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"""simple docstring"""
import unittest
from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers.models.trocr.modeling_trocr import TrOCRDecoder, TrOCRForCausalLM
@require_torch
class lowerCamelCase__ :
def __init__( self : List[Any] , _lowercase : Dict , _lowercase : int=99 , _lowercase : List[Any]=13 , _lowercase : str=16 , _lowercase : Union[str, Any]=7 , _lowercase : str=True , _lowercase : Optional[Any]=True , _lowercase : Any=True , _lowercase : int=False , _lowercase : int=True , _lowercase : Optional[Any]=2 , _lowercase : Dict=32 , _lowercase : str=4 , _lowercase : List[str]=4 , _lowercase : str=30 , _lowercase : List[str]=0 , _lowercase : List[Any]=1 , _lowercase : int=2 , _lowercase : List[str]=None , ):
A = parent
A = batch_size
A = decoder_seq_length
# For common tests
A = self.decoder_seq_length
A = is_training
A = use_attention_mask
A = use_labels
A = vocab_size
A = d_model
A = d_model
A = decoder_layers
A = decoder_layers
A = decoder_ffn_dim
A = decoder_attention_heads
A = decoder_attention_heads
A = eos_token_id
A = bos_token_id
A = pad_token_id
A = decoder_start_token_id
A = use_cache
A = max_position_embeddings
A = None
A = decoder_seq_length
A = 2
A = 1
def __a ( self : Optional[Any] ):
A = ids_tensor([self.batch_size, self.decoder_seq_length] , self.vocab_size )
A = None
if self.use_attention_mask:
A = ids_tensor([self.batch_size, self.decoder_seq_length] , vocab_size=2 )
A = None
if self.use_labels:
A = ids_tensor([self.batch_size, self.decoder_seq_length] , self.vocab_size )
A = TrOCRConfig(
vocab_size=self.vocab_size , d_model=self.d_model , decoder_layers=self.decoder_layers , decoder_ffn_dim=self.decoder_ffn_dim , decoder_attention_heads=self.decoder_attention_heads , eos_token_id=self.eos_token_id , bos_token_id=self.bos_token_id , use_cache=self.use_cache , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , max_position_embeddings=self.max_position_embeddings , )
return (config, input_ids, attention_mask, lm_labels)
def __a ( self : List[str] , _lowercase : Any , _lowercase : List[Any] , _lowercase : Any , _lowercase : int , ):
A = True
A = TrOCRDecoder(config=_lowercase ).to(_lowercase ).eval()
A = input_ids[:2]
input_ids[input_ids == 0] += 1
# first forward pass
A = model(_lowercase , use_cache=_lowercase )
A = model(_lowercase )
A = model(_lowercase , use_cache=_lowercase )
self.parent.assertTrue(len(_lowercase ) == len(_lowercase ) )
self.parent.assertTrue(len(_lowercase ) == len(_lowercase ) + 1 )
A = outputs['past_key_values']
# create hypothetical next token and extent to next_input_ids
A = ids_tensor((2, 1) , config.vocab_size - 1 ) + 1
# append to next input_ids and
A = torch.cat([input_ids, next_tokens] , dim=-1 )
A = model(_lowercase )['last_hidden_state']
A = model(_lowercase , past_key_values=_lowercase )['last_hidden_state']
# select random slice
A = ids_tensor((1,) , output_from_past.shape[-1] ).item()
A = output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
A = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
assert torch.allclose(_lowercase , _lowercase , atol=1e-3 )
def __a ( self : Tuple ):
A = self.prepare_config_and_inputs()
A , A , A , A = config_and_inputs
A = {'input_ids': input_ids, 'attention_mask': attention_mask}
return config, inputs_dict
@require_torch
class lowerCamelCase__ ( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , unittest.TestCase ):
lowerCAmelCase = (TrOCRDecoder, TrOCRForCausalLM) if is_torch_available() else ()
lowerCAmelCase = (TrOCRForCausalLM,) if is_torch_available() else ()
lowerCAmelCase = {"""text-generation""": TrOCRForCausalLM} if is_torch_available() else {}
lowerCAmelCase = True
lowerCAmelCase = False
def __a ( self : Union[str, Any] ):
A = TrOCRStandaloneDecoderModelTester(self , is_training=_lowercase )
A = ConfigTester(self , config_class=_lowercase )
def __a ( self : Union[str, Any] ):
pass
def __a ( self : Union[str, Any] ):
pass
def __a ( self : List[Any] ):
pass
def __a ( self : List[str] ):
self.config_tester.run_common_tests()
def __a ( self : List[Any] ):
A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past(*_lowercase )
def __a ( self : Optional[int] ):
return
@unittest.skip('The model doesn\'t support left padding' ) # and it's not used enough to be worth fixing :)
def __a ( self : Union[str, Any] ):
pass
| 690 |
"""simple docstring"""
import os
import sys
UpperCamelCase : Optional[int] = os.path.join(os.path.dirname(__file__), "src")
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
AutoModelForSequenceClassification,
AutoTokenizer,
add_start_docstrings,
)
UpperCamelCase : Dict = [
"torch",
"numpy",
"tokenizers",
"filelock",
"requests",
"tqdm",
"regex",
"sentencepiece",
"sacremoses",
"importlib_metadata",
"huggingface_hub",
]
@add_start_docstrings(AutoConfig.__doc__ )
def __snake_case ( *UpperCamelCase__ , **UpperCamelCase__ ) -> Dict:
"""simple docstring"""
return AutoConfig.from_pretrained(*UpperCamelCase__ , **UpperCamelCase__ )
@add_start_docstrings(AutoTokenizer.__doc__ )
def __snake_case ( *UpperCamelCase__ , **UpperCamelCase__ ) -> List[str]:
"""simple docstring"""
return AutoTokenizer.from_pretrained(*UpperCamelCase__ , **UpperCamelCase__ )
@add_start_docstrings(AutoModel.__doc__ )
def __snake_case ( *UpperCamelCase__ , **UpperCamelCase__ ) -> str:
"""simple docstring"""
return AutoModel.from_pretrained(*UpperCamelCase__ , **UpperCamelCase__ )
@add_start_docstrings(AutoModelForCausalLM.__doc__ )
def __snake_case ( *UpperCamelCase__ , **UpperCamelCase__ ) -> List[Any]:
"""simple docstring"""
return AutoModelForCausalLM.from_pretrained(*UpperCamelCase__ , **UpperCamelCase__ )
@add_start_docstrings(AutoModelForMaskedLM.__doc__ )
def __snake_case ( *UpperCamelCase__ , **UpperCamelCase__ ) -> Optional[Any]:
"""simple docstring"""
return AutoModelForMaskedLM.from_pretrained(*UpperCamelCase__ , **UpperCamelCase__ )
@add_start_docstrings(AutoModelForSequenceClassification.__doc__ )
def __snake_case ( *UpperCamelCase__ , **UpperCamelCase__ ) -> List[str]:
"""simple docstring"""
return AutoModelForSequenceClassification.from_pretrained(*UpperCamelCase__ , **UpperCamelCase__ )
@add_start_docstrings(AutoModelForQuestionAnswering.__doc__ )
def __snake_case ( *UpperCamelCase__ , **UpperCamelCase__ ) -> int:
"""simple docstring"""
return AutoModelForQuestionAnswering.from_pretrained(*UpperCamelCase__ , **UpperCamelCase__ )
| 690 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCamelCase : Optional[int] = logging.get_logger(__name__)
UpperCamelCase : Dict = {
"junnyu/roformer_chinese_small": "https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json",
"junnyu/roformer_chinese_base": "https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json",
"junnyu/roformer_chinese_char_small": (
"https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json"
),
"junnyu/roformer_chinese_char_base": (
"https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json"
),
"junnyu/roformer_small_discriminator": (
"https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json"
),
"junnyu/roformer_small_generator": (
"https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json"
),
# See all RoFormer models at https://huggingface.co/models?filter=roformer
}
class lowerCamelCase__ ( UpperCAmelCase_ ):
lowerCAmelCase = """roformer"""
def __init__( self : Optional[Any] , _lowercase : Any=50_000 , _lowercase : List[Any]=None , _lowercase : Tuple=768 , _lowercase : Optional[int]=12 , _lowercase : List[str]=12 , _lowercase : Any=3_072 , _lowercase : int="gelu" , _lowercase : List[str]=0.1 , _lowercase : str=0.1 , _lowercase : Optional[int]=1_536 , _lowercase : str=2 , _lowercase : Optional[Any]=0.0_2 , _lowercase : List[Any]=1e-12 , _lowercase : Optional[Any]=0 , _lowercase : Optional[int]=False , _lowercase : Union[str, Any]=True , **_lowercase : str , ):
super().__init__(pad_token_id=_lowercase , **_lowercase )
A = vocab_size
A = hidden_size if embedding_size is None else embedding_size
A = hidden_size
A = num_hidden_layers
A = num_attention_heads
A = hidden_act
A = intermediate_size
A = hidden_dropout_prob
A = attention_probs_dropout_prob
A = max_position_embeddings
A = type_vocab_size
A = initializer_range
A = layer_norm_eps
A = rotary_value
A = use_cache
class lowerCamelCase__ ( UpperCAmelCase_ ):
@property
def __a ( self : Dict ):
if self.task == "multiple-choice":
A = {0: 'batch', 1: 'choice', 2: 'sequence'}
else:
A = {0: 'batch', 1: 'sequence'}
A = {0: 'batch', 1: 'sequence'}
return OrderedDict(
[
('input_ids', dynamic_axis),
('attention_mask', dynamic_axis),
('token_type_ids', dynamic_axis),
] )
| 690 |
"""simple docstring"""
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging
if is_torch_available():
import torch
UpperCamelCase : List[str] = logging.get_logger(__name__)
class lowerCamelCase__ ( UpperCAmelCase_ ):
lowerCAmelCase = ["""pixel_values"""]
def __init__( self : Tuple , _lowercase : bool = True , _lowercase : Optional[Dict[str, int]] = None , _lowercase : PILImageResampling = PILImageResampling.BILINEAR , _lowercase : bool = True , _lowercase : Dict[str, int] = None , _lowercase : bool = True , _lowercase : Union[int, float] = 1 / 255 , _lowercase : bool = True , _lowercase : Optional[Union[float, List[float]]] = None , _lowercase : Optional[Union[float, List[float]]] = None , **_lowercase : List[str] , ):
super().__init__(**_lowercase )
A = size if size is not None else {'shortest_edge': 256}
A = get_size_dict(_lowercase , default_to_square=_lowercase )
A = crop_size if crop_size is not None else {'height': 224, 'width': 224}
A = get_size_dict(_lowercase , param_name='crop_size' )
A = do_resize
A = size
A = resample
A = do_center_crop
A = crop_size
A = do_rescale
A = rescale_factor
A = do_normalize
A = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
A = image_std if image_std is not None else IMAGENET_STANDARD_STD
def __a ( self : Any , _lowercase : np.ndarray , _lowercase : Dict[str, int] , _lowercase : PILImageResampling = PILImageResampling.BICUBIC , _lowercase : Optional[Union[str, ChannelDimension]] = None , **_lowercase : Tuple , ):
A = get_size_dict(_lowercase , default_to_square=_lowercase )
if "shortest_edge" not in size:
raise ValueError(f'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}' )
A = get_resize_output_image_size(_lowercase , size=size['shortest_edge'] , default_to_square=_lowercase )
return resize(_lowercase , size=_lowercase , resample=_lowercase , data_format=_lowercase , **_lowercase )
def __a ( self : List[Any] , _lowercase : np.ndarray , _lowercase : Dict[str, int] , _lowercase : Optional[Union[str, ChannelDimension]] = None , **_lowercase : Optional[int] , ):
A = get_size_dict(_lowercase )
if "height" not in size or "width" not in size:
raise ValueError(f'The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}' )
return center_crop(_lowercase , size=(size['height'], size['width']) , data_format=_lowercase , **_lowercase )
def __a ( self : int , _lowercase : np.ndarray , _lowercase : float , _lowercase : Optional[Union[str, ChannelDimension]] = None , **_lowercase : Tuple ):
return rescale(_lowercase , scale=_lowercase , data_format=_lowercase , **_lowercase )
def __a ( self : int , _lowercase : np.ndarray , _lowercase : Union[float, List[float]] , _lowercase : Union[float, List[float]] , _lowercase : Optional[Union[str, ChannelDimension]] = None , **_lowercase : str , ):
return normalize(_lowercase , mean=_lowercase , std=_lowercase , data_format=_lowercase , **_lowercase )
def __a ( self : Any , _lowercase : ImageInput , _lowercase : Optional[bool] = None , _lowercase : Dict[str, int] = None , _lowercase : PILImageResampling = None , _lowercase : bool = None , _lowercase : Dict[str, int] = None , _lowercase : Optional[bool] = None , _lowercase : Optional[float] = None , _lowercase : Optional[bool] = None , _lowercase : Optional[Union[float, List[float]]] = None , _lowercase : Optional[Union[float, List[float]]] = None , _lowercase : Optional[Union[str, TensorType]] = None , _lowercase : Union[str, ChannelDimension] = ChannelDimension.FIRST , **_lowercase : Any , ):
A = do_resize if do_resize is not None else self.do_resize
A = size if size is not None else self.size
A = get_size_dict(_lowercase , default_to_square=_lowercase )
A = resample if resample is not None else self.resample
A = do_center_crop if do_center_crop is not None else self.do_center_crop
A = crop_size if crop_size is not None else self.crop_size
A = get_size_dict(_lowercase , param_name='crop_size' )
A = do_rescale if do_rescale is not None else self.do_rescale
A = rescale_factor if rescale_factor is not None else self.rescale_factor
A = do_normalize if do_normalize is not None else self.do_normalize
A = image_mean if image_mean is not None else self.image_mean
A = image_std if image_std is not None else self.image_std
A = make_list_of_images(_lowercase )
if not valid_images(_lowercase ):
raise ValueError(
'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
'torch.Tensor, tf.Tensor or jax.ndarray.' )
if do_resize and size is None:
raise ValueError('Size must be specified if do_resize is True.' )
if do_center_crop and crop_size is None:
raise ValueError('Crop size must be specified if do_center_crop is True.' )
if do_rescale and rescale_factor is None:
raise ValueError('Rescale factor must be specified if do_rescale is True.' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('Image mean and std must be specified if do_normalize is True.' )
# All transformations expect numpy arrays.
A = [to_numpy_array(_lowercase ) for image in images]
if do_resize:
A = [self.resize(image=_lowercase , size=_lowercase , resample=_lowercase ) for image in images]
if do_center_crop:
A = [self.center_crop(image=_lowercase , size=_lowercase ) for image in images]
if do_rescale:
A = [self.rescale(image=_lowercase , scale=_lowercase ) for image in images]
if do_normalize:
A = [self.normalize(image=_lowercase , mean=_lowercase , std=_lowercase ) for image in images]
A = [to_channel_dimension_format(_lowercase , _lowercase ) for image in images]
A = {'pixel_values': images}
return BatchFeature(data=_lowercase , tensor_type=_lowercase )
def __a ( self : int , _lowercase : List[str] , _lowercase : List[Tuple] = None ):
A = outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(_lowercase ) != len(_lowercase ):
raise ValueError(
'Make sure that you pass in as many target sizes as the batch dimension of the logits' )
if is_torch_tensor(_lowercase ):
A = target_sizes.numpy()
A = []
for idx in range(len(_lowercase ) ):
A = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) , size=target_sizes[idx] , mode='bilinear' , align_corners=_lowercase )
A = resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(_lowercase )
else:
A = logits.argmax(dim=1 )
A = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation
| 690 | 1 |
"""simple docstring"""
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 lowerCamelCase__ ( unittest.TestCase ):
@property
def __a ( self : Optional[Any] ):
torch.manual_seed(0 )
A = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=('DownBlock2D', 'AttnDownBlock2D') , up_block_types=('AttnUpBlock2D', 'UpBlock2D') , )
return model
def __a ( self : Dict ):
A = self.dummy_uncond_unet
A = KarrasVeScheduler()
A = KarrasVePipeline(unet=_lowercase , scheduler=_lowercase )
pipe.to(_lowercase )
pipe.set_progress_bar_config(disable=_lowercase )
A = torch.manual_seed(0 )
A = pipe(num_inference_steps=2 , generator=_lowercase , output_type='numpy' ).images
A = torch.manual_seed(0 )
A = pipe(num_inference_steps=2 , generator=_lowercase , output_type='numpy' , return_dict=_lowercase )[0]
A = image[0, -3:, -3:, -1]
A = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
A = 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 lowerCamelCase__ ( unittest.TestCase ):
def __a ( self : Optional[Any] ):
A = 'google/ncsnpp-celebahq-256'
A = UNetaDModel.from_pretrained(_lowercase )
A = KarrasVeScheduler()
A = KarrasVePipeline(unet=_lowercase , scheduler=_lowercase )
pipe.to(_lowercase )
pipe.set_progress_bar_config(disable=_lowercase )
A = torch.manual_seed(0 )
A = pipe(num_inference_steps=20 , generator=_lowercase , output_type='numpy' ).images
A = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
A = np.array([0.5_7_8, 0.5_8_1_1, 0.5_9_2_4, 0.5_8_0_9, 0.5_8_7, 0.5_8_8_6, 0.5_8_6_1, 0.5_8_0_2, 0.5_8_6] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
| 690 |
"""simple docstring"""
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def __snake_case ( UpperCamelCase__ = "laptop" ) -> DataFrame:
"""simple docstring"""
A = f'https://www.amazon.in/laptop/s?k={product}'
A = {
'User-Agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36\n (KHTML, like Gecko)Chrome/44.0.2403.157 Safari/537.36',
'Accept-Language': 'en-US, en;q=0.5',
}
A = BeautifulSoup(requests.get(UpperCamelCase__ , headers=UpperCamelCase__ ).text )
# Initialize a Pandas dataframe with the column titles
A = DataFrame(
columns=[
'Product Title',
'Product Link',
'Current Price of the product',
'Product Rating',
'MRP of the product',
'Discount',
] )
# Loop through each entry and store them in the dataframe
for item, _ in zip_longest(
soup.find_all(
'div' , attrs={'class': 's-result-item', 'data-component-type': 's-search-result'} , ) , soup.find_all('div' , attrs={'class': 'a-row a-size-base a-color-base'} ) , ):
try:
A = item.ha.text
A = 'https://www.amazon.in/' + item.ha.a['href']
A = item.find('span' , attrs={'class': 'a-offscreen'} ).text
try:
A = item.find('span' , attrs={'class': 'a-icon-alt'} ).text
except AttributeError:
A = 'Not available'
try:
A = (
'₹'
+ item.find(
'span' , attrs={'class': 'a-price a-text-price'} ).text.split('₹' )[1]
)
except AttributeError:
A = ''
try:
A = float(
(
(
float(product_mrp.strip('₹' ).replace(',' , '' ) )
- float(product_price.strip('₹' ).replace(',' , '' ) )
)
/ float(product_mrp.strip('₹' ).replace(',' , '' ) )
)
* 100 )
except ValueError:
A = float('nan' )
except AttributeError:
pass
A = [
product_title,
product_link,
product_price,
product_rating,
product_mrp,
discount,
]
A = ' '
A = ' '
data_frame.index += 1
return data_frame
if __name__ == "__main__":
UpperCamelCase : Any = "headphones"
get_amazon_product_data(product).to_csv(F"""Amazon Product Data for {product}.csv""")
| 690 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCamelCase : Optional[Any] = {
"configuration_canine": ["CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP", "CanineConfig"],
"tokenization_canine": ["CanineTokenizer"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase : List[Any] = [
"CANINE_PRETRAINED_MODEL_ARCHIVE_LIST",
"CanineForMultipleChoice",
"CanineForQuestionAnswering",
"CanineForSequenceClassification",
"CanineForTokenClassification",
"CanineLayer",
"CanineModel",
"CaninePreTrainedModel",
"load_tf_weights_in_canine",
]
if TYPE_CHECKING:
from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
from .tokenization_canine import CanineTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_canine import (
CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
CanineForMultipleChoice,
CanineForQuestionAnswering,
CanineForSequenceClassification,
CanineForTokenClassification,
CanineLayer,
CanineModel,
CaninePreTrainedModel,
load_tf_weights_in_canine,
)
else:
import sys
UpperCamelCase : Optional[Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 690 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import DonutImageProcessor
class lowerCamelCase__ ( unittest.TestCase ):
def __init__( self : List[str] , _lowercase : Optional[Any] , _lowercase : int=7 , _lowercase : List[str]=3 , _lowercase : Tuple=18 , _lowercase : Dict=30 , _lowercase : Any=400 , _lowercase : int=True , _lowercase : List[Any]=None , _lowercase : Tuple=True , _lowercase : List[Any]=False , _lowercase : str=True , _lowercase : List[str]=True , _lowercase : int=[0.5, 0.5, 0.5] , _lowercase : Optional[int]=[0.5, 0.5, 0.5] , ):
A = parent
A = batch_size
A = num_channels
A = image_size
A = min_resolution
A = max_resolution
A = do_resize
A = size if size is not None else {'height': 18, 'width': 20}
A = do_thumbnail
A = do_align_axis
A = do_pad
A = do_normalize
A = image_mean
A = image_std
def __a ( self : Any ):
return {
"do_resize": self.do_resize,
"size": self.size,
"do_thumbnail": self.do_thumbnail,
"do_align_long_axis": self.do_align_axis,
"do_pad": self.do_pad,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
}
@require_torch
@require_vision
class lowerCamelCase__ ( UpperCAmelCase_ , unittest.TestCase ):
lowerCAmelCase = DonutImageProcessor if is_vision_available() else None
def __a ( self : List[str] ):
A = DonutImageProcessingTester(self )
@property
def __a ( self : int ):
return self.image_processor_tester.prepare_image_processor_dict()
def __a ( self : Union[str, Any] ):
A = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(_lowercase , 'do_resize' ) )
self.assertTrue(hasattr(_lowercase , 'size' ) )
self.assertTrue(hasattr(_lowercase , 'do_thumbnail' ) )
self.assertTrue(hasattr(_lowercase , 'do_align_long_axis' ) )
self.assertTrue(hasattr(_lowercase , 'do_pad' ) )
self.assertTrue(hasattr(_lowercase , 'do_normalize' ) )
self.assertTrue(hasattr(_lowercase , 'image_mean' ) )
self.assertTrue(hasattr(_lowercase , 'image_std' ) )
def __a ( self : int ):
A = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'height': 18, 'width': 20} )
A = self.image_processing_class.from_dict(self.image_processor_dict , size=42 )
self.assertEqual(image_processor.size , {'height': 42, 'width': 42} )
# Previous config had dimensions in (width, height) order
A = self.image_processing_class.from_dict(self.image_processor_dict , size=(42, 84) )
self.assertEqual(image_processor.size , {'height': 84, 'width': 42} )
def __a ( self : Any ):
pass
@is_flaky()
def __a ( self : int ):
# Initialize image_processing
A = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
A = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowercase )
for image in image_inputs:
self.assertIsInstance(_lowercase , Image.Image )
# Test not batched input
A = 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 = image_processing(_lowercase , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
) , )
@is_flaky()
def __a ( self : List[str] ):
# Initialize image_processing
A = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
A = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowercase , numpify=_lowercase )
for image in image_inputs:
self.assertIsInstance(_lowercase , np.ndarray )
# Test not batched input
A = 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 = image_processing(_lowercase , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
) , )
@is_flaky()
def __a ( self : List[Any] ):
# Initialize image_processing
A = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
A = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowercase , torchify=_lowercase )
for image in image_inputs:
self.assertIsInstance(_lowercase , torch.Tensor )
# Test not batched input
A = 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 = image_processing(_lowercase , 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'],
) , )
| 690 | 1 |
"""simple docstring"""
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
UpperCamelCase : Optional[Any] = logging.get_logger(__name__) # pylint: disable=invalid-name
def __snake_case ( UpperCamelCase__ ) -> Union[str, Any]:
"""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 ):
A = [image]
if isinstance(image[0] , PIL.Image.Image ):
A , A = image[0].size
A , A = (x - x % 8 for x in (w, h)) # resize to integer multiple of 8
A = [np.array(i.resize((w, h) , resample=PIL_INTERPOLATION['lanczos'] ) )[None, :] for i in image]
A = np.concatenate(UpperCamelCase__ , axis=0 )
A = np.array(UpperCamelCase__ ).astype(np.floataa ) / 2_5_5.0
A = image.transpose(0 , 3 , 1 , 2 )
A = 2.0 * image - 1.0
A = torch.from_numpy(UpperCamelCase__ )
elif isinstance(image[0] , torch.Tensor ):
A = torch.cat(UpperCamelCase__ , dim=0 )
return image
def __snake_case ( UpperCamelCase__ ) -> Any:
"""simple docstring"""
if isinstance(UpperCamelCase__ , torch.Tensor ):
return mask
elif isinstance(UpperCamelCase__ , PIL.Image.Image ):
A = [mask]
if isinstance(mask[0] , PIL.Image.Image ):
A , A = mask[0].size
A , A = (x - x % 32 for x in (w, h)) # resize to integer multiple of 32
A = [np.array(m.convert('L' ).resize((w, h) , resample=PIL_INTERPOLATION['nearest'] ) )[None, :] for m in mask]
A = np.concatenate(UpperCamelCase__ , axis=0 )
A = mask.astype(np.floataa ) / 2_5_5.0
A = 0
A = 1
A = torch.from_numpy(UpperCamelCase__ )
elif isinstance(mask[0] , torch.Tensor ):
A = torch.cat(UpperCamelCase__ , dim=0 )
return mask
class lowerCamelCase__ ( UpperCAmelCase_ ):
lowerCAmelCase = 42
lowerCAmelCase = 42
def __init__( self : Optional[Any] , _lowercase : Any , _lowercase : List[Any] ):
super().__init__()
self.register_modules(unet=_lowercase , scheduler=_lowercase )
@torch.no_grad()
def __call__( self : Optional[Any] , _lowercase : Union[torch.Tensor, PIL.Image.Image] , _lowercase : Union[torch.Tensor, PIL.Image.Image] , _lowercase : int = 250 , _lowercase : float = 0.0 , _lowercase : int = 10 , _lowercase : int = 10 , _lowercase : Optional[Union[torch.Generator, List[torch.Generator]]] = None , _lowercase : Optional[str] = "pil" , _lowercase : bool = True , ):
A = image
A = _preprocess_image(_lowercase )
A = original_image.to(device=self.device , dtype=self.unet.dtype )
A = _preprocess_mask(_lowercase )
A = mask_image.to(device=self.device , dtype=self.unet.dtype )
A = original_image.shape[0]
# sample gaussian noise to begin the loop
if isinstance(_lowercase , _lowercase ) and len(_lowercase ) != batch_size:
raise ValueError(
f'You have passed a list of generators of length {len(_lowercase )}, but requested an effective batch'
f' size of {batch_size}. Make sure the batch size matches the length of the generators.' )
A = original_image.shape
A = randn_tensor(_lowercase , generator=_lowercase , device=self.device , dtype=self.unet.dtype )
# set step values
self.scheduler.set_timesteps(_lowercase , _lowercase , _lowercase , self.device )
A = eta
A = self.scheduler.timesteps[0] + 1
A = generator[0] if isinstance(_lowercase , _lowercase ) else generator
for i, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
if t < t_last:
# predict the noise residual
A = self.unet(_lowercase , _lowercase ).sample
# compute previous image: x_t -> x_t-1
A = self.scheduler.step(_lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ).prev_sample
else:
# compute the reverse: x_t-1 -> x_t
A = self.scheduler.undo_step(_lowercase , _lowercase , _lowercase )
A = t
A = (image / 2 + 0.5).clamp(0 , 1 )
A = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
A = self.numpy_to_pil(_lowercase )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=_lowercase )
| 690 |
"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class lowerCamelCase__ :
def __init__( self : Optional[Any] , _lowercase : int=2 , _lowercase : Optional[Any]=3 , _lowercase : Any=64 , _lowercase : Tuple=None ):
A = np.random.default_rng(_lowercase )
A = length
A = rng.normal(size=(length,) ).astype(np.floataa )
A = a * self.x + b + rng.normal(scale=0.1 , size=(length,) ).astype(np.floataa )
def __len__( self : str ):
return self.length
def __getitem__( self : List[str] , _lowercase : int ):
return {"x": self.x[i], "y": self.y[i]}
class lowerCamelCase__ ( torch.nn.Module ):
def __init__( self : Optional[int] , _lowercase : Any=0 , _lowercase : List[Any]=0 , _lowercase : Optional[int]=False ):
super().__init__()
A = torch.nn.Parameter(torch.tensor([2, 3] ).float() )
A = torch.nn.Parameter(torch.tensor([2, 3] ).float() )
A = True
def __a ( self : Optional[Any] , _lowercase : str=None ):
if self.first_batch:
print(f'Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}' )
A = False
return x * self.a[0] + self.b[0]
class lowerCamelCase__ ( torch.nn.Module ):
def __init__( self : Optional[Any] , _lowercase : Any=0 , _lowercase : List[str]=0 , _lowercase : str=False ):
super().__init__()
A = torch.nn.Parameter(torch.tensor(_lowercase ).float() )
A = torch.nn.Parameter(torch.tensor(_lowercase ).float() )
A = True
def __a ( self : int , _lowercase : Tuple=None ):
if self.first_batch:
print(f'Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}' )
A = False
return x * self.a + self.b
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ = 16 ) -> Optional[Any]:
"""simple docstring"""
from datasets import load_dataset
from transformers import AutoTokenizer
A = AutoTokenizer.from_pretrained('bert-base-cased' )
A = {'train': 'tests/test_samples/MRPC/train.csv', 'validation': 'tests/test_samples/MRPC/dev.csv'}
A = load_dataset('csv' , data_files=UpperCamelCase__ )
A = datasets['train'].unique('label' )
A = {v: i for i, v in enumerate(UpperCamelCase__ )}
def tokenize_function(UpperCamelCase__ ):
# max_length=None => use the model max length (it's actually the default)
A = tokenizer(
examples['sentence1'] , examples['sentence2'] , truncation=UpperCamelCase__ , max_length=UpperCamelCase__ , padding='max_length' )
if "label" in examples:
A = [label_to_id[l] for l in examples['label']]
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
A = datasets.map(
UpperCamelCase__ , batched=UpperCamelCase__ , remove_columns=['sentence1', 'sentence2', 'label'] , )
def collate_fn(UpperCamelCase__ ):
# On TPU it's best to pad everything to the same length or training will be very slow.
if accelerator.distributed_type == DistributedType.TPU:
return tokenizer.pad(UpperCamelCase__ , padding='max_length' , max_length=128 , return_tensors='pt' )
return tokenizer.pad(UpperCamelCase__ , padding='longest' , return_tensors='pt' )
# Instantiate dataloaders.
A = DataLoader(tokenized_datasets['train'] , shuffle=UpperCamelCase__ , collate_fn=UpperCamelCase__ , batch_size=2 )
A = DataLoader(tokenized_datasets['validation'] , shuffle=UpperCamelCase__ , collate_fn=UpperCamelCase__ , batch_size=1 )
return train_dataloader, eval_dataloader
| 690 | 1 |
"""simple docstring"""
def __snake_case ( UpperCamelCase__ = 10**9 ) -> int:
"""simple docstring"""
A = 1
A = 2
A = 0
A = 0
A = 0
while perimeter <= max_perimeter:
perimeters_sum += perimeter
prev_value += 2 * value
value += prev_value
A = 2 * value + 2 if i % 2 == 0 else 2 * value - 2
i += 1
return perimeters_sum
if __name__ == "__main__":
print(F"""{solution() = }""")
| 690 |
"""simple docstring"""
from __future__ import annotations
def __snake_case ( UpperCamelCase__ ) -> list[int]: # This function is recursive
"""simple docstring"""
A = len(UpperCamelCase__ )
# If the array contains only one element, we return it (it's the stop condition of
# recursion)
if array_length <= 1:
return array
# Else
A = array[0]
A = False
A = 1
A = []
while not is_found and i < array_length:
if array[i] < pivot:
A = True
A = [element for element in array[i:] if element >= array[i]]
A = longest_subsequence(UpperCamelCase__ )
if len(UpperCamelCase__ ) > len(UpperCamelCase__ ):
A = temp_array
else:
i += 1
A = [element for element in array[1:] if element >= pivot]
A = [pivot, *longest_subsequence(UpperCamelCase__ )]
if len(UpperCamelCase__ ) > len(UpperCamelCase__ ):
return temp_array
else:
return longest_subseq
if __name__ == "__main__":
import doctest
doctest.testmod()
| 690 | 1 |
"""simple docstring"""
from collections import namedtuple
UpperCamelCase : Dict = namedtuple("from_to", "from_ to")
UpperCamelCase : str = {
"cubicmeter": from_to(1, 1),
"litre": from_to(0.001, 1_000),
"kilolitre": from_to(1, 1),
"gallon": from_to(0.00_454, 264.172),
"cubicyard": from_to(0.76_455, 1.30_795),
"cubicfoot": from_to(0.028, 35.3_147),
"cup": from_to(0.000_236_588, 4_226.75),
}
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> float:
"""simple docstring"""
if from_type not in METRIC_CONVERSION:
raise ValueError(
f'Invalid \'from_type\' value: {from_type!r} Supported values are:\n'
+ ', '.join(UpperCamelCase__ ) )
if to_type not in METRIC_CONVERSION:
raise ValueError(
f'Invalid \'to_type\' value: {to_type!r}. Supported values are:\n'
+ ', '.join(UpperCamelCase__ ) )
return value * METRIC_CONVERSION[from_type].from_ * METRIC_CONVERSION[to_type].to
if __name__ == "__main__":
import doctest
doctest.testmod()
| 690 |
"""simple docstring"""
from __future__ import annotations
import typing
from collections.abc import Iterable
import numpy as np
UpperCamelCase : Tuple = typing.Union[Iterable[float], Iterable[int], np.ndarray] # noqa: UP007
UpperCamelCase : Optional[int] = typing.Union[np.floataa, int, float] # noqa: UP007
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ ) -> VectorOut:
"""simple docstring"""
return np.sqrt(np.sum((np.asarray(UpperCamelCase__ ) - np.asarray(UpperCamelCase__ )) ** 2 ) )
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ ) -> VectorOut:
"""simple docstring"""
return sum((va - va) ** 2 for va, va in zip(UpperCamelCase__ , UpperCamelCase__ ) ) ** (1 / 2)
if __name__ == "__main__":
def __snake_case ( ) -> None:
"""simple docstring"""
from timeit import timeit
print('Without Numpy' )
print(
timeit(
'euclidean_distance_no_np([1, 2, 3], [4, 5, 6])' , number=10000 , globals=globals() , ) )
print('With Numpy' )
print(
timeit(
'euclidean_distance([1, 2, 3], [4, 5, 6])' , number=10000 , globals=globals() , ) )
benchmark()
| 690 | 1 |
"""simple docstring"""
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def __snake_case ( UpperCamelCase__ ) -> str:
"""simple docstring"""
def wrapper(*UpperCamelCase__ , **UpperCamelCase__ ):
A = timeit.default_timer()
A = func(*UpperCamelCase__ , **UpperCamelCase__ )
A = timeit.default_timer() - starttime
return delta
A = func.__name__
return wrapper
def __snake_case ( UpperCamelCase__ , UpperCamelCase__=100 , UpperCamelCase__=None ) -> Optional[int]:
"""simple docstring"""
A = []
A = seq_shapes or {}
for i in range(UpperCamelCase__ ):
A = {}
for col_id, (k, v) in enumerate(features.items() ):
if isinstance(UpperCamelCase__ , _ArrayXD ):
A = np.random.rand(*v.shape ).astype(v.dtype )
elif isinstance(UpperCamelCase__ , datasets.Value ):
if v.dtype == "string":
A = 'The small grey turtle was surprisingly fast when challenged.'
