = None , **UpperCAmelCase_ : Optional[int] , ):
super().__init__(**UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ = size if size is not None else {"shortest_edge": 224}
SCREAMING_SNAKE_CASE__ = get_size_dict(UpperCAmelCase_ , default_to_square=UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ = crop_size if crop_size is not None else {"height": 224, "width": 224}
SCREAMING_SNAKE_CASE__ = get_size_dict(UpperCAmelCase_ , param_name='crop_size' )
SCREAMING_SNAKE_CASE__ = do_resize
SCREAMING_SNAKE_CASE__ = size
SCREAMING_SNAKE_CASE__ = crop_pct
SCREAMING_SNAKE_CASE__ = resample
SCREAMING_SNAKE_CASE__ = do_center_crop
SCREAMING_SNAKE_CASE__ = crop_size
SCREAMING_SNAKE_CASE__ = do_rescale
SCREAMING_SNAKE_CASE__ = rescale_factor
SCREAMING_SNAKE_CASE__ = do_normalize
SCREAMING_SNAKE_CASE__ = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
SCREAMING_SNAKE_CASE__ = image_std if image_std is not None else IMAGENET_DEFAULT_STD
def A_ ( self : Dict , UpperCAmelCase_ : Union[str, Any] , UpperCAmelCase_ : List[Any] , UpperCAmelCase_ : Any = None , UpperCAmelCase_ : List[Any] = PILImageResampling.BICUBIC , UpperCAmelCase_ : Union[str, Any] = None , **UpperCAmelCase_ : Dict , ):
SCREAMING_SNAKE_CASE__ = get_size_dict(UpperCAmelCase_ , default_to_square=UpperCAmelCase_ )
if "shortest_edge" not in size and ("height" not in size or "width" not in size):
raise ValueError(F'size must contain \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}' )
if crop_pct is not None:
if "shortest_edge" in size:
SCREAMING_SNAKE_CASE__ = int(size['shortest_edge'] / crop_pct )
elif "height" in size and "width" in size:
if size["height"] == size["width"]:
SCREAMING_SNAKE_CASE__ = int(size['height'] / crop_pct )
else:
SCREAMING_SNAKE_CASE__ = (int(size['height'] / crop_pct ), int(size['width'] / crop_pct ))
else:
raise ValueError('Invalid size for resize: {}'.format(UpperCAmelCase_ ) )
SCREAMING_SNAKE_CASE__ = get_resize_output_image_size(UpperCAmelCase_ , size=UpperCAmelCase_ , default_to_square=UpperCAmelCase_ )
else:
if "shortest_edge" in size:
SCREAMING_SNAKE_CASE__ = get_resize_output_image_size(UpperCAmelCase_ , size=size['shortest_edge'] , default_to_square=UpperCAmelCase_ )
elif "height" in size and "width" in size:
SCREAMING_SNAKE_CASE__ = (size["height"], size["width"])
else:
raise ValueError('Invalid size for resize: {}'.format(UpperCAmelCase_ ) )
return resize(UpperCAmelCase_ , size=UpperCAmelCase_ , resample=UpperCAmelCase_ , data_format=UpperCAmelCase_ , **UpperCAmelCase_ )
def A_ ( self : str , UpperCAmelCase_ : str , UpperCAmelCase_ : Tuple , UpperCAmelCase_ : List[str] = None , **UpperCAmelCase_ : int , ):
SCREAMING_SNAKE_CASE__ = get_size_dict(UpperCAmelCase_ )
if "height" not in size or "width" not in size:
raise ValueError(F'size must contain \'height\' and \'width\' as keys. Got {size.keys()}' )
return center_crop(UpperCAmelCase_ , size=(size['height'], size['width']) , data_format=UpperCAmelCase_ , **UpperCAmelCase_ )
def A_ ( self : Optional[Any] , UpperCAmelCase_ : Union[str, Any] , UpperCAmelCase_ : List[str] , UpperCAmelCase_ : Optional[Any] = None , **UpperCAmelCase_ : Tuple , ):
return rescale(UpperCAmelCase_ , scale=UpperCAmelCase_ , data_format=UpperCAmelCase_ , **UpperCAmelCase_ )
def A_ ( self : int , UpperCAmelCase_ : Dict , UpperCAmelCase_ : str , UpperCAmelCase_ : Union[str, Any] , UpperCAmelCase_ : List[Any] = None , **UpperCAmelCase_ : Any , ):
return normalize(UpperCAmelCase_ , mean=UpperCAmelCase_ , std=UpperCAmelCase_ , data_format=UpperCAmelCase_ , **UpperCAmelCase_ )
def A_ ( self : Union[str, Any] , UpperCAmelCase_ : List[str] , UpperCAmelCase_ : List[Any] = None , UpperCAmelCase_ : Union[str, Any] = None , UpperCAmelCase_ : Union[str, Any] = None , UpperCAmelCase_ : Union[str, Any] = None , UpperCAmelCase_ : Optional[int] = None , UpperCAmelCase_ : Dict = None , UpperCAmelCase_ : Union[str, Any] = None , UpperCAmelCase_ : List[Any] = None , UpperCAmelCase_ : Tuple = None , UpperCAmelCase_ : List[Any] = None , UpperCAmelCase_ : Any = None , UpperCAmelCase_ : Tuple = None , UpperCAmelCase_ : Tuple = ChannelDimension.FIRST , **UpperCAmelCase_ : Optional[int] , ):
SCREAMING_SNAKE_CASE__ = do_resize if do_resize is not None else self.do_resize
SCREAMING_SNAKE_CASE__ = crop_pct if crop_pct is not None else self.crop_pct
SCREAMING_SNAKE_CASE__ = resample if resample is not None else self.resample
SCREAMING_SNAKE_CASE__ = do_center_crop if do_center_crop is not None else self.do_center_crop
SCREAMING_SNAKE_CASE__ = do_rescale if do_rescale is not None else self.do_rescale
SCREAMING_SNAKE_CASE__ = rescale_factor if rescale_factor is not None else self.rescale_factor
SCREAMING_SNAKE_CASE__ = do_normalize if do_normalize is not None else self.do_normalize
SCREAMING_SNAKE_CASE__ = image_mean if image_mean is not None else self.image_mean
SCREAMING_SNAKE_CASE__ = image_std if image_std is not None else self.image_std
SCREAMING_SNAKE_CASE__ = size if size is not None else self.size
SCREAMING_SNAKE_CASE__ = get_size_dict(UpperCAmelCase_ , default_to_square=UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ = crop_size if crop_size is not None else self.crop_size
SCREAMING_SNAKE_CASE__ = get_size_dict(UpperCAmelCase_ , param_name='crop_size' )
SCREAMING_SNAKE_CASE__ = make_list_of_images(UpperCAmelCase_ )
if not valid_images(UpperCAmelCase_ ):
raise ValueError(
'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
'torch.Tensor, tf.Tensor or jax.ndarray.' )
if do_resize and size is None or resample is None:
raise ValueError('Size and resample must be specified if do_resize is True.' )
if do_center_crop and crop_pct is None:
raise ValueError('Crop_pct 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.
SCREAMING_SNAKE_CASE__ = [to_numpy_array(UpperCAmelCase_ ) for image in images]
if do_resize:
SCREAMING_SNAKE_CASE__ = [self.resize(image=UpperCAmelCase_ , size=UpperCAmelCase_ , crop_pct=UpperCAmelCase_ , resample=UpperCAmelCase_ ) for image in images]
if do_center_crop:
SCREAMING_SNAKE_CASE__ = [self.center_crop(image=UpperCAmelCase_ , size=UpperCAmelCase_ ) for image in images]
if do_rescale:
SCREAMING_SNAKE_CASE__ = [self.rescale(image=UpperCAmelCase_ , scale=UpperCAmelCase_ ) for image in images]
if do_normalize:
SCREAMING_SNAKE_CASE__ = [self.normalize(image=UpperCAmelCase_ , mean=UpperCAmelCase_ , std=UpperCAmelCase_ ) 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 |
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def lowerCAmelCase__( lowercase : Dict ) -> str: # picklable for multiprocessing
return i + 1
@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
def lowerCAmelCase__( ) -> List[Any]:
with parallel_backend("spark" ):
assert ParallelBackendConfig.backend_name == "spark"
__snake_case : Any = [1, 2, 3]
with pytest.raises(lowercase ):
with parallel_backend("unsupported backend" ):
map_nested(lowercase , lowercase , num_proc=2 )
with pytest.raises(lowercase ):
with parallel_backend("unsupported backend" ):
map_nested(lowercase , lowercase , num_proc=-1 )
@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
@pytest.mark.parametrize("num_proc" , [2, -1] )
def lowerCAmelCase__( lowercase : Dict ) -> Dict:
__snake_case : Any = [1, 2]
__snake_case : Dict = {"a": 1, "b": 2}
__snake_case : Optional[int] = {"a": [1, 2], "b": [3, 4]}
__snake_case : int = {"a": {"1": 1}, "b": 2}
__snake_case : str = {"a": 1, "b": 2, "c": 3, "d": 4}
__snake_case : Dict = [2, 3]
__snake_case : Tuple = {"a": 2, "b": 3}
__snake_case : int = {"a": [2, 3], "b": [4, 5]}
__snake_case : Dict = {"a": {"1": 2}, "b": 3}
__snake_case : str = {"a": 2, "b": 3, "c": 4, "d": 5}
with parallel_backend("spark" ):
assert map_nested(lowercase , lowercase , num_proc=lowercase ) == expected_map_nested_sa
assert map_nested(lowercase , lowercase , num_proc=lowercase ) == expected_map_nested_sa
assert map_nested(lowercase , lowercase , num_proc=lowercase ) == expected_map_nested_sa
assert map_nested(lowercase , lowercase , num_proc=lowercase ) == expected_map_nested_sa
assert map_nested(lowercase , lowercase , num_proc=lowercase ) == expected_map_nested_sa
| 326 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import DistilBertConfig, 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.models.distilbert.modeling_tf_distilbert import (
TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFDistilBertForMaskedLM,
TFDistilBertForMultipleChoice,
TFDistilBertForQuestionAnswering,
TFDistilBertForSequenceClassification,
TFDistilBertForTokenClassification,
TFDistilBertModel,
)
class lowercase :
"""simple docstring"""
def __init__( self ,a_ ,) -> Dict:
_UpperCAmelCase : Tuple = parent
_UpperCAmelCase : Tuple = 13
_UpperCAmelCase : Union[str, Any] = 7
_UpperCAmelCase : List[str] = True
_UpperCAmelCase : Tuple = True
_UpperCAmelCase : int = False
_UpperCAmelCase : List[str] = True
_UpperCAmelCase : Optional[int] = 99
_UpperCAmelCase : int = 32
_UpperCAmelCase : Any = 2
_UpperCAmelCase : List[Any] = 4
_UpperCAmelCase : Optional[int] = 37
_UpperCAmelCase : List[str] = """gelu"""
_UpperCAmelCase : List[Any] = 0.1
_UpperCAmelCase : List[Any] = 0.1
_UpperCAmelCase : int = 512
_UpperCAmelCase : Tuple = 16
_UpperCAmelCase : Dict = 2
_UpperCAmelCase : Optional[int] = 0.02
_UpperCAmelCase : int = 3
_UpperCAmelCase : List[Any] = 4
_UpperCAmelCase : Dict = None
def _snake_case ( self ) -> Any:
_UpperCAmelCase : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
_UpperCAmelCase : List[Any] = None
if self.use_input_mask:
_UpperCAmelCase : int = random_attention_mask([self.batch_size, self.seq_length] )
_UpperCAmelCase : Union[str, Any] = None
_UpperCAmelCase : str = None
_UpperCAmelCase : Union[str, Any] = None
if self.use_labels:
_UpperCAmelCase : Any = ids_tensor([self.batch_size] ,self.type_sequence_label_size )
_UpperCAmelCase : int = ids_tensor([self.batch_size, self.seq_length] ,self.num_labels )
_UpperCAmelCase : str = ids_tensor([self.batch_size] ,self.num_choices )
_UpperCAmelCase : int = DistilBertConfig(
vocab_size=self.vocab_size ,dim=self.hidden_size ,n_layers=self.num_hidden_layers ,n_heads=self.num_attention_heads ,hidden_dim=self.intermediate_size ,hidden_act=self.hidden_act ,dropout=self.hidden_dropout_prob ,attention_dropout=self.attention_probs_dropout_prob ,max_position_embeddings=self.max_position_embeddings ,initializer_range=self.initializer_range ,)
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def _snake_case ( self ,a_ ,a_ ,a_ ,a_ ,a_ ,a_ ) -> Union[str, Any]:
_UpperCAmelCase : List[Any] = TFDistilBertModel(config=a_ )
_UpperCAmelCase : Any = {"""input_ids""": input_ids, """attention_mask""": input_mask}
_UpperCAmelCase : Tuple = model(a_ )
_UpperCAmelCase : List[str] = [input_ids, input_mask]
_UpperCAmelCase : str = model(a_ )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
def _snake_case ( self ,a_ ,a_ ,a_ ,a_ ,a_ ,a_ ) -> str:
_UpperCAmelCase : Tuple = TFDistilBertForMaskedLM(config=a_ )
_UpperCAmelCase : int = {"""input_ids""": input_ids, """attention_mask""": input_mask}
_UpperCAmelCase : Union[str, Any] = model(a_ )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.vocab_size) )
def _snake_case ( self ,a_ ,a_ ,a_ ,a_ ,a_ ,a_ ) -> List[Any]:
_UpperCAmelCase : Union[str, Any] = TFDistilBertForQuestionAnswering(config=a_ )
_UpperCAmelCase : Union[str, Any] = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
}
_UpperCAmelCase : str = 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 _snake_case ( self ,a_ ,a_ ,a_ ,a_ ,a_ ,a_ ) -> int:
_UpperCAmelCase : str = self.num_labels
_UpperCAmelCase : Union[str, Any] = TFDistilBertForSequenceClassification(a_ )
_UpperCAmelCase : str = {"""input_ids""": input_ids, """attention_mask""": input_mask}
_UpperCAmelCase : Union[str, Any] = model(a_ )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_labels) )
def _snake_case ( self ,a_ ,a_ ,a_ ,a_ ,a_ ,a_ ) -> List[Any]:
_UpperCAmelCase : Union[str, Any] = self.num_choices
_UpperCAmelCase : Optional[int] = TFDistilBertForMultipleChoice(a_ )
_UpperCAmelCase : List[str] = tf.tile(tf.expand_dims(a_ ,1 ) ,(1, self.num_choices, 1) )
_UpperCAmelCase : Optional[int] = tf.tile(tf.expand_dims(a_ ,1 ) ,(1, self.num_choices, 1) )
_UpperCAmelCase : Dict = {
"""input_ids""": multiple_choice_inputs_ids,
"""attention_mask""": multiple_choice_input_mask,
}
_UpperCAmelCase : Tuple = model(a_ )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_choices) )
def _snake_case ( self ,a_ ,a_ ,a_ ,a_ ,a_ ,a_ ) -> int:
_UpperCAmelCase : Optional[Any] = self.num_labels
_UpperCAmelCase : List[Any] = TFDistilBertForTokenClassification(a_ )
_UpperCAmelCase : Tuple = {"""input_ids""": input_ids, """attention_mask""": input_mask}
_UpperCAmelCase : Optional[int] = model(a_ )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.num_labels) )
def _snake_case ( self ) -> int:
_UpperCAmelCase : Any = self.prepare_config_and_inputs()
((_UpperCAmelCase) ,(_UpperCAmelCase) ,(_UpperCAmelCase) ,(_UpperCAmelCase) ,(_UpperCAmelCase) ,(_UpperCAmelCase)) : str = config_and_inputs
_UpperCAmelCase : int = {"""input_ids""": input_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_tf
class lowercase ( _lowerCamelCase , _lowerCamelCase , unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase = (
(
TFDistilBertModel,
TFDistilBertForMaskedLM,
TFDistilBertForQuestionAnswering,
TFDistilBertForSequenceClassification,
TFDistilBertForTokenClassification,
TFDistilBertForMultipleChoice,
)
if is_tf_available()
else None
)
UpperCAmelCase = (
{
"""feature-extraction""": TFDistilBertModel,
"""fill-mask""": TFDistilBertForMaskedLM,
"""question-answering""": TFDistilBertForQuestionAnswering,
"""text-classification""": TFDistilBertForSequenceClassification,
"""token-classification""": TFDistilBertForTokenClassification,
"""zero-shot""": TFDistilBertForSequenceClassification,
}
if is_tf_available()
else {}
)
UpperCAmelCase = False
UpperCAmelCase = False
def _snake_case ( self ) -> int:
_UpperCAmelCase : Any = TFDistilBertModelTester(self )
_UpperCAmelCase : Any = ConfigTester(self ,config_class=a_ ,dim=37 )
def _snake_case ( self ) -> Tuple:
self.config_tester.run_common_tests()
def _snake_case ( self ) -> List[Any]:
_UpperCAmelCase : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_model(*a_ )
def _snake_case ( self ) -> List[str]:
_UpperCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_masked_lm(*a_ )
def _snake_case ( self ) -> Optional[int]:
_UpperCAmelCase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_question_answering(*a_ )
def _snake_case ( self ) -> Union[str, Any]:
_UpperCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_sequence_classification(*a_ )
def _snake_case ( self ) -> Dict:
_UpperCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_multiple_choice(*a_ )
def _snake_case ( self ) -> str:
_UpperCAmelCase : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_token_classification(*a_ )
@slow
def _snake_case ( self ) -> List[Any]:
for model_name in list(TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1] ):
_UpperCAmelCase : Optional[int] = TFDistilBertModel.from_pretrained(a_ )
self.assertIsNotNone(a_ )
@require_tf
class lowercase ( unittest.TestCase ):
"""simple docstring"""
@slow
def _snake_case ( self ) -> Any:
_UpperCAmelCase : Tuple = TFDistilBertModel.from_pretrained("""distilbert-base-uncased""" )
_UpperCAmelCase : Tuple = tf.constant([[0, 1, 2, 3, 4, 5]] )
_UpperCAmelCase : List[str] = model(a_ )[0]
_UpperCAmelCase : int = [1, 6, 768]
self.assertEqual(output.shape ,a_ )
_UpperCAmelCase : Tuple = tf.constant(
[
[
[0.1926_1885, -0.1373_2955, 0.411_9799],
[0.2215_0156, -0.0742_2661, 0.3903_7204],
[0.2275_6018, -0.089_6414, 0.370_1467],
]
] )
tf.debugging.assert_near(output[:, :3, :3] ,a_ ,atol=1E-4 )
| 349 |
'''simple docstring'''
import argparse
from typing import List
import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset
# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to perform Cross Validation,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
A_ : Any = 1_6
A_ : Union[str, Any] = 3_2
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ = 16 )-> Optional[int]:
'''simple docstring'''
_UpperCAmelCase : Union[str, Any] = AutoTokenizer.from_pretrained("""bert-base-cased""" )
_UpperCAmelCase : str = DatasetDict(
{
"""train""": dataset["""train"""].select(lowerCAmelCase_ ),
"""validation""": dataset["""train"""].select(lowerCAmelCase_ ),
"""test""": dataset["""validation"""],
} )
def tokenize_function(lowerCAmelCase_ ):
# max_length=None => use the model max length (it's actually the default)
_UpperCAmelCase : List[str] = tokenizer(examples["""sentence1"""] , examples["""sentence2"""] , truncation=lowerCAmelCase_ , max_length=lowerCAmelCase_ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
_UpperCAmelCase : Optional[int] = datasets.map(
lowerCAmelCase_ , batched=lowerCAmelCase_ , remove_columns=["""idx""", """sentence1""", """sentence2"""] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
_UpperCAmelCase : List[Any] = tokenized_datasets.rename_column("""label""" , """labels""" )
def collate_fn(lowerCAmelCase_ ):
# On TPU it's best to pad everything to the same length or training will be very slow.
_UpperCAmelCase : Tuple = 128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
_UpperCAmelCase : List[str] = 16
elif accelerator.mixed_precision != "no":
_UpperCAmelCase : Any = 8
else:
_UpperCAmelCase : Dict = None
return tokenizer.pad(
lowerCAmelCase_ , padding="""longest""" , max_length=lowerCAmelCase_ , pad_to_multiple_of=lowerCAmelCase_ , return_tensors="""pt""" , )
# Instantiate dataloaders.
_UpperCAmelCase : Union[str, Any] = DataLoader(
tokenized_datasets["""train"""] , shuffle=lowerCAmelCase_ , collate_fn=lowerCAmelCase_ , batch_size=lowerCAmelCase_ )
_UpperCAmelCase : Union[str, Any] = DataLoader(
tokenized_datasets["""validation"""] , shuffle=lowerCAmelCase_ , collate_fn=lowerCAmelCase_ , batch_size=lowerCAmelCase_ )
_UpperCAmelCase : Dict = DataLoader(
tokenized_datasets["""test"""] , shuffle=lowerCAmelCase_ , collate_fn=lowerCAmelCase_ , batch_size=lowerCAmelCase_ )
return train_dataloader, eval_dataloader, test_dataloader
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ )-> Optional[int]:
'''simple docstring'''
_UpperCAmelCase : Optional[int] = []
# Download the dataset
_UpperCAmelCase : Dict = load_dataset("""glue""" , """mrpc""" )
# Create our splits
_UpperCAmelCase : Optional[Any] = StratifiedKFold(n_splits=int(args.num_folds ) )
# Initialize accelerator
_UpperCAmelCase : Union[str, Any] = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
_UpperCAmelCase : Dict = config["""lr"""]
_UpperCAmelCase : List[Any] = int(config["""num_epochs"""] )
_UpperCAmelCase : str = int(config["""seed"""] )
_UpperCAmelCase : List[Any] = int(config["""batch_size"""] )
_UpperCAmelCase : int = evaluate.load("""glue""" , """mrpc""" )
# If the batch size is too big we use gradient accumulation
_UpperCAmelCase : List[Any] = 1
if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
_UpperCAmelCase : Dict = batch_size // MAX_GPU_BATCH_SIZE
_UpperCAmelCase : Tuple = MAX_GPU_BATCH_SIZE
set_seed(lowerCAmelCase_ )
# New Code #
# Create our folds:
_UpperCAmelCase : Any = kfold.split(np.zeros(datasets["""train"""].num_rows ) , datasets["""train"""]["""label"""] )
_UpperCAmelCase : Tuple = []
# Iterate over them
for i, (train_idxs, valid_idxs) in enumerate(lowerCAmelCase_ ):
_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase : Union[str, Any] = get_fold_dataloaders(
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
_UpperCAmelCase : Tuple = AutoModelForSequenceClassification.from_pretrained("""bert-base-cased""" , return_dict=lowerCAmelCase_ )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
_UpperCAmelCase : List[Any] = model.to(accelerator.device )
# Instantiate optimizer
_UpperCAmelCase : int = AdamW(params=model.parameters() , lr=lowerCAmelCase_ )
# Instantiate scheduler
_UpperCAmelCase : Dict = get_linear_schedule_with_warmup(
optimizer=lowerCAmelCase_ , num_warmup_steps=100 , num_training_steps=(len(lowerCAmelCase_ ) * num_epochs) // gradient_accumulation_steps , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase : Any = accelerator.prepare(
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
# Now we train the model
for epoch in range(lowerCAmelCase_ ):
model.train()
for step, batch in enumerate(lowerCAmelCase_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
_UpperCAmelCase : Union[str, Any] = model(**lowerCAmelCase_ )
_UpperCAmelCase : Dict = outputs.loss
_UpperCAmelCase : int = loss / gradient_accumulation_steps
accelerator.backward(lowerCAmelCase_ )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(lowerCAmelCase_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
_UpperCAmelCase : List[str] = model(**lowerCAmelCase_ )
_UpperCAmelCase : List[Any] = outputs.logits.argmax(dim=-1 )
_UpperCAmelCase ,_UpperCAmelCase : Union[str, Any] = accelerator.gather_for_metrics((predictions, batch["""labels"""]) )
metric.add_batch(
predictions=lowerCAmelCase_ , references=lowerCAmelCase_ , )
_UpperCAmelCase : List[Any] = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F'''epoch {epoch}:''' , lowerCAmelCase_ )
# New Code #
# We also run predictions on the test set at the very end
_UpperCAmelCase : Tuple = []
for step, batch in enumerate(lowerCAmelCase_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
_UpperCAmelCase : List[Any] = model(**lowerCAmelCase_ )
_UpperCAmelCase : Any = outputs.logits
_UpperCAmelCase ,_UpperCAmelCase : List[Any] = accelerator.gather_for_metrics((predictions, batch["""labels"""]) )
fold_predictions.append(predictions.cpu() )
if i == 0:
# We need all of the test predictions
test_references.append(references.cpu() )
# Use accelerator.print to print only on the main process.
test_predictions.append(torch.cat(lowerCAmelCase_ , dim=0 ) )
# We now need to release all our memory and get rid of the current model, optimizer, etc
accelerator.free_memory()
# New Code #
# Finally we check the accuracy of our folded results:
_UpperCAmelCase : List[Any] = torch.cat(lowerCAmelCase_ , dim=0 )
_UpperCAmelCase : Union[str, Any] = torch.stack(lowerCAmelCase_ , dim=0 ).sum(dim=0 ).div(int(args.num_folds ) ).argmax(dim=-1 )
_UpperCAmelCase : List[str] = metric.compute(predictions=lowerCAmelCase_ , references=lowerCAmelCase_ )
accelerator.print("""Average test metrics from all folds:""" , lowerCAmelCase_ )
def snake_case_ ( )-> Any:
'''simple docstring'''
_UpperCAmelCase : List[str] = argparse.ArgumentParser(description="""Simple example of training script.""" )
parser.add_argument(
"""--mixed_precision""" , type=lowerCAmelCase_ , default=lowerCAmelCase_ , choices=["""no""", """fp16""", """bf16""", """fp8"""] , help="""Whether to use mixed precision. Choose"""
"""between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
"""and an Nvidia Ampere GPU.""" , )
parser.add_argument("""--cpu""" , action="""store_true""" , help="""If passed, will train on the CPU.""" )
# New Code #
parser.add_argument("""--num_folds""" , type=lowerCAmelCase_ , default=3 , help="""The number of splits to perform across the dataset""" )
_UpperCAmelCase : Optional[int] = parser.parse_args()
_UpperCAmelCase : Tuple = {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
training_function(lowerCAmelCase_ , lowerCAmelCase_ )
if __name__ == "__main__":
main()
| 349 | 1 |
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
@slow
@require_flax
class snake_case_( unittest.TestCase ):
def lowerCamelCase__ ( self : int ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
def lowerCamelCase__ ( self : Optional[Any] ):
lowerCAmelCase, lowerCAmelCase : Optional[int] = FlaxStableDiffusionPipeline.from_pretrained(
'''stabilityai/stable-diffusion-2''' , revision='''bf16''' , dtype=jnp.bfloataa , )
lowerCAmelCase : Optional[int] = '''A painting of a squirrel eating a burger'''
lowerCAmelCase : List[str] = jax.device_count()
lowerCAmelCase : Optional[int] = num_samples * [prompt]
lowerCAmelCase : Any = sd_pipe.prepare_inputs(UpperCamelCase_ )
lowerCAmelCase : Optional[int] = replicate(UpperCamelCase_ )
lowerCAmelCase : Union[str, Any] = shard(UpperCamelCase_ )
lowerCAmelCase : Optional[int] = jax.random.PRNGKey(0 )
lowerCAmelCase : Optional[Any] = jax.random.split(UpperCamelCase_ , jax.device_count() )
lowerCAmelCase : str = sd_pipe(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , num_inference_steps=2_5 , jit=UpperCamelCase_ )[0]
assert images.shape == (jax.device_count(), 1, 7_6_8, 7_6_8, 3)
lowerCAmelCase : str = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
lowerCAmelCase : List[str] = images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]
lowerCAmelCase : Dict = jnp.asarray(jax.device_get(image_slice.flatten() ) )
lowerCAmelCase : List[str] = jnp.array([0.4_238, 0.4_414, 0.4_395, 0.4_453, 0.4_629, 0.4_590, 0.4_531, 0.45_508, 0.4_512] )
print(F'''output_slice: {output_slice}''' )
assert jnp.abs(output_slice - expected_slice ).max() < 1E-2
def lowerCamelCase__ ( self : Union[str, Any] ):
lowerCAmelCase : Union[str, Any] = '''stabilityai/stable-diffusion-2'''
lowerCAmelCase, lowerCAmelCase : Dict = FlaxDPMSolverMultistepScheduler.from_pretrained(UpperCamelCase_ , subfolder='''scheduler''' )
lowerCAmelCase, lowerCAmelCase : int = FlaxStableDiffusionPipeline.from_pretrained(
UpperCamelCase_ , scheduler=UpperCamelCase_ , revision='''bf16''' , dtype=jnp.bfloataa , )
lowerCAmelCase : List[Any] = scheduler_params
lowerCAmelCase : List[Any] = '''A painting of a squirrel eating a burger'''
lowerCAmelCase : Any = jax.device_count()
lowerCAmelCase : int = num_samples * [prompt]
lowerCAmelCase : int = sd_pipe.prepare_inputs(UpperCamelCase_ )
lowerCAmelCase : Dict = replicate(UpperCamelCase_ )
lowerCAmelCase : Tuple = shard(UpperCamelCase_ )
lowerCAmelCase : int = jax.random.PRNGKey(0 )
lowerCAmelCase : Optional[int] = jax.random.split(UpperCamelCase_ , jax.device_count() )
lowerCAmelCase : Tuple = sd_pipe(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , num_inference_steps=2_5 , jit=UpperCamelCase_ )[0]
assert images.shape == (jax.device_count(), 1, 7_6_8, 7_6_8, 3)
lowerCAmelCase : Any = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
lowerCAmelCase : str = images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]
lowerCAmelCase : Optional[int] = jnp.asarray(jax.device_get(image_slice.flatten() ) )
lowerCAmelCase : Tuple = jnp.array([0.4_336, 0.42_969, 0.4_453, 0.4_199, 0.4_297, 0.4_531, 0.4_434, 0.4_434, 0.4_297] )
print(F'''output_slice: {output_slice}''' )
assert jnp.abs(output_slice - expected_slice ).max() < 1E-2
| 60 | """simple docstring"""
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__lowerCamelCase = {
"configuration_vivit": ["VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "VivitConfig"],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase = ["VivitImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase = [
"VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
"VivitModel",
"VivitPreTrainedModel",
"VivitForVideoClassification",
]
if TYPE_CHECKING:
from .configuration_vivit import VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, VivitConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_vivit import VivitImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vivit import (
VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
VivitForVideoClassification,
VivitModel,
VivitPreTrainedModel,
)
else:
import sys
__lowerCamelCase = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 221 | 0 |
import argparse
import logging
import pickle
import random
import time
import numpy as np
from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
__lowercase = logging.getLogger(__name__)
def lowerCamelCase ( ):
'''simple docstring'''
__UpperCamelCase :Optional[int] = argparse.ArgumentParser(
description='''Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids).''' )
parser.add_argument('''--file_path''' , type=SCREAMING_SNAKE_CASE , default='''data/dump.txt''' , help='''The path to the data.''' )
parser.add_argument('''--tokenizer_type''' , type=SCREAMING_SNAKE_CASE , default='''bert''' , choices=['''bert''', '''roberta''', '''gpt2'''] )
parser.add_argument('''--tokenizer_name''' , type=SCREAMING_SNAKE_CASE , default='''bert-base-uncased''' , help='''The tokenizer to use.''' )
parser.add_argument('''--dump_file''' , type=SCREAMING_SNAKE_CASE , default='''data/dump''' , help='''The dump file prefix.''' )
__UpperCamelCase :List[Any] = parser.parse_args()
logger.info(f"""Loading Tokenizer ({args.tokenizer_name})""" )
if args.tokenizer_type == "bert":
__UpperCamelCase :Tuple = BertTokenizer.from_pretrained(args.tokenizer_name )
__UpperCamelCase :List[str] = tokenizer.special_tokens_map['''cls_token'''] # `[CLS]`
__UpperCamelCase :Optional[int] = tokenizer.special_tokens_map['''sep_token'''] # `[SEP]`
elif args.tokenizer_type == "roberta":
__UpperCamelCase :str = RobertaTokenizer.from_pretrained(args.tokenizer_name )
__UpperCamelCase :Union[str, Any] = tokenizer.special_tokens_map['''cls_token'''] # `<s>`
__UpperCamelCase :Any = tokenizer.special_tokens_map['''sep_token'''] # `</s>`
elif args.tokenizer_type == "gpt2":
__UpperCamelCase :List[str] = GPTaTokenizer.from_pretrained(args.tokenizer_name )
__UpperCamelCase :List[Any] = tokenizer.special_tokens_map['''bos_token'''] # `<|endoftext|>`
__UpperCamelCase :Dict = tokenizer.special_tokens_map['''eos_token'''] # `<|endoftext|>`
logger.info(f"""Loading text from {args.file_path}""" )
with open(args.file_path , '''r''' , encoding='''utf8''' ) as fp:
__UpperCamelCase :Dict = fp.readlines()
logger.info('''Start encoding''' )
logger.info(f"""{len(SCREAMING_SNAKE_CASE )} examples to process.""" )
__UpperCamelCase :List[str] = []
__UpperCamelCase :Optional[int] = 0
__UpperCamelCase :Optional[int] = 10_000
__UpperCamelCase :Any = time.time()
for text in data:
__UpperCamelCase :Optional[Any] = f"""{bos} {text.strip()} {sep}"""
__UpperCamelCase :Dict = tokenizer.encode(SCREAMING_SNAKE_CASE , add_special_tokens=SCREAMING_SNAKE_CASE )
rslt.append(SCREAMING_SNAKE_CASE )
iter += 1
if iter % interval == 0:
__UpperCamelCase :str = time.time()
logger.info(f"""{iter} examples processed. - {(end-start):.2f}s/{interval}expl""" )
__UpperCamelCase :str = time.time()
logger.info('''Finished binarization''' )
logger.info(f"""{len(SCREAMING_SNAKE_CASE )} examples processed.""" )
__UpperCamelCase :Union[str, Any] = f"""{args.dump_file}.{args.tokenizer_name}.pickle"""
__UpperCamelCase :Union[str, Any] = tokenizer.vocab_size
if vocab_size < (1 << 16):
__UpperCamelCase :Dict = [np.uintaa(SCREAMING_SNAKE_CASE ) for d in rslt]
else:
__UpperCamelCase :Tuple = [np.intaa(SCREAMING_SNAKE_CASE ) for d in rslt]
random.shuffle(rslt_ )
logger.info(f"""Dump to {dp_file}""" )
with open(SCREAMING_SNAKE_CASE , '''wb''' ) as handle:
pickle.dump(rslt_ , SCREAMING_SNAKE_CASE , protocol=pickle.HIGHEST_PROTOCOL )
if __name__ == "__main__":
main()
| 105 | import argparse
from torch import nn
# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)
from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging
__lowercase = logging.get_logger(__name__)
logging.set_verbosity_info()
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
if "xprophetnet" in prophetnet_checkpoint_path:
__UpperCamelCase :Dict = XLMProphetNetForConditionalGenerationOld.from_pretrained(SCREAMING_SNAKE_CASE )
__UpperCamelCase , __UpperCamelCase :int = XLMProphetNetForConditionalGeneration.from_pretrained(
SCREAMING_SNAKE_CASE , output_loading_info=SCREAMING_SNAKE_CASE )
else:
__UpperCamelCase :Union[str, Any] = ProphetNetForConditionalGenerationOld.from_pretrained(SCREAMING_SNAKE_CASE )
__UpperCamelCase , __UpperCamelCase :Union[str, Any] = ProphetNetForConditionalGeneration.from_pretrained(
SCREAMING_SNAKE_CASE , output_loading_info=SCREAMING_SNAKE_CASE )
__UpperCamelCase :Dict = ['''key_proj''', '''value_proj''', '''query_proj''']
__UpperCamelCase :Optional[Any] = {
'''self_attn''': '''ngram_self_attn''',
'''cross_attn''': '''encoder_attn''',
'''cross_attn_layer_norm''': '''encoder_attn_layer_norm''',
'''feed_forward_layer_norm''': '''final_layer_norm''',
'''feed_forward''': '''''',
'''intermediate''': '''fc1''',
'''output''': '''fc2''',
'''key_proj''': '''k_proj''',
'''query_proj''': '''q_proj''',
'''value_proj''': '''v_proj''',
'''word_embeddings''': '''embed_tokens''',
'''embeddings_layer_norm''': '''emb_layer_norm''',
'''relative_pos_embeddings''': '''relative_linear''',
'''ngram_embeddings''': '''ngram_input_embed''',
'''position_embeddings''': '''embed_positions''',
}
for key in loading_info["missing_keys"]:
__UpperCamelCase :Tuple = key.split('''.''' )
if attributes[0] == "lm_head":
__UpperCamelCase :Union[str, Any] = prophet
__UpperCamelCase :Any = prophet_old
else:
__UpperCamelCase :Any = prophet.prophetnet
__UpperCamelCase :int = prophet_old.model
__UpperCamelCase :Optional[Any] = False
for attribute in attributes:
if attribute in mapping:
__UpperCamelCase :str = mapping[attribute]
if not hasattr(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) and len(SCREAMING_SNAKE_CASE ) > 0:
__UpperCamelCase :Optional[int] = attribute
elif hasattr(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
__UpperCamelCase :List[Any] = attribute
if attribute == "weight":
assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
__UpperCamelCase :Tuple = old_model.weight
logger.info(f"""{attribute} is initialized.""" )
__UpperCamelCase :Union[str, Any] = True
break
elif attribute == "bias":
assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
__UpperCamelCase :Union[str, Any] = old_model.bias
logger.info(f"""{attribute} is initialized""" )
__UpperCamelCase :List[Any] = True
break
elif attribute in special_keys and hasattr(SCREAMING_SNAKE_CASE , '''in_proj_weight''' ):
__UpperCamelCase :str = old_model.in_proj_weight.shape[0] // 3
__UpperCamelCase :Optional[Any] = getattr(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
if attribute == "query_proj":
__UpperCamelCase :Optional[int] = nn.Parameter(old_model.in_proj_weight[:embed_dim, :] )
__UpperCamelCase :Tuple = nn.Parameter(old_model.in_proj_bias[:embed_dim] )
elif attribute == "key_proj":
__UpperCamelCase :List[Any] = nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :] )
__UpperCamelCase :Dict = nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim] )
elif attribute == "value_proj":
__UpperCamelCase :Tuple = nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :] )
__UpperCamelCase :Dict = nn.Parameter(old_model.in_proj_bias[2 * embed_dim :] )
__UpperCamelCase :Optional[int] = True
break
elif attribute == "position_embeddings":
assert (
model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
), "Hidden size has to match"
assert model.position_embeddings.weight.shape[0] == 512, "We want 512 position_embeddings."
__UpperCamelCase :Optional[int] = nn.Parameter(old_model.embed_positions.weight[:512, :] )
__UpperCamelCase :List[Any] = True
break
if attribute.isdigit():
__UpperCamelCase :List[Any] = model[int(SCREAMING_SNAKE_CASE )]
__UpperCamelCase :Optional[int] = old_model[int(SCREAMING_SNAKE_CASE )]
else:
__UpperCamelCase :Optional[Any] = getattr(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
if old_attribute == "":
__UpperCamelCase :Any = old_model
else:
if not hasattr(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
raise ValueError(f"""{old_model} does not have {old_attribute}""" )
__UpperCamelCase :Any = getattr(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
if not is_key_init:
raise ValueError(f"""{key} was not correctly initialized!""" )
print(f"""Saving model to {pytorch_dump_folder_path}""" )
prophet.save_pretrained(SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
__lowercase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--prophetnet_checkpoint_path''', default=None, type=str, required=True, help='''Path the official PyTorch dump.'''
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
__lowercase = parser.parse_args()
convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)
| 105 | 1 |
"""simple docstring"""
def __lowerCAmelCase ( lowercase : int ) -> bool:
"""simple docstring"""
if p < 2:
raise ValueError("p should not be less than 2!" )
elif p == 2:
return True
snake_case : Dict = 4
snake_case : str = (1 << p) - 1
for _ in range(p - 2 ):
snake_case : Any = ((s * s) - 2) % m
return s == 0
if __name__ == "__main__":
print(lucas_lehmer_test(7))
print(lucas_lehmer_test(11))
| 203 |
"""simple docstring"""
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,
)
__snake_case = """hf-internal-testing/tiny-random-bert"""
__snake_case = os.path.join(TRANSFORMERS_CACHE, """models--hf-internal-testing--tiny-random-bert""")
__snake_case = """9b8c223d42b2188cb49d29af482996f9d0f3e5a6"""
class _lowerCAmelCase ( unittest.TestCase ):
def lowerCamelCase ( self ) -> str:
'''simple docstring'''
snake_case : int = cached_file(UpperCamelCase__ , UpperCamelCase__ )
# Should have downloaded the file in here
self.assertTrue(os.path.isdir(UpperCamelCase__ ) )
# Cache should contain at least those three subfolders:
for subfolder in ["blobs", "refs", "snapshots"]:
self.assertTrue(os.path.isdir(os.path.join(UpperCamelCase__ , UpperCamelCase__ ) ) )
with open(os.path.join(UpperCamelCase__ , "refs" , "main" ) ) as f:
snake_case : Dict = f.read()
self.assertEqual(UpperCamelCase__ , os.path.join(UpperCamelCase__ , "snapshots" , UpperCamelCase__ , UpperCamelCase__ ) )
self.assertTrue(os.path.isfile(UpperCamelCase__ ) )
# File is cached at the same place the second time.
snake_case : List[str] = cached_file(UpperCamelCase__ , UpperCamelCase__ )
self.assertEqual(UpperCamelCase__ , UpperCamelCase__ )
# Using a specific revision to test the full commit hash.
snake_case : Any = cached_file(UpperCamelCase__ , UpperCamelCase__ , revision="9b8c223" )
self.assertEqual(UpperCamelCase__ , os.path.join(UpperCamelCase__ , "snapshots" , UpperCamelCase__ , UpperCamelCase__ ) )
def lowerCamelCase ( self ) -> Any:
'''simple docstring'''
with self.assertRaisesRegex(UpperCamelCase__ , "is not a valid model identifier" ):
snake_case : Optional[Any] = cached_file("tiny-random-bert" , UpperCamelCase__ )
with self.assertRaisesRegex(UpperCamelCase__ , "is not a valid git identifier" ):
snake_case : Optional[Any] = cached_file(UpperCamelCase__ , UpperCamelCase__ , revision="aaaa" )
with self.assertRaisesRegex(UpperCamelCase__ , "does not appear to have a file named" ):
snake_case : List[Any] = cached_file(UpperCamelCase__ , "conf" )
def lowerCamelCase ( self ) -> Tuple:
'''simple docstring'''
with self.assertRaisesRegex(UpperCamelCase__ , "does not appear to have a file named" ):
snake_case : Tuple = cached_file(UpperCamelCase__ , "conf" )
with open(os.path.join(UpperCamelCase__ , "refs" , "main" ) ) as f:
snake_case : Any = f.read()
self.assertTrue(os.path.isfile(os.path.join(UpperCamelCase__ , ".no_exist" , UpperCamelCase__ , "conf" ) ) )
snake_case : Optional[Any] = cached_file(UpperCamelCase__ , "conf" , _raise_exceptions_for_missing_entries=UpperCamelCase__ )
self.assertIsNone(UpperCamelCase__ )
snake_case : Any = cached_file(UpperCamelCase__ , "conf" , local_files_only=UpperCamelCase__ , _raise_exceptions_for_missing_entries=UpperCamelCase__ )
self.assertIsNone(UpperCamelCase__ )
snake_case : Any = mock.Mock()
snake_case : List[Any] = 500
snake_case : int = {}
snake_case : Optional[int] = HTTPError
snake_case : Tuple = {}
# Under the mock environment we get a 500 error when trying to reach the tokenizer.
with mock.patch("requests.Session.request" , return_value=UpperCamelCase__ ) as mock_head:
snake_case : Tuple = cached_file(UpperCamelCase__ , "conf" , _raise_exceptions_for_connection_errors=UpperCamelCase__ )
self.assertIsNone(UpperCamelCase__ )
# This check we did call the fake head request
mock_head.assert_called()
def lowerCamelCase ( self ) -> Any:
'''simple docstring'''
self.assertTrue(has_file("hf-internal-testing/tiny-bert-pt-only" , UpperCamelCase__ ) )
self.assertFalse(has_file("hf-internal-testing/tiny-bert-pt-only" , UpperCamelCase__ ) )
self.assertFalse(has_file("hf-internal-testing/tiny-bert-pt-only" , UpperCamelCase__ ) )
def lowerCamelCase ( self ) -> str:
'''simple docstring'''
self.assertIsNone(get_file_from_repo("bert-base-cased" , "ahah.txt" ) )
# The function raises if the repository does not exist.
with self.assertRaisesRegex(UpperCamelCase__ , "is not a valid model identifier" ):
get_file_from_repo("bert-base-case" , UpperCamelCase__ )
# The function raises if the revision does not exist.
with self.assertRaisesRegex(UpperCamelCase__ , "is not a valid git identifier" ):
get_file_from_repo("bert-base-cased" , UpperCamelCase__ , revision="ahaha" )
snake_case : int = get_file_from_repo("bert-base-cased" , UpperCamelCase__ )
# The name is the cached name which is not very easy to test, so instead we load the content.
snake_case : str = json.loads(open(UpperCamelCase__ , "r" ).read() )
self.assertEqual(config["hidden_size"] , 768 )
def lowerCamelCase ( self ) -> Optional[Any]:
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmp_dir:
snake_case : int = Path(UpperCamelCase__ ) / "a.txt"
filename.touch()
self.assertEqual(get_file_from_repo(UpperCamelCase__ , "a.txt" ) , str(UpperCamelCase__ ) )
self.assertIsNone(get_file_from_repo(UpperCamelCase__ , "b.txt" ) )
| 203 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase , _UpperCAmelCase , unittest.TestCase ):
A_ : Optional[int] = CycleDiffusionPipeline
A_ : List[Any] = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
'negative_prompt',
'height',
'width',
'negative_prompt_embeds',
}
A_ : List[str] = PipelineTesterMixin.required_optional_params - {'latents'}
A_ : List[str] = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'source_prompt'} )
A_ : List[str] = IMAGE_TO_IMAGE_IMAGE_PARAMS
A_ : Any = IMAGE_TO_IMAGE_IMAGE_PARAMS
def a (self : Dict ):
"""simple docstring"""
torch.manual_seed(0 )
__snake_case = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') , up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''') , cross_attention_dim=32 , )
__snake_case = DDIMScheduler(
beta_start=0.0_0_0_8_5 , beta_end=0.0_1_2 , beta_schedule='''scaled_linear''' , num_train_timesteps=1000 , clip_sample=a__ , set_alpha_to_one=a__ , )
torch.manual_seed(0 )
__snake_case = 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 )
__snake_case = 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 , )
__snake_case = CLIPTextModel(a__ )
__snake_case = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
__snake_case = {
'''unet''': unet,
'''scheduler''': scheduler,
'''vae''': vae,
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''safety_checker''': None,
'''feature_extractor''': None,
}
return components
def a (self : List[str] , a__ : Tuple , a__ : Optional[Any]=0 ):
"""simple docstring"""
__snake_case = floats_tensor((1, 3, 32, 32) , rng=random.Random(a__ ) ).to(a__ )
__snake_case = image / 2 + 0.5
if str(a__ ).startswith('''mps''' ):
__snake_case = torch.manual_seed(a__ )
else:
__snake_case = torch.Generator(device=a__ ).manual_seed(a__ )
__snake_case = {
'''prompt''': '''An astronaut riding an elephant''',
'''source_prompt''': '''An astronaut riding a horse''',
'''image''': image,
'''generator''': generator,
'''num_inference_steps''': 2,
'''eta''': 0.1,
'''strength''': 0.8,
'''guidance_scale''': 3,
'''source_guidance_scale''': 1,
'''output_type''': '''numpy''',
}
return inputs
def a (self : str ):
"""simple docstring"""
__snake_case = '''cpu''' # ensure determinism for the device-dependent torch.Generator
__snake_case = self.get_dummy_components()
__snake_case = CycleDiffusionPipeline(**a__ )
__snake_case = pipe.to(a__ )
pipe.set_progress_bar_config(disable=a__ )
__snake_case = self.get_dummy_inputs(a__ )
__snake_case = pipe(**a__ )
__snake_case = output.images
__snake_case = images[0, -3:, -3:, -1]
assert images.shape == (1, 32, 32, 3)
__snake_case = np.array([0.4_4_5_9, 0.4_9_4_3, 0.4_5_4_4, 0.6_6_4_3, 0.5_4_7_4, 0.4_3_2_7, 0.5_7_0_1, 0.5_9_5_9, 0.5_1_7_9] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
@unittest.skipIf(torch_device != '''cuda''' , '''This test requires a GPU''' )
def a (self : List[str] ):
"""simple docstring"""
__snake_case = self.get_dummy_components()
for name, module in components.items():
if hasattr(a__ , '''half''' ):
__snake_case = module.half()
__snake_case = CycleDiffusionPipeline(**a__ )
__snake_case = pipe.to(a__ )
pipe.set_progress_bar_config(disable=a__ )
__snake_case = self.get_dummy_inputs(a__ )
__snake_case = pipe(**a__ )
__snake_case = output.images
__snake_case = images[0, -3:, -3:, -1]
assert images.shape == (1, 32, 32, 3)
__snake_case = np.array([0.3_5_0_6, 0.4_5_4_3, 0.4_4_6, 0.4_5_7_5, 0.5_1_9_5, 0.4_1_5_5, 0.5_2_7_3, 0.5_1_8, 0.4_1_1_6] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
@skip_mps
def a (self : Any ):
"""simple docstring"""
return super().test_save_load_local()
@unittest.skip('''non-deterministic pipeline''' )
def a (self : Any ):
"""simple docstring"""
return super().test_inference_batch_single_identical()
@skip_mps
def a (self : Any ):
"""simple docstring"""
return super().test_dict_tuple_outputs_equivalent()
@skip_mps
def a (self : str ):
"""simple docstring"""
return super().test_save_load_optional_components()
@skip_mps
def a (self : Dict ):
"""simple docstring"""
return super().test_attention_slicing_forward_pass()
@slow
@require_torch_gpu
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
def a (self : Union[str, Any] ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def a (self : Tuple ):
"""simple docstring"""
__snake_case = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/cycle-diffusion/black_colored_car.png''' )
__snake_case = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy''' )
__snake_case = init_image.resize((512, 512) )
__snake_case = '''CompVis/stable-diffusion-v1-4'''
__snake_case = DDIMScheduler.from_pretrained(a__ , subfolder='''scheduler''' )
__snake_case = CycleDiffusionPipeline.from_pretrained(
a__ , scheduler=a__ , safety_checker=a__ , torch_dtype=torch.floataa , revision='''fp16''' )
pipe.to(a__ )
pipe.set_progress_bar_config(disable=a__ )
pipe.enable_attention_slicing()
__snake_case = '''A black colored car'''
__snake_case = '''A blue colored car'''
__snake_case = torch.manual_seed(0 )
__snake_case = pipe(
prompt=a__ , source_prompt=a__ , image=a__ , num_inference_steps=100 , eta=0.1 , strength=0.8_5 , guidance_scale=3 , source_guidance_scale=1 , generator=a__ , output_type='''np''' , )
__snake_case = output.images
# the values aren't exactly equal, but the images look the same visually
assert np.abs(image - expected_image ).max() < 5E-1
def a (self : Tuple ):
"""simple docstring"""
__snake_case = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/cycle-diffusion/black_colored_car.png''' )
__snake_case = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy''' )
__snake_case = init_image.resize((512, 512) )
__snake_case = '''CompVis/stable-diffusion-v1-4'''
__snake_case = DDIMScheduler.from_pretrained(a__ , subfolder='''scheduler''' )
__snake_case = CycleDiffusionPipeline.from_pretrained(a__ , scheduler=a__ , safety_checker=a__ )
pipe.to(a__ )
pipe.set_progress_bar_config(disable=a__ )
pipe.enable_attention_slicing()
__snake_case = '''A black colored car'''
__snake_case = '''A blue colored car'''
__snake_case = torch.manual_seed(0 )
__snake_case = pipe(
prompt=a__ , source_prompt=a__ , image=a__ , num_inference_steps=100 , eta=0.1 , strength=0.8_5 , guidance_scale=3 , source_guidance_scale=1 , generator=a__ , output_type='''np''' , )
__snake_case = output.images
assert np.abs(image - expected_image ).max() < 2E-2
| 356 |
# Algorithm for the pigeonhole sorting
def lowerCamelCase__ ( snake_case_ : int ) -> Optional[int]:
__snake_case = min(snake_case_ ) # min() finds the minimum value
__snake_case = max(snake_case_ ) # max() finds the maximum value
__snake_case = max_val - min_val + 1 # size is difference of max and min values plus one
# list of pigeonholes of size equal to the variable size
__snake_case = [0] * size
# Populate the pigeonholes.
for x in a:
assert isinstance(snake_case_ , snake_case_ ), "integers only please"
holes[x - min_val] += 1
# Putting the elements back into the array in an order.
__snake_case = 0
for count in range(snake_case_ ):
while holes[count] > 0:
holes[count] -= 1
__snake_case = count + min_val
i += 1
def lowerCamelCase__ ( ) -> Union[str, Any]:
__snake_case = [8, 3, 2, 7, 4, 6, 8]
pigeonhole_sort(snake_case_ )
print('''Sorted order is:''' , ''' '''.join(snake_case_ ) )
if __name__ == "__main__":
main()
| 238 | 0 |
import unittest
import numpy as np
def __lowerCAmelCase ( a__ , a__ , a__ , a__ = None , ) -> np.ndarray:
__a = np.shape(a__ )
__a = np.shape(a__ )
__a = np.shape(a__ )
if shape_a[0] != shape_b[0]:
__a = (
'''Expected the same number of rows for A and B. '''
F"""Instead found A of size {shape_a} and B of size {shape_b}"""
)
raise ValueError(a__ )
if shape_b[1] != shape_c[1]:
__a = (
'''Expected the same number of columns for B and C. '''
F"""Instead found B of size {shape_b} and C of size {shape_c}"""
)
raise ValueError(a__ )
__a = pseudo_inv
if a_inv is None:
try:
__a = np.linalg.inv(a__ )
except np.linalg.LinAlgError:
raise ValueError(
'''Input matrix A is not invertible. Cannot compute Schur complement.''' )
return mat_c - mat_b.T @ a_inv @ mat_b
class __A( unittest.TestCase ):
def SCREAMING_SNAKE_CASE_ ( self ) -> None:
'''simple docstring'''
__a = np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]] )
__a = np.array([[0, 3], [3, 0], [2, 3]] )
__a = np.array([[2, 1], [6, 3]] )
__a = schur_complement(_snake_case , _snake_case , _snake_case )
__a = np.block([[a, b], [b.T, c]] )
__a = np.linalg.det(_snake_case )
__a = np.linalg.det(_snake_case )
__a = np.linalg.det(_snake_case )
self.assertAlmostEqual(_snake_case , det_a * det_s )
def SCREAMING_SNAKE_CASE_ ( self ) -> None:
'''simple docstring'''
__a = np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]] )
__a = np.array([[0, 3], [3, 0], [2, 3]] )
__a = np.array([[2, 1], [6, 3]] )
with self.assertRaises(_snake_case ):
schur_complement(_snake_case , _snake_case , _snake_case )
def SCREAMING_SNAKE_CASE_ ( self ) -> None:
'''simple docstring'''
__a = np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]] )
__a = np.array([[0, 3], [3, 0], [2, 3]] )
__a = np.array([[2, 1, 3], [6, 3, 5]] )
with self.assertRaises(_snake_case ):
schur_complement(_snake_case , _snake_case , _snake_case )
if __name__ == "__main__":
import doctest
doctest.testmod()
unittest.main() | 6 |
from math import pi
def SCREAMING_SNAKE_CASE__ ( __a , __a ):
return 2 * pi * radius * (angle / 3_60)
if __name__ == "__main__":
print(arc_length(90, 10))
| 327 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
lowerCamelCase = {
"""configuration_owlvit""": [
"""OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""OwlViTConfig""",
"""OwlViTOnnxConfig""",
"""OwlViTTextConfig""",
"""OwlViTVisionConfig""",
],
"""processing_owlvit""": ["""OwlViTProcessor"""],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase = ["""OwlViTFeatureExtractor"""]
lowerCamelCase = ["""OwlViTImageProcessor"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase = [
"""OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""OwlViTModel""",
"""OwlViTPreTrainedModel""",
"""OwlViTTextModel""",
"""OwlViTVisionModel""",
"""OwlViTForObjectDetection""",
]
if TYPE_CHECKING:
from .configuration_owlvit import (
OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
OwlViTConfig,
OwlViTOnnxConfig,
OwlViTTextConfig,
OwlViTVisionConfig,
)
from .processing_owlvit import OwlViTProcessor
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_owlvit import OwlViTFeatureExtractor
from .image_processing_owlvit import OwlViTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_owlvit import (
OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
OwlViTForObjectDetection,
OwlViTModel,
OwlViTPreTrainedModel,
OwlViTTextModel,
OwlViTVisionModel,
)
else:
import sys
lowerCamelCase = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 241 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase = logging.get_logger(__name__)
lowerCamelCase = {
"""RWKV/rwkv-4-169m-pile""": """https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json""",
"""RWKV/rwkv-4-430m-pile""": """https://huggingface.co/RWKV/rwkv-4-430m-pile/resolve/main/config.json""",
"""RWKV/rwkv-4-1b5-pile""": """https://huggingface.co/RWKV/rwkv-4-1b5-pile/resolve/main/config.json""",
"""RWKV/rwkv-4-3b-pile""": """https://huggingface.co/RWKV/rwkv-4-3b-pile/resolve/main/config.json""",
"""RWKV/rwkv-4-7b-pile""": """https://huggingface.co/RWKV/rwkv-4-7b-pile/resolve/main/config.json""",
"""RWKV/rwkv-4-14b-pile""": """https://huggingface.co/RWKV/rwkv-4-14b-pile/resolve/main/config.json""",
"""RWKV/rwkv-raven-1b5""": """https://huggingface.co/RWKV/rwkv-raven-1b5/resolve/main/config.json""",
"""RWKV/rwkv-raven-3b""": """https://huggingface.co/RWKV/rwkv-raven-3b/resolve/main/config.json""",
"""RWKV/rwkv-raven-7b""": """https://huggingface.co/RWKV/rwkv-raven-7b/resolve/main/config.json""",
"""RWKV/rwkv-raven-14b""": """https://huggingface.co/RWKV/rwkv-raven-14b/resolve/main/config.json""",
}
class lowercase__ ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
UpperCamelCase = '''rwkv'''
UpperCamelCase = {'''max_position_embeddings''': '''context_length'''}
def __init__( self : str , _UpperCAmelCase : int=50277 , _UpperCAmelCase : Optional[Any]=1024 , _UpperCAmelCase : str=4096 , _UpperCAmelCase : Any=32 , _UpperCAmelCase : Any=None , _UpperCAmelCase : Optional[int]=None , _UpperCAmelCase : Tuple=1e-5 , _UpperCAmelCase : str=0 , _UpperCAmelCase : Any=0 , _UpperCAmelCase : Union[str, Any]=6 , _UpperCAmelCase : Dict=False , _UpperCAmelCase : Any=True , **_UpperCAmelCase : Optional[Any] , ) -> str:
'''simple docstring'''
UpperCAmelCase_ = vocab_size
UpperCAmelCase_ = context_length
UpperCAmelCase_ = hidden_size
UpperCAmelCase_ = num_hidden_layers
UpperCAmelCase_ = attention_hidden_size if attention_hidden_size is not None else hidden_size
UpperCAmelCase_ = intermediate_size if intermediate_size is not None else 4 * hidden_size
UpperCAmelCase_ = layer_norm_epsilon
UpperCAmelCase_ = rescale_every
UpperCAmelCase_ = use_cache
UpperCAmelCase_ = bos_token_id
UpperCAmelCase_ = eos_token_id
super().__init__(
tie_word_embeddings=_UpperCAmelCase , bos_token_id=_UpperCAmelCase , eos_token_id=_UpperCAmelCase , **_UpperCAmelCase )
| 241 | 1 |
from datetime import datetime as dt
import os
from github import Github
__lowerCamelCase : Optional[Any] = [
'good first issue',
'good second issue',
'good difficult issue',
'feature request',
'new model',
'wip',
]
def A_ ( ) -> Union[str, Any]:
UpperCamelCase : List[Any] = Github(os.environ["GITHUB_TOKEN"] )
UpperCamelCase : Any = g.get_repo("huggingface/transformers" )
UpperCamelCase : Tuple = repo.get_issues(state="open" )
for issue in open_issues:
UpperCamelCase : Any = sorted([comment for comment in issue.get_comments()] , key=lambda _lowerCAmelCase : i.created_at , reverse=snake_case_ )
UpperCamelCase : Union[str, Any] = comments[0] if len(snake_case_ ) > 0 else None
if (
last_comment is not None
and last_comment.user.login == "github-actions[bot]"
and (dt.utcnow() - issue.updated_at).days > 7
and (dt.utcnow() - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# print(f"Would close issue {issue.number} since it has been 7 days of inactivity since bot mention.")
issue.edit(state="closed" )
elif (
(dt.utcnow() - issue.updated_at).days > 23
and (dt.utcnow() - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# print(f"Would add stale comment to {issue.number}")
issue.create_comment(
"This issue has been automatically marked as stale because it has not had "
"recent activity. If you think this still needs to be addressed "
"please comment on this thread.\n\nPlease note that issues that do not follow the "
"[contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) "
"are likely to be ignored." )
if __name__ == "__main__":
main()
| 52 | '''simple docstring'''
from typing import Callable, Dict, Optional, Tuple
import torch
from torch import nn
from torch.distributions import (
AffineTransform,
Distribution,
Independent,
NegativeBinomial,
Normal,
StudentT,
TransformedDistribution,
)
class __A ( UpperCamelCase__ ):
def __init__(self : int , __a : Distribution , __a : Dict=None , __a : int=None , __a : Any=0 ):
UpperCAmelCase_ = 1.0 if scale is None else scale
UpperCAmelCase_ = 0.0 if loc is None else loc
super().__init__(__a , [AffineTransform(loc=self.loc , scale=self.scale , event_dim=__a )] )
@property
def _lowercase (self : Union[str, Any] ):
return self.base_dist.mean * self.scale + self.loc
@property
def _lowercase (self : List[Any] ):
return self.base_dist.variance * self.scale**2
@property
def _lowercase (self : List[Any] ):
return self.variance.sqrt()
class __A ( nn.Module ):
def __init__(self : Optional[int] , __a : int , __a : Dict[str, int] , __a : Callable[..., Tuple[torch.Tensor]] , **__a : List[str] ):
super().__init__(**__a )
UpperCAmelCase_ = args_dim
UpperCAmelCase_ = nn.ModuleList([nn.Linear(__a , __a ) for dim in args_dim.values()] )
UpperCAmelCase_ = domain_map
def _lowercase (self : List[str] , __a : torch.Tensor ):
UpperCAmelCase_ = [proj(__a ) for proj in self.proj]
return self.domain_map(*__a )
class __A ( nn.Module ):
def __init__(self : Union[str, Any] , __a : List[str] ):
super().__init__()
UpperCAmelCase_ = function
def _lowercase (self : Optional[int] , __a : List[str] , *__a : Optional[int] ):
return self.function(__a , *__a )
class __A :
a__ : type
a__ : int
a__ : Dict[str, int]
def __init__(self : List[Any] , __a : int = 1 ):
UpperCAmelCase_ = dim
UpperCAmelCase_ = {k: dim * self.args_dim[k] for k in self.args_dim}
def _lowercase (self : Any , __a : Any ):
if self.dim == 1:
return self.distribution_class(*__a )
else:
return Independent(self.distribution_class(*__a ) , 1 )
def _lowercase (self : List[str] , __a : Union[str, Any] , __a : Optional[torch.Tensor] = None , __a : Optional[torch.Tensor] = None , ):
UpperCAmelCase_ = self._base_distribution(__a )
if loc is None and scale is None:
return distr
else:
return AffineTransformed(__a , loc=__a , scale=__a , event_dim=self.event_dim )
@property
def _lowercase (self : Any ):
return () if self.dim == 1 else (self.dim,)
@property
def _lowercase (self : Dict ):
return len(self.event_shape )
@property
def _lowercase (self : Tuple ):
return 0.0
def _lowercase (self : List[str] , __a : int ):
return ParameterProjection(
in_features=__a , args_dim=self.args_dim , domain_map=LambdaLayer(self.domain_map ) , )
def _lowercase (self : Optional[int] , *__a : torch.Tensor ):
raise NotImplementedError()
@staticmethod
def _lowercase (__a : torch.Tensor ):
return (x + torch.sqrt(torch.square(__a ) + 4.0 )) / 2.0
class __A ( UpperCamelCase__ ):
a__ : Dict[str, int] = {"df": 1, "loc": 1, "scale": 1}
a__ : type = StudentT
@classmethod
def _lowercase (cls : Union[str, Any] , __a : torch.Tensor , __a : torch.Tensor , __a : torch.Tensor ):
UpperCAmelCase_ = cls.squareplus(__a ).clamp_min(torch.finfo(scale.dtype ).eps )
UpperCAmelCase_ = 2.0 + cls.squareplus(__a )
return df.squeeze(-1 ), loc.squeeze(-1 ), scale.squeeze(-1 )
class __A ( UpperCamelCase__ ):
a__ : Dict[str, int] = {"loc": 1, "scale": 1}
a__ : type = Normal
@classmethod
def _lowercase (cls : Tuple , __a : torch.Tensor , __a : torch.Tensor ):
UpperCAmelCase_ = cls.squareplus(__a ).clamp_min(torch.finfo(scale.dtype ).eps )
return loc.squeeze(-1 ), scale.squeeze(-1 )
class __A ( UpperCamelCase__ ):
a__ : Dict[str, int] = {"total_count": 1, "logits": 1}
a__ : type = NegativeBinomial
@classmethod
def _lowercase (cls : Optional[Any] , __a : torch.Tensor , __a : torch.Tensor ):
UpperCAmelCase_ = cls.squareplus(__a )
return total_count.squeeze(-1 ), logits.squeeze(-1 )
def _lowercase (self : List[str] , __a : str ):
UpperCAmelCase_ , UpperCAmelCase_ = distr_args
if self.dim == 1:
return self.distribution_class(total_count=__a , logits=__a )
else:
return Independent(self.distribution_class(total_count=__a , logits=__a ) , 1 )
def _lowercase (self : Optional[Any] , __a : int , __a : Optional[torch.Tensor] = None , __a : Optional[torch.Tensor] = None ):
UpperCAmelCase_ , UpperCAmelCase_ = distr_args
if scale is not None:
# See scaling property of Gamma.
logits += scale.log()
return self._base_distribution((total_count, logits) )
| 1 | 0 |
lowerCamelCase : Tuple =[0, 2, 4, 6, 8]
lowerCamelCase : str =[1, 3, 5, 7, 9]
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> int:
if remaining_length == 0:
if digits[0] == 0 or digits[-1] == 0:
return 0
for i in range(length // 2 - 1 , -1 , -1 ):
remainder += digits[i] + digits[length - i - 1]
if remainder % 2 == 0:
return 0
remainder //= 10
return 1
if remaining_length == 1:
if remainder % 2 == 0:
return 0
UpperCamelCase__ : Any = 0
for digit in range(10 ):
UpperCamelCase__ : Optional[Any] = digit
result += reversible_numbers(
0 , (remainder + 2 * digit) // 10 , __lowerCAmelCase , __lowerCAmelCase )
return result
UpperCamelCase__ : str = 0
for digita in range(10 ):
UpperCamelCase__ : List[Any] = digita
if (remainder + digita) % 2 == 0:
UpperCamelCase__ : List[str] = ODD_DIGITS
else:
UpperCamelCase__ : List[Any] = EVEN_DIGITS
for digita in other_parity_digits:
UpperCamelCase__ : int = digita
result += reversible_numbers(
remaining_length - 2 , (remainder + digita + digita) // 10 , __lowerCAmelCase , __lowerCAmelCase , )
return result
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase = 9 ) -> int:
UpperCamelCase__ : Optional[Any] = 0
for length in range(1 , max_power + 1 ):
result += reversible_numbers(__lowerCAmelCase , 0 , [0] * length , __lowerCAmelCase )
return result
if __name__ == "__main__":
print(F"""{solution() = }""") | 196 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=A__ )
class __a ( A__ ):
_lowerCAmelCase : str = field(default='''language-modeling''' , metadata={'''include_in_asdict_even_if_is_default''': True} )
_lowerCAmelCase : ClassVar[Features] = Features({'''text''': Value('''string''' )} )
_lowerCAmelCase : ClassVar[Features] = Features({} )
_lowerCAmelCase : str = "text"
@property
def __lowercase ( self : str ):
'''simple docstring'''
return {self.text_column: "text"} | 196 | 1 |
from __future__ import annotations
import math
def UpperCAmelCase ( a_ ) -> int:
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(a_ ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def UpperCAmelCase ( a_ ) -> Tuple:
"""simple docstring"""
__A = str(a_ )
__A = [n]
for i in range(1 , len(a_ ) ):
list_nums.append(int(str_num[i:] ) )
list_nums.append(int(str_num[:-i] ) )
return list_nums
def UpperCAmelCase ( a_ ) -> Optional[int]:
"""simple docstring"""
if len(str(a_ ) ) > 3:
if not is_prime(int(str(a_ )[-3:] ) ) or not is_prime(int(str(a_ )[:3] ) ):
return False
return True
def UpperCAmelCase ( a_ = 1_1 ) -> str:
"""simple docstring"""
__A = []
__A = 1_3
while len(a_ ) != count:
if validate(a_ ):
__A = list_truncated_nums(a_ )
if all(is_prime(a_ ) for i in list_nums ):
list_truncated_primes.append(a_ )
num += 2
return list_truncated_primes
def UpperCAmelCase ( ) -> Optional[int]:
"""simple docstring"""
return sum(compute_truncated_primes(1_1 ) )
if __name__ == "__main__":
print(f'''{sum(compute_truncated_primes(11)) = }''')
| 15 |
"""simple docstring"""
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configuration_fsmt import FSMTConfig
from transformers.optimization import (
Adafactor,
AdamW,
get_constant_schedule,
get_constant_schedule_with_warmup,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
)
from transformers.trainer_pt_utils import get_tpu_sampler
from transformers.training_args import ParallelMode
from transformers.utils import is_torch_tpu_available
if is_fairscale_available():
from fairscale.optim import OSS
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
'''linear''': get_linear_schedule_with_warmup,
'''cosine''': get_cosine_schedule_with_warmup,
'''cosine_w_restarts''': get_cosine_with_hard_restarts_schedule_with_warmup,
'''polynomial''': get_polynomial_decay_schedule_with_warmup,
'''constant''': get_constant_schedule,
'''constant_w_warmup''': get_constant_schedule_with_warmup,
}
class SCREAMING_SNAKE_CASE__ ( lowercase ):
"""simple docstring"""
def __init__( self , snake_case__=None , snake_case__=None , *snake_case__ , **snake_case__ ):
"""simple docstring"""
super().__init__(*snake_case__ , **snake_case__ )
if config is None:
assert isinstance(self.model , snake_case__ ), (
"If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
f""" {self.model.__class__}"""
)
lowerCAmelCase : Optional[int] = self.model.config
else:
lowerCAmelCase : List[str] = config
lowerCAmelCase : Any = data_args
lowerCAmelCase : Tuple = self.config.tgt_vocab_size if isinstance(self.config , snake_case__ ) else self.config.vocab_size
if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
assert self.config.pad_token_id is not None, (
"Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss"
" calculation or doing label smoothing."
)
if self.config.pad_token_id is None and self.config.eos_token_id is not None:
logger.warning(
f"""The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for"""
" padding.." )
if self.args.label_smoothing == 0:
lowerCAmelCase : int = torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id )
else:
# dynamically import label_smoothed_nll_loss
from utils import label_smoothed_nll_loss
lowerCAmelCase : Tuple = label_smoothed_nll_loss
def lowercase__ ( self , snake_case__ ):
"""simple docstring"""
if self.optimizer is None:
lowerCAmelCase : Optional[int] = ["bias", "LayerNorm.weight"]
lowerCAmelCase : str = [
{
"params": [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay )],
"weight_decay": self.args.weight_decay,
},
{
"params": [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay )],
"weight_decay": 0.0,
},
]
lowerCAmelCase : Union[str, Any] = Adafactor if self.args.adafactor else AdamW
if self.args.adafactor:
lowerCAmelCase : Dict = Adafactor
lowerCAmelCase : Optional[int] = {"scale_parameter": False, "relative_step": False}
else:
lowerCAmelCase : int = AdamW
lowerCAmelCase : int = {
"betas": (self.args.adam_betaa, self.args.adam_betaa),
"eps": self.args.adam_epsilon,
}
lowerCAmelCase : Any = self.args.learning_rate
if self.sharded_ddp:
lowerCAmelCase : int = OSS(
params=snake_case__ , optim=snake_case__ , **snake_case__ , )
else:
lowerCAmelCase : Any = optimizer_cls(snake_case__ , **snake_case__ )
if self.lr_scheduler is None:
lowerCAmelCase : Tuple = self._get_lr_scheduler(snake_case__ )
else: # ignoring --lr_scheduler
logger.warning("scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored." )
def lowercase__ ( self , snake_case__ ):
"""simple docstring"""
lowerCAmelCase : Optional[int] = arg_to_scheduler[self.args.lr_scheduler]
if self.args.lr_scheduler == "constant":
lowerCAmelCase : Tuple = schedule_func(self.optimizer )
elif self.args.lr_scheduler == "constant_w_warmup":
lowerCAmelCase : Any = schedule_func(self.optimizer , num_warmup_steps=self.args.warmup_steps )
else:
lowerCAmelCase : str = schedule_func(
self.optimizer , num_warmup_steps=self.args.warmup_steps , num_training_steps=snake_case__ )
return scheduler
def lowercase__ ( self ):
"""simple docstring"""
if isinstance(self.train_dataset , torch.utils.data.IterableDataset ):
return None
elif is_torch_tpu_available():
return get_tpu_sampler(self.train_dataset )
else:
if self.args.sortish_sampler:
self.train_dataset.make_sortish_sampler(
self.args.per_device_train_batch_size , distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED) , )
return (
RandomSampler(self.train_dataset )
if self.args.local_rank == -1
else DistributedSampler(self.train_dataset )
)
def lowercase__ ( self , snake_case__ , snake_case__ , snake_case__ ):
"""simple docstring"""
if self.args.label_smoothing == 0:
if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
# force training to ignore pad token
lowerCAmelCase : Dict = model(**snake_case__ , use_cache=snake_case__ )[0]
lowerCAmelCase : List[Any] = self.loss_fn(logits.view(-1 , logits.shape[-1] ) , labels.view(-1 ) )
else:
# compute usual loss via models
lowerCAmelCase , lowerCAmelCase : str = model(**snake_case__ , labels=snake_case__ , use_cache=snake_case__ )[:2]
else:
# compute label smoothed loss
lowerCAmelCase : int = model(**snake_case__ , use_cache=snake_case__ )[0]
lowerCAmelCase : List[Any] = torch.nn.functional.log_softmax(snake_case__ , dim=-1 )
lowerCAmelCase , lowerCAmelCase : str = self.loss_fn(snake_case__ , snake_case__ , self.args.label_smoothing , ignore_index=self.config.pad_token_id )
return loss, logits
def lowercase__ ( self , snake_case__ , snake_case__ ):
"""simple docstring"""
lowerCAmelCase : Tuple = inputs.pop("labels" )
lowerCAmelCase , lowerCAmelCase : str = self._compute_loss(snake_case__ , snake_case__ , snake_case__ )
return loss
def lowercase__ ( self , snake_case__ , snake_case__ , snake_case__ , snake_case__ = None , ):
"""simple docstring"""
lowerCAmelCase : List[str] = self._prepare_inputs(snake_case__ )
lowerCAmelCase : Union[str, Any] = {
"max_length": self.data_args.val_max_target_length
if self.data_args is not None
else self.config.max_length,
"num_beams": self.data_args.eval_beams if self.data_args is not None else self.config.num_beams,
}
if self.args.predict_with_generate and not self.args.prediction_loss_only:
lowerCAmelCase : Dict = self.model.generate(
inputs["input_ids"] , attention_mask=inputs["attention_mask"] , **snake_case__ , )
# in case the batch is shorter than max length, the output should be padded
if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
lowerCAmelCase : Dict = self._pad_tensors_to_max_len(snake_case__ , gen_kwargs["max_length"] )
lowerCAmelCase : Optional[Any] = inputs.pop("labels" )
with torch.no_grad():
# compute loss on predict data
lowerCAmelCase , lowerCAmelCase : Dict = self._compute_loss(snake_case__ , snake_case__ , snake_case__ )
lowerCAmelCase : List[str] = loss.mean().detach()
if self.args.prediction_loss_only:
return (loss, None, None)
lowerCAmelCase : int = generated_tokens if self.args.predict_with_generate else logits
if labels.shape[-1] < gen_kwargs["max_length"]:
lowerCAmelCase : Optional[int] = self._pad_tensors_to_max_len(snake_case__ , gen_kwargs["max_length"] )
return (loss, logits, labels)
def lowercase__ ( self , snake_case__ , snake_case__ ):
"""simple docstring"""
lowerCAmelCase : List[Any] = self.config.pad_token_id if self.config.pad_token_id is not None else self.config.eos_token_id
if pad_token_id is None:
raise ValueError(
"Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be"
f""" padded to `max_length`={max_length}""" )
lowerCAmelCase : Optional[Any] = pad_token_id * torch.ones(
(tensor.shape[0], max_length) , dtype=tensor.dtype , device=tensor.device )
lowerCAmelCase : int = tensor
return padded_tensor
| 108 | 0 |
import inspect
import unittest
from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, 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 ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel
from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
class SCREAMING_SNAKE_CASE__ :
def __init__( self , a , a=13 , a=64 , a=2 , a=3 , a=True , a=True , a=32 , a=5 , a=4 , a=37 , a="gelu" , a=0.1 , a=0.1 , a=10 , a=0.02 , a=[1, 16, 4, 4] , a=None , ):
lowercase__ : Any = parent
lowercase__ : Dict = batch_size
lowercase__ : List[Any] = image_size
lowercase__ : Dict = patch_size
lowercase__ : Union[str, Any] = num_channels
lowercase__ : Any = is_training
lowercase__ : List[str] = use_labels
lowercase__ : Optional[Any] = hidden_size
lowercase__ : Tuple = num_hidden_layers
lowercase__ : Union[str, Any] = num_attention_heads
lowercase__ : Union[str, Any] = intermediate_size
lowercase__ : List[Any] = hidden_act
lowercase__ : List[str] = hidden_dropout_prob
lowercase__ : List[Any] = attention_probs_dropout_prob
lowercase__ : Optional[int] = type_sequence_label_size
lowercase__ : str = initializer_range
lowercase__ : List[str] = scope
lowercase__ : int = backbone_featmap_shape
# in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
# the number of patches is based on the feature map of the backbone, which by default uses an output stride
# of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size
lowercase__ : str = (self.image_size // 32) ** 2
lowercase__ : str = num_patches + 1
def snake_case_ ( self):
lowercase__ : str = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
lowercase__ : Optional[Any] = None
if self.use_labels:
lowercase__ : List[str] = ids_tensor([self.batch_size] , self.type_sequence_label_size)
lowercase__ : Any = self.get_config()
return config, pixel_values, labels
def snake_case_ ( self):
lowercase__ : List[str] = {
'global_padding': 'same',
'layer_type': 'bottleneck',
'depths': [3, 4, 9],
'out_features': ['stage1', 'stage2', 'stage3'],
'embedding_dynamic_padding': True,
'hidden_sizes': [4, 8, 16, 32],
'num_groups': 2,
}
return ViTHybridConfig(
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=a , initializer_range=self.initializer_range , backbone_featmap_shape=self.backbone_featmap_shape , backbone_config=a , )
def snake_case_ ( self , a , a , a):
lowercase__ : Dict = ViTHybridModel(config=a)
model.to(a)
model.eval()
lowercase__ : Any = model(a)
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size))
def snake_case_ ( self , a , a , a):
lowercase__ : int = self.type_sequence_label_size
lowercase__ : Dict = ViTHybridForImageClassification(a)
model.to(a)
model.eval()
lowercase__ : Optional[int] = model(a , labels=a)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size))
def snake_case_ ( self):
lowercase__ : Optional[int] = self.prepare_config_and_inputs()
lowercase__ , lowercase__ , lowercase__ : str = config_and_inputs
lowercase__ : Tuple = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class SCREAMING_SNAKE_CASE__ (__snake_case , __snake_case , unittest.TestCase ):
__lowerCamelCase : int = (ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
__lowerCamelCase : Optional[Any] = (
{"""feature-extraction""": ViTHybridModel, """image-classification""": ViTHybridForImageClassification}
if is_torch_available()
else {}
)
__lowerCamelCase : List[str] = False
__lowerCamelCase : Union[str, Any] = False
__lowerCamelCase : List[str] = False
def snake_case_ ( self):
lowercase__ : str = ViTHybridModelTester(self)
lowercase__ : Dict = ConfigTester(self , config_class=a , has_text_modality=a , hidden_size=37)
def snake_case_ ( self):
self.config_tester.run_common_tests()
@unittest.skip(reason='ViT does not use inputs_embeds')
def snake_case_ ( self):
pass
def snake_case_ ( self):
lowercase__ , lowercase__ : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ : int = model_class(a)
self.assertIsInstance(model.get_input_embeddings() , (nn.Module))
lowercase__ : str = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(a , nn.Linear))
def snake_case_ ( self):
lowercase__ , lowercase__ : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ : List[Any] = model_class(a)
lowercase__ : Optional[int] = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase__ : Any = [*signature.parameters.keys()]
lowercase__ : Optional[Any] = ['pixel_values']
self.assertListEqual(arg_names[:1] , a)
def snake_case_ ( self):
lowercase__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*a)
def snake_case_ ( self):
lowercase__ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*a)
def snake_case_ ( self):
lowercase__ , lowercase__ : Any = self.model_tester.prepare_config_and_inputs_for_common()
lowercase__ : Union[str, Any] = _config_zero_init(a)
for model_class in self.all_model_classes:
lowercase__ : List[Any] = model_class(config=a)
# Skip the check for the backbone
for name, module in model.named_modules():
if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
lowercase__ : Any = [f"""{name}.{key}""" for key in module.state_dict().keys()]
break
for name, param in model.named_parameters():
if param.requires_grad:
if name in backbone_params:
continue
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""" , )
@slow
def snake_case_ ( self):
for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase__ : Any = ViTHybridModel.from_pretrained(a)
self.assertIsNotNone(a)
def snake_case__ ( ):
'''simple docstring'''
lowercase__ : List[Any] = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_torch
@require_vision
class SCREAMING_SNAKE_CASE__ (unittest.TestCase ):
@cached_property
def snake_case_ ( self):
return (
ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0])
if is_vision_available()
else None
)
@slow
def snake_case_ ( self):
lowercase__ : Optional[Any] = ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(
a)
lowercase__ : Union[str, Any] = self.default_image_processor
lowercase__ : str = prepare_img()
lowercase__ : Union[str, Any] = image_processor(images=a , return_tensors='pt').to(a)
# forward pass
with torch.no_grad():
lowercase__ : Optional[Any] = model(**a)
# verify the logits
lowercase__ : str = torch.Size((1, 1000))
self.assertEqual(outputs.logits.shape , a)
lowercase__ : Optional[Any] = torch.tensor([-1.9_090, -0.4_993, -0.2_389]).to(a)
self.assertTrue(torch.allclose(outputs.logits[0, :3] , a , atol=1e-4))
@slow
@require_accelerate
def snake_case_ ( self):
lowercase__ : Optional[Any] = ViTHybridImageProcessor.from_pretrained('google/vit-hybrid-base-bit-384')
lowercase__ : int = ViTHybridForImageClassification.from_pretrained('google/vit-hybrid-base-bit-384' , device_map='auto')
lowercase__ : Dict = prepare_img()
lowercase__ : List[Any] = image_processor(images=a , return_tensors='pt')
lowercase__ : List[str] = model(**a)
lowercase__ : Optional[Any] = outputs.logits
# model predicts one of the 1000 ImageNet classes
lowercase__ : Optional[int] = logits.argmax(-1).item()
self.assertTrue(model.config.idalabel[predicted_class_idx] , 'tabby, tabby cat')
| 216 |
from __future__ import annotations
import math
def snake_case__ ( SCREAMING_SNAKE_CASE_ : int ):
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(SCREAMING_SNAKE_CASE_ ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def snake_case__ ( SCREAMING_SNAKE_CASE_ : int ):
'''simple docstring'''
lowercase__ : int = str(SCREAMING_SNAKE_CASE_ )
lowercase__ : Union[str, Any] = [n]
for i in range(1 , len(SCREAMING_SNAKE_CASE_ ) ):
list_nums.append(int(str_num[i:] ) )
list_nums.append(int(str_num[:-i] ) )
return list_nums
def snake_case__ ( SCREAMING_SNAKE_CASE_ : int ):
'''simple docstring'''
if len(str(SCREAMING_SNAKE_CASE_ ) ) > 3:
if not is_prime(int(str(SCREAMING_SNAKE_CASE_ )[-3:] ) ) or not is_prime(int(str(SCREAMING_SNAKE_CASE_ )[:3] ) ):
return False
return True
def snake_case__ ( SCREAMING_SNAKE_CASE_ : int = 11 ):
'''simple docstring'''
lowercase__ : list[int] = []
lowercase__ : Tuple = 13
while len(SCREAMING_SNAKE_CASE_ ) != count:
if validate(SCREAMING_SNAKE_CASE_ ):
lowercase__ : Optional[int] = list_truncated_nums(SCREAMING_SNAKE_CASE_ )
if all(is_prime(SCREAMING_SNAKE_CASE_ ) for i in list_nums ):
list_truncated_primes.append(SCREAMING_SNAKE_CASE_ )
num += 2
return list_truncated_primes
def snake_case__ ( ):
'''simple docstring'''
return sum(compute_truncated_primes(11 ) )
if __name__ == "__main__":
print(F'''{sum(compute_truncated_primes(11)) = }''')
| 216 | 1 |
"""simple docstring"""
from __future__ import annotations
from math import pi
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> dict[str, float]:
'''simple docstring'''
if (inductance, frequency, reactance).count(0 ) != 1:
raise ValueError("""One and only one argument must be 0""" )
if inductance < 0:
raise ValueError("""Inductance cannot be negative""" )
if frequency < 0:
raise ValueError("""Frequency cannot be negative""" )
if reactance < 0:
raise ValueError("""Inductive reactance cannot be negative""" )
if inductance == 0:
return {"inductance": reactance / (2 * pi * frequency)}
elif frequency == 0:
return {"frequency": reactance / (2 * pi * inductance)}
elif reactance == 0:
return {"reactance": 2 * pi * frequency * inductance}
else:
raise ValueError("""Exactly one argument must be 0""" )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 136 |
"""simple docstring"""
from math import pi, sqrt, tan
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> float:
'''simple docstring'''
if side_length < 0:
raise ValueError("""surface_area_cube() only accepts non-negative values""" )
return 6 * side_length**2
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> float:
'''simple docstring'''
if length < 0 or breadth < 0 or height < 0:
raise ValueError("""surface_area_cuboid() only accepts non-negative values""" )
return 2 * ((length * breadth) + (breadth * height) + (length * height))
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> float:
'''simple docstring'''
if radius < 0:
raise ValueError("""surface_area_sphere() only accepts non-negative values""" )
return 4 * pi * radius**2
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> float:
'''simple docstring'''
if radius < 0:
raise ValueError("""surface_area_hemisphere() only accepts non-negative values""" )
return 3 * pi * radius**2
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> float:
'''simple docstring'''
if radius < 0 or height < 0:
raise ValueError("""surface_area_cone() only accepts non-negative values""" )
return pi * radius * (radius + (height**2 + radius**2) ** 0.5)
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> float:
'''simple docstring'''
if radius_a < 0 or radius_a < 0 or height < 0:
raise ValueError(
"""surface_area_conical_frustum() only accepts non-negative values""" )
lowercase_ = (height**2 + (radius_a - radius_a) ** 2) ** 0.5
return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> float:
'''simple docstring'''
if radius < 0 or height < 0:
raise ValueError("""surface_area_cylinder() only accepts non-negative values""" )
return 2 * pi * radius * (height + radius)
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> float:
'''simple docstring'''
if torus_radius < 0 or tube_radius < 0:
raise ValueError("""surface_area_torus() only accepts non-negative values""" )
if torus_radius < tube_radius:
raise ValueError(
"""surface_area_torus() does not support spindle or self intersecting tori""" )
return 4 * pow(__lowerCAmelCase , 2 ) * torus_radius * tube_radius
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> float:
'''simple docstring'''
if length < 0 or width < 0:
raise ValueError("""area_rectangle() only accepts non-negative values""" )
return length * width
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> float:
'''simple docstring'''
if side_length < 0:
raise ValueError("""area_square() only accepts non-negative values""" )
return side_length**2
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> float:
'''simple docstring'''
if base < 0 or height < 0:
raise ValueError("""area_triangle() only accepts non-negative values""" )
return (base * height) / 2
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> float:
'''simple docstring'''
if sidea < 0 or sidea < 0 or sidea < 0:
raise ValueError("""area_triangle_three_sides() only accepts non-negative values""" )
elif sidea + sidea < sidea or sidea + sidea < sidea or sidea + sidea < sidea:
raise ValueError("""Given three sides do not form a triangle""" )
lowercase_ = (sidea + sidea + sidea) / 2
lowercase_ = sqrt(
semi_perimeter
* (semi_perimeter - sidea)
* (semi_perimeter - sidea)
* (semi_perimeter - sidea) )
return area
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> float:
'''simple docstring'''
if base < 0 or height < 0:
raise ValueError("""area_parallelogram() only accepts non-negative values""" )
return base * height
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> float:
'''simple docstring'''
if basea < 0 or basea < 0 or height < 0:
raise ValueError("""area_trapezium() only accepts non-negative values""" )
return 1 / 2 * (basea + basea) * height
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> float:
'''simple docstring'''
if radius < 0:
raise ValueError("""area_circle() only accepts non-negative values""" )
return pi * radius**2
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> float:
'''simple docstring'''
if radius_x < 0 or radius_y < 0:
raise ValueError("""area_ellipse() only accepts non-negative values""" )
return pi * radius_x * radius_y
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> float:
'''simple docstring'''
if diagonal_a < 0 or diagonal_a < 0:
raise ValueError("""area_rhombus() only accepts non-negative values""" )
return 1 / 2 * diagonal_a * diagonal_a
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> float:
'''simple docstring'''
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ) or sides < 3:
raise ValueError(
"""area_reg_polygon() only accepts integers greater than or \
equal to three as number of sides""" )
elif length < 0:
raise ValueError(
"""area_reg_polygon() only accepts non-negative values as \
length of a side""" )
return (sides * length**2) / (4 * tan(pi / sides ))
return (sides * length**2) / (4 * tan(pi / sides ))
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True) # verbose so we can see methods missing tests
print("[DEMO] Areas of various geometric shapes: \n")
print(F"Rectangle: {area_rectangle(10, 20) = }")
print(F"Square: {area_square(10) = }")
print(F"Triangle: {area_triangle(10, 10) = }")
print(F"Triangle: {area_triangle_three_sides(5, 12, 13) = }")
print(F"Parallelogram: {area_parallelogram(10, 20) = }")
print(F"Rhombus: {area_rhombus(10, 20) = }")
print(F"Trapezium: {area_trapezium(10, 20, 30) = }")
print(F"Circle: {area_circle(20) = }")
print(F"Ellipse: {area_ellipse(10, 20) = }")
print("\nSurface Areas of various geometric shapes: \n")
print(F"Cube: {surface_area_cube(20) = }")
print(F"Cuboid: {surface_area_cuboid(10, 20, 30) = }")
print(F"Sphere: {surface_area_sphere(20) = }")
print(F"Hemisphere: {surface_area_hemisphere(20) = }")
print(F"Cone: {surface_area_cone(10, 20) = }")
print(F"Conical Frustum: {surface_area_conical_frustum(10, 20, 30) = }")
print(F"Cylinder: {surface_area_cylinder(10, 20) = }")
print(F"Torus: {surface_area_torus(20, 10) = }")
print(F"Equilateral Triangle: {area_reg_polygon(3, 10) = }")
print(F"Square: {area_reg_polygon(4, 10) = }")
print(F"Reqular Pentagon: {area_reg_polygon(5, 10) = }")
| 136 | 1 |
import unittest
from transformers import (
MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TextGenerationPipeline,
logging,
pipeline,
)
from transformers.testing_utils import (
CaptureLogger,
is_pipeline_test,
require_accelerate,
require_tf,
require_torch,
require_torch_gpu,
require_torch_or_tf,
)
from .test_pipelines_common import ANY
@is_pipeline_test
@require_torch_or_tf
class a ( unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase : str = MODEL_FOR_CAUSAL_LM_MAPPING
lowerCAmelCase : Optional[int] = TF_MODEL_FOR_CAUSAL_LM_MAPPING
@require_torch
def lowerCamelCase_ ( self : Optional[Any] ):
UpperCAmelCase_ = pipeline(task='''text-generation''' , model='''sshleifer/tiny-ctrl''' , framework='''pt''' )
# Using `do_sample=False` to force deterministic output
UpperCAmelCase_ = text_generator('''This is a test''' , do_sample=__snake_case )
self.assertEqual(
__snake_case , [
{
'''generated_text''': (
'''This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope.'''