else:
A = np.random.randint(10 , size=1 ).astype(v.dtype ).item()
elif isinstance(UpperCamelCase__ , datasets.Sequence ):
while isinstance(UpperCamelCase__ , datasets.Sequence ):
A = v.feature
A = seq_shapes[k]
A = np.random.rand(*UpperCamelCase__ ).astype(v.dtype )
A = data
dummy_data.append((i, example) )
return dummy_data
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__=100 , UpperCamelCase__=None ) -> Optional[Any]:
"""simple docstring"""
A = generate_examples(UpperCamelCase__ , num_examples=UpperCamelCase__ , seq_shapes=UpperCamelCase__ )
with ArrowWriter(features=UpperCamelCase__ , path=UpperCamelCase__ ) as writer:
for key, record in dummy_data:
A = features.encode_example(UpperCamelCase__ )
writer.write(UpperCamelCase__ )
A , A = writer.finalize()
if not num_final_examples == num_examples:
raise ValueError(
f'Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}.' )
A = datasets.Dataset.from_file(filename=UpperCamelCase__ , info=datasets.DatasetInfo(features=UpperCamelCase__ ) )
return dataset
| 690 |
"""simple docstring"""
import argparse
import os
import shutil
from pathlib import Path
import onnx
import torch
from packaging import version
from torch.onnx import export
from diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline, StableDiffusionPipeline
UpperCamelCase : List[Any] = version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__=False , ) -> Any:
"""simple docstring"""
output_path.parent.mkdir(parents=UpperCamelCase__ , exist_ok=UpperCamelCase__ )
# PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
# so we check the torch version for backwards compatibility
if is_torch_less_than_1_11:
export(
UpperCamelCase__ , UpperCamelCase__ , f=output_path.as_posix() , input_names=UpperCamelCase__ , output_names=UpperCamelCase__ , dynamic_axes=UpperCamelCase__ , do_constant_folding=UpperCamelCase__ , use_external_data_format=UpperCamelCase__ , enable_onnx_checker=UpperCamelCase__ , opset_version=UpperCamelCase__ , )
else:
export(
UpperCamelCase__ , UpperCamelCase__ , f=output_path.as_posix() , input_names=UpperCamelCase__ , output_names=UpperCamelCase__ , dynamic_axes=UpperCamelCase__ , do_constant_folding=UpperCamelCase__ , opset_version=UpperCamelCase__ , )
@torch.no_grad()
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = False ) -> str:
"""simple docstring"""
A = torch.floataa if fpaa else torch.floataa
if fpaa and torch.cuda.is_available():
A = 'cuda'
elif fpaa and not torch.cuda.is_available():
raise ValueError('`float16` model export is only supported on GPUs with CUDA' )
else:
A = 'cpu'
A = StableDiffusionPipeline.from_pretrained(UpperCamelCase__ , torch_dtype=UpperCamelCase__ ).to(UpperCamelCase__ )
A = Path(UpperCamelCase__ )
# TEXT ENCODER
A = pipeline.text_encoder.config.max_position_embeddings
A = pipeline.text_encoder.config.hidden_size
A = pipeline.tokenizer(
'A sample prompt' , padding='max_length' , max_length=pipeline.tokenizer.model_max_length , truncation=UpperCamelCase__ , return_tensors='pt' , )
onnx_export(
pipeline.text_encoder , model_args=(text_input.input_ids.to(device=UpperCamelCase__ , dtype=torch.intaa )) , output_path=output_path / 'text_encoder' / 'model.onnx' , ordered_input_names=['input_ids'] , output_names=['last_hidden_state', 'pooler_output'] , dynamic_axes={
'input_ids': {0: 'batch', 1: 'sequence'},
} , opset=UpperCamelCase__ , )
del pipeline.text_encoder
# UNET
A = pipeline.unet.config.in_channels
A = pipeline.unet.config.sample_size
A = output_path / 'unet' / 'model.onnx'
onnx_export(
pipeline.unet , model_args=(
torch.randn(2 , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ).to(device=UpperCamelCase__ , dtype=UpperCamelCase__ ),
torch.randn(2 ).to(device=UpperCamelCase__ , dtype=UpperCamelCase__ ),
torch.randn(2 , UpperCamelCase__ , UpperCamelCase__ ).to(device=UpperCamelCase__ , dtype=UpperCamelCase__ ),
False,
) , output_path=UpperCamelCase__ , ordered_input_names=['sample', 'timestep', 'encoder_hidden_states', 'return_dict'] , output_names=['out_sample'] , dynamic_axes={
'sample': {0: 'batch', 1: 'channels', 2: 'height', 3: 'width'},
'timestep': {0: 'batch'},
'encoder_hidden_states': {0: 'batch', 1: 'sequence'},
} , opset=UpperCamelCase__ , use_external_data_format=UpperCamelCase__ , )
A = str(unet_path.absolute().as_posix() )
A = os.path.dirname(UpperCamelCase__ )
A = onnx.load(UpperCamelCase__ )
# clean up existing tensor files
shutil.rmtree(UpperCamelCase__ )
os.mkdir(UpperCamelCase__ )
# collate external tensor files into one
onnx.save_model(
UpperCamelCase__ , UpperCamelCase__ , save_as_external_data=UpperCamelCase__ , all_tensors_to_one_file=UpperCamelCase__ , location='weights.pb' , convert_attribute=UpperCamelCase__ , )
del pipeline.unet
# VAE ENCODER
A = pipeline.vae
A = vae_encoder.config.in_channels
A = vae_encoder.config.sample_size
# need to get the raw tensor output (sample) from the encoder
A = lambda UpperCamelCase__ , UpperCamelCase__ : vae_encoder.encode(UpperCamelCase__ , UpperCamelCase__ )[0].sample()
onnx_export(
UpperCamelCase__ , model_args=(
torch.randn(1 , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ).to(device=UpperCamelCase__ , dtype=UpperCamelCase__ ),
False,
) , output_path=output_path / 'vae_encoder' / 'model.onnx' , ordered_input_names=['sample', 'return_dict'] , output_names=['latent_sample'] , dynamic_axes={
'sample': {0: 'batch', 1: 'channels', 2: 'height', 3: 'width'},
} , opset=UpperCamelCase__ , )
# VAE DECODER
A = pipeline.vae
A = vae_decoder.config.latent_channels
A = vae_decoder.config.out_channels
# forward only through the decoder part
A = vae_encoder.decode
onnx_export(
UpperCamelCase__ , model_args=(
torch.randn(1 , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ).to(device=UpperCamelCase__ , dtype=UpperCamelCase__ ),
False,
) , output_path=output_path / 'vae_decoder' / 'model.onnx' , ordered_input_names=['latent_sample', 'return_dict'] , output_names=['sample'] , dynamic_axes={
'latent_sample': {0: 'batch', 1: 'channels', 2: 'height', 3: 'width'},
} , opset=UpperCamelCase__ , )
del pipeline.vae
# SAFETY CHECKER
if pipeline.safety_checker is not None:
A = pipeline.safety_checker
A = safety_checker.config.vision_config.num_channels
A = safety_checker.config.vision_config.image_size
A = safety_checker.forward_onnx
onnx_export(
pipeline.safety_checker , model_args=(
torch.randn(
1 , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , ).to(device=UpperCamelCase__ , dtype=UpperCamelCase__ ),
torch.randn(1 , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ).to(device=UpperCamelCase__ , dtype=UpperCamelCase__ ),
) , output_path=output_path / 'safety_checker' / 'model.onnx' , ordered_input_names=['clip_input', 'images'] , output_names=['out_images', 'has_nsfw_concepts'] , dynamic_axes={
'clip_input': {0: 'batch', 1: 'channels', 2: 'height', 3: 'width'},
'images': {0: 'batch', 1: 'height', 2: 'width', 3: 'channels'},
} , opset=UpperCamelCase__ , )
del pipeline.safety_checker
A = OnnxRuntimeModel.from_pretrained(output_path / 'safety_checker' )
A = pipeline.feature_extractor
else:
A = None
A = None
A = OnnxStableDiffusionPipeline(
vae_encoder=OnnxRuntimeModel.from_pretrained(output_path / 'vae_encoder' ) , vae_decoder=OnnxRuntimeModel.from_pretrained(output_path / 'vae_decoder' ) , text_encoder=OnnxRuntimeModel.from_pretrained(output_path / 'text_encoder' ) , tokenizer=pipeline.tokenizer , unet=OnnxRuntimeModel.from_pretrained(output_path / 'unet' ) , scheduler=pipeline.scheduler , safety_checker=UpperCamelCase__ , feature_extractor=UpperCamelCase__ , requires_safety_checker=safety_checker is not None , )
onnx_pipeline.save_pretrained(UpperCamelCase__ )
print('ONNX pipeline saved to' , UpperCamelCase__ )
del pipeline
del onnx_pipeline
A = OnnxStableDiffusionPipeline.from_pretrained(UpperCamelCase__ , provider='CPUExecutionProvider' )
print('ONNX pipeline is loadable' )
if __name__ == "__main__":
UpperCamelCase : Optional[int] = argparse.ArgumentParser()
parser.add_argument(
"--model_path",
type=str,
required=True,
help="Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).",
)
parser.add_argument("--output_path", type=str, required=True, help="Path to the output model.")
parser.add_argument(
"--opset",
default=14,
type=int,
help="The version of the ONNX operator set to use.",
)
parser.add_argument("--fp16", action="store_true", default=False, help="Export the models in `float16` mode")
UpperCamelCase : str = parser.parse_args()
convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
| 690 | 1 |
"""simple docstring"""
import copy
import tempfile
import unittest
from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError
from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test
class lowerCamelCase__ ( unittest.TestCase ):
@parameterized.expand([(None,), ('foo.json',)] )
def __a ( self : Dict , _lowercase : Tuple ):
A = GenerationConfig(
do_sample=_lowercase , temperature=0.7 , length_penalty=1.0 , bad_words_ids=[[1, 2, 3], [4, 5]] , )
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(_lowercase , config_name=_lowercase )
A = GenerationConfig.from_pretrained(_lowercase , config_name=_lowercase )
# Checks parameters that were specified
self.assertEqual(loaded_config.do_sample , _lowercase )
self.assertEqual(loaded_config.temperature , 0.7 )
self.assertEqual(loaded_config.length_penalty , 1.0 )
self.assertEqual(loaded_config.bad_words_ids , [[1, 2, 3], [4, 5]] )
# Checks parameters that were not specified (defaults)
self.assertEqual(loaded_config.top_k , 50 )
self.assertEqual(loaded_config.max_length , 20 )
self.assertEqual(loaded_config.max_time , _lowercase )
def __a ( self : Optional[Any] ):
A = AutoConfig.from_pretrained('gpt2' )
A = GenerationConfig.from_model_config(_lowercase )
A = GenerationConfig()
# The generation config has loaded a few non-default parameters from the model config
self.assertNotEqual(_lowercase , _lowercase )
# One of those parameters is eos_token_id -- check if it matches
self.assertNotEqual(generation_config_from_model.eos_token_id , default_generation_config.eos_token_id )
self.assertEqual(generation_config_from_model.eos_token_id , model_config.eos_token_id )
def __a ( self : Dict ):
A = GenerationConfig()
A = {
'max_new_tokens': 1_024,
'foo': 'bar',
}
A = copy.deepcopy(_lowercase )
A = generation_config.update(**_lowercase )
# update_kwargs was not modified (no side effects)
self.assertEqual(_lowercase , _lowercase )
# update_kwargs was used to update the config on valid attributes
self.assertEqual(generation_config.max_new_tokens , 1_024 )
# `.update()` returns a dictionary of unused kwargs
self.assertEqual(_lowercase , {'foo': 'bar'} )
def __a ( self : int ):
A = GenerationConfig()
A = 'bar'
with tempfile.TemporaryDirectory('test-generation-config' ) as tmp_dir:
generation_config.save_pretrained(_lowercase )
A = GenerationConfig.from_pretrained(_lowercase )
# update_kwargs was used to update the config on valid attributes
self.assertEqual(new_config.foo , 'bar' )
A = GenerationConfig.from_model_config(_lowercase )
assert not hasattr(_lowercase , 'foo' ) # no new kwargs should be initialized if from config
def __a ( self : str ):
A = GenerationConfig()
self.assertEqual(default_config.temperature , 1.0 )
self.assertEqual(default_config.do_sample , _lowercase )
self.assertEqual(default_config.num_beams , 1 )
A = GenerationConfig(
do_sample=_lowercase , temperature=0.7 , length_penalty=1.0 , bad_words_ids=[[1, 2, 3], [4, 5]] , )
self.assertEqual(config.temperature , 0.7 )
self.assertEqual(config.do_sample , _lowercase )
self.assertEqual(config.num_beams , 1 )
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(_lowercase )
A = GenerationConfig.from_pretrained(_lowercase , temperature=1.0 )
self.assertEqual(loaded_config.temperature , 1.0 )
self.assertEqual(loaded_config.do_sample , _lowercase )
self.assertEqual(loaded_config.num_beams , 1 ) # default value
@is_staging_test
class lowerCamelCase__ ( unittest.TestCase ):
@classmethod
def __a ( cls : List[str] ):
A = TOKEN
HfFolder.save_token(_lowercase )
@classmethod
def __a ( cls : Dict ):
try:
delete_repo(token=cls._token , repo_id='test-generation-config' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='valid_org/test-generation-config-org' )
except HTTPError:
pass
def __a ( self : List[Any] ):
A = GenerationConfig(
do_sample=_lowercase , temperature=0.7 , length_penalty=1.0 , )
config.push_to_hub('test-generation-config' , use_auth_token=self._token )
A = GenerationConfig.from_pretrained(f'{USER}/test-generation-config' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(_lowercase , getattr(_lowercase , _lowercase ) )
# Reset repo
delete_repo(token=self._token , repo_id='test-generation-config' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
_lowercase , repo_id='test-generation-config' , push_to_hub=_lowercase , use_auth_token=self._token )
A = GenerationConfig.from_pretrained(f'{USER}/test-generation-config' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(_lowercase , getattr(_lowercase , _lowercase ) )
def __a ( self : Tuple ):
A = GenerationConfig(
do_sample=_lowercase , temperature=0.7 , length_penalty=1.0 , )
config.push_to_hub('valid_org/test-generation-config-org' , use_auth_token=self._token )
A = GenerationConfig.from_pretrained('valid_org/test-generation-config-org' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(_lowercase , getattr(_lowercase , _lowercase ) )
# Reset repo
delete_repo(token=self._token , repo_id='valid_org/test-generation-config-org' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
_lowercase , repo_id='valid_org/test-generation-config-org' , push_to_hub=_lowercase , use_auth_token=self._token )
A = GenerationConfig.from_pretrained('valid_org/test-generation-config-org' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(_lowercase , getattr(_lowercase , _lowercase ) )
| 690 |
"""simple docstring"""
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
UpperCamelCase : List[str] = Lock()
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> List[str]:
"""simple docstring"""
global process_lock
# we perform n swaps since after n swaps we know we are sorted
# we *could* stop early if we are sorted already, but it takes as long to
# find out we are sorted as it does to sort the list with this algorithm
for i in range(0 , 10 ):
if (i + position) % 2 == 0 and r_send is not None:
# send your value to your right neighbor
process_lock.acquire()
r_send[1].send(UpperCamelCase__ )
process_lock.release()
# receive your right neighbor's value
process_lock.acquire()
A = rr_cv[0].recv()
process_lock.release()
# take the lower value since you are on the left
A = min(UpperCamelCase__ , UpperCamelCase__ )
elif (i + position) % 2 != 0 and l_send is not None:
# send your value to your left neighbor
process_lock.acquire()
l_send[1].send(UpperCamelCase__ )
process_lock.release()
# receive your left neighbor's value
process_lock.acquire()
A = lr_cv[0].recv()
process_lock.release()
# take the higher value since you are on the right
A = max(UpperCamelCase__ , UpperCamelCase__ )
# after all swaps are performed, send the values back to main
result_pipe[1].send(UpperCamelCase__ )
def __snake_case ( UpperCamelCase__ ) -> List[str]:
"""simple docstring"""
A = []
A = []
# initialize the list of pipes where the values will be retrieved
for _ in arr:
result_pipe.append(Pipe() )
# creates the processes
# the first and last process only have one neighbor so they are made outside
# of the loop
A = Pipe()
A = Pipe()
process_array_.append(
Process(
target=UpperCamelCase__ , args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0]) , ) )
A = temp_rs
A = temp_rr
for i in range(1 , len(UpperCamelCase__ ) - 1 ):
A = Pipe()
A = Pipe()
process_array_.append(
Process(
target=UpperCamelCase__ , args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i]) , ) )
A = temp_rs
A = temp_rr
process_array_.append(
Process(
target=UpperCamelCase__ , args=(
len(UpperCamelCase__ ) - 1,
arr[len(UpperCamelCase__ ) - 1],
temp_ls,
None,
temp_lr,
None,
result_pipe[len(UpperCamelCase__ ) - 1],
) , ) )
# start the processes
for p in process_array_:
p.start()
# wait for the processes to end and write their values to the list
for p in range(0 , len(UpperCamelCase__ ) ):
A = result_pipe[p][0].recv()
process_array_[p].join()
return arr
def __snake_case ( ) -> Optional[Any]:
"""simple docstring"""
A = list(range(10 , 0 , -1 ) )
print('Initial List' )
print(*UpperCamelCase__ )
A = odd_even_transposition(UpperCamelCase__ )
print('Sorted List\n' )
print(*UpperCamelCase__ )
if __name__ == "__main__":
main()
| 690 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCamelCase : Tuple = {
"configuration_chinese_clip": [
"CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"ChineseCLIPConfig",
"ChineseCLIPOnnxConfig",
"ChineseCLIPTextConfig",
"ChineseCLIPVisionConfig",
],
"processing_chinese_clip": ["ChineseCLIPProcessor"],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase : Union[str, Any] = ["ChineseCLIPFeatureExtractor"]
UpperCamelCase : Tuple = ["ChineseCLIPImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase : Union[str, Any] = [
"CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
"ChineseCLIPModel",
"ChineseCLIPPreTrainedModel",
"ChineseCLIPTextModel",
"ChineseCLIPVisionModel",
]
if TYPE_CHECKING:
from .configuration_chinese_clip import (
CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
ChineseCLIPConfig,
ChineseCLIPOnnxConfig,
ChineseCLIPTextConfig,
ChineseCLIPVisionConfig,
)
from .processing_chinese_clip import ChineseCLIPProcessor
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_chinese_clip import ChineseCLIPFeatureExtractor, ChineseCLIPImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_chinese_clip import (
CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
ChineseCLIPModel,
ChineseCLIPPreTrainedModel,
ChineseCLIPTextModel,
ChineseCLIPVisionModel,
)
else:
import sys
UpperCamelCase : Optional[int] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 690 |
"""simple docstring"""
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures
# Importing the dataset
UpperCamelCase : int = pd.read_csv(
"https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/"
"position_salaries.csv"
)
UpperCamelCase : List[Any] = dataset.iloc[:, 1:2].values
UpperCamelCase : Any = dataset.iloc[:, 2].values
UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase : str = train_test_split(X, y, test_size=0.2, random_state=0)
UpperCamelCase : List[str] = PolynomialFeatures(degree=4)
UpperCamelCase : Optional[int] = poly_reg.fit_transform(X)
UpperCamelCase : List[Any] = LinearRegression()
pol_reg.fit(X_poly, y)
def __snake_case ( ) -> Optional[int]:
"""simple docstring"""
plt.scatter(UpperCamelCase__ , UpperCamelCase__ , color='red' )
plt.plot(UpperCamelCase__ , pol_reg.predict(poly_reg.fit_transform(UpperCamelCase__ ) ) , color='blue' )
plt.title('Truth or Bluff (Linear Regression)' )
plt.xlabel('Position level' )
plt.ylabel('Salary' )
plt.show()
if __name__ == "__main__":
viz_polymonial()
# Predicting a new result with Polymonial Regression
pol_reg.predict(poly_reg.fit_transform([[5.5]]))
# output should be 132148.43750003
| 690 | 1 |
"""simple docstring"""
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
UpperCamelCase : str = logging.get_logger(__name__)
def __snake_case ( UpperCamelCase__ ) -> List[Any]:
"""simple docstring"""
A = torch.load(UpperCamelCase__ , map_location='cpu' )
if "model" in sd.keys():
A = torch.load(UpperCamelCase__ , map_location='cpu' )['model']
# pop unnecessary weights
A = [
'decoder.version',
'decoder.output_projection.weight',
]
for key in keys_to_delete:
if key in sd:
sd.pop(UpperCamelCase__ )
A = {
'decoder.project_in_dim.weight': 'decoder.project_in.weight',
'decoder.project_out_dim.weight': 'decoder.project_out.weight',
'decoder.layer_norm.weight': 'decoder.final_layer_norm.weight',
'decoder.layer_norm.bias': 'decoder.final_layer_norm.bias',
}
for old_key, new_key in keys_to_rename.items():
if old_key in sd:
A = sd.pop(UpperCamelCase__ )
A = list(sd.keys() )
for key in keys:
if ".qkv_proj." in key:
A = sd[key]
# We split QKV in separate Q,K,V
A = key.replace('.qkv_proj.' , '.q_proj.' )
A = key.replace('.qkv_proj.' , '.k_proj.' )
A = key.replace('.qkv_proj.' , '.v_proj.' )
A = value.shape[0]
assert depth % 3 == 0
# `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
# https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
A , A , A = torch.split(UpperCamelCase__ , depth // 3 , dim=0 )
A = q
A = k
A = v
del sd[key]
return sd
@torch.no_grad()
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__=None ) -> Optional[Any]:
"""simple docstring"""
A = load_checkpoint(UpperCamelCase__ )
if config is not None:
A = OPTConfig.from_pretrained(UpperCamelCase__ )
else:
A = OPTConfig()
A = OPTModel(UpperCamelCase__ ).half().eval()
model.load_state_dict(UpperCamelCase__ )
# Check results
Path(UpperCamelCase__ ).mkdir(exist_ok=UpperCamelCase__ )
model.save_pretrained(UpperCamelCase__ )
if __name__ == "__main__":
UpperCamelCase : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--fairseq_path",
type=str,
help=(
"path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:"
" https://huggingface.co/models?other=opt_metasq"
),
)
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
parser.add_argument("--hf_config", default=None, type=str, help="Define HF config.")
UpperCamelCase : int = parser.parse_args()
convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)
| 690 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPENAI_CLIP_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
UpperCamelCase : Optional[Any] = logging.get_logger(__name__)
if is_vision_available():
import PIL
class lowerCamelCase__ ( UpperCAmelCase_ ):
lowerCAmelCase = ["""pixel_values"""]
def __init__( self : List[str] , _lowercase : bool = True , _lowercase : Dict[str, int] = None , _lowercase : PILImageResampling = PILImageResampling.BICUBIC , _lowercase : bool = True , _lowercase : Dict[str, int] = None , _lowercase : bool = True , _lowercase : Union[int, float] = 1 / 255 , _lowercase : bool = True , _lowercase : Optional[Union[float, List[float]]] = None , _lowercase : Optional[Union[float, List[float]]] = None , _lowercase : bool = True , **_lowercase : Tuple , ):
super().__init__(**_lowercase )
A = size if size is not None else {'shortest_edge': 224}
A = get_size_dict(_lowercase , default_to_square=_lowercase )
A = crop_size if crop_size is not None else {'height': 224, 'width': 224}
A = get_size_dict(_lowercase , default_to_square=_lowercase , param_name='crop_size' )
A = do_resize
A = size
A = resample
A = do_center_crop
A = crop_size
A = do_rescale
A = rescale_factor
A = do_normalize
A = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
A = image_std if image_std is not None else OPENAI_CLIP_STD
A = do_convert_rgb
def __a ( self : str , _lowercase : np.ndarray , _lowercase : Dict[str, int] , _lowercase : PILImageResampling = PILImageResampling.BICUBIC , _lowercase : Optional[Union[str, ChannelDimension]] = None , **_lowercase : List[str] , ):
A = get_size_dict(_lowercase , default_to_square=_lowercase )
if "shortest_edge" not in size:
raise ValueError(f'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}' )
A = get_resize_output_image_size(_lowercase , size=size['shortest_edge'] , default_to_square=_lowercase )
return resize(_lowercase , size=_lowercase , resample=_lowercase , data_format=_lowercase , **_lowercase )
def __a ( self : int , _lowercase : np.ndarray , _lowercase : Dict[str, int] , _lowercase : Optional[Union[str, ChannelDimension]] = None , **_lowercase : str , ):
A = get_size_dict(_lowercase )
if "height" not in size or "width" not in size:
raise ValueError(f'The `size` parameter must contain the keys (height, width). Got {size.keys()}' )
return center_crop(_lowercase , size=(size['height'], size['width']) , data_format=_lowercase , **_lowercase )
def __a ( self : List[str] , _lowercase : np.ndarray , _lowercase : Union[int, float] , _lowercase : Optional[Union[str, ChannelDimension]] = None , **_lowercase : List[str] , ):
return rescale(_lowercase , scale=_lowercase , data_format=_lowercase , **_lowercase )
def __a ( self : List[str] , _lowercase : np.ndarray , _lowercase : Union[float, List[float]] , _lowercase : Union[float, List[float]] , _lowercase : Optional[Union[str, ChannelDimension]] = None , **_lowercase : Union[str, Any] , ):
return normalize(_lowercase , mean=_lowercase , std=_lowercase , data_format=_lowercase , **_lowercase )
def __a ( self : Optional[int] , _lowercase : ImageInput , _lowercase : bool = None , _lowercase : Dict[str, int] = None , _lowercase : PILImageResampling = None , _lowercase : bool = None , _lowercase : int = None , _lowercase : bool = None , _lowercase : float = None , _lowercase : bool = None , _lowercase : Optional[Union[float, List[float]]] = None , _lowercase : Optional[Union[float, List[float]]] = None , _lowercase : bool = None , _lowercase : Optional[Union[str, TensorType]] = None , _lowercase : Optional[ChannelDimension] = ChannelDimension.FIRST , **_lowercase : int , ):
A = do_resize if do_resize is not None else self.do_resize
A = size if size is not None else self.size
A = get_size_dict(_lowercase , param_name='size' , default_to_square=_lowercase )
A = resample if resample is not None else self.resample
A = do_center_crop if do_center_crop is not None else self.do_center_crop
A = crop_size if crop_size is not None else self.crop_size
A = get_size_dict(_lowercase , param_name='crop_size' , default_to_square=_lowercase )
A = do_rescale if do_rescale is not None else self.do_rescale
A = rescale_factor if rescale_factor is not None else self.rescale_factor
A = do_normalize if do_normalize is not None else self.do_normalize
A = image_mean if image_mean is not None else self.image_mean
A = image_std if image_std is not None else self.image_std
A = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
A = make_list_of_images(_lowercase )
if not valid_images(_lowercase ):
raise ValueError(
'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
'torch.Tensor, tf.Tensor or jax.ndarray.' )
if do_resize and size is None:
raise ValueError('Size must be specified if do_resize is True.' )
if do_center_crop and crop_size is None:
raise ValueError('Crop size must be specified if do_center_crop is True.' )
if do_rescale and rescale_factor is None:
raise ValueError('Rescale factor must be specified if do_rescale is True.' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('Image mean and std must be specified if do_normalize is True.' )
# PIL RGBA images are converted to RGB
if do_convert_rgb:
A = [convert_to_rgb(_lowercase ) for image in images]
# All transformations expect numpy arrays.
A = [to_numpy_array(_lowercase ) for image in images]
if do_resize:
A = [self.resize(image=_lowercase , size=_lowercase , resample=_lowercase ) for image in images]
if do_center_crop:
A = [self.center_crop(image=_lowercase , size=_lowercase ) for image in images]
if do_rescale:
A = [self.rescale(image=_lowercase , scale=_lowercase ) for image in images]
if do_normalize:
A = [self.normalize(image=_lowercase , mean=_lowercase , std=_lowercase ) for image in images]
A = [to_channel_dimension_format(_lowercase , _lowercase ) for image in images]
A = {'pixel_values': images}
return BatchFeature(data=_lowercase , tensor_type=_lowercase )
| 690 | 1 |
"""simple docstring"""
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
from ..tf_utils import stable_softmax
UpperCamelCase : Union[str, Any] = logging.get_logger(__name__)
@add_end_docstrings(UpperCAmelCase_ )
class lowerCamelCase__ ( UpperCAmelCase_ ):
def __init__( self : Tuple , **_lowercase : Any ):
super().__init__(**_lowercase )
requires_backends(self , 'vision' )
self.check_model_type(
TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
if self.framework == 'tf'
else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING )
def __call__( self : List[str] , _lowercase : Union[str, List[str], "Image", List["Image"]] , **_lowercase : str ):
return super().__call__(_lowercase , **_lowercase )
def __a ( self : Union[str, Any] , **_lowercase : Tuple ):
A = {}
if "candidate_labels" in kwargs:
A = kwargs['candidate_labels']
if "hypothesis_template" in kwargs:
A = kwargs['hypothesis_template']
return preprocess_params, {}, {}
def __a ( self : List[str] , _lowercase : List[str] , _lowercase : str=None , _lowercase : List[Any]="This is a photo of {}." ):
A = load_image(_lowercase )
A = self.image_processor(images=[image] , return_tensors=self.framework )
A = candidate_labels
A = [hypothesis_template.format(_lowercase ) for x in candidate_labels]
A = self.tokenizer(_lowercase , return_tensors=self.framework , padding=_lowercase )
A = [text_inputs]
return inputs
def __a ( self : Union[str, Any] , _lowercase : Any ):
A = model_inputs.pop('candidate_labels' )
A = model_inputs.pop('text_inputs' )
if isinstance(text_inputs[0] , _lowercase ):
A = text_inputs[0]
else:
# Batching case.
A = text_inputs[0][0]
A = self.model(**_lowercase , **_lowercase )
A = {
'candidate_labels': candidate_labels,
'logits': outputs.logits_per_image,
}
return model_outputs
def __a ( self : Any , _lowercase : List[Any] ):
A = model_outputs.pop('candidate_labels' )
A = model_outputs['logits'][0]
if self.framework == "pt":
A = logits.softmax(dim=-1 ).squeeze(-1 )
A = probs.tolist()
if not isinstance(_lowercase , _lowercase ):
A = [scores]
elif self.framework == "tf":
A = stable_softmax(_lowercase , axis=-1 )
A = probs.numpy().tolist()
else:
raise ValueError(f'Unsupported framework: {self.framework}' )
A = [
{'score': score, 'label': candidate_label}
for score, candidate_label in sorted(zip(_lowercase , _lowercase ) , key=lambda _lowercase : -x[0] )
]
return result
| 690 |
"""simple docstring"""
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class lowerCamelCase__ ( unittest.TestCase ):
def __a ( self : Union[str, Any] ):
A = torch.nn.Linear(10 , 10 )
A = torch.optim.SGD(model.parameters() , 0.1 )
A = Accelerator()
A = accelerator.prepare(_lowercase )
try:
pickle.loads(pickle.dumps(_lowercase ) )
except Exception as e:
self.fail(f'Accelerated optimizer pickling failed with {e}' )
AcceleratorState._reset_state()
| 690 | 1 |
"""simple docstring"""
from abc import ABC, abstractmethod
from typing import Optional, Union
from .. import Dataset, DatasetDict, Features, IterableDataset, IterableDatasetDict, NamedSplit
from ..utils.typing import NestedDataStructureLike, PathLike
class lowerCamelCase__ ( UpperCAmelCase_ ):
def __init__( self : List[Any] , _lowercase : Optional[NestedDataStructureLike[PathLike]] = None , _lowercase : Optional[NamedSplit] = None , _lowercase : Optional[Features] = None , _lowercase : str = None , _lowercase : bool = False , _lowercase : bool = False , _lowercase : Optional[int] = None , **_lowercase : str , ):
A = path_or_paths
A = split if split or isinstance(_lowercase , _lowercase ) else 'train'
A = features
A = cache_dir
A = keep_in_memory
A = streaming
A = num_proc
A = kwargs
@abstractmethod
def __a ( self : List[Any] ):
pass
class lowerCamelCase__ ( UpperCAmelCase_ ):
def __init__( self : List[Any] , _lowercase : Optional[Features] = None , _lowercase : str = None , _lowercase : bool = False , _lowercase : bool = False , _lowercase : Optional[int] = None , **_lowercase : Any , ):
A = features
A = cache_dir
A = keep_in_memory
A = streaming
A = num_proc
A = kwargs
@abstractmethod
def __a ( self : Tuple ):
pass
| 690 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCamelCase : Optional[int] = logging.get_logger(__name__)
UpperCamelCase : Optional[Any] = {
"YituTech/conv-bert-base": "https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json",
"YituTech/conv-bert-medium-small": (
"https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json"
),
"YituTech/conv-bert-small": "https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json",
# See all ConvBERT models at https://huggingface.co/models?filter=convbert
}
class lowerCamelCase__ ( UpperCAmelCase_ ):
lowerCAmelCase = """convbert"""
def __init__( self : Optional[int] , _lowercase : List[Any]=30_522 , _lowercase : List[str]=768 , _lowercase : Optional[Any]=12 , _lowercase : Any=12 , _lowercase : str=3_072 , _lowercase : List[str]="gelu" , _lowercase : Dict=0.1 , _lowercase : Dict=0.1 , _lowercase : Any=512 , _lowercase : List[str]=2 , _lowercase : Tuple=0.0_2 , _lowercase : List[Any]=1e-12 , _lowercase : List[str]=1 , _lowercase : Tuple=0 , _lowercase : Any=2 , _lowercase : Union[str, Any]=768 , _lowercase : str=2 , _lowercase : Any=9 , _lowercase : Union[str, Any]=1 , _lowercase : Dict=None , **_lowercase : Union[str, Any] , ):
super().__init__(
pad_token_id=_lowercase , bos_token_id=_lowercase , eos_token_id=_lowercase , **_lowercase , )
A = vocab_size
A = hidden_size
A = num_hidden_layers
A = num_attention_heads
A = intermediate_size
A = hidden_act
A = hidden_dropout_prob
A = attention_probs_dropout_prob
A = max_position_embeddings
A = type_vocab_size
A = initializer_range
A = layer_norm_eps
A = embedding_size
A = head_ratio
A = conv_kernel_size
A = num_groups
A = classifier_dropout
class lowerCamelCase__ ( UpperCAmelCase_ ):
@property
def __a ( self : str ):
if self.task == "multiple-choice":
A = {0: 'batch', 1: 'choice', 2: 'sequence'}
else:
A = {0: 'batch', 1: 'sequence'}
return OrderedDict(
[
('input_ids', dynamic_axis),
('attention_mask', dynamic_axis),
('token_type_ids', dynamic_axis),
] )
| 690 | 1 |
"""simple docstring"""
from timeit import timeit
UpperCamelCase : Union[str, Any] = {
"MALAYALAM": True,
"String": False,
"rotor": True,
"level": True,
"A": True,
"BB": True,
"ABC": False,
"amanaplanacanalpanama": True, # "a man a plan a canal panama"
}
# Ensure our test data is valid
assert all((key == key[::-1]) is value for key, value in test_data.items())
def __snake_case ( UpperCamelCase__ ) -> bool:
"""simple docstring"""
A = 0
A = len(UpperCamelCase__ ) - 1
while start_i < end_i:
if s[start_i] == s[end_i]:
start_i += 1
end_i -= 1
else:
return False
return True
def __snake_case ( UpperCamelCase__ ) -> bool:
"""simple docstring"""
A = len(UpperCamelCase__ ) // 2
A = len(UpperCamelCase__ )
# We need to traverse till half of the length of string
# as we can get access of the i'th last element from
# i'th index.
# eg: [0,1,2,3,4,5] => 4th index can be accessed
# with the help of 1st index (i==n-i-1)
# where n is length of string
return all(s[i] == s[n - i - 1] for i in range(UpperCamelCase__ ) )
def __snake_case ( UpperCamelCase__ ) -> bool:
"""simple docstring"""
if len(UpperCamelCase__ ) <= 2:
return True
if s[0] == s[len(UpperCamelCase__ ) - 1]:
return is_palindrome_recursive(s[1:-1] )
else:
return False
def __snake_case ( UpperCamelCase__ ) -> bool:
"""simple docstring"""
return s == s[::-1]
def __snake_case ( UpperCamelCase__ ) -> None:
"""simple docstring"""
A = f'all({name}(key) is value for key, value in test_data.items())'
A = f'from __main__ import test_data, {name}'
A = 500000
A = timeit(stmt=UpperCamelCase__ , setup=UpperCamelCase__ , number=UpperCamelCase__ )
print(f'{name:<35} finished {number:,} runs in {result:.5f} seconds' )
if __name__ == "__main__":
for key, value in test_data.items():
assert is_palindrome(key) is is_palindrome_recursive(key)
assert is_palindrome(key) is is_palindrome_slice(key)
print(F"""{key:21} {value}""")
print("a man a plan a canal panama")
# finished 500,000 runs in 0.46793 seconds
benchmark_function("is_palindrome_slice")
# finished 500,000 runs in 0.85234 seconds
benchmark_function("is_palindrome")
# finished 500,000 runs in 1.32028 seconds
benchmark_function("is_palindrome_recursive")
# finished 500,000 runs in 2.08679 seconds
benchmark_function("is_palindrome_traversal")
| 690 |
"""simple docstring"""
from .constants import (
MODEL_NAME,
OPTIMIZER_NAME,
RNG_STATE_NAME,
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
SCALER_NAME,
SCHEDULER_NAME,
TORCH_LAUNCH_PARAMS,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
)
from .dataclasses import (
BnbQuantizationConfig,
ComputeEnvironment,
CustomDtype,
DeepSpeedPlugin,
DistributedDataParallelKwargs,
DistributedType,
DynamoBackend,
FPaRecipeKwargs,
FullyShardedDataParallelPlugin,
GradientAccumulationPlugin,
GradScalerKwargs,
InitProcessGroupKwargs,
KwargsHandler,
LoggerType,
MegatronLMPlugin,
PrecisionType,
ProjectConfiguration,
RNGType,
SageMakerDistributedType,
TensorInformation,
TorchDynamoPlugin,
)
from .environment import get_int_from_env, parse_choice_from_env, parse_flag_from_env
from .imports import (
get_ccl_version,
is_abit_bnb_available,
is_abit_bnb_available,
is_aim_available,
is_bfaa_available,
is_bnb_available,
is_botoa_available,
is_ccl_available,
is_comet_ml_available,
is_datasets_available,
is_deepspeed_available,
is_fpa_available,
is_ipex_available,
is_megatron_lm_available,
is_mlflow_available,
is_mps_available,
is_npu_available,
is_rich_available,
is_safetensors_available,
is_sagemaker_available,
is_tensorboard_available,
is_tpu_available,
is_transformers_available,
is_wandb_available,
is_xpu_available,
)
from .modeling import (
check_device_map,
check_tied_parameters_in_config,
check_tied_parameters_on_same_device,
compute_module_sizes,
convert_file_size_to_int,
dtype_byte_size,
find_tied_parameters,
get_balanced_memory,
get_max_layer_size,
get_max_memory,
get_mixed_precision_context_manager,
id_tensor_storage,
infer_auto_device_map,
load_checkpoint_in_model,
load_offloaded_weights,
load_state_dict,
named_module_tensors,
retie_parameters,
set_module_tensor_to_device,
shard_checkpoint,
)
from .offload import (
OffloadedWeightsLoader,
PrefixedDataset,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_weight,
save_offload_index,
)
from .operations import (
broadcast,
broadcast_object_list,
concatenate,
convert_outputs_to_fpaa,
convert_to_fpaa,
find_batch_size,
find_device,
gather,
gather_object,
get_data_structure,
honor_type,
initialize_tensors,
is_namedtuple,
is_tensor_information,
is_torch_tensor,
listify,
pad_across_processes,
recursively_apply,
reduce,
send_to_device,
slice_tensors,
)
from .versions import compare_versions, is_torch_version
if is_deepspeed_available():
from .deepspeed import (
DeepSpeedEngineWrapper,
DeepSpeedOptimizerWrapper,
DeepSpeedSchedulerWrapper,
DummyOptim,
DummyScheduler,
HfDeepSpeedConfig,
)
from .bnb import has_abit_bnb_layers, load_and_quantize_model
from .fsdp_utils import load_fsdp_model, load_fsdp_optimizer, save_fsdp_model, save_fsdp_optimizer
from .launch import (
PrepareForLaunch,
_filter_args,
prepare_deepspeed_cmd_env,
prepare_multi_gpu_env,
prepare_sagemager_args_inputs,
prepare_simple_launcher_cmd_env,
prepare_tpu,
)
from .megatron_lm import (
AbstractTrainStep,
BertTrainStep,
GPTTrainStep,
MegatronEngine,
MegatronLMDummyDataLoader,
MegatronLMDummyScheduler,
MegatronLMOptimizerWrapper,
MegatronLMSchedulerWrapper,
TaTrainStep,
avg_losses_across_data_parallel_group,
gather_across_data_parallel_groups,
)
from .megatron_lm import initialize as megatron_lm_initialize
from .megatron_lm import prepare_data_loader as megatron_lm_prepare_data_loader
from .megatron_lm import prepare_model as megatron_lm_prepare_model
from .megatron_lm import prepare_optimizer as megatron_lm_prepare_optimizer
from .megatron_lm import prepare_scheduler as megatron_lm_prepare_scheduler
from .memory import find_executable_batch_size, release_memory
from .other import (
extract_model_from_parallel,
get_pretty_name,
is_port_in_use,
merge_dicts,
patch_environment,
save,
wait_for_everyone,
write_basic_config,
)
from .random import set_seed, synchronize_rng_state, synchronize_rng_states
from .torch_xla import install_xla
from .tqdm import tqdm
from .transformer_engine import convert_model, has_transformer_engine_layers
| 690 | 1 |
"""simple docstring"""
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
get_image_size,
is_torch_available,
is_torch_tensor,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_torch_available():
import torch
if is_vision_available():
import PIL
UpperCamelCase : Union[str, Any] = logging.get_logger(__name__)
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> Tuple[int, int]:
"""simple docstring"""
def constraint_to_multiple_of(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__=0 , UpperCamelCase__=None ):
A = round(val / multiple ) * multiple
if max_val is not None and x > max_val:
A = math.floor(val / multiple ) * multiple
if x < min_val:
A = math.ceil(val / multiple ) * multiple
return x
A = (output_size, output_size) if isinstance(UpperCamelCase__ , UpperCamelCase__ ) else output_size
A , A = get_image_size(UpperCamelCase__ )
A , A = output_size
# determine new height and width
A = output_height / input_height
A = output_width / input_width
if keep_aspect_ratio:
# scale as little as possible
if abs(1 - scale_width ) < abs(1 - scale_height ):
# fit width
A = scale_width
else:
# fit height
A = scale_height
A = constraint_to_multiple_of(scale_height * input_height , multiple=UpperCamelCase__ )
A = constraint_to_multiple_of(scale_width * input_width , multiple=UpperCamelCase__ )
return (new_height, new_width)
class lowerCamelCase__ ( UpperCAmelCase_ ):
lowerCAmelCase = ["""pixel_values"""]
def __init__( self : Tuple , _lowercase : bool = True , _lowercase : Dict[str, int] = None , _lowercase : PILImageResampling = PILImageResampling.BILINEAR , _lowercase : bool = False , _lowercase : int = 1 , _lowercase : bool = True , _lowercase : Union[int, float] = 1 / 255 , _lowercase : bool = True , _lowercase : Optional[Union[float, List[float]]] = None , _lowercase : Optional[Union[float, List[float]]] = None , **_lowercase : str , ):
super().__init__(**_lowercase )
A = size if size is not None else {'height': 384, 'width': 384}
A = get_size_dict(_lowercase )
A = do_resize
A = size
A = keep_aspect_ratio
A = ensure_multiple_of
A = resample
A = do_rescale
A = rescale_factor
A = do_normalize
A = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
A = image_std if image_std is not None else IMAGENET_STANDARD_STD
def __a ( self : str , _lowercase : np.ndarray , _lowercase : Dict[str, int] , _lowercase : bool = False , _lowercase : int = 1 , _lowercase : PILImageResampling = PILImageResampling.BICUBIC , _lowercase : Optional[Union[str, ChannelDimension]] = None , **_lowercase : int , ):
A = get_size_dict(_lowercase )
if "height" not in size or "width" not in size:
raise ValueError(f'The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}' )
A = get_resize_output_image_size(
_lowercase , output_size=(size['height'], size['width']) , keep_aspect_ratio=_lowercase , multiple=_lowercase , )
return resize(_lowercase , size=_lowercase , resample=_lowercase , data_format=_lowercase , **_lowercase )
def __a ( self : Any , _lowercase : np.ndarray , _lowercase : Union[int, float] , _lowercase : Optional[Union[str, ChannelDimension]] = None , **_lowercase : str , ):
return rescale(_lowercase , scale=_lowercase , data_format=_lowercase , **_lowercase )
def __a ( self : Optional[int] , _lowercase : np.ndarray , _lowercase : Union[float, List[float]] , _lowercase : Union[float, List[float]] , _lowercase : Optional[Union[str, ChannelDimension]] = None , **_lowercase : Union[str, Any] , ):
return normalize(_lowercase , mean=_lowercase , std=_lowercase , data_format=_lowercase , **_lowercase )
def __a ( self : List[Any] , _lowercase : ImageInput , _lowercase : bool = None , _lowercase : int = None , _lowercase : bool = None , _lowercase : int = None , _lowercase : PILImageResampling = None , _lowercase : bool = None , _lowercase : float = None , _lowercase : bool = None , _lowercase : Optional[Union[float, List[float]]] = None , _lowercase : Optional[Union[float, List[float]]] = None , _lowercase : Optional[Union[str, TensorType]] = None , _lowercase : ChannelDimension = ChannelDimension.FIRST , **_lowercase : Any , ):
A = do_resize if do_resize is not None else self.do_resize
A = size if size is not None else self.size
A = get_size_dict(_lowercase )
A = keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
A = ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
A = resample if resample is not None else self.resample
A = do_rescale if do_rescale is not None else self.do_rescale
A = rescale_factor if rescale_factor is not None else self.rescale_factor
A = do_normalize if do_normalize is not None else self.do_normalize
A = image_mean if image_mean is not None else self.image_mean
A = image_std if image_std is not None else self.image_std
A = make_list_of_images(_lowercase )
if not valid_images(_lowercase ):
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_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.
A = [to_numpy_array(_lowercase ) for image in images]
if do_resize:
A = [self.resize(image=_lowercase , size=_lowercase , resample=_lowercase ) for image in images]
if do_rescale:
A = [self.rescale(image=_lowercase , scale=_lowercase ) for image in images]
if do_normalize:
A = [self.normalize(image=_lowercase , mean=_lowercase , std=_lowercase ) for image in images]
A = [to_channel_dimension_format(_lowercase , _lowercase ) for image in images]
A = {'pixel_values': images}
return BatchFeature(data=_lowercase , tensor_type=_lowercase )
def __a ( self : Any , _lowercase : Dict , _lowercase : List[Tuple] = None ):
A = outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(_lowercase ) != len(_lowercase ):
raise ValueError(
'Make sure that you pass in as many target sizes as the batch dimension of the logits' )
if is_torch_tensor(_lowercase ):
A = target_sizes.numpy()
A = []
for idx in range(len(_lowercase ) ):
A = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) , size=target_sizes[idx] , mode='bilinear' , align_corners=_lowercase )
A = resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(_lowercase )
else:
A = logits.argmax(dim=1 )
A = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation
| 690 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
def __snake_case ( UpperCamelCase__ ) -> int:
"""simple docstring"""
if not postfix_notation:
return 0
A = {'+', '-', '*', '/'}
A = []
for token in postfix_notation:
if token in operations:
A , A = stack.pop(), stack.pop()
if token == "+":
stack.append(a + b )
elif token == "-":
stack.append(a - b )
elif token == "*":
stack.append(a * b )
else:
if a * b < 0 and a % b != 0:
stack.append(a // b + 1 )
else:
stack.append(a // b )
else:
stack.append(int(UpperCamelCase__ ) )
return stack.pop()
if __name__ == "__main__":
import doctest
doctest.testmod()
| 690 | 1 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_rembert import RemBertTokenizer
else:
UpperCamelCase : Any = None
UpperCamelCase : str = logging.get_logger(__name__)
UpperCamelCase : List[str] = {"vocab_file": "sentencepiece.model", "tokenizer_file": "tokenizer.json"}
UpperCamelCase : Any = {
"vocab_file": {
"google/rembert": "https://huggingface.co/google/rembert/resolve/main/sentencepiece.model",
},
"tokenizer_file": {
"google/rembert": "https://huggingface.co/google/rembert/resolve/main/tokenizer.json",
},
}
UpperCamelCase : int = {
"google/rembert": 256,
}
UpperCamelCase : List[Any] = "▁"
class lowerCamelCase__ ( UpperCAmelCase_ ):
lowerCAmelCase = VOCAB_FILES_NAMES
lowerCAmelCase = PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCAmelCase = RemBertTokenizer
def __init__( self : List[str] , _lowercase : Union[str, Any]=None , _lowercase : Any=None , _lowercase : List[str]=True , _lowercase : List[Any]=True , _lowercase : int=False , _lowercase : Union[str, Any]="[CLS]" , _lowercase : List[Any]="[SEP]" , _lowercase : Optional[Any]="<unk>" , _lowercase : List[Any]="[SEP]" , _lowercase : Any="<pad>" , _lowercase : Optional[Any]="[CLS]" , _lowercase : int="[MASK]" , **_lowercase : Optional[Any] , ):
# Mask token behave like a normal word, i.e. include the space before it
A = AddedToken(_lowercase , lstrip=_lowercase , rstrip=_lowercase ) if isinstance(_lowercase , _lowercase ) else mask_token
super().__init__(
_lowercase , tokenizer_file=_lowercase , do_lower_case=_lowercase , remove_space=_lowercase , keep_accents=_lowercase , bos_token=_lowercase , eos_token=_lowercase , unk_token=_lowercase , sep_token=_lowercase , pad_token=_lowercase , cls_token=_lowercase , mask_token=_lowercase , **_lowercase , )
A = do_lower_case
A = remove_space
A = keep_accents
A = vocab_file
A = False if not self.vocab_file else True
def __a ( self : Union[str, Any] , _lowercase : List[int] , _lowercase : Optional[List[int]] = None ):
A = [self.sep_token_id]
A = [self.cls_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def __a ( self : List[Any] , _lowercase : List[int] , _lowercase : Optional[List[int]] = None , _lowercase : bool = False ):
if already_has_special_tokens:
if token_ids_a is not None:
raise ValueError(
'You should not supply a second sequence if the provided sequence of '
'ids is already formatted with special tokens for the model.' )
return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]
if token_ids_a is not None:
return [1] + ([0] * len(_lowercase )) + [1] + ([0] * len(_lowercase )) + [1]
return [1] + ([0] * len(_lowercase )) + [1]
def __a ( self : Tuple , _lowercase : List[int] , _lowercase : Optional[List[int]] = None ):
A = [self.sep_token_id]
A = [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 __a ( self : int , _lowercase : str , _lowercase : Optional[str] = None ):
if not os.path.isdir(_lowercase ):
logger.error('Vocabulary path ({}) should be a directory'.format(_lowercase ) )
return
A = os.path.join(
_lowercase , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_lowercase ):
copyfile(self.vocab_file , _lowercase )
return (out_vocab_file,)
| 690 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_xlnet import XLNetTokenizer
else:
UpperCamelCase : Any = None
UpperCamelCase : int = logging.get_logger(__name__)
UpperCamelCase : Union[str, Any] = {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}
UpperCamelCase : str = {
"vocab_file": {
"xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model",
"xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model",
},
"tokenizer_file": {
"xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json",
"xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json",
},
}
UpperCamelCase : Optional[int] = {
"xlnet-base-cased": None,
"xlnet-large-cased": None,
}
UpperCamelCase : str = "▁"
# Segments (not really needed)
UpperCamelCase : str = 0
UpperCamelCase : int = 1
UpperCamelCase : List[Any] = 2
UpperCamelCase : Union[str, Any] = 3
UpperCamelCase : Optional[Any] = 4
class lowerCamelCase__ ( UpperCAmelCase_ ):
lowerCAmelCase = VOCAB_FILES_NAMES
lowerCAmelCase = PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCAmelCase = """left"""
lowerCAmelCase = XLNetTokenizer
def __init__( self : Tuple , _lowercase : List[Any]=None , _lowercase : Any=None , _lowercase : int=False , _lowercase : Tuple=True , _lowercase : Union[str, Any]=False , _lowercase : int="<s>" , _lowercase : Optional[int]="</s>" , _lowercase : Dict="<unk>" , _lowercase : Optional[int]="<sep>" , _lowercase : int="<pad>" , _lowercase : Dict="<cls>" , _lowercase : str="<mask>" , _lowercase : List[str]=["<eop>", "<eod>"] , **_lowercase : Any , ):
# Mask token behave like a normal word, i.e. include the space before it
A = AddedToken(_lowercase , lstrip=_lowercase , rstrip=_lowercase ) if isinstance(_lowercase , _lowercase ) else mask_token
super().__init__(
vocab_file=_lowercase , tokenizer_file=_lowercase , do_lower_case=_lowercase , remove_space=_lowercase , keep_accents=_lowercase , bos_token=_lowercase , eos_token=_lowercase , unk_token=_lowercase , sep_token=_lowercase , pad_token=_lowercase , cls_token=_lowercase , mask_token=_lowercase , additional_special_tokens=_lowercase , **_lowercase , )
A = 3
A = do_lower_case
A = remove_space
A = keep_accents
A = vocab_file
A = False if not self.vocab_file else True
def __a ( self : List[Any] , _lowercase : List[int] , _lowercase : Optional[List[int]] = None ):
A = [self.sep_token_id]
A = [self.cls_token_id]
if token_ids_a is None:
return token_ids_a + sep + cls
return token_ids_a + sep + token_ids_a + sep + cls
def __a ( self : Tuple , _lowercase : List[int] , _lowercase : Optional[List[int]] = None ):
A = [self.sep_token_id]
A = [2]
if token_ids_a is None:
return len(token_ids_a + sep ) * [0] + cls_segment_id
return len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] + cls_segment_id
def __a ( self : Optional[Any] , _lowercase : str , _lowercase : Optional[str] = None ):
if not self.can_save_slow_tokenizer:
raise ValueError(
'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
'tokenizer.' )
if not os.path.isdir(_lowercase ):
logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
return
A = os.path.join(
_lowercase , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_lowercase ):
copyfile(self.vocab_file , _lowercase )
return (out_vocab_file,)
| 690 | 1 |
"""simple docstring"""
import math
from typing import Callable, List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale import StableDiffusionUpscalePipeline
from diffusers.schedulers import DDIMScheduler, DDPMScheduler, LMSDiscreteScheduler, PNDMScheduler
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__=[] ) -> Optional[Any]:
"""simple docstring"""
A = size[0] - overlap_pixels * 2
A = size[1] - overlap_pixels * 2
for letter in ["l", "r"]:
if letter in remove_borders:
size_x += overlap_pixels
for letter in ["t", "b"]:
if letter in remove_borders:
size_y += overlap_pixels
A = np.ones((size_y, size_x) , dtype=np.uinta ) * 255
A = np.pad(UpperCamelCase__ , mode='linear_ramp' , pad_width=UpperCamelCase__ , end_values=0 )
if "l" in remove_borders:
A = mask[:, overlap_pixels : mask.shape[1]]
if "r" in remove_borders:
A = mask[:, 0 : mask.shape[1] - overlap_pixels]
if "t" in remove_borders:
A = mask[overlap_pixels : mask.shape[0], :]
if "b" in remove_borders:
A = mask[0 : mask.shape[0] - overlap_pixels, :]
return mask
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> Optional[int]:
"""simple docstring"""
return max(UpperCamelCase__ , min(UpperCamelCase__ , UpperCamelCase__ ) )
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> Union[str, Any]:
"""simple docstring"""
return (
clamp(rect[0] , min[0] , max[0] ),
clamp(rect[1] , min[1] , max[1] ),
clamp(rect[2] , min[0] , max[0] ),
clamp(rect[3] , min[1] , max[1] ),
)
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> List[Any]:
"""simple docstring"""
A = list(UpperCamelCase__ )
rect[0] -= overlap
rect[1] -= overlap
rect[2] += overlap
rect[3] += overlap
A = clamp_rect(UpperCamelCase__ , [0, 0] , [image_size[0], image_size[1]] )
return rect
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> Optional[Any]:
"""simple docstring"""
A = Image.new('RGB' , (tile.size[0] + original_slice, tile.size[1]) )
result.paste(
original_image.resize((tile.size[0], tile.size[1]) , Image.BICUBIC ).crop(
(slice_x, 0, slice_x + original_slice, tile.size[1]) ) , (0, 0) , )
result.paste(UpperCamelCase__ , (original_slice, 0) )
return result
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ ) -> Optional[int]:
"""simple docstring"""
A = (original_image_slice * 4, 0, tile.size[0], tile.size[1])
A = tile.crop(UpperCamelCase__ )
return tile
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ ) -> int:
"""simple docstring"""
A = n % d
return n - divisor
class lowerCamelCase__ ( UpperCAmelCase_ ):
def __init__( self : int , _lowercase : AutoencoderKL , _lowercase : CLIPTextModel , _lowercase : CLIPTokenizer , _lowercase : UNetaDConditionModel , _lowercase : DDPMScheduler , _lowercase : Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler] , _lowercase : int = 350 , ):
super().__init__(
vae=_lowercase , text_encoder=_lowercase , tokenizer=_lowercase , unet=_lowercase , low_res_scheduler=_lowercase , scheduler=_lowercase , max_noise_level=_lowercase , )
def __a ( self : Dict , _lowercase : List[str] , _lowercase : List[str] , _lowercase : Any , _lowercase : Optional[Any] , _lowercase : str , _lowercase : List[Any] , _lowercase : Union[str, Any] , **_lowercase : int ):
torch.manual_seed(0 )
A = (
min(image.size[0] - (tile_size + original_image_slice) , x * tile_size ),
min(image.size[1] - (tile_size + original_image_slice) , y * tile_size ),
min(image.size[0] , (x + 1) * tile_size ),
min(image.size[1] , (y + 1) * tile_size ),
)
A = add_overlap_rect(_lowercase , _lowercase , image.size )
A = image.crop(_lowercase )
A = ((crop_rect[0] + ((crop_rect[2] - crop_rect[0]) / 2)) / image.size[0]) * tile.size[0]
A = translated_slice_x - (original_image_slice / 2)
A = max(0 , _lowercase )
A = squeeze_tile(_lowercase , _lowercase , _lowercase , _lowercase )
A = to_input.size
A = to_input.resize((tile_size, tile_size) , Image.BICUBIC )
A = super(_lowercase , self ).__call__(image=_lowercase , **_lowercase ).images[0]
A = upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4) , Image.BICUBIC )
A = unsqueeze_tile(_lowercase , _lowercase )
A = upscaled_tile.resize((tile.size[0] * 4, tile.size[1] * 4) , Image.BICUBIC )
A = []
if x == 0:
remove_borders.append('l' )
elif crop_rect[2] == image.size[0]:
remove_borders.append('r' )
if y == 0:
remove_borders.append('t' )
elif crop_rect[3] == image.size[1]:
remove_borders.append('b' )
A = Image.fromarray(
make_transparency_mask(
(upscaled_tile.size[0], upscaled_tile.size[1]) , tile_border * 4 , remove_borders=_lowercase ) , mode='L' , )
final_image.paste(
_lowercase , (crop_rect_with_overlap[0] * 4, crop_rect_with_overlap[1] * 4) , _lowercase )
@torch.no_grad()
def __call__( self : str , _lowercase : Union[str, List[str]] , _lowercase : Union[PIL.Image.Image, List[PIL.Image.Image]] , _lowercase : int = 75 , _lowercase : float = 9.0 , _lowercase : int = 50 , _lowercase : Optional[Union[str, List[str]]] = None , _lowercase : Optional[int] = 1 , _lowercase : float = 0.0 , _lowercase : Optional[torch.Generator] = None , _lowercase : Optional[torch.FloatTensor] = None , _lowercase : Optional[Callable[[int, int, torch.FloatTensor], None]] = None , _lowercase : int = 1 , _lowercase : int = 128 , _lowercase : int = 32 , _lowercase : int = 32 , ):
A = Image.new('RGB' , (image.size[0] * 4, image.size[1] * 4) )
A = math.ceil(image.size[0] / tile_size )
A = math.ceil(image.size[1] / tile_size )
A = tcx * tcy
A = 0
for y in range(_lowercase ):
for x in range(_lowercase ):
self._process_tile(
_lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , prompt=_lowercase , num_inference_steps=_lowercase , guidance_scale=_lowercase , noise_level=_lowercase , negative_prompt=_lowercase , num_images_per_prompt=_lowercase , eta=_lowercase , generator=_lowercase , latents=_lowercase , )
current_count += 1
if callback is not None:
callback({'progress': current_count / total_tile_count, 'image': final_image} )
return final_image
def __snake_case ( ) -> List[Any]:
"""simple docstring"""
A = 'stabilityai/stable-diffusion-x4-upscaler'
A = StableDiffusionTiledUpscalePipeline.from_pretrained(UpperCamelCase__ , revision='fp16' , torch_dtype=torch.floataa )
A = pipe.to('cuda' )
A = Image.open('../../docs/source/imgs/diffusers_library.jpg' )
def callback(UpperCamelCase__ ):
print(f'progress: {obj["progress"]:.4f}' )
obj["image"].save('diffusers_library_progress.jpg' )
A = pipe(image=UpperCamelCase__ , prompt='Black font, white background, vector' , noise_level=40 , callback=UpperCamelCase__ )
final_image.save('diffusers_library.jpg' )
if __name__ == "__main__":
main()
| 690 |
"""simple docstring"""
from __future__ import annotations
UpperCamelCase : Any = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , ) -> tuple[list[list[int]], list[list[int]]]:
"""simple docstring"""
A = [
[0 for col in range(len(grid[0] ) )] for row in range(len(UpperCamelCase__ ) )
] # the reference grid
A = 1
A = [
[0 for col in range(len(grid[0] ) )] for row in range(len(UpperCamelCase__ ) )
] # the action grid
A = init[0]
A = init[1]
A = 0
A = g + heuristic[x][y] # cost from starting cell to destination cell
A = [[f, g, x, y]]
A = False # flag that is set when search is complete
A = False # flag set if we can't find expand
while not found and not resign:
if len(UpperCamelCase__ ) == 0:
raise ValueError('Algorithm is unable to find solution' )
else: # to choose the least costliest action so as to move closer to the goal
cell.sort()
cell.reverse()
A = cell.pop()
A = next_cell[2]
A = next_cell[3]
A = next_cell[1]
if x == goal[0] and y == goal[1]:
A = True
else:
for i in range(len(UpperCamelCase__ ) ): # to try out different valid actions
A = x + DIRECTIONS[i][0]
A = y + DIRECTIONS[i][1]
if xa >= 0 and xa < len(UpperCamelCase__ ) and ya >= 0 and ya < len(grid[0] ):
if closed[xa][ya] == 0 and grid[xa][ya] == 0:
A = g + cost
A = ga + heuristic[xa][ya]
cell.append([fa, ga, xa, ya] )
A = 1
A = i
A = []
A = goal[0]
A = goal[1]
invpath.append([x, y] ) # we get the reverse path from here
while x != init[0] or y != init[1]:
A = x - DIRECTIONS[action[x][y]][0]
A = y - DIRECTIONS[action[x][y]][1]
A = xa
A = ya
invpath.append([x, y] )
A = []
for i in range(len(UpperCamelCase__ ) ):
path.append(invpath[len(UpperCamelCase__ ) - 1 - i] )
return path, action
if __name__ == "__main__":
UpperCamelCase : Any = [
[0, 1, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 1, 0, 0, 0, 0],
[0, 1, 0, 0, 1, 0],
[0, 0, 0, 0, 1, 0],
]
UpperCamelCase : List[Any] = [0, 0]
# all coordinates are given in format [y,x]
UpperCamelCase : int = [len(grid) - 1, len(grid[0]) - 1]
UpperCamelCase : Tuple = 1
# the cost map which pushes the path closer to the goal
UpperCamelCase : Union[str, Any] = [[0 for row in range(len(grid[0]))] for col in range(len(grid))]
for i in range(len(grid)):
for j in range(len(grid[0])):
UpperCamelCase : List[str] = abs(i - goal[0]) + abs(j - goal[1])
if grid[i][j] == 1:
# added extra penalty in the heuristic map
UpperCamelCase : Dict = 99
UpperCamelCase , UpperCamelCase : Optional[Any] = search(grid, init, goal, cost, heuristic)
print("ACTION MAP")
for i in range(len(action)):
print(action[i])
for i in range(len(path)):
print(path[i])
| 690 | 1 |
"""simple docstring"""
import importlib.metadata
from typing import Union
from packaging.version import Version, parse
from .constants import STR_OPERATION_TO_FUNC
UpperCamelCase : Tuple = parse(importlib.metadata.version("torch"))
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> Optional[int]:
"""simple docstring"""
if operation not in STR_OPERATION_TO_FUNC.keys():
raise ValueError(f'`operation` must be one of {list(STR_OPERATION_TO_FUNC.keys() )}, received {operation}' )
A = STR_OPERATION_TO_FUNC[operation]
if isinstance(UpperCamelCase__ , UpperCamelCase__ ):
A = parse(importlib.metadata.version(UpperCamelCase__ ) )
return operation(UpperCamelCase__ , parse(UpperCamelCase__ ) )
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ ) -> List[str]:
"""simple docstring"""
return compare_versions(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
| 690 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCamelCase : Optional[int] = logging.get_logger(__name__)
UpperCamelCase : int = {"vocab_file": "sentencepiece.model"}
UpperCamelCase : Union[str, Any] = {
"vocab_file": {
"google/rembert": "https://huggingface.co/google/rembert/resolve/main/sentencepiece.model",
},
}
UpperCamelCase : Union[str, Any] = {
"google/rembert": 256,
}
class lowerCamelCase__ ( UpperCAmelCase_ ):
lowerCAmelCase = VOCAB_FILES_NAMES
lowerCAmelCase = PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__( self : Dict , _lowercase : Optional[Any] , _lowercase : Optional[Any]=False , _lowercase : Dict=True , _lowercase : List[str]=True , _lowercase : int="[CLS]" , _lowercase : str="[SEP]" , _lowercase : List[str]="[UNK]" , _lowercase : List[Any]="[SEP]" , _lowercase : Union[str, Any]="[PAD]" , _lowercase : List[str]="[CLS]" , _lowercase : Any="[MASK]" , **_lowercase : Optional[Any] , ):
super().__init__(
do_lower_case=_lowercase , remove_space=_lowercase , keep_accents=_lowercase , bos_token=_lowercase , eos_token=_lowercase , unk_token=_lowercase , sep_token=_lowercase , pad_token=_lowercase , cls_token=_lowercase , mask_token=_lowercase , **_lowercase , )
A = do_lower_case
A = remove_space
A = keep_accents
A = vocab_file
A = spm.SentencePieceProcessor()
self.sp_model.Load(_lowercase )
@property
def __a ( self : Tuple ):
return len(self.sp_model )
def __a ( self : List[str] ):
A = {self.convert_ids_to_tokens(_lowercase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self : Tuple ):
A = self.__dict__.copy()
A = None
return state
def __setstate__( self : List[str] , _lowercase : int ):
A = d
A = spm.SentencePieceProcessor()
self.sp_model.Load(self.vocab_file )
def __a ( self : Dict , _lowercase : Union[str, Any] , _lowercase : Dict=False ):
A = self.sp_model.EncodeAsPieces(_lowercase )
return pieces
def __a ( self : Dict , _lowercase : Tuple ):
return self.sp_model.PieceToId(_lowercase )
def __a ( self : str , _lowercase : Optional[int] ):
return self.sp_model.IdToPiece(_lowercase )
def __a ( self : Optional[int] , _lowercase : Optional[int] ):
A = self.sp_model.decode_pieces(_lowercase )
return out_string
def __a ( self : Optional[int] , _lowercase : List[int] , _lowercase : Optional[List[int]] = None ):
A = [self.sep_token_id]
A = [self.cls_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def __a ( self : Any , _lowercase : List[int] , _lowercase : Optional[List[int]] = None , _lowercase : bool = False ):
if already_has_special_tokens:
if token_ids_a is not None:
raise ValueError(
'You should not supply a second sequence if the provided sequence of '
'ids is already formatted with special tokens for the model.' )
return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]
if token_ids_a is not None:
return [1] + ([0] * len(_lowercase )) + [1] + ([0] * len(_lowercase )) + [1]
return [1] + ([0] * len(_lowercase )) + [1]
def __a ( self : str , _lowercase : List[int] , _lowercase : Optional[List[int]] = None ):
A = [self.sep_token_id]
A = [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 __a ( self : Optional[Any] , _lowercase : str , _lowercase : Optional[str] = None ):
if not os.path.isdir(_lowercase ):
logger.error('Vocabulary path ({}) should be a directory'.format(_lowercase ) )
return
A = os.path.join(
_lowercase , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_lowercase ):
copyfile(self.vocab_file , _lowercase )
return (out_vocab_file,)
| 690 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase : Dict = {
"configuration_luke": ["LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP", "LukeConfig"],
"tokenization_luke": ["LukeTokenizer"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase : Dict = [
"LUKE_PRETRAINED_MODEL_ARCHIVE_LIST",
"LukeForEntityClassification",
"LukeForEntityPairClassification",
"LukeForEntitySpanClassification",
"LukeForMultipleChoice",
"LukeForQuestionAnswering",
"LukeForSequenceClassification",
"LukeForTokenClassification",
"LukeForMaskedLM",
"LukeModel",
"LukePreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
from .tokenization_luke import LukeTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_luke import (
LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
LukeForEntityClassification,
LukeForEntityPairClassification,
LukeForEntitySpanClassification,
LukeForMaskedLM,
LukeForMultipleChoice,
LukeForQuestionAnswering,
LukeForSequenceClassification,
LukeForTokenClassification,
LukeModel,
LukePreTrainedModel,
)
else:
import sys
UpperCamelCase : Tuple = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 690 |
"""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_mobilebert import MobileBertTokenizer
UpperCamelCase : str = logging.get_logger(__name__)
UpperCamelCase : List[str] = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
UpperCamelCase : List[Any] = {
"vocab_file": {"mobilebert-uncased": "https://huggingface.co/google/mobilebert-uncased/resolve/main/vocab.txt"},
"tokenizer_file": {
"mobilebert-uncased": "https://huggingface.co/google/mobilebert-uncased/resolve/main/tokenizer.json"
},
}
UpperCamelCase : Any = {"mobilebert-uncased": 512}
UpperCamelCase : Any = {}
class lowerCamelCase__ ( UpperCAmelCase_ ):
lowerCAmelCase = VOCAB_FILES_NAMES
lowerCAmelCase = PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase = PRETRAINED_INIT_CONFIGURATION
lowerCAmelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCAmelCase = MobileBertTokenizer
def __init__( self : Optional[int] , _lowercase : Optional[int]=None , _lowercase : Any=None , _lowercase : Optional[int]=True , _lowercase : int="[UNK]" , _lowercase : Dict="[SEP]" , _lowercase : Any="[PAD]" , _lowercase : str="[CLS]" , _lowercase : Union[str, Any]="[MASK]" , _lowercase : List[Any]=True , _lowercase : Any=None , **_lowercase : Optional[Any] , ):
super().__init__(
_lowercase , tokenizer_file=_lowercase , do_lower_case=_lowercase , unk_token=_lowercase , sep_token=_lowercase , pad_token=_lowercase , cls_token=_lowercase , mask_token=_lowercase , tokenize_chinese_chars=_lowercase , strip_accents=_lowercase , **_lowercase , )
A = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('lowercase' , _lowercase ) != do_lower_case
or normalizer_state.get('strip_accents' , _lowercase ) != strip_accents
or normalizer_state.get('handle_chinese_chars' , _lowercase ) != tokenize_chinese_chars
):
A = getattr(_lowercase , normalizer_state.pop('type' ) )
A = do_lower_case
A = strip_accents
A = tokenize_chinese_chars
A = normalizer_class(**_lowercase )
A = do_lower_case
def __a ( self : List[Any] , _lowercase : Tuple , _lowercase : Any=None ):
A = [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 __a ( self : Any , _lowercase : List[int] , _lowercase : Optional[List[int]] = None ):
A = [self.sep_token_id]
A = [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 __a ( self : Dict , _lowercase : str , _lowercase : Optional[str] = None ):
A = self._tokenizer.model.save(_lowercase , name=_lowercase )
return tuple(_lowercase )
| 690 | 1 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_xlnet import XLNetTokenizer
else:
UpperCamelCase : Any = None
UpperCamelCase : int = logging.get_logger(__name__)
UpperCamelCase : Union[str, Any] = {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}
UpperCamelCase : str = {
"vocab_file": {
"xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model",
"xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model",
},
"tokenizer_file": {
"xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json",
"xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json",
},
}
UpperCamelCase : Optional[int] = {
"xlnet-base-cased": None,
"xlnet-large-cased": None,
}
UpperCamelCase : str = "▁"
# Segments (not really needed)
UpperCamelCase : str = 0
UpperCamelCase : int = 1
UpperCamelCase : List[Any] = 2
UpperCamelCase : Union[str, Any] = 3
UpperCamelCase : Optional[Any] = 4
class lowerCamelCase__ ( UpperCAmelCase_ ):
lowerCAmelCase = VOCAB_FILES_NAMES
lowerCAmelCase = PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCAmelCase = """left"""
lowerCAmelCase = XLNetTokenizer
def __init__( self : Tuple , _lowercase : List[Any]=None , _lowercase : Any=None , _lowercase : int=False , _lowercase : Tuple=True , _lowercase : Union[str, Any]=False , _lowercase : int="<s>" , _lowercase : Optional[int]="</s>" , _lowercase : Dict="<unk>" , _lowercase : Optional[int]="<sep>" , _lowercase : int="<pad>" , _lowercase : Dict="<cls>" , _lowercase : str="<mask>" , _lowercase : List[str]=["<eop>", "<eod>"] , **_lowercase : Any , ):
# Mask token behave like a normal word, i.e. include the space before it
A = AddedToken(_lowercase , lstrip=_lowercase , rstrip=_lowercase ) if isinstance(_lowercase , _lowercase ) else mask_token
super().__init__(
vocab_file=_lowercase , tokenizer_file=_lowercase , do_lower_case=_lowercase , remove_space=_lowercase , keep_accents=_lowercase , bos_token=_lowercase , eos_token=_lowercase , unk_token=_lowercase , sep_token=_lowercase , pad_token=_lowercase , cls_token=_lowercase , mask_token=_lowercase , additional_special_tokens=_lowercase , **_lowercase , )
A = 3
A = do_lower_case
A = remove_space
A = keep_accents
A = vocab_file
A = False if not self.vocab_file else True
def __a ( self : List[Any] , _lowercase : List[int] , _lowercase : Optional[List[int]] = None ):
A = [self.sep_token_id]
A = [self.cls_token_id]
if token_ids_a is None:
return token_ids_a + sep + cls
return token_ids_a + sep + token_ids_a + sep + cls
def __a ( self : Tuple , _lowercase : List[int] , _lowercase : Optional[List[int]] = None ):
A = [self.sep_token_id]
A = [2]
if token_ids_a is None:
return len(token_ids_a + sep ) * [0] + cls_segment_id
return len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] + cls_segment_id
def __a ( self : Optional[Any] , _lowercase : str , _lowercase : Optional[str] = None ):
if not self.can_save_slow_tokenizer:
raise ValueError(
'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
'tokenizer.' )
if not os.path.isdir(_lowercase ):
logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
return
A = os.path.join(
_lowercase , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_lowercase ):
copyfile(self.vocab_file , _lowercase )
return (out_vocab_file,)
| 690 |
"""simple docstring"""
def __snake_case ( UpperCamelCase__ ) -> list[int]:
"""simple docstring"""
A = [0 for i in range(len(UpperCamelCase__ ) )]
# initialize interval's left pointer and right pointer
A , A = 0, 0
for i in range(1 , len(UpperCamelCase__ ) ):
# case when current index is inside the interval
if i <= right_pointer:
A = min(right_pointer - i + 1 , z_result[i - left_pointer] )
A = min_edge
while go_next(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
z_result[i] += 1
# if new index's result gives us more right interval,
# we've to update left_pointer and right_pointer
if i + z_result[i] - 1 > right_pointer:
A , A = i, i + z_result[i] - 1
return z_result
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> bool:
"""simple docstring"""
return i + z_result[i] < len(UpperCamelCase__ ) and s[z_result[i]] == s[i + z_result[i]]
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ ) -> int:
"""simple docstring"""
A = 0
# concatenate 'pattern' and 'input_str' and call z_function
# with concatenated string
A = z_function(pattern + input_str )
for val in z_result:
# if value is greater then length of the pattern string
# that means this index is starting position of substring
# which is equal to pattern string
if val >= len(UpperCamelCase__ ):
answer += 1
return answer
if __name__ == "__main__":
import doctest
doctest.testmod()
| 690 | 1 |
"""simple docstring"""
import fire
from utils import calculate_rouge, save_json
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__=None , **UpperCamelCase__ ) -> Optional[int]:
"""simple docstring"""
A = [x.strip() for x in open(UpperCamelCase__ ).readlines()]
A = [x.strip() for x in open(UpperCamelCase__ ).readlines()][: len(UpperCamelCase__ )]
A = calculate_rouge(UpperCamelCase__ , UpperCamelCase__ , **UpperCamelCase__ )
if save_path is not None:
save_json(UpperCamelCase__ , UpperCamelCase__ , indent=UpperCamelCase__ )
return metrics # these print nicely
if __name__ == "__main__":
fire.Fire(calculate_rouge_path)
| 690 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
enable_full_determinism,
load_numpy,
nightly,
require_torch_gpu,
slow,
torch_device,
)
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class lowerCamelCase__ ( UpperCAmelCase_ , unittest.TestCase ):
lowerCAmelCase = LDMTextToImagePipeline
lowerCAmelCase = TEXT_TO_IMAGE_PARAMS - {
"""negative_prompt""",
"""negative_prompt_embeds""",
"""cross_attention_kwargs""",
"""prompt_embeds""",
}
lowerCAmelCase = PipelineTesterMixin.required_optional_params - {
"""num_images_per_prompt""",
"""callback""",
"""callback_steps""",
}
lowerCAmelCase = TEXT_TO_IMAGE_BATCH_PARAMS
lowerCAmelCase = False
def __a ( self : Dict ):
torch.manual_seed(0 )
A = 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 , )
A = DDIMScheduler(
beta_start=0.0_0_0_8_5 , beta_end=0.0_1_2 , beta_schedule='scaled_linear' , clip_sample=_lowercase , set_alpha_to_one=_lowercase , )
torch.manual_seed(0 )
A = 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 )
A = 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=1_000 , )
A = CLIPTextModel(_lowercase )
A = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
A = {
'unet': unet,
'scheduler': scheduler,
'vqvae': vae,
'bert': text_encoder,
'tokenizer': tokenizer,
}
return components
def __a ( self : Union[str, Any] , _lowercase : Union[str, Any] , _lowercase : Union[str, Any]=0 ):
if str(_lowercase ).startswith('mps' ):
A = torch.manual_seed(_lowercase )
else:
A = torch.Generator(device=_lowercase ).manual_seed(_lowercase )
A = {
'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 __a ( self : Any ):
A = 'cpu' # ensure determinism for the device-dependent torch.Generator
A = self.get_dummy_components()
A = LDMTextToImagePipeline(**_lowercase )
pipe.to(_lowercase )
pipe.set_progress_bar_config(disable=_lowercase )
A = self.get_dummy_inputs(_lowercase )
A = pipe(**_lowercase ).images
A = image[0, -3:, -3:, -1]
assert image.shape == (1, 16, 16, 3)
A = np.array([0.6_1_0_1, 0.6_1_5_6, 0.5_6_2_2, 0.4_8_9_5, 0.6_6_6_1, 0.3_8_0_4, 0.5_7_4_8, 0.6_1_3_6, 0.5_0_1_4] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
@slow
@require_torch_gpu
class lowerCamelCase__ ( unittest.TestCase ):
def __a ( self : Optional[Any] ):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __a ( self : int , _lowercase : List[Any] , _lowercase : int=torch.floataa , _lowercase : int=0 ):
A = torch.manual_seed(_lowercase )
A = np.random.RandomState(_lowercase ).standard_normal((1, 4, 32, 32) )
A = torch.from_numpy(_lowercase ).to(device=_lowercase , dtype=_lowercase )
A = {
'prompt': 'A painting of a squirrel eating a burger',
'latents': latents,
'generator': generator,
'num_inference_steps': 3,
'guidance_scale': 6.0,
'output_type': 'numpy',
}
return inputs
def __a ( self : Union[str, Any] ):
A = LDMTextToImagePipeline.from_pretrained('CompVis/ldm-text2im-large-256' ).to(_lowercase )
pipe.set_progress_bar_config(disable=_lowercase )
A = self.get_inputs(_lowercase )
A = pipe(**_lowercase ).images
A = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 256, 256, 3)
A = np.array([0.5_1_8_2_5, 0.5_2_8_5_0, 0.5_2_5_4_3, 0.5_4_2_5_8, 0.5_2_3_0_4, 0.5_2_5_6_9, 0.5_4_3_6_3, 0.5_5_2_7_6, 0.5_6_8_7_8] )
A = np.abs(expected_slice - image_slice ).max()
assert max_diff < 1e-3
@nightly
@require_torch_gpu
class lowerCamelCase__ ( unittest.TestCase ):
def __a ( self : List[Any] ):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __a ( self : List[Any] , _lowercase : Optional[Any] , _lowercase : Tuple=torch.floataa , _lowercase : Optional[Any]=0 ):
A = torch.manual_seed(_lowercase )
A = np.random.RandomState(_lowercase ).standard_normal((1, 4, 32, 32) )
A = torch.from_numpy(_lowercase ).to(device=_lowercase , dtype=_lowercase )
A = {
'prompt': 'A painting of a squirrel eating a burger',
'latents': latents,
'generator': generator,
'num_inference_steps': 50,
'guidance_scale': 6.0,
'output_type': 'numpy',
}
return inputs
def __a ( self : List[str] ):
A = LDMTextToImagePipeline.from_pretrained('CompVis/ldm-text2im-large-256' ).to(_lowercase )
pipe.set_progress_bar_config(disable=_lowercase )
A = self.get_inputs(_lowercase )
A = pipe(**_lowercase ).images[0]
A = load_numpy(
'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/ldm_text2img/ldm_large_256_ddim.npy' )
A = np.abs(expected_image - image ).max()
assert max_diff < 1e-3
| 690 | 1 |
"""simple docstring"""
import random
import torch
from huggingface_hub import HfApi
from diffusers import UNetaDModel
UpperCamelCase : Optional[int] = HfApi()
UpperCamelCase : Optional[Any] = {}
# fmt: off
UpperCamelCase : Optional[int] = torch.tensor([
-0.7_515, -1.6_883, 0.2_420, 0.0_300, 0.6_347, 1.3_433, -1.1_743, -3.7_467,
1.2_342, -2.2_485, 0.4_636, 0.8_076, -0.7_991, 0.3_969, 0.8_498, 0.9_189,
-1.8_887, -3.3_522, 0.7_639, 0.2_040, 0.6_271, -2.7_148, -1.6_316, 3.0_839,
0.3_186, 0.2_721, -0.9_759, -1.2_461, 2.6_257, 1.3_557
])
UpperCamelCase : Optional[Any] = torch.tensor([
-2.3_639, -2.5_344, 0.0_054, -0.6_674, 1.5_990, 1.0_158, 0.3_124, -2.1_436,
1.8_795, -2.5_429, -0.1_566, -0.3_973, 1.2_490, 2.6_447, 1.2_283, -0.5_208,
-2.8_154, -3.5_119, 2.3_838, 1.2_033, 1.7_201, -2.1_256, -1.4_576, 2.7_948,
2.4_204, -0.9_752, -1.2_546, 0.8_027, 3.2_758, 3.1_365
])
UpperCamelCase : Dict = torch.tensor([
-0.6_531, -0.6_891, -0.3_172, -0.5_375, -0.9_140, -0.5_367, -0.1_175, -0.7_869,
-0.3_808, -0.4_513, -0.2_098, -0.0_083, 0.3_183, 0.5_140, 0.2_247, -0.1_304,
-0.1_302, -0.2_802, -0.2_084, -0.2_025, -0.4_967, -0.4_873, -0.0_861, 0.6_925,
0.0_250, 0.1_290, -0.1_543, 0.6_316, 1.0_460, 1.4_943
])
UpperCamelCase : Tuple = torch.tensor([
0.0_911, 0.1_107, 0.0_182, 0.0_435, -0.0_805, -0.0_608, 0.0_381, 0.2_172,
-0.0_280, 0.1_327, -0.0_299, -0.0_255, -0.0_050, -0.1_170, -0.1_046, 0.0_309,
0.1_367, 0.1_728, -0.0_533, -0.0_748, -0.0_534, 0.1_624, 0.0_384, -0.1_805,
-0.0_707, 0.0_642, 0.0_220, -0.0_134, -0.1_333, -0.1_505
])
UpperCamelCase : Tuple = torch.tensor([
0.1_321, 0.1_337, 0.0_440, 0.0_622, -0.0_591, -0.0_370, 0.0_503, 0.2_133,
-0.0_177, 0.1_415, -0.0_116, -0.0_112, 0.0_044, -0.0_980, -0.0_789, 0.0_395,
0.1_502, 0.1_785, -0.0_488, -0.0_514, -0.0_404, 0.1_539, 0.0_454, -0.1_559,
-0.0_665, 0.0_659, 0.0_383, -0.0_005, -0.1_266, -0.1_386
])
UpperCamelCase : Dict = torch.tensor([
0.1_154, 0.1_218, 0.0_307, 0.0_526, -0.0_711, -0.0_541, 0.0_366, 0.2_078,
-0.0_267, 0.1_317, -0.0_226, -0.0_193, -0.0_014, -0.1_055, -0.0_902, 0.0_330,
0.1_391, 0.1_709, -0.0_562, -0.0_693, -0.0_560, 0.1_482, 0.0_381, -0.1_683,
-0.0_681, 0.0_661, 0.0_331, -0.0_046, -0.1_268, -0.1_431
])
UpperCamelCase : int = torch.tensor([
0.1_192, 0.1_240, 0.0_414, 0.0_606, -0.0_557, -0.0_412, 0.0_430, 0.2_042,
-0.0_200, 0.1_385, -0.0_115, -0.0_132, 0.0_017, -0.0_965, -0.0_802, 0.0_398,
0.1_433, 0.1_747, -0.0_458, -0.0_533, -0.0_407, 0.1_545, 0.0_419, -0.1_574,
-0.0_645, 0.0_626, 0.0_341, -0.0_010, -0.1_199, -0.1_390
])
UpperCamelCase : Tuple = torch.tensor([
0.1_075, 0.1_074, 0.0_205, 0.0_431, -0.0_774, -0.0_607, 0.0_298, 0.2_042,
-0.0_320, 0.1_267, -0.0_281, -0.0_250, -0.0_064, -0.1_091, -0.0_946, 0.0_290,
0.1_328, 0.1_650, -0.0_580, -0.0_738, -0.0_586, 0.1_440, 0.0_337, -0.1_746,
-0.0_712, 0.0_605, 0.0_250, -0.0_099, -0.1_316, -0.1_473
])
UpperCamelCase : Optional[int] = torch.tensor([
-1.4_572, -2.0_481, -0.0_414, -0.6_005, 1.4_136, 0.5_848, 0.4_028, -2.7_330,
1.2_212, -2.1_228, 0.2_155, 0.4_039, 0.7_662, 2.0_535, 0.7_477, -0.3_243,
-2.1_758, -2.7_648, 1.6_947, 0.7_026, 1.2_338, -1.6_078, -0.8_682, 2.2_810,
1.8_574, -0.5_718, -0.5_586, -0.0_186, 2.3_415, 2.1_251])
UpperCamelCase : int = torch.tensor([
-1.3_690, -1.9_720, -0.4_090, -0.6_966, 1.4_660, 0.9_938, -0.1_385, -2.7_324,
0.7_736, -1.8_917, 0.2_923, 0.4_293, 0.1_693, 1.4_112, 1.1_887, -0.3_181,
-2.2_160, -2.6_381, 1.3_170, 0.8_163, 0.9_240, -1.6_544, -0.6_099, 2.5_259,
1.6_430, -0.9_090, -0.9_392, -0.0_126, 2.4_268, 2.3_266
])
UpperCamelCase : Tuple = torch.tensor([
-1.3_525, -1.9_628, -0.3_956, -0.6_860, 1.4_664, 1.0_014, -0.1_259, -2.7_212,
0.7_772, -1.8_811, 0.2_996, 0.4_388, 0.1_704, 1.4_029, 1.1_701, -0.3_027,
-2.2_053, -2.6_287, 1.3_350, 0.8_131, 0.9_274, -1.6_292, -0.6_098, 2.5_131,
1.6_505, -0.8_958, -0.9_298, -0.0_151, 2.4_257, 2.3_355
])
UpperCamelCase : Any = torch.tensor([
-2.0_585, -2.7_897, -0.2_850, -0.8_940, 1.9_052, 0.5_702, 0.6_345, -3.8_959,
1.5_932, -3.2_319, 0.1_974, 0.0_287, 1.7_566, 2.6_543, 0.8_387, -0.5_351,
-3.2_736, -4.3_375, 2.9_029, 1.6_390, 1.4_640, -2.1_701, -1.9_013, 2.9_341,
3.4_981, -0.6_255, -1.1_644, -0.1_591, 3.7_097, 3.2_066
])
UpperCamelCase : Tuple = torch.tensor([
-2.3_139, -2.5_594, -0.0_197, -0.6_785, 1.7_001, 1.1_606, 0.3_075, -2.1_740,
1.8_071, -2.5_630, -0.0_926, -0.3_811, 1.2_116, 2.6_246, 1.2_731, -0.5_398,
-2.8_153, -3.6_140, 2.3_893, 1.3_262, 1.6_258, -2.1_856, -1.3_267, 2.8_395,
2.3_779, -1.0_623, -1.2_468, 0.8_959, 3.3_367, 3.2_243
])
UpperCamelCase : Dict = torch.tensor([
-2.0_628, -2.7_667, -0.2_089, -0.8_263, 2.0_539, 0.5_992, 0.6_495, -3.8_336,
1.6_025, -3.2_817, 0.1_721, -0.0_633, 1.7_516, 2.7_039, 0.8_100, -0.5_908,
-3.2_113, -4.4_343, 2.9_257, 1.3_632, 1.5_562, -2.1_489, -1.9_894, 3.0_560,
3.3_396, -0.7_328, -1.0_417, 0.0_383, 3.7_093, 3.2_343
])
UpperCamelCase : Dict = torch.tensor([
-1.4_574, -2.0_569, -0.0_473, -0.6_117, 1.4_018, 0.5_769, 0.4_129, -2.7_344,
1.2_241, -2.1_397, 0.2_000, 0.3_937, 0.7_616, 2.0_453, 0.7_324, -0.3_391,
-2.1_746, -2.7_744, 1.6_963, 0.6_921, 1.2_187, -1.6_172, -0.8_877, 2.2_439,
1.8_471, -0.5_839, -0.5_605, -0.0_464, 2.3_250, 2.1_219
])
# fmt: on
UpperCamelCase : int = api.list_models(filter="diffusers")
for mod in models:
if "google" in mod.author or mod.modelId == "CompVis/ldm-celebahq-256":
UpperCamelCase : str = "/home/patrick/google_checkpoints/" + mod.modelId.split("/")[-1]
print(F"""Started running {mod.modelId}!!!""")