''' oscope. FiliFili@@'''
)
}
] , )
UpperCAmelCase_ = text_generator(['''This is a test''', '''This is a second test'''] )
self.assertEqual(
__snake_case , [
[
{
'''generated_text''': (
'''This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope.'''
''' oscope. FiliFili@@'''
)
}
],
[
{
'''generated_text''': (
'''This is a second test ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy'''
''' oscope. oscope. FiliFili@@'''
)
}
],
] , )
UpperCAmelCase_ = text_generator('''This is a test''' , do_sample=__snake_case , num_return_sequences=2 , return_tensors=__snake_case )
self.assertEqual(
__snake_case , [
{'''generated_token_ids''': ANY(__snake_case )},
{'''generated_token_ids''': ANY(__snake_case )},
] , )
UpperCAmelCase_ = text_generator.model.config.eos_token_id
UpperCAmelCase_ = '''<pad>'''
UpperCAmelCase_ = text_generator(
['''This is a test''', '''This is a second test'''] , do_sample=__snake_case , num_return_sequences=2 , batch_size=2 , return_tensors=__snake_case , )
self.assertEqual(
__snake_case , [
[
{'''generated_token_ids''': ANY(__snake_case )},
{'''generated_token_ids''': ANY(__snake_case )},
],
[
{'''generated_token_ids''': ANY(__snake_case )},
{'''generated_token_ids''': ANY(__snake_case )},
],
] , )
@require_tf
def lowerCamelCase_ ( self : Optional[int] ):
UpperCAmelCase_ = pipeline(task='''text-generation''' , model='''sshleifer/tiny-ctrl''' , framework='''tf''' )
# Using `do_sample=False` to force deterministic output
UpperCAmelCase_ = text_generator('''This is a test''' , do_sample=__snake_case )
self.assertEqual(
__snake_case , [
{
'''generated_text''': (
'''This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵'''
''' please,'''
)
}
] , )
UpperCAmelCase_ = text_generator(['''This is a test''', '''This is a second test'''] , do_sample=__snake_case )
self.assertEqual(
__snake_case , [
[
{
'''generated_text''': (
'''This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵'''
''' please,'''
)
}
],
[
{
'''generated_text''': (
'''This is a second test Chieftain Chieftain prefecture prefecture prefecture Cannes Cannes'''
''' Cannes 閲閲Cannes Cannes Cannes 攵 please,'''
)
}
],
] , )
def lowerCamelCase_ ( self : List[Any] , __snake_case : Optional[Any] , __snake_case : int , __snake_case : List[str] ):
UpperCAmelCase_ = TextGenerationPipeline(model=__snake_case , tokenizer=__snake_case )
return text_generator, ["This is a test", "Another test"]
def lowerCamelCase_ ( self : Optional[int] ):
UpperCAmelCase_ = '''Hello I believe in'''
UpperCAmelCase_ = pipeline('''text-generation''' , model='''hf-internal-testing/tiny-random-gpt2''' )
UpperCAmelCase_ = text_generator(__snake_case )
self.assertEqual(
__snake_case , [{'''generated_text''': '''Hello I believe in fe fe fe fe fe fe fe fe fe fe fe fe'''}] , )
UpperCAmelCase_ = text_generator(__snake_case , stop_sequence=''' fe''' )
self.assertEqual(__snake_case , [{'''generated_text''': '''Hello I believe in fe'''}] )
def lowerCamelCase_ ( self : Any , __snake_case : Optional[Any] , __snake_case : Union[str, Any] ):
UpperCAmelCase_ = text_generator.model
UpperCAmelCase_ = text_generator.tokenizer
UpperCAmelCase_ = text_generator('''This is a test''' )
self.assertEqual(__snake_case , [{'''generated_text''': ANY(__snake_case )}] )
self.assertTrue(outputs[0]['''generated_text'''].startswith('''This is a test''' ) )
UpperCAmelCase_ = text_generator('''This is a test''' , return_full_text=__snake_case )
self.assertEqual(__snake_case , [{'''generated_text''': ANY(__snake_case )}] )
self.assertNotIn('''This is a test''' , outputs[0]['''generated_text'''] )
UpperCAmelCase_ = pipeline(task='''text-generation''' , model=__snake_case , tokenizer=__snake_case , return_full_text=__snake_case )
UpperCAmelCase_ = text_generator('''This is a test''' )
self.assertEqual(__snake_case , [{'''generated_text''': ANY(__snake_case )}] )
self.assertNotIn('''This is a test''' , outputs[0]['''generated_text'''] )
UpperCAmelCase_ = text_generator('''This is a test''' , return_full_text=__snake_case )
self.assertEqual(__snake_case , [{'''generated_text''': ANY(__snake_case )}] )
self.assertTrue(outputs[0]['''generated_text'''].startswith('''This is a test''' ) )
UpperCAmelCase_ = text_generator(['''This is great !''', '''Something else'''] , num_return_sequences=2 , do_sample=__snake_case )
self.assertEqual(
__snake_case , [
[{'''generated_text''': ANY(__snake_case )}, {'''generated_text''': ANY(__snake_case )}],
[{'''generated_text''': ANY(__snake_case )}, {'''generated_text''': ANY(__snake_case )}],
] , )
if text_generator.tokenizer.pad_token is not None:
UpperCAmelCase_ = text_generator(
['''This is great !''', '''Something else'''] , num_return_sequences=2 , batch_size=2 , do_sample=__snake_case )
self.assertEqual(
__snake_case , [
[{'''generated_text''': ANY(__snake_case )}, {'''generated_text''': ANY(__snake_case )}],
[{'''generated_text''': ANY(__snake_case )}, {'''generated_text''': ANY(__snake_case )}],
] , )
with self.assertRaises(__snake_case ):
UpperCAmelCase_ = text_generator('''test''' , return_full_text=__snake_case , return_text=__snake_case )
with self.assertRaises(__snake_case ):
UpperCAmelCase_ = text_generator('''test''' , return_full_text=__snake_case , return_tensors=__snake_case )
with self.assertRaises(__snake_case ):
UpperCAmelCase_ = text_generator('''test''' , return_text=__snake_case , return_tensors=__snake_case )
# Empty prompt is slighly special
# it requires BOS token to exist.
# Special case for Pegasus which will always append EOS so will
# work even without BOS.
if (
text_generator.tokenizer.bos_token_id is not None
or "Pegasus" in tokenizer.__class__.__name__
or "Git" in model.__class__.__name__
):
UpperCAmelCase_ = text_generator('''''' )
self.assertEqual(__snake_case , [{'''generated_text''': ANY(__snake_case )}] )
else:
with self.assertRaises((ValueError, AssertionError) ):
UpperCAmelCase_ = text_generator('''''' )
if text_generator.framework == "tf":
# TF generation does not support max_new_tokens, and it's impossible
# to control long generation with only max_length without
# fancy calculation, dismissing tests for now.
return
# We don't care about infinite range models.
# They already work.
# Skip this test for XGLM, since it uses sinusoidal positional embeddings which are resized on-the-fly.
UpperCAmelCase_ = ['''RwkvForCausalLM''', '''XGLMForCausalLM''', '''GPTNeoXForCausalLM''']
if (
tokenizer.model_max_length < 1_00_00
and text_generator.model.__class__.__name__ not in EXTRA_MODELS_CAN_HANDLE_LONG_INPUTS
):
# Handling of large generations
with self.assertRaises((RuntimeError, IndexError, ValueError, AssertionError) ):
text_generator('''This is a test''' * 5_00 , max_new_tokens=20 )
UpperCAmelCase_ = text_generator('''This is a test''' * 5_00 , handle_long_generation='''hole''' , max_new_tokens=20 )
# Hole strategy cannot work
with self.assertRaises(__snake_case ):
text_generator(
'''This is a test''' * 5_00 , handle_long_generation='''hole''' , max_new_tokens=tokenizer.model_max_length + 10 , )
@require_torch
@require_accelerate
@require_torch_gpu
def lowerCamelCase_ ( self : List[Any] ):
import torch
# Classic `model_kwargs`
UpperCAmelCase_ = pipeline(
model='''hf-internal-testing/tiny-random-bloom''' , model_kwargs={'''device_map''': '''auto''', '''torch_dtype''': torch.bfloataa} , )
self.assertEqual(pipe.model.device , torch.device(0 ) )
self.assertEqual(pipe.model.lm_head.weight.dtype , torch.bfloataa )
UpperCAmelCase_ = pipe('''This is a test''' )
self.assertEqual(
__snake_case , [
{
'''generated_text''': (
'''This is a test test test test test test test test test test test test test test test test'''
''' test'''
)
}
] , )
# Upgraded those two to real pipeline arguments (they just get sent for the model as they're unlikely to mean anything else.)
UpperCAmelCase_ = pipeline(model='''hf-internal-testing/tiny-random-bloom''' , device_map='''auto''' , torch_dtype=torch.bfloataa )
self.assertEqual(pipe.model.device , torch.device(0 ) )
self.assertEqual(pipe.model.lm_head.weight.dtype , torch.bfloataa )
UpperCAmelCase_ = pipe('''This is a test''' )
self.assertEqual(
__snake_case , [
{
'''generated_text''': (
'''This is a test test test test test test test test test test test test test test test test'''
''' test'''
)
}
] , )
# torch_dtype will be automatically set to float32 if not provided - check: https://github.com/huggingface/transformers/pull/20602
UpperCAmelCase_ = pipeline(model='''hf-internal-testing/tiny-random-bloom''' , device_map='''auto''' )
self.assertEqual(pipe.model.device , torch.device(0 ) )
self.assertEqual(pipe.model.lm_head.weight.dtype , torch.floataa )
UpperCAmelCase_ = pipe('''This is a test''' )
self.assertEqual(
__snake_case , [
{
'''generated_text''': (
'''This is a test test test test test test test test test test test test test test test test'''
''' test'''
)
}
] , )
@require_torch
@require_torch_gpu
def lowerCamelCase_ ( self : str ):
import torch
UpperCAmelCase_ = pipeline(model='''hf-internal-testing/tiny-random-bloom''' , device=0 , torch_dtype=torch.floataa )
pipe('''This is a test''' )
@require_torch
@require_accelerate
@require_torch_gpu
def lowerCamelCase_ ( self : Any ):
import torch
UpperCAmelCase_ = pipeline(model='''hf-internal-testing/tiny-random-bloom''' , device_map='''auto''' , torch_dtype=torch.floataa )
pipe('''This is a test''' , do_sample=__snake_case , top_p=0.5 )
def lowerCamelCase_ ( self : Optional[Any] ):
UpperCAmelCase_ = '''Hello world'''
UpperCAmelCase_ = pipeline('''text-generation''' , model='''hf-internal-testing/tiny-random-gpt2''' )
if text_generator.model.framework == "tf":
UpperCAmelCase_ = logging.get_logger('''transformers.generation.tf_utils''' )
else:
UpperCAmelCase_ = logging.get_logger('''transformers.generation.utils''' )
UpperCAmelCase_ = '''Both `max_new_tokens`''' # The beggining of the message to be checked in this test
# Both are set by the user -> log warning
with CaptureLogger(__snake_case ) as cl:
UpperCAmelCase_ = text_generator(__snake_case , max_length=10 , max_new_tokens=1 )
self.assertIn(__snake_case , cl.out )
# The user only sets one -> no warning
with CaptureLogger(__snake_case ) as cl:
UpperCAmelCase_ = text_generator(__snake_case , max_new_tokens=1 )
self.assertNotIn(__snake_case , cl.out )
with CaptureLogger(__snake_case ) as cl:
UpperCAmelCase_ = text_generator(__snake_case , max_length=10 )
self.assertNotIn(__snake_case , cl.out )
| 177 |
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : Dict=2_8123 ) -> str:
UpperCAmelCase_ = [1] * (limit + 1)
for i in range(2 , int(limit**0.5 ) + 1 ):
sum_divs[i * i] += i
for k in range(i + 1 , limit // i + 1 ):
sum_divs[k * i] += k + i
UpperCAmelCase_ = set()
UpperCAmelCase_ = 0
for n in range(1 , limit + 1 ):
if sum_divs[n] > n:
abundants.add(__UpperCamelCase )
if not any((n - a in abundants) for a in abundants ):
res += n
return res
if __name__ == "__main__":
print(solution())
| 177 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import DistilBertConfig, 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.models.distilbert.modeling_tf_distilbert import (
TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFDistilBertForMaskedLM,
TFDistilBertForMultipleChoice,
TFDistilBertForQuestionAnswering,
TFDistilBertForSequenceClassification,
TFDistilBertForTokenClassification,
TFDistilBertModel,
)
class UpperCAmelCase__ :
def __init__( self , lowercase , ) -> Union[str, Any]:
__UpperCamelCase = parent
__UpperCamelCase = 1_3
__UpperCamelCase = 7
__UpperCamelCase = True
__UpperCamelCase = True
__UpperCamelCase = False
__UpperCamelCase = True
__UpperCamelCase = 9_9
__UpperCamelCase = 3_2
__UpperCamelCase = 2
__UpperCamelCase = 4
__UpperCamelCase = 3_7
__UpperCamelCase = """gelu"""
__UpperCamelCase = 0.1
__UpperCamelCase = 0.1
__UpperCamelCase = 5_1_2
__UpperCamelCase = 1_6
__UpperCamelCase = 2
__UpperCamelCase = 0.02
__UpperCamelCase = 3
__UpperCamelCase = 4
__UpperCamelCase = None
def __lowerCamelCase ( self ) -> List[str]:
__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
__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 = DistilBertConfig(
vocab_size=self.vocab_size , dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , hidden_dim=self.intermediate_size , hidden_act=self.hidden_act , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , )
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def __lowerCamelCase ( self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase ) -> Dict:
__UpperCamelCase = TFDistilBertModel(config=lowercase )
__UpperCamelCase = {"""input_ids""": input_ids, """attention_mask""": input_mask}
__UpperCamelCase = model(lowercase )
__UpperCamelCase = [input_ids, input_mask]
__UpperCamelCase = model(lowercase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __lowerCamelCase ( self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase ) -> Optional[Any]:
__UpperCamelCase = TFDistilBertForMaskedLM(config=lowercase )
__UpperCamelCase = {"""input_ids""": input_ids, """attention_mask""": input_mask}
__UpperCamelCase = model(lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __lowerCamelCase ( self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase ) -> Tuple:
__UpperCamelCase = TFDistilBertForQuestionAnswering(config=lowercase )
__UpperCamelCase = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
}
__UpperCamelCase = model(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) )
def __lowerCamelCase ( self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase ) -> Tuple:
__UpperCamelCase = self.num_labels
__UpperCamelCase = TFDistilBertForSequenceClassification(lowercase )
__UpperCamelCase = {"""input_ids""": input_ids, """attention_mask""": input_mask}
__UpperCamelCase = model(lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __lowerCamelCase ( self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase ) -> int:
__UpperCamelCase = self.num_choices
__UpperCamelCase = TFDistilBertForMultipleChoice(lowercase )
__UpperCamelCase = tf.tile(tf.expand_dims(lowercase , 1 ) , (1, self.num_choices, 1) )
__UpperCamelCase = tf.tile(tf.expand_dims(lowercase , 1 ) , (1, self.num_choices, 1) )
__UpperCamelCase = {
"""input_ids""": multiple_choice_inputs_ids,
"""attention_mask""": multiple_choice_input_mask,
}
__UpperCamelCase = model(lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def __lowerCamelCase ( self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase ) -> Optional[int]:
__UpperCamelCase = self.num_labels
__UpperCamelCase = TFDistilBertForTokenClassification(lowercase )
__UpperCamelCase = {"""input_ids""": input_ids, """attention_mask""": input_mask}
__UpperCamelCase = model(lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __lowerCamelCase ( self ) -> Dict:
__UpperCamelCase = self.prepare_config_and_inputs()
((__UpperCamelCase) , (__UpperCamelCase) , (__UpperCamelCase) , (__UpperCamelCase) , (__UpperCamelCase) , (__UpperCamelCase)) = config_and_inputs
__UpperCamelCase = {"""input_ids""": input_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_tf
class UpperCAmelCase__ ( UpperCAmelCase_ , UpperCAmelCase_ , unittest.TestCase):
__SCREAMING_SNAKE_CASE = (
(
TFDistilBertModel,
TFDistilBertForMaskedLM,
TFDistilBertForQuestionAnswering,
TFDistilBertForSequenceClassification,
TFDistilBertForTokenClassification,
TFDistilBertForMultipleChoice,
)
if is_tf_available()
else None
)
__SCREAMING_SNAKE_CASE = (
{
'''feature-extraction''': TFDistilBertModel,
'''fill-mask''': TFDistilBertForMaskedLM,
'''question-answering''': TFDistilBertForQuestionAnswering,
'''text-classification''': TFDistilBertForSequenceClassification,
'''token-classification''': TFDistilBertForTokenClassification,
'''zero-shot''': TFDistilBertForSequenceClassification,
}
if is_tf_available()
else {}
)
__SCREAMING_SNAKE_CASE = False
__SCREAMING_SNAKE_CASE = False
def __lowerCamelCase ( self ) -> Optional[Any]:
__UpperCamelCase = TFDistilBertModelTester(self )
__UpperCamelCase = ConfigTester(self , config_class=lowercase , dim=3_7 )
def __lowerCamelCase ( self ) -> Any:
self.config_tester.run_common_tests()
def __lowerCamelCase ( self ) -> Dict:
__UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_model(*lowercase )
def __lowerCamelCase ( self ) -> Union[str, Any]:
__UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_masked_lm(*lowercase )
def __lowerCamelCase ( self ) -> int:
__UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_question_answering(*lowercase )
def __lowerCamelCase ( self ) -> Any:
__UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_sequence_classification(*lowercase )
def __lowerCamelCase ( self ) -> Optional[Any]:
__UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_multiple_choice(*lowercase )
def __lowerCamelCase ( self ) -> Union[str, Any]:
__UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_token_classification(*lowercase )
@slow
def __lowerCamelCase ( self ) -> Tuple:
for model_name in list(TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1] ):
__UpperCamelCase = TFDistilBertModel.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
@require_tf
class UpperCAmelCase__ ( unittest.TestCase):
@slow
def __lowerCamelCase ( self ) -> Optional[int]:
__UpperCamelCase = TFDistilBertModel.from_pretrained("""distilbert-base-uncased""" )
__UpperCamelCase = tf.constant([[0, 1, 2, 3, 4, 5]] )
__UpperCamelCase = model(lowercase )[0]
__UpperCamelCase = [1, 6, 7_6_8]
self.assertEqual(output.shape , lowercase )
__UpperCamelCase = tf.constant(
[
[
[0.19_261_885, -0.13_732_955, 0.4_119_799],
[0.22_150_156, -0.07_422_661, 0.39_037_204],
[0.22_756_018, -0.0_896_414, 0.3_701_467],
]
] )
tf.debugging.assert_near(output[:, :3, :3] , lowercase , atol=1E-4 )
| 349 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
a__ : int = {
'configuration_layoutlmv3': [
'LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP',
'LayoutLMv3Config',
'LayoutLMv3OnnxConfig',
],
'processing_layoutlmv3': ['LayoutLMv3Processor'],
'tokenization_layoutlmv3': ['LayoutLMv3Tokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ : Dict = ['LayoutLMv3TokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ : Any = [
'LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST',
'LayoutLMv3ForQuestionAnswering',
'LayoutLMv3ForSequenceClassification',
'LayoutLMv3ForTokenClassification',
'LayoutLMv3Model',
'LayoutLMv3PreTrainedModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ : str = [
'TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFLayoutLMv3ForQuestionAnswering',
'TFLayoutLMv3ForSequenceClassification',
'TFLayoutLMv3ForTokenClassification',
'TFLayoutLMv3Model',
'TFLayoutLMv3PreTrainedModel',
]
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ : List[Any] = ['LayoutLMv3FeatureExtractor']
a__ : str = ['LayoutLMv3ImageProcessor']
if TYPE_CHECKING:
from .configuration_layoutlmva import (
LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP,
LayoutLMvaConfig,
LayoutLMvaOnnxConfig,
)
from .processing_layoutlmva import LayoutLMvaProcessor
from .tokenization_layoutlmva import LayoutLMvaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_layoutlmva import (
LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
LayoutLMvaForQuestionAnswering,
LayoutLMvaForSequenceClassification,
LayoutLMvaForTokenClassification,
LayoutLMvaModel,
LayoutLMvaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_layoutlmva import (
TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
TFLayoutLMvaForQuestionAnswering,
TFLayoutLMvaForSequenceClassification,
TFLayoutLMvaForTokenClassification,
TFLayoutLMvaModel,
TFLayoutLMvaPreTrainedModel,
)
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
else:
import sys
a__ : List[str] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 349 | 1 |
'''simple docstring'''
from __future__ import annotations
def __lowerCAmelCase (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
if days_between_payments <= 0:
raise ValueError("days_between_payments must be > 0" )
if daily_interest_rate < 0:
raise ValueError("daily_interest_rate must be >= 0" )
if principal <= 0:
raise ValueError("principal must be > 0" )
return principal * daily_interest_rate * days_between_payments
def __lowerCAmelCase (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , ):
if number_of_compounding_periods <= 0:
raise ValueError("number_of_compounding_periods must be > 0" )
if nominal_annual_interest_rate_percentage < 0:
raise ValueError("nominal_annual_interest_rate_percentage must be >= 0" )
if principal <= 0:
raise ValueError("principal must be > 0" )
return principal * (
(1 + nominal_annual_interest_rate_percentage) ** number_of_compounding_periods
- 1
)
def __lowerCAmelCase (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , ):
if number_of_years <= 0:
raise ValueError("number_of_years must be > 0" )
if nominal_annual_percentage_rate < 0:
raise ValueError("nominal_annual_percentage_rate must be >= 0" )
if principal <= 0:
raise ValueError("principal must be > 0" )
return compound_interest(
__lowerCAmelCase , nominal_annual_percentage_rate / 365 , number_of_years * 365 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 322 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.albert.modeling_flax_albert import (
FlaxAlbertForMaskedLM,
FlaxAlbertForMultipleChoice,
FlaxAlbertForPreTraining,
FlaxAlbertForQuestionAnswering,
FlaxAlbertForSequenceClassification,
FlaxAlbertForTokenClassification,
FlaxAlbertModel,
)
class lowerCAmelCase__ ( unittest.TestCase ):
def __init__( self : Optional[Any] , lowerCamelCase__ : Union[str, Any] , lowerCamelCase__ : List[str]=13 , lowerCamelCase__ : Optional[Any]=7 , lowerCamelCase__ : List[str]=True , lowerCamelCase__ : Any=True , lowerCamelCase__ : List[Any]=True , lowerCamelCase__ : Any=True , lowerCamelCase__ : int=99 , lowerCamelCase__ : int=32 , lowerCamelCase__ : List[str]=5 , lowerCamelCase__ : Optional[Any]=4 , lowerCamelCase__ : Optional[int]=37 , lowerCamelCase__ : Tuple="gelu" , lowerCamelCase__ : Any=0.1 , lowerCamelCase__ : Union[str, Any]=0.1 , lowerCamelCase__ : Optional[int]=5_12 , lowerCamelCase__ : Optional[int]=16 , lowerCamelCase__ : str=2 , lowerCamelCase__ : Union[str, Any]=0.0_2 , lowerCamelCase__ : Tuple=4 , ) ->Union[str, Any]:
'''simple docstring'''
_UpperCAmelCase : List[Any] = parent
_UpperCAmelCase : List[Any] = batch_size
_UpperCAmelCase : Optional[int] = seq_length
_UpperCAmelCase : int = is_training
_UpperCAmelCase : Dict = use_attention_mask
_UpperCAmelCase : Optional[Any] = use_token_type_ids
_UpperCAmelCase : int = use_labels
_UpperCAmelCase : Optional[int] = vocab_size
_UpperCAmelCase : Any = hidden_size
_UpperCAmelCase : Any = num_hidden_layers
_UpperCAmelCase : List[Any] = num_attention_heads
_UpperCAmelCase : Tuple = intermediate_size
_UpperCAmelCase : int = hidden_act
_UpperCAmelCase : int = hidden_dropout_prob
_UpperCAmelCase : Union[str, Any] = attention_probs_dropout_prob
_UpperCAmelCase : Union[str, Any] = max_position_embeddings
_UpperCAmelCase : Tuple = type_vocab_size
_UpperCAmelCase : List[Any] = type_sequence_label_size
_UpperCAmelCase : Optional[int] = initializer_range
_UpperCAmelCase : Dict = num_choices
def lowerCAmelCase__ ( self : List[Any] ) ->Any:
'''simple docstring'''
_UpperCAmelCase : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_UpperCAmelCase : Dict = None
if self.use_attention_mask:
_UpperCAmelCase : Union[str, Any] = random_attention_mask([self.batch_size, self.seq_length] )
_UpperCAmelCase : Union[str, Any] = None
if self.use_token_type_ids:
_UpperCAmelCase : int = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_UpperCAmelCase : int = AlbertConfig(
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=lowerCamelCase__ , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def lowerCAmelCase__ ( self : Any ) ->List[str]:
'''simple docstring'''
_UpperCAmelCase : Tuple = self.prepare_config_and_inputs()
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase : List[Any] = config_and_inputs
_UpperCAmelCase : str = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
return config, inputs_dict
@require_flax
class lowerCAmelCase__ ( UpperCAmelCase__ , unittest.TestCase ):
lowerCAmelCase : Optional[int] = (
(
FlaxAlbertModel,
FlaxAlbertForPreTraining,
FlaxAlbertForMaskedLM,
FlaxAlbertForMultipleChoice,
FlaxAlbertForQuestionAnswering,
FlaxAlbertForSequenceClassification,
FlaxAlbertForTokenClassification,
FlaxAlbertForQuestionAnswering,
)
if is_flax_available()
else ()
)
def lowerCAmelCase__ ( self : Optional[int] ) ->Optional[Any]:
'''simple docstring'''
_UpperCAmelCase : int = FlaxAlbertModelTester(self )
@slow
def lowerCAmelCase__ ( self : Any ) ->List[str]:
'''simple docstring'''
for model_class_name in self.all_model_classes:
_UpperCAmelCase : List[str] = model_class_name.from_pretrained("albert-base-v2" )
_UpperCAmelCase : Optional[int] = model(np.ones((1, 1) ) )
self.assertIsNotNone(lowerCamelCase__ )
@require_flax
class lowerCAmelCase__ ( unittest.TestCase ):
@slow
def lowerCAmelCase__ ( self : Tuple ) ->Optional[Any]:
'''simple docstring'''
_UpperCAmelCase : str = FlaxAlbertModel.from_pretrained("albert-base-v2" )
_UpperCAmelCase : List[Any] = np.array([[0, 3_45, 2_32, 3_28, 7_40, 1_40, 16_95, 69, 60_78, 15_88, 2]] )
_UpperCAmelCase : Optional[int] = np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
_UpperCAmelCase : Dict = model(lowerCamelCase__ , attention_mask=lowerCamelCase__ )[0]
_UpperCAmelCase : List[Any] = (1, 11, 7_68)
self.assertEqual(output.shape , lowerCamelCase__ )
_UpperCAmelCase : str = np.array(
[[[-0.6_5_1_3, 1.5_0_3_5, -0.2_7_6_6], [-0.6_5_1_5, 1.5_0_4_6, -0.2_7_8_0], [-0.6_5_1_2, 1.5_0_4_9, -0.2_7_8_4]]] )
self.assertTrue(jnp.allclose(output[:, 1:4, 1:4] , lowerCamelCase__ , atol=1E-4 ) )
| 322 | 1 |
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import (
VersatileDiffusionDualGuidedPipeline,
VersatileDiffusionImageVariationPipeline,
VersatileDiffusionPipeline,
VersatileDiffusionTextToImagePipeline,
)
else:
from .modeling_text_unet import UNetFlatConditionModel
from .pipeline_versatile_diffusion import VersatileDiffusionPipeline
from .pipeline_versatile_diffusion_dual_guided import VersatileDiffusionDualGuidedPipeline
from .pipeline_versatile_diffusion_image_variation import VersatileDiffusionImageVariationPipeline
from .pipeline_versatile_diffusion_text_to_image import VersatileDiffusionTextToImagePipeline
| 105 |
"""simple docstring"""
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_detectrona,
require_pytesseract,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
from transformers.image_utils import load_image
else:
class __UpperCamelCase :
@staticmethod
def __a ( *lowerCAmelCase__ , **lowerCAmelCase__ ) -> str:
pass
def _SCREAMING_SNAKE_CASE ( _lowercase : Tuple ) ->Dict:
'''simple docstring'''
return None
# This is a pinned image from a specific revision of a document question answering space, hosted by HuggingFace,
# so we can expect it to be available.
a : Optional[Any] = (
'''https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png'''
)
@is_pipeline_test
@require_torch
@require_vision
class __UpperCamelCase ( unittest.TestCase ):
lowerCamelCase : Union[str, Any] =MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING
@require_pytesseract
@require_vision
def __a ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> Tuple:
a : Tuple = pipeline(
"document-question-answering" , model=lowerCAmelCase__ , tokenizer=lowerCAmelCase__ , image_processor=lowerCAmelCase__ )
a : Optional[int] = INVOICE_URL
a : str = list(zip(*apply_tesseract(load_image(lowerCAmelCase__ ) , lowerCAmelCase__ , "" ) ) )
a : Union[str, Any] = "What is the placebo?"
a : Dict = [
{
"image": load_image(lowerCAmelCase__ ),
"question": question,
},
{
"image": image,
"question": question,
},
{
"image": image,
"question": question,
"word_boxes": word_boxes,
},
]
return dqa_pipeline, examples
def __a ( self , lowerCAmelCase__ , lowerCAmelCase__ ) -> Dict:
a : Tuple = dqa_pipeline(lowerCAmelCase__ , top_k=2 )
self.assertEqual(
lowerCAmelCase__ , [
[
{"score": ANY(lowerCAmelCase__ ), "answer": ANY(lowerCAmelCase__ ), "start": ANY(lowerCAmelCase__ ), "end": ANY(lowerCAmelCase__ )},
{"score": ANY(lowerCAmelCase__ ), "answer": ANY(lowerCAmelCase__ ), "start": ANY(lowerCAmelCase__ ), "end": ANY(lowerCAmelCase__ )},
]
]
* 3 , )
@require_torch
@require_detectrona
@require_pytesseract
def __a ( self ) -> List[Any]:
a : List[Any] = pipeline("document-question-answering" , model="hf-internal-testing/tiny-random-layoutlmv2" )
a : Dict = INVOICE_URL
a : List[str] = "How many cats are there?"
a : Tuple = [
{"score": 0.0_001, "answer": "oy 2312/2019", "start": 38, "end": 39},
{"score": 0.0_001, "answer": "oy 2312/2019 DUE", "start": 38, "end": 40},
]
a : Optional[int] = dqa_pipeline(image=lowerCAmelCase__ , question=lowerCAmelCase__ , top_k=2 )
self.assertEqual(nested_simplify(lowerCAmelCase__ , decimals=4 ) , lowerCAmelCase__ )
a : Optional[int] = dqa_pipeline({"image": image, "question": question} , top_k=2 )
self.assertEqual(nested_simplify(lowerCAmelCase__ , decimals=4 ) , lowerCAmelCase__ )
# This image does not detect ANY text in it, meaning layoutlmv2 should fail.
# Empty answer probably
a : List[Any] = "./tests/fixtures/tests_samples/COCO/000000039769.png"
a : Any = dqa_pipeline(image=lowerCAmelCase__ , question=lowerCAmelCase__ , top_k=2 )
self.assertEqual(lowerCAmelCase__ , [] )
# We can optionnally pass directly the words and bounding boxes
a : Optional[int] = "./tests/fixtures/tests_samples/COCO/000000039769.png"
a : Tuple = []
a : Optional[int] = []
a : List[str] = dqa_pipeline(image=lowerCAmelCase__ , question=lowerCAmelCase__ , words=lowerCAmelCase__ , boxes=lowerCAmelCase__ , top_k=2 )
self.assertEqual(lowerCAmelCase__ , [] )
@slow
@require_torch
@require_detectrona
@require_pytesseract
def __a ( self ) -> Tuple:
a : int = pipeline(
"document-question-answering" , model="tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa" , revision="9977165" , )
a : List[str] = INVOICE_URL
a : List[Any] = "What is the invoice number?"
a : int = dqa_pipeline(image=lowerCAmelCase__ , question=lowerCAmelCase__ , top_k=2 )
self.assertEqual(
nested_simplify(lowerCAmelCase__ , decimals=4 ) , [
{"score": 0.9_944, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0_009, "answer": "us-001", "start": 16, "end": 16},
] , )
a : str = dqa_pipeline({"image": image, "question": question} , top_k=2 )
self.assertEqual(
nested_simplify(lowerCAmelCase__ , decimals=4 ) , [
{"score": 0.9_944, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0_009, "answer": "us-001", "start": 16, "end": 16},
] , )
a : Any = dqa_pipeline(
[{"image": image, "question": question}, {"image": image, "question": question}] , top_k=2 )
self.assertEqual(
nested_simplify(lowerCAmelCase__ , decimals=4 ) , [
[
{"score": 0.9_944, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0_009, "answer": "us-001", "start": 16, "end": 16},
],
]
* 2 , )
@slow
@require_torch
@require_detectrona
@require_pytesseract
def __a ( self ) -> Optional[int]:
a : List[str] = pipeline(
"document-question-answering" , model="tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa" , revision="9977165" , max_seq_len=50 , )
a : Optional[Any] = INVOICE_URL
a : Tuple = "What is the invoice number?"
a : List[Any] = dqa_pipeline(image=lowerCAmelCase__ , question=lowerCAmelCase__ , top_k=2 )
self.assertEqual(
nested_simplify(lowerCAmelCase__ , decimals=4 ) , [
{"score": 0.9_974, "answer": "1110212019", "start": 23, "end": 23},
{"score": 0.9_948, "answer": "us-001", "start": 16, "end": 16},
] , )
a : str = dqa_pipeline({"image": image, "question": question} , top_k=2 )
self.assertEqual(
nested_simplify(lowerCAmelCase__ , decimals=4 ) , [
{"score": 0.9_974, "answer": "1110212019", "start": 23, "end": 23},
{"score": 0.9_948, "answer": "us-001", "start": 16, "end": 16},
] , )
a : Tuple = dqa_pipeline(
[{"image": image, "question": question}, {"image": image, "question": question}] , top_k=2 )
self.assertEqual(
nested_simplify(lowerCAmelCase__ , decimals=4 ) , [
[
{"score": 0.9_974, "answer": "1110212019", "start": 23, "end": 23},
{"score": 0.9_948, "answer": "us-001", "start": 16, "end": 16},
]
]
* 2 , )
@slow
@require_torch
@require_pytesseract
@require_vision
def __a ( self ) -> str:
a : Optional[int] = AutoTokenizer.from_pretrained(
"impira/layoutlm-document-qa" , revision="3dc6de3" , add_prefix_space=lowerCAmelCase__ )
a : int = pipeline(
"document-question-answering" , model="impira/layoutlm-document-qa" , tokenizer=lowerCAmelCase__ , revision="3dc6de3" , )
a : List[Any] = INVOICE_URL
a : Tuple = "What is the invoice number?"
a : List[str] = dqa_pipeline(image=lowerCAmelCase__ , question=lowerCAmelCase__ , top_k=2 )
self.assertEqual(
nested_simplify(lowerCAmelCase__ , decimals=4 ) , [
{"score": 0.4_251, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0_819, "answer": "1110212019", "start": 23, "end": 23},
] , )
a : Dict = dqa_pipeline({"image": image, "question": question} , top_k=2 )
self.assertEqual(
nested_simplify(lowerCAmelCase__ , decimals=4 ) , [
{"score": 0.4_251, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0_819, "answer": "1110212019", "start": 23, "end": 23},
] , )
a : List[Any] = dqa_pipeline(
[{"image": image, "question": question}, {"image": image, "question": question}] , top_k=2 )
self.assertEqual(
nested_simplify(lowerCAmelCase__ , decimals=4 ) , [
[
{"score": 0.4_251, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0_819, "answer": "1110212019", "start": 23, "end": 23},
]
]
* 2 , )
a : Dict = list(zip(*apply_tesseract(load_image(lowerCAmelCase__ ) , lowerCAmelCase__ , "" ) ) )
# This model should also work if `image` is set to None
a : Optional[Any] = dqa_pipeline({"image": None, "word_boxes": word_boxes, "question": question} , top_k=2 )
self.assertEqual(
nested_simplify(lowerCAmelCase__ , decimals=4 ) , [
{"score": 0.4_251, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0_819, "answer": "1110212019", "start": 23, "end": 23},
] , )
@slow
@require_torch
@require_pytesseract
@require_vision
def __a ( self ) -> Tuple:
a : int = AutoTokenizer.from_pretrained(
"impira/layoutlm-document-qa" , revision="3dc6de3" , add_prefix_space=lowerCAmelCase__ )
a : Tuple = pipeline(
"document-question-answering" , model="impira/layoutlm-document-qa" , tokenizer=lowerCAmelCase__ , revision="3dc6de3" , max_seq_len=50 , )
a : List[str] = INVOICE_URL
a : Union[str, Any] = "What is the invoice number?"
a : List[Any] = dqa_pipeline(image=lowerCAmelCase__ , question=lowerCAmelCase__ , top_k=2 )
self.assertEqual(
nested_simplify(lowerCAmelCase__ , decimals=4 ) , [
{"score": 0.9_999, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.9_998, "answer": "us-001", "start": 16, "end": 16},
] , )
a : List[str] = dqa_pipeline(
[{"image": image, "question": question}, {"image": image, "question": question}] , top_k=2 )
self.assertEqual(
nested_simplify(lowerCAmelCase__ , decimals=4 ) , [
[
{"score": 0.9_999, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.9_998, "answer": "us-001", "start": 16, "end": 16},
]
]
* 2 , )
a : List[Any] = list(zip(*apply_tesseract(load_image(lowerCAmelCase__ ) , lowerCAmelCase__ , "" ) ) )
# This model should also work if `image` is set to None
a : Any = dqa_pipeline({"image": None, "word_boxes": word_boxes, "question": question} , top_k=2 )
self.assertEqual(
nested_simplify(lowerCAmelCase__ , decimals=4 ) , [
{"score": 0.9_999, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.9_998, "answer": "us-001", "start": 16, "end": 16},
] , )
@slow
@require_torch
def __a ( self ) -> int:
a : Tuple = pipeline(
"document-question-answering" , model="naver-clova-ix/donut-base-finetuned-docvqa" , tokenizer=AutoTokenizer.from_pretrained("naver-clova-ix/donut-base-finetuned-docvqa" ) , feature_extractor="naver-clova-ix/donut-base-finetuned-docvqa" , )
a : Optional[Any] = INVOICE_URL
a : Tuple = "What is the invoice number?"
a : List[Any] = dqa_pipeline(image=lowerCAmelCase__ , question=lowerCAmelCase__ , top_k=2 )
self.assertEqual(nested_simplify(lowerCAmelCase__ , decimals=4 ) , [{"answer": "us-001"}] )
@require_tf
@unittest.skip("Document question answering not implemented in TF" )
def __a ( self ) -> int:
pass
| 105 | 1 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Callable
def __lowerCAmelCase ( lowercase : Callable[[int | float], int | float] , lowercase : int | float , lowercase : int | float , lowercase : int = 100 , ) -> float:
"""simple docstring"""
snake_case : Union[str, Any] = x_start
snake_case : List[Any] = fnc(lowercase )
snake_case : str = 0.0
for _ in range(lowercase ):
# Approximates small segments of curve as linear and solve
# for trapezoidal area
snake_case : List[Any] = (x_end - x_start) / steps + xa
snake_case : Optional[int] = fnc(lowercase )
area += abs(fxa + fxa ) * (xa - xa) / 2
# Increment step
snake_case : Any = xa
snake_case : str = fxa
return area
if __name__ == "__main__":
def __lowerCAmelCase ( lowercase : int ) -> Tuple:
"""simple docstring"""
return x**3 + x**2
print("""f(x) = x^3 + x^2""")
print("""The area between the curve, x = -5, x = 5 and the x axis is:""")
__snake_case = 10
while i <= 100000:
print(F'''with {i} steps: {trapezoidal_area(f, -5, 5, i)}''')
i *= 10
| 112 |
"""simple docstring"""
import math
import sys
def __lowerCAmelCase ( lowercase : int ) -> int:
"""simple docstring"""
if number != int(lowercase ):
raise ValueError("the value of input must be a natural number" )
if number < 0:
raise ValueError("the value of input must not be a negative number" )
if number == 0:
return 1
snake_case : Optional[Any] = [-1] * (number + 1)
snake_case : str = 0
for i in range(1 , number + 1 ):
snake_case : List[Any] = sys.maxsize
snake_case : Union[str, Any] = int(math.sqrt(lowercase ) )
for j in range(1 , root + 1 ):
snake_case : List[str] = 1 + answers[i - (j**2)]
snake_case : Optional[Any] = min(lowercase , lowercase )
snake_case : Any = answer
return answers[number]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 112 | 1 |
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 : Optional[int] = [
"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 : Optional[Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 219 |
"""simple docstring"""
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
from ..processors.squad import SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
_lowercase : Optional[int] = logging.get_logger(__name__)
_lowercase : Tuple = list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
_lowercase : Tuple = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class __SCREAMING_SNAKE_CASE :
'''simple docstring'''
_a = field(
default=lowerCAmelCase_ , metadata={'help': 'Model type selected in the list: ' + ', '.join(lowerCAmelCase_ )} )
_a = field(
default=lowerCAmelCase_ , metadata={'help': 'The input data dir. Should contain the .json files for the SQuAD task.'} )
_a = field(
default=1_2_8 , metadata={
'help': (
'The maximum total input sequence length after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
_a = field(
default=1_2_8 , metadata={'help': 'When splitting up a long document into chunks, how much stride to take between chunks.'} , )
_a = field(
default=6_4 , metadata={
'help': (
'The maximum number of tokens for the question. Questions longer than this will '
'be truncated to this length.'
)
} , )
_a = field(
default=3_0 , metadata={
'help': (
'The maximum length of an answer that can be generated. This is needed because the start '
'and end predictions are not conditioned on one another.'
)
} , )
_a = field(
default=lowerCAmelCase_ , metadata={'help': 'Overwrite the cached training and evaluation sets'} )
_a = field(
default=lowerCAmelCase_ , metadata={'help': 'If true, the SQuAD examples contain some that do not have an answer.'} )
_a = field(
default=0.0 , metadata={'help': 'If null_score - best_non_null is greater than the threshold predict null.'} )
_a = field(
default=2_0 , metadata={'help': 'If null_score - best_non_null is greater than the threshold predict null.'} )
_a = field(
default=0 , metadata={
'help': (
'language id of input for language-specific xlm models (see'
' tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)'
)
} , )
_a = field(default=1 , metadata={'help': 'multiple threads for converting example to features'} )
class __SCREAMING_SNAKE_CASE ( lowerCAmelCase_ ):
'''simple docstring'''
_a = 'train'
_a = 'dev'
class __SCREAMING_SNAKE_CASE ( lowerCAmelCase_ ):
'''simple docstring'''
_a = 42
_a = 42
_a = 42
_a = 42
def __init__( self : Dict, lowerCamelCase : SquadDataTrainingArguments, lowerCamelCase : PreTrainedTokenizer, lowerCamelCase : Optional[int] = None, lowerCamelCase : Union[str, Split] = Split.train, lowerCamelCase : Optional[bool] = False, lowerCamelCase : Optional[str] = None, lowerCamelCase : Optional[str] = "pt", )-> List[str]:
lowerCamelCase__ : List[str] =args
lowerCamelCase__ : Optional[Any] =is_language_sensitive
lowerCamelCase__ : Any =SquadVaProcessor() if args.version_2_with_negative else SquadVaProcessor()
if isinstance(lowerCamelCase, lowerCamelCase ):
try:
lowerCamelCase__ : int =Split[mode]
except KeyError:
raise KeyError('''mode is not a valid split name''' )
lowerCamelCase__ : Optional[Any] =mode
# Load data features from cache or dataset file
lowerCamelCase__ : Any ='''v2''' if args.version_2_with_negative else '''v1'''
lowerCamelCase__ : Optional[int] =os.path.join(
cache_dir if cache_dir is not None else args.data_dir, F'''cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{version_tag}''', )
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
lowerCamelCase__ : Optional[Any] =cached_features_file + '''.lock'''
with FileLock(lowerCamelCase ):
if os.path.exists(lowerCamelCase ) and not args.overwrite_cache:
lowerCamelCase__ : Tuple =time.time()
lowerCamelCase__ : str =torch.load(lowerCamelCase )
# Legacy cache files have only features, while new cache files
# will have dataset and examples also.
lowerCamelCase__ : int =self.old_features['''features''']
lowerCamelCase__ : Optional[int] =self.old_features.get('''dataset''', lowerCamelCase )
lowerCamelCase__ : int =self.old_features.get('''examples''', lowerCamelCase )
logger.info(
F'''Loading features from cached file {cached_features_file} [took %.3f s]''', time.time() - start )
if self.dataset is None or self.examples is None:
logger.warning(
F'''Deleting cached file {cached_features_file} will allow dataset and examples to be cached in'''
''' future run''' )
else:
if mode == Split.dev:
lowerCamelCase__ : Tuple =self.processor.get_dev_examples(args.data_dir )
else:
lowerCamelCase__ : Optional[int] =self.processor.get_train_examples(args.data_dir )
lowerCamelCase__ , lowerCamelCase__ : List[str] =squad_convert_examples_to_features(
examples=self.examples, tokenizer=lowerCamelCase, max_seq_length=args.max_seq_length, doc_stride=args.doc_stride, max_query_length=args.max_query_length, is_training=mode == Split.train, threads=args.threads, return_dataset=lowerCamelCase, )
lowerCamelCase__ : List[str] =time.time()
torch.save(
{'''features''': self.features, '''dataset''': self.dataset, '''examples''': self.examples}, lowerCamelCase, )
# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
logger.info(
F'''Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]''' )
def __len__( self : Optional[int] )-> Dict:
return len(self.features )
def __getitem__( self : List[str], lowerCamelCase : List[Any] )-> Dict[str, torch.Tensor]:
# Convert to Tensors and build dataset
lowerCamelCase__ : List[Any] =self.features[i]
lowerCamelCase__ : List[Any] =torch.tensor(feature.input_ids, dtype=torch.long )
lowerCamelCase__ : List[str] =torch.tensor(feature.attention_mask, dtype=torch.long )
lowerCamelCase__ : List[Any] =torch.tensor(feature.token_type_ids, dtype=torch.long )
lowerCamelCase__ : Optional[int] =torch.tensor(feature.cls_index, dtype=torch.long )
lowerCamelCase__ : Optional[int] =torch.tensor(feature.p_mask, dtype=torch.float )
lowerCamelCase__ : List[Any] =torch.tensor(feature.is_impossible, dtype=torch.float )
lowerCamelCase__ : Optional[Any] ={
'''input_ids''': input_ids,
'''attention_mask''': attention_mask,
'''token_type_ids''': token_type_ids,
}
if self.args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
del inputs["token_type_ids"]
if self.args.model_type in ["xlnet", "xlm"]:
inputs.update({'''cls_index''': cls_index, '''p_mask''': p_mask} )
if self.args.version_2_with_negative:
inputs.update({'''is_impossible''': is_impossible} )
if self.is_language_sensitive:
inputs.update({'''langs''': (torch.ones(input_ids.shape, dtype=torch.intaa ) * self.args.lang_id)} )
if self.mode == Split.train:
lowerCamelCase__ : str =torch.tensor(feature.start_position, dtype=torch.long )
lowerCamelCase__ : int =torch.tensor(feature.end_position, dtype=torch.long )
inputs.update({'''start_positions''': start_positions, '''end_positions''': end_positions} )
return inputs
| 238 | 0 |
import os
import random
import sys
from . import cryptomath_module as cryptomath
from . import rabin_miller
_SCREAMING_SNAKE_CASE = 3
def lowercase( UpperCamelCase_ ) -> int:
'''simple docstring'''
print("""Generating primitive root of p""" )
while True:
UpperCamelCase = random.randrange(3 , UpperCamelCase_ )
if pow(UpperCamelCase_ , 2 , UpperCamelCase_ ) == 1:
continue
if pow(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ) == 1:
continue
return g
def lowercase( UpperCamelCase_ ) -> tuple[tuple[int, int, int, int], tuple[int, int]]:
'''simple docstring'''
print("""Generating prime p...""" )
UpperCamelCase = rabin_miller.generate_large_prime(UpperCamelCase_ ) # select large prime number.
UpperCamelCase = primitive_root(UpperCamelCase_ ) # one primitive root on modulo p.
UpperCamelCase = random.randrange(3 , UpperCamelCase_ ) # private_key -> have to be greater than 2 for safety.
UpperCamelCase = cryptomath.find_mod_inverse(pow(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ) , UpperCamelCase_ )
UpperCamelCase = (key_size, e_a, e_a, p)
UpperCamelCase = (key_size, d)
return public_key, private_key
def lowercase( UpperCamelCase_ , UpperCamelCase_ ) -> None:
'''simple docstring'''
if os.path.exists(f"""{name}_pubkey.txt""" ) or os.path.exists(f"""{name}_privkey.txt""" ):
print("""\nWARNING:""" )
print(
f"""\"{name}_pubkey.txt\" or \"{name}_privkey.txt\" already exists. \n"""
"""Use a different name or delete these files and re-run this program.""" )
sys.exit()
UpperCamelCase , UpperCamelCase = generate_key(UpperCamelCase_ )
print(f"""\nWriting public key to file {name}_pubkey.txt...""" )
with open(f"""{name}_pubkey.txt""" , """w""" ) as fo:
fo.write(f"""{public_key[0]},{public_key[1]},{public_key[2]},{public_key[3]}""" )
print(f"""Writing private key to file {name}_privkey.txt...""" )
with open(f"""{name}_privkey.txt""" , """w""" ) as fo:
fo.write(f"""{private_key[0]},{private_key[1]}""" )
def lowercase( ) -> None:
'''simple docstring'''
print("""Making key files...""" )
make_key_files("""elgamal""" , 2048 )
print("""Key files generation successful""" )
if __name__ == "__main__":
main()
| 365 | 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
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
@add_end_docstrings(
__lowerCAmelCase , 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_ ( __lowerCAmelCase ):
def lowerCamelCase_ ( self : Optional[int] , lowerCamelCase_ : GenericTensor ):
"""simple docstring"""
if self.framework == "tf":
UpperCamelCase = tf.where(input_ids == self.tokenizer.mask_token_id ).numpy()
elif self.framework == "pt":
UpperCamelCase = torch.nonzero(input_ids == self.tokenizer.mask_token_id , as_tuple=lowerCamelCase_ )
else:
raise ValueError("""Unsupported framework""" )
return masked_index
def lowerCamelCase_ ( self : Any , lowerCamelCase_ : GenericTensor ):
"""simple docstring"""
UpperCamelCase = self.get_masked_index(lowerCamelCase_ )
UpperCamelCase = 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 lowerCamelCase_ ( self : Union[str, Any] , lowerCamelCase_ : GenericTensor ):
"""simple docstring"""
if isinstance(lowerCamelCase_ , lowerCamelCase_ ):
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(lowerCamelCase_ )
def lowerCamelCase_ ( self : Any , lowerCamelCase_ : Union[str, Any] , lowerCamelCase_ : Optional[Any]=None , **lowerCamelCase_ : List[str] ):
"""simple docstring"""
if return_tensors is None:
UpperCamelCase = self.framework
UpperCamelCase = self.tokenizer(lowerCamelCase_ , return_tensors=lowerCamelCase_ )
self.ensure_exactly_one_mask_token(lowerCamelCase_ )
return model_inputs
def lowerCamelCase_ ( self : Dict , lowerCamelCase_ : str ):
"""simple docstring"""
UpperCamelCase = self.model(**lowerCamelCase_ )
UpperCamelCase = model_inputs["""input_ids"""]
return model_outputs
def lowerCamelCase_ ( self : str , lowerCamelCase_ : Dict , lowerCamelCase_ : str=5 , lowerCamelCase_ : Optional[int]=None ):
"""simple docstring"""
if target_ids is not None and target_ids.shape[0] < top_k:
UpperCamelCase = target_ids.shape[0]
UpperCamelCase = model_outputs["""input_ids"""][0]
UpperCamelCase = model_outputs["""logits"""]
if self.framework == "tf":
UpperCamelCase = tf.where(input_ids == self.tokenizer.mask_token_id ).numpy()[:, 0]
UpperCamelCase = outputs.numpy()
UpperCamelCase = outputs[0, masked_index, :]
UpperCamelCase = stable_softmax(lowerCamelCase_ , axis=-1 )
if target_ids is not None:
UpperCamelCase = tf.gather_nd(tf.squeeze(lowerCamelCase_ , 0 ) , target_ids.reshape(-1 , 1 ) )
UpperCamelCase = tf.expand_dims(lowerCamelCase_ , 0 )
UpperCamelCase = tf.math.top_k(lowerCamelCase_ , k=lowerCamelCase_ )
UpperCamelCase , UpperCamelCase = topk.values.numpy(), topk.indices.numpy()
else:
UpperCamelCase = torch.nonzero(input_ids == self.tokenizer.mask_token_id , as_tuple=lowerCamelCase_ ).squeeze(-1 )
# Fill mask pipeline supports only one ${mask_token} per sample
UpperCamelCase = outputs[0, masked_index, :]
UpperCamelCase = logits.softmax(dim=-1 )
if target_ids is not None:
UpperCamelCase = probs[..., target_ids]
UpperCamelCase , UpperCamelCase = probs.topk(lowerCamelCase_ )
UpperCamelCase = []
UpperCamelCase = values.shape[0] == 1
for i, (_values, _predictions) in enumerate(zip(values.tolist() , predictions.tolist() ) ):
UpperCamelCase = []
for v, p in zip(_values , _predictions ):
# Copy is important since we're going to modify this array in place
UpperCamelCase = input_ids.numpy().copy()
if target_ids is not None:
UpperCamelCase = target_ids[p].tolist()
UpperCamelCase = p
# Filter padding out:
UpperCamelCase = 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
UpperCamelCase = self.tokenizer.decode(lowerCamelCase_ , skip_special_tokens=lowerCamelCase_ )
UpperCamelCase = {"""score""": v, """token""": p, """token_str""": self.tokenizer.decode([p] ), """sequence""": sequence}
row.append(lowerCamelCase_ )
result.append(lowerCamelCase_ )
if single_mask:
return result[0]
return result
def lowerCamelCase_ ( self : Optional[Any] , lowerCamelCase_ : Union[str, Any] , lowerCamelCase_ : List[Any]=None ):
"""simple docstring"""
if isinstance(lowerCamelCase_ , lowerCamelCase_ ):
UpperCamelCase = [targets]
try:
UpperCamelCase = self.tokenizer.get_vocab()
except Exception:
UpperCamelCase = {}
UpperCamelCase = []
for target in targets:
UpperCamelCase = vocab.get(lowerCamelCase_ , lowerCamelCase_ )
if id_ is None:
UpperCamelCase = self.tokenizer(
lowerCamelCase_ , add_special_tokens=lowerCamelCase_ , return_attention_mask=lowerCamelCase_ , return_token_type_ids=lowerCamelCase_ , max_length=1 , truncation=lowerCamelCase_ , )["""input_ids"""]
if len(lowerCamelCase_ ) == 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
UpperCamelCase = 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_ )
UpperCamelCase = list(set(lowerCamelCase_ ) )
if len(lowerCamelCase_ ) == 0:
raise ValueError("""At least one target must be provided when passed.""" )
UpperCamelCase = np.array(lowerCamelCase_ )
return target_ids
def lowerCamelCase_ ( self : Any , lowerCamelCase_ : Union[str, Any]=None , lowerCamelCase_ : Any=None ):
"""simple docstring"""
UpperCamelCase = {}
if targets is not None:
UpperCamelCase = self.get_target_ids(lowerCamelCase_ , lowerCamelCase_ )
UpperCamelCase = target_ids
if top_k is not None:
UpperCamelCase = 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 : Tuple , lowerCamelCase_ : str , *lowerCamelCase_ : Tuple , **lowerCamelCase_ : List[Any] ):
"""simple docstring"""
UpperCamelCase = super().__call__(lowerCamelCase_ , **lowerCamelCase_ )
if isinstance(lowerCamelCase_ , lowerCamelCase_ ) and len(lowerCamelCase_ ) == 1:
return outputs[0]
return outputs
| 165 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_lowerCamelCase : Dict = {
'''configuration_blip''': [
'''BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''BlipConfig''',
'''BlipTextConfig''',
'''BlipVisionConfig''',
],
'''processing_blip''': ['''BlipProcessor'''],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase : Any = ['''BlipImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase : str = [
'''BLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''BlipModel''',
'''BlipPreTrainedModel''',
'''BlipForConditionalGeneration''',
'''BlipForQuestionAnswering''',
'''BlipVisionModel''',
'''BlipTextModel''',
'''BlipForImageTextRetrieval''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase : List[str] = [
'''TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFBlipModel''',
'''TFBlipPreTrainedModel''',
'''TFBlipForConditionalGeneration''',
'''TFBlipForQuestionAnswering''',
'''TFBlipVisionModel''',
'''TFBlipTextModel''',
'''TFBlipForImageTextRetrieval''',
]
if TYPE_CHECKING:
from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
from .processing_blip import BlipProcessor
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_blip import BlipImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blip import (
BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
BlipForConditionalGeneration,
BlipForImageTextRetrieval,
BlipForQuestionAnswering,
BlipModel,
BlipPreTrainedModel,
BlipTextModel,
BlipVisionModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_blip import (
TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
TFBlipForConditionalGeneration,
TFBlipForImageTextRetrieval,
TFBlipForQuestionAnswering,
TFBlipModel,
TFBlipPreTrainedModel,
TFBlipTextModel,
TFBlipVisionModel,
)
else:
import sys
_lowerCamelCase : List[Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__) | 282 |
'''simple docstring'''
import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
MODEL_MAPPING,
DeiTForImageClassification,
DeiTForImageClassificationWithTeacher,
DeiTForMaskedImageModeling,
DeiTModel,
)
from transformers.models.deit.modeling_deit import DEIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import DeiTImageProcessor
class A :
def __init__( self , lowerCamelCase__ , lowerCamelCase__=13 , lowerCamelCase__=30 , lowerCamelCase__=2 , lowerCamelCase__=3 , lowerCamelCase__=True , lowerCamelCase__=True , lowerCamelCase__=32 , lowerCamelCase__=5 , lowerCamelCase__=4 , lowerCamelCase__=37 , lowerCamelCase__="gelu" , lowerCamelCase__=0.1 , lowerCamelCase__=0.1 , lowerCamelCase__=10 , lowerCamelCase__=0.02 , lowerCamelCase__=3 , lowerCamelCase__=None , lowerCamelCase__=2 , ) -> Optional[int]:
'''simple docstring'''
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__ = scope
lowercase__ = encoder_stride
# in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
lowercase__ = (image_size // patch_size) ** 2
lowercase__ = num_patches + 2
def A__ ( self ) -> Any:
'''simple docstring'''
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 A__ ( self ) -> Optional[Any]:
'''simple docstring'''
return DeiTConfig(
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=lowerCamelCase__ , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , )
def A__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> Dict:
'''simple docstring'''
lowercase__ = DeiTModel(config=lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
lowercase__ = model(lowerCamelCase__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def A__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> Any:
'''simple docstring'''
lowercase__ = DeiTForMaskedImageModeling(config=lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
lowercase__ = model(lowerCamelCase__ )
self.parent.assertEqual(
result.reconstruction.shape , (self.batch_size, self.num_channels, self.image_size, self.image_size) )
# test greyscale images
lowercase__ = 1
lowercase__ = DeiTForMaskedImageModeling(lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
lowercase__ = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
lowercase__ = model(lowerCamelCase__ )
self.parent.assertEqual(result.reconstruction.shape , (self.batch_size, 1, self.image_size, self.image_size) )
def A__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> str:
'''simple docstring'''
lowercase__ = self.type_sequence_label_size
lowercase__ = DeiTForImageClassification(lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
lowercase__ = model(lowerCamelCase__ , labels=lowerCamelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
lowercase__ = 1
lowercase__ = DeiTForImageClassification(lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
lowercase__ = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
lowercase__ = model(lowerCamelCase__ , labels=lowerCamelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def A__ ( self ) -> List[str]:
'''simple docstring'''
lowercase__ = self.prepare_config_and_inputs()
(
(
lowercase__
) , (
lowercase__
) , (
lowercase__
) ,
) = config_and_inputs
lowercase__ = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class A ( __UpperCAmelCase , __UpperCAmelCase , unittest.TestCase ):
lowerCamelCase : int = (
(
DeiTModel,
DeiTForImageClassification,
DeiTForImageClassificationWithTeacher,
DeiTForMaskedImageModeling,
)
if is_torch_available()
else ()
)
lowerCamelCase : Tuple = (
{
"""feature-extraction""": DeiTModel,
"""image-classification""": (DeiTForImageClassification, DeiTForImageClassificationWithTeacher),
}
if is_torch_available()
else {}
)
lowerCamelCase : Any = False
lowerCamelCase : str = False
lowerCamelCase : str = False
def A__ ( self ) -> List[str]:
'''simple docstring'''
lowercase__ = DeiTModelTester(self )
lowercase__ = ConfigTester(self , config_class=lowerCamelCase__ , has_text_modality=lowerCamelCase__ , hidden_size=37 )
def A__ ( self ) -> str:
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason="""DeiT does not use inputs_embeds""" )
def A__ ( self ) -> Union[str, Any]:
'''simple docstring'''
pass
def A__ ( self ) -> Any:
'''simple docstring'''
lowercase__ , lowercase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ = model_class(lowerCamelCase__ )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
lowercase__ = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(lowerCamelCase__ , nn.Linear ) )
def A__ ( self ) -> Union[str, Any]:
'''simple docstring'''
lowercase__ , lowercase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ = model_class(lowerCamelCase__ )
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] , lowerCamelCase__ )
def A__ ( self ) -> int:
'''simple docstring'''
lowercase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCamelCase__ )
def A__ ( self ) -> List[Any]:
'''simple docstring'''
lowercase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*lowerCamelCase__ )
def A__ ( self ) -> Union[str, Any]:
'''simple docstring'''
lowercase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*lowerCamelCase__ )
def A__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__=False ) -> Dict:
'''simple docstring'''
lowercase__ = super()._prepare_for_class(lowerCamelCase__ , lowerCamelCase__ , return_labels=lowerCamelCase__ )
if return_labels:
if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
del inputs_dict["labels"]
return inputs_dict
def A__ ( self ) -> Any:
'''simple docstring'''
if not self.model_tester.is_training:
return
lowercase__ , lowercase__ = self.model_tester.prepare_config_and_inputs_for_common()
lowercase__ = True
for model_class in self.all_model_classes:
# DeiTForImageClassificationWithTeacher supports inference-only
if (
model_class in get_values(lowerCamelCase__ )
or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
):
continue
lowercase__ = model_class(lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.train()
lowercase__ = self._prepare_for_class(lowerCamelCase__ , lowerCamelCase__ , return_labels=lowerCamelCase__ )
lowercase__ = model(**lowerCamelCase__ ).loss
loss.backward()
def A__ ( self ) -> int:
'''simple docstring'''
lowercase__ , lowercase__ = self.model_tester.prepare_config_and_inputs_for_common()
if not self.model_tester.is_training:
return
lowercase__ = False
lowercase__ = True
for model_class in self.all_model_classes:
if model_class in get_values(lowerCamelCase__ ) or not model_class.supports_gradient_checkpointing:
continue
# DeiTForImageClassificationWithTeacher supports inference-only
if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
continue
lowercase__ = model_class(lowerCamelCase__ )
model.gradient_checkpointing_enable()
model.to(lowerCamelCase__ )
model.train()
lowercase__ = self._prepare_for_class(lowerCamelCase__ , lowerCamelCase__ , return_labels=lowerCamelCase__ )
lowercase__ = model(**lowerCamelCase__ ).loss
loss.backward()
def A__ ( self ) -> int:
'''simple docstring'''
lowercase__ , lowercase__ = self.model_tester.prepare_config_and_inputs_for_common()
lowercase__ = [
{"""title""": """multi_label_classification""", """num_labels""": 2, """dtype""": torch.float},
{"""title""": """single_label_classification""", """num_labels""": 1, """dtype""": torch.long},
{"""title""": """regression""", """num_labels""": 1, """dtype""": torch.float},
]
for model_class in self.all_model_classes:
if (
model_class
not in [
*get_values(lowerCamelCase__ ),
*get_values(lowerCamelCase__ ),
]
or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
):
continue
for problem_type in problem_types:
with self.subTest(msg=F'''Testing {model_class} with {problem_type['title']}''' ):
lowercase__ = problem_type["""title"""]
lowercase__ = problem_type["""num_labels"""]
lowercase__ = model_class(lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.train()
lowercase__ = self._prepare_for_class(lowerCamelCase__ , lowerCamelCase__ , return_labels=lowerCamelCase__ )
if problem_type["num_labels"] > 1:
lowercase__ = inputs["""labels"""].unsqueeze(1 ).repeat(1 , problem_type["""num_labels"""] )
lowercase__ = inputs["""labels"""].to(problem_type["""dtype"""] )
# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
# they have the same size." which is a symptom something in wrong for the regression problem.
# See https://github.com/huggingface/transformers/issues/11780
with warnings.catch_warnings(record=lowerCamelCase__ ) as warning_list:
lowercase__ = model(**lowerCamelCase__ ).loss
for w in warning_list:
if "Using a target size that is different to the input size" in str(w.message ):
raise ValueError(
F'''Something is going wrong in the regression problem: intercepted {w.message}''' )
loss.backward()
@slow
def A__ ( self ) -> Any:
'''simple docstring'''
for model_name in DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase__ = DeiTModel.from_pretrained(lowerCamelCase__ )
self.assertIsNotNone(lowerCamelCase__ )
def _A ( ):
lowercase__ = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_torch
@require_vision
class A ( unittest.TestCase ):
@cached_property
def A__ ( self ) -> int:
'''simple docstring'''
return (
DeiTImageProcessor.from_pretrained("""facebook/deit-base-distilled-patch16-224""" )
if is_vision_available()
else None
)
@slow
def A__ ( self ) -> Optional[int]:
'''simple docstring'''
lowercase__ = DeiTForImageClassificationWithTeacher.from_pretrained("""facebook/deit-base-distilled-patch16-224""" ).to(
lowerCamelCase__ )
lowercase__ = self.default_image_processor
lowercase__ = prepare_img()
lowercase__ = image_processor(images=lowerCamelCase__ , return_tensors="""pt""" ).to(lowerCamelCase__ )
# forward pass
with torch.no_grad():
lowercase__ = model(**lowerCamelCase__ )
# verify the logits
lowercase__ = torch.Size((1, 1_000) )
self.assertEqual(outputs.logits.shape , lowerCamelCase__ )
lowercase__ = torch.tensor([-1.02_66, 0.19_12, -1.28_61] ).to(lowerCamelCase__ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , lowerCamelCase__ , atol=1e-4 ) )
@slow
@require_accelerate
@require_torch_gpu
def A__ ( self ) -> List[Any]:
'''simple docstring'''
lowercase__ = DeiTModel.from_pretrained(
"""facebook/deit-base-distilled-patch16-224""" , torch_dtype=torch.floataa , device_map="""auto""" )
lowercase__ = self.default_image_processor
lowercase__ = prepare_img()
lowercase__ = image_processor(images=lowerCamelCase__ , return_tensors="""pt""" )
lowercase__ = inputs.pixel_values.to(lowerCamelCase__ )
# forward pass to make sure inference works in fp16
with torch.no_grad():
lowercase__ = model(lowerCamelCase__ )
| 164 | 0 |
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 : Any = logging.get_logger(__name__)
__UpperCamelCase : Union[str, Any] = {
"salesforce/blip2-opt-2.7b": "https://huggingface.co/salesforce/blip2-opt-2.7b/resolve/main/config.json",
}
class __magic_name__ ( _SCREAMING_SNAKE_CASE):
A: Tuple = "blip_2_vision_model"
def __init__( self : List[str] , lowerCamelCase__ : int=1408 , lowerCamelCase__ : str=6144 , lowerCamelCase__ : str=39 , lowerCamelCase__ : List[str]=16 , lowerCamelCase__ : List[str]=224 , lowerCamelCase__ : List[Any]=14 , lowerCamelCase__ : Dict="gelu" , lowerCamelCase__ : int=0.0_0001 , lowerCamelCase__ : Optional[int]=0.0 , lowerCamelCase__ : Optional[int]=1E-1_0 , lowerCamelCase__ : Dict=True , **lowerCamelCase__ : Optional[Any] , ) -> int:
'''simple docstring'''
super().__init__(**lowerCamelCase__ )
UpperCamelCase__ : Optional[int] = hidden_size
UpperCamelCase__ : str = intermediate_size
UpperCamelCase__ : Dict = num_hidden_layers
UpperCamelCase__ : Optional[Any] = num_attention_heads
UpperCamelCase__ : int = patch_size
UpperCamelCase__ : List[Any] = image_size
UpperCamelCase__ : Dict = initializer_range
UpperCamelCase__ : str = attention_dropout
UpperCamelCase__ : Dict = layer_norm_eps
UpperCamelCase__ : Any = hidden_act
UpperCamelCase__ : Any = qkv_bias
@classmethod
def UpperCAmelCase__ ( cls : Tuple , lowerCamelCase__ : Union[str, os.PathLike] , **lowerCamelCase__ : Union[str, Any] ) -> "PretrainedConfig":
'''simple docstring'''
cls._set_token_in_kwargs(lowerCamelCase__ )
UpperCamelCase__ : Optional[Any] = cls.get_config_dict(lowerCamelCase__ , **lowerCamelCase__ )
# get the vision config dict if we are loading from Blip2Config
if config_dict.get('''model_type''' ) == "blip-2":
UpperCamelCase__ : Union[str, 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(lowerCamelCase__ , **lowerCamelCase__ )
class __magic_name__ ( _SCREAMING_SNAKE_CASE):
A: Tuple = "blip_2_qformer"
def __init__( self : Optional[Any] , lowerCamelCase__ : Optional[Any]=30522 , lowerCamelCase__ : List[Any]=768 , lowerCamelCase__ : Optional[int]=12 , lowerCamelCase__ : Tuple=12 , lowerCamelCase__ : Union[str, Any]=3072 , lowerCamelCase__ : List[str]="gelu" , lowerCamelCase__ : Tuple=0.1 , lowerCamelCase__ : List[Any]=0.1 , lowerCamelCase__ : Any=512 , lowerCamelCase__ : Any=0.02 , lowerCamelCase__ : Union[str, Any]=1E-1_2 , lowerCamelCase__ : Tuple=0 , lowerCamelCase__ : Dict="absolute" , lowerCamelCase__ : List[Any]=2 , lowerCamelCase__ : List[Any]=1408 , **lowerCamelCase__ : str , ) -> Union[str, Any]:
'''simple docstring'''
super().__init__(pad_token_id=lowerCamelCase__ , **lowerCamelCase__ )
UpperCamelCase__ : str = vocab_size
UpperCamelCase__ : Dict = hidden_size
UpperCamelCase__ : str = num_hidden_layers
UpperCamelCase__ : Tuple = num_attention_heads
UpperCamelCase__ : Any = hidden_act
UpperCamelCase__ : Optional[Any] = intermediate_size
UpperCamelCase__ : Dict = hidden_dropout_prob
UpperCamelCase__ : Any = attention_probs_dropout_prob
UpperCamelCase__ : Optional[Any] = max_position_embeddings
UpperCamelCase__ : List[Any] = initializer_range
UpperCamelCase__ : Any = layer_norm_eps
UpperCamelCase__ : Tuple = position_embedding_type
UpperCamelCase__ : Optional[int] = cross_attention_frequency
UpperCamelCase__ : Optional[Any] = encoder_hidden_size
@classmethod
def UpperCAmelCase__ ( cls : Dict , lowerCamelCase__ : Union[str, os.PathLike] , **lowerCamelCase__ : Any ) -> "PretrainedConfig":
'''simple docstring'''
cls._set_token_in_kwargs(lowerCamelCase__ )
UpperCamelCase__ : Optional[int] = cls.get_config_dict(lowerCamelCase__ , **lowerCamelCase__ )
# get the qformer config dict if we are loading from Blip2Config
if config_dict.get('''model_type''' ) == "blip-2":
UpperCamelCase__ : Optional[Any] = 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(lowerCamelCase__ , **lowerCamelCase__ )
class __magic_name__ ( _SCREAMING_SNAKE_CASE):
A: List[str] = "blip-2"
A: Tuple = True
def __init__( self : Union[str, Any] , lowerCamelCase__ : Any=None , lowerCamelCase__ : List[str]=None , lowerCamelCase__ : List[Any]=None , lowerCamelCase__ : Dict=32 , **lowerCamelCase__ : List[str] ) -> Union[str, Any]:
'''simple docstring'''
super().__init__(**lowerCamelCase__ )
if vision_config is None:
UpperCamelCase__ : List[str] = {}
logger.info('''vision_config is None. initializing the Blip2VisionConfig with default values.''' )
if qformer_config is None:
UpperCamelCase__ : Any = {}
logger.info('''qformer_config is None. Initializing the Blip2QFormerConfig with default values.''' )
if text_config is None:
UpperCamelCase__ : Union[str, Any] = {}
logger.info('''text_config is None. Initializing the text config with default values (`OPTConfig`).''' )
UpperCamelCase__ : Optional[int] = BlipaVisionConfig(**lowerCamelCase__ )
UpperCamelCase__ : List[str] = BlipaQFormerConfig(**lowerCamelCase__ )
UpperCamelCase__ : List[str] = text_config["""model_type"""] if """model_type""" in text_config else """opt"""
UpperCamelCase__ : List[str] = CONFIG_MAPPING[text_model_type](**lowerCamelCase__ )
UpperCamelCase__ : Union[str, Any] = self.text_config.tie_word_embeddings
UpperCamelCase__ : Dict = self.text_config.is_encoder_decoder
UpperCamelCase__ : Union[str, Any] = num_query_tokens
UpperCamelCase__ : List[Any] = self.vision_config.hidden_size
UpperCamelCase__ : int = self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
UpperCamelCase__ : str = 1.0
UpperCamelCase__ : str = 0.02
@classmethod
def UpperCAmelCase__ ( cls : int , lowerCamelCase__ : BlipaVisionConfig , lowerCamelCase__ : BlipaQFormerConfig , lowerCamelCase__ : PretrainedConfig , **lowerCamelCase__ : Any , ) -> List[str]:
'''simple docstring'''
return cls(
vision_config=vision_config.to_dict() , qformer_config=qformer_config.to_dict() , text_config=text_config.to_dict() , **lowerCamelCase__ , )
def UpperCAmelCase__ ( self : List[str] ) -> int:
'''simple docstring'''
UpperCamelCase__ : Union[str, Any] = copy.deepcopy(self.__dict__ )
UpperCamelCase__ : Union[str, Any] = self.vision_config.to_dict()
UpperCamelCase__ : Optional[Any] = self.qformer_config.to_dict()
UpperCamelCase__ : Dict = self.text_config.to_dict()
UpperCamelCase__ : Optional[int] = self.__class__.model_type
return output
| 350 |
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 __magic_name__ ( unittest.TestCase):
def UpperCAmelCase__ ( self : List[str] ) -> Union[str, Any]:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@property
def UpperCAmelCase__ ( self : str ) -> Any:
'''simple docstring'''
torch.manual_seed(0 )
UpperCamelCase__ : Union[str, Any] = UNetaDModel(
sample_size=(32, 64) , in_channels=1 , out_channels=1 , layers_per_block=2 , block_out_channels=(128, 128) , down_block_types=('''AttnDownBlock2D''', '''DownBlock2D''') , up_block_types=('''UpBlock2D''', '''AttnUpBlock2D''') , )
return model
@property
def UpperCAmelCase__ ( self : str ) -> Optional[Any]:
'''simple docstring'''
torch.manual_seed(0 )
UpperCamelCase__ : Optional[Any] = UNetaDConditionModel(
sample_size=(64, 32) , in_channels=1 , out_channels=1 , layers_per_block=2 , block_out_channels=(128, 128) , down_block_types=('''CrossAttnDownBlock2D''', '''DownBlock2D''') , up_block_types=('''UpBlock2D''', '''CrossAttnUpBlock2D''') , cross_attention_dim=10 , )
return model
@property
def UpperCAmelCase__ ( self : Union[str, Any] ) -> Tuple:
'''simple docstring'''
torch.manual_seed(0 )
UpperCamelCase__ : Any = AutoencoderKL(
sample_size=(128, 64) , in_channels=1 , out_channels=1 , latent_channels=1 , layers_per_block=2 , block_out_channels=(128, 128) , down_block_types=('''DownEncoderBlock2D''', '''DownEncoderBlock2D''') , up_block_types=('''UpDecoderBlock2D''', '''UpDecoderBlock2D''') , )
UpperCamelCase__ : Union[str, Any] = UNetaDModel(
sample_size=(64, 32) , in_channels=1 , out_channels=1 , layers_per_block=2 , block_out_channels=(128, 128) , down_block_types=('''AttnDownBlock2D''', '''DownBlock2D''') , up_block_types=('''UpBlock2D''', '''AttnUpBlock2D''') , )
return vqvae, unet
@slow
def UpperCAmelCase__ ( self : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
UpperCamelCase__ : List[Any] = '''cpu''' # ensure determinism for the device-dependent torch.Generator
UpperCamelCase__ : List[Any] = Mel(
x_res=self.dummy_unet.config.sample_size[1] , y_res=self.dummy_unet.config.sample_size[0] , )
UpperCamelCase__ : Dict = DDPMScheduler()
UpperCamelCase__ : List[Any] = AudioDiffusionPipeline(vqvae=lowerCamelCase__ , unet=self.dummy_unet , mel=lowerCamelCase__ , scheduler=lowerCamelCase__ )
UpperCamelCase__ : Optional[int] = pipe.to(lowerCamelCase__ )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
UpperCamelCase__ : Union[str, Any] = torch.Generator(device=lowerCamelCase__ ).manual_seed(42 )
UpperCamelCase__ : List[str] = pipe(generator=lowerCamelCase__ , steps=4 )
UpperCamelCase__ : Union[str, Any] = output.audios[0]
UpperCamelCase__ : Any = output.images[0]
UpperCamelCase__ : Optional[int] = torch.Generator(device=lowerCamelCase__ ).manual_seed(42 )
UpperCamelCase__ : int = pipe(generator=lowerCamelCase__ , steps=4 , return_dict=lowerCamelCase__ )
UpperCamelCase__ : Optional[int] = output[0][0]
assert audio.shape == (1, (self.dummy_unet.config.sample_size[1] - 1) * mel.hop_length)
assert (
image.height == self.dummy_unet.config.sample_size[0]
and image.width == self.dummy_unet.config.sample_size[1]
)
UpperCamelCase__ : Optional[Any] = np.frombuffer(image.tobytes() , dtype='''uint8''' )[:10]
UpperCamelCase__ : Any = np.frombuffer(image_from_tuple.tobytes() , dtype='''uint8''' )[:10]
UpperCamelCase__ : str = np.array([69, 255, 255, 255, 0, 0, 77, 181, 12, 127] )
assert np.abs(image_slice.flatten() - expected_slice ).max() == 0
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() == 0
UpperCamelCase__ : List[str] = 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] , )
UpperCamelCase__ : Optional[Any] = DDIMScheduler()
UpperCamelCase__ : Union[str, Any] = self.dummy_vqvae_and_unet
UpperCamelCase__ : Union[str, Any] = AudioDiffusionPipeline(
vqvae=self.dummy_vqvae_and_unet[0] , unet=dummy_vqvae_and_unet[1] , mel=lowerCamelCase__ , scheduler=lowerCamelCase__ )
UpperCamelCase__ : str = pipe.to(lowerCamelCase__ )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
np.random.seed(0 )
UpperCamelCase__ : Optional[int] = np.random.uniform(-1 , 1 , ((dummy_vqvae_and_unet[0].config.sample_size[1] - 1) * mel.hop_length,) )
UpperCamelCase__ : Optional[int] = torch.Generator(device=lowerCamelCase__ ).manual_seed(42 )
UpperCamelCase__ : Union[str, Any] = pipe(raw_audio=lowerCamelCase__ , generator=lowerCamelCase__ , start_step=5 , steps=10 )
UpperCamelCase__ : int = 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]
)
UpperCamelCase__ : List[Any] = np.frombuffer(image.tobytes() , dtype='''uint8''' )[:10]
UpperCamelCase__ : Union[str, Any] = np.array([120, 117, 110, 109, 138, 167, 138, 148, 132, 121] )
assert np.abs(image_slice.flatten() - expected_slice ).max() == 0
UpperCamelCase__ : Any = self.dummy_unet_condition
UpperCamelCase__ : str = AudioDiffusionPipeline(
vqvae=self.dummy_vqvae_and_unet[0] , unet=lowerCamelCase__ , mel=lowerCamelCase__ , scheduler=lowerCamelCase__ )
UpperCamelCase__ : Any = pipe.to(lowerCamelCase__ )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
np.random.seed(0 )
UpperCamelCase__ : Union[str, Any] = torch.rand((1, 1, 10) )
UpperCamelCase__ : int = pipe(generator=lowerCamelCase__ , encoding=lowerCamelCase__ )
UpperCamelCase__ : Optional[int] = output.images[0]
UpperCamelCase__ : List[Any] = np.frombuffer(image.tobytes() , dtype='''uint8''' )[:10]
UpperCamelCase__ : str = np.array([107, 103, 120, 127, 142, 122, 113, 122, 97, 111] )
assert np.abs(image_slice.flatten() - expected_slice ).max() == 0
@slow
@require_torch_gpu
class __magic_name__ ( unittest.TestCase):
def UpperCAmelCase__ ( self : str ) -> List[Any]:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCAmelCase__ ( self : str ) -> Optional[Any]:
'''simple docstring'''
UpperCamelCase__ : Union[str, Any] = torch_device
UpperCamelCase__ : Dict = DiffusionPipeline.from_pretrained('''teticio/audio-diffusion-ddim-256''' )
UpperCamelCase__ : Optional[int] = pipe.to(lowerCamelCase__ )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
UpperCamelCase__ : Optional[int] = torch.Generator(device=lowerCamelCase__ ).manual_seed(42 )
UpperCamelCase__ : Optional[Any] = pipe(generator=lowerCamelCase__ )
UpperCamelCase__ : Dict = output.audios[0]
UpperCamelCase__ : Optional[Any] = output.images[0]
assert audio.shape == (1, (pipe.unet.config.sample_size[1] - 1) * pipe.mel.hop_length)
assert image.height == pipe.unet.config.sample_size[0] and image.width == pipe.unet.config.sample_size[1]
UpperCamelCase__ : Dict = np.frombuffer(image.tobytes() , dtype='''uint8''' )[:10]
UpperCamelCase__ : List[Any] = np.array([151, 167, 154, 144, 122, 134, 121, 105, 70, 26] )
assert np.abs(image_slice.flatten() - expected_slice ).max() == 0
| 51 | 0 |
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class __lowerCAmelCase ( lowerCamelCase__ , unittest.TestCase ):
__lowerCamelCase = DiTPipeline
__lowerCamelCase = CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
__lowerCamelCase = PipelineTesterMixin.required_optional_params - {
'''latents''',
'''num_images_per_prompt''',
'''callback''',
'''callback_steps''',
}
__lowerCamelCase = CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
__lowerCamelCase = False
def snake_case ( self ):
"""simple docstring"""
torch.manual_seed(0 )
_lowerCAmelCase = TransformeraDModel(
sample_size=16 , num_layers=2 , patch_size=4 , attention_head_dim=8 , num_attention_heads=2 , in_channels=4 , out_channels=8 , attention_bias=_snake_case , activation_fn="""gelu-approximate""" , num_embeds_ada_norm=1000 , norm_type="""ada_norm_zero""" , norm_elementwise_affine=_snake_case , )
_lowerCAmelCase = AutoencoderKL()
_lowerCAmelCase = DDIMScheduler()
_lowerCAmelCase = {"""transformer""": transformer.eval(), """vae""": vae.eval(), """scheduler""": scheduler}
return components
def snake_case ( self , _snake_case , _snake_case=0 ):
"""simple docstring"""
if str(_snake_case ).startswith("""mps""" ):
_lowerCAmelCase = torch.manual_seed(_snake_case )
else:
_lowerCAmelCase = torch.Generator(device=_snake_case ).manual_seed(_snake_case )
_lowerCAmelCase = {
"""class_labels""": [1],
"""generator""": generator,
"""num_inference_steps""": 2,
"""output_type""": """numpy""",
}
return inputs
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = """cpu"""
_lowerCAmelCase = self.get_dummy_components()
_lowerCAmelCase = self.pipeline_class(**_snake_case )
pipe.to(_snake_case )
pipe.set_progress_bar_config(disable=_snake_case )
_lowerCAmelCase = self.get_dummy_inputs(_snake_case )
_lowerCAmelCase = pipe(**_snake_case ).images
_lowerCAmelCase = image[0, -3:, -3:, -1]
self.assertEqual(image.shape , (1, 16, 16, 3) )
_lowerCAmelCase = np.array([0.2946, 0.6601, 0.4329, 0.3296, 0.4144, 0.5319, 0.7273, 0.5013, 0.4457] )
_lowerCAmelCase = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(_snake_case , 1e-3 )
def snake_case ( self ):
"""simple docstring"""
self._test_inference_batch_single_identical(relax_max_difference=_snake_case , expected_max_diff=1e-3 )
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() , reason="""XFormers attention is only available with CUDA and `xformers` installed""" , )
def snake_case ( self ):
"""simple docstring"""
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3 )
@require_torch_gpu
@slow
class __lowerCAmelCase ( unittest.TestCase ):
def snake_case ( self ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = torch.manual_seed(0 )
_lowerCAmelCase = DiTPipeline.from_pretrained("""facebook/DiT-XL-2-256""" )
pipe.to("""cuda""" )
_lowerCAmelCase = ["""vase""", """umbrella""", """white shark""", """white wolf"""]
_lowerCAmelCase = pipe.get_label_ids(_snake_case )
_lowerCAmelCase = pipe(_snake_case , generator=_snake_case , num_inference_steps=40 , output_type="""np""" ).images
for word, image in zip(_snake_case , _snake_case ):
_lowerCAmelCase = load_numpy(
F'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy' )
assert np.abs((expected_image - image).max() ) < 1e-2
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = DiTPipeline.from_pretrained("""facebook/DiT-XL-2-512""" )
_lowerCAmelCase = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.to("""cuda""" )
_lowerCAmelCase = ["""vase""", """umbrella"""]
_lowerCAmelCase = pipe.get_label_ids(_snake_case )
_lowerCAmelCase = torch.manual_seed(0 )
_lowerCAmelCase = pipe(_snake_case , generator=_snake_case , num_inference_steps=25 , output_type="""np""" ).images
for word, image in zip(_snake_case , _snake_case ):
_lowerCAmelCase = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
F'/dit/{word}_512.npy' )
assert np.abs((expected_image - image).max() ) < 1e-1
| 82 |
"""simple docstring"""
def A ( snake_case :int ) -> int:
__UpperCamelCase = [1]
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase = 0, 0, 0
__UpperCamelCase = ugly_nums[ia] * 2
__UpperCamelCase = ugly_nums[ia] * 3
__UpperCamelCase = ugly_nums[ia] * 5
for _ in range(1 , snake_case ):
__UpperCamelCase = min(snake_case , snake_case , snake_case )
ugly_nums.append(snake_case )
if next_num == next_a:
ia += 1
__UpperCamelCase = ugly_nums[ia] * 2
if next_num == next_a:
ia += 1
__UpperCamelCase = ugly_nums[ia] * 3
if next_num == next_a:
ia += 1
__UpperCamelCase = ugly_nums[ia] * 5
return ugly_nums[-1]
if __name__ == "__main__":
from doctest import testmod
testmod(verbose=True)
print(f'''{ugly_numbers(2_0_0) = }''')
| 316 | 0 |
'''simple docstring'''
import pytest
import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize('''dataset_size''', [None, 4_0_0 * 2**2_0, 6_0_0 * 2**2_0])
@pytest.mark.parametrize('''input_in_memory_max_size''', ['''default''', 0, 1_0_0 * 2**2_0, 9_0_0 * 2**2_0])
def __magic_name__( lowerCamelCase, lowerCamelCase, lowerCamelCase):
if input_in_memory_max_size != "default":
monkeypatch.setattr(datasets.config, '''IN_MEMORY_MAX_SIZE''', lowerCamelCase)
__lowerCAmelCase = datasets.config.IN_MEMORY_MAX_SIZE
if input_in_memory_max_size == "default":
assert in_memory_max_size == 0
else:
assert in_memory_max_size == input_in_memory_max_size
if dataset_size and in_memory_max_size:
__lowerCAmelCase = dataset_size < in_memory_max_size
else:
__lowerCAmelCase = False
__lowerCAmelCase = is_small_dataset(lowerCamelCase)
assert result == expected
| 9 |
'''simple docstring'''
# Imports
import numpy as np
class a__ :
"""simple docstring"""
def __init__(self , __lowercase=None , __lowercase=None , __lowercase=None , __lowercase=None , __lowercase=None ):
self.set_matricies(red=__lowercase , green=__lowercase , blue=__lowercase , red_edge=__lowercase , nir=__lowercase )
def _snake_case (self , __lowercase=None , __lowercase=None , __lowercase=None , __lowercase=None , __lowercase=None ):
if red is not None:
__lowerCAmelCase = red
if green is not None:
__lowerCAmelCase = green
if blue is not None:
__lowerCAmelCase = blue
if red_edge is not None:
__lowerCAmelCase = red_edge
if nir is not None:
__lowerCAmelCase = nir
return True
def _snake_case (self , __lowercase="" , __lowercase=None , __lowercase=None , __lowercase=None , __lowercase=None , __lowercase=None ):
self.set_matricies(red=__lowercase , green=__lowercase , blue=__lowercase , red_edge=__lowercase , nir=__lowercase )
__lowerCAmelCase = {
'''ARVI2''': self.arvaa,
'''CCCI''': self.ccci,
'''CVI''': self.cvi,
'''GLI''': self.gli,
'''NDVI''': self.ndvi,
'''BNDVI''': self.bndvi,
'''redEdgeNDVI''': self.red_edge_ndvi,
'''GNDVI''': self.gndvi,
'''GBNDVI''': self.gbndvi,
'''GRNDVI''': self.grndvi,
'''RBNDVI''': self.rbndvi,
'''PNDVI''': self.pndvi,
'''ATSAVI''': self.atsavi,
'''BWDRVI''': self.bwdrvi,
'''CIgreen''': self.ci_green,
'''CIrededge''': self.ci_rededge,
'''CI''': self.ci,
'''CTVI''': self.ctvi,
'''GDVI''': self.gdvi,
'''EVI''': self.evi,
'''GEMI''': self.gemi,
'''GOSAVI''': self.gosavi,
'''GSAVI''': self.gsavi,
'''Hue''': self.hue,
'''IVI''': self.ivi,
'''IPVI''': self.ipvi,
'''I''': self.i,
'''RVI''': self.rvi,
'''MRVI''': self.mrvi,
'''MSAVI''': self.m_savi,
'''NormG''': self.norm_g,
'''NormNIR''': self.norm_nir,
'''NormR''': self.norm_r,
'''NGRDI''': self.ngrdi,
'''RI''': self.ri,
'''S''': self.s,
'''IF''': self._if,
'''DVI''': self.dvi,
'''TVI''': self.tvi,
'''NDRE''': self.ndre,
}
try:
return funcs[index]()
except KeyError:
print('''Index not in the list!''' )
return False
def _snake_case (self ):
return -0.1_8 + (1.1_7 * ((self.nir - self.red) / (self.nir + self.red)))
def _snake_case (self ):
return ((self.nir - self.redEdge) / (self.nir + self.redEdge)) / (
(self.nir - self.red) / (self.nir + self.red)
)
def _snake_case (self ):
return self.nir * (self.red / (self.green**2))
def _snake_case (self ):
return (2 * self.green - self.red - self.blue) / (
2 * self.green + self.red + self.blue
)
def _snake_case (self ):
return (self.nir - self.red) / (self.nir + self.red)
def _snake_case (self ):
return (self.nir - self.blue) / (self.nir + self.blue)
def _snake_case (self ):
return (self.redEdge - self.red) / (self.redEdge + self.red)
def _snake_case (self ):
return (self.nir - self.green) / (self.nir + self.green)
def _snake_case (self ):
return (self.nir - (self.green + self.blue)) / (
self.nir + (self.green + self.blue)
)
def _snake_case (self ):
return (self.nir - (self.green + self.red)) / (
self.nir + (self.green + self.red)
)
def _snake_case (self ):
return (self.nir - (self.blue + self.red)) / (self.nir + (self.blue + self.red))
def _snake_case (self ):
return (self.nir - (self.green + self.red + self.blue)) / (
self.nir + (self.green + self.red + self.blue)
)
def _snake_case (self , __lowercase=0.0_8 , __lowercase=1.2_2 , __lowercase=0.0_3 ):
return a * (
(self.nir - a * self.red - b)
/ (a * self.nir + self.red - a * b + x * (1 + a**2))
)
def _snake_case (self ):
return (0.1 * self.nir - self.blue) / (0.1 * self.nir + self.blue)
def _snake_case (self ):
return (self.nir / self.green) - 1
def _snake_case (self ):
return (self.nir / self.redEdge) - 1
def _snake_case (self ):
return (self.red - self.blue) / self.red
def _snake_case (self ):
__lowerCAmelCase = self.ndvi()
return ((ndvi + 0.5) / (abs(ndvi + 0.5 ))) * (abs(ndvi + 0.5 ) ** (1 / 2))
def _snake_case (self ):
return self.nir - self.green
def _snake_case (self ):
return 2.5 * (
(self.nir - self.red) / (self.nir + 6 * self.red - 7.5 * self.blue + 1)
)
def _snake_case (self ):
__lowerCAmelCase = (2 * (self.nir**2 - self.red**2) + 1.5 * self.nir + 0.5 * self.red) / (
self.nir + self.red + 0.5
)
return n * (1 - 0.2_5 * n) - (self.red - 0.1_2_5) / (1 - self.red)
def _snake_case (self , __lowercase=0.1_6 ):
return (self.nir - self.green) / (self.nir + self.green + y)
def _snake_case (self , __lowercase=0.5 ):
return ((self.nir - self.green) / (self.nir + self.green + n)) * (1 + n)
def _snake_case (self ):
return np.arctan(
((2 * self.red - self.green - self.blue) / 3_0.5) * (self.green - self.blue) )
def _snake_case (self , __lowercase=None , __lowercase=None ):
return (self.nir - b) / (a * self.red)
def _snake_case (self ):
return (self.nir / ((self.nir + self.red) / 2)) * (self.ndvi() + 1)
def _snake_case (self ):
return (self.red + self.green + self.blue) / 3_0.5
def _snake_case (self ):
return self.nir / self.red
def _snake_case (self ):
return (self.rvi() - 1) / (self.rvi() + 1)
def _snake_case (self ):
return (
(2 * self.nir + 1)
- ((2 * self.nir + 1) ** 2 - 8 * (self.nir - self.red)) ** (1 / 2)
) / 2
def _snake_case (self ):
return self.green / (self.nir + self.red + self.green)
def _snake_case (self ):
return self.nir / (self.nir + self.red + self.green)
def _snake_case (self ):
return self.red / (self.nir + self.red + self.green)
def _snake_case (self ):
return (self.green - self.red) / (self.green + self.red)
def _snake_case (self ):
return (self.red - self.green) / (self.red + self.green)
def _snake_case (self ):
__lowerCAmelCase = np.max([np.max(self.red ), np.max(self.green ), np.max(self.blue )] )
__lowerCAmelCase = np.min([np.min(self.red ), np.min(self.green ), np.min(self.blue )] )
return (max_value - min_value) / max_value
def _snake_case (self ):
return (2 * self.red - self.green - self.blue) / (self.green - self.blue)
def _snake_case (self ):
return self.nir / self.red
def _snake_case (self ):
return (self.ndvi() + 0.5) ** (1 / 2)
def _snake_case (self ):
return (self.nir - self.redEdge) / (self.nir + self.redEdge)
| 9 | 1 |
import baseaa
import io
import json
import os
from copy import deepcopy
from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler
class __snake_case :
def __init__( self : List[str] , A_ : Union[str, Any]):
if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__):
# Don't modify user's data should they want to reuse it (e.g. in tests), because once we
# modified it, it will not be accepted here again, since `auto` values would have been overridden
lowerCAmelCase_ : Tuple = deepcopy(SCREAMING_SNAKE_CASE__)
elif os.path.exists(SCREAMING_SNAKE_CASE__):
with io.open(SCREAMING_SNAKE_CASE__ , '''r''' , encoding='''utf-8''') as f:
lowerCAmelCase_ : Optional[Any] = json.load(SCREAMING_SNAKE_CASE__)
else:
try:
lowerCAmelCase_ : Union[str, Any] = baseaa.urlsafe_baadecode(SCREAMING_SNAKE_CASE__).decode('''utf-8''')
lowerCAmelCase_ : Optional[Any] = json.loads(SCREAMING_SNAKE_CASE__)
except (UnicodeDecodeError, AttributeError, ValueError):
raise ValueError(
F"""Expected a string path to an existing deepspeed config, or a dictionary, or a base64 encoded string. Received: {config_file_or_dict}""")
lowerCAmelCase_ : Dict = config
self.set_stage_and_offload()
def UpperCAmelCase__ ( self : str):
# zero stage - this is done as early as possible, before model is created, to allow
# ``is_deepspeed_zero3_enabled`` query and getting to the early deepspeed config object
# during ``zero.Init()`` which needs to know the dtype, and some other hparams.
lowerCAmelCase_ : List[Any] = self.get_value('''zero_optimization.stage''' , -1)
# offload
lowerCAmelCase_ : List[Any] = False
if self.is_zeroa() or self.is_zeroa():
lowerCAmelCase_ : Tuple = set(['''cpu''', '''nvme'''])
lowerCAmelCase_ : Union[str, Any] = set(
[
self.get_value('''zero_optimization.offload_optimizer.device'''),
self.get_value('''zero_optimization.offload_param.device'''),
])
if len(offload_devices & offload_devices_valid) > 0:
lowerCAmelCase_ : str = True
def UpperCAmelCase__ ( self : str , A_ : str):
lowerCAmelCase_ : Union[str, Any] = self.config
# find the config node of interest if it exists
lowerCAmelCase_ : List[str] = ds_key_long.split('''.''')
lowerCAmelCase_ : Union[str, Any] = nodes.pop()
for node in nodes:
lowerCAmelCase_ : Any = config.get(SCREAMING_SNAKE_CASE__)
if config is None:
return None, ds_key
return config, ds_key
def UpperCAmelCase__ ( self : List[str] , A_ : Tuple , A_ : Optional[Any]=None):
lowerCAmelCase_ : Dict = self.find_config_node(SCREAMING_SNAKE_CASE__)
if config is None:
return default
return config.get(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__)
def UpperCAmelCase__ ( self : Optional[int] , A_ : Any , A_ : Tuple=False):
lowerCAmelCase_ : List[Any] = self.config
# find the config node of interest if it exists
lowerCAmelCase_ : str = ds_key_long.split('''.''')
for node in nodes:
lowerCAmelCase_ : str = config
lowerCAmelCase_ : List[Any] = config.get(SCREAMING_SNAKE_CASE__)
if config is None:
if must_exist:
raise ValueError(F"""Can\'t find {ds_key_long} entry in the config: {self.config}""")
else:
return
# if found remove it
if parent_config is not None:
parent_config.pop(SCREAMING_SNAKE_CASE__)
def UpperCAmelCase__ ( self : Optional[int] , A_ : Tuple):
lowerCAmelCase_ : int = self.get_value(SCREAMING_SNAKE_CASE__)
return False if value is None else bool(SCREAMING_SNAKE_CASE__)
def UpperCAmelCase__ ( self : int , A_ : int):
lowerCAmelCase_ : List[str] = self.get_value(SCREAMING_SNAKE_CASE__)
return False if value is None else not bool(SCREAMING_SNAKE_CASE__)
def UpperCAmelCase__ ( self : Any):
return self._stage == 2
def UpperCAmelCase__ ( self : Union[str, Any]):
return self._stage == 3
def UpperCAmelCase__ ( self : List[Any]):
return self._offload
class __snake_case :
def __init__( self : Optional[Any] , A_ : Tuple):
lowerCAmelCase_ : Optional[int] = engine
def UpperCAmelCase__ ( self : Tuple , A_ : Optional[Any] , **A_ : Union[str, Any]):
# runs backpropagation and handles mixed precision
self.engine.backward(SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__)
# Deepspeed's `engine.step` performs the following operations:
# - gradient accumulation check
# - gradient clipping
# - optimizer step
# - zero grad
# - checking overflow
# - lr_scheduler step (only if engine.lr_scheduler is not None)
self.engine.step()
# and this plugin overrides the above calls with no-ops when Accelerate runs under
# Deepspeed, but allows normal functionality for non-Deepspeed cases thus enabling a simple
# training loop that works transparently under many training regimes.
class __snake_case ( _a ):
def __init__( self : Tuple , A_ : Dict):
super().__init__(SCREAMING_SNAKE_CASE__ , device_placement=SCREAMING_SNAKE_CASE__ , scaler=SCREAMING_SNAKE_CASE__)
lowerCAmelCase_ : Union[str, Any] = hasattr(self.optimizer , '''overflow''')
def UpperCAmelCase__ ( self : Union[str, Any] , A_ : Tuple=None):
pass # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed
def UpperCAmelCase__ ( self : List[Any]):
pass # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed
@property
def UpperCAmelCase__ ( self : List[str]):
if self.__has_overflow__:
return self.optimizer.overflow
return False
class __snake_case ( _a ):
def __init__( self : List[str] , A_ : List[Any] , A_ : Optional[int]):
super().__init__(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__)
def UpperCAmelCase__ ( self : Dict):
pass # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed
class __snake_case :
def __init__( self : Optional[Any] , A_ : List[Any] , A_ : Tuple=0.001 , A_ : List[str]=0 , **A_ : List[str]):
lowerCAmelCase_ : Optional[Any] = params
lowerCAmelCase_ : List[str] = lr
lowerCAmelCase_ : List[str] = weight_decay
lowerCAmelCase_ : Optional[Any] = kwargs
class __snake_case :
def __init__( self : Tuple , A_ : List[Any] , A_ : Any=None , A_ : Dict=0 , **A_ : Any):
lowerCAmelCase_ : Dict = optimizer
lowerCAmelCase_ : List[Any] = total_num_steps
lowerCAmelCase_ : str = warmup_num_steps
lowerCAmelCase_ : Union[str, Any] = kwargs
| 103 |
'''simple docstring'''
# Logistic Regression from scratch
# In[62]:
# In[63]:
# importing all the required libraries
import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets
def A_ ( snake_case ):
return 1 / (1 + np.exp(-z ))
def A_ ( snake_case , snake_case ):
return (-y * np.log(snake_case ) - (1 - y) * np.log(1 - h )).mean()
def A_ ( snake_case , snake_case , snake_case ):
SCREAMING_SNAKE_CASE:Dict = np.dot(snake_case , snake_case )
return np.sum(y * scores - np.log(1 + np.exp(snake_case ) ) )
def A_ ( snake_case , snake_case , snake_case , snake_case=70000 ):
SCREAMING_SNAKE_CASE:List[str] = np.zeros(x.shape[1] )
for iterations in range(snake_case ):
SCREAMING_SNAKE_CASE:Union[str, Any] = np.dot(snake_case , snake_case )
SCREAMING_SNAKE_CASE:Dict = sigmoid_function(snake_case )
SCREAMING_SNAKE_CASE:List[str] = np.dot(x.T , h - y ) / y.size
SCREAMING_SNAKE_CASE:Any = theta - alpha * gradient # updating the weights
SCREAMING_SNAKE_CASE:Dict = np.dot(snake_case , snake_case )
SCREAMING_SNAKE_CASE:Union[str, Any] = sigmoid_function(snake_case )
SCREAMING_SNAKE_CASE:Dict = cost_function(snake_case , snake_case )
if iterations % 100 == 0:
print(F'''loss: {j} \t''' ) # printing the loss after every 100 iterations
return theta
# In[68]:
if __name__ == "__main__":
A_ = datasets.load_iris()
A_ = iris.data[:, :2]
A_ = (iris.target != 0) * 1
A_ = 0.1
A_ = logistic_reg(alpha, x, y, max_iterations=7_00_00)
print("theta: ", theta) # printing the theta i.e our weights vector
def A_ ( snake_case ):
return sigmoid_function(
np.dot(snake_case , snake_case ) ) # predicting the value of probability from the logistic regression algorithm
plt.figure(figsize=(10, 6))
plt.scatter(x[y == 0][:, 0], x[y == 0][:, 1], color="b", label="0")
plt.scatter(x[y == 1][:, 0], x[y == 1][:, 1], color="r", label="1")
((A_) , (A_)) = (x[:, 0].min(), x[:, 0].max())
((A_) , (A_)) = (x[:, 1].min(), x[:, 1].max())
((A_) , (A_)) = np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
A_ = np.c_[xxa.ravel(), xxa.ravel()]
A_ = predict_prob(grid).reshape(xxa.shape)
plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors="black")
plt.legend()
plt.show()
| 139 | 0 |
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 lowerCamelCase__ ( unittest.TestCase ):
@slow
def _lowerCamelCase ( self : Any ):
a__: Optional[Any] =TFAutoModelForSeqaSeqLM.from_pretrained("google/mt5-small" )
a__: List[str] =AutoTokenizer.from_pretrained("google/mt5-small" )
a__: List[str] =tokenizer("Hello there" , return_tensors="tf" ).input_ids
a__: Dict =tokenizer("Hi I am" , return_tensors="tf" ).input_ids
a__: List[Any] =model(lowerCAmelCase__ , labels=lowerCAmelCase__ ).loss
a__: int =-tf.math.reduce_mean(lowerCAmelCase__ ).numpy()
a__: Dict =-2_1.2_2_8_1_6_8
self.assertTrue(abs(mtf_score - EXPECTED_SCORE ) < 2e-4 )
| 362 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
from ...utils import (
BaseOutput,
is_accelerate_available,
logging,
randn_tensor,
replace_example_docstring,
)
from .renderer import ShapERenderer
__UpperCAmelCase = logging.get_logger(__name__) # pylint: disable=invalid-name
__UpperCAmelCase = '''
Examples:
```py
>>> from PIL import Image
>>> import torch
>>> from diffusers import DiffusionPipeline
>>> from diffusers.utils import export_to_gif, load_image
>>> device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
>>> repo = "openai/shap-e-img2img"
>>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)
>>> pipe = pipe.to(device)
>>> guidance_scale = 3.0
>>> image_url = "https://hf.co/datasets/diffusers/docs-images/resolve/main/shap-e/corgi.png"
>>> image = load_image(image_url).convert("RGB")
>>> images = pipe(
... image,
... guidance_scale=guidance_scale,
... num_inference_steps=64,
... frame_size=256,
... ).images
>>> gif_path = export_to_gif(images[0], "corgi_3d.gif")
```
'''
@dataclass
class lowerCamelCase__ ( _a ):
_lowerCAmelCase = 42
class lowerCamelCase__ ( _a ):
def __init__( self : Optional[Any] , _a : PriorTransformer , _a : CLIPVisionModel , _a : CLIPImageProcessor , _a : HeunDiscreteScheduler , _a : ShapERenderer , ):
super().__init__()
self.register_modules(
prior=_a , image_encoder=_a , image_processor=_a , scheduler=_a , renderer=_a , )
def _lowerCamelCase ( self : Tuple , _a : Tuple , _a : Tuple , _a : Any , _a : Any , _a : List[str] , _a : Any ):
if latents is None:
a__: Any =randn_tensor(_a , generator=_a , device=_a , dtype=_a )
else:
if latents.shape != shape:
raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {shape}" )
a__: List[str] =latents.to(_a )
a__: int =latents * scheduler.init_noise_sigma
return latents
def _lowerCamelCase ( self : Dict , _a : str=0 ):
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError("Please install accelerate via `pip install accelerate`" )
a__: List[Any] =torch.device(F"cuda:{gpu_id}" )
a__: List[Any] =[self.image_encoder, self.prior]
for cpu_offloaded_model in models:
if cpu_offloaded_model is not None:
cpu_offload(_a , _a )
@property
def _lowerCamelCase ( self : Optional[Any] ):
if self.device != torch.device("meta" ) or not hasattr(self.image_encoder , "_hf_hook" ):
return self.device
for module in self.image_encoder.modules():
if (
hasattr(_a , "_hf_hook" )
and hasattr(module._hf_hook , "execution_device" )
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device )
return self.device
def _lowerCamelCase ( self : Any , _a : List[str] , _a : Optional[int] , _a : Union[str, Any] , _a : List[str] , ):
if isinstance(_a , _a ) and isinstance(image[0] , torch.Tensor ):
a__: Optional[int] =torch.cat(_a , axis=0 ) if image[0].ndim == 4 else torch.stack(_a , axis=0 )
if not isinstance(_a , torch.Tensor ):
a__: Optional[Any] =self.image_processor(_a , return_tensors="pt" ).pixel_values[0].unsqueeze(0 )
a__: int =image.to(dtype=self.image_encoder.dtype , device=_a )
a__: str =self.image_encoder(_a )["last_hidden_state"]
a__: Tuple =image_embeds[:, 1:, :].contiguous() # batch_size, dim, 256
a__: Optional[Any] =image_embeds.repeat_interleave(_a , dim=0 )
if do_classifier_free_guidance:
a__: Union[str, Any] =torch.zeros_like(_a )
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
a__: int =torch.cat([negative_image_embeds, image_embeds] )
return image_embeds
@torch.no_grad()
@replace_example_docstring(_a )
def __call__( self : List[Any] , _a : Union[PIL.Image.Image, List[PIL.Image.Image]] , _a : int = 1 , _a : int = 2_5 , _a : Optional[Union[torch.Generator, List[torch.Generator]]] = None , _a : Optional[torch.FloatTensor] = None , _a : float = 4.0 , _a : int = 6_4 , _a : Optional[str] = "pil" , _a : bool = True , ):
if isinstance(_a , PIL.Image.Image ):
a__: List[str] =1
elif isinstance(_a , torch.Tensor ):
a__: List[Any] =image.shape[0]
elif isinstance(_a , _a ) and isinstance(image[0] , (torch.Tensor, PIL.Image.Image) ):
a__: int =len(_a )
else:
raise ValueError(
F"`image` has to be of type `PIL.Image.Image`, `torch.Tensor`, `List[PIL.Image.Image]` or `List[torch.Tensor]` but is {type(_a )}" )
a__: Optional[int] =self._execution_device
a__: Optional[int] =batch_size * num_images_per_prompt
a__: Any =guidance_scale > 1.0
a__: int =self._encode_image(_a , _a , _a , _a )
# prior
self.scheduler.set_timesteps(_a , device=_a )
a__: int =self.scheduler.timesteps
a__: str =self.prior.config.num_embeddings
a__: Dict =self.prior.config.embedding_dim
a__: Dict =self.prepare_latents(
(batch_size, num_embeddings * embedding_dim) , image_embeds.dtype , _a , _a , _a , self.scheduler , )
# YiYi notes: for testing only to match ldm, we can directly create a latents with desired shape: batch_size, num_embeddings, embedding_dim
a__: int =latents.reshape(latents.shape[0] , _a , _a )
for i, t in enumerate(self.progress_bar(_a ) ):
# expand the latents if we are doing classifier free guidance
a__: Union[str, Any] =torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
a__: Tuple =self.scheduler.scale_model_input(_a , _a )
a__: Tuple =self.prior(
_a , timestep=_a , proj_embedding=_a , ).predicted_image_embedding
# remove the variance
a__ , a__: List[str] =noise_pred.split(
scaled_model_input.shape[2] , dim=2 ) # batch_size, num_embeddings, embedding_dim
if do_classifier_free_guidance is not None:
a__ , a__: Union[str, Any] =noise_pred.chunk(2 )
a__: Union[str, Any] =noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)
a__: Any =self.scheduler.step(
_a , timestep=_a , sample=_a , ).prev_sample
if output_type == "latent":
return ShapEPipelineOutput(images=_a )
a__: List[str] =[]
for i, latent in enumerate(_a ):
print()
a__: Any =self.renderer.decode(
latent[None, :] , _a , size=_a , ray_batch_size=4_0_9_6 , n_coarse_samples=6_4 , n_fine_samples=1_2_8 , )
images.append(_a )
a__: Tuple =torch.stack(_a )
if output_type not in ["np", "pil"]:
raise ValueError(F"Only the output types `pil` and `np` are supported not output_type={output_type}" )
a__: Dict =images.cpu().numpy()
if output_type == "pil":
a__: Optional[int] =[self.numpy_to_pil(_a ) for image in images]
# Offload last model to CPU
if hasattr(self , "final_offload_hook" ) and self.final_offload_hook is not None:
self.final_offload_hook.offload()
if not return_dict:
return (images,)
return ShapEPipelineOutput(images=_a )
| 42 | 0 |
'''simple docstring'''
def _lowerCAmelCase ( __snake_case : int = 1_00 ) -> int:
__A : List[Any] = n * (n + 1) * (2 * n + 1) / 6
__A : Optional[Any] = (n * (n + 1) / 2) ** 2
return int(square_of_sum - sum_of_squares )
if __name__ == "__main__":
print(f"""{solution() = }""") | 190 |
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_OBJECT_DETECTION_MAPPING,
AutoFeatureExtractor,
AutoModelForObjectDetection,
ObjectDetectionPipeline,
is_vision_available,
pipeline,
)
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_pytesseract,
require_tf,
require_timm,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
else:
class SCREAMING_SNAKE_CASE :
@staticmethod
def SCREAMING_SNAKE_CASE ( *_UpperCAmelCase , **_UpperCAmelCase):
'''simple docstring'''
pass
@is_pipeline_test
@require_vision
@require_timm
@require_torch
class SCREAMING_SNAKE_CASE (unittest.TestCase ):
lowerCAmelCase = MODEL_FOR_OBJECT_DETECTION_MAPPING
def SCREAMING_SNAKE_CASE ( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase):
'''simple docstring'''
__A : Dict = ObjectDetectionPipeline(model=_UpperCAmelCase , image_processor=_UpperCAmelCase)
return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]
def SCREAMING_SNAKE_CASE ( self , _UpperCAmelCase , _UpperCAmelCase):
'''simple docstring'''
__A : str = object_detector('./tests/fixtures/tests_samples/COCO/000000039769.png' , threshold=0.0)
self.assertGreater(len(_UpperCAmelCase) , 0)
for detected_object in outputs:
self.assertEqual(
_UpperCAmelCase , {
'score': ANY(_UpperCAmelCase),
'label': ANY(_UpperCAmelCase),
'box': {'xmin': ANY(_UpperCAmelCase), 'ymin': ANY(_UpperCAmelCase), 'xmax': ANY(_UpperCAmelCase), 'ymax': ANY(_UpperCAmelCase)},
} , )
import datasets
__A : Tuple = datasets.load_dataset('hf-internal-testing/fixtures_image_utils' , 'image' , split='test')
__A : List[str] = [
Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'),
'http://images.cocodataset.org/val2017/000000039769.jpg',
# RGBA
dataset[0]['file'],
# LA
dataset[1]['file'],
# L
dataset[2]['file'],
]
__A : Union[str, Any] = object_detector(_UpperCAmelCase , threshold=0.0)
self.assertEqual(len(_UpperCAmelCase) , len(_UpperCAmelCase))
for outputs in batch_outputs:
self.assertGreater(len(_UpperCAmelCase) , 0)
for detected_object in outputs:
self.assertEqual(
_UpperCAmelCase , {
'score': ANY(_UpperCAmelCase),
'label': ANY(_UpperCAmelCase),
'box': {'xmin': ANY(_UpperCAmelCase), 'ymin': ANY(_UpperCAmelCase), 'xmax': ANY(_UpperCAmelCase), 'ymax': ANY(_UpperCAmelCase)},
} , )
@require_tf
@unittest.skip('Object detection not implemented in TF')
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
pass
@require_torch
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
__A : List[str] = 'hf-internal-testing/tiny-detr-mobilenetsv3'
__A : Any = AutoModelForObjectDetection.from_pretrained(_UpperCAmelCase)
__A : int = AutoFeatureExtractor.from_pretrained(_UpperCAmelCase)
__A : Any = ObjectDetectionPipeline(model=_UpperCAmelCase , feature_extractor=_UpperCAmelCase)
__A : Dict = object_detector('http://images.cocodataset.org/val2017/000000039769.jpg' , threshold=0.0)
self.assertEqual(
nested_simplify(_UpperCAmelCase , decimals=4) , [
{'score': 0.3376, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
{'score': 0.3376, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
] , )
__A : Optional[Any] = object_detector(
[
'http://images.cocodataset.org/val2017/000000039769.jpg',
'http://images.cocodataset.org/val2017/000000039769.jpg',
] , threshold=0.0 , )
self.assertEqual(
nested_simplify(_UpperCAmelCase , decimals=4) , [
[
{'score': 0.3376, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
{'score': 0.3376, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
],
[
{'score': 0.3376, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
{'score': 0.3376, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
],
] , )
@require_torch
@slow
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
__A : Optional[Any] = 'facebook/detr-resnet-50'
__A : Union[str, Any] = AutoModelForObjectDetection.from_pretrained(_UpperCAmelCase)
__A : Tuple = AutoFeatureExtractor.from_pretrained(_UpperCAmelCase)
__A : List[Any] = ObjectDetectionPipeline(model=_UpperCAmelCase , feature_extractor=_UpperCAmelCase)
__A : Union[str, Any] = object_detector('http://images.cocodataset.org/val2017/000000039769.jpg')
self.assertEqual(
nested_simplify(_UpperCAmelCase , decimals=4) , [
{'score': 0.9982, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
{'score': 0.9960, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
{'score': 0.9955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
{'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
{'score': 0.9987, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
] , )
__A : Tuple = object_detector(
[
'http://images.cocodataset.org/val2017/000000039769.jpg',
'http://images.cocodataset.org/val2017/000000039769.jpg',
])
self.assertEqual(
nested_simplify(_UpperCAmelCase , decimals=4) , [
[
{'score': 0.9982, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
{'score': 0.9960, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
{'score': 0.9955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
{'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
{'score': 0.9987, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
],
[
{'score': 0.9982, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
{'score': 0.9960, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
{'score': 0.9955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
{'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
{'score': 0.9987, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
],
] , )
@require_torch
@slow
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
__A : List[Any] = 'facebook/detr-resnet-50'
__A : str = pipeline('object-detection' , model=_UpperCAmelCase)
__A : Any = object_detector('http://images.cocodataset.org/val2017/000000039769.jpg')
self.assertEqual(
nested_simplify(_UpperCAmelCase , decimals=4) , [
{'score': 0.9982, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
{'score': 0.9960, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
{'score': 0.9955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
{'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
{'score': 0.9987, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
] , )
__A : str = object_detector(
[
'http://images.cocodataset.org/val2017/000000039769.jpg',
'http://images.cocodataset.org/val2017/000000039769.jpg',
])
self.assertEqual(
nested_simplify(_UpperCAmelCase , decimals=4) , [
[
{'score': 0.9982, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
{'score': 0.9960, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
{'score': 0.9955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
{'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
{'score': 0.9987, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
],
[
{'score': 0.9982, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
{'score': 0.9960, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
{'score': 0.9955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
{'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
{'score': 0.9987, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
],
] , )
@require_torch
@slow
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
__A : List[Any] = 0.9985
__A : List[Any] = 'facebook/detr-resnet-50'
__A : List[str] = pipeline('object-detection' , model=_UpperCAmelCase)
__A : List[Any] = object_detector('http://images.cocodataset.org/val2017/000000039769.jpg' , threshold=_UpperCAmelCase)
self.assertEqual(
nested_simplify(_UpperCAmelCase , decimals=4) , [
{'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
{'score': 0.9987, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
] , )
@require_torch
@require_pytesseract
@slow
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
__A : Optional[int] = 'Narsil/layoutlmv3-finetuned-funsd'
__A : Tuple = 0.9993
__A : str = pipeline('object-detection' , model=_UpperCAmelCase , threshold=_UpperCAmelCase)
__A : Optional[int] = object_detector(
'https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png')
self.assertEqual(
nested_simplify(_UpperCAmelCase , decimals=4) , [
{'score': 0.9993, 'label': 'I-ANSWER', 'box': {'xmin': 294, 'ymin': 254, 'xmax': 343, 'ymax': 264}},
{'score': 0.9993, 'label': 'I-ANSWER', 'box': {'xmin': 294, 'ymin': 254, 'xmax': 343, 'ymax': 264}},
] , ) | 190 | 1 |
'''simple docstring'''
def snake_case_ (UpperCamelCase : int , UpperCamelCase : int ):
'''simple docstring'''
return abs(UpperCamelCase ) if a == 0 else greatest_common_divisor(b % a , UpperCamelCase )
def snake_case_ (UpperCamelCase : int , UpperCamelCase : int ):
'''simple docstring'''
while y: # --> when y=0 then loop will terminate and return x as final GCD.
_a , _a = y, x % y
return abs(UpperCamelCase )
def snake_case_ ():
'''simple docstring'''
try:
_a = input('''Enter two integers separated by comma (,): ''' ).split(''',''' )
_a = int(nums[0] )
_a = int(nums[1] )
print(
f'greatest_common_divisor({num_a}, {num_a}) = '
f'{greatest_common_divisor(UpperCamelCase , UpperCamelCase )}' )
print(f'By iterative gcd({num_a}, {num_a}) = {gcd_by_iterative(UpperCamelCase , UpperCamelCase )}' )
except (IndexError, UnboundLocalError, ValueError):
print('''Wrong input''' )
if __name__ == "__main__":
main()
| 179 |
'''simple docstring'''
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
AutoConfig,
AutoFeatureExtractor,
WavaVecaConfig,
WavaVecaFeatureExtractor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir
sys.path.append(str(Path(__file__).parent.parent.parent.parent / 'utils'))
from test_module.custom_configuration import CustomConfig # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor # noqa E402
_snake_case : int = get_tests_dir('fixtures')
_snake_case : Tuple = get_tests_dir('fixtures/dummy_feature_extractor_config.json')
_snake_case : Optional[int] = get_tests_dir('fixtures/dummy-config.json')
class A ( unittest.TestCase ):
def __lowerCAmelCase ( self : int ) -> List[Any]:
"""simple docstring"""
_a = 0
def __lowerCAmelCase ( self : List[str] ) -> int:
"""simple docstring"""
_a = AutoFeatureExtractor.from_pretrained('''facebook/wav2vec2-base-960h''' )
self.assertIsInstance(lowerCAmelCase_ , lowerCAmelCase_ )
def __lowerCAmelCase ( self : str ) -> Tuple:
"""simple docstring"""
_a = AutoFeatureExtractor.from_pretrained(lowerCAmelCase_ )
self.assertIsInstance(lowerCAmelCase_ , lowerCAmelCase_ )
def __lowerCAmelCase ( self : List[str] ) -> Any:
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmpdirname:
_a = WavaVecaConfig()
# remove feature_extractor_type to make sure config.json alone is enough to load feature processor locally
_a = AutoFeatureExtractor.from_pretrained(lowerCAmelCase_ ).to_dict()
config_dict.pop('''feature_extractor_type''' )
_a = WavaVecaFeatureExtractor(**lowerCAmelCase_ )
# save in new folder
model_config.save_pretrained(lowerCAmelCase_ )
config.save_pretrained(lowerCAmelCase_ )
_a = AutoFeatureExtractor.from_pretrained(lowerCAmelCase_ )
# make sure private variable is not incorrectly saved
_a = json.loads(config.to_json_string() )
self.assertTrue('''_processor_class''' not in dict_as_saved )
self.assertIsInstance(lowerCAmelCase_ , lowerCAmelCase_ )
def __lowerCAmelCase ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
_a = AutoFeatureExtractor.from_pretrained(lowerCAmelCase_ )
self.assertIsInstance(lowerCAmelCase_ , lowerCAmelCase_ )
def __lowerCAmelCase ( self : Optional[int] ) -> str:
"""simple docstring"""
with self.assertRaisesRegex(
lowerCAmelCase_ , '''bert-base is not a local folder and is not a valid model identifier''' ):
_a = AutoFeatureExtractor.from_pretrained('''bert-base''' )
def __lowerCAmelCase ( self : Tuple ) -> Union[str, Any]:
"""simple docstring"""
with self.assertRaisesRegex(
lowerCAmelCase_ , R'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)''' ):
_a = AutoFeatureExtractor.from_pretrained(lowerCAmelCase_ , revision='''aaaaaa''' )
def __lowerCAmelCase ( self : Any ) -> Dict:
"""simple docstring"""
with self.assertRaisesRegex(
lowerCAmelCase_ , '''hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.''' , ):
_a = AutoFeatureExtractor.from_pretrained('''hf-internal-testing/config-no-model''' )
def __lowerCAmelCase ( self : List[Any] ) -> Any:
"""simple docstring"""
with self.assertRaises(lowerCAmelCase_ ):
_a = AutoFeatureExtractor.from_pretrained(
'''hf-internal-testing/test_dynamic_feature_extractor''' )
# If remote code is disabled, we can't load this config.
with self.assertRaises(lowerCAmelCase_ ):
_a = AutoFeatureExtractor.from_pretrained(
'''hf-internal-testing/test_dynamic_feature_extractor''' , trust_remote_code=lowerCAmelCase_ )
_a = AutoFeatureExtractor.from_pretrained(
'''hf-internal-testing/test_dynamic_feature_extractor''' , trust_remote_code=lowerCAmelCase_ )
self.assertEqual(feature_extractor.__class__.__name__ , '''NewFeatureExtractor''' )
# Test feature extractor can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
feature_extractor.save_pretrained(lowerCAmelCase_ )
_a = AutoFeatureExtractor.from_pretrained(lowerCAmelCase_ , trust_remote_code=lowerCAmelCase_ )
self.assertEqual(reloaded_feature_extractor.__class__.__name__ , '''NewFeatureExtractor''' )
def __lowerCAmelCase ( self : int ) -> Optional[Any]:
"""simple docstring"""
try:
AutoConfig.register('''custom''' , lowerCAmelCase_ )
AutoFeatureExtractor.register(lowerCAmelCase_ , lowerCAmelCase_ )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(lowerCAmelCase_ ):
AutoFeatureExtractor.register(lowerCAmelCase_ , lowerCAmelCase_ )
# Now that the config is registered, it can be used as any other config with the auto-API
_a = CustomFeatureExtractor.from_pretrained(lowerCAmelCase_ )
with tempfile.TemporaryDirectory() as tmp_dir:
feature_extractor.save_pretrained(lowerCAmelCase_ )
_a = AutoFeatureExtractor.from_pretrained(lowerCAmelCase_ )
self.assertIsInstance(lowerCAmelCase_ , lowerCAmelCase_ )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
def __lowerCAmelCase ( self : Optional[int] ) -> Any:
"""simple docstring"""
class A ( _a ):
lowercase_ = True
try:
AutoConfig.register('''custom''' , lowerCAmelCase_ )
AutoFeatureExtractor.register(lowerCAmelCase_ , lowerCAmelCase_ )
# If remote code is not set, the default is to use local
_a = AutoFeatureExtractor.from_pretrained(
'''hf-internal-testing/test_dynamic_feature_extractor''' )
self.assertEqual(feature_extractor.__class__.__name__ , '''NewFeatureExtractor''' )
self.assertTrue(feature_extractor.is_local )
# If remote code is disabled, we load the local one.
_a = AutoFeatureExtractor.from_pretrained(
'''hf-internal-testing/test_dynamic_feature_extractor''' , trust_remote_code=lowerCAmelCase_ )
self.assertEqual(feature_extractor.__class__.__name__ , '''NewFeatureExtractor''' )
self.assertTrue(feature_extractor.is_local )
# If remote is enabled, we load from the Hub
_a = AutoFeatureExtractor.from_pretrained(
'''hf-internal-testing/test_dynamic_feature_extractor''' , trust_remote_code=lowerCAmelCase_ )
self.assertEqual(feature_extractor.__class__.__name__ , '''NewFeatureExtractor''' )
self.assertTrue(not hasattr(lowerCAmelCase_ , '''is_local''' ) )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
| 179 | 1 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import os
from accelerate.test_utils import execute_subprocess_async
def _a ( lowerCamelCase=None ):
if subparsers is not None:
lowerCamelCase : int = subparsers.add_parser("""test""" )
else:
lowerCamelCase : int = argparse.ArgumentParser("""Accelerate test command""" )
parser.add_argument(
"""--config_file""", default=lowerCamelCase, help=(
"""The path to use to store the config file. Will default to a file named default_config.yaml in the cache """
"""location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have """
"""such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed """
"""with 'huggingface'."""
), )
if subparsers is not None:
parser.set_defaults(func=lowerCamelCase )
return parser
def _a ( lowerCamelCase ):
lowerCamelCase : Optional[Any] = os.path.sep.join(__file__.split(os.path.sep )[:-2] + ["""test_utils""", """scripts""", """test_script.py"""] )
if args.config_file is None:
lowerCamelCase : Optional[int] = script_name
else:
lowerCamelCase : int = F'''--config_file={args.config_file} {script_name}'''
lowerCamelCase : Union[str, Any] = ["""accelerate-launch"""] + test_args.split()
lowerCamelCase : Optional[int] = execute_subprocess_async(lowerCamelCase, env=os.environ.copy() )
if result.returncode == 0:
print("""Test is a success! You are ready for your distributed training!""" )
def _a ( ):
lowerCamelCase : str = test_command_parser()
lowerCamelCase : Dict = parser.parse_args()
test_command(lowerCamelCase )
if __name__ == "__main__":
main()
| 287 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase =logging.get_logger(__name__)
_lowerCamelCase ={
"""BridgeTower/bridgetower-base""": """https://huggingface.co/BridgeTower/bridgetower-base/blob/main/config.json""",
"""BridgeTower/bridgetower-base-itm-mlm""": (
"""https://huggingface.co/BridgeTower/bridgetower-base-itm-mlm/blob/main/config.json"""
),
}
class A__ ( __SCREAMING_SNAKE_CASE):
_UpperCAmelCase : Dict = """bridgetower_vision_model"""
def __init__( self , __magic_name__=7_6_8 , __magic_name__=1_2 , __magic_name__=3 , __magic_name__=1_6 , __magic_name__=2_8_8 , __magic_name__=1 , __magic_name__=1e-05 , __magic_name__=False , __magic_name__=True , __magic_name__=False , **__magic_name__ , ):
super().__init__(**__magic_name__ )
lowerCamelCase : Dict = hidden_size
lowerCamelCase : str = num_hidden_layers
lowerCamelCase : Optional[int] = num_channels
lowerCamelCase : List[str] = patch_size
lowerCamelCase : Tuple = image_size
lowerCamelCase : Any = initializer_factor
lowerCamelCase : Tuple = layer_norm_eps
lowerCamelCase : Tuple = stop_gradient
lowerCamelCase : Optional[int] = share_layernorm
lowerCamelCase : str = remove_last_layer
@classmethod
def UpperCamelCase__ ( cls , __magic_name__ , **__magic_name__ ):
lowerCamelCase , lowerCamelCase : int = cls.get_config_dict(__magic_name__ , **__magic_name__ )
if config_dict.get("""model_type""" ) == "bridgetower":
lowerCamelCase : str = config_dict["""text_config"""]
if "model_type" in config_dict and hasattr(cls , """model_type""" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(__magic_name__ , **__magic_name__ )
class A__ ( __SCREAMING_SNAKE_CASE):
_UpperCAmelCase : Union[str, Any] = """bridgetower_text_model"""
def __init__( self , __magic_name__=5_0_2_6_5 , __magic_name__=7_6_8 , __magic_name__=1_2 , __magic_name__=1_2 , __magic_name__=1 , __magic_name__=3_0_7_2 , __magic_name__="gelu" , __magic_name__=0.1 , __magic_name__=0.1 , __magic_name__=5_1_4 , __magic_name__=1 , __magic_name__=1e-05 , __magic_name__=1 , __magic_name__=0 , __magic_name__=2 , __magic_name__="absolute" , __magic_name__=True , **__magic_name__ , ):
super().__init__(**__magic_name__ )
lowerCamelCase : int = vocab_size
lowerCamelCase : int = hidden_size
lowerCamelCase : Any = num_hidden_layers
lowerCamelCase : Union[str, Any] = num_attention_heads
lowerCamelCase : Tuple = hidden_act
lowerCamelCase : Optional[int] = initializer_factor
lowerCamelCase : Any = intermediate_size
lowerCamelCase : List[str] = hidden_dropout_prob
lowerCamelCase : Dict = attention_probs_dropout_prob
lowerCamelCase : str = max_position_embeddings
lowerCamelCase : Union[str, Any] = type_vocab_size
lowerCamelCase : Optional[int] = layer_norm_eps
lowerCamelCase : Optional[int] = position_embedding_type
lowerCamelCase : List[str] = use_cache
lowerCamelCase : List[str] = pad_token_id
lowerCamelCase : List[str] = bos_token_id
lowerCamelCase : Optional[int] = eos_token_id
@classmethod
def UpperCamelCase__ ( cls , __magic_name__ , **__magic_name__ ):
lowerCamelCase , lowerCamelCase : int = cls.get_config_dict(__magic_name__ , **__magic_name__ )
if config_dict.get("""model_type""" ) == "bridgetower":
lowerCamelCase : Optional[int] = config_dict["""text_config"""]
if "model_type" in config_dict and hasattr(cls , """model_type""" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(__magic_name__ , **__magic_name__ )
class A__ ( __SCREAMING_SNAKE_CASE):
_UpperCAmelCase : Dict = """bridgetower"""
def __init__( self , __magic_name__=True , __magic_name__="gelu" , __magic_name__=7_6_8 , __magic_name__=1 , __magic_name__=1e-05 , __magic_name__=False , __magic_name__="add" , __magic_name__=1_2 , __magic_name__=6 , __magic_name__=False , __magic_name__=False , __magic_name__=None , __magic_name__=None , **__magic_name__ , ):
# TODO: remove this once the Hub files are updated.
lowerCamelCase : int = kwargs.pop("""text_config_dict""" , __magic_name__ )
lowerCamelCase : str = kwargs.pop("""vision_config_dict""" , __magic_name__ )
super().__init__(**__magic_name__ )
lowerCamelCase : str = share_cross_modal_transformer_layers
lowerCamelCase : Union[str, Any] = hidden_act
lowerCamelCase : str = hidden_size
lowerCamelCase : Tuple = initializer_factor
lowerCamelCase : List[str] = layer_norm_eps
lowerCamelCase : int = share_link_tower_layers
lowerCamelCase : List[Any] = link_tower_type
lowerCamelCase : Tuple = num_attention_heads
lowerCamelCase : int = num_hidden_layers
lowerCamelCase : Union[str, Any] = tie_word_embeddings
lowerCamelCase : Tuple = init_layernorm_from_vision_encoder
if text_config is None:
lowerCamelCase : Any = {}
logger.info("""`text_config` is `None`. Initializing the `BridgeTowerTextConfig` with default values.""" )
if vision_config is None:
lowerCamelCase : int = {}
logger.info("""`vision_config` is `None`. Initializing the `BridgeTowerVisionConfig` with default values.""" )
lowerCamelCase : Any = BridgeTowerTextConfig(**__magic_name__ )
lowerCamelCase : Optional[Any] = BridgeTowerVisionConfig(**__magic_name__ )
@classmethod
def UpperCamelCase__ ( cls , __magic_name__ , __magic_name__ , **__magic_name__ ):
return cls(text_config=text_config.to_dict() , vision_config=vision_config.to_dict() , **__magic_name__ )
def UpperCamelCase__ ( self ):
lowerCamelCase : str = copy.deepcopy(self.__dict__ )
lowerCamelCase : int = self.text_config.to_dict()
lowerCamelCase : Dict = self.vision_config.to_dict()
lowerCamelCase : List[str] = self.__class__.model_type
return output
| 287 | 1 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCLIPImgaImgPipeline, UNetaDConditionModel
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
enable_full_determinism,
floats_tensor,
load_image,
load_numpy,
require_torch_gpu,
skip_mps,
slow,
torch_device,
)
from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import (
PipelineKarrasSchedulerTesterMixin,
PipelineLatentTesterMixin,
PipelineTesterMixin,
assert_mean_pixel_difference,
)
enable_full_determinism()
class __UpperCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , unittest.TestCase ):
A_ = StableUnCLIPImgaImgPipeline
A_ = TEXT_GUIDED_IMAGE_VARIATION_PARAMS
A_ = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
A_ = frozenset(
[] ) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
A_ = frozenset([] )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Any = 32
__a : List[Any] = embedder_hidden_size
# image encoding components
__a : List[Any] = CLIPImageProcessor(crop_size=32 , size=32 )
torch.manual_seed(0 )
__a : Union[str, Any] = CLIPVisionModelWithProjection(
CLIPVisionConfig(
hidden_size=__a , projection_dim=__a , num_hidden_layers=5 , num_attention_heads=4 , image_size=32 , intermediate_size=37 , patch_size=1 , ) )
# regular denoising components
torch.manual_seed(0 )
__a : Optional[int] = StableUnCLIPImageNormalizer(embedding_dim=__a )
__a : Any = DDPMScheduler(beta_schedule='squaredcos_cap_v2' )
torch.manual_seed(0 )
__a : Tuple = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
torch.manual_seed(0 )
__a : Union[str, Any] = CLIPTextModel(
CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=__a , projection_dim=32 , intermediate_size=37 , layer_norm_eps=1E-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , ) )
torch.manual_seed(0 )
__a : Tuple = UNetaDConditionModel(
sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=('CrossAttnDownBlock2D', 'DownBlock2D') , up_block_types=('UpBlock2D', 'CrossAttnUpBlock2D') , block_out_channels=(32, 64) , attention_head_dim=(2, 4) , class_embed_type='projection' , projection_class_embeddings_input_dim=embedder_projection_dim * 2 , cross_attention_dim=__a , layers_per_block=1 , upcast_attention=__a , use_linear_projection=__a , )
torch.manual_seed(0 )
__a : Any = DDIMScheduler(
beta_schedule='scaled_linear' , beta_start=0.00085 , beta_end=0.012 , prediction_type='v_prediction' , set_alpha_to_one=__a , steps_offset=1 , )
torch.manual_seed(0 )
__a : Optional[Any] = AutoencoderKL()
__a : Optional[int] = {
# image encoding components
'feature_extractor': feature_extractor,
'image_encoder': image_encoder.eval(),
# image noising components
'image_normalizer': image_normalizer.eval(),
'image_noising_scheduler': image_noising_scheduler,
# regular denoising components
'tokenizer': tokenizer,
'text_encoder': text_encoder.eval(),
'unet': unet.eval(),
'scheduler': scheduler,
'vae': vae.eval(),
}
return components
def __UpperCAmelCase ( self , __a , __a=0 , __a=True ):
'''simple docstring'''
if str(__a ).startswith('mps' ):
__a : Optional[int] = torch.manual_seed(__a )
else:
__a : List[Any] = torch.Generator(device=__a ).manual_seed(__a )
__a : List[Any] = floats_tensor((1, 3, 32, 32) , rng=random.Random(__a ) ).to(__a )
if pil_image:
__a : List[str] = input_image * 0.5 + 0.5
__a : Optional[Any] = input_image.clamp(0 , 1 )
__a : str = input_image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
__a : Any = DiffusionPipeline.numpy_to_pil(__a )[0]
return {
"prompt": "An anime racoon running a marathon",
"image": input_image,
"generator": generator,
"num_inference_steps": 2,
"output_type": "np",
}
@skip_mps
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Dict = 'cpu' # ensure determinism for the device-dependent torch.Generator
__a : List[Any] = self.get_dummy_components()
__a : Any = StableUnCLIPImgaImgPipeline(**__a )
__a : int = sd_pipe.to(__a )
sd_pipe.set_progress_bar_config(disable=__a )
__a : List[str] = self.get_dummy_inputs(__a )
inputs.update({'image_embeds': None} )
__a : Optional[int] = sd_pipe(**__a ).images
__a : Union[str, Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
__a : List[Any] = np.array([0.3872, 0.7224, 0.5601, 0.4741, 0.6872, 0.5814, 0.4636, 0.3867, 0.5078] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Optional[int] = torch_device in ['cpu', 'mps']
self._test_attention_slicing_forward_pass(test_max_difference=__a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Tuple = torch_device in ['cpu', 'mps']
self._test_inference_batch_single_identical(test_max_difference=__a )
@unittest.skipIf(
torch_device != 'cuda' or not is_xformers_available() , reason='XFormers attention is only available with CUDA and `xformers` installed' , )
def __UpperCAmelCase ( self ):
'''simple docstring'''
self._test_xformers_attention_forwardGenerator_pass(test_max_difference=__a )
@slow
@require_torch_gpu
class __UpperCamelCase ( unittest.TestCase ):
def __UpperCAmelCase ( self ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Optional[int] = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png' )
__a : Optional[Any] = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_img2img_anime_turtle_fp16.npy' )
__a : Optional[Any] = StableUnCLIPImgaImgPipeline.from_pretrained(
'fusing/stable-unclip-2-1-l-img2img' , torch_dtype=torch.floataa )
pipe.to(__a )
pipe.set_progress_bar_config(disable=__a )
# stable unclip will oom when integration tests are run on a V100,
# so turn on memory savings
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
__a : Any = torch.Generator(device='cpu' ).manual_seed(0 )
__a : int = pipe(__a , 'anime turle' , generator=__a , output_type='np' )
__a : int = output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(__a , __a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Any = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png' )
__a : int = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_h_img2img_anime_turtle_fp16.npy' )
__a : List[str] = StableUnCLIPImgaImgPipeline.from_pretrained(
'fusing/stable-unclip-2-1-h-img2img' , torch_dtype=torch.floataa )
pipe.to(__a )
pipe.set_progress_bar_config(disable=__a )
# stable unclip will oom when integration tests are run on a V100,
# so turn on memory savings
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
__a : Dict = torch.Generator(device='cpu' ).manual_seed(0 )
__a : Tuple = pipe(__a , 'anime turle' , generator=__a , output_type='np' )
__a : List[Any] = output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(__a , __a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Any = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png' )
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
__a : List[Any] = StableUnCLIPImgaImgPipeline.from_pretrained(
'fusing/stable-unclip-2-1-h-img2img' , torch_dtype=torch.floataa )
__a : Tuple = pipe.to(__a )
pipe.set_progress_bar_config(disable=__a )
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
__a : str = pipe(
__a , 'anime turtle' , num_inference_steps=2 , output_type='np' , )
__a : List[str] = torch.cuda.max_memory_allocated()
# make sure that less than 7 GB is allocated
assert mem_bytes < 7 * 10**9
| 294 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_pytesseract_available():
from PIL import Image
from transformers import LayoutLMvaImageProcessor
class __UpperCamelCase ( unittest.TestCase ):
def __init__( self , __a , __a=7 , __a=3 , __a=18 , __a=30 , __a=400 , __a=True , __a=None , __a=True , ):
'''simple docstring'''
__a : List[Any] = size if size is not None else {'height': 18, 'width': 18}
__a : int = parent
__a : Dict = batch_size
__a : Optional[int] = num_channels
__a : List[Any] = image_size
__a : Tuple = min_resolution
__a : str = max_resolution
__a : str = do_resize
__a : Optional[Any] = size
__a : str = apply_ocr
def __UpperCAmelCase ( self ):
'''simple docstring'''
return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}
@require_torch
@require_pytesseract
class __UpperCamelCase ( lowerCAmelCase_ , unittest.TestCase ):
A_ = LayoutLMvaImageProcessor if is_pytesseract_available() else None
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Dict = LayoutLMvaImageProcessingTester(self )
@property
def __UpperCAmelCase ( self ):
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : str = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(__a , 'do_resize' ) )
self.assertTrue(hasattr(__a , 'size' ) )
self.assertTrue(hasattr(__a , 'apply_ocr' ) )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Tuple = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'height': 18, 'width': 18} )
__a : Optional[int] = self.image_processing_class.from_dict(self.image_processor_dict , size=42 )
self.assertEqual(image_processor.size , {'height': 42, 'width': 42} )
def __UpperCAmelCase ( self ):
'''simple docstring'''
pass
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Dict = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
__a : Optional[int] = prepare_image_inputs(self.image_processor_tester , equal_resolution=__a )
for image in image_inputs:
self.assertIsInstance(__a , Image.Image )
# Test not batched input
__a : Union[str, Any] = image_processing(image_inputs[0] , return_tensors='pt' )
self.assertEqual(
encoding.pixel_values.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
) , )
self.assertIsInstance(encoding.words , __a )
self.assertIsInstance(encoding.boxes , __a )
# Test batched
__a : 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'],
) , )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : int = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
__a : Union[str, Any] = 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
__a : Optional[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
__a : Tuple = 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'],
) , )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : str = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
__a : List[str] = 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
__a : 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
__a : List[str] = 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'],
) , )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : List[str] = LayoutLMvaImageProcessor()
from datasets import load_dataset
__a : str = load_dataset('hf-internal-testing/fixtures_docvqa' , split='test' )
__a : Tuple = Image.open(ds[0]['file'] ).convert('RGB' )
__a : Optional[Any] = image_processing(__a , return_tensors='pt' )
self.assertEqual(encoding.pixel_values.shape , (1, 3, 224, 224) )
self.assertEqual(len(encoding.words ) , len(encoding.boxes ) )
# fmt: off
# the words and boxes were obtained with Tesseract 4.1.1
__a : Optional[Any] = [['11:14', 'to', '11:39', 'a.m', '11:39', 'to', '11:44', 'a.m.', '11:44', 'a.m.', 'to', '12:25', 'p.m.', '12:25', 'to', '12:58', 'p.m.', '12:58', 'to', '4:00', 'p.m.', '2:00', 'to', '5:00', 'p.m.', 'Coffee', 'Break', 'Coffee', 'will', 'be', 'served', 'for', 'men', 'and', 'women', 'in', 'the', 'lobby', 'adjacent', 'to', 'exhibit', 'area.', 'Please', 'move', 'into', 'exhibit', 'area.', '(Exhibits', 'Open)', 'TRRF', 'GENERAL', 'SESSION', '(PART', '|)', 'Presiding:', 'Lee', 'A.', 'Waller', 'TRRF', 'Vice', 'President', '“Introductory', 'Remarks”', 'Lee', 'A.', 'Waller,', 'TRRF', 'Vice', 'Presi-', 'dent', 'Individual', 'Interviews', 'with', 'TRRF', 'Public', 'Board', 'Members', 'and', 'Sci-', 'entific', 'Advisory', 'Council', 'Mem-', 'bers', 'Conducted', 'by', 'TRRF', 'Treasurer', 'Philip', 'G.', 'Kuehn', 'to', 'get', 'answers', 'which', 'the', 'public', 'refrigerated', 'warehousing', 'industry', 'is', 'looking', 'for.', 'Plus', 'questions', 'from', 'the', 'floor.', 'Dr.', 'Emil', 'M.', 'Mrak,', 'University', 'of', 'Cal-', 'ifornia,', 'Chairman,', 'TRRF', 'Board;', 'Sam', 'R.', 'Cecil,', 'University', 'of', 'Georgia', 'College', 'of', 'Agriculture;', 'Dr.', 'Stanley', 'Charm,', 'Tufts', 'University', 'School', 'of', 'Medicine;', 'Dr.', 'Robert', 'H.', 'Cotton,', 'ITT', 'Continental', 'Baking', 'Company;', 'Dr.', 'Owen', 'Fennema,', 'University', 'of', 'Wis-', 'consin;', 'Dr.', 'Robert', 'E.', 'Hardenburg,', 'USDA.', 'Questions', 'and', 'Answers', 'Exhibits', 'Open', 'Capt.', 'Jack', 'Stoney', 'Room', 'TRRF', 'Scientific', 'Advisory', 'Council', 'Meeting', 'Ballroom', 'Foyer']] # noqa: E231
__a : Union[str, Any] = [[[141, 57, 214, 69], [228, 58, 252, 69], [141, 75, 216, 88], [230, 79, 280, 88], [142, 260, 218, 273], [230, 261, 255, 273], [143, 279, 218, 290], [231, 282, 290, 291], [143, 342, 218, 354], [231, 345, 289, 355], [202, 362, 227, 373], [143, 379, 220, 392], [231, 382, 291, 394], [144, 714, 220, 726], [231, 715, 256, 726], [144, 732, 220, 745], [232, 736, 291, 747], [144, 769, 218, 782], [231, 770, 256, 782], [141, 788, 202, 801], [215, 791, 274, 804], [143, 826, 204, 838], [215, 826, 240, 838], [142, 844, 202, 857], [215, 847, 274, 859], [334, 57, 427, 69], [440, 57, 522, 69], [369, 75, 461, 88], [469, 75, 516, 88], [528, 76, 562, 88], [570, 76, 667, 88], [675, 75, 711, 87], [721, 79, 778, 88], [789, 75, 840, 88], [369, 97, 470, 107], [484, 94, 507, 106], [518, 94, 562, 107], [576, 94, 655, 110], [668, 94, 792, 109], [804, 95, 829, 107], [369, 113, 465, 125], [477, 116, 547, 125], [562, 113, 658, 125], [671, 116, 748, 125], [761, 113, 811, 125], [369, 131, 465, 143], [477, 133, 548, 143], [563, 130, 698, 145], [710, 130, 802, 146], [336, 171, 412, 183], [423, 171, 572, 183], [582, 170, 716, 184], [728, 171, 817, 187], [829, 171, 844, 186], [338, 197, 482, 212], [507, 196, 557, 209], [569, 196, 595, 208], [610, 196, 702, 209], [505, 214, 583, 226], [595, 214, 656, 227], [670, 215, 807, 227], [335, 259, 543, 274], [556, 259, 708, 272], [372, 279, 422, 291], [435, 279, 460, 291], [474, 279, 574, 292], [587, 278, 664, 291], [676, 278, 738, 291], [751, 279, 834, 291], [372, 298, 434, 310], [335, 341, 483, 354], [497, 341, 655, 354], [667, 341, 728, 354], [740, 341, 825, 354], [335, 360, 430, 372], [442, 360, 534, 372], [545, 359, 687, 372], [697, 360, 754, 372], [765, 360, 823, 373], [334, 378, 428, 391], [440, 378, 577, 394], [590, 378, 705, 391], [720, 378, 801, 391], [334, 397, 400, 409], [370, 416, 529, 429], [544, 416, 576, 432], [587, 416, 665, 428], [677, 416, 814, 429], [372, 435, 452, 450], [465, 434, 495, 447], [511, 434, 600, 447], [611, 436, 637, 447], [649, 436, 694, 451], [705, 438, 824, 447], [369, 453, 452, 466], [464, 454, 509, 466], [522, 453, 611, 469], [625, 453, 792, 469], [370, 472, 556, 488], [570, 472, 684, 487], [697, 472, 718, 485], [732, 472, 835, 488], [369, 490, 411, 503], [425, 490, 484, 503], [496, 490, 635, 506], [645, 490, 707, 503], [718, 491, 761, 503], [771, 490, 840, 503], [336, 510, 374, 521], [388, 510, 447, 522], [460, 510, 489, 521], [503, 510, 580, 522], [592, 509, 736, 525], [745, 509, 770, 522], [781, 509, 840, 522], [338, 528, 434, 541], [448, 528, 596, 541], [609, 527, 687, 540], [700, 528, 792, 541], [336, 546, 397, 559], [407, 546, 431, 559], [443, 546, 525, 560], [537, 546, 680, 562], [688, 546, 714, 559], [722, 546, 837, 562], [336, 565, 449, 581], [461, 565, 485, 577], [497, 565, 665, 581], [681, 565, 718, 577], [732, 565, 837, 580], [337, 584, 438, 597], [452, 583, 521, 596], [535, 584, 677, 599], [690, 583, 787, 596], [801, 583, 825, 596], [338, 602, 478, 615], [492, 602, 530, 614], [543, 602, 638, 615], [650, 602, 676, 614], [688, 602, 788, 615], [802, 602, 843, 614], [337, 621, 502, 633], [516, 621, 615, 637], [629, 621, 774, 636], [789, 621, 827, 633], [337, 639, 418, 652], [432, 640, 571, 653], [587, 639, 731, 655], [743, 639, 769, 652], [780, 639, 841, 652], [338, 658, 440, 673], [455, 658, 491, 670], [508, 658, 602, 671], [616, 658, 638, 670], [654, 658, 835, 674], [337, 677, 429, 689], [337, 714, 482, 726], [495, 714, 548, 726], [561, 714, 683, 726], [338, 770, 461, 782], [474, 769, 554, 785], [489, 788, 562, 803], [576, 788, 643, 801], [656, 787, 751, 804], [764, 788, 844, 801], [334, 825, 421, 838], [430, 824, 574, 838], [584, 824, 723, 841], [335, 844, 450, 857], [464, 843, 583, 860], [628, 862, 755, 875], [769, 861, 848, 878]]] # noqa: E231
# fmt: on
self.assertListEqual(encoding.words , __a )
self.assertListEqual(encoding.boxes , __a )
# with apply_OCR = False
__a : List[Any] = LayoutLMvaImageProcessor(apply_ocr=__a )
__a : List[Any] = image_processing(__a , return_tensors='pt' )
self.assertEqual(encoding.pixel_values.shape , (1, 3, 224, 224) )
| 294 | 1 |
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
__lowerCAmelCase : List[str] =get_tests_dir('fixtures/test_sentencepiece.model')
@require_sentencepiece
@require_tokenizers
class _lowercase ( A__ , unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : str = XLMRobertaTokenizer
SCREAMING_SNAKE_CASE__ : List[str] = XLMRobertaTokenizerFast
SCREAMING_SNAKE_CASE__ : str = True
SCREAMING_SNAKE_CASE__ : int = True
def __magic_name__( self :List[str] ) -> List[Any]:
super().setUp()
# We have a SentencePiece fixture for testing
__SCREAMING_SNAKE_CASE : Optional[int] = XLMRobertaTokenizer(lowerCAmelCase__ , keep_accents=lowerCAmelCase__ )
tokenizer.save_pretrained(self.tmpdirname )
def __magic_name__( self :int ) -> str:
__SCREAMING_SNAKE_CASE : Optional[Any] = '''<pad>'''
__SCREAMING_SNAKE_CASE : Optional[int] = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCAmelCase__ ) , lowerCAmelCase__ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCAmelCase__ ) , lowerCAmelCase__ )
def __magic_name__( self :Optional[Any] ) -> Optional[Any]:
__SCREAMING_SNAKE_CASE : List[str] = 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(lowerCAmelCase__ ) , 1_002 )
def __magic_name__( self :int ) -> str:
self.assertEqual(self.get_tokenizer().vocab_size , 1_002 )
def __magic_name__( self :Any ) -> Union[str, Any]:
__SCREAMING_SNAKE_CASE : Optional[int] = XLMRobertaTokenizer(lowerCAmelCase__ , keep_accents=lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : Optional[Any] = tokenizer.tokenize('''This is a test''' )
self.assertListEqual(lowerCAmelCase__ , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(lowerCAmelCase__ ) , [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] , )
__SCREAMING_SNAKE_CASE : Union[str, Any] = 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''',
'''.''',
] , )
__SCREAMING_SNAKE_CASE : Optional[int] = tokenizer.convert_tokens_to_ids(lowerCAmelCase__ )
self.assertListEqual(
lowerCAmelCase__ , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
# ^ unk: 2 + 1 = 3 unk: 2 + 1 = 3 ^
] , )
__SCREAMING_SNAKE_CASE : int = 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 __magic_name__( self :Optional[Any] ) -> Optional[int]:
if not self.test_slow_tokenizer:
# as we don't have a slow version, we can't compare the outputs between slow and fast versions
return
__SCREAMING_SNAKE_CASE : List[Any] = (self.rust_tokenizer_class, '''hf-internal-testing/tiny-xlm-roberta''', {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
__SCREAMING_SNAKE_CASE : Tuple = self.rust_tokenizer_class.from_pretrained(lowerCAmelCase__ , **lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : str = self.tokenizer_class.from_pretrained(lowerCAmelCase__ , **lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : int = tempfile.mkdtemp()
__SCREAMING_SNAKE_CASE : Tuple = tokenizer_r.save_pretrained(lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : Dict = tokenizer_p.save_pretrained(lowerCAmelCase__ )
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files ) )
__SCREAMING_SNAKE_CASE : List[str] = tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f )
self.assertSequenceEqual(lowerCAmelCase__ , lowerCAmelCase__ )
# Checks everything loads correctly in the same way
__SCREAMING_SNAKE_CASE : Union[str, Any] = tokenizer_r.from_pretrained(lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : str = tokenizer_p.from_pretrained(lowerCAmelCase__ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(lowerCAmelCase__ , lowerCAmelCase__ ) )
# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))
shutil.rmtree(lowerCAmelCase__ )
# Save tokenizer rust, legacy_format=True
__SCREAMING_SNAKE_CASE : List[str] = tempfile.mkdtemp()
__SCREAMING_SNAKE_CASE : Union[str, Any] = tokenizer_r.save_pretrained(lowerCAmelCase__ , legacy_format=lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : List[str] = tokenizer_p.save_pretrained(lowerCAmelCase__ )
# Checks it save with the same files
self.assertSequenceEqual(lowerCAmelCase__ , lowerCAmelCase__ )
# Checks everything loads correctly in the same way
__SCREAMING_SNAKE_CASE : Any = tokenizer_r.from_pretrained(lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : Optional[int] = tokenizer_p.from_pretrained(lowerCAmelCase__ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(lowerCAmelCase__ , lowerCAmelCase__ ) )
shutil.rmtree(lowerCAmelCase__ )
# Save tokenizer rust, legacy_format=False
__SCREAMING_SNAKE_CASE : List[str] = tempfile.mkdtemp()
__SCREAMING_SNAKE_CASE : List[Any] = tokenizer_r.save_pretrained(lowerCAmelCase__ , legacy_format=lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : str = tokenizer_p.save_pretrained(lowerCAmelCase__ )
# Checks it saved the tokenizer.json file
self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files ) )
# Checks everything loads correctly in the same way
__SCREAMING_SNAKE_CASE : Tuple = tokenizer_r.from_pretrained(lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : Dict = tokenizer_p.from_pretrained(lowerCAmelCase__ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(lowerCAmelCase__ , lowerCAmelCase__ ) )
shutil.rmtree(lowerCAmelCase__ )
@cached_property
def __magic_name__( self :List[str] ) -> Union[str, Any]:
return XLMRobertaTokenizer.from_pretrained('''xlm-roberta-base''' )
def __magic_name__( self :Union[str, Any] ) -> Tuple:
with tempfile.NamedTemporaryFile() as f:
shutil.copyfile(lowerCAmelCase__ , f.name )
__SCREAMING_SNAKE_CASE : Tuple = XLMRobertaTokenizer(f.name , keep_accents=lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : Tuple = pickle.dumps(lowerCAmelCase__ )
pickle.loads(lowerCAmelCase__ )
def __magic_name__( self :Optional[int] ) -> int:
if not self.test_rust_tokenizer:
return
__SCREAMING_SNAKE_CASE : int = self.get_tokenizer()
__SCREAMING_SNAKE_CASE : Any = self.get_rust_tokenizer()
__SCREAMING_SNAKE_CASE : int = '''I was born in 92000, and this is falsé.'''
__SCREAMING_SNAKE_CASE : str = tokenizer.tokenize(lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : List[str] = rust_tokenizer.tokenize(lowerCAmelCase__ )
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : Union[str, Any] = tokenizer.encode(lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : List[str] = rust_tokenizer.encode(lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : Optional[int] = self.get_rust_tokenizer()
__SCREAMING_SNAKE_CASE : str = tokenizer.encode(lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : Any = rust_tokenizer.encode(lowerCAmelCase__ )
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__ )
@slow
def __magic_name__( self :Optional[int] ) -> int:
__SCREAMING_SNAKE_CASE : Tuple = '''Hello World!'''
__SCREAMING_SNAKE_CASE : Optional[int] = [0, 35_378, 6_661, 38, 2]
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base') # xlmr.large has same tokenizer
# xlmr.eval()
# xlmr.encode(symbols)
self.assertListEqual(lowerCAmelCase__ , self.big_tokenizer.encode(lowerCAmelCase__ ) )
@slow
def __magic_name__( self :Dict ) -> int:
__SCREAMING_SNAKE_CASE : List[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'''
)
__SCREAMING_SNAKE_CASE : Any = [
0,
3_293,
83,
10,
4_552,
4_989,
7_986,
678,
10,
5_915,
111,
179_459,
124_850,
4,
6_044,
237,
12,
6,
5,
6,
4,
6_780,
705,
15,
1_388,
44,
378,
10_114,
711,
152,
20,
6,
5,
22_376,
642,
1_221,
15_190,
34_153,
450,
5_608,
959,
1_119,
57_702,
136,
186,
47,
1_098,
29_367,
47,
# 4426, # What fairseq tokenizes from "<unk>": "_<"
# 3678, # What fairseq tokenizes from "<unk>": "unk"
# 2740, # What fairseq tokenizes from "<unk>": ">"
3, # What we tokenize from "<unk>": "<unk>"
6, # Residue from the tokenization: an extra sentencepiece underline
4,
6_044,
237,
6_284,
50_901,
528,
31,
90,
34,
927,
2,
]
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base') # xlmr.large has same tokenizer
# xlmr.eval()
# xlmr.encode(symbols)
self.assertListEqual(lowerCAmelCase__ , self.big_tokenizer.encode(lowerCAmelCase__ ) )
@slow
def __magic_name__( self :Dict ) -> List[Any]:
# fmt: off
__SCREAMING_SNAKE_CASE : Tuple = {'''input_ids''': [[0, 11_062, 82_772, 7, 15, 82_772, 538, 51_529, 237, 17_198, 1_290, 206, 9, 215_175, 1_314, 136, 17_198, 1_290, 206, 9, 56_359, 42, 122_009, 9, 16_466, 16, 87_344, 4_537, 9, 4_717, 78_381, 6, 159_958, 7, 15, 24_480, 618, 4, 527, 22_693, 5_428, 4, 2_777, 24_480, 9_874, 4, 43_523, 594, 4, 803, 18_392, 33_189, 18, 4, 43_523, 24_447, 12_399, 100, 24_955, 83_658, 9_626, 144_057, 15, 839, 22_335, 16, 136, 24_955, 83_658, 83_479, 15, 39_102, 724, 16, 678, 645, 2_789, 1_328, 4_589, 42, 122_009, 115_774, 23, 805, 1_328, 46_876, 7, 136, 53_894, 1_940, 42_227, 41_159, 17_721, 823, 425, 4, 27_512, 98_722, 206, 136, 5_531, 4_970, 919, 17_336, 5, 2], [0, 20_080, 618, 83, 82_775, 47, 479, 9, 1_517, 73, 53_894, 333, 80_581, 110_117, 18_811, 5_256, 1_295, 51, 152_526, 297, 7_986, 390, 124_416, 538, 35_431, 214, 98, 15_044, 25_737, 136, 7_108, 43_701, 23, 756, 135_355, 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], [0, 581, 63_773, 119_455, 6, 147_797, 88_203, 7, 645, 70, 21, 3_285, 10_269, 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=lowerCAmelCase__ , model_name='''xlm-roberta-base''' , revision='''d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3''' , )
| 9 |
'''simple docstring'''
import string
def lowerCAmelCase_ ( _lowerCamelCase: str ):
__SCREAMING_SNAKE_CASE : Dict = """"""
for i in sequence:
__SCREAMING_SNAKE_CASE : Any = ord(_lowerCamelCase )
if 65 <= extract <= 90:
output += chr(1_55 - extract )
elif 97 <= extract <= 1_22:
output += chr(2_19 - extract )
else:
output += i
return output
def lowerCAmelCase_ ( _lowerCamelCase: str ):
__SCREAMING_SNAKE_CASE : Optional[Any] = string.ascii_letters
__SCREAMING_SNAKE_CASE : Union[str, Any] = string.ascii_lowercase[::-1] + string.ascii_uppercase[::-1]
return "".join(
letters_reversed[letters.index(_lowerCamelCase )] if c in letters else c for c in sequence )
def lowerCAmelCase_ ( ):
from timeit import timeit
print("""Running performance benchmarks...""" )
__SCREAMING_SNAKE_CASE : Union[str, Any] = """from string import printable ; from __main__ import atbash, atbash_slow"""
print(F"> atbash_slow(): {timeit('atbash_slow(printable)' , setup=_lowerCamelCase )} seconds" )
print(F"> atbash(): {timeit('atbash(printable)' , setup=_lowerCamelCase )} seconds" )
if __name__ == "__main__":
for example in ("ABCDEFGH", "123GGjj", "testStringtest", "with space"):
print(f"{example} encrypted in atbash: {atbash(example)}")
benchmark() | 112 | 0 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class UpperCamelCase ( metaclass=_UpperCAmelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> Optional[int]:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> List[str]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_UpperCAmelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> Any:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Optional[int]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> str:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_UpperCAmelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> Dict:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> List[str]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> List[str]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_UpperCAmelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> List[str]:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> int:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_UpperCAmelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> Dict:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Dict:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> int:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_UpperCAmelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> Any:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> List[str]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> List[str]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_UpperCAmelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> int:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Tuple:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> List[Any]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_UpperCAmelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> str:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Optional[Any]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_UpperCAmelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> List[str]:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> List[str]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> int:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_UpperCAmelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> Optional[int]:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> List[str]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> int:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_UpperCAmelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> List[str]:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> List[Any]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
def lowercase__( *__SCREAMING_SNAKE_CASE : Tuple , **__SCREAMING_SNAKE_CASE : List[Any] ):
requires_backends(UpperCAmelCase__ , ['torch'] )
def lowercase__( *__SCREAMING_SNAKE_CASE : Dict , **__SCREAMING_SNAKE_CASE : Union[str, Any] ):
requires_backends(UpperCAmelCase__ , ['torch'] )
def lowercase__( *__SCREAMING_SNAKE_CASE : List[Any] , **__SCREAMING_SNAKE_CASE : Any ):
requires_backends(UpperCAmelCase__ , ['torch'] )
def lowercase__( *__SCREAMING_SNAKE_CASE : str , **__SCREAMING_SNAKE_CASE : Optional[int] ):
requires_backends(UpperCAmelCase__ , ['torch'] )
def lowercase__( *__SCREAMING_SNAKE_CASE : Optional[Any] , **__SCREAMING_SNAKE_CASE : Tuple ):
requires_backends(UpperCAmelCase__ , ['torch'] )
def lowercase__( *__SCREAMING_SNAKE_CASE : int , **__SCREAMING_SNAKE_CASE : Optional[int] ):
requires_backends(UpperCAmelCase__ , ['torch'] )
def lowercase__( *__SCREAMING_SNAKE_CASE : Optional[int] , **__SCREAMING_SNAKE_CASE : Optional[Any] ):
requires_backends(UpperCAmelCase__ , ['torch'] )
class UpperCamelCase ( metaclass=_UpperCAmelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> str:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Dict:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_UpperCAmelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> str:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> int:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_UpperCAmelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> Dict:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> str:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_UpperCAmelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> str:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Dict:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> List[str]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_UpperCAmelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Dict:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> int:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_UpperCAmelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> Optional[int]:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> List[str]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Optional[int]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_UpperCAmelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> Dict:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> str:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> str:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_UpperCAmelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> Optional[Any]:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> int:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Optional[Any]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_UpperCAmelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> Dict:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> int:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Any:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_UpperCAmelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> List[Any]:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Optional[int]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> str:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_UpperCAmelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> Any:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Tuple:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_UpperCAmelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> int:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> List[str]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Optional[Any]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_UpperCAmelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> str:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Optional[int]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Any:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_UpperCAmelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> str:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Optional[int]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_UpperCAmelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> Optional[int]:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> List[str]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_UpperCAmelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> Tuple:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Optional[Any]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_UpperCAmelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> Optional[Any]:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Dict:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Dict:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_UpperCAmelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> Tuple:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> int:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Optional[int]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_UpperCAmelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> List[str]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> List[Any]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_UpperCAmelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> List[str]:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> List[str]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Any:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_UpperCAmelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> List[str]:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Any:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_UpperCAmelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> str:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Optional[int]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_UpperCAmelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> Optional[int]:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Optional[int]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> List[str]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_UpperCAmelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> List[Any]:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Dict:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> str:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_UpperCAmelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> List[str]:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Optional[int]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> str:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_UpperCAmelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> Optional[Any]:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Any:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Optional[Any]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_UpperCAmelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> Optional[int]:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Any:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_UpperCAmelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Tuple:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> List[str]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_UpperCAmelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> Optional[Any]:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Tuple:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_UpperCAmelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> List[str]:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Tuple:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> List[str]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_UpperCAmelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> Tuple:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Optional[Any]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Optional[int]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_UpperCAmelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> List[str]:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Dict:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> List[str]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_UpperCAmelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> Optional[int]:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_UpperCAmelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> Optional[int]:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> str:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> str:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_UpperCAmelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> int:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Optional[Any]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_UpperCAmelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> Dict:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> str:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Tuple:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_UpperCAmelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> Any:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> List[Any]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Optional[int]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_UpperCAmelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> Any:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Optional[Any]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> str:
'''simple docstring'''
requires_backends(cls ,['torch'] )
class UpperCamelCase ( metaclass=_UpperCAmelCase ):
lowercase = ['torch']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> List[Any]:
'''simple docstring'''
requires_backends(self ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> List[str]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
@classmethod
def _UpperCAmelCase ( cls ,*__UpperCamelCase ,**__UpperCamelCase ) -> Optional[Any]:
'''simple docstring'''
requires_backends(cls ,['torch'] )
| 371 | """simple docstring"""
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, 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 EsmForMaskedLM, EsmForSequenceClassification, EsmForTokenClassification, EsmModel
from transformers.models.esm.modeling_esm import (
ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
EsmEmbeddings,
create_position_ids_from_input_ids,
)
class UpperCamelCase :
def __init__( self ,__UpperCamelCase ,__UpperCamelCase=13 ,__UpperCamelCase=7 ,__UpperCamelCase=False ,__UpperCamelCase=True ,__UpperCamelCase=False ,__UpperCamelCase=True ,__UpperCamelCase=33 ,__UpperCamelCase=32 ,__UpperCamelCase=5 ,__UpperCamelCase=4 ,__UpperCamelCase=37 ,__UpperCamelCase="gelu" ,__UpperCamelCase=0.1 ,__UpperCamelCase=0.1 ,__UpperCamelCase=512 ,__UpperCamelCase=16 ,__UpperCamelCase=2 ,__UpperCamelCase=0.02 ,__UpperCamelCase=3 ,__UpperCamelCase=4 ,__UpperCamelCase=None ,) -> List[Any]:
'''simple docstring'''
lowercase_ : Any = parent
lowercase_ : str = batch_size
lowercase_ : List[Any] = seq_length
lowercase_ : Dict = is_training
lowercase_ : Tuple = use_input_mask
lowercase_ : Optional[Any] = use_token_type_ids
lowercase_ : List[str] = use_labels
lowercase_ : Any = vocab_size
lowercase_ : List[str] = hidden_size
lowercase_ : Optional[int] = num_hidden_layers
lowercase_ : int = num_attention_heads
lowercase_ : int = intermediate_size
lowercase_ : List[Any] = hidden_act
lowercase_ : Optional[int] = hidden_dropout_prob
lowercase_ : Tuple = attention_probs_dropout_prob
lowercase_ : Tuple = max_position_embeddings
lowercase_ : Optional[int] = type_vocab_size
lowercase_ : Optional[int] = type_sequence_label_size
lowercase_ : Dict = initializer_range
lowercase_ : int = num_labels
lowercase_ : Any = num_choices
lowercase_ : int = scope
def _UpperCAmelCase ( self ) -> Any:
'''simple docstring'''
lowercase_ : Optional[int] = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
lowercase_ : Dict = None
if self.use_input_mask:
lowercase_ : List[Any] = random_attention_mask([self.batch_size, self.seq_length] )
lowercase_ : Tuple = None
lowercase_ : Tuple = None
lowercase_ : Tuple = None
if self.use_labels:
lowercase_ : List[Any] = ids_tensor([self.batch_size] ,self.type_sequence_label_size )
lowercase_ : str = ids_tensor([self.batch_size, self.seq_length] ,self.num_labels )
lowercase_ : int = ids_tensor([self.batch_size] ,self.num_choices )
lowercase_ : str = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def _UpperCAmelCase ( self ) -> List[str]:
'''simple docstring'''
return EsmConfig(
vocab_size=self.vocab_size ,hidden_size=self.hidden_size ,pad_token_id=1 ,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 ,initializer_range=self.initializer_range ,)
def _UpperCAmelCase ( self ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ) -> Tuple:
'''simple docstring'''
lowercase_ : List[Any] = EsmModel(config=__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
lowercase_ : Tuple = model(__UpperCamelCase ,attention_mask=__UpperCamelCase )
lowercase_ : Union[str, Any] = model(__UpperCamelCase )
lowercase_ : int = model(__UpperCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape ,(self.batch_size, self.hidden_size) )
def _UpperCAmelCase ( self ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ) -> Union[str, Any]:
'''simple docstring'''
lowercase_ : Dict = EsmForMaskedLM(config=__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
lowercase_ : int = 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]:
'''simple docstring'''
lowercase_ : str = self.num_labels
lowercase_ : int = EsmForTokenClassification(config=__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
lowercase_ : List[Any] = 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 ) -> str:
'''simple docstring'''
lowercase_ : Any = self.prepare_config_and_inputs()
(
(
lowercase_
) , (
lowercase_
) , (
lowercase_
) , (
lowercase_
) , (
lowercase_
) , (
lowercase_
) ,
) : Optional[int] = config_and_inputs
lowercase_ : Dict = {'input_ids': input_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class UpperCamelCase ( lowercase_ , lowercase_ , unittest.TestCase ):
lowercase = False
lowercase = (
(
EsmForMaskedLM,
EsmModel,
EsmForSequenceClassification,
EsmForTokenClassification,
)
if is_torch_available()
else ()
)
lowercase = ()
lowercase = (
{
'feature-extraction': EsmModel,
'fill-mask': EsmForMaskedLM,
'text-classification': EsmForSequenceClassification,
'token-classification': EsmForTokenClassification,
'zero-shot': EsmForSequenceClassification,
}
if is_torch_available()
else {}
)
lowercase = True
def _UpperCAmelCase ( self ) -> List[str]:
'''simple docstring'''
lowercase_ : Dict = EsmModelTester(self )
lowercase_ : List[Any] = ConfigTester(self ,config_class=__UpperCamelCase ,hidden_size=37 )
def _UpperCAmelCase ( self ) -> Any:
'''simple docstring'''
self.config_tester.run_common_tests()
def _UpperCAmelCase ( self ) -> Optional[Any]:
'''simple docstring'''
lowercase_ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__UpperCamelCase )
def _UpperCAmelCase ( self ) -> List[str]:
'''simple docstring'''
lowercase_ : List[str] = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
lowercase_ : Optional[Any] = type
self.model_tester.create_and_check_model(*__UpperCamelCase )
def _UpperCAmelCase ( self ) -> int:
'''simple docstring'''
lowercase_ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*__UpperCamelCase )
def _UpperCAmelCase ( self ) -> Dict:
'''simple docstring'''
lowercase_ : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*__UpperCamelCase )
@slow
def _UpperCAmelCase ( self ) -> str:
'''simple docstring'''
for model_name in ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase_ : List[str] = EsmModel.from_pretrained(__UpperCamelCase )
self.assertIsNotNone(__UpperCamelCase )
def _UpperCAmelCase ( self ) -> List[Any]:
'''simple docstring'''
lowercase_ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()[0]
lowercase_ : str = EsmEmbeddings(config=__UpperCamelCase )
lowercase_ : Tuple = torch.as_tensor([[12, 31, 13, model.padding_idx]] )
lowercase_ : List[Any] = torch.as_tensor(
[
[
0 + model.padding_idx + 1,
1 + model.padding_idx + 1,
2 + model.padding_idx + 1,
model.padding_idx,
]
] )
lowercase_ : Tuple = create_position_ids_from_input_ids(__UpperCamelCase ,model.padding_idx )
self.assertEqual(position_ids.shape ,expected_positions.shape )
self.assertTrue(torch.all(torch.eq(__UpperCamelCase ,__UpperCamelCase ) ) )
def _UpperCAmelCase ( self ) -> Union[str, Any]:
'''simple docstring'''
lowercase_ : List[str] = self.model_tester.prepare_config_and_inputs()[0]
lowercase_ : List[Any] = EsmEmbeddings(config=__UpperCamelCase )
lowercase_ : List[Any] = torch.empty(2 ,4 ,30 )
lowercase_ : List[str] = [
0 + embeddings.padding_idx + 1,
1 + embeddings.padding_idx + 1,
2 + embeddings.padding_idx + 1,
3 + embeddings.padding_idx + 1,
]
lowercase_ : List[str] = torch.as_tensor([expected_single_positions, expected_single_positions] )
lowercase_ : List[str] = embeddings.create_position_ids_from_inputs_embeds(__UpperCamelCase )
self.assertEqual(position_ids.shape ,expected_positions.shape )
self.assertTrue(torch.all(torch.eq(__UpperCamelCase ,__UpperCamelCase ) ) )
@unittest.skip('Esm does not support embedding resizing' )
def _UpperCAmelCase ( self ) -> str:
'''simple docstring'''
pass
@unittest.skip('Esm does not support embedding resizing' )
def _UpperCAmelCase ( self ) -> Optional[Any]:
'''simple docstring'''
pass
@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
def _UpperCAmelCase ( self ) -> Optional[Any]:
'''simple docstring'''
pass
@require_torch
class UpperCamelCase ( lowercase_ ):
@slow
def _UpperCAmelCase ( self ) -> Optional[Any]:
'''simple docstring'''
with torch.no_grad():
lowercase_ : Any = EsmForMaskedLM.from_pretrained('facebook/esm2_t6_8M_UR50D' )
model.eval()
lowercase_ : List[Any] = torch.tensor([[0, 1, 2, 3, 4, 5]] )
lowercase_ : List[str] = model(__UpperCamelCase )[0]
lowercase_ : Optional[int] = 33
lowercase_ : Union[str, Any] = torch.Size((1, 6, vocab_size) )
self.assertEqual(output.shape ,__UpperCamelCase )
lowercase_ : List[str] = torch.tensor(
[[[8.9215, -10.5898, -6.4671], [-6.3967, -13.9114, -1.1212], [-7.7812, -13.9516, -3.7406]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] ,__UpperCamelCase ,atol=1e-4 ) )
@slow
def _UpperCAmelCase ( self ) -> Union[str, Any]:
'''simple docstring'''
with torch.no_grad():
lowercase_ : int = EsmModel.from_pretrained('facebook/esm2_t6_8M_UR50D' )
model.eval()
lowercase_ : Tuple = torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]] )
lowercase_ : Dict = model(__UpperCamelCase )[0]
# compare the actual values for a slice.
lowercase_ : Any = torch.tensor(
[[[0.1444, 0.5413, 0.3248], [0.3034, 0.0053, 0.3108], [0.3228, -0.2499, 0.3415]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] ,__UpperCamelCase ,atol=1e-4 ) )
| 321 | 0 |
'''simple docstring'''
import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path
import timm
import torch
from huggingface_hub import hf_hub_download
from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
__A = logging.get_logger()
def _A ( lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ = True ):
print(f'''Converting {name}...''' )
with torch.no_grad():
if hidden_sizes == 128:
if name[-1] == "S":
lowercase__ = timm.create_model("""levit_128s""" , pretrained=snake_case__ )
else:
lowercase__ = timm.create_model("""levit_128""" , pretrained=snake_case__ )
if hidden_sizes == 192:
lowercase__ = timm.create_model("""levit_192""" , pretrained=snake_case__ )
if hidden_sizes == 256:
lowercase__ = timm.create_model("""levit_256""" , pretrained=snake_case__ )
if hidden_sizes == 384:
lowercase__ = timm.create_model("""levit_384""" , pretrained=snake_case__ )
from_model.eval()
lowercase__ = LevitForImageClassificationWithTeacher(snake_case__ ).eval()
lowercase__ = OrderedDict()
lowercase__ = from_model.state_dict()
lowercase__ = list(from_model.state_dict().keys() )
lowercase__ = list(our_model.state_dict().keys() )
print(len(snake_case__ ) , len(snake_case__ ) )
for i in range(len(snake_case__ ) ):
lowercase__ = weights[og_keys[i]]
our_model.load_state_dict(snake_case__ )
lowercase__ = torch.randn((2, 3, 224, 224) )
lowercase__ = from_model(snake_case__ )
lowercase__ = our_model(snake_case__ ).logits
assert torch.allclose(snake_case__ , snake_case__ ), "The model logits don't match the original one."
lowercase__ = name
print(snake_case__ )
if push_to_hub:
our_model.save_pretrained(save_directory / checkpoint_name )
lowercase__ = LevitImageProcessor()
image_processor.save_pretrained(save_directory / checkpoint_name )
print(f'''Pushed {checkpoint_name}''' )
def _A ( lowercase__ , lowercase__ = None , lowercase__ = True ):
lowercase__ = """imagenet-1k-id2label.json"""
lowercase__ = 1000
lowercase__ = (1, num_labels)
lowercase__ = """huggingface/label-files"""
lowercase__ = num_labels
lowercase__ = json.load(open(hf_hub_download(snake_case__ , snake_case__ , repo_type="""dataset""" ) , """r""" ) )
lowercase__ = {int(snake_case__ ): v for k, v in idalabel.items()}
lowercase__ = idalabel
lowercase__ = {v: k for k, v in idalabel.items()}
lowercase__ = partial(snake_case__ , num_labels=snake_case__ , idalabel=snake_case__ , labelaid=snake_case__ )
lowercase__ = {
"""levit-128S""": 128,
"""levit-128""": 128,
"""levit-192""": 192,
"""levit-256""": 256,
"""levit-384""": 384,
}
lowercase__ = {
"""levit-128S""": ImageNetPreTrainedConfig(
hidden_sizes=[128, 256, 384] , num_attention_heads=[4, 6, 8] , depths=[2, 3, 4] , key_dim=[16, 16, 16] , drop_path_rate=0 , ),
"""levit-128""": ImageNetPreTrainedConfig(
hidden_sizes=[128, 256, 384] , num_attention_heads=[4, 8, 12] , depths=[4, 4, 4] , key_dim=[16, 16, 16] , drop_path_rate=0 , ),
"""levit-192""": ImageNetPreTrainedConfig(
hidden_sizes=[192, 288, 384] , num_attention_heads=[3, 5, 6] , depths=[4, 4, 4] , key_dim=[32, 32, 32] , drop_path_rate=0 , ),
"""levit-256""": ImageNetPreTrainedConfig(
hidden_sizes=[256, 384, 512] , num_attention_heads=[4, 6, 8] , depths=[4, 4, 4] , key_dim=[32, 32, 32] , drop_path_rate=0 , ),
"""levit-384""": ImageNetPreTrainedConfig(
hidden_sizes=[384, 512, 768] , num_attention_heads=[6, 9, 12] , depths=[4, 4, 4] , key_dim=[32, 32, 32] , drop_path_rate=0.1 , ),
}
if model_name:
convert_weight_and_push(
names_to_hidden_sizes[model_name] , snake_case__ , names_to_config[model_name] , snake_case__ , snake_case__ )
else:
for model_name, config in names_to_config.items():
convert_weight_and_push(names_to_hidden_sizes[model_name] , snake_case__ , snake_case__ , snake_case__ , snake_case__ )
return config, expected_shape
if __name__ == "__main__":
__A = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default=None,
type=str,
help="The name of the model you wish to convert, it must be one of the supported Levit* architecture,",
)
parser.add_argument(
"--pytorch_dump_folder_path",
default="levit-dump-folder/",
type=Path,
required=False,
help="Path to the output PyTorch model directory.",
)
parser.add_argument("--push_to_hub", action="store_true", help="Push model and image processor to the hub")
parser.add_argument(
"--no-push_to_hub",
dest="push_to_hub",
action="store_false",
help="Do not push model and image processor to the hub",
)
__A = parser.parse_args()
__A = args.pytorch_dump_folder_path
pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 164 |
"""simple docstring"""
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def A ( snake_case__ ):
'''simple docstring'''
return (data["data"], data["target"])
def A ( snake_case__ , snake_case__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = XGBClassifier()
classifier.fit(snake_case__ , snake_case__ )
return classifier
def A ( ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = load_iris()
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = data_handling(snake_case__ )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = train_test_split(
snake_case__ , snake_case__ , test_size=0.25 )
SCREAMING_SNAKE_CASE__ = iris["""target_names"""]
# Create an XGBoost Classifier from the training data
SCREAMING_SNAKE_CASE__ = xgboost(snake_case__ , snake_case__ )
# Display the confusion matrix of the classifier with both training and test sets
ConfusionMatrixDisplay.from_estimator(
snake_case__ , snake_case__ , snake_case__ , display_labels=snake_case__ , cmap="""Blues""" , normalize="""true""" , )
plt.title("""Normalized Confusion Matrix - IRIS Dataset""" )
plt.show()
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
main()
| 165 | 0 |
'''simple docstring'''
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configuration_fsmt import FSMTConfig
from transformers.optimization import (
Adafactor,
AdamW,
get_constant_schedule,
get_constant_schedule_with_warmup,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
)
from transformers.trainer_pt_utils import get_tpu_sampler
from transformers.training_args import ParallelMode
from transformers.utils import is_torch_tpu_available
if is_fairscale_available():
from fairscale.optim import OSS
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''linear''': get_linear_schedule_with_warmup,
'''cosine''': get_cosine_schedule_with_warmup,
'''cosine_w_restarts''': get_cosine_with_hard_restarts_schedule_with_warmup,
'''polynomial''': get_polynomial_decay_schedule_with_warmup,
'''constant''': get_constant_schedule,
'''constant_w_warmup''': get_constant_schedule_with_warmup,
}
class _UpperCAmelCase ( UpperCAmelCase_ ):
"""simple docstring"""
def __init__( self : str , __UpperCAmelCase : Any=None , __UpperCAmelCase : Optional[Any]=None , *__UpperCAmelCase : Optional[Any] , **__UpperCAmelCase : Optional[int] ):
'''simple docstring'''
super().__init__(*__lowercase , **__lowercase )
if config is None:
assert isinstance(self.model , __lowercase ), (
"If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
f''' {self.model.__class__}'''
)
_A = self.model.config
else:
_A = config
_A = data_args
_A = self.config.tgt_vocab_size if isinstance(self.config , __lowercase ) else self.config.vocab_size
if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
assert self.config.pad_token_id is not None, (
"Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss"
" calculation or doing label smoothing."
)
if self.config.pad_token_id is None and self.config.eos_token_id is not None:
logger.warning(
f'''The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for'''
" padding.." )
if self.args.label_smoothing == 0:
_A = torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id )
else:
# dynamically import label_smoothed_nll_loss
from utils import label_smoothed_nll_loss
_A = label_smoothed_nll_loss
def lowerCAmelCase ( self : Optional[int] , __UpperCAmelCase : List[str] ):
'''simple docstring'''
if self.optimizer is None:
_A = ['''bias''', '''LayerNorm.weight''']
_A = [
{
'''params''': [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay )],
'''weight_decay''': self.args.weight_decay,
},
{
'''params''': [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay )],
'''weight_decay''': 0.0,
},
]
_A = Adafactor if self.args.adafactor else AdamW
if self.args.adafactor:
_A = Adafactor
_A = {'''scale_parameter''': False, '''relative_step''': False}
else:
_A = AdamW
_A = {
'''betas''': (self.args.adam_betaa, self.args.adam_betaa),
'''eps''': self.args.adam_epsilon,
}
_A = self.args.learning_rate
if self.sharded_ddp:
_A = OSS(
params=__lowercase , optim=__lowercase , **__lowercase , )
else:
_A = optimizer_cls(__lowercase , **__lowercase )
if self.lr_scheduler is None:
_A = self._get_lr_scheduler(__lowercase )
else: # ignoring --lr_scheduler
logger.warning("scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored." )
def lowerCAmelCase ( self : Optional[Any] , __UpperCAmelCase : List[Any] ):
'''simple docstring'''
_A = arg_to_scheduler[self.args.lr_scheduler]
if self.args.lr_scheduler == "constant":
_A = schedule_func(self.optimizer )
elif self.args.lr_scheduler == "constant_w_warmup":
_A = schedule_func(self.optimizer , num_warmup_steps=self.args.warmup_steps )
else:
_A = schedule_func(
self.optimizer , num_warmup_steps=self.args.warmup_steps , num_training_steps=__lowercase )
return scheduler
def lowerCAmelCase ( self : List[str] ):
'''simple docstring'''
if isinstance(self.train_dataset , torch.utils.data.IterableDataset ):
return None
elif is_torch_tpu_available():
return get_tpu_sampler(self.train_dataset )
else:
if self.args.sortish_sampler:
self.train_dataset.make_sortish_sampler(
self.args.per_device_train_batch_size , distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED) , )
return (
RandomSampler(self.train_dataset )
if self.args.local_rank == -1
else DistributedSampler(self.train_dataset )
)
def lowerCAmelCase ( self : int , __UpperCAmelCase : Optional[int] , __UpperCAmelCase : Dict , __UpperCAmelCase : str ):
'''simple docstring'''
if self.args.label_smoothing == 0:
if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
# force training to ignore pad token
_A = model(**__lowercase , use_cache=__lowercase )[0]
_A = self.loss_fn(logits.view(-1 , logits.shape[-1] ) , labels.view(-1 ) )
else:
# compute usual loss via models
_A = model(**__lowercase , labels=__lowercase , use_cache=__lowercase )[:2]
else:
# compute label smoothed loss
_A = model(**__lowercase , use_cache=__lowercase )[0]
_A = torch.nn.functional.log_softmax(__lowercase , dim=-1 )
_A = self.loss_fn(__lowercase , __lowercase , self.args.label_smoothing , ignore_index=self.config.pad_token_id )
return loss, logits
def lowerCAmelCase ( self : Dict , __UpperCAmelCase : int , __UpperCAmelCase : Any ):
'''simple docstring'''
_A = inputs.pop("labels" )
_A = self._compute_loss(__lowercase , __lowercase , __lowercase )
return loss
def lowerCAmelCase ( self : Tuple , __UpperCAmelCase : Dict , __UpperCAmelCase : Optional[Any] , __UpperCAmelCase : Optional[Any] , __UpperCAmelCase : Any = None , ):
'''simple docstring'''
_A = self._prepare_inputs(__lowercase )
_A = {
'''max_length''': self.data_args.val_max_target_length
if self.data_args is not None
else self.config.max_length,
'''num_beams''': self.data_args.eval_beams if self.data_args is not None else self.config.num_beams,
}
if self.args.predict_with_generate and not self.args.prediction_loss_only:
_A = self.model.generate(
inputs["input_ids"] , attention_mask=inputs["attention_mask"] , **__lowercase , )
# in case the batch is shorter than max length, the output should be padded
if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
_A = self._pad_tensors_to_max_len(__lowercase , gen_kwargs["max_length"] )
_A = inputs.pop("labels" )
with torch.no_grad():
# compute loss on predict data
_A = self._compute_loss(__lowercase , __lowercase , __lowercase )
_A = loss.mean().detach()
if self.args.prediction_loss_only:
return (loss, None, None)
_A = generated_tokens if self.args.predict_with_generate else logits
if labels.shape[-1] < gen_kwargs["max_length"]:
_A = self._pad_tensors_to_max_len(__lowercase , gen_kwargs["max_length"] )
return (loss, logits, labels)
def lowerCAmelCase ( self : int , __UpperCAmelCase : Union[str, Any] , __UpperCAmelCase : Union[str, Any] ):
'''simple docstring'''
_A = self.config.pad_token_id if self.config.pad_token_id is not None else self.config.eos_token_id
if pad_token_id is None:
raise ValueError(
"Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be"
f''' padded to `max_length`={max_length}''' )
_A = pad_token_id * torch.ones(
(tensor.shape[0], max_length) , dtype=tensor.dtype , device=tensor.device )
_A = tensor
return padded_tensor
| 367 |
'''simple docstring'''
import operator
def __lowercase ( __lowercase , __lowercase = False , __lowercase = None ) -> list:
'''simple docstring'''
_A = operator.lt if reverse else operator.gt
_A = solution or []
if not arr:
return solution
_A = [arr.pop(0 )]
for i, item in enumerate(__lowercase ):
if _operator(__lowercase , sublist[-1] ):
sublist.append(__lowercase )
arr.pop(__lowercase )
# merging sublist into solution list
if not solution:
solution.extend(__lowercase )
else:
while sublist:
_A = sublist.pop(0 )
for i, xx in enumerate(__lowercase ):
if not _operator(__lowercase , __lowercase ):
solution.insert(__lowercase , __lowercase )
break
else:
solution.append(__lowercase )
strand_sort(__lowercase , __lowercase , __lowercase )
return solution
if __name__ == "__main__":
assert strand_sort([4, 3, 5, 1, 2]) == [1, 2, 3, 4, 5]
assert strand_sort([4, 3, 5, 1, 2], reverse=True) == [5, 4, 3, 2, 1]
| 174 | 0 |
import logging
from transformers.configuration_utils import PretrainedConfig
UpperCamelCase = logging.getLogger(__name__)
class snake_case_ ( __A ):
__A : List[Any] = "masked_bert"
def __init__( self : Optional[int] , lowercase_ : List[str]=3_05_22 , lowercase_ : Optional[Any]=7_68 , lowercase_ : Tuple=12 , lowercase_ : str=12 , lowercase_ : List[Any]=30_72 , lowercase_ : Union[str, Any]="gelu" , lowercase_ : Dict=0.1 , lowercase_ : Union[str, Any]=0.1 , lowercase_ : List[Any]=5_12 , lowercase_ : List[str]=2 , lowercase_ : List[str]=0.02 , lowercase_ : int=1E-12 , lowercase_ : Any=0 , lowercase_ : List[Any]="topK" , lowercase_ : List[str]="constant" , lowercase_ : Dict=0.0 , **lowercase_ : Union[str, Any] , ) -> Optional[Any]:
super().__init__(pad_token_id=lowercase_ , **lowercase_ )
lowercase__ : int = vocab_size
lowercase__ : Optional[int] = hidden_size
lowercase__ : Any = num_hidden_layers
lowercase__ : Dict = num_attention_heads
lowercase__ : Optional[int] = hidden_act
lowercase__ : Any = intermediate_size
lowercase__ : Optional[Any] = hidden_dropout_prob
lowercase__ : int = attention_probs_dropout_prob
lowercase__ : str = max_position_embeddings
lowercase__ : str = type_vocab_size
lowercase__ : Optional[int] = initializer_range
lowercase__ : Optional[int] = layer_norm_eps
lowercase__ : Tuple = pruning_method
lowercase__ : Union[str, Any] = mask_init
lowercase__ : List[str] = mask_scale
| 87 |
import os
import unittest
from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class __snake_case ( a , unittest.TestCase ):
UpperCAmelCase__ : Any = PhobertTokenizer
UpperCAmelCase__ : List[str] = False
def lowerCamelCase ( self : str):
"""simple docstring"""
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
UpperCAmelCase_ = ['''T@@''', '''i''', '''I''', '''R@@''', '''r''', '''e@@''']
UpperCAmelCase_ = dict(zip(_snake_case , range(len(_snake_case))))
UpperCAmelCase_ = ['''#version: 0.2''', '''l à</w>''']
UpperCAmelCase_ = {'''unk_token''': '''<unk>'''}
UpperCAmelCase_ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''])
UpperCAmelCase_ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''merges_file'''])
with open(self.vocab_file , '''w''' , encoding='''utf-8''') as fp:
for token in vocab_tokens:
fp.write(F"""{token} {vocab_tokens[token]}\n""")
with open(self.merges_file , '''w''' , encoding='''utf-8''') as fp:
fp.write('''\n'''.join(_snake_case))
def lowerCamelCase ( self : int , **_snake_case : Any):
"""simple docstring"""
kwargs.update(self.special_tokens_map)
return PhobertTokenizer.from_pretrained(self.tmpdirname , **_snake_case)
def lowerCamelCase ( self : Optional[Any] , _snake_case : Union[str, Any]):
"""simple docstring"""
UpperCAmelCase_ = '''Tôi là VinAI Research'''
UpperCAmelCase_ = '''T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>'''
return input_text, output_text
def lowerCamelCase ( self : List[str]):
"""simple docstring"""
UpperCAmelCase_ = PhobertTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map)
UpperCAmelCase_ = '''Tôi là VinAI Research'''
UpperCAmelCase_ = '''T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h'''.split()
UpperCAmelCase_ = tokenizer.tokenize(_snake_case)
print(_snake_case)
self.assertListEqual(_snake_case , _snake_case)
UpperCAmelCase_ = tokens + [tokenizer.unk_token]
UpperCAmelCase_ = [4, 3, 5, 3, 3, 3, 3, 3, 3, 6, 7, 9, 3, 9, 3, 3, 3, 3, 3]
self.assertListEqual(tokenizer.convert_tokens_to_ids(_snake_case) , _snake_case)
| 51 | 0 |
'''simple docstring'''
class _lowerCAmelCase :
'''simple docstring'''
def __init__(self , UpperCAmelCase ) -> List[Any]:
_snake_case = val
_snake_case = None
_snake_case = None
def lowercase (self , UpperCAmelCase ) -> Tuple:
if self.val:
if val < self.val:
if self.left is None:
_snake_case = Node(UpperCAmelCase )
else:
self.left.insert(UpperCAmelCase )
elif val > self.val:
if self.right is None:
_snake_case = Node(UpperCAmelCase )
else:
self.right.insert(UpperCAmelCase )
else:
_snake_case = val
def __SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
# Recursive traversal
if root:
inorder(root.left , _SCREAMING_SNAKE_CASE )
res.append(root.val )
inorder(root.right , _SCREAMING_SNAKE_CASE )
def __SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE ):
# Build BST
if len(_SCREAMING_SNAKE_CASE ) == 0:
return arr
_snake_case = Node(arr[0] )
for i in range(1 , len(_SCREAMING_SNAKE_CASE ) ):
root.insert(arr[i] )
# Traverse BST in order.
_snake_case = []
inorder(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
return res
if __name__ == "__main__":
print(tree_sort([10, 1, 3, 2, 9, 14, 13])) | 270 |
'''simple docstring'''
import qiskit
def __SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
_snake_case = qiskit.Aer.get_backend("""aer_simulator""" )
# Create a Quantum Circuit acting on the q register
_snake_case = qiskit.QuantumCircuit(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# 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
_snake_case = qiskit.execute(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , shots=1000 )
# Return the histogram data of the results of the experiment.
return job.result().get_counts(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
__lowerCAmelCase = single_qubit_measure(2, 2)
print(f'''Total count for various states are: {counts}''') | 270 | 1 |
import pytest
import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize('''dataset_size''' , [None, 400 * 2**20, 600 * 2**20] )
@pytest.mark.parametrize('''input_in_memory_max_size''' , ['''default''', 0, 100 * 2**20, 900 * 2**20] )
def _UpperCamelCase ( lowercase__ , lowercase__ , lowercase__ ):
if input_in_memory_max_size != "default":
monkeypatch.setattr(datasets.config , '''IN_MEMORY_MAX_SIZE''' , lowercase__ )
__SCREAMING_SNAKE_CASE : Any = datasets.config.IN_MEMORY_MAX_SIZE
if input_in_memory_max_size == "default":
assert in_memory_max_size == 0
else:
assert in_memory_max_size == input_in_memory_max_size
if dataset_size and in_memory_max_size:
__SCREAMING_SNAKE_CASE : List[Any] = dataset_size < in_memory_max_size
else:
__SCREAMING_SNAKE_CASE : Optional[Any] = False
__SCREAMING_SNAKE_CASE : Any = is_small_dataset(lowercase__ )
assert result == expected
| 9 |
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
__lowerCAmelCase : List[str] ='true'
def _UpperCamelCase ( lowercase__ , lowercase__=82 , lowercase__=16 ):
set_seed(42 )
__SCREAMING_SNAKE_CASE : Optional[int] = RegressionModel()
__SCREAMING_SNAKE_CASE : Optional[int] = deepcopy(lowercase__ )
__SCREAMING_SNAKE_CASE : Any = RegressionDataset(length=lowercase__ )
__SCREAMING_SNAKE_CASE : Union[str, Any] = DataLoader(lowercase__ , batch_size=lowercase__ )
model.to(accelerator.device )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Optional[Any] = accelerator.prepare(lowercase__ , lowercase__ )
return model, ddp_model, dataloader
def _UpperCamelCase ( lowercase__ , lowercase__=False ):
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoTokenizer.from_pretrained('''hf-internal-testing/mrpc-bert-base-cased''' )
__SCREAMING_SNAKE_CASE : str = load_dataset('''glue''' , '''mrpc''' , split='''validation''' )
def tokenize_function(lowercase__ ):
__SCREAMING_SNAKE_CASE : Dict = tokenizer(examples['''sentence1'''] , examples['''sentence2'''] , truncation=lowercase__ , max_length=lowercase__ )
return outputs
with accelerator.main_process_first():
__SCREAMING_SNAKE_CASE : Tuple = dataset.map(
lowercase__ , batched=lowercase__ , remove_columns=['''idx''', '''sentence1''', '''sentence2'''] , )
__SCREAMING_SNAKE_CASE : List[Any] = tokenized_datasets.rename_column('''label''' , '''labels''' )
def collate_fn(lowercase__ ):
if use_longest:
return tokenizer.pad(lowercase__ , padding='''longest''' , return_tensors='''pt''' )
return tokenizer.pad(lowercase__ , padding='''max_length''' , max_length=128 , return_tensors='''pt''' )
return DataLoader(lowercase__ , shuffle=lowercase__ , collate_fn=lowercase__ , batch_size=16 )
def _UpperCamelCase ( lowercase__ , lowercase__ ):
__SCREAMING_SNAKE_CASE : str = Accelerator(dispatch_batches=lowercase__ , split_batches=lowercase__ )
__SCREAMING_SNAKE_CASE : Optional[int] = get_dataloader(lowercase__ , not dispatch_batches )
__SCREAMING_SNAKE_CASE : List[str] = AutoModelForSequenceClassification.from_pretrained(
'''hf-internal-testing/mrpc-bert-base-cased''' , return_dict=lowercase__ )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : List[str] = accelerator.prepare(lowercase__ , lowercase__ )
return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator
def _UpperCamelCase ( lowercase__ , lowercase__ , lowercase__ ):
__SCREAMING_SNAKE_CASE : List[str] = []
for batch in dataloader:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Optional[Any] = batch.values()
with torch.no_grad():
__SCREAMING_SNAKE_CASE : Dict = model(lowercase__ )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : str = accelerator.gather_for_metrics((logit, target) )
logits_and_targets.append((logit, target) )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : str = [], []
for logit, targ in logits_and_targets:
logits.append(lowercase__ )
targs.append(lowercase__ )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Any = torch.cat(lowercase__ ), torch.cat(lowercase__ )
return logits, targs
def _UpperCamelCase ( lowercase__ , lowercase__=82 , lowercase__=False , lowercase__=False , lowercase__=16 ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Tuple = get_basic_setup(lowercase__ , lowercase__ , lowercase__ )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : List[str] = generate_predictions(lowercase__ , lowercase__ , lowercase__ )
assert (
len(lowercase__ ) == num_samples
), F'''Unexpected number of inputs:\n Expected: {num_samples}\n Actual: {len(lowercase__ )}'''
def _UpperCamelCase ( lowercase__ = False , lowercase__ = False ):
__SCREAMING_SNAKE_CASE : Optional[Any] = evaluate.load('''glue''' , '''mrpc''' )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : List[Any] = get_mrpc_setup(lowercase__ , lowercase__ )
# First do baseline
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Optional[Any] = setup['''no''']
model.to(lowercase__ )
model.eval()
for batch in dataloader:
batch.to(lowercase__ )
with torch.inference_mode():
__SCREAMING_SNAKE_CASE : Dict = model(**lowercase__ )
__SCREAMING_SNAKE_CASE : Dict = outputs.logits.argmax(dim=-1 )
metric.add_batch(predictions=lowercase__ , references=batch['''labels'''] )
__SCREAMING_SNAKE_CASE : int = metric.compute()
# Then do distributed
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Dict = setup['''ddp''']
model.eval()
for batch in dataloader:
with torch.inference_mode():
__SCREAMING_SNAKE_CASE : int = model(**lowercase__ )
__SCREAMING_SNAKE_CASE : str = outputs.logits.argmax(dim=-1 )
__SCREAMING_SNAKE_CASE : Any = batch['''labels''']
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Dict = accelerator.gather_for_metrics((preds, references) )
metric.add_batch(predictions=lowercase__ , references=lowercase__ )
__SCREAMING_SNAKE_CASE : List[Any] = 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 _UpperCamelCase ( ):
__SCREAMING_SNAKE_CASE : Dict = Accelerator(split_batches=lowercase__ , dispatch_batches=lowercase__ )
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(lowercase__ , lowercase__ )
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 : List[Any] = Accelerator(split_batches=lowercase__ , dispatch_batches=lowercase__ )
if accelerator.is_local_main_process:
print(F'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99''' )
test_torch_metrics(lowercase__ , 99 )
accelerator.state._reset_state()
if accelerator.is_local_main_process:
print('''**Test last batch is not dropped when perfectly divisible**''' )
__SCREAMING_SNAKE_CASE : Tuple = Accelerator()
test_torch_metrics(lowercase__ , 512 )
accelerator.state._reset_state()
def _UpperCamelCase ( lowercase__ ):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 9 | 1 |
import unittest
from transformers import DebertaConfig, 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
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
DebertaForMaskedLM,
DebertaForQuestionAnswering,
DebertaForSequenceClassification,
DebertaForTokenClassification,
DebertaModel,
)
from transformers.models.deberta.modeling_deberta import DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST
class __lowercase ( A ):
'''simple docstring'''
def __init__( self : List[Any] , _a : List[str] , _a : List[str]=13 , _a : Union[str, Any]=7 , _a : Union[str, Any]=True , _a : Dict=True , _a : Union[str, Any]=True , _a : Union[str, Any]=True , _a : Optional[Any]=99 , _a : Optional[Any]=32 , _a : List[Any]=5 , _a : str=4 , _a : str=37 , _a : Any="gelu" , _a : Any=0.1 , _a : Tuple=0.1 , _a : str=512 , _a : List[str]=16 , _a : Union[str, Any]=2 , _a : List[Any]=0.02 , _a : Dict=False , _a : Any=True , _a : Optional[int]="None" , _a : Optional[Any]=3 , _a : Union[str, Any]=4 , _a : int=None , ):
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__ = relative_attention
UpperCamelCase__ = position_biased_input
UpperCamelCase__ = pos_att_type
UpperCamelCase__ = scope
def A_ ( self : Optional[int] ):
UpperCamelCase__ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCamelCase__ = None
if self.use_input_mask:
UpperCamelCase__ = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
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 : List[str] ):
return DebertaConfig(
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 , initializer_range=self.initializer_range , relative_attention=self.relative_attention , position_biased_input=self.position_biased_input , pos_att_type=self.pos_att_type , )
def A_ ( self : Dict ):
UpperCamelCase__ = self.get_config()
UpperCamelCase__ = 300
return config
def A_ ( self : Dict , _a : List[str] ):
self.parent.assertListEqual(list(result.loss.size() ) , [] )
def A_ ( self : Dict , _a : Dict , _a : int , _a : Dict , _a : List[str] , _a : List[str] , _a : Any , _a : Dict ):
UpperCamelCase__ = DebertaModel(config=_a )
model.to(_a )
model.eval()
UpperCamelCase__ = model(_a , attention_mask=_a , token_type_ids=_a )[0]
UpperCamelCase__ = model(_a , token_type_ids=_a )[0]
UpperCamelCase__ = model(_a )[0]
self.parent.assertListEqual(list(sequence_output.size() ) , [self.batch_size, self.seq_length, self.hidden_size] )
def A_ ( self : Dict , _a : List[str] , _a : Optional[int] , _a : str , _a : List[Any] , _a : Dict , _a : Dict , _a : Dict ):
UpperCamelCase__ = DebertaForMaskedLM(config=_a )
model.to(_a )
model.eval()
UpperCamelCase__ = model(_a , attention_mask=_a , token_type_ids=_a , labels=_a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def A_ ( self : Union[str, Any] , _a : Union[str, Any] , _a : int , _a : Tuple , _a : List[Any] , _a : Any , _a : List[Any] , _a : Dict ):
UpperCamelCase__ = self.num_labels
UpperCamelCase__ = DebertaForSequenceClassification(_a )
model.to(_a )
model.eval()
UpperCamelCase__ = model(_a , attention_mask=_a , token_type_ids=_a , labels=_a )
self.parent.assertListEqual(list(result.logits.size() ) , [self.batch_size, self.num_labels] )
self.check_loss_output(_a )
def A_ ( self : List[Any] , _a : List[str] , _a : Union[str, Any] , _a : Any , _a : Dict , _a : List[str] , _a : Optional[int] , _a : Union[str, Any] ):
UpperCamelCase__ = self.num_labels
UpperCamelCase__ = DebertaForTokenClassification(config=_a )
model.to(_a )
model.eval()
UpperCamelCase__ = model(_a , attention_mask=_a , token_type_ids=_a , labels=_a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def A_ ( self : int , _a : int , _a : Tuple , _a : Tuple , _a : int , _a : List[str] , _a : List[Any] , _a : Dict ):
UpperCamelCase__ = DebertaForQuestionAnswering(config=_a )
model.to(_a )
model.eval()
UpperCamelCase__ = model(
_a , attention_mask=_a , token_type_ids=_a , start_positions=_a , end_positions=_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 A_ ( self : List[Any] ):
UpperCamelCase__ = self.prepare_config_and_inputs()
(
(
UpperCamelCase__
) , (
UpperCamelCase__
) , (
UpperCamelCase__
) , (
UpperCamelCase__
) , (
UpperCamelCase__
) , (
UpperCamelCase__
) , (
UpperCamelCase__
) ,
) = config_and_inputs
UpperCamelCase__ = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class __lowercase ( A, A, unittest.TestCase ):
'''simple docstring'''
_A : Optional[Any] = (
(
DebertaModel,
DebertaForMaskedLM,
DebertaForSequenceClassification,
DebertaForTokenClassification,
DebertaForQuestionAnswering,
)
if is_torch_available()
else ()
)
_A : Optional[int] = (
{
'''feature-extraction''': DebertaModel,
'''fill-mask''': DebertaForMaskedLM,
'''question-answering''': DebertaForQuestionAnswering,
'''text-classification''': DebertaForSequenceClassification,
'''token-classification''': DebertaForTokenClassification,
'''zero-shot''': DebertaForSequenceClassification,
}
if is_torch_available()
else {}
)
_A : List[str] = True
_A : List[Any] = False
_A : Optional[Any] = False
_A : Any = False
_A : List[str] = False
def A_ ( self : int ):
UpperCamelCase__ = DebertaModelTester(self )
UpperCamelCase__ = ConfigTester(self , config_class=_a , hidden_size=37 )
def A_ ( self : str ):
self.config_tester.run_common_tests()
def A_ ( self : List[str] ):
UpperCamelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_model(*_a )
def A_ ( self : str ):
UpperCamelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_sequence_classification(*_a )
def A_ ( self : Optional[Any] ):
UpperCamelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_masked_lm(*_a )
def A_ ( self : str ):
UpperCamelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_question_answering(*_a )
def A_ ( self : Optional[Any] ):
UpperCamelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_token_classification(*_a )
@slow
def A_ ( self : Tuple ):
for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCamelCase__ = DebertaModel.from_pretrained(_a )
self.assertIsNotNone(_a )
@require_torch
@require_sentencepiece
@require_tokenizers
class __lowercase ( unittest.TestCase ):
'''simple docstring'''
@unittest.skip(reason='''Model not available yet''' )
def A_ ( self : List[Any] ):
pass
@slow
def A_ ( self : Optional[Any] ):
UpperCamelCase__ = DebertaModel.from_pretrained('''microsoft/deberta-base''' )
UpperCamelCase__ = torch.tensor([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]] )
UpperCamelCase__ = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
UpperCamelCase__ = model(_a , attention_mask=_a )[0]
# compare the actual values for a slice.
UpperCamelCase__ = torch.tensor(
[[[-0.5986, -0.8055, -0.8462], [1.4484, -0.9348, -0.8059], [0.3123, 0.0032, -1.4131]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , _a , atol=1E-4 ) , F"""{output[:, 1:4, 1:4]}""" )
| 35 | import warnings
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
lowercase = logging.get_logger(__name__) # pylint: disable=invalid-name
def lowerCamelCase_ ( UpperCamelCase__ : Union[List, PIL.Image.Image, torch.Tensor] ):
'''simple docstring'''
warnings.warn(
'''The preprocess method is deprecated and will be removed in a future version. Please'''
''' use VaeImageProcessor.preprocess instead''', UpperCamelCase__, )
if isinstance(UpperCamelCase__, torch.Tensor ):
return image
elif isinstance(UpperCamelCase__, PIL.Image.Image ):
UpperCamelCase__ = [image]
if isinstance(image[0], PIL.Image.Image ):
UpperCamelCase__ , UpperCamelCase__ = image[0].size
UpperCamelCase__ , UpperCamelCase__ = (x - x % 8 for x in (w, h)) # resize to integer multiple of 8
UpperCamelCase__ = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION['''lanczos'''] ) )[None, :] for i in image]
UpperCamelCase__ = np.concatenate(UpperCamelCase__, axis=0 )
UpperCamelCase__ = np.array(UpperCamelCase__ ).astype(np.floataa ) / 255.0
UpperCamelCase__ = image.transpose(0, 3, 1, 2 )
UpperCamelCase__ = 2.0 * image - 1.0
UpperCamelCase__ = torch.from_numpy(UpperCamelCase__ )
elif isinstance(image[0], torch.Tensor ):
UpperCamelCase__ = torch.cat(UpperCamelCase__, dim=0 )
return image
def lowerCamelCase_ ( UpperCamelCase__ : Union[List, PIL.Image.Image, torch.Tensor] ):
'''simple docstring'''
if isinstance(UpperCamelCase__, torch.Tensor ):
return mask
elif isinstance(UpperCamelCase__, PIL.Image.Image ):
UpperCamelCase__ = [mask]
if isinstance(mask[0], PIL.Image.Image ):
UpperCamelCase__ , UpperCamelCase__ = mask[0].size
UpperCamelCase__ , UpperCamelCase__ = (x - x % 32 for x in (w, h)) # resize to integer multiple of 32
UpperCamelCase__ = [np.array(m.convert('''L''' ).resize((w, h), resample=PIL_INTERPOLATION['''nearest'''] ) )[None, :] for m in mask]
UpperCamelCase__ = np.concatenate(UpperCamelCase__, axis=0 )
UpperCamelCase__ = mask.astype(np.floataa ) / 255.0
UpperCamelCase__ = 0
UpperCamelCase__ = 1
UpperCamelCase__ = torch.from_numpy(UpperCamelCase__ )
elif isinstance(mask[0], torch.Tensor ):
UpperCamelCase__ = torch.cat(UpperCamelCase__, dim=0 )
return mask
class __lowercase ( A ):
'''simple docstring'''
_A : UNetaDModel
_A : RePaintScheduler
def __init__( self : Tuple , _a : Any , _a : Tuple ):
super().__init__()
self.register_modules(unet=_a , scheduler=_a )
@torch.no_grad()
def __call__( self : Optional[int] , _a : Union[torch.Tensor, PIL.Image.Image] , _a : Union[torch.Tensor, PIL.Image.Image] , _a : int = 250 , _a : float = 0.0 , _a : int = 10 , _a : int = 10 , _a : Optional[Union[torch.Generator, List[torch.Generator]]] = None , _a : Optional[str] = "pil" , _a : bool = True , ):
UpperCamelCase__ = image
UpperCamelCase__ = _preprocess_image(_a )
UpperCamelCase__ = original_image.to(device=self.device , dtype=self.unet.dtype )
UpperCamelCase__ = _preprocess_mask(_a )
UpperCamelCase__ = mask_image.to(device=self.device , dtype=self.unet.dtype )
UpperCamelCase__ = original_image.shape[0]
# sample gaussian noise to begin the loop
if isinstance(_a , _a ) and len(_a ) != batch_size:
raise ValueError(
F"""You have passed a list of generators of length {len(_a )}, but requested an effective batch"""
F""" size of {batch_size}. Make sure the batch size matches the length of the generators.""" )
UpperCamelCase__ = original_image.shape
UpperCamelCase__ = randn_tensor(_a , generator=_a , device=self.device , dtype=self.unet.dtype )
# set step values
self.scheduler.set_timesteps(_a , _a , _a , self.device )
UpperCamelCase__ = eta
UpperCamelCase__ = self.scheduler.timesteps[0] + 1
UpperCamelCase__ = generator[0] if isinstance(_a , _a ) else generator
for i, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
if t < t_last:
# predict the noise residual
UpperCamelCase__ = self.unet(_a , _a ).sample
# compute previous image: x_t -> x_t-1
UpperCamelCase__ = self.scheduler.step(_a , _a , _a , _a , _a , _a ).prev_sample
else:
# compute the reverse: x_t-1 -> x_t
UpperCamelCase__ = self.scheduler.undo_step(_a , _a , _a )
UpperCamelCase__ = t
UpperCamelCase__ = (image / 2 + 0.5).clamp(0 , 1 )
UpperCamelCase__ = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
UpperCamelCase__ = self.numpy_to_pil(_a )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=_a )
| 35 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
snake_case_ = {
"""configuration_bloom""": ["""BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BloomConfig""", """BloomOnnxConfig"""],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case_ = ["""BloomTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case_ = [
"""BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""BloomForCausalLM""",
"""BloomModel""",
"""BloomPreTrainedModel""",
"""BloomForSequenceClassification""",
"""BloomForTokenClassification""",
"""BloomForQuestionAnswering""",
]
if TYPE_CHECKING:
from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_bloom_fast import BloomTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_bloom import (
BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
BloomForCausalLM,
BloomForQuestionAnswering,
BloomForSequenceClassification,
BloomForTokenClassification,
BloomModel,
BloomPreTrainedModel,
)
else:
import sys
snake_case_ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 78 |
'''simple docstring'''
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
lowercase : List[str] = logging.get_logger("transformers.models.speecht5")
def SCREAMING_SNAKE_CASE__ ( __A , __A , __A ) -> Dict:
hf_model.apply_weight_norm()
_snake_case = checkpoint['input_conv.weight_g']
_snake_case = checkpoint['input_conv.weight_v']
_snake_case = checkpoint['input_conv.bias']
for i in range(len(config.upsample_rates ) ):
_snake_case = checkpoint[F'upsamples.{i}.1.weight_g']
_snake_case = checkpoint[F'upsamples.{i}.1.weight_v']
_snake_case = checkpoint[F'upsamples.{i}.1.bias']
for i in range(len(config.upsample_rates ) * len(config.resblock_kernel_sizes ) ):
for j in range(len(config.resblock_dilation_sizes ) ):
_snake_case = checkpoint[F'blocks.{i}.convs1.{j}.1.weight_g']
_snake_case = checkpoint[F'blocks.{i}.convs1.{j}.1.weight_v']
_snake_case = checkpoint[F'blocks.{i}.convs1.{j}.1.bias']
_snake_case = checkpoint[F'blocks.{i}.convs2.{j}.1.weight_g']
_snake_case = checkpoint[F'blocks.{i}.convs2.{j}.1.weight_v']
_snake_case = checkpoint[F'blocks.{i}.convs2.{j}.1.bias']
_snake_case = checkpoint['output_conv.1.weight_g']
_snake_case = checkpoint['output_conv.1.weight_v']
_snake_case = checkpoint['output_conv.1.bias']
hf_model.remove_weight_norm()
@torch.no_grad()
def SCREAMING_SNAKE_CASE__ ( __A , __A , __A , __A=None , __A=None , ) -> List[Any]:
if config_path is not None:
_snake_case = SpeechTaHifiGanConfig.from_pretrained(__A )
else:
_snake_case = SpeechTaHifiGanConfig()
_snake_case = SpeechTaHifiGan(__A )
_snake_case = torch.load(__A )
load_weights(orig_checkpoint['model']['generator'] , __A , __A )
_snake_case = np.load(__A )
_snake_case = stats[0].reshape(-1 )
_snake_case = stats[1].reshape(-1 )
_snake_case = torch.from_numpy(__A ).float()
_snake_case = torch.from_numpy(__A ).float()
model.save_pretrained(__A )
if repo_id:
print('Pushing to the hub...' )
model.push_to_hub(__A )
if __name__ == "__main__":
lowercase : Dict = argparse.ArgumentParser()
parser.add_argument("--checkpoint_path", required=True, default=None, type=str, help="Path to original checkpoint")
parser.add_argument("--stats_path", required=True, default=None, type=str, help="Path to stats.npy file")
parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
parser.add_argument(
"--pytorch_dump_folder_path", required=True, default=None, type=str, help="Path to the output PyTorch model."
)
parser.add_argument(
"--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
)
lowercase : List[Any] = parser.parse_args()
convert_hifigan_checkpoint(
args.checkpoint_path,
args.stats_path,
args.pytorch_dump_folder_path,
args.config_path,
args.push_to_hub,
)
| 42 | 0 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
class UpperCamelCase__( lowerCAmelCase , unittest.TestCase ):
__magic_name__ : Any = ShapEPipeline
__magic_name__ : Tuple = ["prompt"]
__magic_name__ : Optional[int] = ["prompt"]
__magic_name__ : Dict = [
"num_images_per_prompt",
"num_inference_steps",
"generator",
"latents",
"guidance_scale",
"frame_size",
"output_type",
"return_dict",
]
__magic_name__ : Optional[int] = False
@property
def a__( self : Optional[Any] )-> Dict:
"""simple docstring"""
return 32
@property
def a__( self : Dict )-> Dict:
"""simple docstring"""
return 32
@property
def a__( self : Optional[Any] )-> List[str]:
"""simple docstring"""
return self.time_input_dim * 4
@property
def a__( self : List[str] )-> str:
"""simple docstring"""
return 8
@property
def a__( self : int )-> Optional[int]:
"""simple docstring"""
UpperCAmelCase = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
return tokenizer
@property
def a__( self : Tuple )-> Optional[int]:
"""simple docstring"""
torch.manual_seed(0 )
UpperCAmelCase = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=self.text_embedder_hidden_size , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
return CLIPTextModelWithProjection(lowerCAmelCase )
@property
def a__( self : str )-> List[str]:
"""simple docstring"""
torch.manual_seed(0 )
UpperCAmelCase = {
'''num_attention_heads''': 2,
'''attention_head_dim''': 16,
'''embedding_dim''': self.time_input_dim,
'''num_embeddings''': 32,
'''embedding_proj_dim''': self.text_embedder_hidden_size,
'''time_embed_dim''': self.time_embed_dim,
'''num_layers''': 1,
'''clip_embed_dim''': self.time_input_dim * 2,
'''additional_embeddings''': 0,
'''time_embed_act_fn''': '''gelu''',
'''norm_in_type''': '''layer''',
'''encoder_hid_proj_type''': None,
'''added_emb_type''': None,
}
UpperCAmelCase = PriorTransformer(**lowerCAmelCase )
return model
@property
def a__( self : List[Any] )-> List[Any]:
"""simple docstring"""
torch.manual_seed(0 )
UpperCAmelCase = {
'''param_shapes''': (
(self.renderer_dim, 93),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
),
'''d_latent''': self.time_input_dim,
'''d_hidden''': self.renderer_dim,
'''n_output''': 12,
'''background''': (
0.1,
0.1,
0.1,
),
}
UpperCAmelCase = ShapERenderer(**lowerCAmelCase )
return model
def a__( self : Any )-> Tuple:
"""simple docstring"""
UpperCAmelCase = self.dummy_prior
UpperCAmelCase = self.dummy_text_encoder
UpperCAmelCase = self.dummy_tokenizer
UpperCAmelCase = self.dummy_renderer
UpperCAmelCase = HeunDiscreteScheduler(
beta_schedule='''exp''' , num_train_timesteps=1024 , prediction_type='''sample''' , use_karras_sigmas=lowerCAmelCase , clip_sample=lowerCAmelCase , clip_sample_range=1.0 , )
UpperCAmelCase = {
'''prior''': prior,
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''renderer''': renderer,
'''scheduler''': scheduler,
}
return components
def a__( self : Tuple , lowerCAmelCase : Union[str, Any] , lowerCAmelCase : List[Any]=0 )-> Optional[Any]:
"""simple docstring"""
if str(lowerCAmelCase ).startswith('''mps''' ):
UpperCAmelCase = torch.manual_seed(lowerCAmelCase )
else:
UpperCAmelCase = torch.Generator(device=lowerCAmelCase ).manual_seed(lowerCAmelCase )
UpperCAmelCase = {
'''prompt''': '''horse''',
'''generator''': generator,
'''num_inference_steps''': 1,
'''frame_size''': 32,
'''output_type''': '''np''',
}
return inputs
def a__( self : int )-> Dict:
"""simple docstring"""
UpperCAmelCase = '''cpu'''
UpperCAmelCase = self.get_dummy_components()
UpperCAmelCase = self.pipeline_class(**lowerCAmelCase )
UpperCAmelCase = pipe.to(lowerCAmelCase )
pipe.set_progress_bar_config(disable=lowerCAmelCase )
UpperCAmelCase = pipe(**self.get_dummy_inputs(lowerCAmelCase ) )
UpperCAmelCase = output.images[0]
UpperCAmelCase = image[0, -3:, -3:, -1]
assert image.shape == (20, 32, 32, 3)
UpperCAmelCase = np.array(
[
0.00039216,
0.00039216,
0.00039216,
0.00039216,
0.00039216,
0.00039216,
0.00039216,
0.00039216,
0.00039216,
] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def a__( self : Union[str, Any] )-> Optional[int]:
"""simple docstring"""
self._test_inference_batch_consistent(batch_sizes=[1, 2] )
def a__( self : Optional[int] )-> str:
"""simple docstring"""
UpperCAmelCase = torch_device == '''cpu'''
UpperCAmelCase = True
self._test_inference_batch_single_identical(
batch_size=2 , test_max_difference=lowerCAmelCase , relax_max_difference=lowerCAmelCase , )
def a__( self : int )-> List[str]:
"""simple docstring"""
UpperCAmelCase = self.get_dummy_components()
UpperCAmelCase = self.pipeline_class(**lowerCAmelCase )
UpperCAmelCase = pipe.to(lowerCAmelCase )
pipe.set_progress_bar_config(disable=lowerCAmelCase )
UpperCAmelCase = 1
UpperCAmelCase = 2
UpperCAmelCase = self.get_dummy_inputs(lowerCAmelCase )
for key in inputs.keys():
if key in self.batch_params:
UpperCAmelCase = batch_size * [inputs[key]]
UpperCAmelCase = pipe(**lowerCAmelCase , num_images_per_prompt=lowerCAmelCase )[0]
assert images.shape[0] == batch_size * num_images_per_prompt
@slow
@require_torch_gpu
class UpperCamelCase__( unittest.TestCase ):
def a__( self : Dict )-> Union[str, Any]:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def a__( self : Optional[Any] )-> Union[str, Any]:
"""simple docstring"""
UpperCAmelCase = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/shap_e/test_shap_e_np_out.npy''' )
UpperCAmelCase = ShapEPipeline.from_pretrained('''openai/shap-e''' )
UpperCAmelCase = pipe.to(lowerCAmelCase )
pipe.set_progress_bar_config(disable=lowerCAmelCase )
UpperCAmelCase = torch.Generator(device=lowerCAmelCase ).manual_seed(0 )
UpperCAmelCase = pipe(
'''a shark''' , generator=lowerCAmelCase , guidance_scale=15.0 , num_inference_steps=64 , frame_size=64 , output_type='''np''' , ).images[0]
assert images.shape == (20, 64, 64, 3)
assert_mean_pixel_difference(lowerCAmelCase , lowerCAmelCase )
| 91 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowercase : List[str] = {
"""configuration_time_series_transformer""": [
"""TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""TimeSeriesTransformerConfig""",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase : Dict = [
"""TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TimeSeriesTransformerForPrediction""",
"""TimeSeriesTransformerModel""",
"""TimeSeriesTransformerPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_time_series_transformer import (
TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
TimeSeriesTransformerConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_time_series_transformer import (
TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TimeSeriesTransformerForPrediction,
TimeSeriesTransformerModel,
TimeSeriesTransformerPreTrainedModel,
)
else:
import sys
_lowercase : Tuple = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 91 | 1 |
"""simple docstring"""
from typing import List
import numpy as np
def lowercase_ ( __UpperCAmelCase ) -> int:
lowerCAmelCase__ : List[Any] = {key: len(__UpperCAmelCase ) for key, value in gen_kwargs.items() if isinstance(__UpperCAmelCase , __UpperCAmelCase )}
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__ : Optional[Any] = max(lists_lengths.values() , default=0 )
return max(1 , __UpperCAmelCase )
def lowercase_ ( __UpperCAmelCase , __UpperCAmelCase ) -> List[range]:
lowerCAmelCase__ : Optional[int] = []
for group_idx in range(__UpperCAmelCase ):
lowerCAmelCase__ : List[Any] = num_shards // max_num_jobs + (group_idx < (num_shards % max_num_jobs))
if num_shards_to_add == 0:
break
lowerCAmelCase__ : Dict = shards_indices_per_group[-1].stop if shards_indices_per_group else 0
lowerCAmelCase__ : Union[str, Any] = range(__UpperCAmelCase , start + num_shards_to_add )
shards_indices_per_group.append(__UpperCAmelCase )
return shards_indices_per_group
def lowercase_ ( __UpperCAmelCase , __UpperCAmelCase ) -> List[dict]:
lowerCAmelCase__ : Union[str, Any] = _number_of_shards_in_gen_kwargs(__UpperCAmelCase )
if num_shards == 1:
return [dict(__UpperCAmelCase )]
else:
lowerCAmelCase__ : List[Any] = _distribute_shards(num_shards=__UpperCAmelCase , max_num_jobs=__UpperCAmelCase )
return [
{
key: [value[shard_idx] for shard_idx in shard_indices_per_group[group_idx]]
if isinstance(__UpperCAmelCase , __UpperCAmelCase )
else value
for key, value in gen_kwargs.items()
}
for group_idx in range(len(__UpperCAmelCase ) )
]
def lowercase_ ( __UpperCAmelCase ) -> dict:
return {
key: [value for gen_kwargs in gen_kwargs_list for value in gen_kwargs[key]]
if isinstance(gen_kwargs_list[0][key] , __UpperCAmelCase )
else gen_kwargs_list[0][key]
for key in gen_kwargs_list[0]
}
def lowercase_ ( __UpperCAmelCase , __UpperCAmelCase ) -> dict:
lowerCAmelCase__ : str = {len(__UpperCAmelCase ) for value in gen_kwargs.values() if isinstance(__UpperCAmelCase , __UpperCAmelCase )}
lowerCAmelCase__ : List[str] = {}
for size in list_sizes:
lowerCAmelCase__ : str = list(range(__UpperCAmelCase ) )
rng.shuffle(indices_per_size[size] )
# Now let's copy the gen_kwargs and shuffle the lists based on their sizes
lowerCAmelCase__ : int = dict(__UpperCAmelCase )
for key, value in shuffled_kwargs.items():
if isinstance(__UpperCAmelCase , __UpperCAmelCase ):
lowerCAmelCase__ : str = [value[i] for i in indices_per_size[len(__UpperCAmelCase )]]
return shuffled_kwargs
| 242 |
"""simple docstring"""
from itertools import product
def lowercase_ ( __UpperCAmelCase , __UpperCAmelCase ) -> list[int]:
lowerCAmelCase__ : Union[str, Any] = sides_number
lowerCAmelCase__ : Optional[int] = max_face_number * dice_number
lowerCAmelCase__ : List[str] = [0] * (max_total + 1)
lowerCAmelCase__ : Union[str, Any] = 1
lowerCAmelCase__ : Optional[int] = range(__UpperCAmelCase , max_face_number + 1 )
for dice_numbers in product(__UpperCAmelCase , repeat=__UpperCAmelCase ):
lowerCAmelCase__ : str = sum(__UpperCAmelCase )
totals_frequencies[total] += 1
return totals_frequencies
def lowercase_ ( ) -> float:
lowerCAmelCase__ : Union[str, Any] = total_frequency_distribution(
sides_number=4 , dice_number=9 )
lowerCAmelCase__ : Tuple = total_frequency_distribution(
sides_number=6 , dice_number=6 )
lowerCAmelCase__ : str = 0
lowerCAmelCase__ : int = 9
lowerCAmelCase__ : Tuple = 4 * 9
lowerCAmelCase__ : Optional[int] = 6
for peter_total in range(__UpperCAmelCase , max_peter_total + 1 ):
peter_wins_count += peter_totals_frequencies[peter_total] * sum(
colin_totals_frequencies[min_colin_total:peter_total] )
lowerCAmelCase__ : Tuple = (4**9) * (6**6)
lowerCAmelCase__ : Union[str, Any] = peter_wins_count / total_games_number
lowerCAmelCase__ : Optional[int] = round(__UpperCAmelCase , ndigits=7 )
return rounded_peter_win_probability
if __name__ == "__main__":
print(f"""{solution() = }""")
| 242 | 1 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
# Register SEW's fairseq modules
from sew_asapp import tasks # noqa: F401
from transformers import (
SEWConfig,
SEWForCTC,
SEWModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = {
'post_extract_proj': 'feature_projection',
'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',
'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',
'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
'self_attn_layer_norm': 'encoder.layers.*.layer_norm',
'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',
'fc2': 'encoder.layers.*.feed_forward.output_dense',
'final_layer_norm': 'encoder.layers.*.final_layer_norm',
'encoder.upsample.0': 'encoder.upsample.projection',
'encoder.layer_norm': 'encoder.layer_norm',
'w2v_model.layer_norm': 'layer_norm',
'w2v_encoder.proj': 'lm_head',
'mask_emb': 'masked_spec_embed',
}
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> List[Any]:
for attribute in key.split(""".""" ):
UpperCamelCase = getattr(__UpperCamelCase , __UpperCamelCase )
if weight_type is not None:
UpperCamelCase = getattr(__UpperCamelCase , __UpperCamelCase ).shape
else:
UpperCamelCase = hf_pointer.shape
assert hf_shape == value.shape, (
F"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"
F" {value.shape} for {full_name}"
)
if weight_type == "weight":
UpperCamelCase = value
elif weight_type == "weight_g":
UpperCamelCase = value
elif weight_type == "weight_v":
UpperCamelCase = value
elif weight_type == "bias":
UpperCamelCase = value
else:
UpperCamelCase = value
logger.info(F"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}." )
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> List[str]:
UpperCamelCase = []
UpperCamelCase = fairseq_model.state_dict()
UpperCamelCase = hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor
for name, value in fairseq_dict.items():
UpperCamelCase = False
if "conv_layers" in name:
load_conv_layer(
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , hf_model.config.feat_extract_norm == """group""" , )
UpperCamelCase = True
else:
for key, mapped_key in MAPPING.items():
UpperCamelCase = """sew.""" + mapped_key if (is_finetuned and mapped_key != """lm_head""") else mapped_key
if key in name or key.split("""w2v_model.""" )[-1] == name.split(""".""" )[0]:
UpperCamelCase = True
if "*" in mapped_key:
UpperCamelCase = name.split(__UpperCamelCase )[0].split(""".""" )[-2]
UpperCamelCase = mapped_key.replace("""*""" , __UpperCamelCase )
if "weight_g" in name:
UpperCamelCase = """weight_g"""
elif "weight_v" in name:
UpperCamelCase = """weight_v"""
elif "weight" in name:
UpperCamelCase = """weight"""
elif "bias" in name:
UpperCamelCase = """bias"""
else:
UpperCamelCase = None
set_recursively(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
continue
if not is_used:
unused_weights.append(__UpperCamelCase )
logger.warning(F"Unused weights: {unused_weights}" )
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> int:
UpperCamelCase = full_name.split("""conv_layers.""" )[-1]
UpperCamelCase = name.split(""".""" )
UpperCamelCase = int(items[0] )
UpperCamelCase = int(items[1] )
if type_id == 0:
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
F"{full_name} has size {value.shape}, but"
F" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."
)
UpperCamelCase = value
logger.info(F"Feat extract conv layer {layer_id} was initialized from {full_name}." )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
F"{full_name} has size {value.shape}, but"
F" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."
)
UpperCamelCase = value
logger.info(F"Feat extract conv layer {layer_id} was initialized from {full_name}." )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
F"{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"
" found."
)
UpperCamelCase = value
logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}." )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
F"{full_name} has size {value.shape}, but"
F" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."
)
UpperCamelCase = value
logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}." )
else:
unused_weights.append(__UpperCamelCase )
def lowercase__ ( __UpperCamelCase , __UpperCamelCase )-> List[str]:
UpperCamelCase = SEWConfig()
if is_finetuned:
UpperCamelCase = model.wav_encoder.wav_model.cfg
else:
UpperCamelCase = model.cfg
UpperCamelCase = fs_config.conv_bias
UpperCamelCase = eval(fs_config.conv_feature_layers )
UpperCamelCase = [x[0] for x in conv_layers]
UpperCamelCase = [x[1] for x in conv_layers]
UpperCamelCase = [x[2] for x in conv_layers]
UpperCamelCase = """gelu"""
UpperCamelCase = """layer""" if fs_config.extractor_mode == """layer_norm""" else """group"""
UpperCamelCase = 0.0
UpperCamelCase = fs_config.activation_fn.name
UpperCamelCase = fs_config.encoder_embed_dim
UpperCamelCase = 0.02
UpperCamelCase = fs_config.encoder_ffn_embed_dim
UpperCamelCase = 1E-5
UpperCamelCase = fs_config.encoder_layerdrop
UpperCamelCase = fs_config.encoder_attention_heads
UpperCamelCase = fs_config.conv_pos_groups
UpperCamelCase = fs_config.conv_pos
UpperCamelCase = len(__UpperCamelCase )
UpperCamelCase = fs_config.encoder_layers
UpperCamelCase = fs_config.squeeze_factor
# take care of any params that are overridden by the Wav2VecCtc model
if is_finetuned:
UpperCamelCase = model.cfg
UpperCamelCase = fs_config.final_dropout
UpperCamelCase = fs_config.layerdrop
UpperCamelCase = fs_config.activation_dropout
UpperCamelCase = fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
UpperCamelCase = fs_config.attention_dropout
UpperCamelCase = fs_config.dropout_input
UpperCamelCase = fs_config.dropout
UpperCamelCase = fs_config.mask_channel_length
UpperCamelCase = fs_config.mask_channel_prob
UpperCamelCase = fs_config.mask_length
UpperCamelCase = fs_config.mask_prob
UpperCamelCase = """Wav2Vec2FeatureExtractor"""
UpperCamelCase = """Wav2Vec2CTCTokenizer"""
return config
@torch.no_grad()
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase=None , __UpperCamelCase=None , __UpperCamelCase=True )-> str:
if is_finetuned:
UpperCamelCase ,UpperCamelCase ,UpperCamelCase = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={"""data""": """/""".join(dict_path.split("""/""" )[:-1] )} )
else:
UpperCamelCase ,UpperCamelCase ,UpperCamelCase = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] )
if config_path is not None:
UpperCamelCase = SEWConfig.from_pretrained(__UpperCamelCase )
else:
UpperCamelCase = convert_config(model[0] , __UpperCamelCase )
UpperCamelCase = model[0].eval()
UpperCamelCase = True if config.feat_extract_norm == """layer""" else False
UpperCamelCase = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=16000 , padding_value=0 , do_normalize=__UpperCamelCase , return_attention_mask=__UpperCamelCase , )
if is_finetuned:
if dict_path:
UpperCamelCase = Dictionary.load(__UpperCamelCase )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
UpperCamelCase = target_dict.pad_index
UpperCamelCase = target_dict.bos_index
UpperCamelCase = target_dict.pad_index
UpperCamelCase = target_dict.bos_index
UpperCamelCase = target_dict.eos_index
UpperCamelCase = len(target_dict.symbols )
UpperCamelCase = os.path.join(__UpperCamelCase , """vocab.json""" )
if not os.path.isdir(__UpperCamelCase ):
logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(__UpperCamelCase ) )
return
os.makedirs(__UpperCamelCase , exist_ok=__UpperCamelCase )
with open(__UpperCamelCase , """w""" , encoding="""utf-8""" ) as vocab_handle:
json.dump(target_dict.indices , __UpperCamelCase )
UpperCamelCase = WavaVecaCTCTokenizer(
__UpperCamelCase , unk_token=target_dict.unk_word , pad_token=target_dict.pad_word , bos_token=target_dict.bos_word , eos_token=target_dict.eos_word , word_delimiter_token="""|""" , do_lower_case=__UpperCamelCase , )
UpperCamelCase = WavaVecaProcessor(feature_extractor=__UpperCamelCase , tokenizer=__UpperCamelCase )
processor.save_pretrained(__UpperCamelCase )
UpperCamelCase = SEWForCTC(__UpperCamelCase )
else:
UpperCamelCase = SEWModel(__UpperCamelCase )
feature_extractor.save_pretrained(__UpperCamelCase )
recursively_load_weights(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
hf_model.save_pretrained(__UpperCamelCase )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ = argparse.ArgumentParser()
parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
parser.add_argument(
'--is_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
)
SCREAMING_SNAKE_CASE__ = parser.parse_args()
convert_sew_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
)
| 369 |
'''simple docstring'''
from timeit import timeit
def lowercase__ ( __UpperCamelCase )-> int:
if number < 0:
raise ValueError("""the value of input must not be negative""" )
UpperCamelCase = 0
while number:
number &= number - 1
result += 1
return result
def lowercase__ ( __UpperCamelCase )-> int:
if number < 0:
raise ValueError("""the value of input must not be negative""" )
UpperCamelCase = 0
while number:
if number % 2 == 1:
result += 1
number >>= 1
return result
def lowercase__ ( )-> None:
def do_benchmark(__UpperCamelCase ) -> None:
UpperCamelCase = """import __main__ as z"""
print(F"Benchmark when {number = }:" )
print(F"{get_set_bits_count_using_modulo_operator(__UpperCamelCase ) = }" )
UpperCamelCase = timeit("""z.get_set_bits_count_using_modulo_operator(25)""" , setup=__UpperCamelCase )
print(F"timeit() runs in {timing} seconds" )
print(F"{get_set_bits_count_using_brian_kernighans_algorithm(__UpperCamelCase ) = }" )
UpperCamelCase = timeit(
"""z.get_set_bits_count_using_brian_kernighans_algorithm(25)""" , setup=__UpperCamelCase , )
print(F"timeit() runs in {timing} seconds" )
for number in (25, 37, 58, 0):
do_benchmark(__UpperCamelCase )
print()
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 183 | 0 |
"""simple docstring"""
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default_hp_space_optuna,
default_hp_space_ray,
default_hp_space_sigopt,
default_hp_space_wandb,
)
from .utils import logging
_snake_case = logging.get_logger(__name__)
class UpperCamelCase :
UpperCamelCase : str
UpperCamelCase : str = None
@staticmethod
def _lowercase ( ) -> Dict:
raise NotImplementedError
def _lowercase ( self : Any , UpperCAmelCase__ : Any , UpperCAmelCase__ : int , UpperCAmelCase__ : str , **UpperCAmelCase__ : Optional[Any] ) -> Union[str, Any]:
raise NotImplementedError
def _lowercase ( self : Dict , UpperCAmelCase__ : List[Any] ) -> Dict:
raise NotImplementedError
def _lowercase ( self : Optional[Any] ) -> List[str]:
if not self.is_available():
raise RuntimeError(
f"""You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}.""" )
@classmethod
def _lowercase ( cls : Union[str, Any] ) -> Dict:
return f"""`pip install {cls.pip_package or cls.name}`"""
class UpperCamelCase ( snake_case_ ):
UpperCamelCase : List[Any] = '''optuna'''
@staticmethod
def _lowercase ( ) -> List[Any]:
return is_optuna_available()
def _lowercase ( self : int , UpperCAmelCase__ : Any , UpperCAmelCase__ : int , UpperCAmelCase__ : str , **UpperCAmelCase__ : Dict ) -> int:
return run_hp_search_optuna(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , **UpperCAmelCase__ )
def _lowercase ( self : int , UpperCAmelCase__ : Optional[Any] ) -> Optional[Any]:
return default_hp_space_optuna(UpperCAmelCase__ )
class UpperCamelCase ( snake_case_ ):
UpperCamelCase : Any = '''ray'''
UpperCamelCase : Any = '''\'ray[tune]\''''
@staticmethod
def _lowercase ( ) -> List[Any]:
return is_ray_available()
def _lowercase ( self : Dict , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : int , UpperCAmelCase__ : str , **UpperCAmelCase__ : str ) -> List[str]:
return run_hp_search_ray(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , **UpperCAmelCase__ )
def _lowercase ( self : List[str] , UpperCAmelCase__ : Union[str, Any] ) -> Optional[int]:
return default_hp_space_ray(UpperCAmelCase__ )
class UpperCamelCase ( snake_case_ ):
UpperCamelCase : Any = '''sigopt'''
@staticmethod
def _lowercase ( ) -> str:
return is_sigopt_available()
def _lowercase ( self : int , UpperCAmelCase__ : Any , UpperCAmelCase__ : int , UpperCAmelCase__ : str , **UpperCAmelCase__ : Dict ) -> List[Any]:
return run_hp_search_sigopt(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , **UpperCAmelCase__ )
def _lowercase ( self : List[str] , UpperCAmelCase__ : Tuple ) -> Union[str, Any]:
return default_hp_space_sigopt(UpperCAmelCase__ )
class UpperCamelCase ( snake_case_ ):
UpperCamelCase : Optional[int] = '''wandb'''
@staticmethod
def _lowercase ( ) -> Optional[Any]:
return is_wandb_available()
def _lowercase ( self : Any , UpperCAmelCase__ : Optional[Any] , UpperCAmelCase__ : int , UpperCAmelCase__ : str , **UpperCAmelCase__ : Optional[Any] ) -> Any:
return run_hp_search_wandb(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , **UpperCAmelCase__ )
def _lowercase ( self : Optional[Any] , UpperCAmelCase__ : List[str] ) -> str:
return default_hp_space_wandb(UpperCAmelCase__ )
_snake_case = {
HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}
def lowerCAmelCase__ ( ):
'''simple docstring'''
_a : Union[str, Any] = [backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
if len(UpperCamelCase__ ) > 0:
_a : List[str] = available_backends[0].name
if len(UpperCamelCase__ ) > 1:
logger.info(
F"""{len(UpperCamelCase__ )} hyperparameter search backends available. Using {name} as the default.""" )
return name
raise RuntimeError(
"""No hyperparameter search backend available.\n"""
+ """\n""".join(
F""" - To install {backend.name} run {backend.pip_install()}"""
for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() ) )
| 294 |
"""simple docstring"""
from collections.abc import Iterable
from typing import Generic, TypeVar
_snake_case = TypeVar('_T')
class UpperCamelCase ( Generic[_T] ):
def __init__( self : Optional[int] , UpperCAmelCase__ : Iterable[_T] | None = None ) -> None:
_a : list[_T] = list(iterable or [] )
_a : list[_T] = []
def __len__( self : str ) -> int:
return len(self._stacka ) + len(self._stacka )
def __repr__( self : List[str] ) -> str:
return f"""Queue({tuple(self._stacka[::-1] + self._stacka )})"""
def _lowercase ( self : Union[str, Any] , UpperCAmelCase__ : _T ) -> None:
self._stacka.append(UpperCAmelCase__ )
def _lowercase ( self : Optional[Any] ) -> _T:
_a : Any = self._stacka.pop
_a : Union[str, Any] = self._stacka.append
if not self._stacka:
while self._stacka:
stacka_append(stacka_pop() )
if not self._stacka:
raise IndexError("""Queue is empty""" )
return self._stacka.pop()
if __name__ == "__main__":
from doctest import testmod
testmod()
| 294 | 1 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path
import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized
from transformers import AutoProcessor
from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.testing_utils import require_pyctcdecode, require_torch, require_torchaudio, slow
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_pyctcdecode_available, is_torch_available
from ..wavaveca.test_feature_extraction_wavaveca import floats_list
if is_pyctcdecode_available():
from huggingface_hub import snapshot_download
from pyctcdecode import BeamSearchDecoderCTC
from transformers.models.wavaveca_with_lm import WavaVecaProcessorWithLM
from transformers.models.wavaveca_with_lm.processing_wavaveca_with_lm import WavaVecaDecoderWithLMOutput
if is_torch_available():
from transformers import WavaVecaForCTC
@require_pyctcdecode
class snake_case__ ( unittest.TestCase ):
def a__ ( self ):
__a = "| <pad> <unk> <s> </s> a b c d e f g h i j k".split()
__a = dict(zip(lowerCamelCase , range(len(lowerCamelCase ) ) ) )
__a = {
"unk_token": "<unk>",
"bos_token": "<s>",
"eos_token": "</s>",
}
__a = {
"feature_size": 1,
"padding_value": 0.0,
"sampling_rate": 16000,
"return_attention_mask": False,
"do_normalize": True,
}
__a = tempfile.mkdtemp()
__a = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] )
__a = os.path.join(self.tmpdirname , lowerCamelCase )
with open(self.vocab_file , "w" , encoding="utf-8" ) as fp:
fp.write(json.dumps(lowerCamelCase ) + "\n" )
with open(self.feature_extraction_file , "w" , encoding="utf-8" ) as fp:
fp.write(json.dumps(lowerCamelCase ) + "\n" )
# load decoder from hub
__a = "hf-internal-testing/ngram-beam-search-decoder"
def a__ ( self , **lowerCamelCase ):
__a = self.add_kwargs_tokens_map.copy()
kwargs.update(lowerCamelCase )
return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname , **lowerCamelCase )
def a__ ( self , **lowerCamelCase ):
return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname , **lowerCamelCase )
def a__ ( self , **lowerCamelCase ):
return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name , **lowerCamelCase )
def a__ ( self ):
shutil.rmtree(self.tmpdirname )
def a__ ( self ):
__a = self.get_tokenizer()
__a = self.get_feature_extractor()
__a = self.get_decoder()
__a = WavaVecaProcessorWithLM(tokenizer=lowerCamelCase , feature_extractor=lowerCamelCase , decoder=lowerCamelCase )
processor.save_pretrained(self.tmpdirname )
__a = WavaVecaProcessorWithLM.from_pretrained(self.tmpdirname )
# tokenizer
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.tokenizer , lowerCamelCase )
# feature extractor
self.assertEqual(processor.feature_extractor.to_json_string() , feature_extractor.to_json_string() )
self.assertIsInstance(processor.feature_extractor , lowerCamelCase )
# decoder
self.assertEqual(processor.decoder._alphabet.labels , decoder._alphabet.labels )
self.assertEqual(
processor.decoder.model_container[decoder._model_key]._unigram_set , decoder.model_container[decoder._model_key]._unigram_set , )
self.assertIsInstance(processor.decoder , lowerCamelCase )
def a__ ( self ):
__a = WavaVecaProcessorWithLM(
tokenizer=self.get_tokenizer() , feature_extractor=self.get_feature_extractor() , decoder=self.get_decoder() )
processor.save_pretrained(self.tmpdirname )
# make sure that error is thrown when decoder alphabet doesn't match
__a = WavaVecaProcessorWithLM.from_pretrained(
self.tmpdirname , alpha=5.0 , beta=3.0 , score_boundary=-7.0 , unk_score_offset=3 )
# decoder
self.assertEqual(processor.language_model.alpha , 5.0 )
self.assertEqual(processor.language_model.beta , 3.0 )
self.assertEqual(processor.language_model.score_boundary , -7.0 )
self.assertEqual(processor.language_model.unk_score_offset , 3 )
def a__ ( self ):
__a = self.get_tokenizer()
# add token to trigger raise
tokenizer.add_tokens(["xx"] )
with self.assertRaisesRegex(lowerCamelCase , "include" ):
WavaVecaProcessorWithLM(
tokenizer=lowerCamelCase , feature_extractor=self.get_feature_extractor() , decoder=self.get_decoder() )
def a__ ( self ):
__a = self.get_feature_extractor()
__a = self.get_tokenizer()
__a = self.get_decoder()
__a = WavaVecaProcessorWithLM(tokenizer=lowerCamelCase , feature_extractor=lowerCamelCase , decoder=lowerCamelCase )
__a = floats_list((3, 1000) )
__a = feature_extractor(lowerCamelCase , return_tensors="np" )
__a = processor(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 a__ ( self ):
__a = self.get_feature_extractor()
__a = self.get_tokenizer()
__a = self.get_decoder()
__a = WavaVecaProcessorWithLM(tokenizer=lowerCamelCase , feature_extractor=lowerCamelCase , decoder=lowerCamelCase )
__a = "This is a test string"
__a = processor(text=lowerCamelCase )
__a = tokenizer(lowerCamelCase )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def a__ ( self , lowerCamelCase=(2, 10, 16) , lowerCamelCase=77 ):
np.random.seed(lowerCamelCase )
return np.random.rand(*lowerCamelCase )
def a__ ( self ):
__a = self.get_feature_extractor()
__a = self.get_tokenizer()
__a = self.get_decoder()
__a = WavaVecaProcessorWithLM(tokenizer=lowerCamelCase , feature_extractor=lowerCamelCase , decoder=lowerCamelCase )
__a = self._get_dummy_logits(shape=(10, 16) , seed=13 )
__a = processor.decode(lowerCamelCase )
__a = decoder.decode_beams(lowerCamelCase )[0]
self.assertEqual(decoded_decoder[0] , decoded_processor.text )
self.assertEqual("</s> <s> </s>" , decoded_processor.text )
self.assertEqual(decoded_decoder[-2] , decoded_processor.logit_score )
self.assertEqual(decoded_decoder[-1] , decoded_processor.lm_score )
@parameterized.expand([[None], ["fork"], ["spawn"]] )
def a__ ( self , lowerCamelCase ):