if mod.modelId.startswith("CompVis"):
UpperCamelCase : Optional[int] = UNetaDModel.from_pretrained(local_checkpoint, subfolder="unet")
else:
UpperCamelCase : Tuple = UNetaDModel.from_pretrained(local_checkpoint)
torch.manual_seed(0)
random.seed(0)
UpperCamelCase : Dict = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
UpperCamelCase : Optional[Any] = torch.tensor([10] * noise.shape[0])
with torch.no_grad():
UpperCamelCase : Union[str, Any] = model(noise, time_step).sample
assert torch.allclose(
logits[0, 0, 0, :30], results["_".join("_".join(mod.modelId.split("/")).split("-"))], atol=1E-3
)
print(F"""{mod.modelId} has passed successfully!!!""")
| 690 |
"""simple docstring"""
import os
import tempfile
import unittest
import numpy as np
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
from diffusers import FlaxDDIMScheduler, FlaxDiffusionPipeline, FlaxStableDiffusionPipeline
@require_flax
class lowerCamelCase__ ( unittest.TestCase ):
def __a ( self : Union[str, Any] ):
with tempfile.TemporaryDirectory() as tmpdirname:
# pipeline has Flax weights
A = FlaxDiffusionPipeline.from_pretrained(
'hf-internal-testing/tiny-stable-diffusion-pipe' , safety_checker=_lowercase , cache_dir=_lowercase )
A = [t[-1] for t in os.walk(os.path.join(_lowercase , os.listdir(_lowercase )[0] , 'snapshots' ) )]
A = [item for sublist in all_root_files for item in sublist]
# None of the downloaded files should be a PyTorch file even if we have some here:
# https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe/blob/main/unet/diffusion_pytorch_model.bin
assert not any(f.endswith('.bin' ) for f in files )
@slow
@require_flax
class lowerCamelCase__ ( unittest.TestCase ):
def __a ( self : Optional[Any] ):
A , A = FlaxStableDiffusionPipeline.from_pretrained(
'hf-internal-testing/tiny-stable-diffusion-pipe' , safety_checker=_lowercase )
A = (
'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
' field, close up, split lighting, cinematic'
)
A = jax.random.PRNGKey(0 )
A = 4
A = jax.device_count()
A = num_samples * [prompt]
A = pipeline.prepare_inputs(_lowercase )
# shard inputs and rng
A = replicate(_lowercase )
A = jax.random.split(_lowercase , _lowercase )
A = shard(_lowercase )
A = pipeline(_lowercase , _lowercase , _lowercase , _lowercase , jit=_lowercase ).images
assert images.shape == (num_samples, 1, 64, 64, 3)
if jax.device_count() == 8:
assert np.abs(np.abs(images[0, 0, :2, :2, -2:] , dtype=np.floataa ).sum() - 4.1_5_1_4_7_4_5 ) < 1e-3
assert np.abs(np.abs(_lowercase , dtype=np.floataa ).sum() - 4_9_9_4_7.8_7_5 ) < 5e-1
A = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:] ) ) )
assert len(_lowercase ) == num_samples
def __a ( self : Dict ):
A , A = FlaxStableDiffusionPipeline.from_pretrained(
'CompVis/stable-diffusion-v1-4' , revision='flax' , safety_checker=_lowercase )
A = (
'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
' field, close up, split lighting, cinematic'
)
A = jax.random.PRNGKey(0 )
A = 50
A = jax.device_count()
A = num_samples * [prompt]
A = pipeline.prepare_inputs(_lowercase )
# shard inputs and rng
A = replicate(_lowercase )
A = jax.random.split(_lowercase , _lowercase )
A = shard(_lowercase )
A = pipeline(_lowercase , _lowercase , _lowercase , _lowercase , jit=_lowercase ).images
assert images.shape == (num_samples, 1, 512, 512, 3)
if jax.device_count() == 8:
assert np.abs((np.abs(images[0, 0, :2, :2, -2:] , dtype=np.floataa ).sum() - 0.0_5_6_5_2_4_0_1) ) < 1e-3
assert np.abs((np.abs(_lowercase , dtype=np.floataa ).sum() - 2_3_8_3_8_0_8.2) ) < 5e-1
def __a ( self : List[str] ):
A , A = FlaxStableDiffusionPipeline.from_pretrained(
'CompVis/stable-diffusion-v1-4' , revision='bf16' , dtype=jnp.bfloataa , safety_checker=_lowercase )
A = (
'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
' field, close up, split lighting, cinematic'
)
A = jax.random.PRNGKey(0 )
A = 50
A = jax.device_count()
A = num_samples * [prompt]
A = pipeline.prepare_inputs(_lowercase )
# shard inputs and rng
A = replicate(_lowercase )
A = jax.random.split(_lowercase , _lowercase )
A = shard(_lowercase )
A = pipeline(_lowercase , _lowercase , _lowercase , _lowercase , jit=_lowercase ).images
assert images.shape == (num_samples, 1, 512, 512, 3)
if jax.device_count() == 8:
assert np.abs((np.abs(images[0, 0, :2, :2, -2:] , dtype=np.floataa ).sum() - 0.0_4_0_0_3_9_0_6) ) < 1e-3
assert np.abs((np.abs(_lowercase , dtype=np.floataa ).sum() - 2_3_7_3_5_1_6.7_5) ) < 5e-1
def __a ( self : str ):
A , A = FlaxStableDiffusionPipeline.from_pretrained(
'CompVis/stable-diffusion-v1-4' , revision='bf16' , dtype=jnp.bfloataa )
A = (
'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
' field, close up, split lighting, cinematic'
)
A = jax.random.PRNGKey(0 )
A = 50
A = jax.device_count()
A = num_samples * [prompt]
A = pipeline.prepare_inputs(_lowercase )
# shard inputs and rng
A = replicate(_lowercase )
A = jax.random.split(_lowercase , _lowercase )
A = shard(_lowercase )
A = pipeline(_lowercase , _lowercase , _lowercase , _lowercase , jit=_lowercase ).images
assert images.shape == (num_samples, 1, 512, 512, 3)
if jax.device_count() == 8:
assert np.abs((np.abs(images[0, 0, :2, :2, -2:] , dtype=np.floataa ).sum() - 0.0_4_0_0_3_9_0_6) ) < 1e-3
assert np.abs((np.abs(_lowercase , dtype=np.floataa ).sum() - 2_3_7_3_5_1_6.7_5) ) < 5e-1
def __a ( self : Any ):
A = FlaxDDIMScheduler(
beta_start=0.0_0_0_8_5 , beta_end=0.0_1_2 , beta_schedule='scaled_linear' , set_alpha_to_one=_lowercase , steps_offset=1 , )
A , A = FlaxStableDiffusionPipeline.from_pretrained(
'CompVis/stable-diffusion-v1-4' , revision='bf16' , dtype=jnp.bfloataa , scheduler=_lowercase , safety_checker=_lowercase , )
A = scheduler.create_state()
A = scheduler_state
A = (
'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
' field, close up, split lighting, cinematic'
)
A = jax.random.PRNGKey(0 )
A = 50
A = jax.device_count()
A = num_samples * [prompt]
A = pipeline.prepare_inputs(_lowercase )
# shard inputs and rng
A = replicate(_lowercase )
A = jax.random.split(_lowercase , _lowercase )
A = shard(_lowercase )
A = pipeline(_lowercase , _lowercase , _lowercase , _lowercase , jit=_lowercase ).images
assert images.shape == (num_samples, 1, 512, 512, 3)
if jax.device_count() == 8:
assert np.abs((np.abs(images[0, 0, :2, :2, -2:] , dtype=np.floataa ).sum() - 0.0_4_5_0_4_3_9_4_5) ) < 1e-3
assert np.abs((np.abs(_lowercase , dtype=np.floataa ).sum() - 2_3_4_7_6_9_3.5) ) < 5e-1
def __a ( self : List[str] ):
A = (
'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
' field, close up, split lighting, cinematic'
)
A = jax.device_count()
A = num_samples * [prompt]
A = jax.random.split(jax.random.PRNGKey(0 ) , _lowercase )
A , A = FlaxStableDiffusionPipeline.from_pretrained(
'CompVis/stable-diffusion-v1-4' , revision='bf16' , dtype=jnp.bfloataa , safety_checker=_lowercase , )
A = replicate(_lowercase )
A = pipeline.prepare_inputs(_lowercase )
A = shard(_lowercase )
A = pipeline(_lowercase , _lowercase , _lowercase , jit=_lowercase ).images
assert images.shape == (num_samples, 1, 512, 512, 3)
A = images[2, 0, 256, 10:17, 1]
# With memory efficient attention
A , A = FlaxStableDiffusionPipeline.from_pretrained(
'CompVis/stable-diffusion-v1-4' , revision='bf16' , dtype=jnp.bfloataa , safety_checker=_lowercase , use_memory_efficient_attention=_lowercase , )
A = replicate(_lowercase )
A = pipeline.prepare_inputs(_lowercase )
A = shard(_lowercase )
A = pipeline(_lowercase , _lowercase , _lowercase , jit=_lowercase ).images
assert images_eff.shape == (num_samples, 1, 512, 512, 3)
A = images[2, 0, 256, 10:17, 1]
# I checked the results visually and they are very similar. However, I saw that the max diff is `1` and the `sum`
# over the 8 images is exactly `256`, which is very suspicious. Testing a random slice for now.
assert abs(slice_eff - slice ).max() < 1e-2
| 690 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
UpperCamelCase : Any = {
"configuration_gpt_neo": ["GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoConfig", "GPTNeoOnnxConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase : Any = [
"GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST",
"GPTNeoForCausalLM",
"GPTNeoForQuestionAnswering",
"GPTNeoForSequenceClassification",
"GPTNeoForTokenClassification",
"GPTNeoModel",
"GPTNeoPreTrainedModel",
"load_tf_weights_in_gpt_neo",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase : Any = [
"FlaxGPTNeoForCausalLM",
"FlaxGPTNeoModel",
"FlaxGPTNeoPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt_neo import (
GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
GPTNeoForCausalLM,
GPTNeoForQuestionAnswering,
GPTNeoForSequenceClassification,
GPTNeoForTokenClassification,
GPTNeoModel,
GPTNeoPreTrainedModel,
load_tf_weights_in_gpt_neo,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel
else:
import sys
UpperCamelCase : List[str] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 690 |
"""simple docstring"""
import os
import sys
UpperCamelCase : Optional[int] = os.path.join(os.path.dirname(__file__), "src")
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
AutoModelForSequenceClassification,
AutoTokenizer,
add_start_docstrings,
)
UpperCamelCase : Dict = [
"torch",
"numpy",
"tokenizers",
"filelock",
"requests",
"tqdm",
"regex",
"sentencepiece",
"sacremoses",
"importlib_metadata",
"huggingface_hub",
]
@add_start_docstrings(AutoConfig.__doc__ )
def __snake_case ( *UpperCamelCase__ , **UpperCamelCase__ ) -> Dict:
"""simple docstring"""
return AutoConfig.from_pretrained(*UpperCamelCase__ , **UpperCamelCase__ )
@add_start_docstrings(AutoTokenizer.__doc__ )
def __snake_case ( *UpperCamelCase__ , **UpperCamelCase__ ) -> List[str]:
"""simple docstring"""
return AutoTokenizer.from_pretrained(*UpperCamelCase__ , **UpperCamelCase__ )
@add_start_docstrings(AutoModel.__doc__ )
def __snake_case ( *UpperCamelCase__ , **UpperCamelCase__ ) -> str:
"""simple docstring"""
return AutoModel.from_pretrained(*UpperCamelCase__ , **UpperCamelCase__ )
@add_start_docstrings(AutoModelForCausalLM.__doc__ )
def __snake_case ( *UpperCamelCase__ , **UpperCamelCase__ ) -> List[Any]:
"""simple docstring"""
return AutoModelForCausalLM.from_pretrained(*UpperCamelCase__ , **UpperCamelCase__ )
@add_start_docstrings(AutoModelForMaskedLM.__doc__ )
def __snake_case ( *UpperCamelCase__ , **UpperCamelCase__ ) -> Optional[Any]:
"""simple docstring"""
return AutoModelForMaskedLM.from_pretrained(*UpperCamelCase__ , **UpperCamelCase__ )
@add_start_docstrings(AutoModelForSequenceClassification.__doc__ )
def __snake_case ( *UpperCamelCase__ , **UpperCamelCase__ ) -> List[str]:
"""simple docstring"""
return AutoModelForSequenceClassification.from_pretrained(*UpperCamelCase__ , **UpperCamelCase__ )
@add_start_docstrings(AutoModelForQuestionAnswering.__doc__ )
def __snake_case ( *UpperCamelCase__ , **UpperCamelCase__ ) -> int:
"""simple docstring"""
return AutoModelForQuestionAnswering.from_pretrained(*UpperCamelCase__ , **UpperCamelCase__ )
| 690 | 1 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class lowerCamelCase__ ( metaclass=UpperCAmelCase_ ):
lowerCAmelCase = ["""torch"""]
def __init__( self : Any , *_lowercase : Optional[Any] , **_lowercase : str ):
requires_backends(self , ['torch'] )
@classmethod
def __a ( cls : Dict , *_lowercase : Tuple , **_lowercase : List[str] ):
requires_backends(cls , ['torch'] )
@classmethod
def __a ( cls : List[str] , *_lowercase : Optional[int] , **_lowercase : List[Any] ):
requires_backends(cls , ['torch'] )
class lowerCamelCase__ ( metaclass=UpperCAmelCase_ ):
lowerCAmelCase = ["""torch"""]
def __init__( self : Dict , *_lowercase : Dict , **_lowercase : Optional[int] ):
requires_backends(self , ['torch'] )
@classmethod
def __a ( cls : Any , *_lowercase : Optional[int] , **_lowercase : Tuple ):
requires_backends(cls , ['torch'] )
@classmethod
def __a ( cls : str , *_lowercase : List[str] , **_lowercase : Optional[Any] ):
requires_backends(cls , ['torch'] )
class lowerCamelCase__ ( metaclass=UpperCAmelCase_ ):
lowerCAmelCase = ["""torch"""]
def __init__( self : Optional[Any] , *_lowercase : Optional[Any] , **_lowercase : List[Any] ):
requires_backends(self , ['torch'] )
@classmethod
def __a ( cls : Any , *_lowercase : Optional[int] , **_lowercase : List[str] ):
requires_backends(cls , ['torch'] )
@classmethod
def __a ( cls : List[Any] , *_lowercase : List[str] , **_lowercase : Union[str, Any] ):
requires_backends(cls , ['torch'] )
class lowerCamelCase__ ( metaclass=UpperCAmelCase_ ):
lowerCAmelCase = ["""torch"""]
def __init__( self : Tuple , *_lowercase : Optional[Any] , **_lowercase : List[Any] ):
requires_backends(self , ['torch'] )
@classmethod
def __a ( cls : List[Any] , *_lowercase : Union[str, Any] , **_lowercase : Any ):
requires_backends(cls , ['torch'] )
@classmethod
def __a ( cls : Optional[Any] , *_lowercase : Optional[Any] , **_lowercase : Dict ):
requires_backends(cls , ['torch'] )
class lowerCamelCase__ ( metaclass=UpperCAmelCase_ ):
lowerCAmelCase = ["""torch"""]
def __init__( self : Optional[int] , *_lowercase : int , **_lowercase : int ):
requires_backends(self , ['torch'] )
@classmethod
def __a ( cls : List[str] , *_lowercase : Union[str, Any] , **_lowercase : Union[str, Any] ):
requires_backends(cls , ['torch'] )
@classmethod
def __a ( cls : int , *_lowercase : Union[str, Any] , **_lowercase : Union[str, Any] ):
requires_backends(cls , ['torch'] )
class lowerCamelCase__ ( metaclass=UpperCAmelCase_ ):
lowerCAmelCase = ["""torch"""]
def __init__( self : str , *_lowercase : Optional[int] , **_lowercase : int ):
requires_backends(self , ['torch'] )
@classmethod
def __a ( cls : str , *_lowercase : Union[str, Any] , **_lowercase : Dict ):
requires_backends(cls , ['torch'] )
@classmethod
def __a ( cls : Dict , *_lowercase : Optional[int] , **_lowercase : Optional[Any] ):
requires_backends(cls , ['torch'] )
class lowerCamelCase__ ( metaclass=UpperCAmelCase_ ):
lowerCAmelCase = ["""torch"""]
def __init__( self : Optional[int] , *_lowercase : Any , **_lowercase : Tuple ):
requires_backends(self , ['torch'] )
@classmethod
def __a ( cls : Union[str, Any] , *_lowercase : Dict , **_lowercase : List[str] ):
requires_backends(cls , ['torch'] )
@classmethod
def __a ( cls : Union[str, Any] , *_lowercase : List[Any] , **_lowercase : int ):
requires_backends(cls , ['torch'] )
class lowerCamelCase__ ( metaclass=UpperCAmelCase_ ):
lowerCAmelCase = ["""torch"""]
def __init__( self : List[Any] , *_lowercase : Tuple , **_lowercase : List[Any] ):
requires_backends(self , ['torch'] )
@classmethod
def __a ( cls : Optional[int] , *_lowercase : Optional[Any] , **_lowercase : Dict ):
requires_backends(cls , ['torch'] )
@classmethod
def __a ( cls : str , *_lowercase : Optional[Any] , **_lowercase : Optional[Any] ):
requires_backends(cls , ['torch'] )
class lowerCamelCase__ ( metaclass=UpperCAmelCase_ ):
lowerCAmelCase = ["""torch"""]
def __init__( self : str , *_lowercase : Union[str, Any] , **_lowercase : Union[str, Any] ):
requires_backends(self , ['torch'] )
@classmethod
def __a ( cls : List[Any] , *_lowercase : int , **_lowercase : str ):
requires_backends(cls , ['torch'] )
@classmethod
def __a ( cls : Tuple , *_lowercase : Union[str, Any] , **_lowercase : Tuple ):
requires_backends(cls , ['torch'] )
class lowerCamelCase__ ( metaclass=UpperCAmelCase_ ):
lowerCAmelCase = ["""torch"""]
def __init__( self : int , *_lowercase : Union[str, Any] , **_lowercase : str ):
requires_backends(self , ['torch'] )
@classmethod
def __a ( cls : str , *_lowercase : Any , **_lowercase : Dict ):
requires_backends(cls , ['torch'] )
@classmethod
def __a ( cls : int , *_lowercase : str , **_lowercase : Union[str, Any] ):
requires_backends(cls , ['torch'] )
class lowerCamelCase__ ( metaclass=UpperCAmelCase_ ):
lowerCAmelCase = ["""torch"""]
def __init__( self : List[str] , *_lowercase : Optional[Any] , **_lowercase : Tuple ):
requires_backends(self , ['torch'] )
@classmethod
def __a ( cls : Optional[int] , *_lowercase : int , **_lowercase : Optional[Any] ):
requires_backends(cls , ['torch'] )
@classmethod
def __a ( cls : Union[str, Any] , *_lowercase : List[Any] , **_lowercase : str ):
requires_backends(cls , ['torch'] )
def __snake_case ( *UpperCamelCase__ , **UpperCamelCase__ ) -> str:
"""simple docstring"""
requires_backends(UpperCamelCase__ , ['torch'] )
def __snake_case ( *UpperCamelCase__ , **UpperCamelCase__ ) -> Optional[int]:
"""simple docstring"""
requires_backends(UpperCamelCase__ , ['torch'] )
def __snake_case ( *UpperCamelCase__ , **UpperCamelCase__ ) -> Optional[int]:
"""simple docstring"""
requires_backends(UpperCamelCase__ , ['torch'] )
def __snake_case ( *UpperCamelCase__ , **UpperCamelCase__ ) -> List[Any]:
"""simple docstring"""
requires_backends(UpperCamelCase__ , ['torch'] )
def __snake_case ( *UpperCamelCase__ , **UpperCamelCase__ ) -> List[Any]:
"""simple docstring"""
requires_backends(UpperCamelCase__ , ['torch'] )
def __snake_case ( *UpperCamelCase__ , **UpperCamelCase__ ) -> Union[str, Any]:
"""simple docstring"""
requires_backends(UpperCamelCase__ , ['torch'] )
def __snake_case ( *UpperCamelCase__ , **UpperCamelCase__ ) -> List[Any]:
"""simple docstring"""
requires_backends(UpperCamelCase__ , ['torch'] )
class lowerCamelCase__ ( metaclass=UpperCAmelCase_ ):
lowerCAmelCase = ["""torch"""]
def __init__( self : int , *_lowercase : List[str] , **_lowercase : Dict ):
requires_backends(self , ['torch'] )
@classmethod
def __a ( cls : Optional[int] , *_lowercase : Any , **_lowercase : Union[str, Any] ):
requires_backends(cls , ['torch'] )
@classmethod
def __a ( cls : List[Any] , *_lowercase : Dict , **_lowercase : Optional[Any] ):
requires_backends(cls , ['torch'] )
class lowerCamelCase__ ( metaclass=UpperCAmelCase_ ):
lowerCAmelCase = ["""torch"""]
def __init__( self : List[str] , *_lowercase : List[str] , **_lowercase : Any ):
requires_backends(self , ['torch'] )
@classmethod
def __a ( cls : Tuple , *_lowercase : List[str] , **_lowercase : Optional[int] ):
requires_backends(cls , ['torch'] )
@classmethod
def __a ( cls : Dict , *_lowercase : List[Any] , **_lowercase : Any ):
requires_backends(cls , ['torch'] )
class lowerCamelCase__ ( metaclass=UpperCAmelCase_ ):
lowerCAmelCase = ["""torch"""]
def __init__( self : Optional[Any] , *_lowercase : List[Any] , **_lowercase : Union[str, Any] ):
requires_backends(self , ['torch'] )
@classmethod
def __a ( cls : str , *_lowercase : Any , **_lowercase : Union[str, Any] ):
requires_backends(cls , ['torch'] )
@classmethod
def __a ( cls : Optional[Any] , *_lowercase : List[str] , **_lowercase : str ):
requires_backends(cls , ['torch'] )
class lowerCamelCase__ ( metaclass=UpperCAmelCase_ ):
lowerCAmelCase = ["""torch"""]
def __init__( self : Union[str, Any] , *_lowercase : List[str] , **_lowercase : Union[str, Any] ):
requires_backends(self , ['torch'] )
@classmethod
def __a ( cls : str , *_lowercase : Optional[Any] , **_lowercase : Optional[int] ):
requires_backends(cls , ['torch'] )
@classmethod
def __a ( cls : Dict , *_lowercase : Dict , **_lowercase : List[Any] ):
requires_backends(cls , ['torch'] )
class lowerCamelCase__ ( metaclass=UpperCAmelCase_ ):
lowerCAmelCase = ["""torch"""]
def __init__( self : Optional[int] , *_lowercase : Any , **_lowercase : str ):
requires_backends(self , ['torch'] )
@classmethod
def __a ( cls : int , *_lowercase : List[Any] , **_lowercase : int ):
requires_backends(cls , ['torch'] )
@classmethod
def __a ( cls : List[Any] , *_lowercase : Any , **_lowercase : int ):
requires_backends(cls , ['torch'] )
class lowerCamelCase__ ( metaclass=UpperCAmelCase_ ):
lowerCAmelCase = ["""torch"""]
def __init__( self : str , *_lowercase : str , **_lowercase : Any ):
requires_backends(self , ['torch'] )
@classmethod
def __a ( cls : int , *_lowercase : Optional[Any] , **_lowercase : List[Any] ):
requires_backends(cls , ['torch'] )
@classmethod
def __a ( cls : List[str] , *_lowercase : Optional[Any] , **_lowercase : Dict ):
requires_backends(cls , ['torch'] )
class lowerCamelCase__ ( metaclass=UpperCAmelCase_ ):
lowerCAmelCase = ["""torch"""]
def __init__( self : List[Any] , *_lowercase : Optional[Any] , **_lowercase : Optional[int] ):
requires_backends(self , ['torch'] )
@classmethod
def __a ( cls : Optional[Any] , *_lowercase : Optional[int] , **_lowercase : Optional[int] ):
requires_backends(cls , ['torch'] )
@classmethod
def __a ( cls : Optional[Any] , *_lowercase : Union[str, Any] , **_lowercase : Tuple ):
requires_backends(cls , ['torch'] )
class lowerCamelCase__ ( metaclass=UpperCAmelCase_ ):
lowerCAmelCase = ["""torch"""]
def __init__( self : Union[str, Any] , *_lowercase : Optional[Any] , **_lowercase : str ):
requires_backends(self , ['torch'] )
@classmethod
def __a ( cls : Optional[Any] , *_lowercase : Tuple , **_lowercase : str ):
requires_backends(cls , ['torch'] )
@classmethod
def __a ( cls : Union[str, Any] , *_lowercase : Dict , **_lowercase : Any ):
requires_backends(cls , ['torch'] )
class lowerCamelCase__ ( metaclass=UpperCAmelCase_ ):
lowerCAmelCase = ["""torch"""]
def __init__( self : Union[str, Any] , *_lowercase : Union[str, Any] , **_lowercase : List[str] ):
requires_backends(self , ['torch'] )
@classmethod
def __a ( cls : Union[str, Any] , *_lowercase : Optional[Any] , **_lowercase : str ):
requires_backends(cls , ['torch'] )
@classmethod
def __a ( cls : Union[str, Any] , *_lowercase : int , **_lowercase : Tuple ):
requires_backends(cls , ['torch'] )
class lowerCamelCase__ ( metaclass=UpperCAmelCase_ ):
lowerCAmelCase = ["""torch"""]
def __init__( self : Optional[Any] , *_lowercase : Dict , **_lowercase : Dict ):
requires_backends(self , ['torch'] )
@classmethod
def __a ( cls : List[str] , *_lowercase : Optional[Any] , **_lowercase : Optional[int] ):
requires_backends(cls , ['torch'] )
@classmethod
def __a ( cls : List[str] , *_lowercase : Dict , **_lowercase : int ):
requires_backends(cls , ['torch'] )
class lowerCamelCase__ ( metaclass=UpperCAmelCase_ ):
lowerCAmelCase = ["""torch"""]
def __init__( self : Optional[Any] , *_lowercase : str , **_lowercase : Any ):
requires_backends(self , ['torch'] )
@classmethod
def __a ( cls : Dict , *_lowercase : List[str] , **_lowercase : Optional[int] ):
requires_backends(cls , ['torch'] )
@classmethod
def __a ( cls : Any , *_lowercase : List[Any] , **_lowercase : Union[str, Any] ):
requires_backends(cls , ['torch'] )
class lowerCamelCase__ ( metaclass=UpperCAmelCase_ ):
lowerCAmelCase = ["""torch"""]
def __init__( self : List[Any] , *_lowercase : Dict , **_lowercase : str ):
requires_backends(self , ['torch'] )
@classmethod
def __a ( cls : Dict , *_lowercase : Union[str, Any] , **_lowercase : Optional[Any] ):
requires_backends(cls , ['torch'] )
@classmethod
def __a ( cls : List[Any] , *_lowercase : Union[str, Any] , **_lowercase : Optional[int] ):
requires_backends(cls , ['torch'] )
class lowerCamelCase__ ( metaclass=UpperCAmelCase_ ):
lowerCAmelCase = ["""torch"""]
def __init__( self : str , *_lowercase : str , **_lowercase : Tuple ):
requires_backends(self , ['torch'] )
@classmethod
def __a ( cls : Tuple , *_lowercase : int , **_lowercase : Dict ):
requires_backends(cls , ['torch'] )
@classmethod
def __a ( cls : Optional[int] , *_lowercase : Dict , **_lowercase : Optional[Any] ):
requires_backends(cls , ['torch'] )
class lowerCamelCase__ ( metaclass=UpperCAmelCase_ ):
lowerCAmelCase = ["""torch"""]
def __init__( self : Optional[Any] , *_lowercase : Optional[Any] , **_lowercase : List[Any] ):
requires_backends(self , ['torch'] )
@classmethod
def __a ( cls : int , *_lowercase : Dict , **_lowercase : Optional[int] ):
requires_backends(cls , ['torch'] )
@classmethod
def __a ( cls : Union[str, Any] , *_lowercase : Optional[int] , **_lowercase : Optional[int] ):
requires_backends(cls , ['torch'] )
class lowerCamelCase__ ( metaclass=UpperCAmelCase_ ):
lowerCAmelCase = ["""torch"""]
def __init__( self : Any , *_lowercase : Dict , **_lowercase : int ):
requires_backends(self , ['torch'] )
@classmethod
def __a ( cls : str , *_lowercase : List[str] , **_lowercase : Optional[int] ):
requires_backends(cls , ['torch'] )
@classmethod
def __a ( cls : List[str] , *_lowercase : int , **_lowercase : Union[str, Any] ):
requires_backends(cls , ['torch'] )
class lowerCamelCase__ ( metaclass=UpperCAmelCase_ ):
lowerCAmelCase = ["""torch"""]
def __init__( self : List[str] , *_lowercase : Dict , **_lowercase : List[str] ):
requires_backends(self , ['torch'] )
@classmethod
def __a ( cls : Union[str, Any] , *_lowercase : List[str] , **_lowercase : Union[str, Any] ):
requires_backends(cls , ['torch'] )
@classmethod
def __a ( cls : Any , *_lowercase : Union[str, Any] , **_lowercase : Optional[Any] ):
requires_backends(cls , ['torch'] )
class lowerCamelCase__ ( metaclass=UpperCAmelCase_ ):
lowerCAmelCase = ["""torch"""]
def __init__( self : List[str] , *_lowercase : List[str] , **_lowercase : int ):
requires_backends(self , ['torch'] )
@classmethod
def __a ( cls : Any , *_lowercase : Optional[int] , **_lowercase : int ):
requires_backends(cls , ['torch'] )
@classmethod
def __a ( cls : List[Any] , *_lowercase : Tuple , **_lowercase : int ):
requires_backends(cls , ['torch'] )
class lowerCamelCase__ ( metaclass=UpperCAmelCase_ ):
lowerCAmelCase = ["""torch"""]
def __init__( self : Union[str, Any] , *_lowercase : Optional[Any] , **_lowercase : Any ):
requires_backends(self , ['torch'] )
@classmethod
def __a ( cls : Tuple , *_lowercase : str , **_lowercase : List[Any] ):
requires_backends(cls , ['torch'] )
@classmethod
def __a ( cls : Any , *_lowercase : Tuple , **_lowercase : List[Any] ):
requires_backends(cls , ['torch'] )
class lowerCamelCase__ ( metaclass=UpperCAmelCase_ ):
lowerCAmelCase = ["""torch"""]
def __init__( self : int , *_lowercase : List[str] , **_lowercase : Dict ):
requires_backends(self , ['torch'] )
@classmethod
def __a ( cls : int , *_lowercase : Any , **_lowercase : Dict ):
requires_backends(cls , ['torch'] )
@classmethod
def __a ( cls : Any , *_lowercase : Dict , **_lowercase : Union[str, Any] ):
requires_backends(cls , ['torch'] )
class lowerCamelCase__ ( metaclass=UpperCAmelCase_ ):
lowerCAmelCase = ["""torch"""]
def __init__( self : Optional[int] , *_lowercase : Optional[Any] , **_lowercase : Any ):
requires_backends(self , ['torch'] )
@classmethod
def __a ( cls : List[Any] , *_lowercase : List[str] , **_lowercase : Tuple ):
requires_backends(cls , ['torch'] )
@classmethod
def __a ( cls : Optional[int] , *_lowercase : Union[str, Any] , **_lowercase : Union[str, Any] ):
requires_backends(cls , ['torch'] )
class lowerCamelCase__ ( metaclass=UpperCAmelCase_ ):
lowerCAmelCase = ["""torch"""]
def __init__( self : List[str] , *_lowercase : List[str] , **_lowercase : Union[str, Any] ):
requires_backends(self , ['torch'] )
@classmethod
def __a ( cls : Any , *_lowercase : Optional[Any] , **_lowercase : Optional[Any] ):
requires_backends(cls , ['torch'] )
@classmethod
def __a ( cls : Tuple , *_lowercase : Tuple , **_lowercase : Optional[Any] ):
requires_backends(cls , ['torch'] )
class lowerCamelCase__ ( metaclass=UpperCAmelCase_ ):
lowerCAmelCase = ["""torch"""]
def __init__( self : Dict , *_lowercase : Union[str, Any] , **_lowercase : Optional[int] ):
requires_backends(self , ['torch'] )
@classmethod
def __a ( cls : str , *_lowercase : Optional[int] , **_lowercase : Any ):
requires_backends(cls , ['torch'] )
@classmethod
def __a ( cls : Optional[int] , *_lowercase : Dict , **_lowercase : Any ):
requires_backends(cls , ['torch'] )
class lowerCamelCase__ ( metaclass=UpperCAmelCase_ ):
lowerCAmelCase = ["""torch"""]
def __init__( self : List[Any] , *_lowercase : Tuple , **_lowercase : Union[str, Any] ):
requires_backends(self , ['torch'] )
@classmethod
def __a ( cls : Union[str, Any] , *_lowercase : str , **_lowercase : Optional[Any] ):
requires_backends(cls , ['torch'] )
@classmethod
def __a ( cls : Any , *_lowercase : str , **_lowercase : int ):
requires_backends(cls , ['torch'] )
class lowerCamelCase__ ( metaclass=UpperCAmelCase_ ):
lowerCAmelCase = ["""torch"""]
def __init__( self : int , *_lowercase : str , **_lowercase : List[Any] ):
requires_backends(self , ['torch'] )
@classmethod
def __a ( cls : Optional[Any] , *_lowercase : int , **_lowercase : Dict ):
requires_backends(cls , ['torch'] )
@classmethod
def __a ( cls : Dict , *_lowercase : Any , **_lowercase : int ):
requires_backends(cls , ['torch'] )
class lowerCamelCase__ ( metaclass=UpperCAmelCase_ ):
lowerCAmelCase = ["""torch"""]
def __init__( self : Any , *_lowercase : int , **_lowercase : List[Any] ):
requires_backends(self , ['torch'] )
@classmethod
def __a ( cls : int , *_lowercase : List[Any] , **_lowercase : int ):
requires_backends(cls , ['torch'] )
@classmethod
def __a ( cls : Any , *_lowercase : Tuple , **_lowercase : Optional[Any] ):
requires_backends(cls , ['torch'] )
class lowerCamelCase__ ( metaclass=UpperCAmelCase_ ):
lowerCAmelCase = ["""torch"""]
def __init__( self : int , *_lowercase : str , **_lowercase : Optional[Any] ):
requires_backends(self , ['torch'] )
@classmethod
def __a ( cls : Dict , *_lowercase : Any , **_lowercase : Dict ):
requires_backends(cls , ['torch'] )
@classmethod
def __a ( cls : Optional[int] , *_lowercase : Optional[Any] , **_lowercase : Dict ):
requires_backends(cls , ['torch'] )
class lowerCamelCase__ ( metaclass=UpperCAmelCase_ ):
lowerCAmelCase = ["""torch"""]
def __init__( self : Optional[Any] , *_lowercase : List[Any] , **_lowercase : Dict ):
requires_backends(self , ['torch'] )
@classmethod
def __a ( cls : Dict , *_lowercase : Any , **_lowercase : int ):
requires_backends(cls , ['torch'] )
@classmethod
def __a ( cls : List[str] , *_lowercase : Optional[int] , **_lowercase : Tuple ):
requires_backends(cls , ['torch'] )
class lowerCamelCase__ ( metaclass=UpperCAmelCase_ ):
lowerCAmelCase = ["""torch"""]
def __init__( self : Dict , *_lowercase : int , **_lowercase : int ):
requires_backends(self , ['torch'] )
@classmethod
def __a ( cls : Optional[Any] , *_lowercase : Dict , **_lowercase : Optional[Any] ):
requires_backends(cls , ['torch'] )
@classmethod
def __a ( cls : Dict , *_lowercase : int , **_lowercase : List[str] ):
requires_backends(cls , ['torch'] )
class lowerCamelCase__ ( metaclass=UpperCAmelCase_ ):
lowerCAmelCase = ["""torch"""]
def __init__( self : Union[str, Any] , *_lowercase : Optional[int] , **_lowercase : List[str] ):
requires_backends(self , ['torch'] )
@classmethod
def __a ( cls : Optional[int] , *_lowercase : Optional[int] , **_lowercase : Optional[Any] ):
requires_backends(cls , ['torch'] )
@classmethod
def __a ( cls : Optional[int] , *_lowercase : str , **_lowercase : Optional[int] ):
requires_backends(cls , ['torch'] )
class lowerCamelCase__ ( metaclass=UpperCAmelCase_ ):
lowerCAmelCase = ["""torch"""]
def __init__( self : Any , *_lowercase : Optional[Any] , **_lowercase : Tuple ):
requires_backends(self , ['torch'] )
@classmethod
def __a ( cls : Optional[Any] , *_lowercase : Union[str, Any] , **_lowercase : List[str] ):
requires_backends(cls , ['torch'] )
@classmethod
def __a ( cls : Optional[int] , *_lowercase : str , **_lowercase : Optional[Any] ):
requires_backends(cls , ['torch'] )
class lowerCamelCase__ ( metaclass=UpperCAmelCase_ ):
lowerCAmelCase = ["""torch"""]
def __init__( self : List[Any] , *_lowercase : int , **_lowercase : Tuple ):
requires_backends(self , ['torch'] )
@classmethod
def __a ( cls : Any , *_lowercase : Tuple , **_lowercase : Dict ):
requires_backends(cls , ['torch'] )
@classmethod
def __a ( cls : List[str] , *_lowercase : Union[str, Any] , **_lowercase : Tuple ):
requires_backends(cls , ['torch'] )
class lowerCamelCase__ ( metaclass=UpperCAmelCase_ ):
lowerCAmelCase = ["""torch"""]
def __init__( self : int , *_lowercase : Optional[Any] , **_lowercase : Dict ):
requires_backends(self , ['torch'] )
@classmethod
def __a ( cls : List[Any] , *_lowercase : Dict , **_lowercase : int ):
requires_backends(cls , ['torch'] )
@classmethod
def __a ( cls : Optional[Any] , *_lowercase : int , **_lowercase : Optional[int] ):
requires_backends(cls , ['torch'] )
class lowerCamelCase__ ( metaclass=UpperCAmelCase_ ):
lowerCAmelCase = ["""torch"""]
def __init__( self : List[Any] , *_lowercase : Union[str, Any] , **_lowercase : str ):
requires_backends(self , ['torch'] )
@classmethod
def __a ( cls : Union[str, Any] , *_lowercase : List[Any] , **_lowercase : int ):
requires_backends(cls , ['torch'] )
@classmethod
def __a ( cls : Optional[Any] , *_lowercase : Tuple , **_lowercase : Optional[int] ):
requires_backends(cls , ['torch'] )
class lowerCamelCase__ ( metaclass=UpperCAmelCase_ ):
lowerCAmelCase = ["""torch"""]
def __init__( self : Dict , *_lowercase : Union[str, Any] , **_lowercase : int ):
requires_backends(self , ['torch'] )
@classmethod
def __a ( cls : Tuple , *_lowercase : Optional[Any] , **_lowercase : Union[str, Any] ):
requires_backends(cls , ['torch'] )
@classmethod
def __a ( cls : List[str] , *_lowercase : Any , **_lowercase : Optional[Any] ):
requires_backends(cls , ['torch'] )
class lowerCamelCase__ ( metaclass=UpperCAmelCase_ ):
lowerCAmelCase = ["""torch"""]
def __init__( self : Union[str, Any] , *_lowercase : List[str] , **_lowercase : str ):
requires_backends(self , ['torch'] )
@classmethod
def __a ( cls : Tuple , *_lowercase : Union[str, Any] , **_lowercase : List[str] ):
requires_backends(cls , ['torch'] )
@classmethod
def __a ( cls : List[Any] , *_lowercase : Optional[Any] , **_lowercase : Tuple ):
requires_backends(cls , ['torch'] )
class lowerCamelCase__ ( metaclass=UpperCAmelCase_ ):
lowerCAmelCase = ["""torch"""]
def __init__( self : Optional[Any] , *_lowercase : int , **_lowercase : List[str] ):
requires_backends(self , ['torch'] )
@classmethod
def __a ( cls : Union[str, Any] , *_lowercase : Tuple , **_lowercase : Any ):
requires_backends(cls , ['torch'] )
@classmethod
def __a ( cls : List[str] , *_lowercase : List[str] , **_lowercase : Tuple ):
requires_backends(cls , ['torch'] )
class lowerCamelCase__ ( metaclass=UpperCAmelCase_ ):
lowerCAmelCase = ["""torch"""]
def __init__( self : Dict , *_lowercase : str , **_lowercase : List[str] ):
requires_backends(self , ['torch'] )
@classmethod
def __a ( cls : Tuple , *_lowercase : List[str] , **_lowercase : Any ):
requires_backends(cls , ['torch'] )
@classmethod
def __a ( cls : Optional[Any] , *_lowercase : List[Any] , **_lowercase : Optional[int] ):
requires_backends(cls , ['torch'] )
class lowerCamelCase__ ( metaclass=UpperCAmelCase_ ):
lowerCAmelCase = ["""torch"""]
def __init__( self : List[str] , *_lowercase : Tuple , **_lowercase : str ):
requires_backends(self , ['torch'] )
@classmethod
def __a ( cls : str , *_lowercase : Optional[int] , **_lowercase : str ):
requires_backends(cls , ['torch'] )
@classmethod
def __a ( cls : Optional[Any] , *_lowercase : Union[str, Any] , **_lowercase : Any ):
requires_backends(cls , ['torch'] )
class lowerCamelCase__ ( metaclass=UpperCAmelCase_ ):
lowerCAmelCase = ["""torch"""]
def __init__( self : Union[str, Any] , *_lowercase : int , **_lowercase : List[Any] ):
requires_backends(self , ['torch'] )
@classmethod
def __a ( cls : int , *_lowercase : Dict , **_lowercase : Union[str, Any] ):
requires_backends(cls , ['torch'] )
@classmethod
def __a ( cls : Dict , *_lowercase : Union[str, Any] , **_lowercase : Optional[Any] ):
requires_backends(cls , ['torch'] )
| 690 |
"""simple docstring"""
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging
if is_torch_available():
import torch
UpperCamelCase : List[str] = logging.get_logger(__name__)
class lowerCamelCase__ ( UpperCAmelCase_ ):
lowerCAmelCase = ["""pixel_values"""]
def __init__( self : Tuple , _lowercase : bool = True , _lowercase : Optional[Dict[str, int]] = None , _lowercase : PILImageResampling = PILImageResampling.BILINEAR , _lowercase : bool = True , _lowercase : Dict[str, int] = None , _lowercase : bool = True , _lowercase : Union[int, float] = 1 / 255 , _lowercase : bool = True , _lowercase : Optional[Union[float, List[float]]] = None , _lowercase : Optional[Union[float, List[float]]] = None , **_lowercase : List[str] , ):
super().__init__(**_lowercase )
A = size if size is not None else {'shortest_edge': 256}
A = get_size_dict(_lowercase , default_to_square=_lowercase )
A = crop_size if crop_size is not None else {'height': 224, 'width': 224}
A = get_size_dict(_lowercase , param_name='crop_size' )
A = do_resize
A = size
A = resample
A = do_center_crop
A = crop_size
A = do_rescale
A = rescale_factor
A = do_normalize
A = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
A = image_std if image_std is not None else IMAGENET_STANDARD_STD
def __a ( self : Any , _lowercase : np.ndarray , _lowercase : Dict[str, int] , _lowercase : PILImageResampling = PILImageResampling.BICUBIC , _lowercase : Optional[Union[str, ChannelDimension]] = None , **_lowercase : Tuple , ):
A = get_size_dict(_lowercase , default_to_square=_lowercase )
if "shortest_edge" not in size:
raise ValueError(f'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}' )
A = get_resize_output_image_size(_lowercase , size=size['shortest_edge'] , default_to_square=_lowercase )
return resize(_lowercase , size=_lowercase , resample=_lowercase , data_format=_lowercase , **_lowercase )
def __a ( self : List[Any] , _lowercase : np.ndarray , _lowercase : Dict[str, int] , _lowercase : Optional[Union[str, ChannelDimension]] = None , **_lowercase : Optional[int] , ):
A = get_size_dict(_lowercase )
if "height" not in size or "width" not in size:
raise ValueError(f'The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}' )
return center_crop(_lowercase , size=(size['height'], size['width']) , data_format=_lowercase , **_lowercase )
def __a ( self : int , _lowercase : np.ndarray , _lowercase : float , _lowercase : Optional[Union[str, ChannelDimension]] = None , **_lowercase : Tuple ):
return rescale(_lowercase , scale=_lowercase , data_format=_lowercase , **_lowercase )
def __a ( self : int , _lowercase : np.ndarray , _lowercase : Union[float, List[float]] , _lowercase : Union[float, List[float]] , _lowercase : Optional[Union[str, ChannelDimension]] = None , **_lowercase : str , ):
return normalize(_lowercase , mean=_lowercase , std=_lowercase , data_format=_lowercase , **_lowercase )
def __a ( self : Any , _lowercase : ImageInput , _lowercase : Optional[bool] = None , _lowercase : Dict[str, int] = None , _lowercase : PILImageResampling = None , _lowercase : bool = None , _lowercase : Dict[str, int] = None , _lowercase : Optional[bool] = None , _lowercase : Optional[float] = None , _lowercase : Optional[bool] = None , _lowercase : Optional[Union[float, List[float]]] = None , _lowercase : Optional[Union[float, List[float]]] = None , _lowercase : Optional[Union[str, TensorType]] = None , _lowercase : Union[str, ChannelDimension] = ChannelDimension.FIRST , **_lowercase : Any , ):
A = do_resize if do_resize is not None else self.do_resize
A = size if size is not None else self.size
A = get_size_dict(_lowercase , default_to_square=_lowercase )
A = resample if resample is not None else self.resample
A = do_center_crop if do_center_crop is not None else self.do_center_crop
A = crop_size if crop_size is not None else self.crop_size
A = get_size_dict(_lowercase , param_name='crop_size' )
A = do_rescale if do_rescale is not None else self.do_rescale
A = rescale_factor if rescale_factor is not None else self.rescale_factor
A = do_normalize if do_normalize is not None else self.do_normalize
A = image_mean if image_mean is not None else self.image_mean
A = image_std if image_std is not None else self.image_std
A = make_list_of_images(_lowercase )
if not valid_images(_lowercase ):
raise ValueError(
'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
'torch.Tensor, tf.Tensor or jax.ndarray.' )
if do_resize and size is None:
raise ValueError('Size must be specified if do_resize is True.' )
if do_center_crop and crop_size is None:
raise ValueError('Crop size must be specified if do_center_crop is True.' )
if do_rescale and rescale_factor is None:
raise ValueError('Rescale factor must be specified if do_rescale is True.' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('Image mean and std must be specified if do_normalize is True.' )
# All transformations expect numpy arrays.
A = [to_numpy_array(_lowercase ) for image in images]
if do_resize:
A = [self.resize(image=_lowercase , size=_lowercase , resample=_lowercase ) for image in images]
if do_center_crop:
A = [self.center_crop(image=_lowercase , size=_lowercase ) for image in images]
if do_rescale:
A = [self.rescale(image=_lowercase , scale=_lowercase ) for image in images]
if do_normalize:
A = [self.normalize(image=_lowercase , mean=_lowercase , std=_lowercase ) for image in images]
A = [to_channel_dimension_format(_lowercase , _lowercase ) for image in images]
A = {'pixel_values': images}
return BatchFeature(data=_lowercase , tensor_type=_lowercase )
def __a ( self : int , _lowercase : List[str] , _lowercase : List[Tuple] = None ):
A = outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(_lowercase ) != len(_lowercase ):
raise ValueError(
'Make sure that you pass in as many target sizes as the batch dimension of the logits' )
if is_torch_tensor(_lowercase ):
A = target_sizes.numpy()
A = []
for idx in range(len(_lowercase ) ):
A = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) , size=target_sizes[idx] , mode='bilinear' , align_corners=_lowercase )
A = resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(_lowercase )
else:
A = logits.argmax(dim=1 )
A = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation
| 690 | 1 |
"""simple docstring"""
from sklearn.metrics import recall_score
import datasets
UpperCamelCase : Tuple = "\nRecall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:\nRecall = TP / (TP + FN)\nWhere TP is the true positives and FN is the false negatives.\n"
UpperCamelCase : Optional[int] = "\nArgs:\n- **predictions** (`list` of `int`): The predicted labels.\n- **references** (`list` of `int`): The ground truth labels.\n- **labels** (`list` of `int`): The set of labels to include when `average` is not set to `binary`, and their order when average is `None`. Labels present in the data can be excluded in this input, for example to calculate a multiclass average ignoring a majority negative class, while labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in y_true and y_pred are used in sorted order. Defaults to None.\n- **pos_label** (`int`): The class label to use as the 'positive class' when calculating the recall. Defaults to `1`.\n- **average** (`string`): This parameter is required for multiclass/multilabel targets. If None, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `'binary'`.\n - `'binary'`: Only report results for the class specified by `pos_label`. This is applicable only if the target labels and predictions are binary.\n - `'micro'`: Calculate metrics globally by counting the total true positives, false negatives, and false positives.\n - `'macro'`: Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.\n - `'weighted'`: Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `'macro'` to account for label imbalance. Note that it can result in an F-score that is not between precision and recall.\n - `'samples'`: Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).\n- **sample_weight** (`list` of `float`): Sample weights Defaults to `None`.\n- **zero_division** (): Sets the value to return when there is a zero division. Defaults to .\n - `'warn'`: If there is a zero division, the return value is `0`, but warnings are also raised.\n - `0`: If there is a zero division, the return value is `0`.\n - `1`: If there is a zero division, the return value is `1`.\n\nReturns:\n- **recall** (`float`, or `array` of `float`): Either the general recall score, or the recall scores for individual classes, depending on the values input to `labels` and `average`. Minimum possible value is 0. Maximum possible value is 1. A higher recall means that more of the positive examples have been labeled correctly. Therefore, a higher recall is generally considered better.\n\nExamples:\n\n Example 1-A simple example with some errors\n >>> recall_metric = datasets.load_metric('recall')\n >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1])\n >>> print(results)\n {'recall': 0.6666666666666666}\n\n Example 2-The same example as Example 1, but with `pos_label=0` instead of the default `pos_label=1`.\n >>> recall_metric = datasets.load_metric('recall')\n >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], pos_label=0)\n >>> print(results)\n {'recall': 0.5}\n\n Example 3-The same example as Example 1, but with `sample_weight` included.\n >>> recall_metric = datasets.load_metric('recall')\n >>> sample_weight = [0.9, 0.2, 0.9, 0.3, 0.8]\n >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], sample_weight=sample_weight)\n >>> print(results)\n {'recall': 0.55}\n\n Example 4-A multiclass example, using different averages.\n >>> recall_metric = datasets.load_metric('recall')\n >>> predictions = [0, 2, 1, 0, 0, 1]\n >>> references = [0, 1, 2, 0, 1, 2]\n >>> results = recall_metric.compute(predictions=predictions, references=references, average='macro')\n >>> print(results)\n {'recall': 0.3333333333333333}\n >>> results = recall_metric.compute(predictions=predictions, references=references, average='micro')\n >>> print(results)\n {'recall': 0.3333333333333333}\n >>> results = recall_metric.compute(predictions=predictions, references=references, average='weighted')\n >>> print(results)\n {'recall': 0.3333333333333333}\n >>> results = recall_metric.compute(predictions=predictions, references=references, average=None)\n >>> print(results)\n {'recall': array([1., 0., 0.])}\n"
UpperCamelCase : Dict = "\n@article{scikit-learn, title={Scikit-learn: Machine Learning in {P}ython}, author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V. and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P. and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.}, journal={Journal of Machine Learning Research}, volume={12}, pages={2825--2830}, year={2011}\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class lowerCamelCase__ ( datasets.Metric ):
def __a ( self : Optional[int] ):
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'predictions': datasets.Sequence(datasets.Value('int32' ) ),
'references': datasets.Sequence(datasets.Value('int32' ) ),
}
if self.config_name == 'multilabel'
else {
'predictions': datasets.Value('int32' ),
'references': datasets.Value('int32' ),
} ) , reference_urls=['https://scikit-learn.org/stable/modules/generated/sklearn.metrics.recall_score.html'] , )
def __a ( self : Tuple , _lowercase : List[str] , _lowercase : Optional[int] , _lowercase : Tuple=None , _lowercase : List[Any]=1 , _lowercase : int="binary" , _lowercase : List[Any]=None , _lowercase : Union[str, Any]="warn" , ):
A = recall_score(
_lowercase , _lowercase , labels=_lowercase , pos_label=_lowercase , average=_lowercase , sample_weight=_lowercase , zero_division=_lowercase , )
return {"recall": float(_lowercase ) if score.size == 1 else score}
| 690 |
"""simple docstring"""
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def __snake_case ( UpperCamelCase__ = "laptop" ) -> DataFrame:
"""simple docstring"""
A = f'https://www.amazon.in/laptop/s?k={product}'
A = {
'User-Agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36\n (KHTML, like Gecko)Chrome/44.0.2403.157 Safari/537.36',
'Accept-Language': 'en-US, en;q=0.5',
}
A = BeautifulSoup(requests.get(UpperCamelCase__ , headers=UpperCamelCase__ ).text )
# Initialize a Pandas dataframe with the column titles
A = DataFrame(
columns=[
'Product Title',
'Product Link',
'Current Price of the product',
'Product Rating',
'MRP of the product',
'Discount',
] )
# Loop through each entry and store them in the dataframe
for item, _ in zip_longest(
soup.find_all(
'div' , attrs={'class': 's-result-item', 'data-component-type': 's-search-result'} , ) , soup.find_all('div' , attrs={'class': 'a-row a-size-base a-color-base'} ) , ):
try:
A = item.ha.text
A = 'https://www.amazon.in/' + item.ha.a['href']
A = item.find('span' , attrs={'class': 'a-offscreen'} ).text
try:
A = item.find('span' , attrs={'class': 'a-icon-alt'} ).text
except AttributeError:
A = 'Not available'
try:
A = (
'₹'
+ item.find(
'span' , attrs={'class': 'a-price a-text-price'} ).text.split('₹' )[1]
)
except AttributeError:
A = ''
try:
A = float(
(
(
float(product_mrp.strip('₹' ).replace(',' , '' ) )
- float(product_price.strip('₹' ).replace(',' , '' ) )
)
/ float(product_mrp.strip('₹' ).replace(',' , '' ) )
)
* 100 )
except ValueError:
A = float('nan' )
except AttributeError:
pass
A = [
product_title,
product_link,
product_price,
product_rating,
product_mrp,
discount,
]
A = ' '
A = ' '
data_frame.index += 1
return data_frame
if __name__ == "__main__":
UpperCamelCase : Any = "headphones"
get_amazon_product_data(product).to_csv(F"""Amazon Product Data for {product}.csv""")
| 690 | 1 |
"""simple docstring"""
def __snake_case ( UpperCamelCase__ ) -> list[list[int]]:
"""simple docstring"""
A = []
if len(UpperCamelCase__ ) == 1:
return [nums.copy()]
for _ in range(len(UpperCamelCase__ ) ):
A = nums.pop(0 )
A = permute(UpperCamelCase__ )
for perm in permutations:
perm.append(UpperCamelCase__ )
result.extend(UpperCamelCase__ )
nums.append(UpperCamelCase__ )
return result
def __snake_case ( UpperCamelCase__ ) -> int:
"""simple docstring"""
def backtrack(UpperCamelCase__ ):
if start == len(UpperCamelCase__ ) - 1:
output.append(nums[:] )
else:
for i in range(UpperCamelCase__ , len(UpperCamelCase__ ) ):
A , A = nums[i], nums[start]
backtrack(start + 1 )
A , A = nums[i], nums[start] # backtrack
A = []
backtrack(0 )
return output
if __name__ == "__main__":
import doctest
# use res to print the data in permute2 function
UpperCamelCase : int = permutea([1, 2, 3])
print(res)
doctest.testmod()
| 690 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import DonutImageProcessor
class lowerCamelCase__ ( unittest.TestCase ):
def __init__( self : List[str] , _lowercase : Optional[Any] , _lowercase : int=7 , _lowercase : List[str]=3 , _lowercase : Tuple=18 , _lowercase : Dict=30 , _lowercase : Any=400 , _lowercase : int=True , _lowercase : List[Any]=None , _lowercase : Tuple=True , _lowercase : List[Any]=False , _lowercase : str=True , _lowercase : List[str]=True , _lowercase : int=[0.5, 0.5, 0.5] , _lowercase : Optional[int]=[0.5, 0.5, 0.5] , ):
A = parent
A = batch_size
A = num_channels
A = image_size
A = min_resolution
A = max_resolution
A = do_resize
A = size if size is not None else {'height': 18, 'width': 20}
A = do_thumbnail
A = do_align_axis
A = do_pad
A = do_normalize
A = image_mean
A = image_std
def __a ( self : Any ):
return {
"do_resize": self.do_resize,
"size": self.size,
"do_thumbnail": self.do_thumbnail,
"do_align_long_axis": self.do_align_axis,
"do_pad": self.do_pad,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
}
@require_torch
@require_vision
class lowerCamelCase__ ( UpperCAmelCase_ , unittest.TestCase ):
lowerCAmelCase = DonutImageProcessor if is_vision_available() else None
def __a ( self : List[str] ):
A = DonutImageProcessingTester(self )
@property
def __a ( self : int ):
return self.image_processor_tester.prepare_image_processor_dict()
def __a ( self : Union[str, Any] ):
A = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(_lowercase , 'do_resize' ) )
self.assertTrue(hasattr(_lowercase , 'size' ) )
self.assertTrue(hasattr(_lowercase , 'do_thumbnail' ) )
self.assertTrue(hasattr(_lowercase , 'do_align_long_axis' ) )
self.assertTrue(hasattr(_lowercase , 'do_pad' ) )
self.assertTrue(hasattr(_lowercase , 'do_normalize' ) )
self.assertTrue(hasattr(_lowercase , 'image_mean' ) )
self.assertTrue(hasattr(_lowercase , 'image_std' ) )
def __a ( self : int ):
A = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'height': 18, 'width': 20} )
A = self.image_processing_class.from_dict(self.image_processor_dict , size=42 )
self.assertEqual(image_processor.size , {'height': 42, 'width': 42} )
# Previous config had dimensions in (width, height) order
A = self.image_processing_class.from_dict(self.image_processor_dict , size=(42, 84) )
self.assertEqual(image_processor.size , {'height': 84, 'width': 42} )
def __a ( self : Any ):
pass
@is_flaky()
def __a ( self : int ):
# Initialize image_processing
A = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
A = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowercase )
for image in image_inputs:
self.assertIsInstance(_lowercase , Image.Image )
# Test not batched input
A = 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 = image_processing(_lowercase , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
) , )
@is_flaky()
def __a ( self : List[str] ):
# Initialize image_processing
A = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
A = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowercase , numpify=_lowercase )
for image in image_inputs:
self.assertIsInstance(_lowercase , np.ndarray )
# Test not batched input
A = 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 = image_processing(_lowercase , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
) , )
@is_flaky()
def __a ( self : List[Any] ):
# Initialize image_processing
A = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
A = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowercase , torchify=_lowercase )
for image in image_inputs:
self.assertIsInstance(_lowercase , torch.Tensor )
# Test not batched input
A = 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 = image_processing(_lowercase , 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'],
) , )
| 690 | 1 |
"""simple docstring"""
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_torch
@require_sentencepiece
@require_tokenizers
class lowerCamelCase__ ( unittest.TestCase ):
@slow
def __a ( self : List[Any] ):
A = AutoModelForSeqaSeqLM.from_pretrained('google/mt5-small' , return_dict=_lowercase ).to(_lowercase )
A = AutoTokenizer.from_pretrained('google/mt5-small' )
A = tokenizer('Hello there' , return_tensors='pt' ).input_ids
A = tokenizer('Hi I am' , return_tensors='pt' ).input_ids
A = model(input_ids.to(_lowercase ) , labels=labels.to(_lowercase ) ).loss
A = -(labels.shape[-1] * loss.item())
A = -8_4.9_1_2_7
self.assertTrue(abs(mtf_score - EXPECTED_SCORE ) < 1e-4 )
| 690 |
"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class lowerCamelCase__ :
def __init__( self : Optional[Any] , _lowercase : int=2 , _lowercase : Optional[Any]=3 , _lowercase : Any=64 , _lowercase : Tuple=None ):
A = np.random.default_rng(_lowercase )
A = length
A = rng.normal(size=(length,) ).astype(np.floataa )
A = a * self.x + b + rng.normal(scale=0.1 , size=(length,) ).astype(np.floataa )
def __len__( self : str ):
return self.length
def __getitem__( self : List[str] , _lowercase : int ):
return {"x": self.x[i], "y": self.y[i]}
class lowerCamelCase__ ( torch.nn.Module ):
def __init__( self : Optional[int] , _lowercase : Any=0 , _lowercase : List[Any]=0 , _lowercase : Optional[int]=False ):
super().__init__()
A = torch.nn.Parameter(torch.tensor([2, 3] ).float() )
A = torch.nn.Parameter(torch.tensor([2, 3] ).float() )
A = True
def __a ( self : Optional[Any] , _lowercase : str=None ):
if self.first_batch:
print(f'Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}' )
A = False
return x * self.a[0] + self.b[0]
class lowerCamelCase__ ( torch.nn.Module ):
def __init__( self : Optional[Any] , _lowercase : Any=0 , _lowercase : List[str]=0 , _lowercase : str=False ):
super().__init__()
A = torch.nn.Parameter(torch.tensor(_lowercase ).float() )
A = torch.nn.Parameter(torch.tensor(_lowercase ).float() )
A = True
def __a ( self : int , _lowercase : Tuple=None ):
if self.first_batch:
print(f'Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}' )
A = False
return x * self.a + self.b
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ = 16 ) -> Optional[Any]:
"""simple docstring"""
from datasets import load_dataset
from transformers import AutoTokenizer
A = AutoTokenizer.from_pretrained('bert-base-cased' )
A = {'train': 'tests/test_samples/MRPC/train.csv', 'validation': 'tests/test_samples/MRPC/dev.csv'}
A = load_dataset('csv' , data_files=UpperCamelCase__ )
A = datasets['train'].unique('label' )
A = {v: i for i, v in enumerate(UpperCamelCase__ )}
def tokenize_function(UpperCamelCase__ ):
# max_length=None => use the model max length (it's actually the default)
A = tokenizer(
examples['sentence1'] , examples['sentence2'] , truncation=UpperCamelCase__ , max_length=UpperCamelCase__ , padding='max_length' )
if "label" in examples:
A = [label_to_id[l] for l in examples['label']]
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
A = datasets.map(
UpperCamelCase__ , batched=UpperCamelCase__ , remove_columns=['sentence1', 'sentence2', 'label'] , )
def collate_fn(UpperCamelCase__ ):
# On TPU it's best to pad everything to the same length or training will be very slow.
if accelerator.distributed_type == DistributedType.TPU:
return tokenizer.pad(UpperCamelCase__ , padding='max_length' , max_length=128 , return_tensors='pt' )
return tokenizer.pad(UpperCamelCase__ , padding='longest' , return_tensors='pt' )
# Instantiate dataloaders.
A = DataLoader(tokenized_datasets['train'] , shuffle=UpperCamelCase__ , collate_fn=UpperCamelCase__ , batch_size=2 )
A = DataLoader(tokenized_datasets['validation'] , shuffle=UpperCamelCase__ , collate_fn=UpperCamelCase__ , batch_size=1 )
return train_dataloader, eval_dataloader
| 690 | 1 |
"""simple docstring"""
import os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path
import pytest
import transformers
from transformers import (
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
AutoTokenizer,
BertConfig,
BertTokenizer,
BertTokenizerFast,
CTRLTokenizer,
GPTaTokenizer,
GPTaTokenizerFast,
PreTrainedTokenizerFast,
RobertaTokenizer,
RobertaTokenizerFast,
is_tokenizers_available,
)
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.auto.tokenization_auto import (
TOKENIZER_MAPPING,
get_tokenizer_config,
tokenizer_class_from_name,
)
from transformers.models.roberta.configuration_roberta import RobertaConfig
from transformers.testing_utils import (
DUMMY_DIFF_TOKENIZER_IDENTIFIER,
DUMMY_UNKNOWN_IDENTIFIER,
SMALL_MODEL_IDENTIFIER,
RequestCounter,
require_tokenizers,
slow,
)
sys.path.append(str(Path(__file__).parent.parent.parent.parent / "utils"))
from test_module.custom_configuration import CustomConfig # noqa E402
from test_module.custom_tokenization import CustomTokenizer # noqa E402
if is_tokenizers_available():
from test_module.custom_tokenization_fast import CustomTokenizerFast
class lowerCamelCase__ ( unittest.TestCase ):
def __a ( self : Optional[int] ):
A = 0
@slow
def __a ( self : List[Any] ):
for model_name in (x for x in BERT_PRETRAINED_CONFIG_ARCHIVE_MAP.keys() if "japanese" not in x):
A = AutoTokenizer.from_pretrained(_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , (BertTokenizer, BertTokenizerFast) )
self.assertGreater(len(_lowercase ) , 0 )
for model_name in GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP.keys():
A = AutoTokenizer.from_pretrained(_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , (GPTaTokenizer, GPTaTokenizerFast) )
self.assertGreater(len(_lowercase ) , 0 )
def __a ( self : Any ):
A = AutoTokenizer.from_pretrained(_lowercase )
self.assertIsInstance(_lowercase , (BertTokenizer, BertTokenizerFast) )
self.assertEqual(tokenizer.vocab_size , 12 )
def __a ( self : Union[str, Any] ):
A = AutoTokenizer.from_pretrained(_lowercase )
self.assertIsInstance(_lowercase , (RobertaTokenizer, RobertaTokenizerFast) )
self.assertEqual(tokenizer.vocab_size , 20 )
def __a ( self : List[Any] ):
A = AutoConfig.from_pretrained(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
# Check that tokenizer_type ≠ model_type
A = AutoTokenizer.from_pretrained(_lowercase , config=_lowercase )
self.assertIsInstance(_lowercase , (BertTokenizer, BertTokenizerFast) )
self.assertEqual(tokenizer.vocab_size , 12 )
def __a ( self : Any ):
with tempfile.TemporaryDirectory() as tmp_dir:
shutil.copy('./tests/fixtures/vocab.txt' , os.path.join(_lowercase , 'vocab.txt' ) )
A = AutoTokenizer.from_pretrained(_lowercase , tokenizer_type='bert' , use_fast=_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
with tempfile.TemporaryDirectory() as tmp_dir:
shutil.copy('./tests/fixtures/vocab.json' , os.path.join(_lowercase , 'vocab.json' ) )
shutil.copy('./tests/fixtures/merges.txt' , os.path.join(_lowercase , 'merges.txt' ) )
A = AutoTokenizer.from_pretrained(_lowercase , tokenizer_type='gpt2' , use_fast=_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
@require_tokenizers
def __a ( self : List[Any] ):
with tempfile.TemporaryDirectory() as tmp_dir:
shutil.copy('./tests/fixtures/vocab.txt' , os.path.join(_lowercase , 'vocab.txt' ) )
A = AutoTokenizer.from_pretrained(_lowercase , tokenizer_type='bert' )
self.assertIsInstance(_lowercase , _lowercase )
with tempfile.TemporaryDirectory() as tmp_dir:
shutil.copy('./tests/fixtures/vocab.json' , os.path.join(_lowercase , 'vocab.json' ) )
shutil.copy('./tests/fixtures/merges.txt' , os.path.join(_lowercase , 'merges.txt' ) )
A = AutoTokenizer.from_pretrained(_lowercase , tokenizer_type='gpt2' )
self.assertIsInstance(_lowercase , _lowercase )
def __a ( self : str ):
with pytest.raises(_lowercase ):
AutoTokenizer.from_pretrained('./' , tokenizer_type='xxx' )
@require_tokenizers
def __a ( self : List[Any] ):
for tokenizer_class in [BertTokenizer, BertTokenizerFast, AutoTokenizer]:
A = tokenizer_class.from_pretrained('wietsedv/bert-base-dutch-cased' )
self.assertIsInstance(_lowercase , (BertTokenizer, BertTokenizerFast) )
if isinstance(_lowercase , _lowercase ):
self.assertEqual(tokenizer.basic_tokenizer.do_lower_case , _lowercase )
else:
self.assertEqual(tokenizer.do_lower_case , _lowercase )
self.assertEqual(tokenizer.model_max_length , 512 )
@require_tokenizers
def __a ( self : str ):
for tokenizer_class in [BertTokenizer, BertTokenizerFast, AutoTokenizer]:
with self.assertRaisesRegex(
_lowercase , 'julien-c/herlolip-not-exists is not a local folder and is not a valid model identifier' , ):
A = tokenizer_class.from_pretrained('julien-c/herlolip-not-exists' )
def __a ( self : str ):
# tests: https://github.com/huggingface/transformers/pull/13251
# 1. models with `-`, e.g. xlm-roberta -> xlm_roberta
# 2. models that don't remap 1-1 from model-name to model file, e.g., openai-gpt -> openai
A = TOKENIZER_MAPPING.values()
A = []
for slow_tok, fast_tok in tokenizers:
if slow_tok is not None:
tokenizer_names.append(slow_tok.__name__ )
if fast_tok is not None:
tokenizer_names.append(fast_tok.__name__ )
for tokenizer_name in tokenizer_names:
# must find the right class
tokenizer_class_from_name(_lowercase )
@require_tokenizers
def __a ( self : Optional[Any] ):
self.assertIsInstance(AutoTokenizer.from_pretrained('bert-base-cased' , use_fast=_lowercase ) , _lowercase )
self.assertIsInstance(AutoTokenizer.from_pretrained('bert-base-cased' ) , _lowercase )
@require_tokenizers
def __a ( self : Tuple ):
A = AutoTokenizer.from_pretrained('distilbert-base-uncased' , do_lower_case=_lowercase )
A = 'Hello, world. How are you?'