__a = self.get_feature_extractor()
__a = self.get_tokenizer()
__a = self.get_decoder()
__a = WavaVecaProcessorWithLM(tokenizer=lowerCamelCase , feature_extractor=lowerCamelCase , decoder=lowerCamelCase )
__a = self._get_dummy_logits()
# note: pool should be instantiated *after* Wav2Vec2ProcessorWithLM.
# otherwise, the LM won't be available to the pool's sub-processes.
# manual logic used to allow parameterized test for both pool=None and pool=Pool(...)
if pool_context is None:
__a = processor.batch_decode(lowerCamelCase )
else:
with get_context(lowerCamelCase ).Pool() as pool:
__a = processor.batch_decode(lowerCamelCase , lowerCamelCase )
__a = list(lowerCamelCase )
with get_context("fork" ).Pool() as p:
__a = decoder.decode_beams_batch(lowerCamelCase , lowerCamelCase )
__a , __a , __a = [], [], []
for beams in decoded_beams:
texts_decoder.append(beams[0][0] )
logit_scores_decoder.append(beams[0][-2] )
lm_scores_decoder.append(beams[0][-1] )
self.assertListEqual(lowerCamelCase , decoded_processor.text )
self.assertListEqual(["<s> <s> </s>", "<s> <s> <s>"] , decoded_processor.text )
self.assertListEqual(lowerCamelCase , decoded_processor.logit_score )
self.assertListEqual(lowerCamelCase , decoded_processor.lm_score )
def a__ ( self ):
__a = self.get_feature_extractor()
__a = self.get_tokenizer()
__a = self.get_decoder()
__a = WavaVecaProcessorWithLM(tokenizer=lowerCamelCase , feature_extractor=lowerCamelCase , decoder=lowerCamelCase )
__a = self._get_dummy_logits()
__a = 15
__a = -20.0
__a = -4.0
__a = processor.batch_decode(
lowerCamelCase , beam_width=lowerCamelCase , beam_prune_logp=lowerCamelCase , token_min_logp=lowerCamelCase , )
__a = decoded_processor_out.text
__a = list(lowerCamelCase )
with get_context("fork" ).Pool() as pool:
__a = decoder.decode_beams_batch(
lowerCamelCase , lowerCamelCase , beam_width=lowerCamelCase , beam_prune_logp=lowerCamelCase , token_min_logp=lowerCamelCase , )
__a = [d[0][0] for d in decoded_decoder_out]
__a = [d[0][2] for d in decoded_decoder_out]
__a = [d[0][3] for d in decoded_decoder_out]
self.assertListEqual(lowerCamelCase , lowerCamelCase )
self.assertListEqual(["</s> <s> <s>", "<s> <s> <s>"] , lowerCamelCase )
self.assertTrue(np.array_equal(lowerCamelCase , decoded_processor_out.logit_score ) )
self.assertTrue(np.allclose([-20.054, -18.447] , lowerCamelCase , atol=1E-3 ) )
self.assertTrue(np.array_equal(lowerCamelCase , decoded_processor_out.lm_score ) )
self.assertTrue(np.allclose([-15.554, -13.9474] , lowerCamelCase , atol=1E-3 ) )
def a__ ( self ):
__a = self.get_feature_extractor()
__a = self.get_tokenizer()
__a = self.get_decoder()
__a = WavaVecaProcessorWithLM(tokenizer=lowerCamelCase , feature_extractor=lowerCamelCase , decoder=lowerCamelCase )
__a = self._get_dummy_logits()
__a = 2.0
__a = 5.0
__a = -20.0
__a = True
__a = processor.batch_decode(
lowerCamelCase , alpha=lowerCamelCase , beta=lowerCamelCase , unk_score_offset=lowerCamelCase , lm_score_boundary=lowerCamelCase , )
__a = decoded_processor_out.text
__a = list(lowerCamelCase )
decoder.reset_params(
alpha=lowerCamelCase , beta=lowerCamelCase , unk_score_offset=lowerCamelCase , lm_score_boundary=lowerCamelCase , )
with get_context("fork" ).Pool() as pool:
__a = decoder.decode_beams_batch(
lowerCamelCase , lowerCamelCase , )
__a = [d[0][0] for d in decoded_decoder_out]
self.assertListEqual(lowerCamelCase , lowerCamelCase )
self.assertListEqual(["<s> </s> <s> </s> </s>", "</s> </s> <s> </s> </s>"] , lowerCamelCase )
__a = processor.decoder.model_container[processor.decoder._model_key]
self.assertEqual(lm_model.alpha , 2.0 )
self.assertEqual(lm_model.beta , 5.0 )
self.assertEqual(lm_model.unk_score_offset , -20.0 )
self.assertEqual(lm_model.score_boundary , lowerCamelCase )
def a__ ( self ):
__a = WavaVecaProcessorWithLM.from_pretrained("hf-internal-testing/processor_with_lm" )
__a = processor.decoder.model_container[processor.decoder._model_key]
__a = Path(language_model._kenlm_model.path.decode("utf-8" ) ).parent.parent.absolute()
__a = os.listdir(lowerCamelCase )
__a = ["alphabet.json", "language_model"]
downloaded_decoder_files.sort()
expected_decoder_files.sort()
# test that only decoder relevant files from
# https://huggingface.co/hf-internal-testing/processor_with_lm/tree/main
# are downloaded and none of the rest (e.g. README.md, ...)
self.assertListEqual(lowerCamelCase , lowerCamelCase )
def a__ ( self ):
__a = snapshot_download("hf-internal-testing/processor_with_lm" )
__a = WavaVecaProcessorWithLM.from_pretrained(lowerCamelCase )
__a = processor.decoder.model_container[processor.decoder._model_key]
__a = Path(language_model._kenlm_model.path.decode("utf-8" ) ).parent.parent.absolute()
__a = os.listdir(lowerCamelCase )
__a = os.listdir(lowerCamelCase )
local_decoder_files.sort()
expected_decoder_files.sort()
# test that both decoder form hub and local files in cache are the same
self.assertListEqual(lowerCamelCase , lowerCamelCase )
def a__ ( self ):
__a = WavaVecaProcessorWithLM.from_pretrained("hf-internal-testing/processor_with_lm" )
__a = AutoProcessor.from_pretrained("hf-internal-testing/processor_with_lm" )
__a = floats_list((3, 1000) )
__a = processor_wavaveca(lowerCamelCase , return_tensors="np" )
__a = processor_auto(lowerCamelCase , return_tensors="np" )
for key in input_wavaveca.keys():
self.assertAlmostEqual(input_wavaveca[key].sum() , input_auto[key].sum() , delta=1E-2 )
__a = self._get_dummy_logits()
__a = processor_wavaveca.batch_decode(lowerCamelCase )
__a = processor_auto.batch_decode(lowerCamelCase )
self.assertListEqual(decoded_wavaveca.text , decoded_auto.text )
def a__ ( self ):
__a = self.get_feature_extractor()
__a = self.get_tokenizer()
__a = self.get_decoder()
__a = WavaVecaProcessorWithLM(tokenizer=lowerCamelCase , feature_extractor=lowerCamelCase , decoder=lowerCamelCase )
self.assertListEqual(
processor.model_input_names , feature_extractor.model_input_names , msg="`processor` and `feature_extractor` model input names do not match" , )
@staticmethod
def a__ ( lowerCamelCase , lowerCamelCase ):
__a = [d[key] for d in offsets]
return retrieved_list
def a__ ( self ):
__a = WavaVecaProcessorWithLM.from_pretrained("hf-internal-testing/processor_with_lm" )
__a = self._get_dummy_logits()[0]
__a = processor.decode(lowerCamelCase , output_word_offsets=lowerCamelCase )
# check Wav2Vec2CTCTokenizerOutput keys for word
self.assertEqual(len(outputs.keys() ) , 4 )
self.assertTrue("text" in outputs )
self.assertTrue("word_offsets" in outputs )
self.assertTrue(isinstance(lowerCamelCase , lowerCamelCase ) )
self.assertEqual(" ".join(self.get_from_offsets(outputs["word_offsets"] , "word" ) ) , outputs.text )
self.assertListEqual(self.get_from_offsets(outputs["word_offsets"] , "word" ) , ["<s>", "<s>", "</s>"] )
self.assertListEqual(self.get_from_offsets(outputs["word_offsets"] , "start_offset" ) , [0, 2, 4] )
self.assertListEqual(self.get_from_offsets(outputs["word_offsets"] , "end_offset" ) , [1, 3, 5] )
def a__ ( self ):
__a = WavaVecaProcessorWithLM.from_pretrained("hf-internal-testing/processor_with_lm" )
__a = self._get_dummy_logits()
__a = processor.batch_decode(lowerCamelCase , output_word_offsets=lowerCamelCase )
# check Wav2Vec2CTCTokenizerOutput keys for word
self.assertEqual(len(outputs.keys() ) , 4 )
self.assertTrue("text" in outputs )
self.assertTrue("word_offsets" in outputs )
self.assertTrue(isinstance(lowerCamelCase , lowerCamelCase ) )
self.assertListEqual(
[" ".join(self.get_from_offsets(lowerCamelCase , "word" ) ) for o in outputs["word_offsets"]] , outputs.text )
self.assertListEqual(self.get_from_offsets(outputs["word_offsets"][0] , "word" ) , ["<s>", "<s>", "</s>"] )
self.assertListEqual(self.get_from_offsets(outputs["word_offsets"][0] , "start_offset" ) , [0, 2, 4] )
self.assertListEqual(self.get_from_offsets(outputs["word_offsets"][0] , "end_offset" ) , [1, 3, 5] )
@slow
@require_torch
@require_torchaudio
def a__ ( self ):
import torch
__a = load_dataset("common_voice" , "en" , split="train" , streaming=lowerCamelCase )
__a = ds.cast_column("audio" , datasets.Audio(sampling_rate=16000 ) )
__a = iter(lowerCamelCase )
__a = next(lowerCamelCase )
__a = AutoProcessor.from_pretrained("patrickvonplaten/wav2vec2-base-100h-with-lm" )
__a = WavaVecaForCTC.from_pretrained("patrickvonplaten/wav2vec2-base-100h-with-lm" )
# compare to filename `common_voice_en_100038.mp3` of dataset viewer on https://huggingface.co/datasets/common_voice/viewer/en/train
__a = processor(sample["audio"]["array"] , return_tensors="pt" ).input_values
with torch.no_grad():
__a = model(lowerCamelCase ).logits.cpu().numpy()
__a = processor.decode(logits[0] , output_word_offsets=lowerCamelCase )
__a = model.config.inputs_to_logits_ratio / processor.feature_extractor.sampling_rate
__a = [
{
"start_time": d["start_offset"] * time_offset,
"end_time": d["end_offset"] * time_offset,
"word": d["word"],
}
for d in output["word_offsets"]
]
__a = "WHY DOES MILISANDRA LOOK LIKE SHE WANTS TO CONSUME JOHN SNOW ON THE RIVER AT THE WALL"
# output words
self.assertEqual(" ".join(self.get_from_offsets(lowerCamelCase , "word" ) ) , lowerCamelCase )
self.assertEqual(" ".join(self.get_from_offsets(lowerCamelCase , "word" ) ) , output.text )
# output times
__a = torch.tensor(self.get_from_offsets(lowerCamelCase , "start_time" ) )
__a = torch.tensor(self.get_from_offsets(lowerCamelCase , "end_time" ) )
# fmt: off
__a = torch.tensor([1.4199, 1.6599, 2.2599, 3.0, 3.24, 3.5999, 3.7999, 4.0999, 4.26, 4.94, 5.28, 5.6599, 5.78, 5.94, 6.32, 6.5399, 6.6599] )
__a = torch.tensor([1.5399, 1.8999, 2.9, 3.16, 3.5399, 3.72, 4.0199, 4.1799, 4.76, 5.1599, 5.5599, 5.6999, 5.86, 6.1999, 6.38, 6.6199, 6.94] )
# fmt: on
self.assertTrue(torch.allclose(lowerCamelCase , lowerCamelCase , atol=0.01 ) )
self.assertTrue(torch.allclose(lowerCamelCase , lowerCamelCase , atol=0.01 ) )
| 268 | """simple docstring"""
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEmbeddings,
BertLayer,
BertPooler,
BertPreTrainedModel,
)
def _lowerCamelCase( a ):
__a = torch.exp(a )
__a = torch.sum(a , dim=1 ) # sum of exp(x_i)
__a = torch.sum(x * exp_x , dim=1 ) # sum of x_i * exp(x_i)
return torch.log(a ) - B / A
class snake_case__ ( nn.Module ):
def __init__( self , lowerCamelCase ):
super().__init__()
__a = config.output_attentions
__a = config.output_hidden_states
__a = nn.ModuleList([BertLayer(lowerCamelCase ) for _ in range(config.num_hidden_layers )] )
__a = nn.ModuleList([BertHighway(lowerCamelCase ) for _ in range(config.num_hidden_layers )] )
__a = [-1 for _ in range(config.num_hidden_layers )]
def a__ ( self , lowerCamelCase ):
if (type(lowerCamelCase ) is float) or (type(lowerCamelCase ) is int):
for i in range(len(self.early_exit_entropy ) ):
__a = x
else:
__a = x
def a__ ( self , lowerCamelCase ):
__a = pooler.state_dict()
for highway in self.highway:
for name, param in highway.pooler.state_dict().items():
param.copy_(loaded_model[name] )
def a__ ( self , lowerCamelCase , lowerCamelCase=None , lowerCamelCase=None , lowerCamelCase=None , lowerCamelCase=None , ):
__a = ()
__a = ()
__a = ()
for i, layer_module in enumerate(self.layer ):
if self.output_hidden_states:
__a = all_hidden_states + (hidden_states,)
__a = layer_module(
lowerCamelCase , lowerCamelCase , head_mask[i] , lowerCamelCase , lowerCamelCase )
__a = layer_outputs[0]
if self.output_attentions:
__a = all_attentions + (layer_outputs[1],)
__a = (hidden_states,)
if self.output_hidden_states:
__a = current_outputs + (all_hidden_states,)
if self.output_attentions:
__a = current_outputs + (all_attentions,)
__a = self.highway[i](lowerCamelCase )
# logits, pooled_output
if not self.training:
__a = highway_exit[0]
__a = entropy(lowerCamelCase )
__a = highway_exit + (highway_entropy,) # logits, hidden_states(?), entropy
__a = all_highway_exits + (highway_exit,)
if highway_entropy < self.early_exit_entropy[i]:
__a = (highway_logits,) + current_outputs[1:] + (all_highway_exits,)
raise HighwayException(lowerCamelCase , i + 1 )
else:
__a = all_highway_exits + (highway_exit,)
# Add last layer
if self.output_hidden_states:
__a = all_hidden_states + (hidden_states,)
__a = (hidden_states,)
if self.output_hidden_states:
__a = outputs + (all_hidden_states,)
if self.output_attentions:
__a = outputs + (all_attentions,)
__a = outputs + (all_highway_exits,)
return outputs # last-layer hidden state, (all hidden states), (all attentions), all highway exits
@add_start_docstrings(
"""The Bert Model transformer with early exiting (DeeBERT). """, snake_case_, )
class snake_case__ ( snake_case_ ):
def __init__( self , lowerCamelCase ):
super().__init__(lowerCamelCase )
__a = config
__a = BertEmbeddings(lowerCamelCase )
__a = DeeBertEncoder(lowerCamelCase )
__a = BertPooler(lowerCamelCase )
self.init_weights()
def a__ ( self ):
self.encoder.init_highway_pooler(self.pooler )
def a__ ( self ):
return self.embeddings.word_embeddings
def a__ ( self , lowerCamelCase ):
__a = value
def a__ ( self , lowerCamelCase ):
for layer, heads in heads_to_prune.items():
self.encoder.layer[layer].attention.prune_heads(lowerCamelCase )
@add_start_docstrings_to_model_forward(lowerCamelCase )
def a__ ( self , lowerCamelCase=None , lowerCamelCase=None , lowerCamelCase=None , lowerCamelCase=None , lowerCamelCase=None , lowerCamelCase=None , lowerCamelCase=None , lowerCamelCase=None , ):
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time" )
elif input_ids is not None:
__a = input_ids.size()
elif inputs_embeds is not None:
__a = inputs_embeds.size()[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds" )
__a = input_ids.device if input_ids is not None else inputs_embeds.device
if attention_mask is None:
__a = torch.ones(lowerCamelCase , device=lowerCamelCase )
if encoder_attention_mask is None:
__a = torch.ones(lowerCamelCase , device=lowerCamelCase )
if token_type_ids is None:
__a = torch.zeros(lowerCamelCase , dtype=torch.long , device=lowerCamelCase )
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
# ourselves in which case we just need to make it broadcastable to all heads.
__a = self.get_extended_attention_mask(lowerCamelCase , lowerCamelCase , lowerCamelCase )
# If a 2D ou 3D attention mask is provided for the cross-attention
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
if encoder_attention_mask.dim() == 3:
__a = encoder_attention_mask[:, None, :, :]
if encoder_attention_mask.dim() == 2:
__a = encoder_attention_mask[:, None, None, :]
__a = encoder_extended_attention_mask.to(
dtype=next(self.parameters() ).dtype ) # fp16 compatibility
__a = (1.0 - encoder_extended_attention_mask) * -1_0000.0
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
__a = self.get_head_mask(lowerCamelCase , self.config.num_hidden_layers )
__a = self.embeddings(
input_ids=lowerCamelCase , position_ids=lowerCamelCase , token_type_ids=lowerCamelCase , inputs_embeds=lowerCamelCase )
__a = self.encoder(
lowerCamelCase , attention_mask=lowerCamelCase , head_mask=lowerCamelCase , encoder_hidden_states=lowerCamelCase , encoder_attention_mask=lowerCamelCase , )
__a = encoder_outputs[0]
__a = self.pooler(lowerCamelCase )
__a = (
sequence_output,
pooled_output,
) + encoder_outputs[
1:
] # add hidden_states and attentions if they are here
return outputs # sequence_output, pooled_output, (hidden_states), (attentions), highway exits
class snake_case__ ( snake_case_ ):
def __init__( self , lowerCamelCase , lowerCamelCase ):
__a = message
__a = exit_layer # start from 1!
class snake_case__ ( nn.Module ):
def __init__( self , lowerCamelCase ):
super().__init__()
__a = BertPooler(lowerCamelCase )
__a = nn.Dropout(config.hidden_dropout_prob )
__a = nn.Linear(config.hidden_size , config.num_labels )
def a__ ( self , lowerCamelCase ):
# Pooler
__a = encoder_outputs[0]
__a = self.pooler(lowerCamelCase )
# "return" pooler_output
# BertModel
__a = (pooler_input, pooler_output) + encoder_outputs[1:]
# "return" bmodel_output
# Dropout and classification
__a = bmodel_output[1]
__a = self.dropout(lowerCamelCase )
__a = self.classifier(lowerCamelCase )
return logits, pooled_output
@add_start_docstrings(
"""Bert Model (with early exiting - DeeBERT) with a classifier on top,
also takes care of multi-layer training. """, snake_case_, )
class snake_case__ ( snake_case_ ):
def __init__( self , lowerCamelCase ):
super().__init__(lowerCamelCase )
__a = config.num_labels
__a = config.num_hidden_layers
__a = DeeBertModel(lowerCamelCase )
__a = nn.Dropout(config.hidden_dropout_prob )
__a = nn.Linear(config.hidden_size , self.config.num_labels )
self.init_weights()
@add_start_docstrings_to_model_forward(lowerCamelCase )
def a__ ( self , lowerCamelCase=None , lowerCamelCase=None , lowerCamelCase=None , lowerCamelCase=None , lowerCamelCase=None , lowerCamelCase=None , lowerCamelCase=None , lowerCamelCase=-1 , lowerCamelCase=False , ):
__a = self.num_layers
try:
__a = self.bert(
lowerCamelCase , attention_mask=lowerCamelCase , token_type_ids=lowerCamelCase , position_ids=lowerCamelCase , head_mask=lowerCamelCase , inputs_embeds=lowerCamelCase , )
# sequence_output, pooled_output, (hidden_states), (attentions), highway exits
__a = outputs[1]
__a = self.dropout(lowerCamelCase )
__a = self.classifier(lowerCamelCase )
__a = (logits,) + outputs[2:] # add hidden states and attention if they are here
except HighwayException as e:
__a = e.message
__a = e.exit_layer
__a = outputs[0]
if not self.training:
__a = entropy(lowerCamelCase )
__a = []
__a = []
if labels is not None:
if self.num_labels == 1:
# We are doing regression
__a = MSELoss()
__a = loss_fct(logits.view(-1 ) , labels.view(-1 ) )
else:
__a = CrossEntropyLoss()
__a = loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
# work with highway exits
__a = []
for highway_exit in outputs[-1]:
__a = highway_exit[0]
if not self.training:
highway_logits_all.append(lowerCamelCase )
highway_entropy.append(highway_exit[2] )
if self.num_labels == 1:
# We are doing regression
__a = MSELoss()
__a = loss_fct(highway_logits.view(-1 ) , labels.view(-1 ) )
else:
__a = CrossEntropyLoss()
__a = loss_fct(highway_logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
highway_losses.append(lowerCamelCase )
if train_highway:
__a = (sum(highway_losses[:-1] ),) + outputs
# exclude the final highway, of course
else:
__a = (loss,) + outputs
if not self.training:
__a = outputs + ((original_entropy, highway_entropy), exit_layer)
if output_layer >= 0:
__a = (
(outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
) # use the highway of the last layer
return outputs # (loss), logits, (hidden_states), (attentions), (highway_exits)
| 268 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case_ = logging.get_logger(__name__)
snake_case_ = {
"""transfo-xl-wt103""": """https://huggingface.co/transfo-xl-wt103/resolve/main/config.json""",
}
class A_ ( SCREAMING_SNAKE_CASE_ ):
"""simple docstring"""
__UpperCamelCase = """transfo-xl"""
__UpperCamelCase = ["""mems"""]
__UpperCamelCase = {
"""n_token""": """vocab_size""",
"""hidden_size""": """d_model""",
"""num_attention_heads""": """n_head""",
"""num_hidden_layers""": """n_layer""",
}
def __init__( self :List[Any] , lowercase_ :Optional[int]=26_77_35 , lowercase_ :Union[str, Any]=[2_00_00, 4_00_00, 20_00_00] , lowercase_ :List[Any]=10_24 , lowercase_ :Optional[Any]=10_24 , lowercase_ :Tuple=16 , lowercase_ :Tuple=64 , lowercase_ :Any=40_96 , lowercase_ :int=4 , lowercase_ :List[str]=False , lowercase_ :Union[str, Any]=18 , lowercase_ :Optional[Any]=16_00 , lowercase_ :Dict=10_00 , lowercase_ :Optional[int]=True , lowercase_ :Tuple=True , lowercase_ :Dict=0 , lowercase_ :Tuple=-1 , lowercase_ :Optional[int]=True , lowercase_ :Optional[int]=0.1 , lowercase_ :str=0.0 , lowercase_ :List[str]=True , lowercase_ :int="normal" , lowercase_ :Dict=0.01 , lowercase_ :Optional[Any]=0.01 , lowercase_ :Dict=0.02 , lowercase_ :Tuple=1E-5 , lowercase_ :str=0 , **lowercase_ :Tuple , ) -> List[str]:
UpperCAmelCase = vocab_size
UpperCAmelCase = []
self.cutoffs.extend(lowercase_ )
if proj_share_all_but_first:
UpperCAmelCase = [False] + [True] * len(self.cutoffs )
else:
UpperCAmelCase = [False] + [False] * len(self.cutoffs )
UpperCAmelCase = d_model
UpperCAmelCase = d_embed
UpperCAmelCase = d_head
UpperCAmelCase = d_inner
UpperCAmelCase = div_val
UpperCAmelCase = pre_lnorm
UpperCAmelCase = n_layer
UpperCAmelCase = n_head
UpperCAmelCase = mem_len
UpperCAmelCase = same_length
UpperCAmelCase = attn_type
UpperCAmelCase = clamp_len
UpperCAmelCase = sample_softmax
UpperCAmelCase = adaptive
UpperCAmelCase = dropout
UpperCAmelCase = dropatt
UpperCAmelCase = untie_r
UpperCAmelCase = init
UpperCAmelCase = init_range
UpperCAmelCase = proj_init_std
UpperCAmelCase = init_std
UpperCAmelCase = layer_norm_epsilon
super().__init__(eos_token_id=lowercase_ , **lowercase_ )
@property
def UpperCAmelCase__ ( self :Union[str, Any] ) -> Any:
# Message copied from Transformer-XL documentation
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 UpperCAmelCase__ ( self :Union[str, Any] , lowercase_ :Any ) -> Tuple:
# Message copied from Transformer-XL documentation
raise NotImplementedError(
f"""The model {self.model_type} is one of the few models that has no sequence length limit.""" )
| 78 |
"""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 |
import faiss # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import requests # noqa: F401 # Here to have a nice missing dependency error message early on
import sklearn # noqa: F401 # Here to have a nice missing dependency error message early on
import tqdm # noqa: F401 # Here to have a nice missing dependency error message early on
from mauve import compute_mauve # From: mauve-text
import datasets
lowerCAmelCase = '\\n@inproceedings{pillutla-etal:mauve:neurips2021,\n title={MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers},\n author={Pillutla, Krishna and Swayamdipta, Swabha and Zellers, Rowan and Thickstun, John and Welleck, Sean and Choi, Yejin and Harchaoui, Zaid},\n booktitle = {NeurIPS},\n year = {2021}\n}\n\n'
lowerCAmelCase = '\\nMAUVE is a library built on PyTorch and HuggingFace Transformers to measure the gap between neural text and human text with the eponymous MAUVE measure.\n\nMAUVE summarizes both Type I and Type II errors measured softly using Kullback–Leibler (KL) divergences.\n\nFor details, see the MAUVE paper: https://arxiv.org/abs/2102.01454 (Neurips, 2021).\n\nThis metrics is a wrapper around the official implementation of MAUVE:\nhttps://github.com/krishnap25/mauve\n'
lowerCAmelCase = '\nCalculates MAUVE scores between two lists of generated text and reference text.\nArgs:\n predictions: list of generated text to score. Each predictions\n should be a string with tokens separated by spaces.\n references: list of reference for each prediction. Each\n reference should be a string with tokens separated by spaces.\nOptional Args:\n num_buckets: the size of the histogram to quantize P and Q. Options: \'auto\' (default) or an integer\n pca_max_data: the number data points to use for PCA dimensionality reduction prior to clustering. If -1, use all the data. Default -1\n kmeans_explained_var: amount of variance of the data to keep in dimensionality reduction by PCA. Default 0.9\n kmeans_num_redo: number of times to redo k-means clustering (the best objective is kept). Default 5\n kmeans_max_iter: maximum number of k-means iterations. Default 500\n featurize_model_name: name of the model from which features are obtained. Default \'gpt2-large\' Use one of [\'gpt2\', \'gpt2-medium\', \'gpt2-large\', \'gpt2-xl\'].\n device_id: Device for featurization. Supply a GPU id (e.g. 0 or 3) to use GPU. If no GPU with this id is found, use CPU\n max_text_length: maximum number of tokens to consider. Default 1024\n divergence_curve_discretization_size: Number of points to consider on the divergence curve. Default 25\n mauve_scaling_factor: "c" from the paper. Default 5.\n verbose: If True (default), print running time updates\n seed: random seed to initialize k-means cluster assignments.\nReturns:\n mauve: MAUVE score, a number between 0 and 1. Larger values indicate that P and Q are closer,\n frontier_integral: Frontier Integral, a number between 0 and 1. Smaller values indicate that P and Q are closer,\n divergence_curve: a numpy.ndarray of shape (m, 2); plot it with matplotlib to view the divergence curve,\n p_hist: a discrete distribution, which is a quantized version of the text distribution p_text,\n q_hist: same as above, but with q_text.\nExamples:\n\n >>> # faiss segfaults in doctest for some reason, so the .compute call is not tested with doctest\n >>> import datasets\n >>> mauve = datasets.load_metric(\'mauve\')\n >>> predictions = ["hello there", "general kenobi"]\n >>> references = ["hello there", "general kenobi"]\n >>> out = mauve.compute(predictions=predictions, references=references) # doctest: +SKIP\n >>> print(out.mauve) # doctest: +SKIP\n 1.0\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class _a ( datasets.Metric ):
def lowerCamelCase_ ( self: Union[str, Any] ) -> Any:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , homepage='''https://github.com/krishnap25/mauve''' , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Value('''string''' , id='''sequence''' ),
'''references''': datasets.Value('''string''' , id='''sequence''' ),
} ) , codebase_urls=['''https://github.com/krishnap25/mauve'''] , reference_urls=[
'''https://arxiv.org/abs/2102.01454''',
'''https://github.com/krishnap25/mauve''',
] , )
def lowerCamelCase_ ( self: int , UpperCamelCase_: Union[str, Any] , UpperCamelCase_: Union[str, Any] , UpperCamelCase_: Tuple=None , UpperCamelCase_: Dict=None , UpperCamelCase_: int=None , UpperCamelCase_: Optional[int]=None , UpperCamelCase_: Dict="auto" , UpperCamelCase_: Optional[int]=-1 , UpperCamelCase_: Union[str, Any]=0.9 , UpperCamelCase_: Any=5 , UpperCamelCase_: Optional[Any]=500 , UpperCamelCase_: Any="gpt2-large" , UpperCamelCase_: List[Any]=-1 , UpperCamelCase_: Dict=1_024 , UpperCamelCase_: str=25 , UpperCamelCase_: int=5 , UpperCamelCase_: int=True , UpperCamelCase_: Any=25 , ) -> List[Any]:
"""simple docstring"""
lowercase__ = compute_mauve(
p_text=UpperCamelCase_ , q_text=UpperCamelCase_ , p_features=UpperCamelCase_ , q_features=UpperCamelCase_ , p_tokens=UpperCamelCase_ , q_tokens=UpperCamelCase_ , num_buckets=UpperCamelCase_ , pca_max_data=UpperCamelCase_ , kmeans_explained_var=UpperCamelCase_ , kmeans_num_redo=UpperCamelCase_ , kmeans_max_iter=UpperCamelCase_ , featurize_model_name=UpperCamelCase_ , device_id=UpperCamelCase_ , max_text_length=UpperCamelCase_ , divergence_curve_discretization_size=UpperCamelCase_ , mauve_scaling_factor=UpperCamelCase_ , verbose=UpperCamelCase_ , seed=UpperCamelCase_ , )
return out
| 365 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer
from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
RobertaSeriesConfig,
RobertaSeriesModelWithTransformation,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class _a ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , unittest.TestCase ):
_lowercase : int = AltDiffusionPipeline
_lowercase : Tuple = TEXT_TO_IMAGE_PARAMS
_lowercase : List[str] = TEXT_TO_IMAGE_BATCH_PARAMS
_lowercase : Union[str, Any] = TEXT_TO_IMAGE_IMAGE_PARAMS
_lowercase : int = TEXT_TO_IMAGE_IMAGE_PARAMS
def lowerCamelCase_ ( self: Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
torch.manual_seed(0 )
lowercase__ = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') , up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''') , cross_attention_dim=32 , )
lowercase__ = DDIMScheduler(
beta_start=0.00085 , beta_end=0.012 , beta_schedule='''scaled_linear''' , clip_sample=UpperCamelCase_ , set_alpha_to_one=UpperCamelCase_ , )
torch.manual_seed(0 )
lowercase__ = 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 , )
# TODO: address the non-deterministic text encoder (fails for save-load tests)
# torch.manual_seed(0)
# text_encoder_config = RobertaSeriesConfig(
# hidden_size=32,
# project_dim=32,
# intermediate_size=37,
# layer_norm_eps=1e-05,
# num_attention_heads=4,
# num_hidden_layers=5,
# vocab_size=5002,
# )
# text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config)
torch.manual_seed(0 )
lowercase__ = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , projection_dim=32 , intermediate_size=37 , layer_norm_eps=1E-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=5_002 , )
lowercase__ = CLIPTextModel(UpperCamelCase_ )
lowercase__ = XLMRobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-xlm-roberta''' )
lowercase__ = 77
lowercase__ = {
'''unet''': unet,
'''scheduler''': scheduler,
'''vae''': vae,
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''safety_checker''': None,
'''feature_extractor''': None,
}
return components
def lowerCamelCase_ ( self: List[str] , UpperCamelCase_: Optional[int] , UpperCamelCase_: str=0 ) -> Union[str, Any]:
"""simple docstring"""
if str(UpperCamelCase_ ).startswith('''mps''' ):
lowercase__ = torch.manual_seed(UpperCamelCase_ )
else:
lowercase__ = torch.Generator(device=UpperCamelCase_ ).manual_seed(UpperCamelCase_ )
lowercase__ = {
'''prompt''': '''A painting of a squirrel eating a burger''',
'''generator''': generator,
'''num_inference_steps''': 2,
'''guidance_scale''': 6.0,
'''output_type''': '''numpy''',
}
return inputs
def lowerCamelCase_ ( self: Optional[Any] ) -> Dict:
"""simple docstring"""
super().test_attention_slicing_forward_pass(expected_max_diff=3E-3 )
def lowerCamelCase_ ( self: Dict ) -> str:
"""simple docstring"""
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
def lowerCamelCase_ ( self: Optional[int] ) -> Any:
"""simple docstring"""
lowercase__ = '''cpu''' # ensure determinism for the device-dependent torch.Generator
lowercase__ = self.get_dummy_components()
torch.manual_seed(0 )
lowercase__ = RobertaSeriesConfig(
hidden_size=32 , project_dim=32 , intermediate_size=37 , layer_norm_eps=1E-0_5 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=5_002 , )
# TODO: remove after fixing the non-deterministic text encoder
lowercase__ = RobertaSeriesModelWithTransformation(UpperCamelCase_ )
lowercase__ = text_encoder
lowercase__ = AltDiffusionPipeline(**UpperCamelCase_ )
lowercase__ = alt_pipe.to(UpperCamelCase_ )
alt_pipe.set_progress_bar_config(disable=UpperCamelCase_ )
lowercase__ = self.get_dummy_inputs(UpperCamelCase_ )
lowercase__ = '''A photo of an astronaut'''
lowercase__ = alt_pipe(**UpperCamelCase_ )
lowercase__ = output.images
lowercase__ = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
lowercase__ = np.array(
[0.5748162, 0.60447145, 0.48821217, 0.50100636, 0.5431185, 0.45763683, 0.49657696, 0.48132733, 0.47573093] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def lowerCamelCase_ ( self: Optional[Any] ) -> Tuple:
"""simple docstring"""
lowercase__ = '''cpu''' # ensure determinism for the device-dependent torch.Generator
lowercase__ = self.get_dummy_components()
lowercase__ = PNDMScheduler(skip_prk_steps=UpperCamelCase_ )
torch.manual_seed(0 )
lowercase__ = RobertaSeriesConfig(
hidden_size=32 , project_dim=32 , intermediate_size=37 , layer_norm_eps=1E-0_5 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=5_002 , )
# TODO: remove after fixing the non-deterministic text encoder
lowercase__ = RobertaSeriesModelWithTransformation(UpperCamelCase_ )
lowercase__ = text_encoder
lowercase__ = AltDiffusionPipeline(**UpperCamelCase_ )
lowercase__ = alt_pipe.to(UpperCamelCase_ )
alt_pipe.set_progress_bar_config(disable=UpperCamelCase_ )
lowercase__ = self.get_dummy_inputs(UpperCamelCase_ )
lowercase__ = alt_pipe(**UpperCamelCase_ )
lowercase__ = output.images
lowercase__ = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
lowercase__ = np.array(
[0.51605093, 0.5707241, 0.47365507, 0.50578886, 0.5633877, 0.4642503, 0.5182081, 0.48763484, 0.49084237] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
@slow
@require_torch_gpu
class _a ( unittest.TestCase ):
def lowerCamelCase_ ( self: Dict ) -> Optional[int]:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase_ ( self: Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = AltDiffusionPipeline.from_pretrained('''BAAI/AltDiffusion''' , safety_checker=UpperCamelCase_ )
lowercase__ = alt_pipe.to(UpperCamelCase_ )
alt_pipe.set_progress_bar_config(disable=UpperCamelCase_ )
lowercase__ = '''A painting of a squirrel eating a burger'''
lowercase__ = torch.manual_seed(0 )
lowercase__ = alt_pipe([prompt] , generator=UpperCamelCase_ , guidance_scale=6.0 , num_inference_steps=20 , output_type='''np''' )
lowercase__ = output.images
lowercase__ = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
lowercase__ = np.array([0.1010, 0.0800, 0.0794, 0.0885, 0.0843, 0.0762, 0.0769, 0.0729, 0.0586] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def lowerCamelCase_ ( self: Union[str, Any] ) -> Dict:
"""simple docstring"""
lowercase__ = DDIMScheduler.from_pretrained('''BAAI/AltDiffusion''' , subfolder='''scheduler''' )
lowercase__ = AltDiffusionPipeline.from_pretrained('''BAAI/AltDiffusion''' , scheduler=UpperCamelCase_ , safety_checker=UpperCamelCase_ )
lowercase__ = alt_pipe.to(UpperCamelCase_ )
alt_pipe.set_progress_bar_config(disable=UpperCamelCase_ )
lowercase__ = '''A painting of a squirrel eating a burger'''
lowercase__ = torch.manual_seed(0 )
lowercase__ = alt_pipe([prompt] , generator=UpperCamelCase_ , num_inference_steps=2 , output_type='''numpy''' )
lowercase__ = output.images
lowercase__ = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
lowercase__ = np.array([0.4019, 0.4052, 0.3810, 0.4119, 0.3916, 0.3982, 0.4651, 0.4195, 0.5323] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
| 93 | 0 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow
from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class __UpperCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , unittest.TestCase ):
A_ = StableDiffusionInpaintPipeline
A_ = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
A_ = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
A_ = frozenset(
[] ) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
A_ = frozenset([] )
def __UpperCAmelCase ( self ):
'''simple docstring'''
torch.manual_seed(0 )
__a : int = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=9 , out_channels=4 , down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') , up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D') , cross_attention_dim=32 , attention_head_dim=(2, 4) , use_linear_projection=__a , )
__a : str = PNDMScheduler(skip_prk_steps=__a )
torch.manual_seed(0 )
__a : 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 , sample_size=128 , )
torch.manual_seed(0 )
__a : List[str] = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , hidden_act='gelu' , projection_dim=512 , )
__a : Dict = CLIPTextModel(__a )
__a : Union[str, Any] = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
__a : Union[str, Any] = {
'unet': unet,
'scheduler': scheduler,
'vae': vae,
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'safety_checker': None,
'feature_extractor': None,
}
return components
def __UpperCAmelCase ( self , __a , __a=0 ):
'''simple docstring'''
__a : Union[str, Any] = floats_tensor((1, 3, 32, 32) , rng=random.Random(__a ) ).to(__a )
__a : List[Any] = image.cpu().permute(0 , 2 , 3 , 1 )[0]
__a : Tuple = Image.fromarray(np.uinta(__a ) ).convert('RGB' ).resize((64, 64) )
__a : Tuple = Image.fromarray(np.uinta(image + 4 ) ).convert('RGB' ).resize((64, 64) )
if str(__a ).startswith('mps' ):
__a : Any = torch.manual_seed(__a )
else:
__a : str = torch.Generator(device=__a ).manual_seed(__a )
__a : Dict = {
'prompt': 'A painting of a squirrel eating a burger',
'image': init_image,
'mask_image': mask_image,
'generator': generator,
'num_inference_steps': 2,
'guidance_scale': 6.0,
'output_type': 'numpy',
}
return inputs
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Dict = 'cpu' # ensure determinism for the device-dependent torch.Generator
__a : str = self.get_dummy_components()
__a : Union[str, Any] = StableDiffusionInpaintPipeline(**__a )
__a : List[Any] = sd_pipe.to(__a )
sd_pipe.set_progress_bar_config(disable=__a )
__a : List[Any] = self.get_dummy_inputs(__a )
__a : Dict = sd_pipe(**__a ).images
__a : Optional[Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
__a : List[Any] = np.array([0.4727, 0.5735, 0.3941, 0.5446, 0.5926, 0.4394, 0.5062, 0.4654, 0.4476] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def __UpperCAmelCase ( self ):
'''simple docstring'''
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
@slow
@require_torch_gpu
class __UpperCamelCase ( unittest.TestCase ):
def __UpperCAmelCase ( self ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : List[str] = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/sd2-inpaint/init_image.png' )
__a : List[str] = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png' )
__a : str = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'
'/yellow_cat_sitting_on_a_park_bench.npy' )
__a : Optional[int] = 'stabilityai/stable-diffusion-2-inpainting'
__a : Optional[int] = StableDiffusionInpaintPipeline.from_pretrained(__a , safety_checker=__a )
pipe.to(__a )
pipe.set_progress_bar_config(disable=__a )
pipe.enable_attention_slicing()
__a : Dict = 'Face of a yellow cat, high resolution, sitting on a park bench'
__a : Tuple = torch.manual_seed(0 )
__a : int = pipe(
prompt=__a , image=__a , mask_image=__a , generator=__a , output_type='np' , )
__a : Dict = output.images[0]
assert image.shape == (512, 512, 3)
assert np.abs(expected_image - image ).max() < 9E-3
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Tuple = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/sd2-inpaint/init_image.png' )
__a : int = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png' )
__a : Any = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'
'/yellow_cat_sitting_on_a_park_bench_fp16.npy' )
__a : str = 'stabilityai/stable-diffusion-2-inpainting'
__a : List[str] = StableDiffusionInpaintPipeline.from_pretrained(
__a , torch_dtype=torch.floataa , safety_checker=__a , )
pipe.to(__a )
pipe.set_progress_bar_config(disable=__a )
pipe.enable_attention_slicing()
__a : Union[str, Any] = 'Face of a yellow cat, high resolution, sitting on a park bench'
__a : int = torch.manual_seed(0 )
__a : Optional[Any] = pipe(
prompt=__a , image=__a , mask_image=__a , generator=__a , output_type='np' , )
__a : int = output.images[0]
assert image.shape == (512, 512, 3)
assert np.abs(expected_image - image ).max() < 5E-1
def __UpperCAmelCase ( self ):
'''simple docstring'''
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
__a : str = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/sd2-inpaint/init_image.png' )
__a : List[Any] = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png' )
__a : str = 'stabilityai/stable-diffusion-2-inpainting'
__a : Any = PNDMScheduler.from_pretrained(__a , subfolder='scheduler' )
__a : str = StableDiffusionInpaintPipeline.from_pretrained(
__a , safety_checker=__a , scheduler=__a , torch_dtype=torch.floataa , )
pipe.to(__a )
pipe.set_progress_bar_config(disable=__a )
pipe.enable_attention_slicing(1 )
pipe.enable_sequential_cpu_offload()
__a : str = 'Face of a yellow cat, high resolution, sitting on a park bench'
__a : Tuple = torch.manual_seed(0 )
__a : str = pipe(
prompt=__a , image=__a , mask_image=__a , generator=__a , num_inference_steps=2 , output_type='np' , )
__a : List[str] = torch.cuda.max_memory_allocated()
# make sure that less than 2.65 GB is allocated
assert mem_bytes < 2.65 * 10**9
| 27 |
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def __UpperCamelCase ( _A = 3 ):
if isinstance(_A , _A ):
raise TypeError('''number of qubits must be a integer.''' )
if number_of_qubits <= 0:
raise ValueError('''number of qubits must be > 0.''' )
if math.floor(_A ) != number_of_qubits:
raise ValueError('''number of qubits must be exact integer.''' )
if number_of_qubits > 10:
raise ValueError('''number of qubits too large to simulate(>10).''' )
lowerCAmelCase_ = QuantumRegister(_A , '''qr''' )
lowerCAmelCase_ = ClassicalRegister(_A , '''cr''' )
lowerCAmelCase_ = QuantumCircuit(_A , _A )
lowerCAmelCase_ = number_of_qubits
for i in range(_A ):
quantum_circuit.h(number_of_qubits - i - 1 )
counter -= 1
for j in range(_A ):
quantum_circuit.cp(np.pi / 2 ** (counter - j) , _A , _A )
for k in range(number_of_qubits // 2 ):
quantum_circuit.swap(_A , number_of_qubits - k - 1 )
# measure all the qubits
quantum_circuit.measure(_A , _A )
# simulate with 10000 shots
lowerCAmelCase_ = Aer.get_backend('''qasm_simulator''' )
lowerCAmelCase_ = execute(_A , _A , shots=10000 )
return job.result().get_counts(_A )
if __name__ == "__main__":
print(
f"Total count for quantum fourier transform state is: \
{quantum_fourier_transform(3)}"
)
| 278 | 0 |
import unittest
from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
LiltForQuestionAnswering,
LiltForSequenceClassification,
LiltForTokenClassification,
LiltModel,
)
from transformers.models.lilt.modeling_lilt import LILT_PRETRAINED_MODEL_ARCHIVE_LIST
class SCREAMING_SNAKE_CASE__ :
"""simple docstring"""
def __init__( self , __UpperCamelCase , __UpperCamelCase=13 , __UpperCamelCase=7 , __UpperCamelCase=True , __UpperCamelCase=True , __UpperCamelCase=True , __UpperCamelCase=True , __UpperCamelCase=99 , __UpperCamelCase=24 , __UpperCamelCase=2 , __UpperCamelCase=6 , __UpperCamelCase=37 , __UpperCamelCase="gelu" , __UpperCamelCase=0.1 , __UpperCamelCase=0.1 , __UpperCamelCase=5_12 , __UpperCamelCase=16 , __UpperCamelCase=2 , __UpperCamelCase=0.02 , __UpperCamelCase=3 , __UpperCamelCase=None , __UpperCamelCase=10_00 , ) -> int:
'''simple docstring'''
__UpperCamelCase : Tuple = parent
__UpperCamelCase : Optional[int] = batch_size
__UpperCamelCase : Tuple = seq_length
__UpperCamelCase : Any = is_training
__UpperCamelCase : Union[str, Any] = use_input_mask
__UpperCamelCase : Optional[int] = use_token_type_ids
__UpperCamelCase : str = use_labels
__UpperCamelCase : Optional[int] = vocab_size
__UpperCamelCase : List[Any] = hidden_size
__UpperCamelCase : int = num_hidden_layers
__UpperCamelCase : Dict = num_attention_heads
__UpperCamelCase : Optional[Any] = intermediate_size
__UpperCamelCase : str = hidden_act
__UpperCamelCase : str = hidden_dropout_prob
__UpperCamelCase : Dict = attention_probs_dropout_prob
__UpperCamelCase : str = max_position_embeddings
__UpperCamelCase : List[str] = type_vocab_size
__UpperCamelCase : int = type_sequence_label_size
__UpperCamelCase : Any = initializer_range
__UpperCamelCase : List[str] = num_labels
__UpperCamelCase : List[str] = scope
__UpperCamelCase : List[Any] = range_bbox
def __lowerCamelCase ( self ) -> str:
'''simple docstring'''
__UpperCamelCase : str = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__UpperCamelCase : int = ids_tensor([self.batch_size, self.seq_length, 4] , self.range_bbox )
# Ensure that bbox is legal
for i in range(bbox.shape[0] ):
for j in range(bbox.shape[1] ):
if bbox[i, j, 3] < bbox[i, j, 1]:
__UpperCamelCase : Optional[Any] = bbox[i, j, 3]
__UpperCamelCase : List[str] = bbox[i, j, 1]
__UpperCamelCase : int = t
if bbox[i, j, 2] < bbox[i, j, 0]:
__UpperCamelCase : int = bbox[i, j, 2]
__UpperCamelCase : List[Any] = bbox[i, j, 0]
__UpperCamelCase : List[Any] = t
__UpperCamelCase : Optional[int] = None
if self.use_input_mask:
__UpperCamelCase : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
__UpperCamelCase : Any = None
if self.use_token_type_ids:
__UpperCamelCase : Any = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__UpperCamelCase : Optional[Any] = None
__UpperCamelCase : str = None
if self.use_labels:
__UpperCamelCase : Any = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__UpperCamelCase : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__UpperCamelCase : Tuple = self.get_config()
return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels
def __lowerCamelCase ( self ) -> Optional[int]:
'''simple docstring'''
return LiltConfig(
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 , initializer_range=self.initializer_range , )
def __lowerCamelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , ) -> Tuple:
'''simple docstring'''
__UpperCamelCase : Dict = LiltModel(config=__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
__UpperCamelCase : int = model(__UpperCamelCase , bbox=__UpperCamelCase , attention_mask=__UpperCamelCase , token_type_ids=__UpperCamelCase )
__UpperCamelCase : List[Any] = model(__UpperCamelCase , bbox=__UpperCamelCase , token_type_ids=__UpperCamelCase )
__UpperCamelCase : int = model(__UpperCamelCase , bbox=__UpperCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def __lowerCamelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , ) -> int:
'''simple docstring'''
__UpperCamelCase : List[Any] = self.num_labels
__UpperCamelCase : int = LiltForTokenClassification(config=__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
__UpperCamelCase : Tuple = model(
__UpperCamelCase , bbox=__UpperCamelCase , attention_mask=__UpperCamelCase , token_type_ids=__UpperCamelCase , labels=__UpperCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __lowerCamelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , ) -> Optional[Any]:
'''simple docstring'''
__UpperCamelCase : str = LiltForQuestionAnswering(config=__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
__UpperCamelCase : Dict = model(
__UpperCamelCase , bbox=__UpperCamelCase , attention_mask=__UpperCamelCase , token_type_ids=__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 __lowerCamelCase ( self ) -> Optional[Any]:
'''simple docstring'''
__UpperCamelCase : Optional[int] = self.prepare_config_and_inputs()
(
(
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) ,
) : List[str] = config_and_inputs
__UpperCamelCase : Tuple = {
"input_ids": input_ids,
"bbox": bbox,
"token_type_ids": token_type_ids,
"attention_mask": input_mask,
}
return config, inputs_dict
@require_torch
class SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , unittest.TestCase ):
"""simple docstring"""
lowercase : Union[str, Any] = (
(
LiltModel,
LiltForSequenceClassification,
LiltForTokenClassification,
LiltForQuestionAnswering,
)
if is_torch_available()
else ()
)
lowercase : Union[str, Any] = (
{
'feature-extraction': LiltModel,
'question-answering': LiltForQuestionAnswering,
'text-classification': LiltForSequenceClassification,
'token-classification': LiltForTokenClassification,
'zero-shot': LiltForSequenceClassification,
}
if is_torch_available()
else {}
)
lowercase : Tuple = False
lowercase : Any = False
def __lowerCamelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ) -> int:
'''simple docstring'''
return True
def __lowerCamelCase ( self ) -> str:
'''simple docstring'''
__UpperCamelCase : int = LiltModelTester(self )
__UpperCamelCase : Tuple = ConfigTester(self , config_class=__UpperCamelCase , hidden_size=37 )
def __lowerCamelCase ( self ) -> Optional[int]:
'''simple docstring'''
self.config_tester.run_common_tests()
def __lowerCamelCase ( self ) -> Optional[int]:
'''simple docstring'''
__UpperCamelCase : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__UpperCamelCase )
def __lowerCamelCase ( self ) -> List[str]:
'''simple docstring'''
__UpperCamelCase : int = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
__UpperCamelCase : Dict = type
self.model_tester.create_and_check_model(*__UpperCamelCase )
def __lowerCamelCase ( self ) -> Optional[Any]:
'''simple docstring'''
__UpperCamelCase : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*__UpperCamelCase )
def __lowerCamelCase ( self ) -> Dict:
'''simple docstring'''
__UpperCamelCase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*__UpperCamelCase )
@slow
def __lowerCamelCase ( self ) -> Tuple:
'''simple docstring'''
for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__UpperCamelCase : List[str] = LiltModel.from_pretrained(__UpperCamelCase )
self.assertIsNotNone(__UpperCamelCase )
@require_torch
@slow
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
"""simple docstring"""
def __lowerCamelCase ( self ) -> List[str]:
'''simple docstring'''
__UpperCamelCase : Union[str, Any] = LiltModel.from_pretrained("SCUT-DLVCLab/lilt-roberta-en-base" ).to(__UpperCamelCase )
__UpperCamelCase : Dict = torch.tensor([[1, 2]] , device=__UpperCamelCase )
__UpperCamelCase : Dict = torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]] , device=__UpperCamelCase )
# forward pass
with torch.no_grad():
__UpperCamelCase : Dict = model(input_ids=__UpperCamelCase , bbox=__UpperCamelCase )
__UpperCamelCase : Any = torch.Size([1, 2, 7_68] )
__UpperCamelCase : List[Any] = torch.tensor(
[[-0.0653, 0.0950, -0.0061], [-0.0545, 0.0926, -0.0324]] , device=__UpperCamelCase , )
self.assertTrue(outputs.last_hidden_state.shape , __UpperCamelCase )
self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :, :3] , __UpperCamelCase , atol=1E-3 ) ) | 171 |
def UpperCAmelCase_ (_lowerCAmelCase : list ):
if len(_lowerCAmelCase ) <= 1:
return lst
__UpperCamelCase : Dict = 1
while i < len(_lowerCAmelCase ):
if lst[i - 1] <= lst[i]:
i += 1
else:
__UpperCamelCase , __UpperCamelCase : Union[str, Any] = lst[i], lst[i - 1]
i -= 1
if i == 0:
__UpperCamelCase : Any = 1
return lst
if __name__ == "__main__":
lowercase : Dict = input("Enter numbers separated by a comma:\n").strip()
lowercase : Union[str, Any] = [int(item) for item in user_input.split(",")]
print(gnome_sort(unsorted)) | 171 | 1 |
from pathlib import Path
import numpy as np
from PIL import Image
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ ):
snake_case_, snake_case_, snake_case_ = rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
return 0.2989 * r + 0.5870 * g + 0.1140 * b
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ ):
return (gray > 127) & (gray <= 255)
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
snake_case_ = np.zeros_like(SCREAMING_SNAKE_CASE__ )
snake_case_ = np.zeros(
(image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1) )
# Copy image to padded image
snake_case_ = image
# Iterate over image & apply kernel
for x in range(image.shape[1] ):
for y in range(image.shape[0] ):
snake_case_ = (
kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
).sum()
snake_case_ = int(summation > 0 )
return output
if __name__ == "__main__":
# read original image
lowerCAmelCase_ = Path(__file__).resolve().parent / '''image_data''' / '''lena.jpg'''
lowerCAmelCase_ = np.array(Image.open(lena_path))
# kernel to be applied
lowerCAmelCase_ = np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
lowerCAmelCase_ = dilation(gray_to_binary(rgb_to_gray(lena)), structuring_element)
# Save the output image
lowerCAmelCase_ = Image.fromarray(output).convert('''RGB''')
pil_img.save('''result_dilation.png''') | 8 | """simple docstring"""
import os
from typing import Dict, List, Tuple, TypeVar, Union
lowercase__ = TypeVar('T')
lowercase__ = Union[List[T], Tuple[T, ...]]
lowercase__ = Union[T, List[T], Dict[str, T]]
lowercase__ = Union[str, bytes, os.PathLike]
| 290 | 0 |
'''simple docstring'''
import pytest
from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
'''kwargs, expected''' , [
({'''num_shards''': 0, '''max_num_jobs''': 1}, []),
({'''num_shards''': 10, '''max_num_jobs''': 1}, [range(10 )]),
({'''num_shards''': 10, '''max_num_jobs''': 10}, [range(a , i + 1 ) for i in range(10 )]),
({'''num_shards''': 1, '''max_num_jobs''': 10}, [range(1 )]),
({'''num_shards''': 10, '''max_num_jobs''': 3}, [range(0 , 4 ), range(4 , 7 ), range(7 , 10 )]),
({'''num_shards''': 3, '''max_num_jobs''': 10}, [range(0 , 1 ), range(1 , 2 ), range(2 , 3 )]),
] , )
def UpperCamelCase ( a , a ) -> Tuple:
'''simple docstring'''
__magic_name__ = _distribute_shards(**a )
assert out == expected
@pytest.mark.parametrize(
'''gen_kwargs, max_num_jobs, expected''' , [
({'''foo''': 0}, 10, [{'''foo''': 0}]),
({'''shards''': [0, 1, 2, 3]}, 1, [{'''shards''': [0, 1, 2, 3]}]),
({'''shards''': [0, 1, 2, 3]}, 4, [{'''shards''': [0]}, {'''shards''': [1]}, {'''shards''': [2]}, {'''shards''': [3]}]),
({'''shards''': [0, 1]}, 4, [{'''shards''': [0]}, {'''shards''': [1]}]),
({'''shards''': [0, 1, 2, 3]}, 2, [{'''shards''': [0, 1]}, {'''shards''': [2, 3]}]),
] , )
def UpperCamelCase ( a , a , a ) -> Optional[int]:
'''simple docstring'''
__magic_name__ = _split_gen_kwargs(a , a )
assert out == expected
@pytest.mark.parametrize(
'''gen_kwargs, expected''' , [
({'''foo''': 0}, 1),
({'''shards''': [0]}, 1),
({'''shards''': [0, 1, 2, 3]}, 4),
({'''shards''': [0, 1, 2, 3], '''foo''': 0}, 4),
({'''shards''': [0, 1, 2, 3], '''other''': (0, 1)}, 4),
({'''shards''': [0, 1, 2, 3], '''shards2''': [0, 1]}, RuntimeError),
] , )
def UpperCamelCase ( a , a ) -> Union[str, Any]:
'''simple docstring'''
if expected is RuntimeError:
with pytest.raises(a ):
_number_of_shards_in_gen_kwargs(a )
else:
__magic_name__ = _number_of_shards_in_gen_kwargs(a )
assert out == expected
| 364 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCAmelCase = {
"configuration_clap": [
"CLAP_PRETRAINED_MODEL_ARCHIVE_LIST",
"ClapAudioConfig",
"ClapConfig",
"ClapTextConfig",
],
"processing_clap": ["ClapProcessor"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCAmelCase = [
"CLAP_PRETRAINED_MODEL_ARCHIVE_LIST",
"ClapModel",
"ClapPreTrainedModel",
"ClapTextModel",
"ClapTextModelWithProjection",
"ClapAudioModel",
"ClapAudioModelWithProjection",
]
_lowerCAmelCase = ["ClapFeatureExtractor"]
if TYPE_CHECKING:
from .configuration_clap import (
CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
ClapAudioConfig,
ClapConfig,
ClapTextConfig,
)
from .processing_clap import ClapProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_clap import ClapFeatureExtractor
from .modeling_clap import (
CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
ClapAudioModel,
ClapAudioModelWithProjection,
ClapModel,
ClapPreTrainedModel,
ClapTextModel,
ClapTextModelWithProjection,
)
else:
import sys
_lowerCAmelCase = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 98 | 0 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor
from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor
def __snake_case( _lowerCAmelCase ) -> Any:
snake_case__ : Any = SwinaSRConfig()
if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
snake_case__ : List[Any] = 4
elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
snake_case__ : Optional[int] = 4
snake_case__ : Any = 48
snake_case__ : List[Any] = """pixelshuffle_aux"""
elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
snake_case__ : Tuple = [6, 6, 6, 6]
snake_case__ : Dict = 60
snake_case__ : str = [6, 6, 6, 6]
snake_case__ : str = """pixelshuffledirect"""
elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
snake_case__ : Dict = 4
snake_case__ : str = """nearest+conv"""
elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
snake_case__ : Any = 1
snake_case__ : Dict = 1
snake_case__ : Tuple = 126
snake_case__ : Dict = 7
snake_case__ : Tuple = 255.0
snake_case__ : Tuple = """"""
return config
def __snake_case( _lowerCAmelCase , _lowerCAmelCase ) -> List[Any]:
if "patch_embed.proj" in name and "layers" not in name:
snake_case__ : Any = name.replace("""patch_embed.proj""" , """embeddings.patch_embeddings.projection""" )
if "patch_embed.norm" in name:
snake_case__ : int = name.replace("""patch_embed.norm""" , """embeddings.patch_embeddings.layernorm""" )
if "layers" in name:
snake_case__ : List[str] = name.replace("""layers""" , """encoder.stages""" )
if "residual_group.blocks" in name:
snake_case__ : Optional[int] = name.replace("""residual_group.blocks""" , """layers""" )
if "attn.proj" in name:
snake_case__ : Optional[Any] = name.replace("""attn.proj""" , """attention.output.dense""" )
if "attn" in name:
snake_case__ : Optional[int] = name.replace("""attn""" , """attention.self""" )
if "norm1" in name:
snake_case__ : str = name.replace("""norm1""" , """layernorm_before""" )
if "norm2" in name:
snake_case__ : List[str] = name.replace("""norm2""" , """layernorm_after""" )
if "mlp.fc1" in name:
snake_case__ : int = name.replace("""mlp.fc1""" , """intermediate.dense""" )
if "mlp.fc2" in name:
snake_case__ : int = name.replace("""mlp.fc2""" , """output.dense""" )
if "q_bias" in name:
snake_case__ : Tuple = name.replace("""q_bias""" , """query.bias""" )
if "k_bias" in name:
snake_case__ : Dict = name.replace("""k_bias""" , """key.bias""" )
if "v_bias" in name:
snake_case__ : str = name.replace("""v_bias""" , """value.bias""" )
if "cpb_mlp" in name:
snake_case__ : Optional[int] = name.replace("""cpb_mlp""" , """continuous_position_bias_mlp""" )
if "patch_embed.proj" in name:
snake_case__ : List[str] = name.replace("""patch_embed.proj""" , """patch_embed.projection""" )
if name == "norm.weight":
snake_case__ : str = """layernorm.weight"""
if name == "norm.bias":
snake_case__ : Tuple = """layernorm.bias"""
if "conv_first" in name:
snake_case__ : int = name.replace("""conv_first""" , """first_convolution""" )
if (
"upsample" in name
or "conv_before_upsample" in name
or "conv_bicubic" in name
or "conv_up" in name
or "conv_hr" in name
or "conv_last" in name
or "aux" in name
):
# heads
if "conv_last" in name:
snake_case__ : str = name.replace("""conv_last""" , """final_convolution""" )
if config.upsampler in ["pixelshuffle", "pixelshuffle_aux", "nearest+conv"]:
if "conv_before_upsample.0" in name:
snake_case__ : List[Any] = name.replace("""conv_before_upsample.0""" , """conv_before_upsample""" )
if "upsample.0" in name:
snake_case__ : List[str] = name.replace("""upsample.0""" , """upsample.convolution_0""" )
if "upsample.2" in name:
snake_case__ : int = name.replace("""upsample.2""" , """upsample.convolution_1""" )
snake_case__ : Dict = """upsample.""" + name
elif config.upsampler == "pixelshuffledirect":
snake_case__ : str = name.replace("""upsample.0.weight""" , """upsample.conv.weight""" )
snake_case__ : Optional[Any] = name.replace("""upsample.0.bias""" , """upsample.conv.bias""" )
else:
pass
else:
snake_case__ : Any = """swin2sr.""" + name
return name
def __snake_case( _lowerCAmelCase , _lowerCAmelCase ) -> Union[str, Any]:
for key in orig_state_dict.copy().keys():
snake_case__ : Tuple = orig_state_dict.pop(_lowerCAmelCase )
if "qkv" in key:
snake_case__ : Optional[int] = key.split(""".""" )
snake_case__ : Tuple = int(key_split[1] )
snake_case__ : List[Any] = int(key_split[4] )
snake_case__ : List[Any] = config.embed_dim
if "weight" in key:
snake_case__ : Any = val[:dim, :]
snake_case__ : Dict = val[dim : dim * 2, :]
snake_case__ : Any = val[-dim:, :]
else:
snake_case__ : str = val[:dim]
snake_case__ : int = val[dim : dim * 2]
snake_case__ : List[Any] = val[-dim:]
pass
else:
snake_case__ : Tuple = val
return orig_state_dict
def __snake_case( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> List[Any]:
snake_case__ : Tuple = get_config(_lowerCAmelCase )
snake_case__ : List[Any] = SwinaSRForImageSuperResolution(_lowerCAmelCase )
model.eval()
snake_case__ : Union[str, Any] = torch.hub.load_state_dict_from_url(_lowerCAmelCase , map_location="""cpu""" )
snake_case__ : Optional[int] = convert_state_dict(_lowerCAmelCase , _lowerCAmelCase )
snake_case__ , snake_case__ : Union[str, Any] = model.load_state_dict(_lowerCAmelCase , strict=_lowerCAmelCase )
if len(_lowerCAmelCase ) > 0:
raise ValueError("""Missing keys when converting: {}""".format(_lowerCAmelCase ) )
for key in unexpected_keys:
if not ("relative_position_index" in key or "relative_coords_table" in key or "self_mask" in key):
raise ValueError(f"Unexpected key {key} in state_dict" )
# verify values
snake_case__ : Optional[Any] = """https://github.com/mv-lab/swin2sr/blob/main/testsets/real-inputs/shanghai.jpg?raw=true"""
snake_case__ : List[Any] = Image.open(requests.get(_lowerCAmelCase , stream=_lowerCAmelCase ).raw ).convert("""RGB""" )
snake_case__ : Tuple = SwinaSRImageProcessor()
# pixel_values = processor(image, return_tensors="pt").pixel_values
snake_case__ : List[Any] = 126 if """Jpeg""" in checkpoint_url else 256
snake_case__ : Tuple = Compose(
[
Resize((image_size, image_size) ),
ToTensor(),
Normalize(mean=[0.485, 0.456, 0.406] , std=[0.229, 0.224, 0.225] ),
] )
snake_case__ : Union[str, Any] = transforms(_lowerCAmelCase ).unsqueeze(0 )
if config.num_channels == 1:
snake_case__ : List[Any] = pixel_values[:, 0, :, :].unsqueeze(1 )
snake_case__ : Optional[int] = model(_lowerCAmelCase )
# assert values
if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
snake_case__ : Optional[Any] = torch.Size([1, 3, 512, 512] )
snake_case__ : Any = torch.tensor(
[[-0.7087, -0.7138, -0.6721], [-0.8340, -0.8095, -0.7298], [-0.9149, -0.8414, -0.7940]] )
elif "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
snake_case__ : List[str] = torch.Size([1, 3, 1_024, 1_024] )
snake_case__ : Any = torch.tensor(
[[-0.7775, -0.8105, -0.8933], [-0.7764, -0.8356, -0.9225], [-0.7976, -0.8686, -0.9579]] )
elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
# TODO values didn't match exactly here
snake_case__ : List[Any] = torch.Size([1, 3, 1_024, 1_024] )
snake_case__ : Tuple = torch.tensor(
[[-0.8035, -0.7504, -0.7491], [-0.8538, -0.8124, -0.7782], [-0.8804, -0.8651, -0.8493]] )
elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
snake_case__ : Any = torch.Size([1, 3, 512, 512] )
snake_case__ : Dict = torch.tensor(
[[-0.7669, -0.8662, -0.8767], [-0.8810, -0.9962, -0.9820], [-0.9340, -1.0322, -1.1149]] )
elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
snake_case__ : Union[str, Any] = torch.Size([1, 3, 1_024, 1_024] )
snake_case__ : Optional[Any] = torch.tensor(
[[-0.5238, -0.5557, -0.6321], [-0.6016, -0.5903, -0.6391], [-0.6244, -0.6334, -0.6889]] )
assert (
outputs.reconstruction.shape == expected_shape
), f"Shape of reconstruction should be {expected_shape}, but is {outputs.reconstruction.shape}"
assert torch.allclose(outputs.reconstruction[0, 0, :3, :3] , _lowerCAmelCase , atol=1e-3 )
print("""Looks ok!""" )
snake_case__ : Optional[Any] = {
"""https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth""": (
"""swin2SR-classical-sr-x2-64"""
),
"""https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X4_64.pth""": (
"""swin2SR-classical-sr-x4-64"""
),
"""https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_CompressedSR_X4_48.pth""": (
"""swin2SR-compressed-sr-x4-48"""
),
"""https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_Lightweight_X2_64.pth""": (
"""swin2SR-lightweight-x2-64"""
),
"""https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR.pth""": (
"""swin2SR-realworld-sr-x4-64-bsrgan-psnr"""
),
}
snake_case__ : Union[str, Any] = url_to_name[checkpoint_url]
if pytorch_dump_folder_path is not None:
print(f"Saving model {model_name} to {pytorch_dump_folder_path}" )
model.save_pretrained(_lowerCAmelCase )
print(f"Saving image processor to {pytorch_dump_folder_path}" )
processor.save_pretrained(_lowerCAmelCase )
if push_to_hub:
model.push_to_hub(f"caidas/{model_name}" )
processor.push_to_hub(f"caidas/{model_name}" )
if __name__ == "__main__":
__a = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--checkpoint_url",
default="https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth",
type=str,
help="URL of the original Swin2SR checkpoint you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
)
parser.add_argument("--push_to_hub", action="store_true", help="Whether to push the converted model to the hub.")
__a = parser.parse_args()
convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)
| 35 |
'''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 __snake_case( _lowerCAmelCase ) -> List[Any]:
snake_case__ : Dict = SwinConfig()
snake_case__ : Optional[Any] = swin_name.split("""_""" )
snake_case__ : Any = name_split[1]
snake_case__ : List[Any] = int(name_split[4] )
snake_case__ : int = int(name_split[3][-1] )
if model_size == "tiny":
snake_case__ : List[Any] = 96
snake_case__ : int = (2, 2, 6, 2)
snake_case__ : int = (3, 6, 12, 24)
elif model_size == "small":
snake_case__ : Union[str, Any] = 96
snake_case__ : Optional[Any] = (2, 2, 18, 2)
snake_case__ : str = (3, 6, 12, 24)
elif model_size == "base":
snake_case__ : Dict = 128
snake_case__ : str = (2, 2, 18, 2)
snake_case__ : Dict = (4, 8, 16, 32)
else:
snake_case__ : List[str] = 192
snake_case__ : str = (2, 2, 18, 2)
snake_case__ : List[Any] = (6, 12, 24, 48)
if "in22k" in swin_name:
snake_case__ : str = 21_841
else:
snake_case__ : List[str] = 1_000
snake_case__ : int = """huggingface/label-files"""
snake_case__ : Any = """imagenet-1k-id2label.json"""
snake_case__ : List[Any] = json.load(open(hf_hub_download(_lowerCAmelCase , _lowerCAmelCase , repo_type="""dataset""" ) , """r""" ) )
snake_case__ : Dict = {int(_lowerCAmelCase ): v for k, v in idalabel.items()}
snake_case__ : Optional[int] = idalabel
snake_case__ : List[Any] = {v: k for k, v in idalabel.items()}
snake_case__ : List[Any] = img_size
snake_case__ : Dict = num_classes
snake_case__ : Dict = embed_dim
snake_case__ : Optional[int] = depths
snake_case__ : int = num_heads
snake_case__ : Optional[int] = window_size
return config
def __snake_case( _lowerCAmelCase ) -> Dict:
if "patch_embed.proj" in name:
snake_case__ : List[str] = name.replace("""patch_embed.proj""" , """embeddings.patch_embeddings.projection""" )
if "patch_embed.norm" in name:
snake_case__ : int = name.replace("""patch_embed.norm""" , """embeddings.norm""" )
if "layers" in name:
snake_case__ : str = """encoder.""" + name
if "attn.proj" in name:
snake_case__ : List[str] = name.replace("""attn.proj""" , """attention.output.dense""" )
if "attn" in name:
snake_case__ : Tuple = name.replace("""attn""" , """attention.self""" )
if "norm1" in name:
snake_case__ : List[str] = name.replace("""norm1""" , """layernorm_before""" )
if "norm2" in name:
snake_case__ : Optional[Any] = name.replace("""norm2""" , """layernorm_after""" )
if "mlp.fc1" in name:
snake_case__ : Union[str, Any] = name.replace("""mlp.fc1""" , """intermediate.dense""" )
if "mlp.fc2" in name:
snake_case__ : Dict = name.replace("""mlp.fc2""" , """output.dense""" )
if name == "norm.weight":
snake_case__ : Tuple = """layernorm.weight"""
if name == "norm.bias":
snake_case__ : Union[str, Any] = """layernorm.bias"""
if "head" in name:
snake_case__ : Optional[int] = name.replace("""head""" , """classifier""" )
else:
snake_case__ : List[str] = """swin.""" + name
return name
def __snake_case( _lowerCAmelCase , _lowerCAmelCase ) -> Optional[Any]:
for key in orig_state_dict.copy().keys():
snake_case__ : Optional[int] = orig_state_dict.pop(_lowerCAmelCase )
if "mask" in key:
continue
elif "qkv" in key:
snake_case__ : Dict = key.split(""".""" )
snake_case__ : Optional[int] = int(key_split[1] )
snake_case__ : Union[str, Any] = int(key_split[3] )
snake_case__ : List[Any] = model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size
if "weight" in key:
snake_case__ : Optional[Any] = val[:dim, :]
snake_case__ : Tuple = val[
dim : dim * 2, :
]
snake_case__ : Dict = val[-dim:, :]
else:
snake_case__ : Tuple = val[
:dim
]
snake_case__ : int = val[
dim : dim * 2
]
snake_case__ : int = val[
-dim:
]
else:
snake_case__ : Union[str, Any] = val
return orig_state_dict
def __snake_case( _lowerCAmelCase , _lowerCAmelCase ) -> int:
snake_case__ : Optional[int] = timm.create_model(_lowerCAmelCase , pretrained=_lowerCAmelCase )
timm_model.eval()
snake_case__ : Optional[int] = get_swin_config(_lowerCAmelCase )
snake_case__ : Optional[Any] = SwinForImageClassification(_lowerCAmelCase )
model.eval()
snake_case__ : str = convert_state_dict(timm_model.state_dict() , _lowerCAmelCase )
model.load_state_dict(_lowerCAmelCase )
snake_case__ : int = """http://images.cocodataset.org/val2017/000000039769.jpg"""
snake_case__ : Dict = AutoImageProcessor.from_pretrained("""microsoft/{}""".format(swin_name.replace("""_""" , """-""" ) ) )
snake_case__ : Dict = Image.open(requests.get(_lowerCAmelCase , stream=_lowerCAmelCase ).raw )
snake_case__ : Optional[int] = image_processor(images=_lowerCAmelCase , return_tensors="""pt""" )
snake_case__ : Optional[Any] = timm_model(inputs["""pixel_values"""] )
snake_case__ : str = model(**_lowerCAmelCase ).logits
assert torch.allclose(_lowerCAmelCase , _lowerCAmelCase , atol=1e-3 )
print(f"Saving model {swin_name} to {pytorch_dump_folder_path}" )
model.save_pretrained(_lowerCAmelCase )
print(f"Saving image processor to {pytorch_dump_folder_path}" )
image_processor.save_pretrained(_lowerCAmelCase )
if __name__ == "__main__":
__a = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--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."
)
__a = parser.parse_args()
convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)
| 35 | 1 |
import functools
import logging
import os
import sys
import threading
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
import huggingface_hub.utils as hf_hub_utils
from tqdm import auto as tqdm_lib
__lowerCamelCase : List[Any] = threading.Lock()
__lowerCamelCase : Optional[logging.Handler] = None
__lowerCamelCase : int = {
"""debug""": logging.DEBUG,
"""info""": logging.INFO,
"""warning""": logging.WARNING,
"""error""": logging.ERROR,
"""critical""": logging.CRITICAL,
}
__lowerCamelCase : List[Any] = logging.WARNING
__lowerCamelCase : Any = True
def A_ ( ) -> Optional[Any]:
UpperCamelCase : str = os.getenv("TRANSFORMERS_VERBOSITY" , _lowerCAmelCase )
if env_level_str:
if env_level_str in log_levels:
return log_levels[env_level_str]
else:
logging.getLogger().warning(
F"""Unknown option TRANSFORMERS_VERBOSITY={env_level_str}, """
F"""has to be one of: { ", ".join(log_levels.keys() ) }""" )
return _default_log_level
def A_ ( ) -> str:
return __name__.split("." )[0]
def A_ ( ) -> logging.Logger:
return logging.getLogger(_get_library_name() )
def A_ ( ) -> None:
global _default_handler
with _lock:
if _default_handler:
# This library has already configured the library root logger.
return
UpperCamelCase : str = logging.StreamHandler() # Set sys.stderr as stream.
UpperCamelCase : Dict = sys.stderr.flush
# Apply our default configuration to the library root logger.
UpperCamelCase : List[Any] = _get_library_root_logger()
library_root_logger.addHandler(_default_handler )
library_root_logger.setLevel(_get_default_logging_level() )
UpperCamelCase : List[str] = False
def A_ ( ) -> None:
global _default_handler
with _lock:
if not _default_handler:
return
UpperCamelCase : Optional[int] = _get_library_root_logger()
library_root_logger.removeHandler(_default_handler )
library_root_logger.setLevel(logging.NOTSET )
UpperCamelCase : Optional[int] = None
def A_ ( ) -> Optional[Any]:
return log_levels
def A_ ( _lowerCAmelCase = None ) -> logging.Logger:
if name is None:
UpperCamelCase : Optional[Any] = _get_library_name()
_configure_library_root_logger()
return logging.getLogger(_lowerCAmelCase )
def A_ ( ) -> int:
_configure_library_root_logger()
return _get_library_root_logger().getEffectiveLevel()
def A_ ( _lowerCAmelCase ) -> None:
_configure_library_root_logger()
_get_library_root_logger().setLevel(_lowerCAmelCase )
def A_ ( ) -> Any:
return set_verbosity(_lowerCAmelCase )
def A_ ( ) -> Any:
return set_verbosity(_lowerCAmelCase )
def A_ ( ) -> int:
return set_verbosity(_lowerCAmelCase )
def A_ ( ) -> Dict:
return set_verbosity(_lowerCAmelCase )
def A_ ( ) -> None:
_configure_library_root_logger()
assert _default_handler is not None
_get_library_root_logger().removeHandler(_default_handler )
def A_ ( ) -> None:
_configure_library_root_logger()
assert _default_handler is not None
_get_library_root_logger().addHandler(_default_handler )
def A_ ( _lowerCAmelCase ) -> None:
_configure_library_root_logger()
assert handler is not None
_get_library_root_logger().addHandler(_lowerCAmelCase )
def A_ ( _lowerCAmelCase ) -> None:
_configure_library_root_logger()
assert handler is not None and handler not in _get_library_root_logger().handlers
_get_library_root_logger().removeHandler(_lowerCAmelCase )
def A_ ( ) -> None:
_configure_library_root_logger()
UpperCamelCase : Tuple = False
def A_ ( ) -> None:
_configure_library_root_logger()
UpperCamelCase : int = True
def A_ ( ) -> None:
UpperCamelCase : Tuple = _get_library_root_logger().handlers
for handler in handlers:
UpperCamelCase : int = logging.Formatter("[%(levelname)s|%(filename)s:%(lineno)s] %(asctime)s >> %(message)s" )
handler.setFormatter(_lowerCAmelCase )
def A_ ( ) -> None:
UpperCamelCase : str = _get_library_root_logger().handlers
for handler in handlers:
handler.setFormatter(_lowerCAmelCase )
def A_ ( self , *_lowerCAmelCase , **_lowerCAmelCase ) -> Optional[Any]:
UpperCamelCase : int = os.getenv("TRANSFORMERS_NO_ADVISORY_WARNINGS" , _lowerCAmelCase )
if no_advisory_warnings:
return
self.warning(*_lowerCAmelCase , **_lowerCAmelCase )
__lowerCamelCase : Optional[Any] = warning_advice
@functools.lru_cache(_lowerCAmelCase )
def A_ ( self , *_lowerCAmelCase , **_lowerCAmelCase ) -> Optional[int]:
self.warning(*_lowerCAmelCase , **_lowerCAmelCase )
__lowerCamelCase : Dict = warning_once
class A__ :
def __init__( self , *A_ , **A_ ): # pylint: disable=unused-argument
'''simple docstring'''
UpperCamelCase : List[Any] = args[0] if args else None
def __iter__( self ):
'''simple docstring'''
return iter(self._iterator )
def __getattr__( self , A_ ):
'''simple docstring'''
def empty_fn(*A_ , **A_ ): # pylint: disable=unused-argument
return
return empty_fn
def __enter__( self ):
'''simple docstring'''
return self
def __exit__( self , A_ , A_ , A_ ):
'''simple docstring'''
return
class A__ :
def __call__( self , *A_ , **A_ ):
'''simple docstring'''
if _tqdm_active:
return tqdm_lib.tqdm(*A_ , **A_ )
else:
return EmptyTqdm(*A_ , **A_ )
def __UpperCamelCase( self , *A_ , **A_ ):
'''simple docstring'''
UpperCamelCase : Dict = None
if _tqdm_active:
return tqdm_lib.tqdm.set_lock(*A_ , **A_ )
def __UpperCamelCase( self ):
'''simple docstring'''
if _tqdm_active:
return tqdm_lib.tqdm.get_lock()
__lowerCamelCase : str = _tqdm_cls()
def A_ ( ) -> bool:
global _tqdm_active
return bool(_tqdm_active )
def A_ ( ) -> List[str]:
global _tqdm_active
UpperCamelCase : Tuple = True
hf_hub_utils.enable_progress_bars()
def A_ ( ) -> List[str]:
global _tqdm_active
UpperCamelCase : List[Any] = False
hf_hub_utils.disable_progress_bars()
| 140 |
from scipy.stats import spearmanr
import datasets
__lowerCamelCase : List[str] = """
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlations imply that as data in dataset x increases, so
does data in dataset y. Negative correlations imply that as x increases,
y decreases. Correlations of -1 or +1 imply an exact monotonic relationship.
Unlike the Pearson correlation, the Spearman correlation does not
assume that both datasets are normally distributed.
The p-value roughly indicates the probability of an uncorrelated system
producing datasets that have a Spearman correlation at least as extreme
as the one computed from these datasets. The p-values are not entirely
reliable but are probably reasonable for datasets larger than 500 or so.
"""
__lowerCamelCase : List[Any] = """
Args:
predictions (`List[float]`): Predicted labels, as returned by a model.
references (`List[float]`): Ground truth labels.
return_pvalue (`bool`): If `True`, returns the p-value. If `False`, returns
only the spearmanr score. Defaults to `False`.
Returns:
spearmanr (`float`): Spearman correlation coefficient.
p-value (`float`): p-value. **Note**: is only returned if `return_pvalue=True` is input.
Examples:
Example 1:
>>> spearmanr_metric = datasets.load_metric(\"spearmanr\")
>>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], predictions=[10, 9, 2.5, 6, 4])
>>> print(results)
{'spearmanr': -0.7}
Example 2:
>>> spearmanr_metric = datasets.load_metric(\"spearmanr\")
>>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5],
... predictions=[10, 9, 2.5, 6, 4],
... return_pvalue=True)
>>> print(results['spearmanr'])
-0.7
>>> print(round(results['spearmanr_pvalue'], 2))
0.19
"""
__lowerCamelCase : Optional[int] = r"""\
@book{kokoska2000crc,
title={CRC standard probability and statistics tables and formulae},
author={Kokoska, Stephen and Zwillinger, Daniel},
year={2000},
publisher={Crc Press}
}
@article{2020SciPy-NMeth,
author = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
Haberland, Matt and Reddy, Tyler and Cournapeau, David and
Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
Kern, Robert and Larson, Eric and Carey, C J and
Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and
{VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
Harris, Charles R. and Archibald, Anne M. and
Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and
{van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
title = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
Computing in Python}},
journal = {Nature Methods},
year = {2020},
volume = {17},
pages = {261--272},
adsurl = {https://rdcu.be/b08Wh},
doi = {10.1038/s41592-019-0686-2},
}
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class A__ ( datasets.Metric ):
def __UpperCamelCase( self ):
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"predictions": datasets.Value("float" ),
"references": datasets.Value("float" ),
} ) , reference_urls=["https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.spearmanr.html"] , )
def __UpperCamelCase( self , A_ , A_ , A_=False ):
'''simple docstring'''
UpperCamelCase : Tuple = spearmanr(A_ , A_ )
if return_pvalue:
return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
else:
return {"spearmanr": results[0]}
| 140 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
UpperCAmelCase_ : Optional[int] = {
"""google/tapas-base-finetuned-sqa""": (
"""https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json"""
),
"""google/tapas-base-finetuned-wtq""": (
"""https://huggingface.co/google/tapas-base-finetuned-wtq/resolve/main/config.json"""
),
"""google/tapas-base-finetuned-wikisql-supervised""": (
"""https://huggingface.co/google/tapas-base-finetuned-wikisql-supervised/resolve/main/config.json"""
),
"""google/tapas-base-finetuned-tabfact""": (
"""https://huggingface.co/google/tapas-base-finetuned-tabfact/resolve/main/config.json"""
),
}
class lowerCAmelCase__ ( UpperCAmelCase__ ):
'''simple docstring'''
__UpperCamelCase = "tapas"
def __init__( self : List[str] , lowercase_ : Tuple=30522 , lowercase_ : str=768 , lowercase_ : List[str]=12 , lowercase_ : str=12 , lowercase_ : str=3072 , lowercase_ : str="gelu" , lowercase_ : Optional[int]=0.1 , lowercase_ : Union[str, Any]=0.1 , lowercase_ : Tuple=1024 , lowercase_ : Union[str, Any]=[3, 256, 256, 2, 256, 256, 10] , lowercase_ : Union[str, Any]=0.02 , lowercase_ : List[Any]=1e-12 , lowercase_ : Dict=0 , lowercase_ : Tuple=10.0 , lowercase_ : Optional[int]=0 , lowercase_ : Optional[int]=1.0 , lowercase_ : List[str]=None , lowercase_ : Optional[Any]=1.0 , lowercase_ : int=False , lowercase_ : Any=None , lowercase_ : List[Any]=1.0 , lowercase_ : List[Any]=1.0 , lowercase_ : Any=False , lowercase_ : Optional[int]=False , lowercase_ : Dict="ratio" , lowercase_ : Tuple=None , lowercase_ : Optional[int]=None , lowercase_ : List[str]=64 , lowercase_ : Tuple=32 , lowercase_ : Optional[int]=False , lowercase_ : int=True , lowercase_ : Any=False , lowercase_ : Optional[int]=False , lowercase_ : str=True , lowercase_ : Optional[Any]=False , lowercase_ : str=None , lowercase_ : str=None , **lowercase_ : List[str] , ):
'''simple docstring'''
super().__init__(pad_token_id=lowercase_ , **lowercase_)
# BERT hyperparameters (with updated max_position_embeddings and type_vocab_sizes)
SCREAMING_SNAKE_CASE_ : Dict = vocab_size
SCREAMING_SNAKE_CASE_ : Optional[Any] = hidden_size
SCREAMING_SNAKE_CASE_ : int = num_hidden_layers
SCREAMING_SNAKE_CASE_ : str = num_attention_heads
SCREAMING_SNAKE_CASE_ : List[Any] = hidden_act
SCREAMING_SNAKE_CASE_ : Optional[Any] = intermediate_size
SCREAMING_SNAKE_CASE_ : Optional[int] = hidden_dropout_prob
SCREAMING_SNAKE_CASE_ : List[Any] = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE_ : str = max_position_embeddings
SCREAMING_SNAKE_CASE_ : Dict = type_vocab_sizes
SCREAMING_SNAKE_CASE_ : List[Any] = initializer_range
SCREAMING_SNAKE_CASE_ : List[Any] = layer_norm_eps
# Fine-tuning task hyperparameters
SCREAMING_SNAKE_CASE_ : Dict = positive_label_weight
SCREAMING_SNAKE_CASE_ : Dict = num_aggregation_labels
SCREAMING_SNAKE_CASE_ : Optional[Any] = aggregation_loss_weight
SCREAMING_SNAKE_CASE_ : List[str] = use_answer_as_supervision
SCREAMING_SNAKE_CASE_ : Tuple = answer_loss_importance
SCREAMING_SNAKE_CASE_ : str = use_normalized_answer_loss
SCREAMING_SNAKE_CASE_ : str = huber_loss_delta
SCREAMING_SNAKE_CASE_ : List[str] = temperature
SCREAMING_SNAKE_CASE_ : Optional[Any] = aggregation_temperature
SCREAMING_SNAKE_CASE_ : Union[str, Any] = use_gumbel_for_cells
SCREAMING_SNAKE_CASE_ : List[str] = use_gumbel_for_aggregation
SCREAMING_SNAKE_CASE_ : Tuple = average_approximation_function
SCREAMING_SNAKE_CASE_ : Any = cell_selection_preference
SCREAMING_SNAKE_CASE_ : Tuple = answer_loss_cutoff
SCREAMING_SNAKE_CASE_ : str = max_num_rows
SCREAMING_SNAKE_CASE_ : Any = max_num_columns
SCREAMING_SNAKE_CASE_ : int = average_logits_per_cell
SCREAMING_SNAKE_CASE_ : Dict = select_one_column
SCREAMING_SNAKE_CASE_ : Union[str, Any] = allow_empty_column_selection
SCREAMING_SNAKE_CASE_ : int = init_cell_selection_weights_to_zero
SCREAMING_SNAKE_CASE_ : Union[str, Any] = reset_position_index_per_cell
SCREAMING_SNAKE_CASE_ : Union[str, Any] = disable_per_token_loss
# Aggregation hyperparameters
SCREAMING_SNAKE_CASE_ : List[str] = aggregation_labels
SCREAMING_SNAKE_CASE_ : Any = no_aggregation_label_index
if isinstance(self.aggregation_labels , lowercase_):
SCREAMING_SNAKE_CASE_ : Dict = {int(lowercase_): v for k, v in aggregation_labels.items()}
| 91 |
"""simple docstring"""
import unittest
from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
LiltForQuestionAnswering,
LiltForSequenceClassification,
LiltForTokenClassification,
LiltModel,
)
from transformers.models.lilt.modeling_lilt import LILT_PRETRAINED_MODEL_ARCHIVE_LIST
class lowerCAmelCase__ :
'''simple docstring'''
def __init__( self : Any , lowercase_ : List[Any] , lowercase_ : List[str]=13 , lowercase_ : int=7 , lowercase_ : Any=True , lowercase_ : str=True , lowercase_ : List[Any]=True , lowercase_ : List[Any]=True , lowercase_ : Dict=99 , lowercase_ : Union[str, Any]=24 , lowercase_ : int=2 , lowercase_ : List[str]=6 , lowercase_ : Any=37 , lowercase_ : Dict="gelu" , lowercase_ : List[str]=0.1 , lowercase_ : Dict=0.1 , lowercase_ : Union[str, Any]=512 , lowercase_ : List[str]=16 , lowercase_ : Any=2 , lowercase_ : Any=0.02 , lowercase_ : List[Any]=3 , lowercase_ : Optional[int]=None , lowercase_ : str=1000 , ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : List[str] = parent
SCREAMING_SNAKE_CASE_ : Optional[Any] = batch_size
SCREAMING_SNAKE_CASE_ : Optional[Any] = seq_length
SCREAMING_SNAKE_CASE_ : List[Any] = is_training
SCREAMING_SNAKE_CASE_ : Optional[int] = use_input_mask
SCREAMING_SNAKE_CASE_ : Optional[Any] = use_token_type_ids
SCREAMING_SNAKE_CASE_ : int = use_labels
SCREAMING_SNAKE_CASE_ : List[Any] = vocab_size
SCREAMING_SNAKE_CASE_ : List[str] = hidden_size
SCREAMING_SNAKE_CASE_ : List[Any] = num_hidden_layers
SCREAMING_SNAKE_CASE_ : List[str] = num_attention_heads
SCREAMING_SNAKE_CASE_ : Tuple = intermediate_size
SCREAMING_SNAKE_CASE_ : Tuple = hidden_act
SCREAMING_SNAKE_CASE_ : int = hidden_dropout_prob
SCREAMING_SNAKE_CASE_ : str = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE_ : List[Any] = max_position_embeddings
SCREAMING_SNAKE_CASE_ : Union[str, Any] = type_vocab_size
SCREAMING_SNAKE_CASE_ : List[str] = type_sequence_label_size
SCREAMING_SNAKE_CASE_ : Any = initializer_range
SCREAMING_SNAKE_CASE_ : Optional[Any] = num_labels
SCREAMING_SNAKE_CASE_ : Tuple = scope
SCREAMING_SNAKE_CASE_ : Optional[int] = range_bbox
def _SCREAMING_SNAKE_CASE ( self : List[str]):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
SCREAMING_SNAKE_CASE_ : Dict = ids_tensor([self.batch_size, self.seq_length, 4] , self.range_bbox)
# Ensure that bbox is legal
for i in range(bbox.shape[0]):
for j in range(bbox.shape[1]):
if bbox[i, j, 3] < bbox[i, j, 1]:
SCREAMING_SNAKE_CASE_ : Optional[int] = bbox[i, j, 3]
SCREAMING_SNAKE_CASE_ : Optional[int] = bbox[i, j, 1]
SCREAMING_SNAKE_CASE_ : str = t
if bbox[i, j, 2] < bbox[i, j, 0]:
SCREAMING_SNAKE_CASE_ : List[str] = bbox[i, j, 2]
SCREAMING_SNAKE_CASE_ : Optional[int] = bbox[i, j, 0]
SCREAMING_SNAKE_CASE_ : List[str] = t
SCREAMING_SNAKE_CASE_ : Tuple = None
if self.use_input_mask:
SCREAMING_SNAKE_CASE_ : List[Any] = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2)
SCREAMING_SNAKE_CASE_ : Union[str, Any] = None
if self.use_token_type_ids:
SCREAMING_SNAKE_CASE_ : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size)
SCREAMING_SNAKE_CASE_ : List[str] = None
SCREAMING_SNAKE_CASE_ : List[str] = None
if self.use_labels:
SCREAMING_SNAKE_CASE_ : int = 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_ : Any = self.get_config()
return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels
def _SCREAMING_SNAKE_CASE ( self : Optional[int]):
'''simple docstring'''
return LiltConfig(
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 , initializer_range=self.initializer_range , )
def _SCREAMING_SNAKE_CASE ( self : Optional[int] , lowercase_ : Optional[Any] , lowercase_ : Optional[Any] , lowercase_ : str , lowercase_ : Optional[Any] , lowercase_ : int , lowercase_ : Optional[Any] , lowercase_ : int , ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : Tuple = LiltModel(config=lowercase_)
model.to(lowercase_)
model.eval()
SCREAMING_SNAKE_CASE_ : List[Any] = model(lowercase_ , bbox=lowercase_ , attention_mask=lowercase_ , token_type_ids=lowercase_)
SCREAMING_SNAKE_CASE_ : List[Any] = model(lowercase_ , bbox=lowercase_ , token_type_ids=lowercase_)
SCREAMING_SNAKE_CASE_ : int = model(lowercase_ , bbox=lowercase_)
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size))
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size))
def _SCREAMING_SNAKE_CASE ( self : int , lowercase_ : Union[str, Any] , lowercase_ : Union[str, Any] , lowercase_ : Union[str, Any] , lowercase_ : Optional[Any] , lowercase_ : Union[str, Any] , lowercase_ : List[Any] , lowercase_ : int , ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : Optional[Any] = self.num_labels
SCREAMING_SNAKE_CASE_ : Optional[Any] = LiltForTokenClassification(config=lowercase_)
model.to(lowercase_)
model.eval()
SCREAMING_SNAKE_CASE_ : Tuple = model(
lowercase_ , bbox=lowercase_ , attention_mask=lowercase_ , token_type_ids=lowercase_ , labels=lowercase_)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels))
def _SCREAMING_SNAKE_CASE ( self : Tuple , lowercase_ : str , lowercase_ : Union[str, Any] , lowercase_ : Any , lowercase_ : Dict , lowercase_ : List[str] , lowercase_ : List[Any] , lowercase_ : str , ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : Union[str, Any] = LiltForQuestionAnswering(config=lowercase_)
model.to(lowercase_)
model.eval()
SCREAMING_SNAKE_CASE_ : Optional[int] = model(
lowercase_ , bbox=lowercase_ , attention_mask=lowercase_ , token_type_ids=lowercase_ , start_positions=lowercase_ , end_positions=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))
def _SCREAMING_SNAKE_CASE ( self : Tuple):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : str = 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_
) ,
) : List[str] = config_and_inputs
SCREAMING_SNAKE_CASE_ : str = {
'''input_ids''': input_ids,
'''bbox''': bbox,
'''token_type_ids''': token_type_ids,
'''attention_mask''': input_mask,
}
return config, inputs_dict
@require_torch
class lowerCAmelCase__ ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
__UpperCamelCase = (
(
LiltModel,
LiltForSequenceClassification,
LiltForTokenClassification,
LiltForQuestionAnswering,
)
if is_torch_available()
else ()
)
__UpperCamelCase = (
{
"feature-extraction": LiltModel,
"question-answering": LiltForQuestionAnswering,
"text-classification": LiltForSequenceClassification,
"token-classification": LiltForTokenClassification,
"zero-shot": LiltForSequenceClassification,
}
if is_torch_available()
else {}
)
__UpperCamelCase = False
__UpperCamelCase = False
def _SCREAMING_SNAKE_CASE ( self : Any , lowercase_ : List[str] , lowercase_ : Optional[int] , lowercase_ : Optional[int] , lowercase_ : str , lowercase_ : str):
'''simple docstring'''
return True
def _SCREAMING_SNAKE_CASE ( self : Tuple):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : Dict = LiltModelTester(self)
SCREAMING_SNAKE_CASE_ : Optional[int] = ConfigTester(self , config_class=lowercase_ , hidden_size=37)
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any]):
'''simple docstring'''
self.config_tester.run_common_tests()
def _SCREAMING_SNAKE_CASE ( self : List[str]):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowercase_)
def _SCREAMING_SNAKE_CASE ( self : Tuple):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : Tuple = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
SCREAMING_SNAKE_CASE_ : Dict = type
self.model_tester.create_and_check_model(*lowercase_)
def _SCREAMING_SNAKE_CASE ( self : str):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*lowercase_)
def _SCREAMING_SNAKE_CASE ( self : Tuple):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*lowercase_)
@slow
def _SCREAMING_SNAKE_CASE ( self : Optional[int]):
'''simple docstring'''
for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE_ : Optional[int] = LiltModel.from_pretrained(lowercase_)
self.assertIsNotNone(lowercase_)
@require_torch
@slow
class lowerCAmelCase__ ( unittest.TestCase ):
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( self : Optional[int]):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : List[str] = LiltModel.from_pretrained('''SCUT-DLVCLab/lilt-roberta-en-base''').to(lowercase_)
SCREAMING_SNAKE_CASE_ : str = torch.tensor([[1, 2]] , device=lowercase_)
SCREAMING_SNAKE_CASE_ : Optional[int] = torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]] , device=lowercase_)
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE_ : Dict = model(input_ids=lowercase_ , bbox=lowercase_)
SCREAMING_SNAKE_CASE_ : str = torch.Size([1, 2, 768])
SCREAMING_SNAKE_CASE_ : Dict = torch.tensor(
[[-0.06_53, 0.09_50, -0.00_61], [-0.05_45, 0.09_26, -0.03_24]] , device=lowercase_ , )
self.assertTrue(outputs.last_hidden_state.shape , lowercase_)
self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :, :3] , lowercase_ , atol=1e-3))
| 91 | 1 |
import inspect
import warnings
from typing import Any, Dict, Optional, Union
from packaging import version
def __snake_case ( *__UpperCamelCase : Union[str, Any] ,__UpperCamelCase : Optional[Union[Dict, Any]] = None ,__UpperCamelCase : Optional[Any]=True ,__UpperCamelCase : Optional[Any]=2 ):
"""simple docstring"""
from .. import __version__
A_ = take_from
A_ = ()
if not isinstance(args[0] ,__UpperCamelCase ):
A_ = (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}''' )
A_ = None
if isinstance(__UpperCamelCase ,__UpperCamelCase ) and attribute in deprecated_kwargs:
values += (deprecated_kwargs.pop(__UpperCamelCase ),)
A_ = f'''The `{attribute}` argument is deprecated and will be removed in version {version_name}.'''
elif hasattr(__UpperCamelCase ,__UpperCamelCase ):
values += (getattr(__UpperCamelCase ,__UpperCamelCase ),)
A_ = f'''The `{attribute}` attribute is deprecated and will be removed in version {version_name}.'''
elif deprecated_kwargs is None:
A_ = f'''`{attribute}` is deprecated and will be removed in version {version_name}.'''
if warning is not None:
A_ = warning + " " if standard_warn else ""
warnings.warn(warning + message ,__UpperCamelCase ,stacklevel=__UpperCamelCase )
if isinstance(__UpperCamelCase ,__UpperCamelCase ) and len(__UpperCamelCase ) > 0:
A_ = inspect.getouterframes(inspect.currentframe() )[1]
A_ = call_frame.filename
A_ = call_frame.lineno
A_ = call_frame.function
A_ , A_ = 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 | 329 |
import os
import tempfile
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from torch import nn
from transformers import (
Adafactor,
AdamW,
get_constant_schedule,
get_constant_schedule_with_warmup,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_inverse_sqrt_schedule,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
)
def __snake_case ( __UpperCamelCase : Optional[Any] ,__UpperCamelCase : Dict=10 ):
"""simple docstring"""
A_ = []
for _ in range(__UpperCamelCase ):
lrs.append(scheduler.get_lr()[0] )
scheduler.step()
return lrs
def __snake_case ( __UpperCamelCase : Any ,__UpperCamelCase : Tuple=10 ):
"""simple docstring"""
A_ = []
for step in range(__UpperCamelCase ):
lrs.append(scheduler.get_lr()[0] )
scheduler.step()
if step == num_steps // 2:
with tempfile.TemporaryDirectory() as tmpdirname:
A_ = os.path.join(__UpperCamelCase ,"schedule.bin" )
torch.save(scheduler.state_dict() ,__UpperCamelCase )
A_ = torch.load(__UpperCamelCase )
scheduler.load_state_dict(__UpperCamelCase )
return lrs
@require_torch
class _a ( unittest.TestCase ):
"""simple docstring"""
def __A ( self : Any , UpperCAmelCase : int , UpperCAmelCase : List[Any] , UpperCAmelCase : List[str] ):
self.assertEqual(len(UpperCAmelCase ) , len(UpperCAmelCase ) )
for a, b in zip(UpperCAmelCase , UpperCAmelCase ):
self.assertAlmostEqual(UpperCAmelCase , UpperCAmelCase , delta=UpperCAmelCase )
def __A ( self : List[Any] ):
A_ = torch.tensor([0.1, -0.2, -0.1] , requires_grad=UpperCAmelCase )
A_ = torch.tensor([0.4, 0.2, -0.5] )
A_ = nn.MSELoss()
# No warmup, constant schedule, no gradient clipping
A_ = AdamW(params=[w] , lr=2E-1 , weight_decay=0.0 )
for _ in range(100 ):
A_ = criterion(UpperCAmelCase , UpperCAmelCase )
loss.backward()
optimizer.step()
w.grad.detach_() # No zero_grad() function on simple tensors. we do it ourselves.
w.grad.zero_()
self.assertListAlmostEqual(w.tolist() , [0.4, 0.2, -0.5] , tol=1E-2 )
def __A ( self : Dict ):
A_ = torch.tensor([0.1, -0.2, -0.1] , requires_grad=UpperCAmelCase )
A_ = torch.tensor([0.4, 0.2, -0.5] )
A_ = nn.MSELoss()
# No warmup, constant schedule, no gradient clipping
A_ = Adafactor(
params=[w] , lr=1E-2 , eps=(1E-30, 1E-3) , clip_threshold=1.0 , decay_rate=-0.8 , betaa=UpperCAmelCase , weight_decay=0.0 , relative_step=UpperCAmelCase , scale_parameter=UpperCAmelCase , warmup_init=UpperCAmelCase , )
for _ in range(1000 ):
A_ = criterion(UpperCAmelCase , UpperCAmelCase )
loss.backward()
optimizer.step()
w.grad.detach_() # No zero_grad() function on simple tensors. we do it ourselves.
w.grad.zero_()
self.assertListAlmostEqual(w.tolist() , [0.4, 0.2, -0.5] , tol=1E-2 )
@require_torch
class _a ( unittest.TestCase ):
"""simple docstring"""
_lowerCamelCase : Optional[int] = nn.Linear(5_0 , 5_0 ) if is_torch_available() else None
_lowerCamelCase : Any = AdamW(m.parameters() , lr=1_0.0 ) if is_torch_available() else None
_lowerCamelCase : Any = 1_0
def __A ( self : str , UpperCAmelCase : int , UpperCAmelCase : Any , UpperCAmelCase : Tuple , UpperCAmelCase : Dict=None ):
self.assertEqual(len(UpperCAmelCase ) , len(UpperCAmelCase ) )
for a, b in zip(UpperCAmelCase , UpperCAmelCase ):
self.assertAlmostEqual(UpperCAmelCase , UpperCAmelCase , delta=UpperCAmelCase , msg=UpperCAmelCase )
def __A ( self : List[Any] ):
A_ = {"num_warmup_steps": 2, "num_training_steps": 10}
# schedulers doct format
# function: (sched_args_dict, expected_learning_rates)
A_ = {
get_constant_schedule: ({}, [10.0] * self.num_steps),
get_constant_schedule_with_warmup: (
{"num_warmup_steps": 4},
[0.0, 2.5, 5.0, 7.5, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0],
),
get_linear_schedule_with_warmup: (
{**common_kwargs},
[0.0, 5.0, 10.0, 8.75, 7.5, 6.25, 5.0, 3.75, 2.5, 1.25],
),
get_cosine_schedule_with_warmup: (
{**common_kwargs},
[0.0, 5.0, 10.0, 9.61, 8.53, 6.91, 5.0, 3.08, 1.46, 0.38],
),
get_cosine_with_hard_restarts_schedule_with_warmup: (
{**common_kwargs, "num_cycles": 2},
[0.0, 5.0, 10.0, 8.53, 5.0, 1.46, 10.0, 8.53, 5.0, 1.46],
),
get_polynomial_decay_schedule_with_warmup: (
{**common_kwargs, "power": 2.0, "lr_end": 1E-7},
[0.0, 5.0, 10.0, 7.656, 5.625, 3.906, 2.5, 1.406, 0.625, 0.156],
),
get_inverse_sqrt_schedule: (
{"num_warmup_steps": 2},
[0.0, 5.0, 10.0, 8.165, 7.071, 6.325, 5.774, 5.345, 5.0, 4.714],
),
}
for scheduler_func, data in scheds.items():
A_ , A_ = data
A_ = scheduler_func(self.optimizer , **UpperCAmelCase )
self.assertEqual(len([scheduler.get_lr()[0]] ) , 1 )
A_ = unwrap_schedule(UpperCAmelCase , self.num_steps )
self.assertListAlmostEqual(
UpperCAmelCase , UpperCAmelCase , tol=1E-2 , msg=f'''failed for {scheduler_func} in normal scheduler''' , )
A_ = scheduler_func(self.optimizer , **UpperCAmelCase )
if scheduler_func.__name__ != "get_constant_schedule":
LambdaScheduleWrapper.wrap_scheduler(UpperCAmelCase ) # wrap to test picklability of the schedule
A_ = unwrap_and_save_reload_schedule(UpperCAmelCase , self.num_steps )
self.assertListEqual(UpperCAmelCase , UpperCAmelCase , msg=f'''failed for {scheduler_func} in save and reload''' )
class _a :
"""simple docstring"""
def __init__( self : List[str] , UpperCAmelCase : List[str] ):
A_ = fn
def __call__( self : Union[str, Any] , *UpperCAmelCase : str , **UpperCAmelCase : Optional[Any] ):
return self.fn(*UpperCAmelCase , **UpperCAmelCase )
@classmethod
def __A ( self : Dict , UpperCAmelCase : List[str] ):
A_ = list(map(self , scheduler.lr_lambdas ) ) | 329 | 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_retribert import RetriBertTokenizer
snake_case_ : List[str] = logging.get_logger(__name__)
snake_case_ : Union[str, Any] = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
snake_case_ : Optional[int] = {
"vocab_file": {
"yjernite/retribert-base-uncased": (
"https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/vocab.txt"
),
},
"tokenizer_file": {
"yjernite/retribert-base-uncased": (
"https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/tokenizer.json"
),
},
}
snake_case_ : Optional[Any] = {
"yjernite/retribert-base-uncased": 5_12,
}
snake_case_ : Union[str, Any] = {
"yjernite/retribert-base-uncased": {"do_lower_case": True},
}
class __a (lowerCamelCase ):
__a : Optional[Any] = VOCAB_FILES_NAMES
__a : Dict = PRETRAINED_VOCAB_FILES_MAP
__a : Dict = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__a : int = PRETRAINED_INIT_CONFIGURATION
__a : Union[str, Any] = RetriBertTokenizer
__a : Optional[int] = ["input_ids", "attention_mask"]
def __init__( self : str , __magic_name__ : List[Any]=None , __magic_name__ : Optional[int]=None , __magic_name__ : Any=True , __magic_name__ : int="[UNK]" , __magic_name__ : List[Any]="[SEP]" , __magic_name__ : List[Any]="[PAD]" , __magic_name__ : Optional[int]="[CLS]" , __magic_name__ : Union[str, Any]="[MASK]" , __magic_name__ : int=True , __magic_name__ : Optional[Any]=None , **__magic_name__ : Any , ) -> List[str]:
"""simple docstring"""
super().__init__(
__magic_name__ , tokenizer_file=__magic_name__ , do_lower_case=__magic_name__ , unk_token=__magic_name__ , sep_token=__magic_name__ , pad_token=__magic_name__ , cls_token=__magic_name__ , mask_token=__magic_name__ , tokenize_chinese_chars=__magic_name__ , strip_accents=__magic_name__ , **__magic_name__ , )
UpperCAmelCase_ : str = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('''lowercase''' , __magic_name__ ) != do_lower_case
or normalizer_state.get('''strip_accents''' , __magic_name__ ) != strip_accents
or normalizer_state.get('''handle_chinese_chars''' , __magic_name__ ) != tokenize_chinese_chars
):
UpperCAmelCase_ : Dict = getattr(__magic_name__ , normalizer_state.pop('''type''' ) )
UpperCAmelCase_ : Optional[int] = do_lower_case
UpperCAmelCase_ : Optional[int] = strip_accents
UpperCAmelCase_ : Tuple = tokenize_chinese_chars
UpperCAmelCase_ : Optional[int] = normalizer_class(**__magic_name__ )
UpperCAmelCase_ : List[str] = do_lower_case
def UpperCAmelCase__ ( self : int , __magic_name__ : List[str] , __magic_name__ : Optional[Any]=None ) -> List[Any]:
"""simple docstring"""
UpperCAmelCase_ : str = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def UpperCAmelCase__ ( self : Optional[Any] , __magic_name__ : List[int] , __magic_name__ : Optional[List[int]] = None ) -> List[int]:
"""simple docstring"""
UpperCAmelCase_ : Dict = [self.sep_token_id]
UpperCAmelCase_ : Optional[int] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def UpperCAmelCase__ ( self : Optional[Any] , __magic_name__ : str , __magic_name__ : Optional[str] = None ) -> Tuple[str]:
"""simple docstring"""
UpperCAmelCase_ : int = self._tokenizer.model.save(__magic_name__ , name=__magic_name__ )
return tuple(__magic_name__ )
| 125 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case_ : Any = logging.get_logger(__name__)
snake_case_ : Dict = {
"weiweishi/roc-bert-base-zh": "https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json",
}
class __a (lowerCamelCase ):
__a : Tuple = "roc_bert"
def __init__( self : Union[str, Any] , __magic_name__ : List[str]=3_05_22 , __magic_name__ : Tuple=7_68 , __magic_name__ : Any=12 , __magic_name__ : Optional[Any]=12 , __magic_name__ : Union[str, Any]=30_72 , __magic_name__ : Optional[int]="gelu" , __magic_name__ : Optional[int]=0.1 , __magic_name__ : Tuple=0.1 , __magic_name__ : Any=5_12 , __magic_name__ : str=2 , __magic_name__ : Any=0.0_2 , __magic_name__ : Dict=1E-12 , __magic_name__ : int=True , __magic_name__ : Optional[int]=0 , __magic_name__ : str="absolute" , __magic_name__ : Tuple=None , __magic_name__ : Any=True , __magic_name__ : Optional[Any]=True , __magic_name__ : List[str]=7_68 , __magic_name__ : List[Any]=9_10 , __magic_name__ : Tuple=5_12 , __magic_name__ : Dict=2_48_58 , __magic_name__ : Any=True , **__magic_name__ : Union[str, Any] , ) -> Dict:
"""simple docstring"""
UpperCAmelCase_ : List[str] = vocab_size
UpperCAmelCase_ : Any = max_position_embeddings
UpperCAmelCase_ : Dict = hidden_size
UpperCAmelCase_ : int = num_hidden_layers
UpperCAmelCase_ : Optional[Any] = num_attention_heads
UpperCAmelCase_ : Optional[int] = intermediate_size
UpperCAmelCase_ : Dict = hidden_act
UpperCAmelCase_ : Tuple = hidden_dropout_prob
UpperCAmelCase_ : Optional[int] = attention_probs_dropout_prob
UpperCAmelCase_ : Dict = initializer_range
UpperCAmelCase_ : Optional[Any] = type_vocab_size
UpperCAmelCase_ : str = layer_norm_eps
UpperCAmelCase_ : Tuple = use_cache
UpperCAmelCase_ : Optional[int] = enable_pronunciation
UpperCAmelCase_ : Union[str, Any] = enable_shape
UpperCAmelCase_ : List[str] = pronunciation_embed_dim
UpperCAmelCase_ : List[str] = pronunciation_vocab_size
UpperCAmelCase_ : int = shape_embed_dim
UpperCAmelCase_ : Optional[int] = shape_vocab_size
UpperCAmelCase_ : Optional[Any] = concat_input
UpperCAmelCase_ : Dict = position_embedding_type
UpperCAmelCase_ : Union[str, Any] = classifier_dropout
super().__init__(pad_token_id=__magic_name__ , **__magic_name__ )
| 125 | 1 |
"""simple docstring"""
from packaging import version
from .import_utils import is_accelerate_available
if is_accelerate_available():
import accelerate
def a__ ( lowerCAmelCase ) -> Dict:
if not is_accelerate_available():
return method
UpperCAmelCase__ : List[Any] = version.parse(accelerate.__version__ ).base_version
if version.parse(lowerCAmelCase ) < version.parse("""0.17.0""" ):
return method
def wrapper(self , *lowerCAmelCase , **lowerCAmelCase ):
if hasattr(self , """_hf_hook""" ) and hasattr(self._hf_hook , """pre_forward""" ):
self._hf_hook.pre_forward(self )
return method(self , *lowerCAmelCase , **lowerCAmelCase )
return wrapper
| 361 |
"""simple docstring"""
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
_A = logging.get_logger(__name__)
def a__ ( lowerCAmelCase ) -> Tuple:
UpperCAmelCase__ : Optional[int] = OrderedDict()
for key, value in state_dict.items():
if key.startswith("""module.encoder""" ):
UpperCAmelCase__ : Dict = key.replace("""module.encoder""" , """glpn.encoder""" )
if key.startswith("""module.decoder""" ):
UpperCAmelCase__ : int = key.replace("""module.decoder""" , """decoder.stages""" )
if "patch_embed" in key:
# replace for example patch_embed1 by patch_embeddings.0
UpperCAmelCase__ : Optional[int] = key[key.find("""patch_embed""" ) + len("""patch_embed""" )]
UpperCAmelCase__ : Union[str, Any] = key.replace(F"""patch_embed{idx}""" , F"""patch_embeddings.{int(lowerCAmelCase )-1}""" )
if "norm" in key:
UpperCAmelCase__ : Optional[Any] = key.replace("""norm""" , """layer_norm""" )
if "glpn.encoder.layer_norm" in key:
# replace for example layer_norm1 by layer_norm.0
UpperCAmelCase__ : int = key[key.find("""glpn.encoder.layer_norm""" ) + len("""glpn.encoder.layer_norm""" )]
UpperCAmelCase__ : Union[str, Any] = key.replace(F"""layer_norm{idx}""" , F"""layer_norm.{int(lowerCAmelCase )-1}""" )
if "layer_norm1" in key:
UpperCAmelCase__ : Any = key.replace("""layer_norm1""" , """layer_norm_1""" )
if "layer_norm2" in key:
UpperCAmelCase__ : Union[str, Any] = key.replace("""layer_norm2""" , """layer_norm_2""" )
if "block" in key:
# replace for example block1 by block.0
UpperCAmelCase__ : int = key[key.find("""block""" ) + len("""block""" )]
UpperCAmelCase__ : List[Any] = key.replace(F"""block{idx}""" , F"""block.{int(lowerCAmelCase )-1}""" )
if "attn.q" in key:
UpperCAmelCase__ : List[Any] = key.replace("""attn.q""" , """attention.self.query""" )
if "attn.proj" in key:
UpperCAmelCase__ : Tuple = key.replace("""attn.proj""" , """attention.output.dense""" )
if "attn" in key:
UpperCAmelCase__ : Union[str, Any] = key.replace("""attn""" , """attention.self""" )
if "fc1" in key:
UpperCAmelCase__ : int = key.replace("""fc1""" , """dense1""" )
if "fc2" in key:
UpperCAmelCase__ : List[Any] = key.replace("""fc2""" , """dense2""" )
if "linear_pred" in key:
UpperCAmelCase__ : Optional[Any] = key.replace("""linear_pred""" , """classifier""" )
if "linear_fuse" in key:
UpperCAmelCase__ : Optional[Any] = key.replace("""linear_fuse.conv""" , """linear_fuse""" )
UpperCAmelCase__ : Optional[Any] = key.replace("""linear_fuse.bn""" , """batch_norm""" )
if "linear_c" in key:
# replace for example linear_c4 by linear_c.3
UpperCAmelCase__ : List[Any] = key[key.find("""linear_c""" ) + len("""linear_c""" )]
UpperCAmelCase__ : int = key.replace(F"""linear_c{idx}""" , F"""linear_c.{int(lowerCAmelCase )-1}""" )
if "bot_conv" in key:
UpperCAmelCase__ : int = key.replace("""bot_conv""" , """0.convolution""" )
if "skip_conv1" in key:
UpperCAmelCase__ : List[Any] = key.replace("""skip_conv1""" , """1.convolution""" )
if "skip_conv2" in key:
UpperCAmelCase__ : List[Any] = key.replace("""skip_conv2""" , """2.convolution""" )
if "fusion1" in key:
UpperCAmelCase__ : Optional[Any] = key.replace("""fusion1""" , """1.fusion""" )
if "fusion2" in key:
UpperCAmelCase__ : List[str] = key.replace("""fusion2""" , """2.fusion""" )
if "fusion3" in key:
UpperCAmelCase__ : int = key.replace("""fusion3""" , """3.fusion""" )
if "fusion" in key and "conv" in key:
UpperCAmelCase__ : Union[str, Any] = key.replace("""conv""" , """convolutional_layer""" )
if key.startswith("""module.last_layer_depth""" ):
UpperCAmelCase__ : Optional[int] = key.replace("""module.last_layer_depth""" , """head.head""" )
UpperCAmelCase__ : Optional[Any] = value
return new_state_dict
def a__ ( lowerCAmelCase , lowerCAmelCase ) -> Dict:
# for each of the encoder blocks:
for i in range(config.num_encoder_blocks ):
for j in range(config.depths[i] ):
# read in weights + bias of keys and values (which is a single matrix in the original implementation)
UpperCAmelCase__ : Dict = state_dict.pop(F"""glpn.encoder.block.{i}.{j}.attention.self.kv.weight""" )
UpperCAmelCase__ : int = state_dict.pop(F"""glpn.encoder.block.{i}.{j}.attention.self.kv.bias""" )
# next, add keys and values (in that order) to the state dict
UpperCAmelCase__ : Optional[int] = kv_weight[
: config.hidden_sizes[i], :
]
UpperCAmelCase__ : int = kv_bias[: config.hidden_sizes[i]]
UpperCAmelCase__ : int = kv_weight[
config.hidden_sizes[i] :, :
]
UpperCAmelCase__ : List[Any] = kv_bias[config.hidden_sizes[i] :]
def a__ ( ) -> int:
UpperCAmelCase__ : Optional[Any] = """http://images.cocodataset.org/val2017/000000039769.jpg"""
UpperCAmelCase__ : int = Image.open(requests.get(lowerCAmelCase , stream=lowerCAmelCase ).raw )
return image
@torch.no_grad()
def a__ ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase=False , lowerCAmelCase=None ) -> Union[str, Any]:
UpperCAmelCase__ : Any = GLPNConfig(hidden_sizes=[64, 1_28, 3_20, 5_12] , decoder_hidden_size=64 , depths=[3, 8, 27, 3] )
# load image processor (only resize + rescale)
UpperCAmelCase__ : Any = GLPNImageProcessor()
# prepare image
UpperCAmelCase__ : List[str] = prepare_img()
UpperCAmelCase__ : Tuple = image_processor(images=lowerCAmelCase , return_tensors="""pt""" ).pixel_values
logger.info("""Converting model...""" )
# load original state dict
UpperCAmelCase__ : Tuple = torch.load(lowerCAmelCase , map_location=torch.device("""cpu""" ) )
# rename keys
UpperCAmelCase__ : Optional[Any] = rename_keys(lowerCAmelCase )
# key and value matrices need special treatment
read_in_k_v(lowerCAmelCase , lowerCAmelCase )
# create HuggingFace model and load state dict
UpperCAmelCase__ : Union[str, Any] = GLPNForDepthEstimation(lowerCAmelCase )
model.load_state_dict(lowerCAmelCase )
model.eval()
# forward pass
UpperCAmelCase__ : Any = model(lowerCAmelCase )
UpperCAmelCase__ : Tuple = outputs.predicted_depth
# verify output
if model_name is not None:
if "nyu" in model_name:
UpperCAmelCase__ : int = torch.tensor(
[[4.4147, 4.0873, 4.0673], [3.7890, 3.2881, 3.1525], [3.7674, 3.5423, 3.4913]] )
elif "kitti" in model_name:
UpperCAmelCase__ : Union[str, Any] = torch.tensor(
[[3.4291, 2.7865, 2.5151], [3.2841, 2.7021, 2.3502], [3.1147, 2.4625, 2.2481]] )
else:
raise ValueError(F"""Unknown model name: {model_name}""" )
UpperCAmelCase__ : Any = torch.Size([1, 4_80, 6_40] )
assert predicted_depth.shape == expected_shape
assert torch.allclose(predicted_depth[0, :3, :3] , lowerCAmelCase , atol=1E-4 )
print("""Looks ok!""" )
# finally, push to hub if required
if push_to_hub:
logger.info("""Pushing model and image processor to the 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__":
_A = argparse.ArgumentParser()
parser.add_argument(
"""--checkpoint_path""",
default=None,
type=str,
help="""Path to the original PyTorch checkpoint (.pth file).""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
)
parser.add_argument(
"""--push_to_hub""", action="""store_true""", help="""Whether to upload the model to the HuggingFace hub."""
)
parser.add_argument(
"""--model_name""",
default="""glpn-kitti""",
type=str,
help="""Name of the model in case you're pushing to the hub.""",
)
_A = parser.parse_args()
convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)
| 166 | 0 |
"""simple docstring"""
import copy
import tempfile
import unittest
from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaMaaaForConditionalGeneration, MaMaaaModel, MaMaaaTokenizer
from transformers.models.mam_aaa.modeling_mam_aaa import MaMaaaDecoder, MaMaaaEncoder
def __lowerCamelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase=None , __UpperCamelCase=None , __UpperCamelCase=None , __UpperCamelCase=None , __UpperCamelCase=None , ) -> Tuple:
"""simple docstring"""
if attention_mask is None:
lowerCAmelCase_ : Any = input_ids.ne(config.pad_token_id )
if decoder_attention_mask is None:
lowerCAmelCase_ : Tuple = decoder_input_ids.ne(config.pad_token_id )
if head_mask is None:
lowerCAmelCase_ : Union[str, Any] = torch.ones(config.encoder_layers , config.encoder_attention_heads , device=__UpperCamelCase )
if decoder_head_mask is None:
lowerCAmelCase_ : Tuple = torch.ones(config.decoder_layers , config.decoder_attention_heads , device=__UpperCamelCase )
if cross_attn_head_mask is None:
lowerCAmelCase_ : List[Any] = torch.ones(config.decoder_layers , config.decoder_attention_heads , device=__UpperCamelCase )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
class __lowerCamelCase :
'''simple docstring'''
def __init__( self : Tuple , a_ : Dict , a_ : Dict=13 , a_ : str=7 , a_ : Optional[Any]=True , a_ : Any=False , a_ : Union[str, Any]=99 , a_ : Optional[int]=16 , a_ : str=2 , a_ : Tuple=4 , a_ : Optional[int]=4 , a_ : Optional[int]="relu" , a_ : Union[str, Any]=0.1 , a_ : Optional[int]=0.1 , a_ : Tuple=0.0 , a_ : Optional[int]=0.0 , a_ : int=20 , a_ : Tuple=2 , a_ : Tuple=1 , a_ : Union[str, Any]=0 , ):
lowerCAmelCase_ : Any = parent
lowerCAmelCase_ : Optional[int] = batch_size
lowerCAmelCase_ : Optional[int] = seq_length
lowerCAmelCase_ : Dict = is_training
lowerCAmelCase_ : Any = use_labels
lowerCAmelCase_ : str = vocab_size
lowerCAmelCase_ : List[Any] = hidden_size
lowerCAmelCase_ : str = num_hidden_layers
lowerCAmelCase_ : Tuple = num_attention_heads
lowerCAmelCase_ : Dict = intermediate_size
lowerCAmelCase_ : Tuple = hidden_act
lowerCAmelCase_ : List[Any] = hidden_dropout_prob
lowerCAmelCase_ : Optional[Any] = attention_probs_dropout_prob
lowerCAmelCase_ : Tuple = encoder_layerdrop
lowerCAmelCase_ : Optional[Any] = decoder_layerdrop
lowerCAmelCase_ : List[Any] = max_position_embeddings
lowerCAmelCase_ : Union[str, Any] = eos_token_id
lowerCAmelCase_ : Optional[Any] = pad_token_id
lowerCAmelCase_ : Dict = bos_token_id
def lowerCamelCase ( self : Tuple ):
lowerCAmelCase_ : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowerCAmelCase_ : Any = self.eos_token_id # Eos Token
lowerCAmelCase_ : Dict = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
# we need to clamp the input ids here to avoid having pad token in between
# this is because for M2M100 the position_ids are prepared such that
# all pad tokens have pos id = 2 and rest are between 2..seq_length
# and the seq_length here is seq_length - num_pad_tokens
# but when using past, there is no way of knowing if the past input ids had
# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
# position_ids being off by num_pad_tokens in past input
lowerCAmelCase_ : List[str] = input_ids.clamp(self.pad_token_id + 1 )
lowerCAmelCase_ : Optional[int] = decoder_input_ids.clamp(self.pad_token_id + 1 )
lowerCAmelCase_ : Optional[int] = self.get_config()
lowerCAmelCase_ : Optional[int] = prepare_mam_aaa_inputs_dict(a_ , a_ , a_ )
return config, inputs_dict
def lowerCamelCase ( self : Optional[int] ):
return MaMaaaConfig(
vocab_size=self.vocab_size , d_model=self.hidden_size , 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 , encoder_layerdrop=self.encoder_layerdrop , decoder_layerdrop=self.decoder_layerdrop , max_position_embeddings=self.max_position_embeddings , eos_token_id=self.eos_token_id , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , )
def lowerCamelCase ( self : Dict ):
lowerCAmelCase_ , lowerCAmelCase_ : Dict = self.prepare_config_and_inputs()
return config, inputs_dict
def lowerCamelCase ( self : Any , a_ : Tuple , a_ : Any ):
lowerCAmelCase_ : List[str] = MaMaaaModel(config=a_ ).get_decoder().to(a_ ).eval()
lowerCAmelCase_ : Dict = inputs_dict["input_ids"]
lowerCAmelCase_ : Any = inputs_dict["attention_mask"]
lowerCAmelCase_ : Dict = inputs_dict["head_mask"]
# first forward pass
lowerCAmelCase_ : Any = model(a_ , attention_mask=a_ , head_mask=a_ , use_cache=a_ )
lowerCAmelCase_ , lowerCAmelCase_ : Tuple = outputs.to_tuple()
# create hypothetical multiple next token and extent to next_input_ids
lowerCAmelCase_ : Dict = ids_tensor((self.batch_size, 3) , config.vocab_size )
lowerCAmelCase_ : Any = ids_tensor((self.batch_size, 3) , 2 )
# append to next input_ids and
lowerCAmelCase_ : int = torch.cat([input_ids, next_tokens] , dim=-1 )
lowerCAmelCase_ : List[str] = torch.cat([attention_mask, next_attn_mask] , dim=-1 )
lowerCAmelCase_ : Any = model(a_ , attention_mask=a_ )["last_hidden_state"]
lowerCAmelCase_ : Optional[int] = model(a_ , attention_mask=a_ , past_key_values=a_ )[
"last_hidden_state"
]
# select random slice
lowerCAmelCase_ : Optional[Any] = ids_tensor((1,) , output_from_past.shape[-1] ).item()
lowerCAmelCase_ : str = output_from_no_past[:, -3:, random_slice_idx].detach()
lowerCAmelCase_ : int = 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-2 ) )
def lowerCamelCase ( self : List[Any] , a_ : str , a_ : List[str] ):
lowerCAmelCase_ : List[Any] = MaMaaaModel(config=a_ ).to(a_ ).eval()
lowerCAmelCase_ : List[str] = model(**a_ )
lowerCAmelCase_ : List[str] = outputs.encoder_last_hidden_state
lowerCAmelCase_ : Tuple = outputs.last_hidden_state
with tempfile.TemporaryDirectory() as tmpdirname:
lowerCAmelCase_ : str = model.get_encoder()
encoder.save_pretrained(a_ )
lowerCAmelCase_ : Optional[int] = MaMaaaEncoder.from_pretrained(a_ ).to(a_ )
lowerCAmelCase_ : Any = encoder(inputs_dict["input_ids"] , attention_mask=inputs_dict["attention_mask"] )[
0
]
self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1e-3 )
with tempfile.TemporaryDirectory() as tmpdirname:
lowerCAmelCase_ : Any = model.get_decoder()
decoder.save_pretrained(a_ )
lowerCAmelCase_ : Tuple = MaMaaaDecoder.from_pretrained(a_ ).to(a_ )
lowerCAmelCase_ : Optional[Any] = decoder(
input_ids=inputs_dict["decoder_input_ids"] , attention_mask=inputs_dict["decoder_attention_mask"] , encoder_hidden_states=a_ , encoder_attention_mask=inputs_dict["attention_mask"] , )[0]
self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1e-3 )
@require_torch
class __lowerCamelCase ( A__ , A__ , A__ , unittest.TestCase ):
'''simple docstring'''
a_ : Dict = (
(
MaMaaaModel,
MaMaaaForConditionalGeneration,
)
if is_torch_available()
else ()
)
a_ : str = (MaMaaaForConditionalGeneration,) if is_torch_available() else ()
a_ : Optional[int] = (
{
"""conversational""": MaMaaaForConditionalGeneration,
"""feature-extraction""": MaMaaaModel,
"""summarization""": MaMaaaForConditionalGeneration,
"""text2text-generation""": MaMaaaForConditionalGeneration,
"""translation""": MaMaaaForConditionalGeneration,
}
if is_torch_available()
else {}
)
a_ : int = True
a_ : Any = True
a_ : List[str] = False
a_ : int = False
def lowerCamelCase ( self : Optional[int] , a_ : Optional[int] , a_ : Union[str, Any] , a_ : int , a_ : Optional[int] , a_ : List[Any] ):
if pipeline_test_casse_name == "TranslationPipelineTests":
# Get `ValueError: Translation requires a `src_lang` and a `tgt_lang` for this model`.
# `M2M100Config` was never used in pipeline tests: cannot create a simple tokenizer.
return True
return False
def lowerCamelCase ( self : List[Any] ):
lowerCAmelCase_ : List[str] = MaMaaaModelTester(self )
lowerCAmelCase_ : Dict = ConfigTester(self , config_class=a_ )
def lowerCamelCase ( self : Optional[int] ):
self.config_tester.run_common_tests()
def lowerCamelCase ( self : str ):
lowerCAmelCase_ , lowerCAmelCase_ : Tuple = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
lowerCAmelCase_ : List[Any] = model_class(a_ )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(a_ )
lowerCAmelCase_ , lowerCAmelCase_ : List[str] = model_class.from_pretrained(a_ , output_loading_info=a_ )
self.assertEqual(info["missing_keys"] , [] )
def lowerCamelCase ( self : Tuple ):
lowerCAmelCase_ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past_large_inputs(*a_ )
def lowerCamelCase ( self : Optional[Any] ):
lowerCAmelCase_ : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_encoder_decoder_model_standalone(*a_ )
def lowerCamelCase ( self : str ):
lowerCAmelCase_ , lowerCAmelCase_ : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
lowerCAmelCase_ : Optional[int] = model_class(a_ )
model.to(a_ )
model.eval()
lowerCAmelCase_ : Union[str, Any] = copy.deepcopy(self._prepare_for_class(a_ , a_ ) )
if not self.is_encoder_decoder:
lowerCAmelCase_ : str = inputs["input_ids"]
del inputs["input_ids"]
else:
lowerCAmelCase_ : List[Any] = inputs["input_ids"]
lowerCAmelCase_ : int = inputs.get("decoder_input_ids" , a_ )
del inputs["input_ids"]
inputs.pop("decoder_input_ids" , a_ )
lowerCAmelCase_ : Dict = model.get_input_embeddings()
if not self.is_encoder_decoder:
lowerCAmelCase_ : Optional[int] = wte(a_ )
else:
lowerCAmelCase_ : List[Any] = wte(a_ )
lowerCAmelCase_ : Optional[Any] = wte(a_ )
with torch.no_grad():
model(**a_ )[0]
def lowerCamelCase ( self : Tuple ):
lowerCAmelCase_ , lowerCAmelCase_ : List[Any] = self.model_tester.prepare_config_and_inputs()
lowerCAmelCase_ : List[str] = input_dict["input_ids"]
lowerCAmelCase_ : Tuple = input_ids.ne(1 ).to(a_ )
lowerCAmelCase_ : Dict = MaMaaaForConditionalGeneration(a_ ).eval().to(a_ )
if torch_device == "cuda":
model.half()
model.generate(a_ , attention_mask=a_ )
model.generate(num_beams=4 , do_sample=a_ , early_stopping=a_ , num_return_sequences=3 )
def __lowerCamelCase ( __UpperCamelCase ) -> Optional[int]:
"""simple docstring"""
return torch.tensor(__UpperCamelCase , dtype=torch.long , device=__UpperCamelCase )
lowercase__ = 1E-4
@require_torch
@require_sentencepiece
@require_tokenizers
@slow
class __lowerCamelCase ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def lowerCamelCase ( self : Tuple ):
return MaMaaaTokenizer.from_pretrained("facebook/m2m100_418M" )
def lowerCamelCase ( self : Union[str, Any] ):
lowerCAmelCase_ : Union[str, Any] = MaMaaaModel.from_pretrained("facebook/m2m100_418M" ).to(a_ )
lowerCAmelCase_ : List[Any] = _long_tensor([[12_80_28, 98, 12, 3_05_27, 27_32, 1_59, 77_55, 6_19_04, 3_91_44, 38, 2]] )
lowerCAmelCase_ : List[Any] = _long_tensor([[2, 12_80_28, 98, 12, 3_05_27, 27_32, 1_59, 77_55, 6_19_04, 3_91_44, 38]] )
lowerCAmelCase_ : Optional[int] = prepare_mam_aaa_inputs_dict(model.config , a_ , a_ )
with torch.no_grad():
lowerCAmelCase_ : str = model(**a_ )[0]
lowerCAmelCase_ : Any = torch.Size((1, 11, 10_24) )
self.assertEqual(output.shape , a_ )
# change to expected output here
lowerCAmelCase_ : Dict = torch.tensor(
[[-0.7780, -0.1676, 0.1038], [-6.7556, -1.3992, 0.0567], [-7.5383, -0.5920, -0.2779]] , device=a_ )
self.assertTrue(torch.allclose(output[:, :3, :3] , a_ , atol=a_ ) )
def lowerCamelCase ( self : int ):
lowerCAmelCase_ : Dict = MaMaaaForConditionalGeneration.from_pretrained("facebook/m2m100_418M" ).to(a_ )
# change to intended input
lowerCAmelCase_ : Optional[int] = _long_tensor([[12_80_28, 98, 12, 3_05_27, 27_32, 1_59, 77_55, 6_19_04, 3_91_44, 38, 2]] )
lowerCAmelCase_ : List[Any] = _long_tensor([[2, 12_80_28, 98, 12, 3_05_27, 27_32, 1_59, 77_55, 6_19_04, 3_91_44, 38]] )
lowerCAmelCase_ : Dict = prepare_mam_aaa_inputs_dict(model.config , a_ , a_ )
with torch.no_grad():
lowerCAmelCase_ : int = model(**a_ )[0]
lowerCAmelCase_ : Optional[int] = torch.Size((1, 11, model.config.vocab_size) )
self.assertEqual(output.shape , a_ )
# change to expected output here
lowerCAmelCase_ : Any = torch.tensor(
[[-1.0448, -1.0411, 3.7992], [-3.2191, -3.2386, -1.3451], [-3.6210, -3.5993, 0.4925]] , device=a_ )
self.assertTrue(torch.allclose(output[:, :3, :3] , a_ , atol=a_ ) )
def lowerCamelCase ( self : List[Any] ):
lowerCAmelCase_ : Tuple = MaMaaaForConditionalGeneration.from_pretrained("facebook/m2m100_418M" ).to(a_ )
lowerCAmelCase_ : List[str] = MaMaaaTokenizer.from_pretrained("facebook/m2m100_418M" , src_lang="fr" , tgt_lang="en" )
lowerCAmelCase_ : Optional[int] = [
"L'affaire NSA souligne l'absence totale de débat sur le renseignement",
"Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.",
"Lorsque François Hollande téléphone à Barack Obama ou quand le ministre des affaires étrangères Laurent"
" Fabius convoque l'ambassadeur des Etats-Unis, ils réagissent à une vraie découverte, qui est celle de"
" l'ampleur de la surveillance américaine sur l'ensemble des communications en France.",
]
# The below article tests that we don't add any hypotheses outside of the top n_beams
lowerCAmelCase_ : Union[str, Any] = tokenizer(a_ , padding=a_ , return_tensors="pt" )
lowerCAmelCase_ : List[str] = model.generate(
input_ids=dct["input_ids"].to(a_ ) , attention_mask=dct["attention_mask"].to(a_ ) , num_beams=5 , forced_bos_token_id=tokenizer.get_lang_id("en" ) , )
lowerCAmelCase_ : Union[str, Any] = [
"The NSA case highlights the total absence of intelligence debate",
"I think there are two levels of response from the French government.",
"When François Hollande calls Barack Obama or when Foreign Minister Laurent Fabius calls the U.S."
" Ambassador, they respond to a real discovery, which is that of the scale of U.S. surveillance on all"
" communications in France.",
]
lowerCAmelCase_ : Optional[int] = tokenizer.batch_decode(
hypotheses_batch.tolist() , clean_up_tokenization_spaces=a_ , skip_special_tokens=a_ )
assert generated == expected_en
| 241 |
"""simple docstring"""
import itertools
from dataclasses import dataclass
from typing import List, Optional
import pyarrow as pa
import pyarrow.parquet as pq
import datasets
from datasets.table import table_cast
lowercase__ = datasets.utils.logging.get_logger(__name__)
@dataclass
class __lowerCamelCase ( datasets.BuilderConfig ):
'''simple docstring'''
a_ : int = 1_0000
a_ : Optional[List[str]] = None
a_ : Optional[datasets.Features] = None
class __lowerCamelCase ( datasets.ArrowBasedBuilder ):
'''simple docstring'''
a_ : Dict = ParquetConfig
def lowerCamelCase ( self : Optional[Any] ):
return datasets.DatasetInfo(features=self.config.features )
def lowerCamelCase ( self : Any , a_ : Optional[Any] ):
if not self.config.data_files:
raise ValueError(f'''At least one data file must be specified, but got data_files={self.config.data_files}''' )
lowerCAmelCase_ : List[str] = dl_manager.download_and_extract(self.config.data_files )
if isinstance(a_ , (str, list, tuple) ):
lowerCAmelCase_ : str = data_files
if isinstance(a_ , a_ ):
lowerCAmelCase_ : Optional[Any] = [files]
# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
lowerCAmelCase_ : str = [dl_manager.iter_files(a_ ) for file in files]
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={"files": files} )]
lowerCAmelCase_ : Optional[Any] = []
for split_name, files in data_files.items():
if isinstance(a_ , a_ ):
lowerCAmelCase_ : Union[str, Any] = [files]
# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
lowerCAmelCase_ : List[str] = [dl_manager.iter_files(a_ ) for file in files]
# Infer features is they are stoed in the arrow schema
if self.info.features is None:
for file in itertools.chain.from_iterable(a_ ):
with open(a_ , "rb" ) as f:
lowerCAmelCase_ : Dict = datasets.Features.from_arrow_schema(pq.read_schema(a_ ) )
break
splits.append(datasets.SplitGenerator(name=a_ , gen_kwargs={"files": files} ) )
return splits
def lowerCamelCase ( self : int , a_ : pa.Table ):
if self.info.features is not None:
# more expensive cast to support nested features with keys in a different order
# allows str <-> int/float or str to Audio for example
lowerCAmelCase_ : Tuple = table_cast(a_ , self.info.features.arrow_schema )
return pa_table
def lowerCamelCase ( self : Dict , a_ : Dict ):
lowerCAmelCase_ : Optional[int] = self.info.features.arrow_schema if self.info.features is not None else None
if self.info.features is not None and self.config.columns is not None:
if sorted(field.name for field in schema ) != sorted(self.config.columns ):
raise ValueError(
f'''Tried to load parquet data with columns \'{self.config.columns}\' with mismatching features \'{self.info.features}\'''' )
for file_idx, file in enumerate(itertools.chain.from_iterable(a_ ) ):
with open(a_ , "rb" ) as f:
lowerCAmelCase_ : Any = pq.ParquetFile(a_ )
try:
for batch_idx, record_batch in enumerate(
parquet_file.iter_batches(batch_size=self.config.batch_size , columns=self.config.columns ) ):
lowerCAmelCase_ : List[str] = pa.Table.from_batches([record_batch] )
# Uncomment for debugging (will print the Arrow table size and elements)
# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
yield f'''{file_idx}_{batch_idx}''', self._cast_table(a_ )
except ValueError as e:
logger.error(f'''Failed to read file \'{file}\' with error {type(a_ )}: {e}''' )
raise
| 241 | 1 |
import shutil
import tempfile
import unittest
from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio
from .test_feature_extraction_clap import floats_list
@require_torchaudio
@require_sentencepiece
class __snake_case ( unittest.TestCase ):
'''simple docstring'''
def UpperCAmelCase__ ( self : Any ):
__snake_case: Union[str, Any] = """laion/clap-htsat-unfused"""
__snake_case: Any = tempfile.mkdtemp()
def UpperCAmelCase__ ( self : Optional[int] , **A : Optional[int] ):
return RobertaTokenizer.from_pretrained(self.checkpoint , **A )
def UpperCAmelCase__ ( self : List[str] , **A : Optional[int] ):
return ClapFeatureExtractor.from_pretrained(self.checkpoint , **A )
def UpperCAmelCase__ ( self : Optional[int] ):
shutil.rmtree(self.tmpdirname )
def UpperCAmelCase__ ( self : Optional[int] ):
__snake_case: int = self.get_tokenizer()
__snake_case: Tuple = self.get_feature_extractor()
__snake_case: Union[str, Any] = ClapProcessor(tokenizer=A , feature_extractor=A )
processor.save_pretrained(self.tmpdirname )
__snake_case: int = ClapProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.tokenizer , A )
self.assertEqual(processor.feature_extractor.to_json_string() , feature_extractor.to_json_string() )
self.assertIsInstance(processor.feature_extractor , A )
def UpperCAmelCase__ ( self : Tuple ):
__snake_case: Union[str, Any] = ClapProcessor(tokenizer=self.get_tokenizer() , feature_extractor=self.get_feature_extractor() )
processor.save_pretrained(self.tmpdirname )
__snake_case: List[Any] = self.get_tokenizer(bos_token="""(BOS)""" , eos_token="""(EOS)""" )
__snake_case: Any = self.get_feature_extractor(do_normalize=A , padding_value=1.0 )
__snake_case: Optional[Any] = ClapProcessor.from_pretrained(
self.tmpdirname , bos_token="""(BOS)""" , eos_token="""(EOS)""" , do_normalize=A , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , A )
self.assertEqual(processor.feature_extractor.to_json_string() , feature_extractor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.feature_extractor , A )
def UpperCAmelCase__ ( self : List[Any] ):
__snake_case: Dict = self.get_feature_extractor()
__snake_case: int = self.get_tokenizer()
__snake_case: Any = ClapProcessor(tokenizer=A , feature_extractor=A )
__snake_case: Optional[int] = floats_list((3, 1_000) )
__snake_case: Tuple = feature_extractor(A , return_tensors="""np""" )
__snake_case: List[str] = processor(audios=A , return_tensors="""np""" )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1E-2 )
def UpperCAmelCase__ ( self : Optional[Any] ):
__snake_case: List[str] = self.get_feature_extractor()
__snake_case: List[Any] = self.get_tokenizer()
__snake_case: Optional[Any] = ClapProcessor(tokenizer=A , feature_extractor=A )
__snake_case: Dict = """This is a test string"""
__snake_case: Tuple = processor(text=A )
__snake_case: Optional[int] = tokenizer(A )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def UpperCAmelCase__ ( self : List[str] ):
__snake_case: str = self.get_feature_extractor()
__snake_case: int = self.get_tokenizer()
__snake_case: Dict = ClapProcessor(tokenizer=A , feature_extractor=A )
__snake_case: Any = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
__snake_case: List[str] = processor.batch_decode(A )
__snake_case: Any = tokenizer.batch_decode(A )
self.assertListEqual(A , A )
def UpperCAmelCase__ ( self : List[str] ):
__snake_case: Union[str, Any] = self.get_feature_extractor()
__snake_case: Optional[Any] = self.get_tokenizer()
__snake_case: List[str] = ClapProcessor(tokenizer=A , feature_extractor=A )
self.assertListEqual(
processor.model_input_names[2:] , feature_extractor.model_input_names , msg="""`processor` and `feature_extractor` model input names do not match""" , )
| 359 |
from __future__ import annotations
from decimal import Decimal
from numpy import array
def A__ ( SCREAMING_SNAKE_CASE__) -> list[list[float]]:
__snake_case: Any = Decimal
# Check if the provided matrix has 2 rows and 2 columns
# since this implementation only works for 2x2 matrices
if len(SCREAMING_SNAKE_CASE__) == 2 and len(matrix[0]) == 2 and len(matrix[1]) == 2:
# Calculate the determinant of the matrix
__snake_case: Tuple = 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
__snake_case: Optional[int] = [[0.0, 0.0], [0.0, 0.0]]
__snake_case , __snake_case: Optional[Any] = matrix[1][1], matrix[0][0]
__snake_case , __snake_case: Union[str, Any] = -matrix[1][0], -matrix[0][1]
# Calculate the inverse of the matrix
return [
[(float(d(SCREAMING_SNAKE_CASE__)) / determinant) or 0.0 for n in row] for row in swapped_matrix
]
elif (
len(SCREAMING_SNAKE_CASE__) == 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
__snake_case: 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
__snake_case: Tuple = [
[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)],
]
__snake_case: Dict = (d(matrix[1][1]) * d(matrix[2][2])) - (
d(matrix[1][2]) * d(matrix[2][1])
)
__snake_case: Tuple = -(
(d(matrix[1][0]) * d(matrix[2][2])) - (d(matrix[1][2]) * d(matrix[2][0]))
)
__snake_case: Optional[int] = (d(matrix[1][0]) * d(matrix[2][1])) - (
d(matrix[1][1]) * d(matrix[2][0])
)
__snake_case: Union[str, Any] = -(
(d(matrix[0][1]) * d(matrix[2][2])) - (d(matrix[0][2]) * d(matrix[2][1]))
)
__snake_case: str = (d(matrix[0][0]) * d(matrix[2][2])) - (
d(matrix[0][2]) * d(matrix[2][0])
)
__snake_case: List[Any] = -(
(d(matrix[0][0]) * d(matrix[2][1])) - (d(matrix[0][1]) * d(matrix[2][0]))
)
__snake_case: Optional[Any] = (d(matrix[0][1]) * d(matrix[1][2])) - (
d(matrix[0][2]) * d(matrix[1][1])
)
__snake_case: List[str] = -(
(d(matrix[0][0]) * d(matrix[1][2])) - (d(matrix[0][2]) * d(matrix[1][0]))
)
__snake_case: Optional[int] = (d(matrix[0][0]) * d(matrix[1][1])) - (
d(matrix[0][1]) * d(matrix[1][0])
)
# Transpose the cofactor matrix (Adjoint matrix)
__snake_case: List[Any] = array(SCREAMING_SNAKE_CASE__)
for i in range(3):
for j in range(3):
__snake_case: Tuple = cofactor_matrix[j][i]
# Inverse of the matrix using the formula (1/determinant) * adjoint matrix
__snake_case: List[Any] = array(SCREAMING_SNAKE_CASE__)
for i in range(3):
for j in range(3):
inverse_matrix[i][j] /= d(SCREAMING_SNAKE_CASE__)
# Calculate the inverse of the matrix
return [[float(d(SCREAMING_SNAKE_CASE__)) or 0.0 for n in row] for row in inverse_matrix]
raise ValueError("""Please provide a matrix of size 2x2 or 3x3.""")
| 293 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
snake_case_ = {
"""configuration_xlm""": ["""XLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMConfig""", """XLMOnnxConfig"""],
"""tokenization_xlm""": ["""XLMTokenizer"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case_ = [
"""XLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""XLMForMultipleChoice""",
"""XLMForQuestionAnswering""",
"""XLMForQuestionAnsweringSimple""",
"""XLMForSequenceClassification""",
"""XLMForTokenClassification""",
"""XLMModel""",
"""XLMPreTrainedModel""",
"""XLMWithLMHeadModel""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case_ = [
"""TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFXLMForMultipleChoice""",
"""TFXLMForQuestionAnsweringSimple""",
"""TFXLMForSequenceClassification""",
"""TFXLMForTokenClassification""",
"""TFXLMMainLayer""",
"""TFXLMModel""",
"""TFXLMPreTrainedModel""",
"""TFXLMWithLMHeadModel""",
]
if TYPE_CHECKING:
from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
from .tokenization_xlm import XLMTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xlm import (
XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
XLMForMultipleChoice,
XLMForQuestionAnswering,
XLMForQuestionAnsweringSimple,
XLMForSequenceClassification,
XLMForTokenClassification,
XLMModel,
XLMPreTrainedModel,
XLMWithLMHeadModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_xlm import (
TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXLMForMultipleChoice,
TFXLMForQuestionAnsweringSimple,
TFXLMForSequenceClassification,
TFXLMForTokenClassification,
TFXLMMainLayer,
TFXLMModel,
TFXLMPreTrainedModel,
TFXLMWithLMHeadModel,
)
else:
import sys
snake_case_ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 78 |
'''simple docstring'''
from __future__ import annotations
import math
class a_ :
def __init__( self , _SCREAMING_SNAKE_CASE ) -> None:
"""simple docstring"""
UpperCamelCase = size
# approximate the overall size of segment tree with given value
UpperCamelCase = [0 for i in range(0 , 4 * size )]
# create array to store lazy update
UpperCamelCase = [0 for i in range(0 , 4 * size )]
UpperCamelCase = [0 for i in range(0 , 4 * size )] # flag for lazy update
def A__ ( self , _SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
return idx * 2
def A__ ( self , _SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
return idx * 2 + 1
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> None:
"""simple docstring"""
if left_element == right_element:
UpperCamelCase = a[left_element - 1]
else:
UpperCamelCase = (left_element + right_element) // 2
self.build(self.left(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
self.build(self.right(_SCREAMING_SNAKE_CASE ) , mid + 1 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
UpperCamelCase = max(
self.segment_tree[self.left(_SCREAMING_SNAKE_CASE )] , self.segment_tree[self.right(_SCREAMING_SNAKE_CASE )] )
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> bool:
"""simple docstring"""
if self.flag[idx] is True:
UpperCamelCase = self.lazy[idx]
UpperCamelCase = False
if left_element != right_element:
UpperCamelCase = self.lazy[idx]
UpperCamelCase = self.lazy[idx]
UpperCamelCase = True
UpperCamelCase = True
if right_element < a or left_element > b:
return True
if left_element >= a and right_element <= b:
UpperCamelCase = val
if left_element != right_element:
UpperCamelCase = val
UpperCamelCase = val
UpperCamelCase = True
UpperCamelCase = True
return True
UpperCamelCase = (left_element + right_element) // 2
self.update(self.left(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
self.update(self.right(_SCREAMING_SNAKE_CASE ) , mid + 1 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
UpperCamelCase = max(
self.segment_tree[self.left(_SCREAMING_SNAKE_CASE )] , self.segment_tree[self.right(_SCREAMING_SNAKE_CASE )] )
return True
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> int | float:
"""simple docstring"""
if self.flag[idx] is True:
UpperCamelCase = self.lazy[idx]
UpperCamelCase = False
if left_element != right_element:
UpperCamelCase = self.lazy[idx]
UpperCamelCase = self.lazy[idx]
UpperCamelCase = True
UpperCamelCase = True
if right_element < a or left_element > b:
return -math.inf
if left_element >= a and right_element <= b:
return self.segment_tree[idx]
UpperCamelCase = (left_element + right_element) // 2
UpperCamelCase = self.query(self.left(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
UpperCamelCase = self.query(self.right(_SCREAMING_SNAKE_CASE ) , mid + 1 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
return max(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def __str__( self ) -> str:
"""simple docstring"""
return str([self.query(1 , 1 , self.size , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) for i in range(1 , self.size + 1 )] )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ = [1, 2, -4, 7, 3, -5, 6, 1_1, -2_0, 9, 1_4, 1_5, 5, 2, -8]
SCREAMING_SNAKE_CASE__ = 1_5
SCREAMING_SNAKE_CASE__ = SegmentTree(size)
segt.build(1, 1, size, A)
print(segt.query(1, 1, size, 4, 6))
print(segt.query(1, 1, size, 7, 1_1))
print(segt.query(1, 1, size, 7, 1_2))
segt.update(1, 1, size, 1, 3, 1_1_1)
print(segt.query(1, 1, size, 1, 1_5))
segt.update(1, 1, size, 7, 8, 2_3_5)
print(segt)
| 321 | 0 |
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging
__SCREAMING_SNAKE_CASE : Union[str, Any] = logging.get_logger(__name__) # pylint: disable=invalid-name
class lowercase_ ( __snake_case ):
def __init__( self , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , ):
super().__init__()
self.register_modules(
vae=lowercase_ , text_encoder=lowercase_ , tokenizer=lowercase_ , unet=lowercase_ , scheduler=lowercase_ , safety_checker=lowercase_ , feature_extractor=lowercase_ , )
def UpperCamelCase ( self , lowercase_ = "auto" ):
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
_snake_case : str = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(lowercase_ )
def UpperCamelCase ( self ):
self.enable_attention_slicing(lowercase_ )
@torch.no_grad()
def __call__( self , lowercase_ , lowercase_ = 512 , lowercase_ = 512 , lowercase_ = 50 , lowercase_ = 7.5 , lowercase_ = None , lowercase_ = 1 , lowercase_ = 0.0 , lowercase_ = None , lowercase_ = None , lowercase_ = "pil" , lowercase_ = True , lowercase_ = None , lowercase_ = 1 , lowercase_ = None , **lowercase_ , ):
if isinstance(lowercase_ , lowercase_ ):
_snake_case : str = 1
elif isinstance(lowercase_ , lowercase_ ):
_snake_case : str = len(lowercase_ )
else:
raise ValueError(f"""`prompt` has to be of type `str` or `list` but is {type(lowercase_ )}""" )
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"""`height` and `width` have to be divisible by 8 but are {height} and {width}.""" )
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(lowercase_ , lowercase_ ) or callback_steps <= 0)
):
raise ValueError(
f"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
f""" {type(lowercase_ )}.""" )
# get prompt text embeddings
_snake_case : Tuple = self.tokenizer(
lowercase_ , padding="max_length" , max_length=self.tokenizer.model_max_length , return_tensors="pt" , )
_snake_case : Union[str, Any] = text_inputs.input_ids
if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
_snake_case : Optional[Any] = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :] )
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f""" {self.tokenizer.model_max_length} tokens: {removed_text}""" )
_snake_case : List[str] = text_input_ids[:, : self.tokenizer.model_max_length]
if text_embeddings is None:
_snake_case : List[Any] = self.text_encoder(text_input_ids.to(self.device ) )[0]
# duplicate text embeddings for each generation per prompt, using mps friendly method
_snake_case ,_snake_case ,_snake_case : List[str] = text_embeddings.shape
_snake_case : List[Any] = text_embeddings.repeat(1 , lowercase_ , 1 )
_snake_case : Tuple = text_embeddings.view(bs_embed * num_images_per_prompt , lowercase_ , -1 )
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
_snake_case : str = guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
_snake_case : List[str]
if negative_prompt is None:
_snake_case : Tuple = [""]
elif type(lowercase_ ) is not type(lowercase_ ):
raise TypeError(
f"""`negative_prompt` should be the same type to `prompt`, but got {type(lowercase_ )} !="""
f""" {type(lowercase_ )}.""" )
elif isinstance(lowercase_ , lowercase_ ):
_snake_case : Any = [negative_prompt]
elif batch_size != len(lowercase_ ):
raise ValueError(
f"""`negative_prompt`: {negative_prompt} has batch size {len(lowercase_ )}, but `prompt`:"""
f""" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"""
" the batch size of `prompt`." )
else:
_snake_case : Tuple = negative_prompt
_snake_case : Any = text_input_ids.shape[-1]
_snake_case : Tuple = self.tokenizer(
lowercase_ , padding="max_length" , max_length=lowercase_ , truncation=lowercase_ , return_tensors="pt" , )
_snake_case : Optional[Any] = self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
_snake_case : Optional[int] = uncond_embeddings.shape[1]
_snake_case : Optional[Any] = uncond_embeddings.repeat(lowercase_ , lowercase_ , 1 )
_snake_case : str = uncond_embeddings.view(batch_size * num_images_per_prompt , lowercase_ , -1 )
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
_snake_case : Any = torch.cat([uncond_embeddings, text_embeddings] )