A = tokenizer.tokenize(_lowercase )
self.assertEqual('[UNK]' , tokens[0] )
A = AutoTokenizer.from_pretrained('microsoft/mpnet-base' , do_lower_case=_lowercase )
A = tokenizer.tokenize(_lowercase )
self.assertEqual('[UNK]' , tokens[0] )
@require_tokenizers
def __a ( self : Tuple ):
A = AutoTokenizer.from_pretrained('robot-test/dummy-tokenizer-fast-with-model-config' )
self.assertEqual(type(_lowercase ) , _lowercase )
self.assertEqual(tokenizer.model_max_length , 512 )
self.assertEqual(tokenizer.vocab_size , 30_000 )
self.assertEqual(tokenizer.unk_token , '[UNK]' )
self.assertEqual(tokenizer.padding_side , 'right' )
self.assertEqual(tokenizer.truncation_side , 'right' )
def __a ( self : int ):
A = AutoTokenizer.from_pretrained(_lowercase )
self.assertIsInstance(_lowercase , (BertTokenizer, BertTokenizerFast) )
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(_lowercase )
A = AutoTokenizer.from_pretrained(_lowercase )
self.assertIsInstance(_lowercase , tokenizer.__class__ )
self.assertEqual(tokenizera.vocab_size , 12 )
def __a ( self : Optional[Any] ):
A = AutoTokenizer.from_pretrained('ctrl' )
# There is no fast CTRL so this always gives us a slow tokenizer.
self.assertIsInstance(_lowercase , _lowercase )
def __a ( self : Union[str, Any] ):
# Check we can load the tokenizer config of an online model.
A = get_tokenizer_config('bert-base-cased' )
A = config.pop('_commit_hash' , _lowercase )
# If we ever update bert-base-cased tokenizer config, this dict here will need to be updated.
self.assertEqual(_lowercase , {'do_lower_case': False} )
# This model does not have a tokenizer_config so we get back an empty dict.
A = get_tokenizer_config(_lowercase )
self.assertDictEqual(_lowercase , {} )
# A tokenizer saved with `save_pretrained` always creates a tokenizer config.
A = AutoTokenizer.from_pretrained(_lowercase )
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(_lowercase )
A = get_tokenizer_config(_lowercase )
# Check the class of the tokenizer was properly saved (note that it always saves the slow class).
self.assertEqual(config['tokenizer_class'] , 'BertTokenizer' )
def __a ( self : Optional[int] ):
try:
AutoConfig.register('custom' , _lowercase )
AutoTokenizer.register(_lowercase , slow_tokenizer_class=_lowercase )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(_lowercase ):
AutoTokenizer.register(_lowercase , slow_tokenizer_class=_lowercase )
A = CustomTokenizer.from_pretrained(_lowercase )
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(_lowercase )
A = AutoTokenizer.from_pretrained(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in TOKENIZER_MAPPING._extra_content:
del TOKENIZER_MAPPING._extra_content[CustomConfig]
@require_tokenizers
def __a ( self : List[Any] ):
try:
AutoConfig.register('custom' , _lowercase )
# Can register in two steps
AutoTokenizer.register(_lowercase , slow_tokenizer_class=_lowercase )
self.assertEqual(TOKENIZER_MAPPING[CustomConfig] , (CustomTokenizer, None) )
AutoTokenizer.register(_lowercase , fast_tokenizer_class=_lowercase )
self.assertEqual(TOKENIZER_MAPPING[CustomConfig] , (CustomTokenizer, CustomTokenizerFast) )
del TOKENIZER_MAPPING._extra_content[CustomConfig]
# Can register in one step
AutoTokenizer.register(
_lowercase , slow_tokenizer_class=_lowercase , fast_tokenizer_class=_lowercase )
self.assertEqual(TOKENIZER_MAPPING[CustomConfig] , (CustomTokenizer, CustomTokenizerFast) )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(_lowercase ):
AutoTokenizer.register(_lowercase , fast_tokenizer_class=_lowercase )
# We pass through a bert tokenizer fast cause there is no converter slow to fast for our new toknizer
# and that model does not have a tokenizer.json
with tempfile.TemporaryDirectory() as tmp_dir:
A = BertTokenizerFast.from_pretrained(_lowercase )
bert_tokenizer.save_pretrained(_lowercase )
A = CustomTokenizerFast.from_pretrained(_lowercase )
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(_lowercase )
A = AutoTokenizer.from_pretrained(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
A = AutoTokenizer.from_pretrained(_lowercase , use_fast=_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in TOKENIZER_MAPPING._extra_content:
del TOKENIZER_MAPPING._extra_content[CustomConfig]
def __a ( self : Any ):
# If remote code is not set, we will time out when asking whether to load the model.
with self.assertRaises(_lowercase ):
A = AutoTokenizer.from_pretrained('hf-internal-testing/test_dynamic_tokenizer' )
# If remote code is disabled, we can't load this config.
with self.assertRaises(_lowercase ):
A = AutoTokenizer.from_pretrained(
'hf-internal-testing/test_dynamic_tokenizer' , trust_remote_code=_lowercase )
A = AutoTokenizer.from_pretrained('hf-internal-testing/test_dynamic_tokenizer' , trust_remote_code=_lowercase )
self.assertTrue(tokenizer.special_attribute_present )
# Test tokenizer can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(_lowercase )
A = AutoTokenizer.from_pretrained(_lowercase , trust_remote_code=_lowercase )
self.assertTrue(reloaded_tokenizer.special_attribute_present )
if is_tokenizers_available():
self.assertEqual(tokenizer.__class__.__name__ , 'NewTokenizerFast' )
self.assertEqual(reloaded_tokenizer.__class__.__name__ , 'NewTokenizerFast' )
# Test we can also load the slow version
A = AutoTokenizer.from_pretrained(
'hf-internal-testing/test_dynamic_tokenizer' , trust_remote_code=_lowercase , use_fast=_lowercase )
self.assertTrue(tokenizer.special_attribute_present )
self.assertEqual(tokenizer.__class__.__name__ , 'NewTokenizer' )
# Test tokenizer can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(_lowercase )
A = AutoTokenizer.from_pretrained(_lowercase , trust_remote_code=_lowercase , use_fast=_lowercase )
self.assertEqual(reloaded_tokenizer.__class__.__name__ , 'NewTokenizer' )
self.assertTrue(reloaded_tokenizer.special_attribute_present )
else:
self.assertEqual(tokenizer.__class__.__name__ , 'NewTokenizer' )
self.assertEqual(reloaded_tokenizer.__class__.__name__ , 'NewTokenizer' )
@require_tokenizers
def __a ( self : Dict ):
class lowerCamelCase__ ( UpperCAmelCase_ ):
lowerCAmelCase = False
class lowerCamelCase__ ( UpperCAmelCase_ ):
lowerCAmelCase = NewTokenizer
lowerCAmelCase = False
try:
AutoConfig.register('custom' , _lowercase )
AutoTokenizer.register(_lowercase , slow_tokenizer_class=_lowercase )
AutoTokenizer.register(_lowercase , fast_tokenizer_class=_lowercase )
# If remote code is not set, the default is to use local
A = AutoTokenizer.from_pretrained('hf-internal-testing/test_dynamic_tokenizer' )
self.assertEqual(tokenizer.__class__.__name__ , 'NewTokenizerFast' )
self.assertFalse(tokenizer.special_attribute_present )
A = AutoTokenizer.from_pretrained('hf-internal-testing/test_dynamic_tokenizer' , use_fast=_lowercase )
self.assertEqual(tokenizer.__class__.__name__ , 'NewTokenizer' )
self.assertFalse(tokenizer.special_attribute_present )
# If remote code is disabled, we load the local one.
A = AutoTokenizer.from_pretrained(
'hf-internal-testing/test_dynamic_tokenizer' , trust_remote_code=_lowercase )
self.assertEqual(tokenizer.__class__.__name__ , 'NewTokenizerFast' )
self.assertFalse(tokenizer.special_attribute_present )
A = AutoTokenizer.from_pretrained(
'hf-internal-testing/test_dynamic_tokenizer' , trust_remote_code=_lowercase , use_fast=_lowercase )
self.assertEqual(tokenizer.__class__.__name__ , 'NewTokenizer' )
self.assertFalse(tokenizer.special_attribute_present )
# If remote is enabled, we load from the Hub
A = AutoTokenizer.from_pretrained(
'hf-internal-testing/test_dynamic_tokenizer' , trust_remote_code=_lowercase )
self.assertEqual(tokenizer.__class__.__name__ , 'NewTokenizerFast' )
self.assertTrue(tokenizer.special_attribute_present )
A = AutoTokenizer.from_pretrained(
'hf-internal-testing/test_dynamic_tokenizer' , trust_remote_code=_lowercase , use_fast=_lowercase )
self.assertEqual(tokenizer.__class__.__name__ , 'NewTokenizer' )
self.assertTrue(tokenizer.special_attribute_present )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in TOKENIZER_MAPPING._extra_content:
del TOKENIZER_MAPPING._extra_content[CustomConfig]
def __a ( self : Tuple ):
A = AutoTokenizer.from_pretrained(
'hf-internal-testing/test_dynamic_tokenizer_legacy' , trust_remote_code=_lowercase )
self.assertTrue(tokenizer.special_attribute_present )
if is_tokenizers_available():
self.assertEqual(tokenizer.__class__.__name__ , 'NewTokenizerFast' )
# Test we can also load the slow version
A = AutoTokenizer.from_pretrained(
'hf-internal-testing/test_dynamic_tokenizer_legacy' , trust_remote_code=_lowercase , use_fast=_lowercase )
self.assertTrue(tokenizer.special_attribute_present )
self.assertEqual(tokenizer.__class__.__name__ , 'NewTokenizer' )
else:
self.assertEqual(tokenizer.__class__.__name__ , 'NewTokenizer' )
def __a ( self : Tuple ):
with self.assertRaisesRegex(
_lowercase , 'bert-base is not a local folder and is not a valid model identifier' ):
A = AutoTokenizer.from_pretrained('bert-base' )
def __a ( self : Dict ):
with self.assertRaisesRegex(
_lowercase , R'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)' ):
A = AutoTokenizer.from_pretrained(_lowercase , revision='aaaaaa' )
def __a ( self : Dict ):
# Make sure we have cached the tokenizer.
A = AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-bert' )
with RequestCounter() as counter:
A = AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-bert' )
self.assertEqual(counter.get_request_count , 0 )
self.assertEqual(counter.head_request_count , 1 )
self.assertEqual(counter.other_request_count , 0 )
| 690 |
"""simple docstring"""
from __future__ import annotations
def __snake_case ( UpperCamelCase__ ) -> list[int]: # This function is recursive
"""simple docstring"""
A = len(UpperCamelCase__ )
# If the array contains only one element, we return it (it's the stop condition of
# recursion)
if array_length <= 1:
return array
# Else
A = array[0]
A = False
A = 1
A = []
while not is_found and i < array_length:
if array[i] < pivot:
A = True
A = [element for element in array[i:] if element >= array[i]]
A = longest_subsequence(UpperCamelCase__ )
if len(UpperCamelCase__ ) > len(UpperCamelCase__ ):
A = temp_array
else:
i += 1
A = [element for element in array[1:] if element >= pivot]
A = [pivot, *longest_subsequence(UpperCamelCase__ )]
if len(UpperCamelCase__ ) > len(UpperCamelCase__ ):
return temp_array
else:
return longest_subseq
if __name__ == "__main__":
import doctest
doctest.testmod()
| 690 | 1 |
"""simple docstring"""
from .imports import is_tqdm_available
if is_tqdm_available():
from tqdm.auto import tqdm as _tqdm
from ..state import PartialState
def __snake_case ( UpperCamelCase__ = True , *UpperCamelCase__ , **UpperCamelCase__ ) -> Dict:
"""simple docstring"""
if not is_tqdm_available():
raise ImportError('Accelerate\'s `tqdm` module requires `tqdm` to be installed. Please run `pip install tqdm`.' )
A = False
if main_process_only:
A = PartialState().local_process_index == 0
return _tqdm(*UpperCamelCase__ , **UpperCamelCase__ , disable=UpperCamelCase__ )
| 690 |
"""simple docstring"""
from __future__ import annotations
import typing
from collections.abc import Iterable
import numpy as np
UpperCamelCase : Tuple = typing.Union[Iterable[float], Iterable[int], np.ndarray] # noqa: UP007
UpperCamelCase : Optional[int] = typing.Union[np.floataa, int, float] # noqa: UP007
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ ) -> VectorOut:
"""simple docstring"""
return np.sqrt(np.sum((np.asarray(UpperCamelCase__ ) - np.asarray(UpperCamelCase__ )) ** 2 ) )
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ ) -> VectorOut:
"""simple docstring"""
return sum((va - va) ** 2 for va, va in zip(UpperCamelCase__ , UpperCamelCase__ ) ) ** (1 / 2)
if __name__ == "__main__":
def __snake_case ( ) -> None:
"""simple docstring"""
from timeit import timeit
print('Without Numpy' )
print(
timeit(
'euclidean_distance_no_np([1, 2, 3], [4, 5, 6])' , number=10000 , globals=globals() , ) )
print('With Numpy' )
print(
timeit(
'euclidean_distance([1, 2, 3], [4, 5, 6])' , number=10000 , globals=globals() , ) )
benchmark()
| 690 | 1 |
"""simple docstring"""
def __snake_case ( UpperCamelCase__ = 50 ) -> int:
"""simple docstring"""
A = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
ways_number[row_length] += ways_number[
row_length - tile_start - tile_length
]
return ways_number[length]
if __name__ == "__main__":
print(F"""{solution() = }""")
| 690 |
"""simple docstring"""
import argparse
import os
import shutil
from pathlib import Path
import onnx
import torch
from packaging import version
from torch.onnx import export
from diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline, StableDiffusionPipeline
UpperCamelCase : List[Any] = version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__=False , ) -> Any:
"""simple docstring"""
output_path.parent.mkdir(parents=UpperCamelCase__ , exist_ok=UpperCamelCase__ )
# PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
# so we check the torch version for backwards compatibility
if is_torch_less_than_1_11:
export(
UpperCamelCase__ , UpperCamelCase__ , f=output_path.as_posix() , input_names=UpperCamelCase__ , output_names=UpperCamelCase__ , dynamic_axes=UpperCamelCase__ , do_constant_folding=UpperCamelCase__ , use_external_data_format=UpperCamelCase__ , enable_onnx_checker=UpperCamelCase__ , opset_version=UpperCamelCase__ , )
else:
export(
UpperCamelCase__ , UpperCamelCase__ , f=output_path.as_posix() , input_names=UpperCamelCase__ , output_names=UpperCamelCase__ , dynamic_axes=UpperCamelCase__ , do_constant_folding=UpperCamelCase__ , opset_version=UpperCamelCase__ , )
@torch.no_grad()
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = False ) -> str:
"""simple docstring"""
A = torch.floataa if fpaa else torch.floataa
if fpaa and torch.cuda.is_available():
A = 'cuda'
elif fpaa and not torch.cuda.is_available():
raise ValueError('`float16` model export is only supported on GPUs with CUDA' )
else:
A = 'cpu'
A = StableDiffusionPipeline.from_pretrained(UpperCamelCase__ , torch_dtype=UpperCamelCase__ ).to(UpperCamelCase__ )
A = Path(UpperCamelCase__ )
# TEXT ENCODER
A = pipeline.text_encoder.config.max_position_embeddings
A = pipeline.text_encoder.config.hidden_size
A = pipeline.tokenizer(
'A sample prompt' , padding='max_length' , max_length=pipeline.tokenizer.model_max_length , truncation=UpperCamelCase__ , return_tensors='pt' , )
onnx_export(
pipeline.text_encoder , model_args=(text_input.input_ids.to(device=UpperCamelCase__ , dtype=torch.intaa )) , output_path=output_path / 'text_encoder' / 'model.onnx' , ordered_input_names=['input_ids'] , output_names=['last_hidden_state', 'pooler_output'] , dynamic_axes={
'input_ids': {0: 'batch', 1: 'sequence'},
} , opset=UpperCamelCase__ , )
del pipeline.text_encoder
# UNET
A = pipeline.unet.config.in_channels
A = pipeline.unet.config.sample_size
A = output_path / 'unet' / 'model.onnx'
onnx_export(
pipeline.unet , model_args=(
torch.randn(2 , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ).to(device=UpperCamelCase__ , dtype=UpperCamelCase__ ),
torch.randn(2 ).to(device=UpperCamelCase__ , dtype=UpperCamelCase__ ),
torch.randn(2 , UpperCamelCase__ , UpperCamelCase__ ).to(device=UpperCamelCase__ , dtype=UpperCamelCase__ ),
False,
) , output_path=UpperCamelCase__ , ordered_input_names=['sample', 'timestep', 'encoder_hidden_states', 'return_dict'] , output_names=['out_sample'] , dynamic_axes={
'sample': {0: 'batch', 1: 'channels', 2: 'height', 3: 'width'},
'timestep': {0: 'batch'},
'encoder_hidden_states': {0: 'batch', 1: 'sequence'},
} , opset=UpperCamelCase__ , use_external_data_format=UpperCamelCase__ , )
A = str(unet_path.absolute().as_posix() )
A = os.path.dirname(UpperCamelCase__ )
A = onnx.load(UpperCamelCase__ )
# clean up existing tensor files
shutil.rmtree(UpperCamelCase__ )
os.mkdir(UpperCamelCase__ )
# collate external tensor files into one
onnx.save_model(
UpperCamelCase__ , UpperCamelCase__ , save_as_external_data=UpperCamelCase__ , all_tensors_to_one_file=UpperCamelCase__ , location='weights.pb' , convert_attribute=UpperCamelCase__ , )
del pipeline.unet
# VAE ENCODER
A = pipeline.vae
A = vae_encoder.config.in_channels
A = vae_encoder.config.sample_size
# need to get the raw tensor output (sample) from the encoder
A = lambda UpperCamelCase__ , UpperCamelCase__ : vae_encoder.encode(UpperCamelCase__ , UpperCamelCase__ )[0].sample()
onnx_export(
UpperCamelCase__ , model_args=(
torch.randn(1 , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ).to(device=UpperCamelCase__ , dtype=UpperCamelCase__ ),
False,
) , output_path=output_path / 'vae_encoder' / 'model.onnx' , ordered_input_names=['sample', 'return_dict'] , output_names=['latent_sample'] , dynamic_axes={
'sample': {0: 'batch', 1: 'channels', 2: 'height', 3: 'width'},
} , opset=UpperCamelCase__ , )
# VAE DECODER
A = pipeline.vae
A = vae_decoder.config.latent_channels
A = vae_decoder.config.out_channels
# forward only through the decoder part
A = vae_encoder.decode
onnx_export(
UpperCamelCase__ , model_args=(
torch.randn(1 , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ).to(device=UpperCamelCase__ , dtype=UpperCamelCase__ ),
False,
) , output_path=output_path / 'vae_decoder' / 'model.onnx' , ordered_input_names=['latent_sample', 'return_dict'] , output_names=['sample'] , dynamic_axes={
'latent_sample': {0: 'batch', 1: 'channels', 2: 'height', 3: 'width'},
} , opset=UpperCamelCase__ , )
del pipeline.vae
# SAFETY CHECKER
if pipeline.safety_checker is not None:
A = pipeline.safety_checker
A = safety_checker.config.vision_config.num_channels
A = safety_checker.config.vision_config.image_size
A = safety_checker.forward_onnx
onnx_export(
pipeline.safety_checker , model_args=(
torch.randn(
1 , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , ).to(device=UpperCamelCase__ , dtype=UpperCamelCase__ ),
torch.randn(1 , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ).to(device=UpperCamelCase__ , dtype=UpperCamelCase__ ),
) , output_path=output_path / 'safety_checker' / 'model.onnx' , ordered_input_names=['clip_input', 'images'] , output_names=['out_images', 'has_nsfw_concepts'] , dynamic_axes={
'clip_input': {0: 'batch', 1: 'channels', 2: 'height', 3: 'width'},
'images': {0: 'batch', 1: 'height', 2: 'width', 3: 'channels'},
} , opset=UpperCamelCase__ , )
del pipeline.safety_checker
A = OnnxRuntimeModel.from_pretrained(output_path / 'safety_checker' )
A = pipeline.feature_extractor
else:
A = None
A = None
A = OnnxStableDiffusionPipeline(
vae_encoder=OnnxRuntimeModel.from_pretrained(output_path / 'vae_encoder' ) , vae_decoder=OnnxRuntimeModel.from_pretrained(output_path / 'vae_decoder' ) , text_encoder=OnnxRuntimeModel.from_pretrained(output_path / 'text_encoder' ) , tokenizer=pipeline.tokenizer , unet=OnnxRuntimeModel.from_pretrained(output_path / 'unet' ) , scheduler=pipeline.scheduler , safety_checker=UpperCamelCase__ , feature_extractor=UpperCamelCase__ , requires_safety_checker=safety_checker is not None , )
onnx_pipeline.save_pretrained(UpperCamelCase__ )
print('ONNX pipeline saved to' , UpperCamelCase__ )
del pipeline
del onnx_pipeline
A = OnnxStableDiffusionPipeline.from_pretrained(UpperCamelCase__ , provider='CPUExecutionProvider' )
print('ONNX pipeline is loadable' )
if __name__ == "__main__":
UpperCamelCase : Optional[int] = argparse.ArgumentParser()
parser.add_argument(
"--model_path",
type=str,
required=True,
help="Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).",
)
parser.add_argument("--output_path", type=str, required=True, help="Path to the output model.")
parser.add_argument(
"--opset",
default=14,
type=int,
help="The version of the ONNX operator set to use.",
)
parser.add_argument("--fp16", action="store_true", default=False, help="Export the models in `float16` mode")
UpperCamelCase : str = parser.parse_args()
convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
| 690 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase : Dict = {
"configuration_blip_2": [
"BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP",
"Blip2Config",
"Blip2QFormerConfig",
"Blip2VisionConfig",
],
"processing_blip_2": ["Blip2Processor"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase : Optional[Any] = [
"BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST",
"Blip2Model",
"Blip2QFormerModel",
"Blip2PreTrainedModel",
"Blip2ForConditionalGeneration",
"Blip2VisionModel",
]
if TYPE_CHECKING:
from .configuration_blip_a import (
BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
BlipaConfig,
BlipaQFormerConfig,
BlipaVisionConfig,
)
from .processing_blip_a import BlipaProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blip_a import (
BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST,
BlipaForConditionalGeneration,
BlipaModel,
BlipaPreTrainedModel,
BlipaQFormerModel,
BlipaVisionModel,
)
else:
import sys
UpperCamelCase : List[Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 690 |
"""simple docstring"""
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
UpperCamelCase : List[str] = Lock()
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> List[str]:
"""simple docstring"""
global process_lock
# we perform n swaps since after n swaps we know we are sorted
# we *could* stop early if we are sorted already, but it takes as long to
# find out we are sorted as it does to sort the list with this algorithm
for i in range(0 , 10 ):
if (i + position) % 2 == 0 and r_send is not None:
# send your value to your right neighbor
process_lock.acquire()
r_send[1].send(UpperCamelCase__ )
process_lock.release()
# receive your right neighbor's value
process_lock.acquire()
A = rr_cv[0].recv()
process_lock.release()
# take the lower value since you are on the left
A = min(UpperCamelCase__ , UpperCamelCase__ )
elif (i + position) % 2 != 0 and l_send is not None:
# send your value to your left neighbor
process_lock.acquire()
l_send[1].send(UpperCamelCase__ )
process_lock.release()
# receive your left neighbor's value
process_lock.acquire()
A = lr_cv[0].recv()
process_lock.release()
# take the higher value since you are on the right
A = max(UpperCamelCase__ , UpperCamelCase__ )
# after all swaps are performed, send the values back to main
result_pipe[1].send(UpperCamelCase__ )
def __snake_case ( UpperCamelCase__ ) -> List[str]:
"""simple docstring"""
A = []
A = []
# initialize the list of pipes where the values will be retrieved
for _ in arr:
result_pipe.append(Pipe() )
# creates the processes
# the first and last process only have one neighbor so they are made outside
# of the loop
A = Pipe()
A = Pipe()
process_array_.append(
Process(
target=UpperCamelCase__ , args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0]) , ) )
A = temp_rs
A = temp_rr
for i in range(1 , len(UpperCamelCase__ ) - 1 ):
A = Pipe()
A = Pipe()
process_array_.append(
Process(
target=UpperCamelCase__ , args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i]) , ) )
A = temp_rs
A = temp_rr
process_array_.append(
Process(
target=UpperCamelCase__ , args=(
len(UpperCamelCase__ ) - 1,
arr[len(UpperCamelCase__ ) - 1],
temp_ls,
None,
temp_lr,
None,
result_pipe[len(UpperCamelCase__ ) - 1],
) , ) )
# start the processes
for p in process_array_:
p.start()
# wait for the processes to end and write their values to the list
for p in range(0 , len(UpperCamelCase__ ) ):
A = result_pipe[p][0].recv()
process_array_[p].join()
return arr
def __snake_case ( ) -> Optional[Any]:
"""simple docstring"""
A = list(range(10 , 0 , -1 ) )
print('Initial List' )
print(*UpperCamelCase__ )
A = odd_even_transposition(UpperCamelCase__ )
print('Sorted List\n' )
print(*UpperCamelCase__ )
if __name__ == "__main__":
main()
| 690 | 1 |
"""simple docstring"""
from __future__ import annotations
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ ) -> bool:
"""simple docstring"""
A = get_failure_array(UpperCamelCase__ )
# 2) Step through text searching for pattern
A , A = 0, 0 # index into text, pattern
while i < len(UpperCamelCase__ ):
if pattern[j] == text[i]:
if j == (len(UpperCamelCase__ ) - 1):
return True
j += 1
# if this is a prefix in our pattern
# just go back far enough to continue
elif j > 0:
A = failure[j - 1]
continue
i += 1
return False
def __snake_case ( UpperCamelCase__ ) -> list[int]:
"""simple docstring"""
A = [0]
A = 0
A = 1
while j < len(UpperCamelCase__ ):
if pattern[i] == pattern[j]:
i += 1
elif i > 0:
A = failure[i - 1]
continue
j += 1
failure.append(UpperCamelCase__ )
return failure
if __name__ == "__main__":
# Test 1)
UpperCamelCase : int = "abc1abc12"
UpperCamelCase : Tuple = "alskfjaldsabc1abc1abc12k23adsfabcabc"
UpperCamelCase : Union[str, Any] = "alskfjaldsk23adsfabcabc"
assert kmp(pattern, texta) and not kmp(pattern, texta)
# Test 2)
UpperCamelCase : Union[str, Any] = "ABABX"
UpperCamelCase : Any = "ABABZABABYABABX"
assert kmp(pattern, text)
# Test 3)
UpperCamelCase : Optional[Any] = "AAAB"
UpperCamelCase : List[Any] = "ABAAAAAB"
assert kmp(pattern, text)
# Test 4)
UpperCamelCase : Tuple = "abcdabcy"
UpperCamelCase : Tuple = "abcxabcdabxabcdabcdabcy"
assert kmp(pattern, text)
# Test 5)
UpperCamelCase : Optional[int] = "aabaabaaa"
assert get_failure_array(pattern) == [0, 1, 0, 1, 2, 3, 4, 5, 2]
| 690 |
"""simple docstring"""
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures
# Importing the dataset
UpperCamelCase : int = pd.read_csv(
"https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/"
"position_salaries.csv"
)
UpperCamelCase : List[Any] = dataset.iloc[:, 1:2].values
UpperCamelCase : Any = dataset.iloc[:, 2].values
UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase : str = train_test_split(X, y, test_size=0.2, random_state=0)
UpperCamelCase : List[str] = PolynomialFeatures(degree=4)
UpperCamelCase : Optional[int] = poly_reg.fit_transform(X)
UpperCamelCase : List[Any] = LinearRegression()
pol_reg.fit(X_poly, y)
def __snake_case ( ) -> Optional[int]:
"""simple docstring"""
plt.scatter(UpperCamelCase__ , UpperCamelCase__ , color='red' )
plt.plot(UpperCamelCase__ , pol_reg.predict(poly_reg.fit_transform(UpperCamelCase__ ) ) , color='blue' )
plt.title('Truth or Bluff (Linear Regression)' )
plt.xlabel('Position level' )
plt.ylabel('Salary' )
plt.show()
if __name__ == "__main__":
viz_polymonial()
# Predicting a new result with Polymonial Regression
pol_reg.predict(poly_reg.fit_transform([[5.5]]))
# output should be 132148.43750003
| 690 | 1 |
"""simple docstring"""
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ ) -> List[Any]:
"""simple docstring"""
A = [1]
for i in range(2 , UpperCamelCase__ ):
factorials.append(factorials[-1] * i )
assert 0 <= k < factorials[-1] * n, "k out of bounds"
A = []
A = list(range(UpperCamelCase__ ) )
# Find permutation
while factorials:
A = factorials.pop()
A , A = divmod(UpperCamelCase__ , UpperCamelCase__ )
permutation.append(elements[number] )
elements.remove(elements[number] )
permutation.append(elements[0] )
return permutation
if __name__ == "__main__":
import doctest
doctest.testmod()
| 690 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPENAI_CLIP_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
UpperCamelCase : Optional[Any] = logging.get_logger(__name__)
if is_vision_available():
import PIL
class lowerCamelCase__ ( UpperCAmelCase_ ):
lowerCAmelCase = ["""pixel_values"""]
def __init__( self : List[str] , _lowercase : bool = True , _lowercase : Dict[str, int] = None , _lowercase : PILImageResampling = PILImageResampling.BICUBIC , _lowercase : bool = True , _lowercase : Dict[str, int] = None , _lowercase : bool = True , _lowercase : Union[int, float] = 1 / 255 , _lowercase : bool = True , _lowercase : Optional[Union[float, List[float]]] = None , _lowercase : Optional[Union[float, List[float]]] = None , _lowercase : bool = True , **_lowercase : Tuple , ):
super().__init__(**_lowercase )
A = size if size is not None else {'shortest_edge': 224}
A = get_size_dict(_lowercase , default_to_square=_lowercase )
A = crop_size if crop_size is not None else {'height': 224, 'width': 224}
A = get_size_dict(_lowercase , default_to_square=_lowercase , param_name='crop_size' )
A = do_resize
A = size
A = resample
A = do_center_crop
A = crop_size
A = do_rescale
A = rescale_factor
A = do_normalize
A = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
A = image_std if image_std is not None else OPENAI_CLIP_STD
A = do_convert_rgb
def __a ( self : str , _lowercase : np.ndarray , _lowercase : Dict[str, int] , _lowercase : PILImageResampling = PILImageResampling.BICUBIC , _lowercase : Optional[Union[str, ChannelDimension]] = None , **_lowercase : List[str] , ):
A = get_size_dict(_lowercase , default_to_square=_lowercase )
if "shortest_edge" not in size:
raise ValueError(f'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}' )
A = get_resize_output_image_size(_lowercase , size=size['shortest_edge'] , default_to_square=_lowercase )
return resize(_lowercase , size=_lowercase , resample=_lowercase , data_format=_lowercase , **_lowercase )
def __a ( self : int , _lowercase : np.ndarray , _lowercase : Dict[str, int] , _lowercase : Optional[Union[str, ChannelDimension]] = None , **_lowercase : str , ):
A = get_size_dict(_lowercase )
if "height" not in size or "width" not in size:
raise ValueError(f'The `size` parameter must contain the keys (height, width). Got {size.keys()}' )
return center_crop(_lowercase , size=(size['height'], size['width']) , data_format=_lowercase , **_lowercase )
def __a ( self : List[str] , _lowercase : np.ndarray , _lowercase : Union[int, float] , _lowercase : Optional[Union[str, ChannelDimension]] = None , **_lowercase : List[str] , ):
return rescale(_lowercase , scale=_lowercase , data_format=_lowercase , **_lowercase )
def __a ( self : List[str] , _lowercase : np.ndarray , _lowercase : Union[float, List[float]] , _lowercase : Union[float, List[float]] , _lowercase : Optional[Union[str, ChannelDimension]] = None , **_lowercase : Union[str, Any] , ):
return normalize(_lowercase , mean=_lowercase , std=_lowercase , data_format=_lowercase , **_lowercase )
def __a ( self : Optional[int] , _lowercase : ImageInput , _lowercase : bool = None , _lowercase : Dict[str, int] = None , _lowercase : PILImageResampling = None , _lowercase : bool = None , _lowercase : int = None , _lowercase : bool = None , _lowercase : float = None , _lowercase : bool = None , _lowercase : Optional[Union[float, List[float]]] = None , _lowercase : Optional[Union[float, List[float]]] = None , _lowercase : bool = None , _lowercase : Optional[Union[str, TensorType]] = None , _lowercase : Optional[ChannelDimension] = ChannelDimension.FIRST , **_lowercase : int , ):
A = do_resize if do_resize is not None else self.do_resize
A = size if size is not None else self.size
A = get_size_dict(_lowercase , param_name='size' , default_to_square=_lowercase )
A = resample if resample is not None else self.resample
A = do_center_crop if do_center_crop is not None else self.do_center_crop
A = crop_size if crop_size is not None else self.crop_size
A = get_size_dict(_lowercase , param_name='crop_size' , default_to_square=_lowercase )
A = do_rescale if do_rescale is not None else self.do_rescale
A = rescale_factor if rescale_factor is not None else self.rescale_factor
A = do_normalize if do_normalize is not None else self.do_normalize
A = image_mean if image_mean is not None else self.image_mean
A = image_std if image_std is not None else self.image_std
A = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
A = make_list_of_images(_lowercase )
if not valid_images(_lowercase ):
raise ValueError(
'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
'torch.Tensor, tf.Tensor or jax.ndarray.' )
if do_resize and size is None:
raise ValueError('Size must be specified if do_resize is True.' )
if do_center_crop and crop_size is None:
raise ValueError('Crop size must be specified if do_center_crop is True.' )
if do_rescale and rescale_factor is None:
raise ValueError('Rescale factor must be specified if do_rescale is True.' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('Image mean and std must be specified if do_normalize is True.' )
# PIL RGBA images are converted to RGB
if do_convert_rgb:
A = [convert_to_rgb(_lowercase ) for image in images]
# All transformations expect numpy arrays.
A = [to_numpy_array(_lowercase ) for image in images]
if do_resize:
A = [self.resize(image=_lowercase , size=_lowercase , resample=_lowercase ) for image in images]
if do_center_crop:
A = [self.center_crop(image=_lowercase , size=_lowercase ) for image in images]
if do_rescale:
A = [self.rescale(image=_lowercase , scale=_lowercase ) for image in images]
if do_normalize:
A = [self.normalize(image=_lowercase , mean=_lowercase , std=_lowercase ) for image in images]
A = [to_channel_dimension_format(_lowercase , _lowercase ) for image in images]
A = {'pixel_values': images}
return BatchFeature(data=_lowercase , tensor_type=_lowercase )
| 690 | 1 |
"""simple docstring"""
from __future__ import annotations
UpperCamelCase : int = 10
def __snake_case ( UpperCamelCase__ ) -> list[int]:
"""simple docstring"""
A = 1
A = max(UpperCamelCase__ )
while placement <= max_digit:
# declare and initialize empty buckets
A = [[] for _ in range(UpperCamelCase__ )]
# split list_of_ints between the buckets
for i in list_of_ints:
A = int((i / placement) % RADIX )
buckets[tmp].append(UpperCamelCase__ )
# put each buckets' contents into list_of_ints
A = 0
for b in range(UpperCamelCase__ ):
for i in buckets[b]:
A = i
a += 1
# move to next
placement *= RADIX
return list_of_ints
if __name__ == "__main__":
import doctest
doctest.testmod()
| 690 |
"""simple docstring"""
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class lowerCamelCase__ ( unittest.TestCase ):
def __a ( self : Union[str, Any] ):
A = torch.nn.Linear(10 , 10 )
A = torch.optim.SGD(model.parameters() , 0.1 )
A = Accelerator()
A = accelerator.prepare(_lowercase )
try:
pickle.loads(pickle.dumps(_lowercase ) )
except Exception as e:
self.fail(f'Accelerated optimizer pickling failed with {e}' )
AcceleratorState._reset_state()
| 690 | 1 |
"""simple docstring"""
from __future__ import annotations
import os
from collections.abc import Mapping
UpperCamelCase : int = tuple[int, int]
class lowerCamelCase__ :
def __init__( self : Optional[Any] , _lowercase : set[int] , _lowercase : Mapping[EdgeT, int] ):
A = vertices
A = {
(min(_lowercase ), max(_lowercase )): weight for edge, weight in edges.items()
}
def __a ( self : Union[str, Any] , _lowercase : EdgeT , _lowercase : int ):
self.vertices.add(edge[0] )
self.vertices.add(edge[1] )
A = weight
def __a ( self : Union[str, Any] ):
A = Graph({min(self.vertices )} , {} )
A = 42
A = 42
A = 42
A = 42
while len(subgraph.vertices ) < len(self.vertices ):
A = max(self.edges.values() ) + 1
for edge, weight in self.edges.items():
if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
if weight < min_weight:
A = edge
A = weight
subgraph.add_edge(_lowercase , _lowercase )
return subgraph
def __snake_case ( UpperCamelCase__ = "p107_network.txt" ) -> int:
"""simple docstring"""
A = os.path.abspath(os.path.dirname(UpperCamelCase__ ) )
A = os.path.join(UpperCamelCase__ , UpperCamelCase__ )
A = {}
A = 42
A = 42
A = 42
with open(UpperCamelCase__ ) as f:
A = f.read().strip().split('\n' )
A = [line.split(',' ) for line in data]
for edgea in range(1 , len(UpperCamelCase__ ) ):
for edgea in range(UpperCamelCase__ ):
if adjaceny_matrix[edgea][edgea] != "-":
A = int(adjaceny_matrix[edgea][edgea] )
A = Graph(set(range(len(UpperCamelCase__ ) ) ) , UpperCamelCase__ )
A = graph.prims_algorithm()
A = sum(graph.edges.values() )
A = sum(subgraph.edges.values() )
return initial_total - optimal_total
if __name__ == "__main__":
print(F"""{solution() = }""")
| 690 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCamelCase : Optional[int] = logging.get_logger(__name__)
UpperCamelCase : Optional[Any] = {
"YituTech/conv-bert-base": "https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json",
"YituTech/conv-bert-medium-small": (
"https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json"
),
"YituTech/conv-bert-small": "https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json",
# See all ConvBERT models at https://huggingface.co/models?filter=convbert
}
class lowerCamelCase__ ( UpperCAmelCase_ ):
lowerCAmelCase = """convbert"""
def __init__( self : Optional[int] , _lowercase : List[Any]=30_522 , _lowercase : List[str]=768 , _lowercase : Optional[Any]=12 , _lowercase : Any=12 , _lowercase : str=3_072 , _lowercase : List[str]="gelu" , _lowercase : Dict=0.1 , _lowercase : Dict=0.1 , _lowercase : Any=512 , _lowercase : List[str]=2 , _lowercase : Tuple=0.0_2 , _lowercase : List[Any]=1e-12 , _lowercase : List[str]=1 , _lowercase : Tuple=0 , _lowercase : Any=2 , _lowercase : Union[str, Any]=768 , _lowercase : str=2 , _lowercase : Any=9 , _lowercase : Union[str, Any]=1 , _lowercase : Dict=None , **_lowercase : Union[str, Any] , ):
super().__init__(
pad_token_id=_lowercase , bos_token_id=_lowercase , eos_token_id=_lowercase , **_lowercase , )
A = vocab_size
A = hidden_size
A = num_hidden_layers
A = num_attention_heads
A = intermediate_size
A = hidden_act
A = hidden_dropout_prob
A = attention_probs_dropout_prob
A = max_position_embeddings
A = type_vocab_size
A = initializer_range
A = layer_norm_eps
A = embedding_size
A = head_ratio
A = conv_kernel_size
A = num_groups
A = classifier_dropout
class lowerCamelCase__ ( UpperCAmelCase_ ):
@property
def __a ( self : str ):
if self.task == "multiple-choice":
A = {0: 'batch', 1: 'choice', 2: 'sequence'}
else:
A = {0: 'batch', 1: 'sequence'}
return OrderedDict(
[
('input_ids', dynamic_axis),
('attention_mask', dynamic_axis),
('token_type_ids', dynamic_axis),
] )
| 690 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCamelCase : str = {
"configuration_tapas": ["TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP", "TapasConfig"],
"tokenization_tapas": ["TapasTokenizer"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase : Union[str, Any] = [
"TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
"TapasForMaskedLM",
"TapasForQuestionAnswering",
"TapasForSequenceClassification",
"TapasModel",
"TapasPreTrainedModel",
"load_tf_weights_in_tapas",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase : Optional[int] = [
"TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFTapasForMaskedLM",
"TFTapasForQuestionAnswering",
"TFTapasForSequenceClassification",
"TFTapasModel",
"TFTapasPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
from .tokenization_tapas import TapasTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tapas import (
TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasPreTrainedModel,
load_tf_weights_in_tapas,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_tapas import (
TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
TFTapasForMaskedLM,
TFTapasForQuestionAnswering,
TFTapasForSequenceClassification,
TFTapasModel,
TFTapasPreTrainedModel,
)
else:
import sys
UpperCamelCase : List[Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 690 |
"""simple docstring"""
from .constants import (
MODEL_NAME,
OPTIMIZER_NAME,
RNG_STATE_NAME,
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
SCALER_NAME,
SCHEDULER_NAME,
TORCH_LAUNCH_PARAMS,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
)
from .dataclasses import (
BnbQuantizationConfig,
ComputeEnvironment,
CustomDtype,
DeepSpeedPlugin,
DistributedDataParallelKwargs,
DistributedType,
DynamoBackend,
FPaRecipeKwargs,
FullyShardedDataParallelPlugin,
GradientAccumulationPlugin,
GradScalerKwargs,
InitProcessGroupKwargs,
KwargsHandler,
LoggerType,
MegatronLMPlugin,
PrecisionType,
ProjectConfiguration,
RNGType,
SageMakerDistributedType,
TensorInformation,
TorchDynamoPlugin,
)
from .environment import get_int_from_env, parse_choice_from_env, parse_flag_from_env
from .imports import (
get_ccl_version,
is_abit_bnb_available,
is_abit_bnb_available,
is_aim_available,
is_bfaa_available,
is_bnb_available,
is_botoa_available,
is_ccl_available,
is_comet_ml_available,
is_datasets_available,
is_deepspeed_available,
is_fpa_available,
is_ipex_available,
is_megatron_lm_available,
is_mlflow_available,
is_mps_available,
is_npu_available,
is_rich_available,
is_safetensors_available,
is_sagemaker_available,
is_tensorboard_available,
is_tpu_available,
is_transformers_available,
is_wandb_available,
is_xpu_available,
)
from .modeling import (
check_device_map,
check_tied_parameters_in_config,
check_tied_parameters_on_same_device,
compute_module_sizes,
convert_file_size_to_int,
dtype_byte_size,
find_tied_parameters,
get_balanced_memory,
get_max_layer_size,
get_max_memory,
get_mixed_precision_context_manager,
id_tensor_storage,
infer_auto_device_map,
load_checkpoint_in_model,
load_offloaded_weights,
load_state_dict,
named_module_tensors,
retie_parameters,
set_module_tensor_to_device,
shard_checkpoint,
)
from .offload import (
OffloadedWeightsLoader,
PrefixedDataset,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_weight,
save_offload_index,
)
from .operations import (
broadcast,
broadcast_object_list,
concatenate,
convert_outputs_to_fpaa,
convert_to_fpaa,
find_batch_size,
find_device,
gather,
gather_object,
get_data_structure,
honor_type,
initialize_tensors,
is_namedtuple,
is_tensor_information,
is_torch_tensor,
listify,
pad_across_processes,
recursively_apply,
reduce,
send_to_device,
slice_tensors,
)
from .versions import compare_versions, is_torch_version
if is_deepspeed_available():
from .deepspeed import (
DeepSpeedEngineWrapper,
DeepSpeedOptimizerWrapper,
DeepSpeedSchedulerWrapper,
DummyOptim,
DummyScheduler,
HfDeepSpeedConfig,
)
from .bnb import has_abit_bnb_layers, load_and_quantize_model
from .fsdp_utils import load_fsdp_model, load_fsdp_optimizer, save_fsdp_model, save_fsdp_optimizer
from .launch import (
PrepareForLaunch,
_filter_args,
prepare_deepspeed_cmd_env,
prepare_multi_gpu_env,
prepare_sagemager_args_inputs,
prepare_simple_launcher_cmd_env,
prepare_tpu,
)
from .megatron_lm import (
AbstractTrainStep,
BertTrainStep,
GPTTrainStep,
MegatronEngine,
MegatronLMDummyDataLoader,
MegatronLMDummyScheduler,
MegatronLMOptimizerWrapper,
MegatronLMSchedulerWrapper,
TaTrainStep,
avg_losses_across_data_parallel_group,
gather_across_data_parallel_groups,
)
from .megatron_lm import initialize as megatron_lm_initialize
from .megatron_lm import prepare_data_loader as megatron_lm_prepare_data_loader
from .megatron_lm import prepare_model as megatron_lm_prepare_model
from .megatron_lm import prepare_optimizer as megatron_lm_prepare_optimizer
from .megatron_lm import prepare_scheduler as megatron_lm_prepare_scheduler
from .memory import find_executable_batch_size, release_memory
from .other import (
extract_model_from_parallel,
get_pretty_name,
is_port_in_use,
merge_dicts,
patch_environment,
save,
wait_for_everyone,
write_basic_config,
)
from .random import set_seed, synchronize_rng_state, synchronize_rng_states
from .torch_xla import install_xla
from .tqdm import tqdm
from .transformer_engine import convert_model, has_transformer_engine_layers
| 690 | 1 |
"""simple docstring"""
from manim import *
class lowerCamelCase__ ( UpperCAmelCase_ ):
def __a ( self : Optional[int] ):
A = Rectangle(height=0.5 , width=0.5 )
A = Rectangle(height=0.4_6 , width=0.4_6 ).set_stroke(width=0 )
A = [mem.copy() for i in range(6 )]
A = [mem.copy() for i in range(6 )]
A = VGroup(*_lowercase ).arrange(_lowercase , buff=0 )
A = VGroup(*_lowercase ).arrange(_lowercase , buff=0 )
A = VGroup(_lowercase , _lowercase ).arrange(_lowercase , buff=0 )
A = Text('CPU' , font_size=24 )
A = Group(_lowercase , _lowercase ).arrange(_lowercase , buff=0.5 , aligned_edge=_lowercase )
cpu.move_to([-2.5, -0.5, 0] )
self.add(_lowercase )
A = [mem.copy() for i in range(1 )]
A = VGroup(*_lowercase ).arrange(_lowercase , buff=0 )
A = Text('GPU' , font_size=24 )
A = Group(_lowercase , _lowercase ).arrange(_lowercase , buff=0.5 , aligned_edge=_lowercase )
gpu.align_to(_lowercase , _lowercase )
gpu.set_x(gpu.get_x() - 1 )
self.add(_lowercase )
A = [mem.copy() for i in range(6 )]
A = VGroup(*_lowercase ).arrange(_lowercase , buff=0 )
A = Text('Model' , font_size=24 )
A = Group(_lowercase , _lowercase ).arrange(_lowercase , buff=0.5 , aligned_edge=_lowercase )
model.move_to([3, -1.0, 0] )
self.play(
Create(_lowercase , run_time=1 ) , Create(_lowercase , run_time=1 ) , Create(_lowercase , run_time=1 ) , )
A = MarkupText(
f'First, an empty model skeleton is loaded\ninto <span fgcolor=\'{YELLOW}\'>memory</span> without using much RAM.' , font_size=24 , )
A = Square(side_length=2.2 )
key.move_to([-5, 2, 0] )
A = MarkupText(
f'<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model' , font_size=18 , )
key_text.move_to([-5, 2.4, 0] )
step_a.move_to([2, 2, 0] )
self.play(Write(_lowercase , run_time=2.5 ) , Write(_lowercase ) , Write(_lowercase ) )
self.add(_lowercase )
A = []
A = []
A = []
for i, rect in enumerate(_lowercase ):
A = Rectangle(height=0.4_6 , width=0.4_6 ).set_stroke(width=0.0 ).set_fill(_lowercase , opacity=0.7 )
cpu_target.move_to(_lowercase )
cpu_target.generate_target()
A = 0.4_6 / 4
A = 0.4_6 / 3
if i == 0:
cpu_target.target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT ) , buff=0.0_2 , direction=_lowercase )
cpu_target.target.set_x(cpu_target.target.get_x() + 0.1 )
elif i == 3:
cpu_target.target.next_to(cpu_targs[0].target , direction=_lowercase , buff=0.0 )
else:
cpu_target.target.next_to(cpu_targs[i - 1].target , direction=_lowercase , buff=0.0 )
cpu_targs.append(_lowercase )
first_animations.append(rect.animate(run_time=0.5 ).set_stroke(_lowercase ) )
second_animations.append(MoveToTarget(_lowercase , run_time=1.5 ) )
self.play(*_lowercase )
self.play(*_lowercase )
self.wait()
| 690 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
def __snake_case ( UpperCamelCase__ ) -> int:
"""simple docstring"""
if not postfix_notation:
return 0
A = {'+', '-', '*', '/'}
A = []
for token in postfix_notation:
if token in operations:
A , A = stack.pop(), stack.pop()
if token == "+":
stack.append(a + b )
elif token == "-":
stack.append(a - b )
elif token == "*":
stack.append(a * b )
else:
if a * b < 0 and a % b != 0:
stack.append(a // b + 1 )
else:
stack.append(a // b )
else:
stack.append(int(UpperCamelCase__ ) )
return stack.pop()
if __name__ == "__main__":
import doctest
doctest.testmod()
| 690 | 1 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
import diffusers
from diffusers import (
AutoencoderKL,
EulerDiscreteScheduler,
StableDiffusionLatentUpscalePipeline,
StableDiffusionPipeline,
UNetaDConditionModel,
)
from diffusers.schedulers import KarrasDiffusionSchedulers
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 ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
def __snake_case ( UpperCamelCase__ ) -> Tuple:
"""simple docstring"""
A = [tensor.shape for tensor in tensor_list]
return all(shape == shapes[0] for shape in shapes[1:] )
class lowerCamelCase__ ( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , unittest.TestCase ):
lowerCAmelCase = StableDiffusionLatentUpscalePipeline
lowerCAmelCase = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
"""height""",
"""width""",
"""cross_attention_kwargs""",
"""negative_prompt_embeds""",
"""prompt_embeds""",
}
lowerCAmelCase = PipelineTesterMixin.required_optional_params - {"""num_images_per_prompt"""}
lowerCAmelCase = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
lowerCAmelCase = frozenset(
[] ) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
lowerCAmelCase = frozenset([] )
lowerCAmelCase = True
@property
def __a ( self : Union[str, Any] ):
A = 1
A = 4
A = (16, 16)
A = floats_tensor((batch_size, num_channels) + sizes , rng=random.Random(0 ) ).to(_lowercase )
return image
def __a ( self : List[str] ):
torch.manual_seed(0 )
A = UNetaDConditionModel(
act_fn='gelu' , attention_head_dim=8 , norm_num_groups=_lowercase , block_out_channels=[32, 32, 64, 64] , time_cond_proj_dim=160 , conv_in_kernel=1 , conv_out_kernel=1 , cross_attention_dim=32 , down_block_types=(
'KDownBlock2D',
'KCrossAttnDownBlock2D',
'KCrossAttnDownBlock2D',
'KCrossAttnDownBlock2D',
) , in_channels=8 , mid_block_type=_lowercase , only_cross_attention=_lowercase , out_channels=5 , resnet_time_scale_shift='scale_shift' , time_embedding_type='fourier' , timestep_post_act='gelu' , up_block_types=('KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KUpBlock2D') , )
A = AutoencoderKL(
block_out_channels=[32, 32, 64, 64] , in_channels=3 , out_channels=3 , down_block_types=[
'DownEncoderBlock2D',
'DownEncoderBlock2D',
'DownEncoderBlock2D',
'DownEncoderBlock2D',
] , up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D', 'UpDecoderBlock2D', 'UpDecoderBlock2D'] , latent_channels=4 , )
A = EulerDiscreteScheduler(prediction_type='sample' )
A = 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=1_000 , hidden_act='quick_gelu' , projection_dim=512 , )
A = CLIPTextModel(_lowercase )
A = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
A = {
'unet': model.eval(),
'vae': vae.eval(),
'scheduler': scheduler,
'text_encoder': text_encoder,
'tokenizer': tokenizer,
}
return components
def __a ( self : List[Any] , _lowercase : List[str] , _lowercase : Optional[int]=0 ):
if str(_lowercase ).startswith('mps' ):
A = torch.manual_seed(_lowercase )
else:
A = torch.Generator(device=_lowercase ).manual_seed(_lowercase )
A = {
'prompt': 'A painting of a squirrel eating a burger',
'image': self.dummy_image.cpu(),
'generator': generator,
'num_inference_steps': 2,
'output_type': 'numpy',
}
return inputs
def __a ( self : List[Any] ):
A = 'cpu'
A = self.get_dummy_components()
A = self.pipeline_class(**_lowercase )
pipe.to(_lowercase )
pipe.set_progress_bar_config(disable=_lowercase )
A = self.get_dummy_inputs(_lowercase )
A = pipe(**_lowercase ).images
A = image[0, -3:, -3:, -1]
self.assertEqual(image.shape , (1, 256, 256, 3) )
A = np.array(
[0.4_7_2_2_2_4_1_2, 0.4_1_9_2_1_6_3_3, 0.4_4_7_1_7_4_3_4, 0.4_6_8_7_4_1_9_2, 0.4_2_5_8_8_2_5_8, 0.4_6_1_5_0_7_2_6, 0.4_6_7_7_5_3_4, 0.4_5_5_8_3_8_3_2, 0.4_8_5_7_9_0_5_5] )
A = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(_lowercase , 1e-3 )
def __a ( self : str ):
super().test_attention_slicing_forward_pass(expected_max_diff=7e-3 )
def __a ( self : Union[str, Any] ):
super().test_cpu_offload_forward_pass(expected_max_diff=3e-3 )
def __a ( self : Optional[int] ):
super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3 )
def __a ( self : Tuple ):
super().test_inference_batch_single_identical(expected_max_diff=7e-3 )
def __a ( self : int ):
super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3e-3 )
def __a ( self : List[str] ):
super().test_save_load_local(expected_max_difference=3e-3 )
def __a ( self : Tuple ):
super().test_save_load_optional_components(expected_max_difference=3e-3 )
def __a ( self : Dict ):
A = [
'DDIMScheduler',
'DDPMScheduler',
'PNDMScheduler',
'HeunDiscreteScheduler',
'EulerAncestralDiscreteScheduler',
'KDPM2DiscreteScheduler',
'KDPM2AncestralDiscreteScheduler',
'DPMSolverSDEScheduler',
]
A = self.get_dummy_components()
A = self.pipeline_class(**_lowercase )
# make sure that PNDM does not need warm-up
pipe.scheduler.register_to_config(skip_prk_steps=_lowercase )
pipe.to(_lowercase )
pipe.set_progress_bar_config(disable=_lowercase )
A = self.get_dummy_inputs(_lowercase )
A = 2
A = []
for scheduler_enum in KarrasDiffusionSchedulers:
if scheduler_enum.name in skip_schedulers:
# no sigma schedulers are not supported
# no schedulers
continue
A = getattr(_lowercase , scheduler_enum.name )
A = scheduler_cls.from_config(pipe.scheduler.config )
A = pipe(**_lowercase )[0]
outputs.append(_lowercase )
assert check_same_shape(_lowercase )
@require_torch_gpu
@slow
class lowerCamelCase__ ( unittest.TestCase ):
def __a ( self : Tuple ):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __a ( self : int ):
A = torch.manual_seed(33 )
A = StableDiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4' , torch_dtype=torch.floataa )
pipe.to('cuda' )
A = StableDiffusionLatentUpscalePipeline.from_pretrained(
'stabilityai/sd-x2-latent-upscaler' , torch_dtype=torch.floataa )
upscaler.to('cuda' )
A = 'a photo of an astronaut high resolution, unreal engine, ultra realistic'
A = pipe(_lowercase , generator=_lowercase , output_type='latent' ).images
A = upscaler(
prompt=_lowercase , image=_lowercase , num_inference_steps=20 , guidance_scale=0 , generator=_lowercase , output_type='np' , ).images[0]
A = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/astronaut_1024.npy' )
assert np.abs((expected_image - image).mean() ) < 5e-2
def __a ( self : int ):
A = torch.manual_seed(33 )
A = StableDiffusionLatentUpscalePipeline.from_pretrained(
'stabilityai/sd-x2-latent-upscaler' , torch_dtype=torch.floataa )
upscaler.to('cuda' )
A = 'the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas'
A = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_512.png' )
A = upscaler(
prompt=_lowercase , image=_lowercase , num_inference_steps=20 , guidance_scale=0 , generator=_lowercase , output_type='np' , ).images[0]
A = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_1024.npy' )
assert np.abs((expected_image - image).max() ) < 5e-2
| 690 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_xlnet import XLNetTokenizer
else:
UpperCamelCase : Any = None
UpperCamelCase : int = logging.get_logger(__name__)
UpperCamelCase : Union[str, Any] = {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}
UpperCamelCase : str = {
"vocab_file": {
"xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model",
"xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model",
},
"tokenizer_file": {
"xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json",
"xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json",
},
}
UpperCamelCase : Optional[int] = {
"xlnet-base-cased": None,
"xlnet-large-cased": None,
}
UpperCamelCase : str = "▁"
# Segments (not really needed)
UpperCamelCase : str = 0
UpperCamelCase : int = 1
UpperCamelCase : List[Any] = 2
UpperCamelCase : Union[str, Any] = 3
UpperCamelCase : Optional[Any] = 4
class lowerCamelCase__ ( UpperCAmelCase_ ):
lowerCAmelCase = VOCAB_FILES_NAMES
lowerCAmelCase = PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCAmelCase = """left"""
lowerCAmelCase = XLNetTokenizer
def __init__( self : Tuple , _lowercase : List[Any]=None , _lowercase : Any=None , _lowercase : int=False , _lowercase : Tuple=True , _lowercase : Union[str, Any]=False , _lowercase : int="<s>" , _lowercase : Optional[int]="</s>" , _lowercase : Dict="<unk>" , _lowercase : Optional[int]="<sep>" , _lowercase : int="<pad>" , _lowercase : Dict="<cls>" , _lowercase : str="<mask>" , _lowercase : List[str]=["<eop>", "<eod>"] , **_lowercase : Any , ):
# Mask token behave like a normal word, i.e. include the space before it
A = AddedToken(_lowercase , lstrip=_lowercase , rstrip=_lowercase ) if isinstance(_lowercase , _lowercase ) else mask_token
super().__init__(
vocab_file=_lowercase , tokenizer_file=_lowercase , do_lower_case=_lowercase , remove_space=_lowercase , keep_accents=_lowercase , bos_token=_lowercase , eos_token=_lowercase , unk_token=_lowercase , sep_token=_lowercase , pad_token=_lowercase , cls_token=_lowercase , mask_token=_lowercase , additional_special_tokens=_lowercase , **_lowercase , )
A = 3
A = do_lower_case
A = remove_space
A = keep_accents
A = vocab_file
A = False if not self.vocab_file else True
def __a ( self : List[Any] , _lowercase : List[int] , _lowercase : Optional[List[int]] = None ):
A = [self.sep_token_id]
A = [self.cls_token_id]
if token_ids_a is None:
return token_ids_a + sep + cls
return token_ids_a + sep + token_ids_a + sep + cls
def __a ( self : Tuple , _lowercase : List[int] , _lowercase : Optional[List[int]] = None ):
A = [self.sep_token_id]
A = [2]
if token_ids_a is None:
return len(token_ids_a + sep ) * [0] + cls_segment_id
return len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] + cls_segment_id
def __a ( self : Optional[Any] , _lowercase : str , _lowercase : Optional[str] = None ):
if not self.can_save_slow_tokenizer:
raise ValueError(
'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
'tokenizer.' )
if not os.path.isdir(_lowercase ):
logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
return
A = os.path.join(
_lowercase , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_lowercase ):
copyfile(self.vocab_file , _lowercase )
return (out_vocab_file,)
| 690 | 1 |
"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset
from ..utils.generic import ModelOutput
class lowerCamelCase__ ( UpperCAmelCase_ ):
def __init__( self : List[Any] , _lowercase : Tuple , _lowercase : int , _lowercase : Union[str, Any] ):
A = dataset
A = process
A = params
def __len__( self : Tuple ):
return len(self.dataset )
def __getitem__( self : Union[str, Any] , _lowercase : List[str] ):
A = self.dataset[i]
A = self.process(_lowercase , **self.params )
return processed
class lowerCamelCase__ ( UpperCAmelCase_ ):
def __init__( self : Union[str, Any] , _lowercase : List[Any] , _lowercase : Optional[int] , _lowercase : Tuple , _lowercase : List[str]=None ):
A = loader
A = infer
A = params
if loader_batch_size == 1:
# Let's spare some time by deactivating altogether
A = None
A = loader_batch_size
# Internal bookkeeping
A = None
A = None
def __len__( self : List[str] ):
return len(self.loader )
def __iter__( self : str ):
A = iter(self.loader )
return self
def __a ( self : Any ):
if isinstance(self._loader_batch_data , torch.Tensor ):
# Batch data is simple tensor, just fetch the slice
A = self._loader_batch_data[self._loader_batch_index]
else:
# Batch data is assumed to be BaseModelOutput (or dict)
A = {}
for k, element in self._loader_batch_data.items():
if isinstance(_lowercase , _lowercase ):
# Convert ModelOutput to tuple first
A = element.to_tuple()
if isinstance(element[0] , torch.Tensor ):
A = tuple(el[self._loader_batch_index].unsqueeze(0 ) for el in element )
elif isinstance(element[0] , np.ndarray ):
A = tuple(np.expand_dims(el[self._loader_batch_index] , 0 ) for el in element )
continue
if k in {"hidden_states", "past_key_values", "attentions"} and isinstance(_lowercase , _lowercase ):
# Those are stored as lists of tensors so need specific unbatching.
if isinstance(element[0] , torch.Tensor ):
A = tuple(el[self._loader_batch_index].unsqueeze(0 ) for el in element )
elif isinstance(element[0] , np.ndarray ):
A = tuple(np.expand_dims(el[self._loader_batch_index] , 0 ) for el in element )
continue
if element is None:
# This can happen for optional data that get passed around
A = None
elif isinstance(element[self._loader_batch_index] , torch.Tensor ):
# Take correct batch data, but make it looked like batch_size=1
# For compatibility with other methods within transformers
A = element[self._loader_batch_index].unsqueeze(0 )
elif isinstance(element[self._loader_batch_index] , np.ndarray ):
# Take correct batch data, but make it looked like batch_size=1
# For compatibility with other methods within transformers
A = np.expand_dims(element[self._loader_batch_index] , 0 )
else:
# This is typically a list, so no need to `unsqueeze`.
A = element[self._loader_batch_index]
# Recreate the element by reusing the original class to make it look
# batch_size=1
A = self._loader_batch_data.__class__(_lowercase )
self._loader_batch_index += 1
return result
def __a ( self : str ):
if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
# We are currently unrolling a batch so we just need to return
# the current item within a batch
return self.loader_batch_item()
# We're out of items within a batch
A = next(self.iterator )
A = self.infer(_lowercase , **self.params )
# We now have a batch of "inferred things".
if self.loader_batch_size is not None:
# Try to infer the size of the batch
if isinstance(_lowercase , torch.Tensor ):
A = processed
else:
A = list(processed.keys() )[0]
A = processed[key]
if isinstance(_lowercase , _lowercase ):
A = len(_lowercase )
else:
A = first_tensor.shape[0]
if 0 < observed_batch_size < self.loader_batch_size:
# could be last batch so we can't unroll as many
# elements.
A = observed_batch_size
# Setting internal index to unwrap the batch
A = processed
A = 0
return self.loader_batch_item()
else:
# We're not unrolling batches
return processed
class lowerCamelCase__ ( UpperCAmelCase_ ):
def __init__( self : Union[str, Any] , _lowercase : List[str] , _lowercase : Union[str, Any] , _lowercase : Tuple , _lowercase : Tuple=None ):
super().__init__(_lowercase , _lowercase , _lowercase )
def __iter__( self : Union[str, Any] ):
A = iter(self.loader )
A = None
return self
def __a ( self : List[str] ):
if self.subiterator is None:
A = self.infer(next(self.iterator ) , **self.params )
try:
# Try to return next item
A = next(self.subiterator )
except StopIteration:
# When a preprocess iterator ends, we can start lookig at the next item
# ChunkIterator will keep feeding until ALL elements of iterator
# all have created their subiterator and have been iterating against.
#
# Another way to look at it, is we're basically flattening lists of lists
# into a single list, but with generators
A = self.infer(next(self.iterator ) , **self.params )
A = next(self.subiterator )
return processed
class lowerCamelCase__ ( UpperCAmelCase_ ):
def __iter__( self : str ):
A = iter(self.loader )
return self
def __a ( self : Optional[int] ):
# Extremely similar to PipelineIterator in its unpacking mechanism
# BUT, we have an extra required item which is the presence of `is_last`
# That is because everything is flattened by `PipelineChunkIterator` we
# need to keep track of how to regroup here in the original `process`
# boundaries so that `process` and `postprocess` see the same data.
# This iterator accumulates items (possibly while unbatching) until it
# its a `is_last` and then just passes it on to the caller.
A = False
A = []
if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
while self._loader_batch_index < self.loader_batch_size:
A = self.loader_batch_item()
A = item.pop('is_last' )
accumulator.append(_lowercase )
if is_last:
return accumulator
while not is_last:
A = self.infer(next(self.iterator ) , **self.params )
if self.loader_batch_size is not None:
if isinstance(_lowercase , torch.Tensor ):
A = processed
else:
A = list(processed.keys() )[0]
A = processed[key]
if isinstance(_lowercase , _lowercase ):
A = len(_lowercase )
else:
A = first_tensor.shape[0]
if 0 < observed_batch_size < self.loader_batch_size:
# could be last batch so we can't unroll as many
# elements.
A = observed_batch_size
A = processed
A = 0
while self._loader_batch_index < self.loader_batch_size:
A = self.loader_batch_item()
A = item.pop('is_last' )
accumulator.append(_lowercase )
if is_last:
return accumulator
else:
A = processed
A = item.pop('is_last' )
accumulator.append(_lowercase )
return accumulator
class lowerCamelCase__ ( UpperCAmelCase_ ):
def __init__( self : Tuple , _lowercase : Dataset , _lowercase : str ):
A = dataset
A = key
def __len__( self : int ):
return len(self.dataset )
def __getitem__( self : List[Any] , _lowercase : Optional[Any] ):
return self.dataset[i][self.key]
class lowerCamelCase__ ( UpperCAmelCase_ ):
def __init__( self : Optional[Any] , _lowercase : Dataset , _lowercase : str , _lowercase : str ):
A = dataset
A = keya
A = keya
def __len__( self : Optional[int] ):
return len(self.dataset )
def __getitem__( self : int , _lowercase : str ):
return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}
| 690 |
"""simple docstring"""
from __future__ import annotations
UpperCamelCase : Any = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , ) -> tuple[list[list[int]], list[list[int]]]:
"""simple docstring"""
A = [
[0 for col in range(len(grid[0] ) )] for row in range(len(UpperCamelCase__ ) )
] # the reference grid
A = 1
A = [
[0 for col in range(len(grid[0] ) )] for row in range(len(UpperCamelCase__ ) )
] # the action grid
A = init[0]
A = init[1]
A = 0
A = g + heuristic[x][y] # cost from starting cell to destination cell
A = [[f, g, x, y]]
A = False # flag that is set when search is complete
A = False # flag set if we can't find expand
while not found and not resign:
if len(UpperCamelCase__ ) == 0:
raise ValueError('Algorithm is unable to find solution' )
else: # to choose the least costliest action so as to move closer to the goal
cell.sort()
cell.reverse()
A = cell.pop()
A = next_cell[2]
A = next_cell[3]
A = next_cell[1]
if x == goal[0] and y == goal[1]:
A = True
else:
for i in range(len(UpperCamelCase__ ) ): # to try out different valid actions
A = x + DIRECTIONS[i][0]
A = y + DIRECTIONS[i][1]
if xa >= 0 and xa < len(UpperCamelCase__ ) and ya >= 0 and ya < len(grid[0] ):
if closed[xa][ya] == 0 and grid[xa][ya] == 0:
A = g + cost
A = ga + heuristic[xa][ya]
cell.append([fa, ga, xa, ya] )
A = 1
A = i
A = []
A = goal[0]
A = goal[1]
invpath.append([x, y] ) # we get the reverse path from here
while x != init[0] or y != init[1]:
A = x - DIRECTIONS[action[x][y]][0]
A = y - DIRECTIONS[action[x][y]][1]
A = xa
A = ya
invpath.append([x, y] )
A = []
for i in range(len(UpperCamelCase__ ) ):
path.append(invpath[len(UpperCamelCase__ ) - 1 - i] )
return path, action
if __name__ == "__main__":
UpperCamelCase : Any = [
[0, 1, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 1, 0, 0, 0, 0],
[0, 1, 0, 0, 1, 0],
[0, 0, 0, 0, 1, 0],
]
UpperCamelCase : List[Any] = [0, 0]
# all coordinates are given in format [y,x]
UpperCamelCase : int = [len(grid) - 1, len(grid[0]) - 1]
UpperCamelCase : Tuple = 1
# the cost map which pushes the path closer to the goal
UpperCamelCase : Union[str, Any] = [[0 for row in range(len(grid[0]))] for col in range(len(grid))]
for i in range(len(grid)):
for j in range(len(grid[0])):
UpperCamelCase : List[str] = abs(i - goal[0]) + abs(j - goal[1])
if grid[i][j] == 1:
# added extra penalty in the heuristic map
UpperCamelCase : Dict = 99
UpperCamelCase , UpperCamelCase : Optional[Any] = search(grid, init, goal, cost, heuristic)
print("ACTION MAP")
for i in range(len(action)):
print(action[i])
for i in range(len(path)):
print(path[i])
| 690 | 1 |
"""simple docstring"""
from ....configuration_utils import PretrainedConfig
from ....utils import logging
UpperCamelCase : str = logging.get_logger(__name__)
UpperCamelCase : List[Any] = {
"Visual-Attention-Network/van-base": (
"https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json"
),
}
class lowerCamelCase__ ( UpperCAmelCase_ ):
lowerCAmelCase = """van"""
def __init__( self : str , _lowercase : Optional[int]=224 , _lowercase : Optional[Any]=3 , _lowercase : List[str]=[7, 3, 3, 3] , _lowercase : List[Any]=[4, 2, 2, 2] , _lowercase : List[str]=[64, 128, 320, 512] , _lowercase : Tuple=[3, 3, 12, 3] , _lowercase : Any=[8, 8, 4, 4] , _lowercase : Dict="gelu" , _lowercase : Dict=0.0_2 , _lowercase : List[Any]=1e-6 , _lowercase : int=1e-2 , _lowercase : int=0.0 , _lowercase : List[str]=0.0 , **_lowercase : int , ):
super().__init__(**_lowercase )
A = image_size
A = num_channels
A = patch_sizes
A = strides
A = hidden_sizes
A = depths
A = mlp_ratios
A = hidden_act
A = initializer_range
A = layer_norm_eps
A = layer_scale_init_value
A = drop_path_rate
A = dropout_rate
| 690 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCamelCase : Optional[int] = logging.get_logger(__name__)
UpperCamelCase : int = {"vocab_file": "sentencepiece.model"}
UpperCamelCase : Union[str, Any] = {
"vocab_file": {
"google/rembert": "https://huggingface.co/google/rembert/resolve/main/sentencepiece.model",
},
}
UpperCamelCase : Union[str, Any] = {
"google/rembert": 256,
}
class lowerCamelCase__ ( UpperCAmelCase_ ):
lowerCAmelCase = VOCAB_FILES_NAMES
lowerCAmelCase = PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__( self : Dict , _lowercase : Optional[Any] , _lowercase : Optional[Any]=False , _lowercase : Dict=True , _lowercase : List[str]=True , _lowercase : int="[CLS]" , _lowercase : str="[SEP]" , _lowercase : List[str]="[UNK]" , _lowercase : List[Any]="[SEP]" , _lowercase : Union[str, Any]="[PAD]" , _lowercase : List[str]="[CLS]" , _lowercase : Any="[MASK]" , **_lowercase : Optional[Any] , ):
super().__init__(
do_lower_case=_lowercase , remove_space=_lowercase , keep_accents=_lowercase , bos_token=_lowercase , eos_token=_lowercase , unk_token=_lowercase , sep_token=_lowercase , pad_token=_lowercase , cls_token=_lowercase , mask_token=_lowercase , **_lowercase , )
A = do_lower_case
A = remove_space
A = keep_accents
A = vocab_file
A = spm.SentencePieceProcessor()
self.sp_model.Load(_lowercase )
@property
def __a ( self : Tuple ):
return len(self.sp_model )
def __a ( self : List[str] ):
A = {self.convert_ids_to_tokens(_lowercase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self : Tuple ):
A = self.__dict__.copy()
A = None
return state
def __setstate__( self : List[str] , _lowercase : int ):
A = d
A = spm.SentencePieceProcessor()
self.sp_model.Load(self.vocab_file )
def __a ( self : Dict , _lowercase : Union[str, Any] , _lowercase : Dict=False ):
A = self.sp_model.EncodeAsPieces(_lowercase )
return pieces
def __a ( self : Dict , _lowercase : Tuple ):
return self.sp_model.PieceToId(_lowercase )
def __a ( self : str , _lowercase : Optional[int] ):
return self.sp_model.IdToPiece(_lowercase )
def __a ( self : Optional[int] , _lowercase : Optional[int] ):
A = self.sp_model.decode_pieces(_lowercase )
return out_string
def __a ( self : Optional[int] , _lowercase : List[int] , _lowercase : Optional[List[int]] = None ):
A = [self.sep_token_id]
A = [self.cls_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def __a ( self : Any , _lowercase : List[int] , _lowercase : Optional[List[int]] = None , _lowercase : bool = False ):
if already_has_special_tokens:
if token_ids_a is not None:
raise ValueError(
'You should not supply a second sequence if the provided sequence of '
'ids is already formatted with special tokens for the model.' )
return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]
if token_ids_a is not None:
return [1] + ([0] * len(_lowercase )) + [1] + ([0] * len(_lowercase )) + [1]
return [1] + ([0] * len(_lowercase )) + [1]
def __a ( self : str , _lowercase : List[int] , _lowercase : Optional[List[int]] = None ):
A = [self.sep_token_id]
A = [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 __a ( self : Optional[Any] , _lowercase : str , _lowercase : Optional[str] = None ):
if not os.path.isdir(_lowercase ):
logger.error('Vocabulary path ({}) should be a directory'.format(_lowercase ) )
return
A = os.path.join(
_lowercase , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_lowercase ):
copyfile(self.vocab_file , _lowercase )
return (out_vocab_file,)
| 690 | 1 |
"""simple docstring"""
import unittest
from transformers import DebertaVaConfig, 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 (
DebertaVaForMaskedLM,
DebertaVaForMultipleChoice,
DebertaVaForQuestionAnswering,
DebertaVaForSequenceClassification,
DebertaVaForTokenClassification,
DebertaVaModel,
)
from transformers.models.deberta_va.modeling_deberta_va import DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST
class lowerCamelCase__ ( UpperCAmelCase_ ):
def __init__( self : Optional[int] , _lowercase : Optional[int] , _lowercase : Tuple=13 , _lowercase : List[str]=7 , _lowercase : Dict=True , _lowercase : List[str]=True , _lowercase : List[Any]=True , _lowercase : List[str]=True , _lowercase : Optional[Any]=99 , _lowercase : Dict=32 , _lowercase : Optional[int]=5 , _lowercase : str=4 , _lowercase : Union[str, Any]=37 , _lowercase : Dict="gelu" , _lowercase : List[str]=0.1 , _lowercase : List[Any]=0.1 , _lowercase : Optional[int]=512 , _lowercase : Tuple=16 , _lowercase : Optional[Any]=2 , _lowercase : Union[str, Any]=0.0_2 , _lowercase : int=False , _lowercase : Optional[int]=True , _lowercase : List[str]="None" , _lowercase : List[str]=3 , _lowercase : int=4 , _lowercase : str=None , ):
A = parent
A = batch_size
A = seq_length
A = is_training
A = use_input_mask
A = use_token_type_ids
A = use_labels
A = vocab_size
A = hidden_size
A = num_hidden_layers
A = num_attention_heads
A = intermediate_size
A = hidden_act
A = hidden_dropout_prob
A = attention_probs_dropout_prob
A = max_position_embeddings
A = type_vocab_size
A = type_sequence_label_size
A = initializer_range
A = num_labels
A = num_choices
A = relative_attention
A = position_biased_input
A = pos_att_type
A = scope
def __a ( self : List[Any] ):
A = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
A = None
if self.use_input_mask:
A = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
A = None
if self.use_token_type_ids:
A = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
A = None
A = None
A = None
if self.use_labels:
A = ids_tensor([self.batch_size] , self.type_sequence_label_size )
A = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
A = ids_tensor([self.batch_size] , self.num_choices )
A = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def __a ( self : Optional[int] ):
return DebertaVaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , 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 , _lowercase : Optional[Any] ):
self.parent.assertListEqual(list(result.loss.size() ) , [] )
def __a ( self : List[str] , _lowercase : str , _lowercase : Tuple , _lowercase : Tuple , _lowercase : Optional[int] , _lowercase : List[str] , _lowercase : Union[str, Any] , _lowercase : str ):
A = DebertaVaModel(config=_lowercase )
model.to(_lowercase )
model.eval()
A = model(_lowercase , attention_mask=_lowercase , token_type_ids=_lowercase )[0]
A = model(_lowercase , token_type_ids=_lowercase )[0]
A = model(_lowercase )[0]
self.parent.assertListEqual(list(sequence_output.size() ) , [self.batch_size, self.seq_length, self.hidden_size] )
def __a ( self : Optional[int] , _lowercase : str , _lowercase : Dict , _lowercase : Any , _lowercase : str , _lowercase : Optional[int] , _lowercase : Union[str, Any] , _lowercase : Any ):
A = DebertaVaForMaskedLM(config=_lowercase )
model.to(_lowercase )
model.eval()
A = model(_lowercase , attention_mask=_lowercase , token_type_ids=_lowercase , labels=_lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __a ( self : Dict , _lowercase : int , _lowercase : str , _lowercase : Tuple , _lowercase : List[str] , _lowercase : int , _lowercase : Tuple , _lowercase : List[str] ):
A = self.num_labels
A = DebertaVaForSequenceClassification(_lowercase )
model.to(_lowercase )
model.eval()
A = model(_lowercase , attention_mask=_lowercase , token_type_ids=_lowercase , labels=_lowercase )
self.parent.assertListEqual(list(result.logits.size() ) , [self.batch_size, self.num_labels] )
self.check_loss_output(_lowercase )
def __a ( self : Tuple , _lowercase : Optional[Any] , _lowercase : Optional[int] , _lowercase : Tuple , _lowercase : Any , _lowercase : Optional[Any] , _lowercase : int , _lowercase : int ):
A = self.num_labels
A = DebertaVaForTokenClassification(config=_lowercase )
model.to(_lowercase )
model.eval()
A = model(_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 __a ( self : List[Any] , _lowercase : Optional[Any] , _lowercase : Tuple , _lowercase : Any , _lowercase : List[str] , _lowercase : Optional[int] , _lowercase : Any , _lowercase : Any ):
A = DebertaVaForQuestionAnswering(config=_lowercase )
model.to(_lowercase )
model.eval()
A = model(
_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 __a ( self : List[Any] , _lowercase : str , _lowercase : int , _lowercase : Any , _lowercase : Any , _lowercase : int , _lowercase : Tuple , _lowercase : int ):
A = DebertaVaForMultipleChoice(config=_lowercase )
model.to(_lowercase )
model.eval()
A = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
A = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
A = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
A = model(
_lowercase , attention_mask=_lowercase , token_type_ids=_lowercase , labels=_lowercase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def __a ( self : Optional[int] ):
A = self.prepare_config_and_inputs()
(
(
A
) , (
A
) , (
A
) , (
A
) , (
A
) , (
A
) , (
A
) ,
) = config_and_inputs
A = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class lowerCamelCase__ ( UpperCAmelCase_ , UpperCAmelCase_ , unittest.TestCase ):
lowerCAmelCase = (
(
DebertaVaModel,
DebertaVaForMaskedLM,
DebertaVaForSequenceClassification,
DebertaVaForTokenClassification,
DebertaVaForQuestionAnswering,
DebertaVaForMultipleChoice,
)
if is_torch_available()
else ()
)
lowerCAmelCase = (
{
"""feature-extraction""": DebertaVaModel,
"""fill-mask""": DebertaVaForMaskedLM,
"""question-answering""": DebertaVaForQuestionAnswering,
"""text-classification""": DebertaVaForSequenceClassification,
"""token-classification""": DebertaVaForTokenClassification,
"""zero-shot""": DebertaVaForSequenceClassification,
}
if is_torch_available()
else {}
)
lowerCAmelCase = True
lowerCAmelCase = False
lowerCAmelCase = False
lowerCAmelCase = False
lowerCAmelCase = False
def __a ( self : str ):
A = DebertaVaModelTester(self )
A = ConfigTester(self , config_class=_lowercase , hidden_size=37 )
def __a ( self : Union[str, Any] ):
self.config_tester.run_common_tests()
def __a ( self : int ):
A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_model(*_lowercase )
def __a ( self : Optional[Any] ):
A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_sequence_classification(*_lowercase )
def __a ( self : Dict ):
A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_masked_lm(*_lowercase )
def __a ( self : Optional[Any] ):
A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_question_answering(*_lowercase )
def __a ( self : Tuple ):
A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_token_classification(*_lowercase )
def __a ( self : Tuple ):
A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_multiple_choice(*_lowercase )
@slow
def __a ( self : Optional[int] ):
for model_name in DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
A = DebertaVaModel.from_pretrained(_lowercase )
self.assertIsNotNone(_lowercase )
@require_torch
@require_sentencepiece
@require_tokenizers
class lowerCamelCase__ ( unittest.TestCase ):
@unittest.skip(reason='Model not available yet' )
def __a ( self : Tuple ):
pass
@slow
def __a ( self : Optional[int] ):
A = DebertaVaModel.from_pretrained('microsoft/deberta-v2-xlarge' )
A = torch.tensor([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]] )
A = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
A = model(_lowercase , attention_mask=_lowercase )[0]