# get the initial random noise unless the user supplied it
# Unlike in other pipelines, latents need to be generated in the target device
# for 1-to-1 results reproducibility with the CompVis implementation.
# However this currently doesn't work in `mps`.
_snake_case : Tuple = (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
_snake_case : str = (batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
_snake_case : Optional[int] = text_embeddings.dtype
if latents is None:
if self.device.type == "mps":
# randn does not exist on mps
_snake_case : Optional[Any] = torch.randn(
lowercase_ , generator=lowercase_ , device="cpu" , dtype=lowercase_ ).to(self.device )
_snake_case : Union[str, Any] = torch.randn(lowercase_ , generator=lowercase_ , device="cpu" , dtype=lowercase_ ).to(
self.device )
else:
_snake_case : Dict = torch.randn(
lowercase_ , generator=lowercase_ , device=self.device , dtype=lowercase_ )
_snake_case : List[str] = torch.randn(lowercase_ , generator=lowercase_ , device=self.device , dtype=lowercase_ )
else:
if latents_reference.shape != latents_shape:
raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}""" )
_snake_case : Optional[Any] = latents_reference.to(self.device )
_snake_case : Tuple = latents.to(self.device )
# This is the key part of the pipeline where we
# try to ensure that the generated images w/ the same seed
# but different sizes actually result in similar images
_snake_case : Union[str, Any] = (latents_shape[3] - latents_shape_reference[3]) // 2
_snake_case : List[Any] = (latents_shape[2] - latents_shape_reference[2]) // 2
_snake_case : Union[str, Any] = latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
_snake_case : Dict = latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
_snake_case : int = 0 if dx < 0 else dx
_snake_case : int = 0 if dy < 0 else dy
_snake_case : Dict = max(-dx , 0 )
_snake_case : List[str] = max(-dy , 0 )
# import pdb
# pdb.set_trace()
_snake_case : Optional[int] = latents_reference[:, :, dy : dy + h, dx : dx + w]
# set timesteps
self.scheduler.set_timesteps(lowercase_ )
# Some schedulers like PNDM have timesteps as arrays
# It's more optimized to move all timesteps to correct device beforehand
_snake_case : List[str] = self.scheduler.timesteps.to(self.device )
# scale the initial noise by the standard deviation required by the scheduler
_snake_case : Optional[Any] = latents * self.scheduler.init_noise_sigma
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
_snake_case : str = "eta" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
_snake_case : Optional[int] = {}
if accepts_eta:
_snake_case : int = eta
for i, t in enumerate(self.progress_bar(lowercase_ ) ):
# expand the latents if we are doing classifier free guidance
_snake_case : Dict = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
_snake_case : List[Any] = self.scheduler.scale_model_input(lowercase_ , lowercase_ )
# predict the noise residual
_snake_case : str = self.unet(lowercase_ , lowercase_ , encoder_hidden_states=lowercase_ ).sample
# perform guidance
if do_classifier_free_guidance:
_snake_case ,_snake_case : List[Any] = noise_pred.chunk(2 )
_snake_case : Optional[int] = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
_snake_case : Any = self.scheduler.step(lowercase_ , lowercase_ , lowercase_ , **lowercase_ ).prev_sample
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(lowercase_ , lowercase_ , lowercase_ )
_snake_case : List[str] = 1 / 0.18_215 * latents
_snake_case : int = self.vae.decode(lowercase_ ).sample
_snake_case : Optional[int] = (image / 2 + 0.5).clamp(0 , 1 )
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
_snake_case : List[Any] = image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if self.safety_checker is not None:
_snake_case : int = self.feature_extractor(self.numpy_to_pil(lowercase_ ) , return_tensors="pt" ).to(
self.device )
_snake_case ,_snake_case : Dict = self.safety_checker(
images=lowercase_ , clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype ) )
else:
_snake_case : Any = None
if output_type == "pil":
_snake_case : Optional[int] = self.numpy_to_pil(lowercase_ )
if not return_dict:
return (image, has_nsfw_concept)
return StableDiffusionPipelineOutput(images=lowercase_ , nsfw_content_detected=lowercase_ ) | 284 | from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
__SCREAMING_SNAKE_CASE : str = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
__SCREAMING_SNAKE_CASE : list[int] = [ord(letter) for letter in string.ascii_lowercase]
__SCREAMING_SNAKE_CASE : set[int] = {ord(char) for char in VALID_CHARS}
__SCREAMING_SNAKE_CASE : list[str] = ["the", "be", "to", "of", "and", "in", "that", "have"]
def snake_case (__lowercase , __lowercase ) -> str | None:
'''simple docstring'''
_snake_case : str = ""
_snake_case : int
_snake_case : int
_snake_case : int
for keychar, cipherchar in zip(cycle(__lowercase ) , __lowercase ):
_snake_case : str = cipherchar ^ keychar
if decodedchar not in VALID_INTS:
return None
decoded += chr(__lowercase )
return decoded
def snake_case (__lowercase ) -> list[str]:
'''simple docstring'''
_snake_case : list[str] = []
for key in product(__lowercase , repeat=3 ):
_snake_case : Union[str, Any] = try_key(__lowercase , __lowercase )
if encoded is not None:
possibles.append(__lowercase )
return possibles
def snake_case (__lowercase , __lowercase ) -> list[str]:
'''simple docstring'''
return [possible for possible in possibles if common_word in possible.lower()]
def snake_case (__lowercase = "p059_cipher.txt" ) -> int:
'''simple docstring'''
_snake_case : list[int]
_snake_case : list[str]
_snake_case : str
_snake_case : str
_snake_case : str = Path(__lowercase ).parent.joinpath(__lowercase ).read_text(encoding="utf-8" )
_snake_case : Dict = [int(__lowercase ) for number in data.strip().split("," )]
_snake_case : Tuple = filter_valid_chars(__lowercase )
for common_word in COMMON_WORDS:
_snake_case : Optional[int] = filter_common_word(__lowercase , __lowercase )
if len(__lowercase ) == 1:
break
_snake_case : int = possibles[0]
return sum(ord(__lowercase ) for char in decoded_text )
if __name__ == "__main__":
print(F'''{solution() = }''') | 284 | 1 |
def _SCREAMING_SNAKE_CASE ( a , a ) -> str:
__A : list[list[str]] = [[] for _ in range(a )]
__A : Dict = key - 1
if key <= 0:
raise ValueError('Height of grid can\'t be 0 or negative' )
if key == 1 or len(a ) <= key:
return input_string
for position, character in enumerate(a ):
__A : List[str] = position % (lowest * 2) # puts it in bounds
__A : Tuple = min(a , lowest * 2 - num ) # creates zigzag pattern
temp_grid[num].append(a )
__A : Union[str, Any] = [''.join(a ) for row in temp_grid]
__A : Optional[int] = ''.join(a )
return output_string
def _SCREAMING_SNAKE_CASE ( a , a ) -> str:
__A : Dict = []
__A : Any = key - 1
if key <= 0:
raise ValueError('Height of grid can\'t be 0 or negative' )
if key == 1:
return input_string
__A : list[list[str]] = [[] for _ in range(a )] # generates template
for position in range(len(a ) ):
__A : Optional[Any] = position % (lowest * 2) # puts it in bounds
__A : Dict = min(a , lowest * 2 - num ) # creates zigzag pattern
temp_grid[num].append('*' )
__A : Dict = 0
for row in temp_grid: # fills in the characters
__A : List[str] = input_string[counter : counter + len(a )]
grid.append(list(a ) )
counter += len(a )
__A : Any = '' # reads as zigzag
for position in range(len(a ) ):
__A : List[Any] = position % (lowest * 2) # puts it in bounds
__A : Optional[Any] = min(a , lowest * 2 - num ) # creates zigzag pattern
output_string += grid[num][0]
grid[num].pop(0 )
return output_string
def _SCREAMING_SNAKE_CASE ( a ) -> dict[int, str]:
__A : int = {}
for key_guess in range(1 , len(a ) ): # tries every key
__A : Tuple = decrypt(a , a )
return results
if __name__ == "__main__":
import doctest
doctest.testmod()
| 280 |
import math
def _SCREAMING_SNAKE_CASE ( a ) -> list[int]:
__A : List[str] = []
__A : Any = 2
__A : Union[str, Any] = int(math.sqrt(a ) ) # Size of every segment
__A : Any = [True] * (end + 1)
__A : List[Any] = []
while start <= end:
if temp[start] is True:
in_prime.append(a )
for i in range(start * start , end + 1 , a ):
__A : Optional[int] = False
start += 1
prime += in_prime
__A : Any = end + 1
__A : Any = min(2 * end , a )
while low <= n:
__A : List[Any] = [True] * (high - low + 1)
for each in in_prime:
__A : List[str] = math.floor(low / each ) * each
if t < low:
t += each
for j in range(a , high + 1 , a ):
__A : Optional[int] = False
for j in range(len(a ) ):
if temp[j] is True:
prime.append(j + low )
__A : Optional[int] = high + 1
__A : Tuple = min(high + end , a )
return prime
print(sieve(10**6))
| 280 | 1 |
'''simple docstring'''
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase : Any = logging.get_logger(__name__)
_lowercase : Optional[int] = {
"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 __magic_name__ ( _UpperCAmelCase):
UpperCamelCase__ = '''encodec'''
def __init__( self : Optional[int] , lowercase_ : Union[str, Any]=[1.5, 3.0, 6.0, 12.0, 24.0] , lowercase_ : Tuple=24000 , lowercase_ : str=1 , lowercase_ : Optional[Any]=False , lowercase_ : Optional[int]=None , lowercase_ : Union[str, Any]=None , lowercase_ : str=128 , lowercase_ : Tuple=32 , lowercase_ : Dict=1 , lowercase_ : Optional[Any]=[8, 5, 4, 2] , lowercase_ : Optional[int]="weight_norm" , lowercase_ : Tuple=7 , lowercase_ : Union[str, Any]=7 , lowercase_ : Dict=3 , lowercase_ : Union[str, Any]=2 , lowercase_ : List[Any]=True , lowercase_ : List[Any]="reflect" , lowercase_ : str=2 , lowercase_ : Any=2 , lowercase_ : Tuple=1.0 , lowercase_ : Dict=1024 , lowercase_ : List[Any]=None , lowercase_ : Dict=True , **lowercase_ : str , ):
lowercase_ : Union[str, Any] = target_bandwidths
lowercase_ : Optional[int] = sampling_rate
lowercase_ : Union[str, Any] = audio_channels
lowercase_ : str = normalize
lowercase_ : Dict = chunk_length_s
lowercase_ : Optional[int] = overlap
lowercase_ : Any = hidden_size
lowercase_ : List[Any] = num_filters
lowercase_ : Tuple = num_residual_layers
lowercase_ : List[Any] = upsampling_ratios
lowercase_ : List[Any] = norm_type
lowercase_ : List[str] = kernel_size
lowercase_ : Tuple = last_kernel_size
lowercase_ : Optional[Any] = residual_kernel_size
lowercase_ : Any = dilation_growth_rate
lowercase_ : Optional[int] = use_causal_conv
lowercase_ : Optional[int] = pad_mode
lowercase_ : str = compress
lowercase_ : Any = num_lstm_layers
lowercase_ : List[str] = trim_right_ratio
lowercase_ : Optional[int] = codebook_size
lowercase_ : Optional[int] = codebook_dim if codebook_dim is not None else hidden_size
lowercase_ : List[Any] = 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__(**lowercase_ )
@property
def SCREAMING_SNAKE_CASE_ ( self : str ):
if self.chunk_length_s is None:
return None
else:
return int(self.chunk_length_s * self.sampling_rate )
@property
def SCREAMING_SNAKE_CASE_ ( self : str ):
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 SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ):
lowercase_ : Union[str, Any] = np.prod(self.upsampling_ratios )
return math.ceil(self.sampling_rate / hop_length )
@property
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
return int(1000 * self.target_bandwidths[-1] // (self.frame_rate * 10) )
| 370 | '''simple docstring'''
def lowerCamelCase ( UpperCAmelCase__ : int = 1000000 ) -> int:
lowercase_ : List[Any] = limit + 1
lowercase_ : Optional[Any] = [0] * limit
for first_term in range(1 , UpperCAmelCase__ ):
for n in range(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ):
lowercase_ : List[Any] = 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
lowercase_ : List[Any] = sum(1 for x in frequency[1:limit] if x == 10 )
return count
if __name__ == "__main__":
print(f"""{solution() = }""")
| 21 | 0 |
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class UpperCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
@property
def __lowercase ( self ) -> List[str]:
torch.manual_seed(0 )
_a : List[Any] = UNetaDModel(
block_out_channels=(3_2, 6_4) , layers_per_block=2 , sample_size=3_2 , in_channels=3 , out_channels=3 , down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''') , up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''') , )
return model
def __lowercase ( self ) -> Tuple:
_a : Any = self.dummy_uncond_unet
_a : Union[str, Any] = KarrasVeScheduler()
_a : Union[str, Any] = KarrasVePipeline(unet=lowerCamelCase__ , scheduler=lowerCamelCase__ )
pipe.to(lowerCamelCase__ )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
_a : Optional[Any] = torch.manual_seed(0 )
_a : Any = pipe(num_inference_steps=2 , generator=lowerCamelCase__ , output_type='''numpy''' ).images
_a : List[str] = torch.manual_seed(0 )
_a : str = pipe(num_inference_steps=2 , generator=lowerCamelCase__ , output_type='''numpy''' , return_dict=lowerCamelCase__ )[0]
_a : Any = image[0, -3:, -3:, -1]
_a : Tuple = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 3_2, 3_2, 3)
_a : Dict = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
@slow
@require_torch
class UpperCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
def __lowercase ( self ) -> Union[str, Any]:
_a : Optional[Any] = '''google/ncsnpp-celebahq-256'''
_a : Tuple = UNetaDModel.from_pretrained(lowerCamelCase__ )
_a : Dict = KarrasVeScheduler()
_a : Union[str, Any] = KarrasVePipeline(unet=lowerCamelCase__ , scheduler=lowerCamelCase__ )
pipe.to(lowerCamelCase__ )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
_a : Dict = torch.manual_seed(0 )
_a : str = pipe(num_inference_steps=2_0 , generator=lowerCamelCase__ , output_type='''numpy''' ).images
_a : Any = image[0, -3:, -3:, -1]
assert image.shape == (1, 2_5_6, 2_5_6, 3)
_a : List[str] = np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
| 235 | """simple docstring"""
import argparse
import logging
import sys
from unittest.mock import patch
import run_glue_deebert
from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow
logging.basicConfig(level=logging.DEBUG)
lowerCAmelCase__ : Optional[Any] = logging.getLogger()
def a_ ( ):
UpperCAmelCase__ = argparse.ArgumentParser()
parser.add_argument('-f' )
UpperCAmelCase__ = parser.parse_args()
return args.f
class snake_case ( __UpperCAmelCase ):
"""simple docstring"""
def __lowerCAmelCase ( self : int ):
UpperCAmelCase__ = logging.StreamHandler(sys.stdout )
logger.addHandler(lowerCamelCase__ )
def __lowerCAmelCase ( self : Optional[Any] ,lowerCamelCase__ : Union[str, Any] ):
UpperCAmelCase__ = get_gpu_count()
if n_gpu > 1:
pass
# XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
# script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
# distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
# cmd = [sys.executable] + distributed_args + args
# execute_subprocess_async(cmd, env=self.get_env())
# XXX: test the results - need to save them first into .json file
else:
args.insert(0 ,'run_glue_deebert.py' )
with patch.object(lowerCamelCase__ ,'argv' ,lowerCamelCase__ ):
UpperCAmelCase__ = run_glue_deebert.main()
for value in result.values():
self.assertGreaterEqual(lowerCamelCase__ ,0.6_6_6 )
@slow
@require_torch_non_multi_gpu
def __lowerCAmelCase ( self : List[str] ):
UpperCAmelCase__ = '\n --model_type roberta\n --model_name_or_path roberta-base\n --task_name MRPC\n --do_train\n --do_eval\n --do_lower_case\n --data_dir ./tests/fixtures/tests_samples/MRPC/\n --max_seq_length 128\n --per_gpu_eval_batch_size=1\n --per_gpu_train_batch_size=8\n --learning_rate 2e-4\n --num_train_epochs 3\n --overwrite_output_dir\n --seed 42\n --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n --plot_data_dir ./examples/deebert/results/\n --save_steps 0\n --overwrite_cache\n --eval_after_first_stage\n '.split()
self.run_and_check(lowerCamelCase__ )
UpperCAmelCase__ = '\n --model_type roberta\n --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n --task_name MRPC\n --do_eval\n --do_lower_case\n --data_dir ./tests/fixtures/tests_samples/MRPC/\n --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n --plot_data_dir ./examples/deebert/results/\n --max_seq_length 128\n --eval_each_highway\n --eval_highway\n --overwrite_cache\n --per_gpu_eval_batch_size=1\n '.split()
self.run_and_check(lowerCamelCase__ )
UpperCAmelCase__ = '\n --model_type roberta\n --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n --task_name MRPC\n --do_eval\n --do_lower_case\n --data_dir ./tests/fixtures/tests_samples/MRPC/\n --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n --plot_data_dir ./examples/deebert/results/\n --max_seq_length 128\n --early_exit_entropy 0.1\n --eval_highway\n --overwrite_cache\n --per_gpu_eval_batch_size=1\n '.split()
self.run_and_check(lowerCamelCase__ )
| 98 | 0 |
def lowerCamelCase__ ( a ) -> list:
if len(a ) <= 1:
return lst
_A: int = 1
while i < len(a ):
if lst[i - 1] <= lst[i]:
i += 1
else:
_A , _A: Optional[int] = lst[i], lst[i - 1]
i -= 1
if i == 0:
_A: Optional[int] = 1
return lst
if __name__ == "__main__":
UpperCAmelCase__ : List[str] = input('Enter numbers separated by a comma:\n').strip()
UpperCAmelCase__ : int = [int(item) for item in user_input.split(',')]
print(gnome_sort(unsorted))
| 301 |
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__ ( a , a=0.999 , a="cosine" , ) -> int:
if alpha_transform_type == "cosine":
def alpha_bar_fn(a ):
return math.cos((t + 0.008) / 1.008 * math.pi / 2 ) ** 2
elif alpha_transform_type == "exp":
def alpha_bar_fn(a ):
return math.exp(t * -12.0 )
else:
raise ValueError(f"""Unsupported alpha_tranform_type: {alpha_transform_type}""" )
_A: Dict = []
for i in range(a ):
_A: Optional[int] = i / num_diffusion_timesteps
_A: Optional[int] = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar_fn(a ) / alpha_bar_fn(a ) , a ) )
return torch.tensor(a , dtype=torch.floataa )
class UpperCAmelCase ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
__UpperCamelCase : Optional[int] = [e.name for e in KarrasDiffusionSchedulers]
__UpperCamelCase : Tuple = 2
@register_to_config
def __init__( self : str , lowerCAmelCase_ : int = 1_0_0_0 , lowerCAmelCase_ : float = 0.00085 , lowerCAmelCase_ : float = 0.012 , lowerCAmelCase_ : str = "linear" , lowerCAmelCase_ : Optional[Union[np.ndarray, List[float]]] = None , lowerCAmelCase_ : str = "epsilon" , lowerCAmelCase_ : Optional[bool] = False , lowerCAmelCase_ : Optional[bool] = False , lowerCAmelCase_ : float = 1.0 , lowerCAmelCase_ : str = "linspace" , lowerCAmelCase_ : int = 0 , ):
"""simple docstring"""
if trained_betas is not None:
_A: Optional[Any] = torch.tensor(lowerCAmelCase_ , dtype=torch.floataa )
elif beta_schedule == "linear":
_A: List[str] = torch.linspace(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , dtype=torch.floataa )
elif beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
_A: Optional[Any] = (
torch.linspace(beta_start**0.5 , beta_end**0.5 , lowerCAmelCase_ , dtype=torch.floataa ) ** 2
)
elif beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
_A: Tuple = betas_for_alpha_bar(lowerCAmelCase_ , alpha_transform_type='''cosine''' )
elif beta_schedule == "exp":
_A: int = betas_for_alpha_bar(lowerCAmelCase_ , alpha_transform_type='''exp''' )
else:
raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}""" )
_A: Union[str, Any] = 1.0 - self.betas
_A: Dict = torch.cumprod(self.alphas , dim=0 )
# set all values
self.set_timesteps(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
_A: str = use_karras_sigmas
def __magic_name__ ( self : List[str] , lowerCAmelCase_ : Any , lowerCAmelCase_ : Optional[int]=None ):
"""simple docstring"""
if schedule_timesteps is None:
_A: List[str] = self.timesteps
_A: int = (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:
_A: Optional[int] = 1 if len(lowerCAmelCase_ ) > 1 else 0
else:
_A: int = timestep.cpu().item() if torch.is_tensor(lowerCAmelCase_ ) else timestep
_A: List[str] = self._index_counter[timestep_int]
return indices[pos].item()
@property
def __magic_name__ ( self : int ):
"""simple docstring"""
# 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 __magic_name__ ( self : List[str] , lowerCAmelCase_ : torch.FloatTensor , lowerCAmelCase_ : Union[float, torch.FloatTensor] , ):
"""simple docstring"""
_A: List[str] = self.index_for_timestep(lowerCAmelCase_ )
_A: str = self.sigmas[step_index]
_A: str = sample / ((sigma**2 + 1) ** 0.5)
return sample
def __magic_name__ ( self : Optional[int] , lowerCAmelCase_ : int , lowerCAmelCase_ : Union[str, torch.device] = None , lowerCAmelCase_ : Optional[int] = None , ):
"""simple docstring"""
_A: Union[str, Any] = num_inference_steps
_A: str = 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":
_A: Optional[Any] = np.linspace(0 , num_train_timesteps - 1 , lowerCAmelCase_ , dtype=lowerCAmelCase_ )[::-1].copy()
elif self.config.timestep_spacing == "leading":
_A: List[Any] = 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
_A: Dict = (np.arange(0 , lowerCAmelCase_ ) * step_ratio).round()[::-1].copy().astype(lowerCAmelCase_ )
timesteps += self.config.steps_offset
elif self.config.timestep_spacing == "trailing":
_A: Union[str, Any] = 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
_A: List[Any] = (np.arange(lowerCAmelCase_ , 0 , -step_ratio )).round().copy().astype(lowerCAmelCase_ )
timesteps -= 1
else:
raise ValueError(
F"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'.""" )
_A: Optional[int] = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
_A: str = np.log(lowerCAmelCase_ )
_A: int = np.interp(lowerCAmelCase_ , np.arange(0 , len(lowerCAmelCase_ ) ) , lowerCAmelCase_ )
if self.config.use_karras_sigmas:
_A: Optional[int] = self._convert_to_karras(in_sigmas=lowerCAmelCase_ , num_inference_steps=self.num_inference_steps )
_A: List[str] = np.array([self._sigma_to_t(lowerCAmelCase_ , lowerCAmelCase_ ) for sigma in sigmas] )
_A: Optional[int] = np.concatenate([sigmas, [0.0]] ).astype(np.floataa )
_A: Optional[Any] = torch.from_numpy(lowerCAmelCase_ ).to(device=lowerCAmelCase_ )
_A: Tuple = torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2 ), sigmas[-1:]] )
_A: str = torch.from_numpy(lowerCAmelCase_ )
_A: str = torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2 )] )
if str(lowerCAmelCase_ ).startswith('''mps''' ):
# mps does not support float64
_A: List[Any] = timesteps.to(lowerCAmelCase_ , dtype=torch.floataa )
else:
_A: Optional[int] = timesteps.to(device=lowerCAmelCase_ )
# empty dt and derivative
_A: Dict = None
_A: List[Any] = None
# for exp beta schedules, such as the one for `pipeline_shap_e.py`
# we need an index counter
_A: Dict = defaultdict(lowerCAmelCase_ )
def __magic_name__ ( self : Dict , lowerCAmelCase_ : Dict , lowerCAmelCase_ : Dict ):
"""simple docstring"""
# get log sigma
_A: Tuple = np.log(lowerCAmelCase_ )
# get distribution
_A: List[str] = log_sigma - log_sigmas[:, np.newaxis]
# get sigmas range
_A: Dict = np.cumsum((dists >= 0) , axis=0 ).argmax(axis=0 ).clip(max=log_sigmas.shape[0] - 2 )
_A: int = low_idx + 1
_A: Optional[int] = log_sigmas[low_idx]
_A: Dict = log_sigmas[high_idx]
# interpolate sigmas
_A: Union[str, Any] = (low - log_sigma) / (low - high)
_A: Optional[Any] = np.clip(lowerCAmelCase_ , 0 , 1 )
# transform interpolation to time range
_A: Any = (1 - w) * low_idx + w * high_idx
_A: List[Any] = t.reshape(sigma.shape )
return t
def __magic_name__ ( self : Any , lowerCAmelCase_ : torch.FloatTensor , lowerCAmelCase_ : Optional[Any] ):
"""simple docstring"""
_A: float = in_sigmas[-1].item()
_A: float = in_sigmas[0].item()
_A: Union[str, Any] = 7.0 # 7.0 is the value used in the paper
_A: Optional[Any] = np.linspace(0 , 1 , lowerCAmelCase_ )
_A: Tuple = sigma_min ** (1 / rho)
_A: Optional[Any] = sigma_max ** (1 / rho)
_A: List[str] = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
return sigmas
@property
def __magic_name__ ( self : Optional[Any] ):
"""simple docstring"""
return self.dt is None
def __magic_name__ ( self : Tuple , lowerCAmelCase_ : Union[torch.FloatTensor, np.ndarray] , lowerCAmelCase_ : Union[float, torch.FloatTensor] , lowerCAmelCase_ : Union[torch.FloatTensor, np.ndarray] , lowerCAmelCase_ : bool = True , ):
"""simple docstring"""
_A: Optional[int] = self.index_for_timestep(lowerCAmelCase_ )
# advance index counter by 1
_A: Union[str, Any] = timestep.cpu().item() if torch.is_tensor(lowerCAmelCase_ ) else timestep
self._index_counter[timestep_int] += 1
if self.state_in_first_order:
_A: Optional[int] = self.sigmas[step_index]
_A: Union[str, Any] = self.sigmas[step_index + 1]
else:
# 2nd order / Heun's method
_A: Union[str, Any] = self.sigmas[step_index - 1]
_A: Optional[int] = 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
_A: List[Any] = 0
_A: Tuple = 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":
_A: Union[str, Any] = sigma_hat if self.state_in_first_order else sigma_next
_A: List[str] = sample - sigma_input * model_output
elif self.config.prediction_type == "v_prediction":
_A: int = sigma_hat if self.state_in_first_order else sigma_next
_A: List[str] = model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
sample / (sigma_input**2 + 1)
)
elif self.config.prediction_type == "sample":
_A: Optional[int] = model_output
else:
raise ValueError(
F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`""" )
if self.config.clip_sample:
_A: Tuple = pred_original_sample.clamp(
-self.config.clip_sample_range , self.config.clip_sample_range )
if self.state_in_first_order:
# 2. Convert to an ODE derivative for 1st order
_A: Optional[int] = (sample - pred_original_sample) / sigma_hat
# 3. delta timestep
_A: List[Any] = sigma_next - sigma_hat
# store for 2nd order step
_A: str = derivative
_A: Any = dt
_A: Dict = sample
else:
# 2. 2nd order / Heun's method
_A: List[str] = (sample - pred_original_sample) / sigma_next
_A: str = (self.prev_derivative + derivative) / 2
# 3. take prev timestep & sample
_A: Dict = self.dt
_A: int = self.sample
# free dt and derivative
# Note, this puts the scheduler in "first order mode"
_A: int = None
_A: int = None
_A: Optional[Any] = None
_A: Optional[Any] = sample + derivative * dt
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=lowerCAmelCase_ )
def __magic_name__ ( self : Any , lowerCAmelCase_ : torch.FloatTensor , lowerCAmelCase_ : torch.FloatTensor , lowerCAmelCase_ : torch.FloatTensor , ):
"""simple docstring"""
# Make sure sigmas and timesteps have the same device and dtype as original_samples
_A: str = self.sigmas.to(device=original_samples.device , dtype=original_samples.dtype )
if original_samples.device.type == "mps" and torch.is_floating_point(lowerCAmelCase_ ):
# mps does not support float64
_A: Optional[int] = self.timesteps.to(original_samples.device , dtype=torch.floataa )
_A: Any = timesteps.to(original_samples.device , dtype=torch.floataa )
else:
_A: Union[str, Any] = self.timesteps.to(original_samples.device )
_A: int = timesteps.to(original_samples.device )
_A: str = [self.index_for_timestep(lowerCAmelCase_ , lowerCAmelCase_ ) for t in timesteps]
_A: Optional[Any] = sigmas[step_indices].flatten()
while len(sigma.shape ) < len(original_samples.shape ):
_A: List[str] = sigma.unsqueeze(-1 )
_A: Any = original_samples + noise * sigma
return noisy_samples
def __len__( self : Dict ):
"""simple docstring"""
return self.config.num_train_timesteps
| 301 | 1 |
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transformers import HfArgumentParser, TrainingArguments
from transformers.hf_argparser import make_choice_type_function, string_to_bool
# Since Python 3.10, we can use the builtin `|` operator for Union types
# See PEP 604: https://peps.python.org/pep-0604
_UpperCAmelCase = sys.version_info >= (3, 10)
def UpperCamelCase ( __lowercase : Dict=None ,__lowercase : Union[str, Any]=None ):
'''simple docstring'''
return field(default_factory=lambda: default ,metadata=__lowercase )
@dataclass
class UpperCAmelCase :
'''simple docstring'''
lowerCamelCase_ = 42
lowerCamelCase_ = 42
lowerCamelCase_ = 42
lowerCamelCase_ = 42
@dataclass
class UpperCAmelCase :
'''simple docstring'''
lowerCamelCase_ = 4_2
lowerCamelCase_ = field(default='''toto''' , metadata={'''help''': '''help message'''} )
@dataclass
class UpperCAmelCase :
'''simple docstring'''
lowerCamelCase_ = False
lowerCamelCase_ = True
lowerCamelCase_ = None
class UpperCAmelCase ( __A ):
'''simple docstring'''
lowerCamelCase_ = '''titi'''
lowerCamelCase_ = '''toto'''
class UpperCAmelCase ( __A ):
'''simple docstring'''
lowerCamelCase_ = '''titi'''
lowerCamelCase_ = '''toto'''
lowerCamelCase_ = 4_2
@dataclass
class UpperCAmelCase :
'''simple docstring'''
lowerCamelCase_ = "toto"
def lowerCAmelCase_ ( self ):
"""simple docstring"""
A_ : List[Any] = BasicEnum(self.foo )
@dataclass
class UpperCAmelCase :
'''simple docstring'''
lowerCamelCase_ = "toto"
def lowerCAmelCase_ ( self ):
"""simple docstring"""
A_ : Any = MixedTypeEnum(self.foo )
@dataclass
class UpperCAmelCase :
'''simple docstring'''
lowerCamelCase_ = None
lowerCamelCase_ = field(default=__A , metadata={'''help''': '''help message'''} )
lowerCamelCase_ = None
lowerCamelCase_ = list_field(default=[] )
lowerCamelCase_ = list_field(default=[] )
@dataclass
class UpperCAmelCase :
'''simple docstring'''
lowerCamelCase_ = list_field(default=[] )
lowerCamelCase_ = list_field(default=[1, 2, 3] )
lowerCamelCase_ = list_field(default=['''Hallo''', '''Bonjour''', '''Hello'''] )
lowerCamelCase_ = list_field(default=[0.1, 0.2, 0.3] )
@dataclass
class UpperCAmelCase :
'''simple docstring'''
lowerCamelCase_ = field()
lowerCamelCase_ = field()
lowerCamelCase_ = field()
def lowerCAmelCase_ ( self ):
"""simple docstring"""
A_ : Optional[Any] = BasicEnum(self.required_enum )
@dataclass
class UpperCAmelCase :
'''simple docstring'''
lowerCamelCase_ = 42
lowerCamelCase_ = field()
lowerCamelCase_ = None
lowerCamelCase_ = field(default='''toto''' , metadata={'''help''': '''help message'''} )
lowerCamelCase_ = list_field(default=['''Hallo''', '''Bonjour''', '''Hello'''] )
if is_python_no_less_than_3_10:
@dataclass
class UpperCAmelCase :
'''simple docstring'''
lowerCamelCase_ = False
lowerCamelCase_ = True
lowerCamelCase_ = None
@dataclass
class UpperCAmelCase :
'''simple docstring'''
lowerCamelCase_ = None
lowerCamelCase_ = field(default=__A , metadata={'''help''': '''help message'''} )
lowerCamelCase_ = None
lowerCamelCase_ = list_field(default=[] )
lowerCamelCase_ = list_field(default=[] )
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def lowerCAmelCase_ ( self , lowercase , lowercase ):
"""simple docstring"""
self.assertEqual(len(a._actions ) , len(b._actions ) )
for x, y in zip(a._actions , b._actions ):
A_ : Dict = {k: v for k, v in vars(lowercase ).items() if k != 'container'}
A_ : Union[str, Any] = {k: v for k, v in vars(lowercase ).items() if k != 'container'}
# Choices with mixed type have custom function as "type"
# So we need to compare results directly for equality
if xx.get('choices' , lowercase ) and yy.get('choices' , lowercase ):
for expected_choice in yy["choices"] + xx["choices"]:
self.assertEqual(xx['type'](lowercase ) , yy['type'](lowercase ) )
del xx["type"], yy["type"]
self.assertEqual(lowercase , lowercase )
def lowerCAmelCase_ ( self ):
"""simple docstring"""
A_ : Optional[Any] = HfArgumentParser(lowercase )
A_ : Optional[Any] = argparse.ArgumentParser()
expected.add_argument('--foo' , type=lowercase , required=lowercase )
expected.add_argument('--bar' , type=lowercase , required=lowercase )
expected.add_argument('--baz' , type=lowercase , required=lowercase )
expected.add_argument('--flag' , type=lowercase , default=lowercase , const=lowercase , nargs='?' )
self.argparsersEqual(lowercase , lowercase )
A_ : int = ['--foo', '1', '--baz', 'quux', '--bar', '0.5']
((A_) , ) : Dict = parser.parse_args_into_dataclasses(lowercase , look_for_args_file=lowercase )
self.assertFalse(example.flag )
def lowerCAmelCase_ ( self ):
"""simple docstring"""
A_ : Any = HfArgumentParser(lowercase )
A_ : Tuple = argparse.ArgumentParser()
expected.add_argument('--foo' , default=4_2 , type=lowercase )
expected.add_argument('--baz' , default='toto' , type=lowercase , help='help message' )
self.argparsersEqual(lowercase , lowercase )
def lowerCAmelCase_ ( self ):
"""simple docstring"""
A_ : Tuple = argparse.ArgumentParser()
expected.add_argument('--foo' , type=lowercase , default=lowercase , const=lowercase , nargs='?' )
expected.add_argument('--baz' , type=lowercase , default=lowercase , const=lowercase , nargs='?' )
# A boolean no_* argument always has to come after its "default: True" regular counter-part
# and its default must be set to False
expected.add_argument('--no_baz' , action='store_false' , default=lowercase , dest='baz' )
expected.add_argument('--opt' , type=lowercase , default=lowercase )
A_ : int = [WithDefaultBoolExample]
if is_python_no_less_than_3_10:
dataclass_types.append(lowercase )
for dataclass_type in dataclass_types:
A_ : Optional[Any] = HfArgumentParser(lowercase )
self.argparsersEqual(lowercase , lowercase )
A_ : Any = parser.parse_args([] )
self.assertEqual(lowercase , Namespace(foo=lowercase , baz=lowercase , opt=lowercase ) )
A_ : List[str] = parser.parse_args(['--foo', '--no_baz'] )
self.assertEqual(lowercase , Namespace(foo=lowercase , baz=lowercase , opt=lowercase ) )
A_ : Optional[int] = parser.parse_args(['--foo', '--baz'] )
self.assertEqual(lowercase , Namespace(foo=lowercase , baz=lowercase , opt=lowercase ) )
A_ : Tuple = parser.parse_args(['--foo', 'True', '--baz', 'True', '--opt', 'True'] )
self.assertEqual(lowercase , Namespace(foo=lowercase , baz=lowercase , opt=lowercase ) )
A_ : int = parser.parse_args(['--foo', 'False', '--baz', 'False', '--opt', 'False'] )
self.assertEqual(lowercase , Namespace(foo=lowercase , baz=lowercase , opt=lowercase ) )
def lowerCAmelCase_ ( self ):
"""simple docstring"""
A_ : List[str] = HfArgumentParser(lowercase )
A_ : str = argparse.ArgumentParser()
expected.add_argument(
'--foo' , default='toto' , choices=['titi', 'toto', 4_2] , type=make_choice_type_function(['titi', 'toto', 4_2] ) , )
self.argparsersEqual(lowercase , lowercase )
A_ : int = parser.parse_args([] )
self.assertEqual(args.foo , 'toto' )
A_ : Union[str, Any] = parser.parse_args_into_dataclasses([] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.toto )
A_ : List[str] = parser.parse_args(['--foo', 'titi'] )
self.assertEqual(args.foo , 'titi' )
A_ : Optional[int] = parser.parse_args_into_dataclasses(['--foo', 'titi'] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.titi )
A_ : Optional[Any] = parser.parse_args(['--foo', '42'] )
self.assertEqual(args.foo , 4_2 )
A_ : Any = parser.parse_args_into_dataclasses(['--foo', '42'] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.fourtytwo )
def lowerCAmelCase_ ( self ):
"""simple docstring"""
@dataclass
class UpperCAmelCase :
'''simple docstring'''
lowerCamelCase_ = "toto"
A_ : int = HfArgumentParser(lowercase )
A_ : int = argparse.ArgumentParser()
expected.add_argument(
'--foo' , default='toto' , choices=('titi', 'toto', 4_2) , type=make_choice_type_function(['titi', 'toto', 4_2] ) , )
self.argparsersEqual(lowercase , lowercase )
A_ : int = parser.parse_args([] )
self.assertEqual(args.foo , 'toto' )
A_ : List[Any] = parser.parse_args(['--foo', 'titi'] )
self.assertEqual(args.foo , 'titi' )
A_ : List[Any] = parser.parse_args(['--foo', '42'] )
self.assertEqual(args.foo , 4_2 )
def lowerCAmelCase_ ( self ):
"""simple docstring"""
A_ : Any = HfArgumentParser(lowercase )
A_ : Tuple = argparse.ArgumentParser()
expected.add_argument('--foo_int' , nargs='+' , default=[] , type=lowercase )
expected.add_argument('--bar_int' , nargs='+' , default=[1, 2, 3] , type=lowercase )
expected.add_argument('--foo_str' , nargs='+' , default=['Hallo', 'Bonjour', 'Hello'] , type=lowercase )
expected.add_argument('--foo_float' , nargs='+' , default=[0.1, 0.2, 0.3] , type=lowercase )
self.argparsersEqual(lowercase , lowercase )
A_ : Tuple = parser.parse_args([] )
self.assertEqual(
lowercase , Namespace(foo_int=[] , bar_int=[1, 2, 3] , foo_str=['Hallo', 'Bonjour', 'Hello'] , foo_float=[0.1, 0.2, 0.3] ) , )
A_ : Dict = parser.parse_args('--foo_int 1 --bar_int 2 3 --foo_str a b c --foo_float 0.1 0.7'.split() )
self.assertEqual(lowercase , Namespace(foo_int=[1] , bar_int=[2, 3] , foo_str=['a', 'b', 'c'] , foo_float=[0.1, 0.7] ) )
def lowerCAmelCase_ ( self ):
"""simple docstring"""
A_ : Union[str, Any] = argparse.ArgumentParser()
expected.add_argument('--foo' , default=lowercase , type=lowercase )
expected.add_argument('--bar' , default=lowercase , type=lowercase , help='help message' )
expected.add_argument('--baz' , default=lowercase , type=lowercase )
expected.add_argument('--ces' , nargs='+' , default=[] , type=lowercase )
expected.add_argument('--des' , nargs='+' , default=[] , type=lowercase )
A_ : int = [OptionalExample]
if is_python_no_less_than_3_10:
dataclass_types.append(lowercase )
for dataclass_type in dataclass_types:
A_ : int = HfArgumentParser(lowercase )
self.argparsersEqual(lowercase , lowercase )
A_ : str = parser.parse_args([] )
self.assertEqual(lowercase , Namespace(foo=lowercase , bar=lowercase , baz=lowercase , ces=[] , des=[] ) )
A_ : List[Any] = parser.parse_args('--foo 12 --bar 3.14 --baz 42 --ces a b c --des 1 2 3'.split() )
self.assertEqual(lowercase , Namespace(foo=1_2 , bar=3.14 , baz='42' , ces=['a', 'b', 'c'] , des=[1, 2, 3] ) )
def lowerCAmelCase_ ( self ):
"""simple docstring"""
A_ : Dict = HfArgumentParser(lowercase )
A_ : List[str] = argparse.ArgumentParser()
expected.add_argument('--required_list' , nargs='+' , type=lowercase , required=lowercase )
expected.add_argument('--required_str' , type=lowercase , required=lowercase )
expected.add_argument(
'--required_enum' , type=make_choice_type_function(['titi', 'toto'] ) , choices=['titi', 'toto'] , required=lowercase , )
self.argparsersEqual(lowercase , lowercase )
def lowerCAmelCase_ ( self ):
"""simple docstring"""
A_ : Optional[Any] = HfArgumentParser(lowercase )
A_ : str = argparse.ArgumentParser()
expected.add_argument('--foo' , type=lowercase , required=lowercase )
expected.add_argument(
'--required_enum' , type=make_choice_type_function(['titi', 'toto'] ) , choices=['titi', 'toto'] , required=lowercase , )
expected.add_argument('--opt' , type=lowercase , default=lowercase )
expected.add_argument('--baz' , default='toto' , type=lowercase , help='help message' )
expected.add_argument('--foo_str' , nargs='+' , default=['Hallo', 'Bonjour', 'Hello'] , type=lowercase )
self.argparsersEqual(lowercase , lowercase )
def lowerCAmelCase_ ( self ):
"""simple docstring"""
A_ : Tuple = HfArgumentParser(lowercase )
A_ : List[Any] = {
'foo': 1_2,
'bar': 3.14,
'baz': '42',
'flag': True,
}
A_ : Union[str, Any] = parser.parse_dict(lowercase )[0]
A_ : List[str] = BasicExample(**lowercase )
self.assertEqual(lowercase , lowercase )
def lowerCAmelCase_ ( self ):
"""simple docstring"""
A_ : Optional[int] = HfArgumentParser(lowercase )
A_ : str = {
'foo': 1_2,
'bar': 3.14,
'baz': '42',
'flag': True,
'extra': 4_2,
}
self.assertRaises(lowercase , parser.parse_dict , lowercase , allow_extra_keys=lowercase )
def lowerCAmelCase_ ( self ):
"""simple docstring"""
A_ : Tuple = HfArgumentParser(lowercase )
A_ : Any = {
'foo': 1_2,
'bar': 3.14,
'baz': '42',
'flag': True,
}
with tempfile.TemporaryDirectory() as tmp_dir:
A_ : List[str] = os.path.join(lowercase , 'temp_json' )
os.mkdir(lowercase )
with open(temp_local_path + '.json' , 'w+' ) as f:
json.dump(lowercase , lowercase )
A_ : List[str] = parser.parse_yaml_file(Path(temp_local_path + '.json' ) )[0]
A_ : Optional[Any] = BasicExample(**lowercase )
self.assertEqual(lowercase , lowercase )
def lowerCAmelCase_ ( self ):
"""simple docstring"""
A_ : Tuple = HfArgumentParser(lowercase )
A_ : str = {
'foo': 1_2,
'bar': 3.14,
'baz': '42',
'flag': True,
}
with tempfile.TemporaryDirectory() as tmp_dir:
A_ : int = os.path.join(lowercase , 'temp_yaml' )
os.mkdir(lowercase )
with open(temp_local_path + '.yaml' , 'w+' ) as f:
yaml.dump(lowercase , lowercase )
A_ : Optional[int] = parser.parse_yaml_file(Path(temp_local_path + '.yaml' ) )[0]
A_ : Optional[Any] = BasicExample(**lowercase )
self.assertEqual(lowercase , lowercase )
def lowerCAmelCase_ ( self ):
"""simple docstring"""
A_ : str = HfArgumentParser(lowercase )
self.assertIsNotNone(lowercase )
| 140 | from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {
"""huggingface/time-series-transformer-tourism-monthly""": (
"""https://huggingface.co/huggingface/time-series-transformer-tourism-monthly/resolve/main/config.json"""
),
# See all TimeSeriesTransformer models at https://huggingface.co/models?filter=time_series_transformer
}
class UpperCAmelCase ( __A ):
'''simple docstring'''
lowerCamelCase_ = '''time_series_transformer'''
lowerCamelCase_ = {
'''hidden_size''': '''d_model''',
'''num_attention_heads''': '''encoder_attention_heads''',
'''num_hidden_layers''': '''encoder_layers''',
}
def __init__( self , lowercase = None , lowercase = None , lowercase = "student_t" , lowercase = "nll" , lowercase = 1 , lowercase = [1, 2, 3, 4, 5, 6, 7] , lowercase = "mean" , lowercase = 0 , lowercase = 0 , lowercase = 0 , lowercase = 0 , lowercase = None , lowercase = None , lowercase = 3_2 , lowercase = 3_2 , lowercase = 2 , lowercase = 2 , lowercase = 2 , lowercase = 2 , lowercase = True , lowercase = "gelu" , lowercase = 6_4 , lowercase = 0.1 , lowercase = 0.1 , lowercase = 0.1 , lowercase = 0.1 , lowercase = 0.1 , lowercase = 1_0_0 , lowercase = 0.02 , lowercase=True , **lowercase , ):
"""simple docstring"""
A_ : Tuple = prediction_length
A_ : Any = context_length or prediction_length
A_ : Any = distribution_output
A_ : Dict = loss
A_ : int = input_size
A_ : Any = num_time_features
A_ : List[str] = lags_sequence
A_ : List[Any] = scaling
A_ : str = num_dynamic_real_features
A_ : List[Any] = num_static_real_features
A_ : Optional[int] = num_static_categorical_features
if cardinality and num_static_categorical_features > 0:
if len(lowercase ) != num_static_categorical_features:
raise ValueError(
'The cardinality should be a list of the same length as `num_static_categorical_features`' )
A_ : Any = cardinality
else:
A_ : Any = [0]
if embedding_dimension and num_static_categorical_features > 0:
if len(lowercase ) != num_static_categorical_features:
raise ValueError(
'The embedding dimension should be a list of the same length as `num_static_categorical_features`' )
A_ : int = embedding_dimension
else:
A_ : Tuple = [min(5_0 , (cat + 1) // 2 ) for cat in self.cardinality]
A_ : Optional[int] = num_parallel_samples
# Transformer architecture configuration
A_ : Any = input_size * len(lowercase ) + self._number_of_features
A_ : List[str] = d_model
A_ : Union[str, Any] = encoder_attention_heads
A_ : int = decoder_attention_heads
A_ : int = encoder_ffn_dim
A_ : str = decoder_ffn_dim
A_ : Tuple = encoder_layers
A_ : Tuple = decoder_layers
A_ : List[str] = dropout
A_ : str = attention_dropout
A_ : List[Any] = activation_dropout
A_ : List[Any] = encoder_layerdrop
A_ : int = decoder_layerdrop
A_ : Optional[int] = activation_function
A_ : str = init_std
A_ : int = use_cache
super().__init__(is_encoder_decoder=lowercase , **lowercase )
@property
def lowerCAmelCase_ ( self ):
"""simple docstring"""
return (
sum(self.embedding_dimension )
+ self.num_dynamic_real_features
+ self.num_time_features
+ self.num_static_real_features
+ self.input_size * 2 # the log1p(abs(loc)) and log(scale) features
)
| 140 | 1 |
__UpperCAmelCase = '0.18.2'
from .configuration_utils import ConfigMixin
from .utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_inflect_available,
is_invisible_watermark_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_librosa_available,
is_note_seq_available,
is_onnx_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
is_transformers_available,
is_transformers_version,
is_unidecode_available,
logging,
)
try:
if not is_onnx_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_onnx_objects import * # noqa F403
else:
from .pipelines import OnnxRuntimeModel
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_pt_objects import * # noqa F403
else:
from .models import (
AutoencoderKL,
ControlNetModel,
ModelMixin,
PriorTransformer,
TaFilmDecoder,
TransformeraDModel,
UNetaDModel,
UNetaDConditionModel,
UNetaDModel,
UNetaDConditionModel,
VQModel,
)
from .optimization import (
get_constant_schedule,
get_constant_schedule_with_warmup,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
get_scheduler,
)
from .pipelines import (
AudioPipelineOutput,
ConsistencyModelPipeline,
DanceDiffusionPipeline,
DDIMPipeline,
DDPMPipeline,
DiffusionPipeline,
DiTPipeline,
ImagePipelineOutput,
KarrasVePipeline,
LDMPipeline,
LDMSuperResolutionPipeline,
PNDMPipeline,
RePaintPipeline,
ScoreSdeVePipeline,
)
from .schedulers import (
CMStochasticIterativeScheduler,
DDIMInverseScheduler,
DDIMParallelScheduler,
DDIMScheduler,
DDPMParallelScheduler,
DDPMScheduler,
DEISMultistepScheduler,
DPMSolverMultistepInverseScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
HeunDiscreteScheduler,
IPNDMScheduler,
KarrasVeScheduler,
KDPMaAncestralDiscreteScheduler,
KDPMaDiscreteScheduler,
PNDMScheduler,
RePaintScheduler,
SchedulerMixin,
ScoreSdeVeScheduler,
UnCLIPScheduler,
UniPCMultistepScheduler,
VQDiffusionScheduler,
)
from .training_utils import EMAModel
try:
if not (is_torch_available() and is_scipy_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_scipy_objects import * # noqa F403
else:
from .schedulers import LMSDiscreteScheduler
try:
if not (is_torch_available() and is_torchsde_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_torchsde_objects import * # noqa F403
else:
from .schedulers import DPMSolverSDEScheduler
try:
if not (is_torch_available() and is_transformers_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .pipelines import (
AltDiffusionImgaImgPipeline,
AltDiffusionPipeline,
AudioLDMPipeline,
CycleDiffusionPipeline,
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
ImageTextPipelineOutput,
KandinskyImgaImgPipeline,
KandinskyInpaintPipeline,
KandinskyPipeline,
KandinskyPriorPipeline,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaControlnetPipeline,
KandinskyVaaImgaImgPipeline,
KandinskyVaaInpaintPipeline,
KandinskyVaaPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
KandinskyVaaPriorPipeline,
LDMTextToImagePipeline,
PaintByExamplePipeline,
SemanticStableDiffusionPipeline,
ShapEImgaImgPipeline,
ShapEPipeline,
StableDiffusionAttendAndExcitePipeline,
StableDiffusionControlNetImgaImgPipeline,
StableDiffusionControlNetInpaintPipeline,
StableDiffusionControlNetPipeline,
StableDiffusionDepthaImgPipeline,
StableDiffusionDiffEditPipeline,
StableDiffusionImageVariationPipeline,
StableDiffusionImgaImgPipeline,
StableDiffusionInpaintPipeline,
StableDiffusionInpaintPipelineLegacy,
StableDiffusionInstructPixaPixPipeline,
StableDiffusionLatentUpscalePipeline,
StableDiffusionLDMaDPipeline,
StableDiffusionModelEditingPipeline,
StableDiffusionPanoramaPipeline,
StableDiffusionParadigmsPipeline,
StableDiffusionPipeline,
StableDiffusionPipelineSafe,
StableDiffusionPixaPixZeroPipeline,
StableDiffusionSAGPipeline,
StableDiffusionUpscalePipeline,
StableUnCLIPImgaImgPipeline,
StableUnCLIPPipeline,
TextToVideoSDPipeline,
TextToVideoZeroPipeline,
UnCLIPImageVariationPipeline,
UnCLIPPipeline,
UniDiffuserModel,
UniDiffuserPipeline,
UniDiffuserTextDecoder,
VersatileDiffusionDualGuidedPipeline,
VersatileDiffusionImageVariationPipeline,
VersatileDiffusionPipeline,
VersatileDiffusionTextToImagePipeline,
VideoToVideoSDPipeline,
VQDiffusionPipeline,
)
try:
if not (is_torch_available() and is_transformers_available() and is_invisible_watermark_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_transformers_and_invisible_watermark_objects import * # noqa F403
else:
from .pipelines import StableDiffusionXLImgaImgPipeline, StableDiffusionXLPipeline
try:
if not (is_torch_available() and is_transformers_available() and is_k_diffusion_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_transformers_and_k_diffusion_objects import * # noqa F403
else:
from .pipelines import StableDiffusionKDiffusionPipeline
try:
if not (is_torch_available() and is_transformers_available() and is_onnx_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_transformers_and_onnx_objects import * # noqa F403
else:
from .pipelines import (
OnnxStableDiffusionImgaImgPipeline,
OnnxStableDiffusionInpaintPipeline,
OnnxStableDiffusionInpaintPipelineLegacy,
OnnxStableDiffusionPipeline,
OnnxStableDiffusionUpscalePipeline,
StableDiffusionOnnxPipeline,
)
try:
if not (is_torch_available() and is_librosa_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_librosa_objects import * # noqa F403
else:
from .pipelines import AudioDiffusionPipeline, Mel
try:
if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_transformers_and_torch_and_note_seq_objects import * # noqa F403
else:
from .pipelines import SpectrogramDiffusionPipeline
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_flax_objects import * # noqa F403
else:
from .models.controlnet_flax import FlaxControlNetModel
from .models.modeling_flax_utils import FlaxModelMixin
from .models.unet_ad_condition_flax import FlaxUNetaDConditionModel
from .models.vae_flax import FlaxAutoencoderKL
from .pipelines import FlaxDiffusionPipeline
from .schedulers import (
FlaxDDIMScheduler,
FlaxDDPMScheduler,
FlaxDPMSolverMultistepScheduler,
FlaxKarrasVeScheduler,
FlaxLMSDiscreteScheduler,
FlaxPNDMScheduler,
FlaxSchedulerMixin,
FlaxScoreSdeVeScheduler,
)
try:
if not (is_flax_available() and is_transformers_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_flax_and_transformers_objects import * # noqa F403
else:
from .pipelines import (
FlaxStableDiffusionControlNetPipeline,
FlaxStableDiffusionImgaImgPipeline,
FlaxStableDiffusionInpaintPipeline,
FlaxStableDiffusionPipeline,
)
try:
if not (is_note_seq_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_note_seq_objects import * # noqa F403
else:
from .pipelines import MidiProcessor
| 364 |
def lowercase__ ( __snake_case : str , __snake_case : int , __snake_case : List[str] ):
'''simple docstring'''
if n == 0:
return 1
elif n % 2 == 1:
return (binary_exponentiation(__snake_case , n - 1 , __snake_case ) * a) % mod
else:
UpperCAmelCase_ : Optional[int] = binary_exponentiation(__snake_case , n / 2 , __snake_case )
return (b * b) % mod
# a prime number
__UpperCAmelCase = 701
__UpperCAmelCase = 1000000000
__UpperCAmelCase = 10
# using binary exponentiation function, O(log(p)):
print((a / b) % p == (a * binary_exponentiation(b, p - 2, p)) % p)
print((a / b) % p == (a * b ** (p - 2)) % p)
| 145 | 0 |
'''simple docstring'''
from __future__ import annotations
from decimal import Decimal
from math import * # noqa: F403
from sympy import diff
def __snake_case( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = 10**-10 ) -> float:
snake_case__ : List[str] = a
while True:
snake_case__ : Union[str, Any] = Decimal(_lowercase ) - (
Decimal(eval(_lowercase ) ) / Decimal(eval(str(diff(_lowercase ) ) ) ) # noqa: S307
)
# This number dictates the accuracy of the answer
if abs(eval(_lowercase ) ) < precision: # noqa: S307
return float(_lowercase )
# Let's Execute
if __name__ == "__main__":
# Find root of trigonometric function
# Find value of pi
print(F"The root of sin(x) = 0 is {newton_raphson('sin(x)', 2)}")
# Find root of polynomial
print(F"The root of x**2 - 5*x + 2 = 0 is {newton_raphson('x**2 - 5*x + 2', 0.4)}")
# Find Square Root of 5
print(F"The root of log(x) - 1 = 0 is {newton_raphson('log(x) - 1', 2)}")
# Exponential Roots
print(F"The root of exp(x) - 1 = 0 is {newton_raphson('exp(x) - 1', 0)}")
| 35 |
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from transformers import BartTokenizer, RagTokenizer, TaTokenizer
def _A ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase=True , _lowercase="pt" ) -> Union[str, Any]:
"""simple docstring"""
__UpperCamelCase = {'add_prefix_space': True} if isinstance(_lowercase , _lowercase ) and not line.startswith(' ' ) else {}
__UpperCamelCase = padding_side
return tokenizer(
[line] , max_length=_lowercase , padding='max_length' if pad_to_max_length else None , truncation=_lowercase , return_tensors=_lowercase , add_special_tokens=_lowercase , **_lowercase , )
def _A ( _lowercase , _lowercase , _lowercase=None , ) -> List[Any]:
"""simple docstring"""
__UpperCamelCase = input_ids.ne(_lowercase ).any(dim=0 )
if attention_mask is None:
return input_ids[:, keep_column_mask]
else:
return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])
class __lowerCamelCase (_a ):
def __init__( self: List[str],A_: str,A_: List[str],A_: List[str],A_: List[str],A_: Tuple="train",A_: Any=None,A_: List[str]=None,A_: List[Any]=None,A_: int="",):
'''simple docstring'''
super().__init__()
__UpperCamelCase = Path(A_ ).joinpath(type_path + '.source' )
__UpperCamelCase = Path(A_ ).joinpath(type_path + '.target' )
__UpperCamelCase = self.get_char_lens(self.src_file )
__UpperCamelCase = max_source_length
__UpperCamelCase = max_target_length
assert min(self.src_lens ) > 0, F'''found empty line in {self.src_file}'''
__UpperCamelCase = tokenizer
__UpperCamelCase = prefix
if n_obs is not None:
__UpperCamelCase = self.src_lens[:n_obs]
__UpperCamelCase = src_lang
__UpperCamelCase = tgt_lang
def __len__( self: Optional[Any] ):
'''simple docstring'''
return len(self.src_lens )
def __getitem__( self: int,A_: Optional[Any] ):
'''simple docstring'''
__UpperCamelCase = index + 1 # linecache starts at 1
__UpperCamelCase = self.prefix + linecache.getline(str(self.src_file ),A_ ).rstrip('\n' )
__UpperCamelCase = linecache.getline(str(self.tgt_file ),A_ ).rstrip('\n' )
assert source_line, F'''empty source line for index {index}'''
assert tgt_line, F'''empty tgt line for index {index}'''
# Need to add eos token manually for T5
if isinstance(self.tokenizer,A_ ):
source_line += self.tokenizer.eos_token
tgt_line += self.tokenizer.eos_token
# Pad source and target to the right
__UpperCamelCase = (
self.tokenizer.question_encoder if isinstance(self.tokenizer,A_ ) else self.tokenizer
)
__UpperCamelCase = self.tokenizer.generator if isinstance(self.tokenizer,A_ ) else self.tokenizer
__UpperCamelCase = encode_line(A_,A_,self.max_source_length,'right' )
__UpperCamelCase = encode_line(A_,A_,self.max_target_length,'right' )
__UpperCamelCase = source_inputs['input_ids'].squeeze()
__UpperCamelCase = target_inputs['input_ids'].squeeze()
__UpperCamelCase = source_inputs['attention_mask'].squeeze()
return {
"input_ids": source_ids,
"attention_mask": src_mask,
"decoder_input_ids": target_ids,
}
@staticmethod
def snake_case_ ( A_: List[Any] ):
'''simple docstring'''
return [len(A_ ) for x in Path(A_ ).open().readlines()]
def snake_case_ ( self: Union[str, Any],A_: Any ):
'''simple docstring'''
__UpperCamelCase = torch.stack([x['input_ids'] for x in batch] )
__UpperCamelCase = torch.stack([x['attention_mask'] for x in batch] )
__UpperCamelCase = torch.stack([x['decoder_input_ids'] for x in batch] )
__UpperCamelCase = (
self.tokenizer.generator.pad_token_id
if isinstance(self.tokenizer,A_ )
else self.tokenizer.pad_token_id
)
__UpperCamelCase = (
self.tokenizer.question_encoder.pad_token_id
if isinstance(self.tokenizer,A_ )
else self.tokenizer.pad_token_id
)
__UpperCamelCase = trim_batch(A_,A_ )
__UpperCamelCase, __UpperCamelCase = trim_batch(A_,A_,attention_mask=A_ )
__UpperCamelCase = {
'input_ids': source_ids,
'attention_mask': source_mask,
'decoder_input_ids': y,
}
return batch
__snake_case = getLogger(__name__)
def _A ( _lowercase ) -> Any:
"""simple docstring"""
return list(itertools.chain.from_iterable(_lowercase ) )
def _A ( _lowercase ) -> None:
"""simple docstring"""
__UpperCamelCase = get_git_info()
save_json(_lowercase , os.path.join(_lowercase , 'git_log.json' ) )
def _A ( _lowercase , _lowercase , _lowercase=4 , **_lowercase ) -> List[Any]:
"""simple docstring"""
with open(_lowercase , 'w' ) as f:
json.dump(_lowercase , _lowercase , indent=_lowercase , **_lowercase )
def _A ( _lowercase ) -> Union[str, Any]:
"""simple docstring"""
with open(_lowercase ) as f:
return json.load(_lowercase )
def _A ( ) -> Dict:
"""simple docstring"""
__UpperCamelCase = git.Repo(search_parent_directories=_lowercase )
__UpperCamelCase = {
'repo_id': str(_lowercase ),
'repo_sha': str(repo.head.object.hexsha ),
'repo_branch': str(repo.active_branch ),
'hostname': str(socket.gethostname() ),
}
return repo_infos
def _A ( _lowercase , _lowercase ) -> List:
"""simple docstring"""
return list(map(_lowercase , _lowercase ) )
def _A ( _lowercase , _lowercase ) -> Tuple:
"""simple docstring"""
with open(_lowercase , 'wb' ) as f:
return pickle.dump(_lowercase , _lowercase )
def _A ( _lowercase ) -> List[Any]:
"""simple docstring"""
def remove_articles(_lowercase ):
return re.sub(r'\b(a|an|the)\b' , ' ' , _lowercase )
def white_space_fix(_lowercase ):
return " ".join(text.split() )
def remove_punc(_lowercase ):
__UpperCamelCase = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(_lowercase ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(_lowercase ) ) ) )
def _A ( _lowercase , _lowercase ) -> int:
"""simple docstring"""
__UpperCamelCase = normalize_answer(_lowercase ).split()
__UpperCamelCase = normalize_answer(_lowercase ).split()
__UpperCamelCase = Counter(_lowercase ) & Counter(_lowercase )
__UpperCamelCase = sum(common.values() )
if num_same == 0:
return 0
__UpperCamelCase = 1.0 * num_same / len(_lowercase )
__UpperCamelCase = 1.0 * num_same / len(_lowercase )
__UpperCamelCase = (2 * precision * recall) / (precision + recall)
return fa
def _A ( _lowercase , _lowercase ) -> Any:
"""simple docstring"""
return normalize_answer(_lowercase ) == normalize_answer(_lowercase )
def _A ( _lowercase , _lowercase ) -> Dict:
"""simple docstring"""
assert len(_lowercase ) == len(_lowercase )
__UpperCamelCase = 0
for hypo, pred in zip(_lowercase , _lowercase ):
em += exact_match_score(_lowercase , _lowercase )
if len(_lowercase ) > 0:
em /= len(_lowercase )
return {"em": em}
def _A ( _lowercase ) -> Optional[Any]:
"""simple docstring"""
return model_prefix.startswith('rag' )
def _A ( _lowercase , _lowercase , _lowercase ) -> Dict:
"""simple docstring"""
__UpperCamelCase = {p: p for p in extra_params}
# T5 models don't have `dropout` param, they have `dropout_rate` instead
__UpperCamelCase = 'dropout_rate'
for p in extra_params:
if getattr(_lowercase , _lowercase , _lowercase ):
if not hasattr(_lowercase , _lowercase ) and not hasattr(_lowercase , equivalent_param[p] ):
logger.info('config doesn\'t have a `{}` attribute'.format(_lowercase ) )
delattr(_lowercase , _lowercase )
continue
__UpperCamelCase = p if hasattr(_lowercase , _lowercase ) else equivalent_param[p]
setattr(_lowercase , _lowercase , getattr(_lowercase , _lowercase ) )
delattr(_lowercase , _lowercase )
return hparams, config
| 310 | 0 |
'''simple docstring'''
class UpperCAmelCase__ :
"""simple docstring"""
def __init__( self : Optional[int] ):
'''simple docstring'''
_a : Any = ''
_a : List[str] = ''
_a : str = []
def __lowercase ( self : Optional[int] ,_a : int ,_a : int ):
'''simple docstring'''
if m == -1:
return n + 1
elif n == -1:
return m + 1
elif self.dp[m][n] > -1:
return self.dp[m][n]
else:
if self.worda[m] == self.worda[n]:
_a : Optional[Any] = self.__min_dist_top_down_dp(m - 1 ,n - 1 )
else:
_a : List[Any] = self.__min_dist_top_down_dp(_a ,n - 1 )
_a : List[Any] = self.__min_dist_top_down_dp(m - 1 ,_a )
_a : int = self.__min_dist_top_down_dp(m - 1 ,n - 1 )
_a : str = 1 + min(_a ,_a ,_a )
return self.dp[m][n]
def __lowercase ( self : Any ,_a : str ,_a : str ):
'''simple docstring'''
_a : List[str] = worda
_a : List[str] = worda
_a : List[Any] = [[-1 for _ in range(len(_a ) )] for _ in range(len(_a ) )]
return self.__min_dist_top_down_dp(len(_a ) - 1 ,len(_a ) - 1 )
def __lowercase ( self : Any ,_a : str ,_a : str ):
'''simple docstring'''
_a : str = worda
_a : Optional[int] = worda
_a : Tuple = len(_a )
_a : List[str] = len(_a )
_a : Tuple = [[0 for _ in range(n + 1 )] for _ in range(m + 1 )]
for i in range(m + 1 ):
for j in range(n + 1 ):
if i == 0: # first string is empty
_a : Optional[Any] = j
elif j == 0: # second string is empty
_a : Union[str, Any] = i
elif worda[i - 1] == worda[j - 1]: # last characters are equal
_a : Union[str, Any] = self.dp[i - 1][j - 1]
else:
_a : List[str] = self.dp[i][j - 1]
_a : Union[str, Any] = self.dp[i - 1][j]
_a : Union[str, Any] = self.dp[i - 1][j - 1]
_a : Optional[int] = 1 + min(_a ,_a ,_a )
return self.dp[m][n]
if __name__ == "__main__":
__lowerCAmelCase = EditDistance()
print("""****************** Testing Edit Distance DP Algorithm ******************""")
print()
__lowerCAmelCase = input("""Enter the first string: """).strip()
__lowerCAmelCase = input("""Enter the second string: """).strip()
print()
print(f'''The minimum edit distance is: {solver.min_dist_top_down(Sa, Sa)}''')
print(f'''The minimum edit distance is: {solver.min_dist_bottom_up(Sa, Sa)}''')
print()
print("""*************** End of Testing Edit Distance DP Algorithm ***************""")
| 5 |
'''simple docstring'''
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def UpperCAmelCase_ (__a : Optional[Any] ):
"""simple docstring"""
_a : int = FileLock(str(tmpdir / 'foo.lock' ) )
_a : List[Any] = FileLock(str(tmpdir / 'foo.lock' ) )
_a : Any = 0.01
with locka.acquire():
with pytest.raises(__a ):
_a : int = time.time()
locka.acquire(__a )
assert time.time() - _start > timeout
def UpperCAmelCase_ (__a : str ):
"""simple docstring"""
_a : Dict = 'a' * 1_0_0_0 + '.lock'
_a : int = FileLock(str(tmpdir / filename ) )
assert locka._lock_file.endswith('.lock' )
assert not locka._lock_file.endswith(__a )
assert len(os.path.basename(locka._lock_file ) ) <= 2_5_5
_a : Dict = FileLock(tmpdir / filename )
with locka.acquire():
with pytest.raises(__a ):
locka.acquire(0 )
| 5 | 1 |
'''simple docstring'''
import argparse
import gc
import json
import os
import shutil
import warnings
import torch
from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer
try:
from transformers import LlamaTokenizerFast
except ImportError as e:
warnings.warn(e)
warnings.warn(
'''The converted tokenizer will be the `slow` tokenizer. To use the fast, update your `tokenizers` library and re-run the tokenizer conversion'''
)
UpperCamelCase__ = None
UpperCamelCase__ = {
'''7B''': 1_1_0_0_8,
'''13B''': 1_3_8_2_4,
'''30B''': 1_7_9_2_0,
'''65B''': 2_2_0_1_6,
'''70B''': 2_8_6_7_2,
}
UpperCamelCase__ = {
'''7B''': 1,
'''7Bf''': 1,
'''13B''': 2,
'''13Bf''': 2,
'''30B''': 4,
'''65B''': 8,
'''70B''': 8,
'''70Bf''': 8,
}
def a__ ( lowerCAmelCase__ , lowerCAmelCase__=1 , lowerCAmelCase__=2_56 ) -> List[str]:
return multiple_of * ((int(ffn_dim_multiplier * int(8 * n / 3 ) ) + multiple_of - 1) // multiple_of)
def a__ ( lowerCAmelCase__ ) -> Dict:
with open(_lowerCAmelCase , '''r''' ) as f:
return json.load(_lowerCAmelCase )
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ ) -> List[Any]:
with open(_lowerCAmelCase , '''w''' ) as f:
json.dump(_lowerCAmelCase , _lowerCAmelCase )
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__=True ) -> List[str]:
os.makedirs(_lowerCAmelCase , exist_ok=_lowerCAmelCase )
UpperCAmelCase__ : Union[str, Any] = os.path.join(_lowerCAmelCase , '''tmp''' )
os.makedirs(_lowerCAmelCase , exist_ok=_lowerCAmelCase )
UpperCAmelCase__ : Optional[Any] = read_json(os.path.join(_lowerCAmelCase , '''params.json''' ) )
UpperCAmelCase__ : Optional[int] = NUM_SHARDS[model_size]
UpperCAmelCase__ : int = params['''n_layers''']
UpperCAmelCase__ : Optional[Any] = params['''n_heads''']
UpperCAmelCase__ : List[Any] = n_heads // num_shards
UpperCAmelCase__ : Optional[Any] = params['''dim''']
UpperCAmelCase__ : int = dim // n_heads
UpperCAmelCase__ : Any = 1_00_00.0
UpperCAmelCase__ : Any = 1.0 / (base ** (torch.arange(0 , _lowerCAmelCase , 2 ).float() / dims_per_head))
if "n_kv_heads" in params:
UpperCAmelCase__ : int = params['''n_kv_heads'''] # for GQA / MQA
UpperCAmelCase__ : Optional[int] = n_heads_per_shard // num_key_value_heads
UpperCAmelCase__ : str = dim // num_key_value_heads
else: # compatibility with other checkpoints
UpperCAmelCase__ : List[Any] = n_heads
UpperCAmelCase__ : Optional[int] = n_heads_per_shard
UpperCAmelCase__ : int = dim
# permute for sliced rotary
def permute(lowerCAmelCase__ , lowerCAmelCase__=n_heads , lowerCAmelCase__=dim , lowerCAmelCase__=dim ):
return w.view(_lowerCAmelCase , dima // n_heads // 2 , 2 , _lowerCAmelCase ).transpose(1 , 2 ).reshape(_lowerCAmelCase , _lowerCAmelCase )
print(F"""Fetching all parameters from the checkpoint at {input_base_path}.""" )
# Load weights
if model_size == "7B":
# Not sharded
# (The sharded implementation would also work, but this is simpler.)
UpperCAmelCase__ : Dict = torch.load(os.path.join(_lowerCAmelCase , '''consolidated.00.pth''' ) , map_location='''cpu''' )
else:
# Sharded
UpperCAmelCase__ : List[str] = [
torch.load(os.path.join(_lowerCAmelCase , F"""consolidated.{i:02d}.pth""" ) , map_location='''cpu''' )
for i in range(_lowerCAmelCase )
]
UpperCAmelCase__ : Optional[int] = 0
UpperCAmelCase__ : str = {'''weight_map''': {}}
for layer_i in range(_lowerCAmelCase ):
UpperCAmelCase__ : Optional[Any] = F"""pytorch_model-{layer_i + 1}-of-{n_layers + 1}.bin"""
if model_size == "7B":
# Unsharded
UpperCAmelCase__ : Union[str, Any] = {
F"""model.layers.{layer_i}.self_attn.q_proj.weight""": permute(
loaded[F"""layers.{layer_i}.attention.wq.weight"""] ),
F"""model.layers.{layer_i}.self_attn.k_proj.weight""": permute(
loaded[F"""layers.{layer_i}.attention.wk.weight"""] ),
F"""model.layers.{layer_i}.self_attn.v_proj.weight""": loaded[F"""layers.{layer_i}.attention.wv.weight"""],
F"""model.layers.{layer_i}.self_attn.o_proj.weight""": loaded[F"""layers.{layer_i}.attention.wo.weight"""],
F"""model.layers.{layer_i}.mlp.gate_proj.weight""": loaded[F"""layers.{layer_i}.feed_forward.w1.weight"""],
F"""model.layers.{layer_i}.mlp.down_proj.weight""": loaded[F"""layers.{layer_i}.feed_forward.w2.weight"""],
F"""model.layers.{layer_i}.mlp.up_proj.weight""": loaded[F"""layers.{layer_i}.feed_forward.w3.weight"""],
F"""model.layers.{layer_i}.input_layernorm.weight""": loaded[F"""layers.{layer_i}.attention_norm.weight"""],
F"""model.layers.{layer_i}.post_attention_layernorm.weight""": loaded[F"""layers.{layer_i}.ffn_norm.weight"""],
}
else:
# Sharded
# Note that attention.w{q,k,v,o}, feed_fordward.w[1,2,3], attention_norm.weight and ffn_norm.weight share
# the same storage object, saving attention_norm and ffn_norm will save other weights too, which is
# redundant as other weights will be stitched from multiple shards. To avoid that, they are cloned.
UpperCAmelCase__ : List[str] = {
F"""model.layers.{layer_i}.input_layernorm.weight""": loaded[0][
F"""layers.{layer_i}.attention_norm.weight"""
].clone(),
F"""model.layers.{layer_i}.post_attention_layernorm.weight""": loaded[0][
F"""layers.{layer_i}.ffn_norm.weight"""
].clone(),
}
UpperCAmelCase__ : List[Any] = permute(
torch.cat(
[
loaded[i][F"""layers.{layer_i}.attention.wq.weight"""].view(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
for i in range(_lowerCAmelCase )
] , dim=0 , ).reshape(_lowerCAmelCase , _lowerCAmelCase ) )
UpperCAmelCase__ : Optional[Any] = permute(
torch.cat(
[
loaded[i][F"""layers.{layer_i}.attention.wk.weight"""].view(
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
for i in range(_lowerCAmelCase )
] , dim=0 , ).reshape(_lowerCAmelCase , _lowerCAmelCase ) , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , )
UpperCAmelCase__ : int = torch.cat(
[
loaded[i][F"""layers.{layer_i}.attention.wv.weight"""].view(
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
for i in range(_lowerCAmelCase )
] , dim=0 , ).reshape(_lowerCAmelCase , _lowerCAmelCase )
UpperCAmelCase__ : Any = torch.cat(
[loaded[i][F"""layers.{layer_i}.attention.wo.weight"""] for i in range(_lowerCAmelCase )] , dim=1 )
UpperCAmelCase__ : Any = torch.cat(
[loaded[i][F"""layers.{layer_i}.feed_forward.w1.weight"""] for i in range(_lowerCAmelCase )] , dim=0 )
UpperCAmelCase__ : Optional[int] = torch.cat(
[loaded[i][F"""layers.{layer_i}.feed_forward.w2.weight"""] for i in range(_lowerCAmelCase )] , dim=1 )
UpperCAmelCase__ : int = torch.cat(
[loaded[i][F"""layers.{layer_i}.feed_forward.w3.weight"""] for i in range(_lowerCAmelCase )] , dim=0 )
UpperCAmelCase__ : Optional[Any] = inv_freq
for k, v in state_dict.items():
UpperCAmelCase__ : Dict = filename
param_count += v.numel()
torch.save(_lowerCAmelCase , os.path.join(_lowerCAmelCase , _lowerCAmelCase ) )
UpperCAmelCase__ : Any = F"""pytorch_model-{n_layers + 1}-of-{n_layers + 1}.bin"""
if model_size == "7B":
# Unsharded
UpperCAmelCase__ : Union[str, Any] = {
'''model.embed_tokens.weight''': loaded['''tok_embeddings.weight'''],
'''model.norm.weight''': loaded['''norm.weight'''],
'''lm_head.weight''': loaded['''output.weight'''],
}
else:
UpperCAmelCase__ : Any = {
'''model.norm.weight''': loaded[0]['''norm.weight'''],
'''model.embed_tokens.weight''': torch.cat(
[loaded[i]['''tok_embeddings.weight'''] for i in range(_lowerCAmelCase )] , dim=1 ),
'''lm_head.weight''': torch.cat([loaded[i]['''output.weight'''] for i in range(_lowerCAmelCase )] , dim=0 ),
}
for k, v in state_dict.items():
UpperCAmelCase__ : Any = filename
param_count += v.numel()
torch.save(_lowerCAmelCase , os.path.join(_lowerCAmelCase , _lowerCAmelCase ) )
# Write configs
UpperCAmelCase__ : Dict = {'''total_size''': param_count * 2}
write_json(_lowerCAmelCase , os.path.join(_lowerCAmelCase , '''pytorch_model.bin.index.json''' ) )
UpperCAmelCase__ : Union[str, Any] = params['''ffn_dim_multiplier'''] if '''ffn_dim_multiplier''' in params else 1
UpperCAmelCase__ : Dict = params['''multiple_of'''] if '''multiple_of''' in params else 2_56
UpperCAmelCase__ : int = LlamaConfig(
hidden_size=_lowerCAmelCase , intermediate_size=compute_intermediate_size(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) , num_attention_heads=params['''n_heads'''] , num_hidden_layers=params['''n_layers'''] , rms_norm_eps=params['''norm_eps'''] , num_key_value_heads=_lowerCAmelCase , )
config.save_pretrained(_lowerCAmelCase )
# Make space so we can load the model properly now.
del state_dict
del loaded
gc.collect()
print('''Loading the checkpoint in a Llama model.''' )
UpperCAmelCase__ : Tuple = LlamaForCausalLM.from_pretrained(_lowerCAmelCase , torch_dtype=torch.floataa , low_cpu_mem_usage=_lowerCAmelCase )
# Avoid saving this as part of the config.
del model.config._name_or_path
print('''Saving in the Transformers format.''' )
model.save_pretrained(_lowerCAmelCase , safe_serialization=_lowerCAmelCase )
shutil.rmtree(_lowerCAmelCase )
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ ) -> List[str]:
UpperCAmelCase__ : Optional[Any] = LlamaTokenizer if LlamaTokenizerFast is None else LlamaTokenizerFast
print(F"""Saving a {tokenizer_class.__name__} to {tokenizer_path}.""" )
UpperCAmelCase__ : Optional[Any] = tokenizer_class(_lowerCAmelCase )
tokenizer.save_pretrained(_lowerCAmelCase )
def a__ ( ) -> Optional[int]:
UpperCAmelCase__ : Optional[Any] = argparse.ArgumentParser()
parser.add_argument(
'''--input_dir''' , help='''Location of LLaMA weights, which contains tokenizer.model and model folders''' , )
parser.add_argument(
'''--model_size''' , choices=['''7B''', '''7Bf''', '''13B''', '''13Bf''', '''30B''', '''65B''', '''70B''', '''70Bf''', '''tokenizer_only'''] , )
parser.add_argument(
'''--output_dir''' , help='''Location to write HF model and tokenizer''' , )
parser.add_argument('''--safe_serialization''' , type=_lowerCAmelCase , help='''Whether or not to save using `safetensors`.''' )
UpperCAmelCase__ : Tuple = parser.parse_args()
if args.model_size != "tokenizer_only":
write_model(
model_path=args.output_dir , input_base_path=os.path.join(args.input_dir , args.model_size ) , model_size=args.model_size , safe_serialization=args.safe_serialization , )
UpperCAmelCase__ : int = os.path.join(args.input_dir , '''tokenizer.model''' )
write_tokenizer(args.output_dir , _lowerCAmelCase )
if __name__ == "__main__":
main()
| 181 |
'''simple docstring'''
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
lowerCamelCase = logging.get_logger(__name__)
lowerCamelCase = {
"""google/umt5-small""": """https://huggingface.co/google/umt5-small/resolve/main/config.json""",
# See all umt5 models at https://huggingface.co/models?filter=umt5
}
class _UpperCamelCase ( A ):
'''simple docstring'''
lowerCAmelCase__ = """umt5"""
lowerCAmelCase__ = ["""past_key_values"""]
def __init__( self : Optional[int] , _lowerCAmelCase : int=2_5_0_1_1_2 , _lowerCAmelCase : Union[str, Any]=5_1_2 , _lowerCAmelCase : List[Any]=6_4 , _lowerCAmelCase : Optional[Any]=1_0_2_4 , _lowerCAmelCase : Union[str, Any]=8 , _lowerCAmelCase : Any=None , _lowerCAmelCase : Tuple=6 , _lowerCAmelCase : str=3_2 , _lowerCAmelCase : List[str]=1_2_8 , _lowerCAmelCase : Tuple=0.1 , _lowerCAmelCase : Tuple=1e-6 , _lowerCAmelCase : List[Any]=1.0 , _lowerCAmelCase : Union[str, Any]="gated-gelu" , _lowerCAmelCase : List[str]=True , _lowerCAmelCase : int=True , _lowerCAmelCase : Tuple="T5Tokenizer" , _lowerCAmelCase : List[Any]=True , _lowerCAmelCase : List[str]=0 , _lowerCAmelCase : Union[str, Any]=1 , _lowerCAmelCase : Any=0 , **_lowerCAmelCase : int , ):
'''simple docstring'''
super().__init__(
is_encoder_decoder=_lowerCAmelCase , tokenizer_class=_lowerCAmelCase , tie_word_embeddings=_lowerCAmelCase , pad_token_id=_lowerCAmelCase , eos_token_id=_lowerCAmelCase , decoder_start_token_id=_lowerCAmelCase , **_lowerCAmelCase , )
__lowercase =vocab_size
__lowercase =d_model
__lowercase =d_kv
__lowercase =d_ff
__lowercase =num_layers
__lowercase =(
num_decoder_layers if num_decoder_layers is not None else self.num_layers
) # default = symmetry
__lowercase =num_heads
__lowercase =relative_attention_num_buckets
__lowercase =relative_attention_max_distance
__lowercase =dropout_rate
__lowercase =layer_norm_epsilon
__lowercase =initializer_factor
__lowercase =feed_forward_proj
__lowercase =use_cache
__lowercase =self.feed_forward_proj.split('-')
__lowercase =act_info[-1]
__lowercase =act_info[0] == 'gated'
if len(_lowerCAmelCase) > 1 and act_info[0] != "gated" or len(_lowerCAmelCase) > 2:
raise ValueError(
f"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
'\'gated-gelu\' or \'relu\'')
if feed_forward_proj == "gated-gelu":
__lowercase ='gelu_new'
@property
def __lowerCamelCase ( self : List[Any]):
'''simple docstring'''
return self.d_model
@property
def __lowerCamelCase ( self : Optional[Any]):
'''simple docstring'''
return self.num_heads
@property
def __lowerCamelCase ( self : int):
'''simple docstring'''
return self.num_layers
class _UpperCamelCase ( A ):
'''simple docstring'''
@property
# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
def __lowerCamelCase ( self : Optional[int]):
'''simple docstring'''
__lowercase ={
'input_ids': {0: 'batch', 1: 'encoder_sequence'},
'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
}
if self.use_past:
__lowercase ='past_encoder_sequence + sequence'
__lowercase ={0: 'batch'}
__lowercase ={0: 'batch', 1: 'past_decoder_sequence + sequence'}
else:
__lowercase ={0: 'batch', 1: 'decoder_sequence'}
__lowercase ={0: 'batch', 1: 'decoder_sequence'}
if self.use_past:
self.fill_with_past_key_values_(_lowerCAmelCase , direction='inputs')
return common_inputs
@property
# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
def __lowerCamelCase ( self : Optional[Any]):
'''simple docstring'''
return 1_3
@property
def __lowerCamelCase ( self : int):
'''simple docstring'''
return 5e-4
| 166 | 0 |
"""simple docstring"""
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
if exponent == 1:
return base
if exponent % 2 == 0:
lowerCamelCase__ : Any = _modexpt(_lowerCamelCase , exponent // 2 , _lowerCamelCase ) % modulo_value
return (x * x) % modulo_value
else:
return (base * _modexpt(_lowerCamelCase , exponent - 1 , _lowerCamelCase )) % modulo_value
def lowerCamelCase_ ( _lowerCamelCase = 1777 , _lowerCamelCase = 1855 , _lowerCamelCase = 8 ):
lowerCamelCase__ : Dict = base
for _ in range(1 , _lowerCamelCase ):
lowerCamelCase__ : List[str] = _modexpt(_lowerCamelCase , _lowerCamelCase , 10**digits )
return result
if __name__ == "__main__":
print(f"{solution() = }")
| 316 |
"""simple docstring"""
import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union
import numpy as np
import pyarrow as pa
import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config import MAX_SHARD_SIZE
from datasets.filesystems import (
is_remote_filesystem,
rename,
)
from datasets.iterable_dataset import _BaseExamplesIterable
from datasets.utils.py_utils import convert_file_size_to_int
A_ : List[Any] = datasets.utils.logging.get_logger(__name__)
if TYPE_CHECKING:
import pyspark
@dataclass
class a_ ( datasets.BuilderConfig ):
'''simple docstring'''
lowerCamelCase__ : Optional[datasets.Features] = None
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase , ):
import pyspark
def generate_fn():
lowerCamelCase__ : Optional[Any] = df.select('*' , pyspark.sql.functions.spark_partition_id().alias('part_id' ) )
for partition_id in partition_order:
lowerCamelCase__ : Dict = df_with_partition_id.select('*' ).where(f'''part_id = {partition_id}''' ).drop('part_id' )
lowerCamelCase__ : Dict = partition_df.collect()
lowerCamelCase__ : int = 0
for row in rows:
yield f'''{partition_id}_{row_id}''', row.asDict()
row_id += 1
return generate_fn
class a_ ( _BaseExamplesIterable ):
'''simple docstring'''
def __init__(self, lowerCamelCase_, lowerCamelCase_=None, ):
'''simple docstring'''
lowerCamelCase__ : Tuple = df
lowerCamelCase__ : Any = partition_order or range(self.df.rdd.getNumPartitions() )
lowerCamelCase__ : List[Any] = _generate_iterable_examples(self.df, self.partition_order )
def __iter__(self ):
'''simple docstring'''
yield from self.generate_examples_fn()
def a__ (self, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : Union[str, Any] = list(range(self.df.rdd.getNumPartitions() ) )
generator.shuffle(lowerCamelCase_ )
return SparkExamplesIterable(self.df, partition_order=lowerCamelCase_ )
def a__ (self, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : Union[str, Any] = self.split_shard_indices_by_worker(lowerCamelCase_, lowerCamelCase_ )
return SparkExamplesIterable(self.df, partition_order=lowerCamelCase_ )
@property
def a__ (self ):
'''simple docstring'''
return len(self.partition_order )
class a_ ( datasets.DatasetBuilder ):
'''simple docstring'''
lowerCamelCase__ : Optional[int] = SparkConfig
def __init__(self, lowerCamelCase_, lowerCamelCase_ = None, lowerCamelCase_ = None, **lowerCamelCase_, ):
'''simple docstring'''
import pyspark
lowerCamelCase__ : str = pyspark.sql.SparkSession.builder.getOrCreate()
lowerCamelCase__ : Optional[Any] = df
lowerCamelCase__ : Dict = working_dir
super().__init__(
cache_dir=lowerCamelCase_, config_name=str(self.df.semanticHash() ), **lowerCamelCase_, )
def a__ (self ):
'''simple docstring'''
def create_cache_and_write_probe(lowerCamelCase_ ):
# makedirs with exist_ok will recursively create the directory. It will not throw an error if directories
# already exist.
os.makedirs(self._cache_dir, exist_ok=lowerCamelCase_ )
lowerCamelCase__ : str = os.path.join(self._cache_dir, 'fs_test' + uuid.uuida().hex )
# Opening the file in append mode will create a new file unless it already exists, in which case it will not
# change the file contents.
open(lowerCamelCase_, 'a' )
return [probe_file]
if self._spark.conf.get('spark.master', '' ).startswith('local' ):
return
# If the cluster is multi-node, make sure that the user provided a cache_dir and that it is on an NFS
# accessible to the driver.
# TODO: Stream batches to the driver using ArrowCollectSerializer instead of throwing an error.
if self._cache_dir:
lowerCamelCase__ : Tuple = (
self._spark.sparkContext.parallelize(range(1 ), 1 ).mapPartitions(lowerCamelCase_ ).collect()
)
if os.path.isfile(probe[0] ):
return
raise ValueError(
'When using Dataset.from_spark on a multi-node cluster, the driver and all workers should be able to access cache_dir' )
def a__ (self ):
'''simple docstring'''
return datasets.DatasetInfo(features=self.config.features )
def a__ (self, lowerCamelCase_ ):
'''simple docstring'''
return [datasets.SplitGenerator(name=datasets.Split.TRAIN )]
def a__ (self, lowerCamelCase_ ):
'''simple docstring'''
import pyspark
def get_arrow_batch_size(lowerCamelCase_ ):
for batch in it:
yield pa.RecordBatch.from_pydict({'batch_bytes': [batch.nbytes]} )
lowerCamelCase__ : List[Any] = self.df.count()
lowerCamelCase__ : List[Any] = df_num_rows if df_num_rows <= 1_0_0 else 1_0_0
# Approximate the size of each row (in Arrow format) by averaging over a max-100-row sample.
lowerCamelCase__ : List[Any] = (
self.df.limit(lowerCamelCase_ )
.repartition(1 )
.mapInArrow(lowerCamelCase_, 'batch_bytes: long' )
.agg(pyspark.sql.functions.sum('batch_bytes' ).alias('sample_bytes' ) )
.collect()[0]
.sample_bytes
/ sample_num_rows
)
lowerCamelCase__ : Dict = approx_bytes_per_row * df_num_rows
if approx_total_size > max_shard_size:
# Make sure there is at least one row per partition.
lowerCamelCase__ : str = min(lowerCamelCase_, int(approx_total_size / max_shard_size ) )
lowerCamelCase__ : List[Any] = self.df.repartition(lowerCamelCase_ )
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, ):
'''simple docstring'''
import pyspark
lowerCamelCase__ : List[str] = ParquetWriter if file_format == 'parquet' else ArrowWriter
lowerCamelCase__ : List[str] = os.path.join(self._working_dir, os.path.basename(lowerCamelCase_ ) ) if self._working_dir else fpath
lowerCamelCase__ : Optional[int] = file_format == 'parquet'
# Define these so that we don't reference self in write_arrow, which will result in a pickling error due to
# pickling the SparkContext.
lowerCamelCase__ : int = self.config.features
lowerCamelCase__ : Dict = self._writer_batch_size
lowerCamelCase__ : Optional[Any] = self._fs.storage_options
def write_arrow(lowerCamelCase_ ):
# Within the same SparkContext, no two task attempts will share the same attempt ID.
lowerCamelCase__ : Any = pyspark.TaskContext().taskAttemptId()
lowerCamelCase__ : str = next(lowerCamelCase_, lowerCamelCase_ )
if first_batch is None:
# Some partitions might not receive any data.
return pa.RecordBatch.from_arrays(
[[task_id], [0], [0]], names=['task_id', 'num_examples', 'num_bytes'], )
lowerCamelCase__ : Tuple = 0
lowerCamelCase__ : Any = writer_class(
features=lowerCamelCase_, path=working_fpath.replace('SSSSS', f'''{shard_id:05d}''' ).replace('TTTTT', f'''{task_id:05d}''' ), writer_batch_size=lowerCamelCase_, storage_options=lowerCamelCase_, embed_local_files=lowerCamelCase_, )
lowerCamelCase__ : List[str] = pa.Table.from_batches([first_batch] )
writer.write_table(lowerCamelCase_ )
for batch in it:
if max_shard_size is not None and writer._num_bytes >= max_shard_size:
lowerCamelCase__ , lowerCamelCase__ : Tuple = writer.finalize()
writer.close()
yield pa.RecordBatch.from_arrays(
[[task_id], [num_examples], [num_bytes]], names=['task_id', 'num_examples', 'num_bytes'], )
shard_id += 1
lowerCamelCase__ : Dict = writer_class(
features=writer._features, path=working_fpath.replace('SSSSS', f'''{shard_id:05d}''' ).replace('TTTTT', f'''{task_id:05d}''' ), writer_batch_size=lowerCamelCase_, storage_options=lowerCamelCase_, embed_local_files=lowerCamelCase_, )
lowerCamelCase__ : Tuple = pa.Table.from_batches([batch] )
writer.write_table(lowerCamelCase_ )
if writer._num_bytes > 0:
lowerCamelCase__ , lowerCamelCase__ : Tuple = writer.finalize()
writer.close()
yield pa.RecordBatch.from_arrays(
[[task_id], [num_examples], [num_bytes]], names=['task_id', 'num_examples', 'num_bytes'], )
if working_fpath != fpath:
for file in os.listdir(os.path.dirname(lowerCamelCase_ ) ):
lowerCamelCase__ : Optional[int] = os.path.join(os.path.dirname(lowerCamelCase_ ), os.path.basename(lowerCamelCase_ ) )
shutil.move(lowerCamelCase_, lowerCamelCase_ )
lowerCamelCase__ : List[str] = (
self.df.mapInArrow(lowerCamelCase_, 'task_id: long, num_examples: long, num_bytes: long' )
.groupBy('task_id' )
.agg(
pyspark.sql.functions.sum('num_examples' ).alias('total_num_examples' ), pyspark.sql.functions.sum('num_bytes' ).alias('total_num_bytes' ), pyspark.sql.functions.count('num_bytes' ).alias('num_shards' ), pyspark.sql.functions.collect_list('num_examples' ).alias('shard_lengths' ), )
.collect()
)
for row in stats:
yield row.task_id, (row.total_num_examples, row.total_num_bytes, row.num_shards, row.shard_lengths)
def a__ (self, lowerCamelCase_, lowerCamelCase_ = "arrow", lowerCamelCase_ = None, lowerCamelCase_ = None, **lowerCamelCase_, ):
'''simple docstring'''
self._validate_cache_dir()
lowerCamelCase__ : Union[str, Any] = convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE )
self._repartition_df_if_needed(lowerCamelCase_ )
lowerCamelCase__ : str = not is_remote_filesystem(self._fs )
lowerCamelCase__ : Any = os.path.join if is_local else posixpath.join
lowerCamelCase__ : Any = '-TTTTT-SSSSS-of-NNNNN'
lowerCamelCase__ : Tuple = f'''{self.name}-{split_generator.name}{SUFFIX}.{file_format}'''
lowerCamelCase__ : Union[str, Any] = path_join(self._output_dir, lowerCamelCase_ )
lowerCamelCase__ : List[str] = 0
lowerCamelCase__ : Dict = 0
lowerCamelCase__ : List[Any] = 0
lowerCamelCase__ : Optional[Any] = []
lowerCamelCase__ : List[str] = []
for task_id, content in self._prepare_split_single(lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
(
(
lowerCamelCase__
) , (
lowerCamelCase__
) , (
lowerCamelCase__
) , (
lowerCamelCase__
) ,
) : int = content
if num_bytes > 0:
total_num_examples += num_examples
total_num_bytes += num_bytes
total_shards += num_shards
task_id_and_num_shards.append((task_id, num_shards) )
all_shard_lengths.extend(lowerCamelCase_ )
lowerCamelCase__ : str = total_num_examples
lowerCamelCase__ : int = total_num_bytes
# should rename everything at the end
logger.debug(f'''Renaming {total_shards} shards.''' )
if total_shards > 1:
lowerCamelCase__ : Union[str, Any] = all_shard_lengths
# Define fs outside of _rename_shard so that we don't reference self in the function, which will result in a
# pickling error due to pickling the SparkContext.
lowerCamelCase__ : Optional[Any] = self._fs
# use the -SSSSS-of-NNNNN pattern
def _rename_shard(
lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, ):
rename(
lowerCamelCase_, fpath.replace('SSSSS', f'''{shard_id:05d}''' ).replace('TTTTT', f'''{task_id:05d}''' ), fpath.replace('TTTTT-SSSSS', f'''{global_shard_id:05d}''' ).replace('NNNNN', f'''{total_shards:05d}''' ), )
lowerCamelCase__ : List[str] = []
lowerCamelCase__ : List[str] = 0
for i in range(len(lowerCamelCase_ ) ):
lowerCamelCase__ , lowerCamelCase__ : Any = task_id_and_num_shards[i]
for shard_id in range(lowerCamelCase_ ):
args.append([task_id, shard_id, global_shard_id] )
global_shard_id += 1
self._spark.sparkContext.parallelize(lowerCamelCase_, len(lowerCamelCase_ ) ).map(lambda lowerCamelCase_ : _rename_shard(*lowerCamelCase_ ) ).collect()
else:
# don't use any pattern
lowerCamelCase__ : List[Any] = 0
lowerCamelCase__ : Dict = task_id_and_num_shards[0][0]
self._rename(
fpath.replace('SSSSS', f'''{shard_id:05d}''' ).replace('TTTTT', f'''{task_id:05d}''' ), fpath.replace(lowerCamelCase_, '' ), )
def a__ (self, lowerCamelCase_, ):
'''simple docstring'''
return SparkExamplesIterable(self.df )
| 316 | 1 |
"""simple docstring"""
import argparse
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_dummies.py
UpperCAmelCase = """src/diffusers"""
# Matches is_xxx_available()
UpperCAmelCase = re.compile(r"""is\_([a-z_]*)_available\(\)""")
# Matches from xxx import bla
UpperCAmelCase = re.compile(r"""\s+from\s+\S*\s+import\s+([^\(\s].*)\n""")
UpperCAmelCase = """
{0} = None
"""
UpperCAmelCase = """
class {0}(metaclass=DummyObject):
_backends = {1}
def __init__(self, *args, **kwargs):
requires_backends(self, {1})
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, {1})
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, {1})
"""
UpperCAmelCase = """
def {0}(*args, **kwargs):
requires_backends({0}, {1})
"""
def lowercase ( a__ : List[Any] ) -> Optional[int]:
_UpperCamelCase = _re_backend.findall(a__ )
if len(a__ ) == 0:
return None
return "_and_".join(a__ )
def lowercase ( ) -> Dict:
with open(os.path.join(a__ , '''__init__.py''' ) , '''r''' , encoding='''utf-8''' , newline='''\n''' ) as f:
_UpperCamelCase = f.readlines()
# Get to the point we do the actual imports for type checking
_UpperCamelCase = 0
_UpperCamelCase = {}
# Go through the end of the file
while line_index < len(a__ ):
# If the line contains is_backend_available, we grab all objects associated with the `else` block
_UpperCamelCase = find_backend(lines[line_index] )
if backend is not None:
while not lines[line_index].startswith('''else:''' ):
line_index += 1
line_index += 1
_UpperCamelCase = []
# Until we unindent, add backend objects to the list
while line_index < len(a__ ) and len(lines[line_index] ) > 1:
_UpperCamelCase = lines[line_index]
_UpperCamelCase = _re_single_line_import.search(a__ )
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(''', ''' ) )
elif line.startswith(''' ''' * 8 ):
objects.append(line[8:-2] )
line_index += 1
if len(a__ ) > 0:
_UpperCamelCase = objects
else:
line_index += 1
return backend_specific_objects
def lowercase ( a__ : List[str] , a__ : List[str] ) -> Dict:
if name.isupper():
return DUMMY_CONSTANT.format(a__ )
elif name.islower():
return DUMMY_FUNCTION.format(a__ , a__ )
else:
return DUMMY_CLASS.format(a__ , a__ )
def lowercase ( a__ : str=None ) -> List[str]:
if backend_specific_objects is None:
_UpperCamelCase = read_init()
# For special correspondence backend to module name as used in the function requires_modulename
_UpperCamelCase = {}
for backend, objects in backend_specific_objects.items():
_UpperCamelCase = '''[''' + ''', '''.join(F'''"{b}"''' for b in backend.split('''_and_''' ) ) + ''']'''
_UpperCamelCase = '''# This file is autogenerated by the command `make fix-copies`, do not edit.\n'''
dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
dummy_file += "\n".join([create_dummy_object(a__ , a__ ) for o in objects] )
_UpperCamelCase = dummy_file
return dummy_files
def lowercase ( a__ : List[str]=False ) -> List[Any]:
_UpperCamelCase = create_dummy_files()
# For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
_UpperCamelCase = {'''torch''': '''pt'''}
# Locate actual dummy modules and read their content.
_UpperCamelCase = os.path.join(a__ , '''utils''' )
_UpperCamelCase = {
backend: os.path.join(a__ , F'''dummy_{short_names.get(a__ , a__ )}_objects.py''' )
for backend in dummy_files.keys()
}
_UpperCamelCase = {}
for backend, file_path in dummy_file_paths.items():
if os.path.isfile(a__ ):
with open(a__ , '''r''' , encoding='''utf-8''' , newline='''\n''' ) as f:
_UpperCamelCase = f.read()
else:
_UpperCamelCase = ''''''
for backend in dummy_files.keys():
if dummy_files[backend] != actual_dummies[backend]:
if overwrite:
print(
F'''Updating diffusers.utils.dummy_{short_names.get(a__ , a__ )}_objects.py as the main '''
'''__init__ has new objects.''' )
with open(dummy_file_paths[backend] , '''w''' , encoding='''utf-8''' , newline='''\n''' ) as f:
f.write(dummy_files[backend] )
else:
raise ValueError(
'''The main __init__ has objects that are not present in '''
F'''diffusers.utils.dummy_{short_names.get(a__ , a__ )}_objects.py. Run `make fix-copies` '''
'''to fix this.''' )
if __name__ == "__main__":
UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
UpperCAmelCase = parser.parse_args()
check_dummies(args.fix_and_overwrite)
| 256 | """simple docstring"""
import inspect
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class UpperCAmelCase_ ( _lowercase):
def __init__( self : List[Any] , __UpperCamelCase : VQModel , __UpperCamelCase : UNetaDModel , __UpperCamelCase : DDIMScheduler ) -> Optional[Any]:
super().__init__()
self.register_modules(vqvae=__UpperCamelCase , unet=__UpperCamelCase , scheduler=__UpperCamelCase )
@torch.no_grad()
def __call__( self : List[Any] , __UpperCamelCase : int = 1 , __UpperCamelCase : Optional[Union[torch.Generator, List[torch.Generator]]] = None , __UpperCamelCase : float = 0.0 , __UpperCamelCase : int = 50 , __UpperCamelCase : Optional[str] = "pil" , __UpperCamelCase : bool = True , **__UpperCamelCase : Optional[int] , ) -> Union[Tuple, ImagePipelineOutput]:
_UpperCamelCase = randn_tensor(
(batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size) , generator=__UpperCamelCase , )
_UpperCamelCase = latents.to(self.device )
# scale the initial noise by the standard deviation required by the scheduler
_UpperCamelCase = latents * self.scheduler.init_noise_sigma
self.scheduler.set_timesteps(__UpperCamelCase )
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
_UpperCamelCase = '''eta''' in set(inspect.signature(self.scheduler.step ).parameters.keys() )
_UpperCamelCase = {}
if accepts_eta:
_UpperCamelCase = eta
for t in self.progress_bar(self.scheduler.timesteps ):
_UpperCamelCase = self.scheduler.scale_model_input(__UpperCamelCase , __UpperCamelCase )
# predict the noise residual
_UpperCamelCase = self.unet(__UpperCamelCase , __UpperCamelCase ).sample
# compute the previous noisy sample x_t -> x_t-1
_UpperCamelCase = self.scheduler.step(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , **__UpperCamelCase ).prev_sample
# decode the image latents with the VAE
_UpperCamelCase = self.vqvae.decode(__UpperCamelCase ).sample
_UpperCamelCase = (image / 2 + 0.5).clamp(0 , 1 )
_UpperCamelCase = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
_UpperCamelCase = self.numpy_to_pil(__UpperCamelCase )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=__UpperCamelCase )
| 256 | 1 |
"""simple docstring"""
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
'''huggingface/informer-tourism-monthly''': (
'''https://huggingface.co/huggingface/informer-tourism-monthly/resolve/main/config.json'''
),
# See all Informer models at https://huggingface.co/models?filter=informer
}
class SCREAMING_SNAKE_CASE__ ( lowercase ):
"""simple docstring"""
a : Any ="informer"
a : str ={
"hidden_size": "d_model",
"num_attention_heads": "encoder_attention_heads",
"num_hidden_layers": "encoder_layers",
}
def __init__( self , snake_case__ = None , snake_case__ = None , snake_case__ = "student_t" , snake_case__ = "nll" , snake_case__ = 1 , snake_case__ = None , snake_case__ = "mean" , snake_case__ = 0 , snake_case__ = 0 , snake_case__ = 0 , snake_case__ = 0 , snake_case__ = None , snake_case__ = None , snake_case__ = 64 , snake_case__ = 32 , snake_case__ = 32 , snake_case__ = 2 , snake_case__ = 2 , snake_case__ = 2 , snake_case__ = 2 , snake_case__ = True , snake_case__ = "gelu" , snake_case__ = 0.05 , snake_case__ = 0.1 , snake_case__ = 0.1 , snake_case__ = 0.1 , snake_case__ = 0.1 , snake_case__ = 100 , snake_case__ = 0.02 , snake_case__=True , snake_case__ = "prob" , snake_case__ = 5 , snake_case__ = True , **snake_case__ , ):
"""simple docstring"""
lowerCAmelCase : int = prediction_length
lowerCAmelCase : List[Any] = context_length or prediction_length
lowerCAmelCase : Dict = distribution_output
lowerCAmelCase : str = loss
lowerCAmelCase : int = input_size
lowerCAmelCase : Dict = num_time_features
lowerCAmelCase : int = lags_sequence if lags_sequence is not None else [1, 2, 3, 4, 5, 6, 7]
lowerCAmelCase : Union[str, Any] = scaling
lowerCAmelCase : str = num_dynamic_real_features
lowerCAmelCase : Any = num_static_real_features
lowerCAmelCase : List[Any] = num_static_categorical_features
# set cardinality
if cardinality and num_static_categorical_features > 0:
if len(snake_case__ ) != num_static_categorical_features:
raise ValueError(
"The cardinality should be a list of the same length as `num_static_categorical_features`" )
lowerCAmelCase : Any = cardinality
else:
lowerCAmelCase : Dict = [0]
# set embedding_dimension
if embedding_dimension and num_static_categorical_features > 0:
if len(snake_case__ ) != num_static_categorical_features:
raise ValueError(
"The embedding dimension should be a list of the same length as `num_static_categorical_features`" )
lowerCAmelCase : Optional[int] = embedding_dimension
else:
lowerCAmelCase : Tuple = [min(50 , (cat + 1) // 2 ) for cat in self.cardinality]
lowerCAmelCase : Any = num_parallel_samples
# Transformer architecture configuration
lowerCAmelCase : Any = input_size * len(self.lags_sequence ) + self._number_of_features
lowerCAmelCase : Union[str, Any] = d_model
lowerCAmelCase : List[Any] = encoder_attention_heads
lowerCAmelCase : List[Any] = decoder_attention_heads
lowerCAmelCase : List[str] = encoder_ffn_dim
lowerCAmelCase : List[Any] = decoder_ffn_dim
lowerCAmelCase : int = encoder_layers
lowerCAmelCase : List[Any] = decoder_layers
lowerCAmelCase : List[Any] = dropout
lowerCAmelCase : Union[str, Any] = attention_dropout
lowerCAmelCase : Optional[Any] = activation_dropout
lowerCAmelCase : Optional[Any] = encoder_layerdrop
lowerCAmelCase : Optional[Any] = decoder_layerdrop
lowerCAmelCase : Union[str, Any] = activation_function
lowerCAmelCase : Optional[int] = init_std
lowerCAmelCase : Union[str, Any] = use_cache
# Informer
lowerCAmelCase : Dict = attention_type
lowerCAmelCase : Any = sampling_factor
lowerCAmelCase : str = distil
super().__init__(is_encoder_decoder=snake_case__ , **snake_case__ )
@property
def lowercase__ ( self ):
"""simple docstring"""
return (
sum(self.embedding_dimension )
+ self.num_dynamic_real_features
+ self.num_time_features
+ self.num_static_real_features
+ self.input_size * 2 # the log1p(abs(loc)) and log(scale) features
)
| 133 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class SCREAMING_SNAKE_CASE__ ( lowercase , unittest.TestCase ):
"""simple docstring"""
a : Optional[Any] =KandinskyVaaImgaImgPipeline
a : Optional[int] =["image_embeds", "negative_image_embeds", "image"]
a : Optional[int] =[
"image_embeds",
"negative_image_embeds",
"image",
]
a : str =[
"generator",
"height",
"width",
"strength",
"guidance_scale",
"num_inference_steps",
"return_dict",
"guidance_scale",
"num_images_per_prompt",
"output_type",
"return_dict",
]
a : Dict =False
@property
def lowercase__ ( self ):
"""simple docstring"""
return 32
@property
def lowercase__ ( self ):
"""simple docstring"""
return 32
@property
def lowercase__ ( self ):
"""simple docstring"""
return self.time_input_dim
@property
def lowercase__ ( self ):
"""simple docstring"""
return self.time_input_dim * 4
@property
def lowercase__ ( self ):
"""simple docstring"""
return 100
@property
def lowercase__ ( self ):
"""simple docstring"""
torch.manual_seed(0 )
lowerCAmelCase : List[str] = {
"in_channels": 4,
# Out channels is double in channels because predicts mean and variance
"out_channels": 8,
"addition_embed_type": "image",
"down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
"up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
"mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
"block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
"layers_per_block": 1,
"encoder_hid_dim": self.text_embedder_hidden_size,
"encoder_hid_dim_type": "image_proj",
"cross_attention_dim": self.cross_attention_dim,
"attention_head_dim": 4,
"resnet_time_scale_shift": "scale_shift",
"class_embed_type": None,
}
lowerCAmelCase : int = UNetaDConditionModel(**snake_case__ )
return model
@property
def lowercase__ ( self ):
"""simple docstring"""
return {
"block_out_channels": [32, 64],
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def lowercase__ ( self ):
"""simple docstring"""
torch.manual_seed(0 )
lowerCAmelCase : Optional[int] = VQModel(**self.dummy_movq_kwargs )
return model
def lowercase__ ( self ):
"""simple docstring"""
lowerCAmelCase : Optional[Any] = self.dummy_unet
lowerCAmelCase : Optional[int] = self.dummy_movq
lowerCAmelCase : List[str] = {
"num_train_timesteps": 1_000,
"beta_schedule": "linear",
"beta_start": 0.00085,
"beta_end": 0.012,
"clip_sample": False,
"set_alpha_to_one": False,
"steps_offset": 0,
"prediction_type": "epsilon",
"thresholding": False,
}
lowerCAmelCase : Tuple = DDIMScheduler(**snake_case__ )
lowerCAmelCase : Optional[Any] = {
"unet": unet,
"scheduler": scheduler,
"movq": movq,
}
return components
def lowercase__ ( self , snake_case__ , snake_case__=0 ):
"""simple docstring"""
lowerCAmelCase : Any = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(snake_case__ ) ).to(snake_case__ )
lowerCAmelCase : Any = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(seed + 1 ) ).to(
snake_case__ )
# create init_image
lowerCAmelCase : List[Any] = floats_tensor((1, 3, 64, 64) , rng=random.Random(snake_case__ ) ).to(snake_case__ )
lowerCAmelCase : List[str] = image.cpu().permute(0 , 2 , 3 , 1 )[0]
lowerCAmelCase : List[str] = Image.fromarray(np.uinta(snake_case__ ) ).convert("RGB" ).resize((256, 256) )
if str(snake_case__ ).startswith("mps" ):
lowerCAmelCase : Optional[int] = torch.manual_seed(snake_case__ )
else:
lowerCAmelCase : Optional[int] = torch.Generator(device=snake_case__ ).manual_seed(snake_case__ )
lowerCAmelCase : List[str] = {
"image": init_image,
"image_embeds": image_embeds,
"negative_image_embeds": negative_image_embeds,
"generator": generator,
"height": 64,
"width": 64,
"num_inference_steps": 10,
"guidance_scale": 7.0,
"strength": 0.2,
"output_type": "np",
}
return inputs
def lowercase__ ( self ):
"""simple docstring"""
lowerCAmelCase : Tuple = "cpu"
lowerCAmelCase : Dict = self.get_dummy_components()
lowerCAmelCase : Union[str, Any] = self.pipeline_class(**snake_case__ )
lowerCAmelCase : Optional[int] = pipe.to(snake_case__ )
pipe.set_progress_bar_config(disable=snake_case__ )
lowerCAmelCase : int = pipe(**self.get_dummy_inputs(snake_case__ ) )
lowerCAmelCase : Union[str, Any] = output.images
lowerCAmelCase : Union[str, Any] = pipe(
**self.get_dummy_inputs(snake_case__ ) , return_dict=snake_case__ , )[0]
lowerCAmelCase : Optional[Any] = image[0, -3:, -3:, -1]
lowerCAmelCase : Optional[int] = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
lowerCAmelCase : int = np.array(
[0.6199778, 0.63984406, 0.46145785, 0.62944984, 0.5622215, 0.47306132, 0.47441456, 0.4607606, 0.48719263] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
), f""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
), f""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""
@slow
@require_torch_gpu
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
"""simple docstring"""
def lowercase__ ( self ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowercase__ ( self ):
"""simple docstring"""
lowerCAmelCase : List[Any] = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/kandinskyv22/kandinskyv22_img2img_frog.npy" )
lowerCAmelCase : Tuple = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png" )
lowerCAmelCase : Optional[Any] = "A red cartoon frog, 4k"
lowerCAmelCase : int = KandinskyVaaPriorPipeline.from_pretrained(
"kandinsky-community/kandinsky-2-2-prior" , torch_dtype=torch.floataa )
pipe_prior.to(snake_case__ )
lowerCAmelCase : List[Any] = KandinskyVaaImgaImgPipeline.from_pretrained(
"kandinsky-community/kandinsky-2-2-decoder" , torch_dtype=torch.floataa )
lowerCAmelCase : Tuple = pipeline.to(snake_case__ )
pipeline.set_progress_bar_config(disable=snake_case__ )
lowerCAmelCase : str = torch.Generator(device="cpu" ).manual_seed(0 )
lowerCAmelCase , lowerCAmelCase : Optional[Any] = pipe_prior(
snake_case__ , generator=snake_case__ , num_inference_steps=5 , negative_prompt="" , ).to_tuple()
lowerCAmelCase : Tuple = pipeline(
image=snake_case__ , image_embeds=snake_case__ , negative_image_embeds=snake_case__ , generator=snake_case__ , num_inference_steps=100 , height=768 , width=768 , strength=0.2 , output_type="np" , )
lowerCAmelCase : Optional[Any] = output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(snake_case__ , snake_case__ )
| 133 | 1 |
# 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.
_SCREAMING_SNAKE_CASE : Union[str, 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 UpperCAmelCase_ ( _A ):
'''simple docstring'''
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 UpperCAmelCase_ ( _A ):
'''simple docstring'''
from transformers.testing_utils import pytest_addoption_shared
pytest_addoption_shared(_A )
def UpperCAmelCase_ ( _A ):
'''simple docstring'''
from transformers.testing_utils import pytest_terminal_summary_main
SCREAMING_SNAKE_CASE__ = terminalreporter.config.getoption('''--make-reports''' )
if make_reports:
pytest_terminal_summary_main(_A , id=_A )
def UpperCAmelCase_ ( _A , _A ):
'''simple docstring'''
if exitstatus == 5:
SCREAMING_SNAKE_CASE__ = 0
# Doctest custom flag to ignore output.
_SCREAMING_SNAKE_CASE : List[Any] = doctest.register_optionflag('''IGNORE_RESULT''')
_SCREAMING_SNAKE_CASE : int = doctest.OutputChecker
class UpperCAmelCase__ ( A__ ):
"""simple docstring"""
def lowercase_ ( self : Union[str, Any] , __lowerCamelCase : Optional[int] , __lowerCamelCase : Optional[Any] , __lowerCamelCase : List[Any] ) -> Optional[Any]:
if IGNORE_RESULT & optionflags:
return True
return OutputChecker.check_output(self , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
_SCREAMING_SNAKE_CASE : Optional[int] = CustomOutputChecker
_SCREAMING_SNAKE_CASE : int = HfDoctestModule
_SCREAMING_SNAKE_CASE : int = HfDocTestParser
| 314 |
from functools import reduce
_SCREAMING_SNAKE_CASE : Any = (
'''73167176531330624919225119674426574742355349194934'''
'''96983520312774506326239578318016984801869478851843'''
'''85861560789112949495459501737958331952853208805511'''
'''12540698747158523863050715693290963295227443043557'''
'''66896648950445244523161731856403098711121722383113'''
'''62229893423380308135336276614282806444486645238749'''
'''30358907296290491560440772390713810515859307960866'''
'''70172427121883998797908792274921901699720888093776'''
'''65727333001053367881220235421809751254540594752243'''
'''52584907711670556013604839586446706324415722155397'''
'''53697817977846174064955149290862569321978468622482'''
'''83972241375657056057490261407972968652414535100474'''
'''82166370484403199890008895243450658541227588666881'''
'''16427171479924442928230863465674813919123162824586'''
'''17866458359124566529476545682848912883142607690042'''
'''24219022671055626321111109370544217506941658960408'''
'''07198403850962455444362981230987879927244284909188'''
'''84580156166097919133875499200524063689912560717606'''
'''05886116467109405077541002256983155200055935729725'''
'''71636269561882670428252483600823257530420752963450'''
)
def UpperCAmelCase_ ( _A = N ):
'''simple docstring'''
return max(
# mypy cannot properly interpret reduce
int(reduce(lambda _A , _A : str(int(_A ) * int(_A ) ) , n[i : i + 13] ) )
for i in range(len(_A ) - 12 ) )
if __name__ == "__main__":
print(F"{solution() = }")
| 314 | 1 |
"""simple docstring"""
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
UpperCamelCase_ =TypeVar("""KEY""")
UpperCamelCase_ =TypeVar("""VAL""")
@dataclass(frozen=_lowerCAmelCase , slots=_lowerCAmelCase )
class _a ( Generic[KEY, VAL] ):
UpperCamelCase = 42
UpperCamelCase = 42
class _a ( _Item ):
def __init__( self : Union[str, Any] ) -> None:
'''simple docstring'''
super().__init__(_SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE )
def __bool__( self : Union[str, Any] ) -> bool:
'''simple docstring'''
return False
UpperCamelCase_ =_DeletedItem()
class _a ( MutableMapping[KEY, VAL] ):
def __init__( self : Union[str, Any], lowerCAmelCase__ : Tuple = 8, lowerCAmelCase__ : str = 0.75 ) -> None:
'''simple docstring'''
_UpperCamelCase : Any = initial_block_size
_UpperCamelCase : list[_Item | None] = [None] * initial_block_size
assert 0.0 < capacity_factor < 1.0
_UpperCamelCase : Optional[Any] = capacity_factor
_UpperCamelCase : Union[str, Any] = 0
def snake_case ( self : Optional[Any], lowerCAmelCase__ : str ) -> int:
'''simple docstring'''
return hash(_SCREAMING_SNAKE_CASE ) % len(self._buckets )
def snake_case ( self : int, lowerCAmelCase__ : Union[str, Any] ) -> int:
'''simple docstring'''
return (ind + 1) % len(self._buckets )
def snake_case ( self : Tuple, lowerCAmelCase__ : Dict, lowerCAmelCase__ : int, lowerCAmelCase__ : Tuple ) -> bool:
'''simple docstring'''
_UpperCamelCase : Union[str, Any] = self._buckets[ind]
if not stored:
_UpperCamelCase : int = _Item(_SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE )
self._len += 1
return True
elif stored.key == key:
_UpperCamelCase : Tuple = _Item(_SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE )
return True
else:
return False
def snake_case ( self : Dict ) -> bool:
'''simple docstring'''
_UpperCamelCase : Union[str, Any] = len(self._buckets ) * self._capacity_factor
return len(self ) >= int(_SCREAMING_SNAKE_CASE )
def snake_case ( self : Tuple ) -> bool:
'''simple docstring'''
if len(self._buckets ) <= self._initial_block_size:
return False
_UpperCamelCase : Optional[int] = len(self._buckets ) * self._capacity_factor / 2
return len(self ) < limit
def snake_case ( self : str, lowerCAmelCase__ : Any ) -> None:
'''simple docstring'''
_UpperCamelCase : Dict = self._buckets
_UpperCamelCase : Union[str, Any] = [None] * new_size
_UpperCamelCase : Optional[int] = 0
for item in old_buckets:
if item:
self._add_item(item.key, item.val )
def snake_case ( self : List[str] ) -> None:
'''simple docstring'''
self._resize(len(self._buckets ) * 2 )
def snake_case ( self : int ) -> None:
'''simple docstring'''
self._resize(len(self._buckets ) // 2 )
def snake_case ( self : Optional[int], lowerCAmelCase__ : List[str] ) -> Iterator[int]:
'''simple docstring'''
_UpperCamelCase : Union[str, Any] = self._get_bucket_index(_SCREAMING_SNAKE_CASE )
for _ in range(len(self._buckets ) ):
yield ind
_UpperCamelCase : str = self._get_next_ind(_SCREAMING_SNAKE_CASE )
def snake_case ( self : Optional[int], lowerCAmelCase__ : int, lowerCAmelCase__ : str ) -> None:
'''simple docstring'''
for ind in self._iterate_buckets(_SCREAMING_SNAKE_CASE ):
if self._try_set(_SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE ):
break
def __setitem__( self : int, lowerCAmelCase__ : Tuple, lowerCAmelCase__ : int ) -> None:
'''simple docstring'''
if self._is_full():
self._size_up()
self._add_item(_SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE )
def __delitem__( self : Optional[int], lowerCAmelCase__ : List[Any] ) -> None:
'''simple docstring'''
for ind in self._iterate_buckets(_SCREAMING_SNAKE_CASE ):
_UpperCamelCase : Union[str, Any] = self._buckets[ind]
if item is None:
raise KeyError(_SCREAMING_SNAKE_CASE )
if item is _deleted:
continue
if item.key == key:
_UpperCamelCase : List[Any] = _deleted
self._len -= 1
break
if self._is_sparse():
self._size_down()
def __getitem__( self : Any, lowerCAmelCase__ : Any ) -> VAL:
'''simple docstring'''
for ind in self._iterate_buckets(_SCREAMING_SNAKE_CASE ):
_UpperCamelCase : List[Any] = self._buckets[ind]
if item is None:
break
if item is _deleted:
continue
if item.key == key:
return item.val
raise KeyError(_SCREAMING_SNAKE_CASE )
def __len__( self : Tuple ) -> int:
'''simple docstring'''
return self._len
def __iter__( self : List[str] ) -> Iterator[KEY]:
'''simple docstring'''
yield from (item.key for item in self._buckets if item)
def __repr__( self : str ) -> str:
'''simple docstring'''
_UpperCamelCase : Tuple = ''' ,'''.join(
f"""{item.key}: {item.val}""" for item in self._buckets if item )
return f"""HashMap({val_string})"""
| 371 |
"""simple docstring"""
from __future__ import annotations
from random import choice
def a_ ( _lowercase ):
return choice(_lowercase )
def a_ ( _lowercase , _lowercase ):
_UpperCamelCase : Optional[int] = random_pivot(_lowercase )
# partition based on pivot
# linear time
_UpperCamelCase : Union[str, Any] = [e for e in lst if e < pivot]
_UpperCamelCase : int = [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(_lowercase ) == k - 1:
return pivot
# pivot is in elements bigger than k
elif len(_lowercase ) < k - 1:
return kth_number(_lowercase , k - len(_lowercase ) - 1 )
# pivot is in elements smaller than k
else:
return kth_number(_lowercase , _lowercase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 128 | 0 |
import os
import re
import shutil
from argparse import ArgumentParser, Namespace
from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger
lowercase__ : Union[str, Any] = "<<<<<<< This should probably be modified because it mentions: "
lowercase__ : Optional[int] = "=======\n>>>>>>>\n"
lowercase__ : str = [
"TextEncoderConfig",
"ByteTextEncoder",
"SubwordTextEncoder",
"encoder_config",
"maybe_build_from_corpus",
"manual_dir",
]
lowercase__ : List[Any] = [
# (pattern, replacement)
# Order is important here for some replacements
(r"tfds\.core", r"datasets"),
(r"tf\.io\.gfile\.GFile", r"open"),
(r"tf\.([\w\d]+)", r"datasets.Value('\1')"),
(r"tfds\.features\.Text\(\)", r"datasets.Value('string')"),
(r"tfds\.features\.Text\(", r"datasets.Value('string'),"),
(r"features\s*=\s*tfds.features.FeaturesDict\(", r"features=datasets.Features("),
(r"tfds\.features\.FeaturesDict\(", r"dict("),
(r"The TensorFlow Datasets Authors", r"The TensorFlow Datasets Authors and the HuggingFace Datasets Authors"),
(r"tfds\.", r"datasets."),
(r"dl_manager\.manual_dir", r"self.config.data_dir"),
(r"self\.builder_config", r"self.config"),
]
def lowerCamelCase__ ( _A ):
'''simple docstring'''
return ConvertCommand(args.tfds_path , args.datasets_directory )
class UpperCAmelCase ( _a ):
'''simple docstring'''
@staticmethod
def snake_case__ ( __lowercase : Any ):
"""simple docstring"""
snake_case_ = parser.add_parser(
"convert" , help="Convert a TensorFlow Datasets dataset to a HuggingFace Datasets dataset." , )
train_parser.add_argument(
"--tfds_path" , type=__lowercase , required=__lowercase , help="Path to a TensorFlow Datasets folder to convert or a single tfds file to convert." , )
train_parser.add_argument(
"--datasets_directory" , type=__lowercase , required=__lowercase , help="Path to the HuggingFace Datasets folder." )
train_parser.set_defaults(func=__lowercase )
def __init__( self : str , __lowercase : int , __lowercase : str , *__lowercase : Dict ):
"""simple docstring"""
snake_case_ = get_logger("datasets-cli/converting" )
snake_case_ = tfds_path
snake_case_ = datasets_directory
def snake_case__ ( self : Union[str, Any] ):
"""simple docstring"""
if os.path.isdir(self._tfds_path ):
snake_case_ = os.path.abspath(self._tfds_path )
elif os.path.isfile(self._tfds_path ):
snake_case_ = os.path.dirname(self._tfds_path )
else:
raise ValueError("--tfds_path is neither a directory nor a file. Please check path." )
snake_case_ = os.path.abspath(self._datasets_directory )
self._logger.info(f"Converting datasets from {abs_tfds_path} to {abs_datasets_path}" )
snake_case_ = []
snake_case_ = []
snake_case_ = {}
if os.path.isdir(self._tfds_path ):
snake_case_ = os.listdir(__lowercase )
else:
snake_case_ = [os.path.basename(self._tfds_path )]
for f_name in file_names:
self._logger.info(f"Looking at file {f_name}" )
snake_case_ = os.path.join(__lowercase , __lowercase )
snake_case_ = os.path.join(__lowercase , __lowercase )
if not os.path.isfile(__lowercase ) or "__init__" in f_name or "_test" in f_name or ".py" not in f_name:
self._logger.info("Skipping file" )
continue
with open(__lowercase , encoding="utf-8" ) as f:
snake_case_ = f.readlines()
snake_case_ = []
snake_case_ = False
snake_case_ = False
snake_case_ = []
for line in lines:
snake_case_ = line
# Convert imports
if "import tensorflow.compat.v2 as tf" in out_line:
continue
elif "@tfds.core" in out_line:
continue
elif "builder=self" in out_line:
continue
elif "import tensorflow_datasets.public_api as tfds" in out_line:
snake_case_ = 'import datasets\n'
elif "import tensorflow" in out_line:
# order is important here
snake_case_ = ''
continue
elif "from absl import logging" in out_line:
snake_case_ = 'from datasets import logging\n'
elif "getLogger" in out_line:
snake_case_ = out_line.replace("getLogger" , "get_logger" )
elif any(expression in out_line for expression in TO_HIGHLIGHT ):
snake_case_ = True
snake_case_ = list(filter(lambda __lowercase : e in out_line , __lowercase ) )
out_lines.append(HIGHLIGHT_MESSAGE_PRE + str(__lowercase ) + "\n" )
out_lines.append(__lowercase )
out_lines.append(__lowercase )
continue
else:
for pattern, replacement in TO_CONVERT:
snake_case_ = re.sub(__lowercase , __lowercase , __lowercase )
# Take care of saving utilities (to later move them together with main script)
if "tensorflow_datasets" in out_line:
snake_case_ = re.match(r"from\stensorflow_datasets.*import\s([^\.\r\n]+)" , __lowercase )
tfds_imports.extend(imp.strip() for imp in match.group(1 ).split("," ) )
snake_case_ = 'from . import ' + match.group(1 )
# Check we have not forget anything
if "tf." in out_line or "tfds." in out_line or "tensorflow_datasets" in out_line:
raise ValueError(f"Error converting {out_line.strip()}" )
if "GeneratorBasedBuilder" in out_line or "BeamBasedBuilder" in out_line:
snake_case_ = True
out_lines.append(__lowercase )
if is_builder or "wmt" in f_name:
# We create a new directory for each dataset
snake_case_ = f_name.replace(".py" , "" )
snake_case_ = os.path.join(__lowercase , __lowercase )
snake_case_ = os.path.join(__lowercase , __lowercase )
os.makedirs(__lowercase , exist_ok=__lowercase )
self._logger.info(f"Adding directory {output_dir}" )
imports_to_builder_map.update({imp: output_dir for imp in tfds_imports} )
else:
# Utilities will be moved at the end
utils_files.append(__lowercase )
if needs_manual_update:
with_manual_update.append(__lowercase )
with open(__lowercase , "w" , encoding="utf-8" ) as f:
f.writelines(__lowercase )
self._logger.info(f"Converted in {output_file}" )
for utils_file in utils_files:
try:
snake_case_ = os.path.basename(__lowercase )
snake_case_ = imports_to_builder_map[f_name.replace(".py" , "" )]
self._logger.info(f"Moving {dest_folder} to {utils_file}" )
shutil.copy(__lowercase , __lowercase )
except KeyError:
self._logger.error(f"Cannot find destination folder for {utils_file}. Please copy manually." )
if with_manual_update:
for file_path in with_manual_update:
self._logger.warning(
f"You need to manually update file {file_path} to remove configurations using \'TextEncoderConfig\'." )
| 187 |
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,
)
import transformers
from transformers import AutoModel, AutoModelForSeqaSeqLM, AutoTokenizer
SCREAMING_SNAKE_CASE : str = "bart"
SCREAMING_SNAKE_CASE : Optional[int] = True
@st.cache(allow_output_mutation=lowerCamelCase_ )
def UpperCamelCase_( ) -> int:
if LOAD_DENSE_INDEX:
_lowercase : str = AutoTokenizer.from_pretrained('yjernite/retribert-base-uncased' )
_lowercase : Union[str, Any] = AutoModel.from_pretrained('yjernite/retribert-base-uncased' ).to('cuda:0' )
_lowercase : str = qar_model.eval()
else:
_lowercase , _lowercase : Any = (None, None)
if MODEL_TYPE == "bart":
_lowercase : Dict = AutoTokenizer.from_pretrained('yjernite/bart_eli5' )
_lowercase : int = AutoModelForSeqaSeqLM.from_pretrained('yjernite/bart_eli5' ).to('cuda:0' )
_lowercase : Any = torch.load('seq2seq_models/eli5_bart_model_blm_2.pth' )
sas_model.load_state_dict(save_dict['model'] )
_lowercase : List[Any] = sas_model.eval()
else:
_lowercase , _lowercase : Union[str, Any] = make_qa_sas_model(
model_name='t5-small' , from_file='seq2seq_models/eli5_t5_model_1024_4.pth' , device='cuda:0' )
return (qar_tokenizer, qar_model, sas_tokenizer, sas_model)
@st.cache(allow_output_mutation=lowerCamelCase_ )
def UpperCamelCase_( ) -> str:
if LOAD_DENSE_INDEX:
_lowercase : Optional[Any] = faiss.StandardGpuResources()
_lowercase : Optional[int] = datasets.load_dataset(path='wiki_snippets' , name='wiki40b_en_100_0' )['train']
_lowercase : Tuple = np.memmap(
'wiki40b_passages_reps_32_l-8_h-768_b-512-512.dat' , dtype='float32' , mode='r' , shape=(wikiaab_passages.num_rows, 128) , )
_lowercase : Any = faiss.IndexFlatIP(128 )
_lowercase : Union[str, Any] = faiss.index_cpu_to_gpu(lowerCamelCase_ , 1 , lowerCamelCase_ )
wikiaab_gpu_index_flat.add(lowerCamelCase_ ) # TODO fix for larger GPU
else:
_lowercase , _lowercase : Any = (None, None)
_lowercase : List[str] = Elasticsearch([{'host': 'localhost', 'port': '9200'}] )
return (wikiaab_passages, wikiaab_gpu_index_flat, es_client)
@st.cache(allow_output_mutation=lowerCamelCase_ )
def UpperCamelCase_( ) -> Any:
_lowercase : List[str] = datasets.load_dataset('eli5' , name='LFQA_reddit' )
_lowercase : Optional[Any] = elia['train_eli5']
_lowercase : Tuple = np.memmap(
'eli5_questions_reps.dat' , dtype='float32' , mode='r' , shape=(elia_train.num_rows, 128) )
_lowercase : Union[str, Any] = faiss.IndexFlatIP(128 )
eli5_train_q_index.add(lowerCamelCase_ )
return (elia_train, eli5_train_q_index)
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Any = load_indexes()
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Optional[Any] = load_models()
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : List[Any] = load_train_data()
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_=10 ) -> List[str]:
_lowercase : Any = embed_questions_for_retrieval([question] , lowerCamelCase_ , lowerCamelCase_ )
_lowercase , _lowercase : List[str] = eli5_train_q_index.search(lowerCamelCase_ , lowerCamelCase_ )
_lowercase : List[str] = [elia_train[int(lowerCamelCase_ )] for i in I[0]]
return nn_examples
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_="wiki40b" , lowerCamelCase_="dense" , lowerCamelCase_=10 ) -> Dict:
if source == "none":
_lowercase , _lowercase : Union[str, Any] = (' <P> '.join(['' for _ in range(11 )] ).strip(), [])
else:
if method == "dense":
_lowercase , _lowercase : Dict = query_qa_dense_index(
lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ )
else:
_lowercase , _lowercase : str = query_es_index(
lowerCamelCase_ , lowerCamelCase_ , index_name='english_wiki40b_snippets_100w' , n_results=lowerCamelCase_ , )
_lowercase : List[Any] = [
(res['article_title'], res['section_title'].strip(), res['score'], res['passage_text']) for res in hit_lst
]
_lowercase : Union[str, Any] = 'question: {} context: {}'.format(lowerCamelCase_ , lowerCamelCase_ )
return question_doc, support_list
@st.cache(
hash_funcs={
torch.Tensor: (lambda lowerCamelCase_ : None),
transformers.models.bart.tokenization_bart.BartTokenizer: (lambda lowerCamelCase_ : None),
} )
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_=64 , lowerCamelCase_=256 , lowerCamelCase_=False , lowerCamelCase_=2 , lowerCamelCase_=0.95 , lowerCamelCase_=0.8 ) -> Dict:
with torch.no_grad():
_lowercase : str = qa_sas_generate(
lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , num_answers=1 , num_beams=lowerCamelCase_ , min_len=lowerCamelCase_ , max_len=lowerCamelCase_ , do_sample=lowerCamelCase_ , temp=lowerCamelCase_ , top_p=lowerCamelCase_ , top_k=lowerCamelCase_ , max_input_length=1024 , device='cuda:0' , )[0]
return (answer, support_list)
st.title("Long Form Question Answering with ELI5")
# Start sidebar
SCREAMING_SNAKE_CASE : Union[str, Any] = "<img src='https://huggingface.co/front/assets/huggingface_logo.svg'>"
SCREAMING_SNAKE_CASE : List[Any] = "\n<html>\n <head>\n <style>\n .img-container {\n padding-left: 90px;\n padding-right: 90px;\n padding-top: 50px;\n padding-bottom: 50px;\n background-color: #f0f3f9;\n }\n </style>\n </head>\n <body>\n <span class=\"img-container\"> <!-- Inline parent element -->\n %s\n </span>\n </body>\n</html>\n" % (
header_html,
)
st.sidebar.markdown(
header_full,
unsafe_allow_html=True,
)
# Long Form QA with ELI5 and Wikipedia
SCREAMING_SNAKE_CASE : Any = "\nThis demo presents a model trained to [provide long-form answers to open-domain questions](https://yjernite.github.io/lfqa.html).\nFirst, a document retriever fetches a set of relevant Wikipedia passages given the question from the [Wiki40b](https://research.google/pubs/pub49029/) dataset,\na pre-processed fixed snapshot of Wikipedia.\n"
st.sidebar.markdown(description, unsafe_allow_html=True)
SCREAMING_SNAKE_CASE : Union[str, Any] = [
"Answer the question",
"View the retrieved document only",
"View the most similar ELI5 question and answer",
"Show me everything, please!",
]
SCREAMING_SNAKE_CASE : Optional[int] = st.sidebar.checkbox("Demo options")
if demo_options:
SCREAMING_SNAKE_CASE : List[str] = st.sidebar.selectbox(
"",
action_list,
index=3,
)
SCREAMING_SNAKE_CASE : Optional[int] = action_list.index(action_st)
SCREAMING_SNAKE_CASE : Tuple = st.sidebar.selectbox(
"",
["Show full text of passages", "Show passage section titles"],
index=0,
)
SCREAMING_SNAKE_CASE : int = show_type == "Show full text of passages"
else:
SCREAMING_SNAKE_CASE : Any = 3
SCREAMING_SNAKE_CASE : Dict = True
SCREAMING_SNAKE_CASE : Union[str, Any] = st.sidebar.checkbox("Retrieval options")
if retrieval_options:
SCREAMING_SNAKE_CASE : Tuple = "\n ### Information retriever options\n\n The **sparse** retriever uses ElasticSearch, while the **dense** retriever uses max-inner-product search between a question and passage embedding\n trained using the [ELI5](https://arxiv.org/abs/1907.09190) questions-answer pairs.\n The answer is then generated by sequence to sequence model which takes the question and retrieved document as input.\n "
st.sidebar.markdown(retriever_info)
SCREAMING_SNAKE_CASE : Dict = st.sidebar.selectbox("Which Wikipedia format should the model use?", ["wiki40b", "none"])
SCREAMING_SNAKE_CASE : Union[str, Any] = st.sidebar.selectbox("Which Wikipedia indexer should the model use?", ["dense", "sparse", "mixed"])
else:
SCREAMING_SNAKE_CASE : int = "wiki40b"
SCREAMING_SNAKE_CASE : int = "dense"
SCREAMING_SNAKE_CASE : str = "beam"
SCREAMING_SNAKE_CASE : Optional[Any] = 2
SCREAMING_SNAKE_CASE : List[str] = 64
SCREAMING_SNAKE_CASE : Union[str, Any] = 256
SCREAMING_SNAKE_CASE : Union[str, Any] = None
SCREAMING_SNAKE_CASE : List[Any] = None
SCREAMING_SNAKE_CASE : str = st.sidebar.checkbox("Generation options")
if generate_options:
SCREAMING_SNAKE_CASE : Any = "\n ### Answer generation options\n\n The sequence-to-sequence model was initialized with [BART](https://huggingface.co/facebook/bart-large)\n weights and fine-tuned on the ELI5 QA pairs and retrieved documents. You can use the model for greedy decoding with\n **beam** search, or **sample** from the decoder's output probabilities.\n "
st.sidebar.markdown(generate_info)
SCREAMING_SNAKE_CASE : List[Any] = st.sidebar.selectbox("Would you like to use beam search or sample an answer?", ["beam", "sampled"])
SCREAMING_SNAKE_CASE : Tuple = st.sidebar.slider(
"Minimum generation length", min_value=8, max_value=256, value=64, step=8, format=None, key=None
)
SCREAMING_SNAKE_CASE : int = st.sidebar.slider(
"Maximum generation length", min_value=64, max_value=512, value=256, step=16, format=None, key=None
)
if sampled == "beam":
SCREAMING_SNAKE_CASE : int = st.sidebar.slider("Beam size", min_value=1, max_value=8, value=2, step=None, format=None, key=None)
else:
SCREAMING_SNAKE_CASE : Union[str, Any] = st.sidebar.slider(
"Nucleus sampling p", min_value=0.1, max_value=1.0, value=0.95, step=0.01, format=None, key=None
)
SCREAMING_SNAKE_CASE : Any = st.sidebar.slider(
"Temperature", min_value=0.1, max_value=1.0, value=0.7, step=0.01, format=None, key=None
)
SCREAMING_SNAKE_CASE : str = None
# start main text
SCREAMING_SNAKE_CASE : List[str] = [
"<MY QUESTION>",
"How do people make chocolate?",
"Why do we get a fever when we are sick?",
"How can different animals perceive different colors?",
"What is natural language processing?",
"What's the best way to treat a sunburn?",
"What exactly are vitamins ?",
"How does nuclear energy provide electricity?",
"What's the difference between viruses and bacteria?",
"Why are flutes classified as woodwinds when most of them are made out of metal ?",
"Why do people like drinking coffee even though it tastes so bad?",
"What happens when wine ages? How does it make the wine taste better?",
"If an animal is an herbivore, where does it get the protein that it needs to survive if it only eats grass?",
"How can we set a date to the beginning or end of an artistic period? Doesn't the change happen gradually?",
"How does New Zealand have so many large bird predators?",
]
SCREAMING_SNAKE_CASE : str = st.selectbox(
"What would you like to ask? ---- select <MY QUESTION> to enter a new query",
questions_list,
index=1,
)
if question_s == "<MY QUESTION>":
SCREAMING_SNAKE_CASE : List[str] = st.text_input("Enter your question here:", "")
else:
SCREAMING_SNAKE_CASE : Optional[int] = question_s
if st.button("Show me!"):
if action in [0, 1, 3]:
if index_type == "mixed":
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Optional[int] = make_support(question, source=wiki_source, method="dense", n_results=10)
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Any = make_support(question, source=wiki_source, method="sparse", n_results=10)
SCREAMING_SNAKE_CASE : Tuple = []
for res_d, res_s in zip(support_list_dense, support_list_sparse):
if tuple(res_d) not in support_list:
support_list += [tuple(res_d)]
if tuple(res_s) not in support_list:
support_list += [tuple(res_s)]
SCREAMING_SNAKE_CASE : Optional[Any] = support_list[:10]
SCREAMING_SNAKE_CASE : int = "<P> " + " <P> ".join([res[-1] for res in support_list])
else:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Any = make_support(question, source=wiki_source, method=index_type, n_results=10)
if action in [0, 3]:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Tuple = answer_question(
question_doc,
sas_model,
sas_tokenizer,
min_len=min_len,
max_len=int(max_len),
sampling=(sampled == "sampled"),
n_beams=n_beams,
top_p=top_p,
temp=temp,
)
st.markdown("### The model generated answer is:")
st.write(answer)
if action in [0, 1, 3] and wiki_source != "none":
st.markdown("--- \n ### The model is drawing information from the following Wikipedia passages:")
for i, res in enumerate(support_list):
SCREAMING_SNAKE_CASE : Optional[Any] = "https://en.wikipedia.org/wiki/{}".format(res[0].replace(" ", "_"))
SCREAMING_SNAKE_CASE : List[Any] = res[1].strip()
if sec_titles == "":
SCREAMING_SNAKE_CASE : Union[str, Any] = "[{}]({})".format(res[0], wiki_url)
else:
SCREAMING_SNAKE_CASE : Any = sec_titles.split(" & ")
SCREAMING_SNAKE_CASE : List[Any] = " & ".join(
["[{}]({}#{})".format(sec.strip(), wiki_url, sec.strip().replace(" ", "_")) for sec in sec_list]
)
st.markdown(
"{0:02d} - **Article**: {1:<18} <br> _Section_: {2}".format(i + 1, res[0], sections),
unsafe_allow_html=True,
)
if show_passages:
st.write(
"> <span style=\"font-family:arial; font-size:10pt;\">" + res[-1] + "</span>", unsafe_allow_html=True
)
if action in [2, 3]:
SCREAMING_SNAKE_CASE : str = find_nearest_training(question)
SCREAMING_SNAKE_CASE : Any = nn_train_list[0]
st.markdown(
"--- \n ### The most similar question in the ELI5 training set was: \n\n {}".format(train_exple["title"])
)
SCREAMING_SNAKE_CASE : str = [
"{}. {}".format(i + 1, " \n".join([line.strip() for line in ans.split("\n") if line.strip() != ""]))
for i, (ans, sc) in enumerate(zip(train_exple["answers"]["text"], train_exple["answers"]["score"]))
if i == 0 or sc > 2
]
st.markdown("##### Its answers were: \n\n {}".format("\n".join(answers_st)))
SCREAMING_SNAKE_CASE : Tuple = "\n---\n\n**Disclaimer**\n\n*The intent of this app is to provide some (hopefully entertaining) insights into the behavior of a current LFQA system.\nEvaluating biases of such a model and ensuring factual generations are still very much open research problems.\nTherefore, until some significant progress is achieved, we caution against using the generated answers for practical purposes.*\n"
st.sidebar.markdown(disclaimer, unsafe_allow_html=True)
| 21 | 0 |
'''simple docstring'''
import os
import sys
import unittest
__lowercase : Any = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init # noqa: E402
# Align TRANSFORMERS_PATH in check_dummies with the current path
__lowercase : Optional[Any] = os.path.join(git_repo_path, 'src', 'diffusers')
class __UpperCamelCase ( unittest.TestCase ):
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Union[str, Any] = find_backend(' if not is_torch_available():' )
self.assertEqual(__a , 'torch' )
# backend_with_underscore = find_backend(" if not is_tensorflow_text_available():")
# self.assertEqual(backend_with_underscore, "tensorflow_text")
__a : str = find_backend(' if not (is_torch_available() and is_transformers_available()):' )
self.assertEqual(__a , 'torch_and_transformers' )
# double_backend_with_underscore = find_backend(
# " if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
# )
# self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text")
__a : Optional[int] = find_backend(
' if not (is_torch_available() and is_transformers_available() and is_onnx_available()):' )
self.assertEqual(__a , 'torch_and_transformers_and_onnx' )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : List[str] = read_init()
# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
self.assertIn('torch' , __a )
self.assertIn('torch_and_transformers' , __a )
self.assertIn('flax_and_transformers' , __a )
self.assertIn('torch_and_transformers_and_onnx' , __a )
# Likewise, we can't assert on the exact content of a key
self.assertIn('UNet2DModel' , objects['torch'] )
self.assertIn('FlaxUNet2DConditionModel' , objects['flax'] )
self.assertIn('StableDiffusionPipeline' , objects['torch_and_transformers'] )
self.assertIn('FlaxStableDiffusionPipeline' , objects['flax_and_transformers'] )
self.assertIn('LMSDiscreteScheduler' , objects['torch_and_scipy'] )
self.assertIn('OnnxStableDiffusionPipeline' , objects['torch_and_transformers_and_onnx'] )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Any = create_dummy_object('CONSTANT' , '\'torch\'' )
self.assertEqual(__a , '\nCONSTANT = None\n' )
__a : Any = create_dummy_object('function' , '\'torch\'' )
self.assertEqual(
__a , '\ndef function(*args, **kwargs):\n requires_backends(function, \'torch\')\n' )
__a : List[Any] = '\nclass FakeClass(metaclass=DummyObject):\n _backends = \'torch\'\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, \'torch\')\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, \'torch\')\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, \'torch\')\n'
__a : Union[str, Any] = create_dummy_object('FakeClass' , '\'torch\'' )
self.assertEqual(__a , __a )
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : Tuple = '# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n requires_backends(function, ["torch"])\n\n\nclass FakeClass(metaclass=DummyObject):\n _backends = ["torch"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, ["torch"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, ["torch"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, ["torch"])\n'
__a : Optional[int] = create_dummy_files({'torch': ['CONSTANT', 'function', 'FakeClass']} )
self.assertEqual(dummy_files['torch'] , __a )
| 294 |
'''simple docstring'''
import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import convert_model
from .versions import is_torch_version
if is_deepspeed_available():
from deepspeed import DeepSpeedEngine
if is_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Optional[Any] ):
if is_torch_version('<' , '2.0.0' ) or not hasattr(_SCREAMING_SNAKE_CASE , '_dynamo' ):
return False
return isinstance(_SCREAMING_SNAKE_CASE , torch._dynamo.eval_frame.OptimizedModule )
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Optional[Any] , _SCREAMING_SNAKE_CASE : bool = True ):
__a : int = (torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)
__a : Any = is_compiled_module(_SCREAMING_SNAKE_CASE )
if is_compiled:
__a : List[Any] = model
__a : Union[str, Any] = model._orig_mod
if is_deepspeed_available():
options += (DeepSpeedEngine,)
while isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__a : Union[str, Any] = model.module
if not keep_fpaa_wrapper:
__a : Optional[Any] = getattr(_SCREAMING_SNAKE_CASE , 'forward' )
__a : str = model.__dict__.pop('_original_forward' , _SCREAMING_SNAKE_CASE )
if original_forward is not None:
while hasattr(_SCREAMING_SNAKE_CASE , '__wrapped__' ):
__a : Any = forward.__wrapped__
if forward == original_forward:
break
__a : str = forward
if getattr(_SCREAMING_SNAKE_CASE , '_converted_to_transformer_engine' , _SCREAMING_SNAKE_CASE ):
convert_model(_SCREAMING_SNAKE_CASE , to_transformer_engine=_SCREAMING_SNAKE_CASE )
if is_compiled:
__a : List[str] = model
__a : Optional[int] = compiled_model
return model
def lowerCamelCase ():
PartialState().wait_for_everyone()
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : Tuple ):
if PartialState().distributed_type == DistributedType.TPU:
xm.save(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
elif PartialState().local_process_index == 0:
torch.save(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
@contextmanager
def lowerCamelCase (**_SCREAMING_SNAKE_CASE : Tuple ):
for key, value in kwargs.items():
__a : Optional[int] = str(_SCREAMING_SNAKE_CASE )
yield
for key in kwargs:
if key.upper() in os.environ:
del os.environ[key.upper()]
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Dict ):
if not hasattr(_SCREAMING_SNAKE_CASE , '__qualname__' ) and not hasattr(_SCREAMING_SNAKE_CASE , '__name__' ):
__a : List[Any] = getattr(_SCREAMING_SNAKE_CASE , '__class__' , _SCREAMING_SNAKE_CASE )
if hasattr(_SCREAMING_SNAKE_CASE , '__qualname__' ):
return obj.__qualname__
if hasattr(_SCREAMING_SNAKE_CASE , '__name__' ):
return obj.__name__
return str(_SCREAMING_SNAKE_CASE )
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : List[str] ):
for key, value in source.items():
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__a : int = destination.setdefault(_SCREAMING_SNAKE_CASE , {} )
merge_dicts(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
else:
__a : Tuple = value
return destination
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int = None ):
if port is None:
__a : List[str] = 29_500
with socket.socket(socket.AF_INET , socket.SOCK_STREAM ) as s:
return s.connect_ex(('localhost', port) ) == 0
| 294 | 1 |
import re
from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P
# Sentinels
_A = object()
# For specifying empty leaf dict `{}`
_A = object()
def lowerCamelCase__ ( a__ : List[Any] , a__ : str ) -> Any:
UpperCamelCase_ = tuple((re.compile(x + """$""" ) for x in qs) )
for i in range(len(__lowerCAmelCase ) - len(__lowerCAmelCase ) + 1 ):
UpperCamelCase_ = [x.match(__lowerCAmelCase ) for x, y in zip(__lowerCAmelCase , ks[i:] )]
if matches and all(__lowerCAmelCase ):
return True
return False
def lowerCamelCase__ ( a__ : Any ) -> Union[str, Any]:
def replace(a__ : Tuple , a__ : str ):
for rule, replacement in rules:
if _match(__lowerCAmelCase , __lowerCAmelCase ):
return replacement
return val
return replace
def lowerCamelCase__ ( ) -> Optional[int]:
return [
# embeddings
(("transformer", "wpe", "embedding"), P("""mp""" , __lowerCAmelCase )),
(("transformer", "wte", "embedding"), P("""mp""" , __lowerCAmelCase )),
# atention
(("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(__lowerCAmelCase , """mp""" )),
(("attention", "out_proj", "kernel"), P("""mp""" , __lowerCAmelCase )),
(("attention", "out_proj", "bias"), None),
# mlp
(("mlp", "c_fc", "kernel"), P(__lowerCAmelCase , """mp""" )),
(("mlp", "c_fc", "bias"), P("""mp""" )),
(("mlp", "c_proj", "kernel"), P("""mp""" , __lowerCAmelCase )),
(("mlp", "c_proj", "bias"), None),
# layer norms
((r"ln_\d+", "bias"), None),
((r"\d+", r"ln_\d+", "scale"), None),
(("ln_f", "bias"), None),
(("ln_f", "scale"), None),
]
def lowerCamelCase__ ( a__ : str ) -> List[Any]:
UpperCamelCase_ = _get_partition_rules()
UpperCamelCase_ = _replacement_rules(__lowerCAmelCase )
UpperCamelCase_ = {k: _unmatched for k in flatten_dict(__lowerCAmelCase )}
UpperCamelCase_ = {k: replace(__lowerCAmelCase , __lowerCAmelCase ) for k, v in initd.items()}
assert _unmatched not in result.values(), "Incomplete partition spec."
return freeze(unflatten_dict(__lowerCAmelCase ) )
| 122 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
snake_case : str = {
'''configuration_rag''': ['''RagConfig'''],
'''retrieval_rag''': ['''RagRetriever'''],
'''tokenization_rag''': ['''RagTokenizer'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case : Any = [
'''RagModel''',
'''RagPreTrainedModel''',
'''RagSequenceForGeneration''',
'''RagTokenForGeneration''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case : Optional[int] = [
'''TFRagModel''',
'''TFRagPreTrainedModel''',
'''TFRagSequenceForGeneration''',
'''TFRagTokenForGeneration''',
]
if TYPE_CHECKING:
from .configuration_rag import RagConfig
from .retrieval_rag import RagRetriever
from .tokenization_rag import RagTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_rag import (
TFRagModel,
TFRagPreTrainedModel,
TFRagSequenceForGeneration,
TFRagTokenForGeneration,
)
else:
import sys
snake_case : int = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 240 | 0 |
import tempfile
import unittest
import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax
if is_flax_available():
import os
from flax.core.frozen_dict import unfreeze
from flax.traverse_util import flatten_dict
from transformers import FlaxBertModel
__lowerCamelCase : str = "0.12" # assumed parallelism: 8
@require_flax
@is_staging_test
class A__ ( unittest.TestCase ):
@classmethod
def __UpperCamelCase( cls ):
'''simple docstring'''
UpperCamelCase : Dict = TOKEN
HfFolder.save_token(_SCREAMING_SNAKE_CASE )
@classmethod
def __UpperCamelCase( cls ):
'''simple docstring'''
try:
delete_repo(token=cls._token , repo_id="test-model-flax" )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="valid_org/test-model-flax-org" )
except HTTPError:
pass
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : int = BertConfig(
vocab_size=99 , hidden_size=32 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=37 )
UpperCamelCase : Dict = FlaxBertModel(_SCREAMING_SNAKE_CASE )
model.push_to_hub("test-model-flax" , use_auth_token=self._token )
UpperCamelCase : Optional[Any] = FlaxBertModel.from_pretrained(F"""{USER}/test-model-flax""" )
UpperCamelCase : Dict = flatten_dict(unfreeze(model.params ) )
UpperCamelCase : List[Any] = flatten_dict(unfreeze(new_model.params ) )
for key in base_params.keys():
UpperCamelCase : Optional[int] = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(_SCREAMING_SNAKE_CASE , 1e-3 , msg=F"""{key} not identical""" )
# Reset repo
delete_repo(token=self._token , repo_id="test-model-flax" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(_SCREAMING_SNAKE_CASE , repo_id="test-model-flax" , push_to_hub=_SCREAMING_SNAKE_CASE , use_auth_token=self._token )
UpperCamelCase : Dict = FlaxBertModel.from_pretrained(F"""{USER}/test-model-flax""" )
UpperCamelCase : Tuple = flatten_dict(unfreeze(model.params ) )
UpperCamelCase : List[Any] = flatten_dict(unfreeze(new_model.params ) )
for key in base_params.keys():
UpperCamelCase : Optional[Any] = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(_SCREAMING_SNAKE_CASE , 1e-3 , msg=F"""{key} not identical""" )
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : Union[str, Any] = BertConfig(
vocab_size=99 , hidden_size=32 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=37 )
UpperCamelCase : List[str] = FlaxBertModel(_SCREAMING_SNAKE_CASE )
model.push_to_hub("valid_org/test-model-flax-org" , use_auth_token=self._token )
UpperCamelCase : Optional[Any] = FlaxBertModel.from_pretrained("valid_org/test-model-flax-org" )
UpperCamelCase : Any = flatten_dict(unfreeze(model.params ) )
UpperCamelCase : str = flatten_dict(unfreeze(new_model.params ) )
for key in base_params.keys():
UpperCamelCase : Any = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(_SCREAMING_SNAKE_CASE , 1e-3 , msg=F"""{key} not identical""" )
# Reset repo
delete_repo(token=self._token , repo_id="valid_org/test-model-flax-org" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(
_SCREAMING_SNAKE_CASE , repo_id="valid_org/test-model-flax-org" , push_to_hub=_SCREAMING_SNAKE_CASE , use_auth_token=self._token )
UpperCamelCase : Optional[int] = FlaxBertModel.from_pretrained("valid_org/test-model-flax-org" )
UpperCamelCase : Any = flatten_dict(unfreeze(model.params ) )
UpperCamelCase : Dict = flatten_dict(unfreeze(new_model.params ) )
for key in base_params.keys():
UpperCamelCase : int = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(_SCREAMING_SNAKE_CASE , 1e-3 , msg=F"""{key} not identical""" )
def A_ ( _lowerCAmelCase , _lowerCAmelCase ) -> Tuple:
UpperCamelCase : Optional[Any] = True
UpperCamelCase : Union[str, Any] = flatten_dict(modela.params )
UpperCamelCase : Tuple = flatten_dict(modela.params )
for key in flat_params_a.keys():
if np.sum(np.abs(flat_params_a[key] - flat_params_a[key] ) ) > 1e-4:
UpperCamelCase : List[str] = False
return models_are_equal
@require_flax
class A__ ( unittest.TestCase ):
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : Dict = BertConfig.from_pretrained("hf-internal-testing/tiny-bert-flax-only" )
UpperCamelCase : Dict = FlaxBertModel(_SCREAMING_SNAKE_CASE )
UpperCamelCase : int = """bert"""
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(os.path.join(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
with self.assertRaises(_SCREAMING_SNAKE_CASE ):
UpperCamelCase : Optional[int] = FlaxBertModel.from_pretrained(_SCREAMING_SNAKE_CASE )
UpperCamelCase : Tuple = FlaxBertModel.from_pretrained(_SCREAMING_SNAKE_CASE , subfolder=_SCREAMING_SNAKE_CASE )
self.assertTrue(check_models_equal(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : List[Any] = BertConfig.from_pretrained("hf-internal-testing/tiny-bert-flax-only" )
UpperCamelCase : Dict = FlaxBertModel(_SCREAMING_SNAKE_CASE )
UpperCamelCase : Optional[Any] = """bert"""
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(os.path.join(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) , max_shard_size="10KB" )
with self.assertRaises(_SCREAMING_SNAKE_CASE ):
UpperCamelCase : str = FlaxBertModel.from_pretrained(_SCREAMING_SNAKE_CASE )
UpperCamelCase : List[str] = FlaxBertModel.from_pretrained(_SCREAMING_SNAKE_CASE , subfolder=_SCREAMING_SNAKE_CASE )
self.assertTrue(check_models_equal(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : Union[str, Any] = """bert"""
UpperCamelCase : Union[str, Any] = """hf-internal-testing/tiny-random-bert-subfolder"""
with self.assertRaises(_SCREAMING_SNAKE_CASE ):
UpperCamelCase : Dict = FlaxBertModel.from_pretrained(_SCREAMING_SNAKE_CASE )
UpperCamelCase : Any = FlaxBertModel.from_pretrained(_SCREAMING_SNAKE_CASE , subfolder=_SCREAMING_SNAKE_CASE )
self.assertIsNotNone(_SCREAMING_SNAKE_CASE )
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : Optional[int] = """bert"""
UpperCamelCase : int = """hf-internal-testing/tiny-random-bert-sharded-subfolder"""
with self.assertRaises(_SCREAMING_SNAKE_CASE ):
UpperCamelCase : int = FlaxBertModel.from_pretrained(_SCREAMING_SNAKE_CASE )
UpperCamelCase : Optional[Any] = FlaxBertModel.from_pretrained(_SCREAMING_SNAKE_CASE , subfolder=_SCREAMING_SNAKE_CASE )
self.assertIsNotNone(_SCREAMING_SNAKE_CASE )
| 360 |
from pathlib import Path
import numpy as np
from PIL import Image
def A_ ( _lowerCAmelCase ) -> np.ndarray:
UpperCamelCase , UpperCamelCase , UpperCamelCase : Optional[Any] = rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
return 0.2_989 * r + 0.5_870 * g + 0.1_140 * b
def A_ ( _lowerCAmelCase ) -> np.ndarray:
return (gray > 127) & (gray <= 255)
def A_ ( _lowerCAmelCase , _lowerCAmelCase ) -> np.ndarray:
UpperCamelCase : Dict = np.zeros_like(_lowerCAmelCase )
UpperCamelCase : List[str] = np.zeros(
(image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1) )
# Copy image to padded image
UpperCamelCase : Tuple = image
# Iterate over image & apply kernel
for x in range(image.shape[1] ):
for y in range(image.shape[0] ):
UpperCamelCase : List[str] = (
kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
).sum()
UpperCamelCase : int = int(summation > 0 )
return output
if __name__ == "__main__":
# read original image
__lowerCamelCase : int = Path(__file__).resolve().parent / """image_data""" / """lena.jpg"""
__lowerCamelCase : Tuple = np.array(Image.open(lena_path))
# kernel to be applied
__lowerCamelCase : Optional[Any] = np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
__lowerCamelCase : Union[str, Any] = dilation(gray_to_binary(rgb_to_gray(lena)), structuring_element)
# Save the output image
__lowerCamelCase : Any = Image.fromarray(output).convert("""RGB""")
pil_img.save("""result_dilation.png""")
| 140 | 0 |
'''simple docstring'''
import argparse
import os
import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints
from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrogram_diffusion import SpectrogramContEncoder, SpectrogramNotesEncoder, TaFilmDecoder
a__ : Union[str, Any] = 'base_with_context'
def _UpperCamelCase ( __A , __A ) -> Optional[Any]:
'''simple docstring'''
UpperCamelCase__ = nn.Parameter(torch.FloatTensor(weights["token_embedder"]["embedding"] ) )
UpperCamelCase__ = nn.Parameter(
torch.FloatTensor(weights["Embed_0"]["embedding"] ) , requires_grad=_lowerCAmelCase )
for lyr_num, lyr in enumerate(model.encoders ):
UpperCamelCase__ = weights[F'''layers_{lyr_num}''']
UpperCamelCase__ = nn.Parameter(
torch.FloatTensor(ly_weight["pre_attention_layer_norm"]["scale"] ) )
UpperCamelCase__ = ly_weight["attention"]
UpperCamelCase__ = nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T ) )
UpperCamelCase__ = nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T ) )
UpperCamelCase__ = nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T ) )
UpperCamelCase__ = nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T ) )
UpperCamelCase__ = nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"] ) )
UpperCamelCase__ = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T ) )
UpperCamelCase__ = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T ) )
UpperCamelCase__ = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T ) )
UpperCamelCase__ = nn.Parameter(torch.FloatTensor(weights["encoder_norm"]["scale"] ) )
return model
def _UpperCamelCase ( __A , __A ) -> Optional[Any]:
'''simple docstring'''
UpperCamelCase__ = nn.Parameter(torch.FloatTensor(weights["input_proj"]["kernel"].T ) )
UpperCamelCase__ = nn.Parameter(
torch.FloatTensor(weights["Embed_0"]["embedding"] ) , requires_grad=_lowerCAmelCase )
for lyr_num, lyr in enumerate(model.encoders ):
UpperCamelCase__ = weights[F'''layers_{lyr_num}''']
UpperCamelCase__ = ly_weight["attention"]
UpperCamelCase__ = nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T ) )
UpperCamelCase__ = nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T ) )
UpperCamelCase__ = nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T ) )
UpperCamelCase__ = nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T ) )
UpperCamelCase__ = nn.Parameter(
torch.FloatTensor(ly_weight["pre_attention_layer_norm"]["scale"] ) )
UpperCamelCase__ = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T ) )
UpperCamelCase__ = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T ) )
UpperCamelCase__ = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T ) )
UpperCamelCase__ = nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"] ) )
UpperCamelCase__ = nn.Parameter(torch.FloatTensor(weights["encoder_norm"]["scale"] ) )
return model
def _UpperCamelCase ( __A , __A ) -> List[Any]:
'''simple docstring'''
UpperCamelCase__ = nn.Parameter(torch.FloatTensor(weights["time_emb_dense0"]["kernel"].T ) )
UpperCamelCase__ = nn.Parameter(torch.FloatTensor(weights["time_emb_dense1"]["kernel"].T ) )
UpperCamelCase__ = nn.Parameter(
torch.FloatTensor(weights["Embed_0"]["embedding"] ) , requires_grad=_lowerCAmelCase )
UpperCamelCase__ = nn.Parameter(
torch.FloatTensor(weights["continuous_inputs_projection"]["kernel"].T ) )
for lyr_num, lyr in enumerate(model.decoders ):
UpperCamelCase__ = weights[F'''layers_{lyr_num}''']
UpperCamelCase__ = nn.Parameter(
torch.FloatTensor(ly_weight["pre_self_attention_layer_norm"]["scale"] ) )
UpperCamelCase__ = nn.Parameter(
torch.FloatTensor(ly_weight["FiLMLayer_0"]["DenseGeneral_0"]["kernel"].T ) )
UpperCamelCase__ = ly_weight["self_attention"]
UpperCamelCase__ = nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T ) )
UpperCamelCase__ = nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T ) )
UpperCamelCase__ = nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T ) )
UpperCamelCase__ = nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T ) )
UpperCamelCase__ = ly_weight["MultiHeadDotProductAttention_0"]
UpperCamelCase__ = nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T ) )
UpperCamelCase__ = nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T ) )
UpperCamelCase__ = nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T ) )
UpperCamelCase__ = nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T ) )
UpperCamelCase__ = nn.Parameter(
torch.FloatTensor(ly_weight["pre_cross_attention_layer_norm"]["scale"] ) )
UpperCamelCase__ = nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"] ) )
UpperCamelCase__ = nn.Parameter(
torch.FloatTensor(ly_weight["FiLMLayer_1"]["DenseGeneral_0"]["kernel"].T ) )
UpperCamelCase__ = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T ) )
UpperCamelCase__ = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T ) )
UpperCamelCase__ = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T ) )
UpperCamelCase__ = nn.Parameter(torch.FloatTensor(weights["decoder_norm"]["scale"] ) )
UpperCamelCase__ = nn.Parameter(torch.FloatTensor(weights["spec_out_dense"]["kernel"].T ) )
return model
def _UpperCamelCase ( __A ) -> Union[str, Any]:
'''simple docstring'''
UpperCamelCase__ = checkpoints.load_tax_checkpoint(args.checkpoint_path )
UpperCamelCase__ = jnp.tree_util.tree_map(onp.array , _lowerCAmelCase )
UpperCamelCase__ = [
"from __gin__ import dynamic_registration",
"from music_spectrogram_diffusion.models.diffusion import diffusion_utils",
"diffusion_utils.ClassifierFreeGuidanceConfig.eval_condition_weight = 2.0",
"diffusion_utils.DiffusionConfig.classifier_free_guidance = @diffusion_utils.ClassifierFreeGuidanceConfig()",
]
UpperCamelCase__ = os.path.join(args.checkpoint_path , ".." , "config.gin" )
UpperCamelCase__ = inference.parse_training_gin_file(_lowerCAmelCase , _lowerCAmelCase )
UpperCamelCase__ = inference.InferenceModel(args.checkpoint_path , _lowerCAmelCase )
UpperCamelCase__ = DDPMScheduler(beta_schedule="squaredcos_cap_v2" , variance_type="fixed_large" )
UpperCamelCase__ = SpectrogramNotesEncoder(
max_length=synth_model.sequence_length["inputs"] , vocab_size=synth_model.model.module.config.vocab_size , d_model=synth_model.model.module.config.emb_dim , dropout_rate=synth_model.model.module.config.dropout_rate , num_layers=synth_model.model.module.config.num_encoder_layers , num_heads=synth_model.model.module.config.num_heads , d_kv=synth_model.model.module.config.head_dim , d_ff=synth_model.model.module.config.mlp_dim , feed_forward_proj="gated-gelu" , )
UpperCamelCase__ = SpectrogramContEncoder(
input_dims=synth_model.audio_codec.n_dims , targets_context_length=synth_model.sequence_length["targets_context"] , d_model=synth_model.model.module.config.emb_dim , dropout_rate=synth_model.model.module.config.dropout_rate , num_layers=synth_model.model.module.config.num_encoder_layers , num_heads=synth_model.model.module.config.num_heads , d_kv=synth_model.model.module.config.head_dim , d_ff=synth_model.model.module.config.mlp_dim , feed_forward_proj="gated-gelu" , )
UpperCamelCase__ = TaFilmDecoder(
input_dims=synth_model.audio_codec.n_dims , targets_length=synth_model.sequence_length["targets_context"] , max_decoder_noise_time=synth_model.model.module.config.max_decoder_noise_time , d_model=synth_model.model.module.config.emb_dim , num_layers=synth_model.model.module.config.num_decoder_layers , num_heads=synth_model.model.module.config.num_heads , d_kv=synth_model.model.module.config.head_dim , d_ff=synth_model.model.module.config.mlp_dim , dropout_rate=synth_model.model.module.config.dropout_rate , )
UpperCamelCase__ = load_notes_encoder(ta_checkpoint["target"]["token_encoder"] , _lowerCAmelCase )
UpperCamelCase__ = load_continuous_encoder(ta_checkpoint["target"]["continuous_encoder"] , _lowerCAmelCase )
UpperCamelCase__ = load_decoder(ta_checkpoint["target"]["decoder"] , _lowerCAmelCase )
UpperCamelCase__ = OnnxRuntimeModel.from_pretrained("kashif/soundstream_mel_decoder" )
UpperCamelCase__ = SpectrogramDiffusionPipeline(
notes_encoder=_lowerCAmelCase , continuous_encoder=_lowerCAmelCase , decoder=_lowerCAmelCase , scheduler=_lowerCAmelCase , melgan=_lowerCAmelCase , )
if args.save:
pipe.save_pretrained(args.output_path )
if __name__ == "__main__":
a__ : Union[str, Any] = argparse.ArgumentParser()
parser.add_argument('--output_path', default=None, type=str, required=True, help='Path to the converted model.')
parser.add_argument(
'--save', default=True, type=bool, required=False, help='Whether to save the converted model or not.'
)
parser.add_argument(
'--checkpoint_path',
default=F"""{MODEL}/checkpoint_500000""",
type=str,
required=False,
help='Path to the original jax model checkpoint.',
)
a__ : List[str] = parser.parse_args()
main(args)
| 80 |
'''simple docstring'''
import argparse
import random
import joblib
import numpy as np
import torch
from igf.igf import (
SecondaryLearner,
collect_objective_set,
compute_perplexity,
generate_datasets,
load_gpta,
recopy_gpta,
set_seed,
train_secondary_learner,
)
from torch.utils.data import DataLoader, RandomSampler
from transformers import GPTaLMHeadModel
def _A ( _lowerCAmelCase=32 , _lowerCAmelCase=10 , _lowerCAmelCase=100 , _lowerCAmelCase=1_026 , _lowerCAmelCase=True , _lowerCAmelCase="data/tokenized_stories_train_wikitext103.jbl" , _lowerCAmelCase="igf_context_pairs.jbl" , ):
"""simple docstring"""
set_seed(3 )
# generate train_data and objective_set
__lowercase , __lowercase =generate_datasets(
_lowerCAmelCase , _lowerCAmelCase , number=_lowerCAmelCase , min_len=1_026 , trim=_lowerCAmelCase )
# keeps model same across runs
set_seed(4 )
# model, lm_optimizer, lm_scheduler = recopy_gpt2(model, device, max_steps) # store original model weights
# can we train on GPU?
__lowercase =torch.device('cuda:0' if torch.cuda.is_available() else 'cpu' )
# load pretrained model
__lowercase =load_gpta('gpt2' ).to(_lowerCAmelCase )
print('computing perplexity on objective set' )
__lowercase =compute_perplexity(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ).item()
print('perplexity on objective set:' , _lowerCAmelCase )
# collect igf pairs and save to file demo.jbl
collect_objective_set(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
# clean up, delete model and data we don't need anymore
del model, train_data, objective_set
torch.cuda.empty_cache()
def _A ( _lowerCAmelCase , _lowerCAmelCase=15 , _lowerCAmelCase=128 , _lowerCAmelCase=100 , _lowerCAmelCase="igf_model.pt" , ):
"""simple docstring"""
set_seed(42 )
# Load pre-trained model
__lowercase =GPTaLMHeadModel.from_pretrained('gpt2' )
# Initialize secondary learner to use embedding weights of model
__lowercase =SecondaryLearner(_lowerCAmelCase )
# Train secondary learner
__lowercase =train_secondary_learner(
_lowerCAmelCase , _lowerCAmelCase , max_epochs=_lowerCAmelCase , batch_size=_lowerCAmelCase , eval_freq=100 , igf_model_path=_lowerCAmelCase , )
del model, secondary_learner_train_data
torch.cuda.empty_cache()
return secondary_learner
def _A ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase=32 , _lowerCAmelCase=1_000 , _lowerCAmelCase=16 , _lowerCAmelCase=1.0 , _lowerCAmelCase=recopy_gpta , _lowerCAmelCase=None , _lowerCAmelCase=10 , _lowerCAmelCase="gpt2_finetuned.pt" , ):
"""simple docstring"""
__lowercase =torch.device('cuda:0' if torch.cuda.is_available() else 'cpu' )
__lowercase =RandomSampler(_lowerCAmelCase )
__lowercase =DataLoader(_lowerCAmelCase , sampler=_lowerCAmelCase )
__lowercase =max_steps // (len(_lowerCAmelCase )) + 1
__lowercase =0
__lowercase =torch.zeros((1, context_len) , dtype=torch.long , device=_lowerCAmelCase )
__lowercase , __lowercase , __lowercase =recopy_model(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
model.train()
if secondary_learner is not None:
secondary_learner.to(_lowerCAmelCase )
secondary_learner.eval()
__lowercase =[]
__lowercase =0
__lowercase =[]
__lowercase =[]
# Compute the performance of the transformer model at the beginning
__lowercase =compute_perplexity(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
test_perps.append(_lowerCAmelCase )
print('Test perplexity, step' , _lowerCAmelCase , ':' , _lowerCAmelCase )
for epoch in range(int(_lowerCAmelCase ) ):
for step, example in enumerate(_lowerCAmelCase ):
torch.cuda.empty_cache()
__lowercase =random.randint(0 , example.size(2 ) - context_len - 1 )
__lowercase =example[0, 0, start : start + context_len]
lm_optimizer.zero_grad()
__lowercase =model(_lowerCAmelCase , labels=_lowerCAmelCase )
__lowercase =True
if secondary_learner is not None:
__lowercase =secondary_learner.forward(
torch.tensor(_lowerCAmelCase , dtype=torch.long , device=_lowerCAmelCase ).unsqueeze(0 ) )[0].item()
observed_qs.append(float(_lowerCAmelCase ) )
# Here we implement the simple non-constant threshold for the predicted IG(X) value
# We will decay the selectivity of our secondary learner filter from
# 1 standard deviation above average to 1 below average after 10 batches.
if global_step == 10:
__lowercase =-1
if predicted_q < threshold:
__lowercase =False
# If we passed the filter, add the context to the batch!
if do_backprop:
contexts.append(np.array(context.cpu() ) )
__lowercase =outputs[0]
lm_loss.backward()
examples += 1
del outputs
# Once the batch is filled with enough contexts, backprop on the batch.
if examples == batch_size:
torch.cuda.empty_cache()
__lowercase =0
# Do LM backprop
torch.nn.utils.clip_grad_norm_(model.parameters() , 3.0 )
lm_optimizer.step()
lm_scheduler.step() # Update learning rate schedule
global_step += 1
# Compute the performance of the transformer model at this batch
if global_step % eval_interval == 0:
__lowercase =compute_perplexity(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
test_perps.append(_lowerCAmelCase )
print('Test perplexity, step' , _lowerCAmelCase , ':' , _lowerCAmelCase )
# Break out of the loop after 60 batches
if max_steps > 0 and global_step > 60:
break
if max_steps > 0 and global_step > 60:
break
# save finetuned transformer model
torch.save(model.state_dict() , _lowerCAmelCase )
torch.cuda.empty_cache()
# Do some cleaning up so we can reinitialize for the next run of this function
del lm_optimizer
del lm_scheduler
return model
def _A ( ):
"""simple docstring"""
__lowercase =argparse.ArgumentParser(description='Fine-tune a transformer model with IGF on a language modeling task' )
# Required parameters
parser.add_argument(
'--data_dir' , default=_lowerCAmelCase , type=_lowerCAmelCase , required=_lowerCAmelCase , help='The input data dir. Should contain data files for WikiText.' , )
parser.add_argument(
'--model_name_or_path' , default=_lowerCAmelCase , type=_lowerCAmelCase , required=_lowerCAmelCase , help='Path to pretrained model or model identifier from huggingface.co/models' , )
parser.add_argument(
'--data_file' , type=_lowerCAmelCase , default=_lowerCAmelCase , help=(
'A jbl file containing tokenized data which can be split as objective dataset, '
'train_dataset and test_dataset.'
) , )
parser.add_argument(
'--igf_data_file' , type=_lowerCAmelCase , default=_lowerCAmelCase , help='A jbl file containing the context and information gain pairs to train secondary learner.' , )
parser.add_argument(
'--output_dir' , default=_lowerCAmelCase , type=_lowerCAmelCase , required=_lowerCAmelCase , help='The output directory where the final fine-tuned model is stored.' , )
parser.add_argument(
'--tokenizer_name' , default=_lowerCAmelCase , type=_lowerCAmelCase , help='Pretrained tokenizer name or path if not the same as model_name' , )
parser.add_argument('--seed' , type=_lowerCAmelCase , default=_lowerCAmelCase , help='A seed for reproducible training.' )
parser.add_argument(
'--context_len' , default=32 , type=_lowerCAmelCase , help=(
'The maximum total input sequence length after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
) , )
parser.add_argument(
'--size_objective_set' , default=100 , type=_lowerCAmelCase , help='number of articles that are long enough to be used as our objective set' , )
parser.add_argument(
'--eval_freq' , default=100 , type=_lowerCAmelCase , help='secondary model evaluation is triggered at eval_freq' )
parser.add_argument('--max_steps' , default=1_000 , type=_lowerCAmelCase , help='To calculate training epochs' )
parser.add_argument(
'--secondary_learner_batch_size' , default=128 , type=_lowerCAmelCase , help='batch size of training data for secondary learner' , )
parser.add_argument(
'--batch_size' , default=16 , type=_lowerCAmelCase , help='batch size of training data of language model(gpt2) ' )
parser.add_argument(
'--eval_interval' , default=10 , type=_lowerCAmelCase , help=(
'decay the selectivity of our secondary learner filter from'
'1 standard deviation above average to 1 below average after 10 batches'
) , )
parser.add_argument(
'--number' , default=100 , type=_lowerCAmelCase , help='The number of examples split to be used as objective_set/test_data' )
parser.add_argument(
'--min_len' , default=1_026 , type=_lowerCAmelCase , help='The minimum length of the article to be used as objective set' )
parser.add_argument(
'--secondary_learner_max_epochs' , default=15 , type=_lowerCAmelCase , help='number of epochs to train secondary learner' )
parser.add_argument('--trim' , default=_lowerCAmelCase , type=_lowerCAmelCase , help='truncate the example if it exceeds context length' )
parser.add_argument(
'--threshold' , default=1.0 , type=_lowerCAmelCase , help=(
'The threshold value used by secondary learner to filter the train_data and allow only'
' informative data as input to the model'
) , )
parser.add_argument('--finetuned_model_name' , default='gpt2_finetuned.pt' , type=_lowerCAmelCase , help='finetuned_model_name' )
parser.add_argument(
'--recopy_model' , default=_lowerCAmelCase , type=_lowerCAmelCase , help='Reset the model to the original pretrained GPT-2 weights after each iteration' , )
# function calls
# Collecting *n* pairs of context and information gain(X, IG(X)) for training the secondary learner
generate_n_pairs(
context_len=32 , max_steps=10 , size_objective_set=100 , min_len=1_026 , trim=_lowerCAmelCase , data_file='data/tokenized_stories_train_wikitext103.jbl' , igf_data_file='igf_context_pairs.jbl' , )
# Load train data for secondary learner
__lowercase =joblib.load('data/IGF_values.jbl' )
# Train secondary learner
__lowercase =training_secondary_learner(
_lowerCAmelCase , secondary_learner_max_epochs=15 , secondary_learner_batch_size=128 , eval_freq=100 , igf_model_path='igf_model.pt' , )
# load pretrained gpt2 model
__lowercase =GPTaLMHeadModel.from_pretrained('gpt2' )
set_seed(42 )
# Generate train and test data to train and evaluate gpt2 model
__lowercase , __lowercase =generate_datasets(
context_len=32 , file='data/tokenized_stories_train_wikitext103.jbl' , number=100 , min_len=1_026 , trim=_lowerCAmelCase )
# fine-tuning of the gpt2 model using igf (Information Gain Filtration)
finetune(
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , context_len=32 , max_steps=1_000 , batch_size=16 , threshold=1.0 , recopy_model=_lowerCAmelCase , secondary_learner=_lowerCAmelCase , eval_interval=10 , finetuned_model_name='gpt2_finetuned.pt' , )
if __name__ == "__main__":
main()
| 166 | 0 |
from typing import List, Optional, Union
import numpy as np
from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ....file_utils import PaddingStrategy, TensorType
from ....utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
class __SCREAMING_SNAKE_CASE ( _a ):
snake_case : Any = ["""input_features""", """attention_mask"""]
def __init__( self , __lowerCAmelCase=80 , __lowerCAmelCase=16000 , __lowerCAmelCase=0.0 , __lowerCAmelCase=10 , __lowerCAmelCase=25 , __lowerCAmelCase="hamming_window" , __lowerCAmelCase=32768.0 , __lowerCAmelCase=0.97 , __lowerCAmelCase=1.0 , __lowerCAmelCase=True , __lowerCAmelCase=True , __lowerCAmelCase=False , **__lowerCAmelCase , ):
super().__init__(feature_size=__lowerCAmelCase , sampling_rate=__lowerCAmelCase , padding_value=__lowerCAmelCase , **__lowerCAmelCase )
UpperCamelCase__ = feature_size
UpperCamelCase__ = sampling_rate
UpperCamelCase__ = padding_value
UpperCamelCase__ = hop_length
UpperCamelCase__ = win_length
UpperCamelCase__ = frame_signal_scale
UpperCamelCase__ = preemphasis_coeff
UpperCamelCase__ = mel_floor
UpperCamelCase__ = normalize_means
UpperCamelCase__ = normalize_vars
UpperCamelCase__ = win_function
UpperCamelCase__ = return_attention_mask
UpperCamelCase__ = win_length * sampling_rate // 1000
UpperCamelCase__ = hop_length * sampling_rate // 1000
UpperCamelCase__ = optimal_fft_length(self.sample_size )
UpperCamelCase__ = (self.n_fft // 2) + 1
def _lowerCamelCase ( self , __lowerCAmelCase ):
if self.win_function == "hamming_window":
UpperCamelCase__ = window_function(window_length=self.sample_size , name=self.win_function , periodic=__lowerCAmelCase )
else:
UpperCamelCase__ = window_function(window_length=self.sample_size , name=self.win_function )
UpperCamelCase__ = mel_filter_bank(
num_frequency_bins=self.n_freqs , num_mel_filters=self.feature_size , min_frequency=0.0 , max_frequency=self.sampling_rate / 2.0 , sampling_rate=self.sampling_rate , )
UpperCamelCase__ = spectrogram(
one_waveform * self.frame_signal_scale , window=__lowerCAmelCase , frame_length=self.sample_size , hop_length=self.sample_stride , fft_length=self.n_fft , center=__lowerCAmelCase , preemphasis=self.preemphasis_coeff , mel_filters=__lowerCAmelCase , mel_floor=self.mel_floor , log_mel="""log""" , )
return msfc_features.T
def _lowerCamelCase ( self , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
# make sure we normalize float32 arrays
if self.normalize_means:
UpperCamelCase__ = x[:input_length].mean(axis=0 )
UpperCamelCase__ = np.subtract(__lowerCAmelCase , __lowerCAmelCase )
if self.normalize_vars:
UpperCamelCase__ = x[:input_length].std(axis=0 )
UpperCamelCase__ = np.divide(__lowerCAmelCase , __lowerCAmelCase )
if input_length < x.shape[0]:
UpperCamelCase__ = padding_value
# make sure array is in float32
UpperCamelCase__ = x.astype(np.floataa )
return x
def _lowerCamelCase ( self , __lowerCAmelCase , __lowerCAmelCase = None ):
UpperCamelCase__ = attention_mask.sum(-1 ) if attention_mask is not None else [x.shape[0] for x in input_features]
return [self._normalize_one(__lowerCAmelCase , __lowerCAmelCase , self.padding_value ) for x, n in zip(__lowerCAmelCase , __lowerCAmelCase )]
def __call__( self , __lowerCAmelCase , __lowerCAmelCase = False , __lowerCAmelCase = None , __lowerCAmelCase = False , __lowerCAmelCase = None , __lowerCAmelCase = None , __lowerCAmelCase = None , __lowerCAmelCase = None , **__lowerCAmelCase , ):
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
f"""The model corresponding to this feature extractor: {self} was trained using a sampling rate of"""
f""" {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with"""
f""" {self.sampling_rate} and not {sampling_rate}.""" )
else:
logger.warning(
"""It is strongly recommended to pass the ``sampling_rate`` argument to this function. """
"""Failing to do so can result in silent errors that might be hard to debug.""" )
UpperCamelCase__ = isinstance(__lowerCAmelCase , np.ndarray ) and len(raw_speech.shape ) > 1
if is_batched_numpy and len(raw_speech.shape ) > 2:
raise ValueError(f"""Only mono-channel audio is supported for input to {self}""" )
UpperCamelCase__ = is_batched_numpy or (
isinstance(__lowerCAmelCase , (list, tuple) ) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
UpperCamelCase__ = [np.asarray(__lowerCAmelCase , dtype=np.floataa ) for speech in raw_speech]
elif not is_batched and not isinstance(__lowerCAmelCase , np.ndarray ):
UpperCamelCase__ = np.asarray(__lowerCAmelCase , dtype=np.floataa )
elif isinstance(__lowerCAmelCase , np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
UpperCamelCase__ = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
UpperCamelCase__ = [raw_speech]
# extract fbank features
UpperCamelCase__ = [self._extract_mfsc_features(__lowerCAmelCase ) for one_waveform in raw_speech]
# convert into correct format for padding
UpperCamelCase__ = BatchFeature({"""input_features""": features} )
UpperCamelCase__ = self.pad(
__lowerCAmelCase , padding=__lowerCAmelCase , max_length=__lowerCAmelCase , truncation=__lowerCAmelCase , pad_to_multiple_of=__lowerCAmelCase , return_attention_mask=__lowerCAmelCase , **__lowerCAmelCase , )
# make sure list is in array format
UpperCamelCase__ = padded_inputs.get("""input_features""" )
if isinstance(input_features[0] , __lowerCAmelCase ):
UpperCamelCase__ = [np.asarray(__lowerCAmelCase , dtype=np.floataa ) for feature in input_features]
UpperCamelCase__ = padded_inputs.get("""attention_mask""" )
if attention_mask is not None:
UpperCamelCase__ = [np.asarray(__lowerCAmelCase , dtype=np.intaa ) for array in attention_mask]
if self.normalize_means or self.normalize_vars:
UpperCamelCase__ = (
np.array(__lowerCAmelCase , dtype=np.intaa )
if self._get_padding_strategies(__lowerCAmelCase , max_length=__lowerCAmelCase ) is not PaddingStrategy.DO_NOT_PAD
and padding
else None
)
UpperCamelCase__ = self.normalize(
padded_inputs["""input_features"""] , attention_mask=__lowerCAmelCase )
if return_tensors is not None:
UpperCamelCase__ = padded_inputs.convert_to_tensors(__lowerCAmelCase )
return padded_inputs
| 368 |
import logging
from transformers.configuration_utils import PretrainedConfig
UpperCamelCase__ = logging.getLogger(__name__)
class __SCREAMING_SNAKE_CASE ( _a ):
snake_case : Optional[Any] = """masked_bert"""
def __init__( self , __lowerCAmelCase=30522 , __lowerCAmelCase=768 , __lowerCAmelCase=12 , __lowerCAmelCase=12 , __lowerCAmelCase=3072 , __lowerCAmelCase="gelu" , __lowerCAmelCase=0.1 , __lowerCAmelCase=0.1 , __lowerCAmelCase=512 , __lowerCAmelCase=2 , __lowerCAmelCase=0.02 , __lowerCAmelCase=1E-12 , __lowerCAmelCase=0 , __lowerCAmelCase="topK" , __lowerCAmelCase="constant" , __lowerCAmelCase=0.0 , **__lowerCAmelCase , ):
super().__init__(pad_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__ = pruning_method
UpperCamelCase__ = mask_init
UpperCamelCase__ = mask_scale
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