# compare the actual values for a slice.
A = torch.tensor(
[[[0.2_3_5_6, 0.1_9_4_8, 0.0_3_6_9], [-0.1_0_6_3, 0.3_5_8_6, -0.5_1_5_2], [-0.6_3_9_9, -0.0_2_5_9, -0.2_5_2_5]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , _lowercase , atol=1e-4 ) , f'{output[:, 1:4, 1:4]}' )
| 690 |
"""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_mobilebert import MobileBertTokenizer
UpperCamelCase : str = logging.get_logger(__name__)
UpperCamelCase : List[str] = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
UpperCamelCase : List[Any] = {
"vocab_file": {"mobilebert-uncased": "https://huggingface.co/google/mobilebert-uncased/resolve/main/vocab.txt"},
"tokenizer_file": {
"mobilebert-uncased": "https://huggingface.co/google/mobilebert-uncased/resolve/main/tokenizer.json"
},
}
UpperCamelCase : Any = {"mobilebert-uncased": 512}
UpperCamelCase : Any = {}
class lowerCamelCase__ ( UpperCAmelCase_ ):
lowerCAmelCase = VOCAB_FILES_NAMES
lowerCAmelCase = PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase = PRETRAINED_INIT_CONFIGURATION
lowerCAmelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCAmelCase = MobileBertTokenizer
def __init__( self : Optional[int] , _lowercase : Optional[int]=None , _lowercase : Any=None , _lowercase : Optional[int]=True , _lowercase : int="[UNK]" , _lowercase : Dict="[SEP]" , _lowercase : Any="[PAD]" , _lowercase : str="[CLS]" , _lowercase : Union[str, Any]="[MASK]" , _lowercase : List[Any]=True , _lowercase : Any=None , **_lowercase : Optional[Any] , ):
super().__init__(
_lowercase , tokenizer_file=_lowercase , do_lower_case=_lowercase , unk_token=_lowercase , sep_token=_lowercase , pad_token=_lowercase , cls_token=_lowercase , mask_token=_lowercase , tokenize_chinese_chars=_lowercase , strip_accents=_lowercase , **_lowercase , )
A = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('lowercase' , _lowercase ) != do_lower_case
or normalizer_state.get('strip_accents' , _lowercase ) != strip_accents
or normalizer_state.get('handle_chinese_chars' , _lowercase ) != tokenize_chinese_chars
):
A = getattr(_lowercase , normalizer_state.pop('type' ) )
A = do_lower_case
A = strip_accents
A = tokenize_chinese_chars
A = normalizer_class(**_lowercase )
A = do_lower_case
def __a ( self : List[Any] , _lowercase : Tuple , _lowercase : Any=None ):
A = [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 __a ( self : Any , _lowercase : List[int] , _lowercase : Optional[List[int]] = None ):
A = [self.sep_token_id]
A = [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 __a ( self : Dict , _lowercase : str , _lowercase : Optional[str] = None ):
A = self._tokenizer.model.save(_lowercase , name=_lowercase )
return tuple(_lowercase )
| 690 | 1 |
"""simple docstring"""
import json
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from transformers import OneFormerImageProcessor
from transformers.models.oneformer.image_processing_oneformer import binary_mask_to_rle
from transformers.models.oneformer.modeling_oneformer import OneFormerForUniversalSegmentationOutput
if is_vision_available():
from PIL import Image
def __snake_case ( UpperCamelCase__ , UpperCamelCase__="shi-labs/oneformer_demo" ) -> List[Any]:
"""simple docstring"""
with open(hf_hub_download(UpperCamelCase__ , UpperCamelCase__ , repo_type='dataset' ) , 'r' ) as f:
A = json.load(UpperCamelCase__ )
A = {}
A = []
A = []
for key, info in class_info.items():
A = info['name']
class_names.append(info['name'] )
if info["isthing"]:
thing_ids.append(int(UpperCamelCase__ ) )
A = thing_ids
A = class_names
return metadata
class lowerCamelCase__ ( unittest.TestCase ):
def __init__( self : List[Any] , _lowercase : Optional[int] , _lowercase : Dict=7 , _lowercase : str=3 , _lowercase : List[Any]=30 , _lowercase : str=400 , _lowercase : int=None , _lowercase : int=True , _lowercase : Optional[int]=True , _lowercase : Tuple=[0.5, 0.5, 0.5] , _lowercase : List[Any]=[0.5, 0.5, 0.5] , _lowercase : Optional[int]=10 , _lowercase : Any=False , _lowercase : List[str]=255 , _lowercase : Optional[Any]="shi-labs/oneformer_demo" , _lowercase : Any="ade20k_panoptic.json" , _lowercase : Tuple=10 , ):
A = parent
A = batch_size
A = num_channels
A = min_resolution
A = max_resolution
A = do_resize
A = {'shortest_edge': 32, 'longest_edge': 1_333} if size is None else size
A = do_normalize
A = image_mean
A = image_std
A = class_info_file
A = prepare_metadata(_lowercase , _lowercase )
A = num_text
A = repo_path
# for the post_process_functions
A = 2
A = 10
A = 10
A = 3
A = 4
A = num_labels
A = do_reduce_labels
A = ignore_index
def __a ( self : Optional[Any] ):
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"num_labels": self.num_labels,
"do_reduce_labels": self.do_reduce_labels,
"ignore_index": self.ignore_index,
"class_info_file": self.class_info_file,
"metadata": self.metadata,
"num_text": self.num_text,
}
def __a ( self : Dict , _lowercase : int , _lowercase : Tuple=False ):
if not batched:
A = image_inputs[0]
if isinstance(_lowercase , Image.Image ):
A , A = image.size
else:
A , A = image.shape[1], image.shape[2]
if w < h:
A = int(self.size['shortest_edge'] * h / w )
A = self.size['shortest_edge']
elif w > h:
A = self.size['shortest_edge']
A = int(self.size['shortest_edge'] * w / h )
else:
A = self.size['shortest_edge']
A = self.size['shortest_edge']
else:
A = []
for image in image_inputs:
A , A = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
A = max(_lowercase , key=lambda _lowercase : item[0] )[0]
A = max(_lowercase , key=lambda _lowercase : item[1] )[1]
return expected_height, expected_width
def __a ( self : str ):
return OneFormerForUniversalSegmentationOutput(
# +1 for null class
class_queries_logits=torch.randn((self.batch_size, self.num_queries, self.num_classes + 1) ) , masks_queries_logits=torch.randn((self.batch_size, self.num_queries, self.height, self.width) ) , )
@require_torch
@require_vision
class lowerCamelCase__ ( UpperCAmelCase_ , unittest.TestCase ):
lowerCAmelCase = OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
# only for test_image_processing_common.test_image_proc_to_json_string
lowerCAmelCase = image_processing_class
def __a ( self : Union[str, Any] ):
A = OneFormerImageProcessorTester(self )
@property
def __a ( self : List[str] ):
return self.image_processing_tester.prepare_image_processor_dict()
def __a ( self : str ):
A = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(_lowercase , 'image_mean' ) )
self.assertTrue(hasattr(_lowercase , 'image_std' ) )
self.assertTrue(hasattr(_lowercase , 'do_normalize' ) )
self.assertTrue(hasattr(_lowercase , 'do_resize' ) )
self.assertTrue(hasattr(_lowercase , 'size' ) )
self.assertTrue(hasattr(_lowercase , 'ignore_index' ) )
self.assertTrue(hasattr(_lowercase , 'class_info_file' ) )
self.assertTrue(hasattr(_lowercase , 'num_text' ) )
self.assertTrue(hasattr(_lowercase , 'repo_path' ) )
self.assertTrue(hasattr(_lowercase , 'metadata' ) )
self.assertTrue(hasattr(_lowercase , 'do_reduce_labels' ) )
def __a ( self : Optional[int] ):
pass
def __a ( self : Dict ):
# Initialize image_processor
A = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
A = prepare_image_inputs(self.image_processing_tester , equal_resolution=_lowercase )
for image in image_inputs:
self.assertIsInstance(_lowercase , Image.Image )
# Test not batched input
A = image_processor(image_inputs[0] , ['semantic'] , return_tensors='pt' ).pixel_values
A , A = self.image_processing_tester.get_expected_values(_lowercase )
self.assertEqual(
encoded_images.shape , (1, self.image_processing_tester.num_channels, expected_height, expected_width) , )
# Test batched
A , A = self.image_processing_tester.get_expected_values(_lowercase , batched=_lowercase )
A = image_processor(
_lowercase , ['semantic'] * len(_lowercase ) , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processing_tester.batch_size,
self.image_processing_tester.num_channels,
expected_height,
expected_width,
) , )
def __a ( self : Any ):
# Initialize image_processor
A = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
A = prepare_image_inputs(self.image_processing_tester , equal_resolution=_lowercase , numpify=_lowercase )
for image in image_inputs:
self.assertIsInstance(_lowercase , np.ndarray )
# Test not batched input
A = image_processor(image_inputs[0] , ['semantic'] , return_tensors='pt' ).pixel_values
A , A = self.image_processing_tester.get_expected_values(_lowercase )
self.assertEqual(
encoded_images.shape , (1, self.image_processing_tester.num_channels, expected_height, expected_width) , )
# Test batched
A , A = self.image_processing_tester.get_expected_values(_lowercase , batched=_lowercase )
A = image_processor(
_lowercase , ['semantic'] * len(_lowercase ) , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processing_tester.batch_size,
self.image_processing_tester.num_channels,
expected_height,
expected_width,
) , )
def __a ( self : List[Any] ):
# Initialize image_processor
A = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
A = prepare_image_inputs(self.image_processing_tester , equal_resolution=_lowercase , torchify=_lowercase )
for image in image_inputs:
self.assertIsInstance(_lowercase , torch.Tensor )
# Test not batched input
A = image_processor(image_inputs[0] , ['semantic'] , return_tensors='pt' ).pixel_values
A , A = self.image_processing_tester.get_expected_values(_lowercase )
self.assertEqual(
encoded_images.shape , (1, self.image_processing_tester.num_channels, expected_height, expected_width) , )
# Test batched
A , A = self.image_processing_tester.get_expected_values(_lowercase , batched=_lowercase )
A = image_processor(
_lowercase , ['semantic'] * len(_lowercase ) , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processing_tester.batch_size,
self.image_processing_tester.num_channels,
expected_height,
expected_width,
) , )
def __a ( self : int , _lowercase : Union[str, Any]=False , _lowercase : List[str]=False , _lowercase : Optional[Any]="np" ):
A = self.image_processing_class(**self.image_processor_dict )
# prepare image and target
A = self.image_processing_tester.num_labels
A = None
A = None
A = prepare_image_inputs(self.image_processing_tester , equal_resolution=_lowercase )
if with_segmentation_maps:
A = num_labels
if is_instance_map:
A = list(range(_lowercase ) ) * 2
A = dict(enumerate(_lowercase ) )
A = [
np.random.randint(0 , high * 2 , (img.size[1], img.size[0]) ).astype(np.uinta ) for img in image_inputs
]
if segmentation_type == "pil":
A = [Image.fromarray(_lowercase ) for annotation in annotations]
A = image_processor(
_lowercase , ['semantic'] * len(_lowercase ) , _lowercase , return_tensors='pt' , instance_id_to_semantic_id=_lowercase , pad_and_return_pixel_mask=_lowercase , )
return inputs
def __a ( self : Any ):
pass
def __a ( self : List[Any] ):
def common(_lowercase : List[str]=False , _lowercase : List[str]=None ):
A = self.comm_get_image_processor_inputs(
with_segmentation_maps=_lowercase , is_instance_map=_lowercase , segmentation_type=_lowercase )
A = inputs['mask_labels']
A = inputs['class_labels']
A = inputs['pixel_values']
A = inputs['text_inputs']
# check the batch_size
for mask_label, class_label, text_input in zip(_lowercase , _lowercase , _lowercase ):
self.assertEqual(mask_label.shape[0] , class_label.shape[0] )
# this ensure padding has happened
self.assertEqual(mask_label.shape[1:] , pixel_values.shape[2:] )
self.assertEqual(len(_lowercase ) , self.image_processing_tester.num_text )
common()
common(is_instance_map=_lowercase )
common(is_instance_map=_lowercase , segmentation_type='pil' )
common(is_instance_map=_lowercase , segmentation_type='pil' )
def __a ( self : Any ):
A = np.zeros((20, 50) )
A = 1
A = 1
A = 1
A = binary_mask_to_rle(_lowercase )
self.assertEqual(len(_lowercase ) , 4 )
self.assertEqual(rle[0] , 21 )
self.assertEqual(rle[1] , 45 )
def __a ( self : int ):
A = self.image_processing_class(
num_labels=self.image_processing_tester.num_classes , max_seq_length=77 , task_seq_length=77 , class_info_file='ade20k_panoptic.json' , num_text=self.image_processing_tester.num_text , repo_path='shi-labs/oneformer_demo' , )
A = self.image_processing_tester.get_fake_oneformer_outputs()
A = fature_extractor.post_process_semantic_segmentation(_lowercase )
self.assertEqual(len(_lowercase ) , self.image_processing_tester.batch_size )
self.assertEqual(
segmentation[0].shape , (
self.image_processing_tester.height,
self.image_processing_tester.width,
) , )
A = [(1, 4) for i in range(self.image_processing_tester.batch_size )]
A = fature_extractor.post_process_semantic_segmentation(_lowercase , target_sizes=_lowercase )
self.assertEqual(segmentation[0].shape , target_sizes[0] )
def __a ( self : List[str] ):
A = self.image_processing_class(
num_labels=self.image_processing_tester.num_classes , max_seq_length=77 , task_seq_length=77 , class_info_file='ade20k_panoptic.json' , num_text=self.image_processing_tester.num_text , repo_path='shi-labs/oneformer_demo' , )
A = self.image_processing_tester.get_fake_oneformer_outputs()
A = image_processor.post_process_instance_segmentation(_lowercase , threshold=0 )
self.assertTrue(len(_lowercase ) == self.image_processing_tester.batch_size )
for el in segmentation:
self.assertTrue('segmentation' in el )
self.assertTrue('segments_info' in el )
self.assertEqual(type(el['segments_info'] ) , _lowercase )
self.assertEqual(
el['segmentation'].shape , (self.image_processing_tester.height, self.image_processing_tester.width) )
def __a ( self : Dict ):
A = self.image_processing_class(
num_labels=self.image_processing_tester.num_classes , max_seq_length=77 , task_seq_length=77 , class_info_file='ade20k_panoptic.json' , num_text=self.image_processing_tester.num_text , repo_path='shi-labs/oneformer_demo' , )
A = self.image_processing_tester.get_fake_oneformer_outputs()
A = image_processor.post_process_panoptic_segmentation(_lowercase , threshold=0 )
self.assertTrue(len(_lowercase ) == self.image_processing_tester.batch_size )
for el in segmentation:
self.assertTrue('segmentation' in el )
self.assertTrue('segments_info' in el )
self.assertEqual(type(el['segments_info'] ) , _lowercase )
self.assertEqual(
el['segmentation'].shape , (self.image_processing_tester.height, self.image_processing_tester.width) )
| 690 |
"""simple docstring"""
def __snake_case ( UpperCamelCase__ ) -> list[int]:
"""simple docstring"""
A = [0 for i in range(len(UpperCamelCase__ ) )]
# initialize interval's left pointer and right pointer
A , A = 0, 0
for i in range(1 , len(UpperCamelCase__ ) ):
# case when current index is inside the interval
if i <= right_pointer:
A = min(right_pointer - i + 1 , z_result[i - left_pointer] )
A = min_edge
while go_next(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
z_result[i] += 1
# if new index's result gives us more right interval,
# we've to update left_pointer and right_pointer
if i + z_result[i] - 1 > right_pointer:
A , A = i, i + z_result[i] - 1
return z_result
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> bool:
"""simple docstring"""
return i + z_result[i] < len(UpperCamelCase__ ) and s[z_result[i]] == s[i + z_result[i]]
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ ) -> int:
"""simple docstring"""
A = 0
# concatenate 'pattern' and 'input_str' and call z_function
# with concatenated string
A = z_function(pattern + input_str )
for val in z_result:
# if value is greater then length of the pattern string
# that means this index is starting position of substring
# which is equal to pattern string
if val >= len(UpperCamelCase__ ):
answer += 1
return answer
if __name__ == "__main__":
import doctest
doctest.testmod()
| 690 | 1 |
"""simple docstring"""
import argparse
import collections
import json
from pathlib import Path
import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTImageProcessor,
MobileViTVaConfig,
MobileViTVaForImageClassification,
MobileViTVaForSemanticSegmentation,
)
from transformers.utils import logging
logging.set_verbosity_info()
UpperCamelCase : Any = logging.get_logger(__name__)
def __snake_case ( UpperCamelCase__ ) -> List[str]:
"""simple docstring"""
print('Loading config file...' )
def flatten_yaml_as_dict(UpperCamelCase__ , UpperCamelCase__="" , UpperCamelCase__="." ):
A = []
for k, v in d.items():
A = parent_key + sep + k if parent_key else k
if isinstance(UpperCamelCase__ , collections.abc.MutableMapping ):
items.extend(flatten_yaml_as_dict(UpperCamelCase__ , UpperCamelCase__ , sep=UpperCamelCase__ ).items() )
else:
items.append((new_key, v) )
return dict(UpperCamelCase__ )
A = argparse.Namespace()
with open(UpperCamelCase__ , 'r' ) as yaml_file:
try:
A = yaml.load(UpperCamelCase__ , Loader=yaml.FullLoader )
A = flatten_yaml_as_dict(UpperCamelCase__ )
for k, v in flat_cfg.items():
setattr(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
except yaml.YAMLError as exc:
logger.error('Error while loading config file: {}. Error message: {}'.format(UpperCamelCase__ , str(UpperCamelCase__ ) ) )
return config
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ ) -> int:
"""simple docstring"""
A = MobileViTVaConfig()
A = False
# dataset
if task_name.startswith('imagenet1k_' ):
A = 1000
if int(task_name.strip().split('_' )[-1] ) == 384:
A = 384
else:
A = 256
A = 'imagenet-1k-id2label.json'
elif task_name.startswith('imagenet21k_to_1k_' ):
A = 21000
if int(task_name.strip().split('_' )[-1] ) == 384:
A = 384
else:
A = 256
A = 'imagenet-22k-id2label.json'
elif task_name.startswith('ade20k_' ):
A = 151
A = 512
A = 'ade20k-id2label.json'
A = True
elif task_name.startswith('voc_' ):
A = 21
A = 512
A = 'pascal-voc-id2label.json'
A = True
# orig_config
A = load_orig_config_file(UpperCamelCase__ )
assert getattr(UpperCamelCase__ , 'model.classification.name' , -1 ) == "mobilevit_v2", "Invalid model"
A = getattr(UpperCamelCase__ , 'model.classification.mitv2.width_multiplier' , 1.0 )
assert (
getattr(UpperCamelCase__ , 'model.classification.mitv2.attn_norm_layer' , -1 ) == "layer_norm_2d"
), "Norm layers other than layer_norm_2d is not supported"
A = getattr(UpperCamelCase__ , 'model.classification.activation.name' , 'swish' )
# config.image_size == getattr(orig_config, 'sampler.bs.crop_size_width', 256)
if is_segmentation_model:
A = getattr(UpperCamelCase__ , 'model.segmentation.output_stride' , 16 )
if "_deeplabv3" in task_name:
A = getattr(UpperCamelCase__ , 'model.segmentation.deeplabv3.aspp_rates' , [12, 24, 36] )
A = getattr(UpperCamelCase__ , 'model.segmentation.deeplabv3.aspp_out_channels' , 512 )
A = getattr(UpperCamelCase__ , 'model.segmentation.deeplabv3.aspp_dropout' , 0.1 )
# id2label
A = 'huggingface/label-files'
A = json.load(open(hf_hub_download(UpperCamelCase__ , UpperCamelCase__ , repo_type='dataset' ) , 'r' ) )
A = {int(UpperCamelCase__ ): v for k, v in idalabel.items()}
A = idalabel
A = {v: k for k, v in idalabel.items()}
return config
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> str:
"""simple docstring"""
A = dct.pop(UpperCamelCase__ )
A = val
def __snake_case ( UpperCamelCase__ , UpperCamelCase__=False ) -> List[str]:
"""simple docstring"""
if base_model:
A = ''
else:
A = 'mobilevitv2.'
A = []
for k in state_dict.keys():
if k[:8] == "encoder.":
A = k[8:]
else:
A = k
if ".block." in k:
A = k_new.replace('.block.' , '.' )
if ".conv." in k:
A = k_new.replace('.conv.' , '.convolution.' )
if ".norm." in k:
A = k_new.replace('.norm.' , '.normalization.' )
if "conv_1." in k:
A = k_new.replace('conv_1.' , f'{model_prefix}conv_stem.' )
for i in [1, 2]:
if f'layer_{i}.' in k:
A = k_new.replace(f'layer_{i}.' , f'{model_prefix}encoder.layer.{i-1}.layer.' )
if ".exp_1x1." in k:
A = k_new.replace('.exp_1x1.' , '.expand_1x1.' )
if ".red_1x1." in k:
A = k_new.replace('.red_1x1.' , '.reduce_1x1.' )
for i in [3, 4, 5]:
if f'layer_{i}.0.' in k:
A = k_new.replace(f'layer_{i}.0.' , f'{model_prefix}encoder.layer.{i-1}.downsampling_layer.' )
if f'layer_{i}.1.local_rep.0.' in k:
A = k_new.replace(f'layer_{i}.1.local_rep.0.' , f'{model_prefix}encoder.layer.{i-1}.conv_kxk.' )
if f'layer_{i}.1.local_rep.1.' in k:
A = k_new.replace(f'layer_{i}.1.local_rep.1.' , f'{model_prefix}encoder.layer.{i-1}.conv_1x1.' )
for i in [3, 4, 5]:
if i == 3:
A = [0, 1]
elif i == 4:
A = [0, 1, 2, 3]
elif i == 5:
A = [0, 1, 2]
for j in j_in:
if f'layer_{i}.1.global_rep.{j}.' in k:
A = k_new.replace(
f'layer_{i}.1.global_rep.{j}.' , f'{model_prefix}encoder.layer.{i-1}.transformer.layer.{j}.' )
if f'layer_{i}.1.global_rep.{j+1}.' in k:
A = k_new.replace(
f'layer_{i}.1.global_rep.{j+1}.' , f'{model_prefix}encoder.layer.{i-1}.layernorm.' )
if f'layer_{i}.1.conv_proj.' in k:
A = k_new.replace(f'layer_{i}.1.conv_proj.' , f'{model_prefix}encoder.layer.{i-1}.conv_projection.' )
if "pre_norm_attn.0." in k:
A = k_new.replace('pre_norm_attn.0.' , 'layernorm_before.' )
if "pre_norm_attn.1." in k:
A = k_new.replace('pre_norm_attn.1.' , 'attention.' )
if "pre_norm_ffn.0." in k:
A = k_new.replace('pre_norm_ffn.0.' , 'layernorm_after.' )
if "pre_norm_ffn.1." in k:
A = k_new.replace('pre_norm_ffn.1.' , 'ffn.conv1.' )
if "pre_norm_ffn.3." in k:
A = k_new.replace('pre_norm_ffn.3.' , 'ffn.conv2.' )
if "classifier.1." in k:
A = k_new.replace('classifier.1.' , 'classifier.' )
if "seg_head." in k:
A = k_new.replace('seg_head.' , 'segmentation_head.' )
if ".aspp_layer." in k:
A = k_new.replace('.aspp_layer.' , '.' )
if ".aspp_pool." in k:
A = k_new.replace('.aspp_pool.' , '.' )
rename_keys.append((k, k_new) )
return rename_keys
def __snake_case ( UpperCamelCase__ ) -> List[Any]:
"""simple docstring"""
A = []
for k in state_dict.keys():
if k.startswith('seg_head.aux_head.' ):
keys_to_ignore.append(UpperCamelCase__ )
for k in keys_to_ignore:
state_dict.pop(UpperCamelCase__ , UpperCamelCase__ )
def __snake_case ( ) -> str:
"""simple docstring"""
A = 'http://images.cocodataset.org/val2017/000000039769.jpg'
# url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
A = Image.open(requests.get(UpperCamelCase__ , stream=UpperCamelCase__ ).raw )
return im
@torch.no_grad()
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> List[Any]:
"""simple docstring"""
A = get_mobilevitva_config(UpperCamelCase__ , UpperCamelCase__ )
# load original state_dict
A = torch.load(UpperCamelCase__ , map_location='cpu' )
# load huggingface model
if task_name.startswith('ade20k_' ) or task_name.startswith('voc_' ):
A = MobileViTVaForSemanticSegmentation(UpperCamelCase__ ).eval()
A = False
else:
A = MobileViTVaForImageClassification(UpperCamelCase__ ).eval()
A = False
# remove and rename some keys of load the original model
A = checkpoint
remove_unused_keys(UpperCamelCase__ )
A = create_rename_keys(UpperCamelCase__ , base_model=UpperCamelCase__ )
for rename_key_src, rename_key_dest in rename_keys:
rename_key(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
# load modified state_dict
model.load_state_dict(UpperCamelCase__ )
# Check outputs on an image, prepared by MobileViTImageProcessor
A = MobileViTImageProcessor(crop_size=config.image_size , size=config.image_size + 32 )
A = image_processor(images=prepare_img() , return_tensors='pt' )
A = model(**UpperCamelCase__ )
# verify classification model
if task_name.startswith('imagenet' ):
A = outputs.logits
A = logits.argmax(-1 ).item()
print('Predicted class:' , model.config.idalabel[predicted_class_idx] )
if task_name.startswith('imagenet1k_256' ) and config.width_multiplier == 1.0:
# expected_logits for base variant
A = torch.tensor([-1.6336E00, -7.3204E-02, -5.1883E-01] )
assert torch.allclose(logits[0, :3] , UpperCamelCase__ , atol=1E-4 )
Path(UpperCamelCase__ ).mkdir(exist_ok=UpperCamelCase__ )
print(f'Saving model {task_name} to {pytorch_dump_folder_path}' )
model.save_pretrained(UpperCamelCase__ )
print(f'Saving image processor to {pytorch_dump_folder_path}' )
image_processor.save_pretrained(UpperCamelCase__ )
if __name__ == "__main__":
UpperCamelCase : str = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--task",
default="imagenet1k_256",
type=str,
help=(
"Name of the task for which the MobileViTV2 model you'd like to convert is trained on . "
"\n Classification (ImageNet-1k)\n - MobileViTV2 (256x256) : imagenet1k_256\n - MobileViTV2 (Trained on 256x256 and Finetuned on 384x384) : imagenet1k_384\n - MobileViTV2 (Trained on ImageNet-21k and Finetuned on ImageNet-1k 256x256) :\n imagenet21k_to_1k_256\n - MobileViTV2 (Trained on ImageNet-21k, Finetuned on ImageNet-1k 256x256, and Finetuned on\n ImageNet-1k 384x384) : imagenet21k_to_1k_384\n Segmentation\n - ADE20K Dataset : ade20k_deeplabv3\n - Pascal VOC 2012 Dataset: voc_deeplabv3\n "
),
choices=[
"imagenet1k_256",
"imagenet1k_384",
"imagenet21k_to_1k_256",
"imagenet21k_to_1k_384",
"ade20k_deeplabv3",
"voc_deeplabv3",
],
)
parser.add_argument(
"--orig_checkpoint_path", required=True, type=str, help="Path to the original state dict (.pt file)."
)
parser.add_argument("--orig_config_path", required=True, type=str, help="Path to the original config file.")
parser.add_argument(
"--pytorch_dump_folder_path", required=True, type=str, help="Path to the output PyTorch model directory."
)
UpperCamelCase : int = parser.parse_args()
convert_mobilevitva_checkpoint(
args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
)
| 690 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
enable_full_determinism,
load_numpy,
nightly,
require_torch_gpu,
slow,
torch_device,
)
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class lowerCamelCase__ ( UpperCAmelCase_ , unittest.TestCase ):
lowerCAmelCase = LDMTextToImagePipeline
lowerCAmelCase = TEXT_TO_IMAGE_PARAMS - {
"""negative_prompt""",
"""negative_prompt_embeds""",
"""cross_attention_kwargs""",
"""prompt_embeds""",
}
lowerCAmelCase = PipelineTesterMixin.required_optional_params - {
"""num_images_per_prompt""",
"""callback""",
"""callback_steps""",
}
lowerCAmelCase = TEXT_TO_IMAGE_BATCH_PARAMS
lowerCAmelCase = False
def __a ( self : Dict ):
torch.manual_seed(0 )
A = 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 , )
A = DDIMScheduler(
beta_start=0.0_0_0_8_5 , beta_end=0.0_1_2 , beta_schedule='scaled_linear' , clip_sample=_lowercase , set_alpha_to_one=_lowercase , )
torch.manual_seed(0 )
A = 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 )
A = 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=1_000 , )
A = CLIPTextModel(_lowercase )
A = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
A = {
'unet': unet,
'scheduler': scheduler,
'vqvae': vae,
'bert': text_encoder,
'tokenizer': tokenizer,
}
return components
def __a ( self : Union[str, Any] , _lowercase : Union[str, Any] , _lowercase : Union[str, Any]=0 ):
if str(_lowercase ).startswith('mps' ):
A = torch.manual_seed(_lowercase )
else:
A = torch.Generator(device=_lowercase ).manual_seed(_lowercase )
A = {
'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 __a ( self : Any ):
A = 'cpu' # ensure determinism for the device-dependent torch.Generator
A = self.get_dummy_components()
A = LDMTextToImagePipeline(**_lowercase )
pipe.to(_lowercase )
pipe.set_progress_bar_config(disable=_lowercase )
A = self.get_dummy_inputs(_lowercase )
A = pipe(**_lowercase ).images
A = image[0, -3:, -3:, -1]
assert image.shape == (1, 16, 16, 3)
A = np.array([0.6_1_0_1, 0.6_1_5_6, 0.5_6_2_2, 0.4_8_9_5, 0.6_6_6_1, 0.3_8_0_4, 0.5_7_4_8, 0.6_1_3_6, 0.5_0_1_4] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
@slow
@require_torch_gpu
class lowerCamelCase__ ( unittest.TestCase ):
def __a ( self : Optional[Any] ):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __a ( self : int , _lowercase : List[Any] , _lowercase : int=torch.floataa , _lowercase : int=0 ):
A = torch.manual_seed(_lowercase )
A = np.random.RandomState(_lowercase ).standard_normal((1, 4, 32, 32) )
A = torch.from_numpy(_lowercase ).to(device=_lowercase , dtype=_lowercase )
A = {
'prompt': 'A painting of a squirrel eating a burger',
'latents': latents,
'generator': generator,
'num_inference_steps': 3,
'guidance_scale': 6.0,
'output_type': 'numpy',
}
return inputs
def __a ( self : Union[str, Any] ):
A = LDMTextToImagePipeline.from_pretrained('CompVis/ldm-text2im-large-256' ).to(_lowercase )
pipe.set_progress_bar_config(disable=_lowercase )
A = self.get_inputs(_lowercase )
A = pipe(**_lowercase ).images
A = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 256, 256, 3)
A = np.array([0.5_1_8_2_5, 0.5_2_8_5_0, 0.5_2_5_4_3, 0.5_4_2_5_8, 0.5_2_3_0_4, 0.5_2_5_6_9, 0.5_4_3_6_3, 0.5_5_2_7_6, 0.5_6_8_7_8] )
A = np.abs(expected_slice - image_slice ).max()
assert max_diff < 1e-3
@nightly
@require_torch_gpu
class lowerCamelCase__ ( unittest.TestCase ):
def __a ( self : List[Any] ):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __a ( self : List[Any] , _lowercase : Optional[Any] , _lowercase : Tuple=torch.floataa , _lowercase : Optional[Any]=0 ):
A = torch.manual_seed(_lowercase )
A = np.random.RandomState(_lowercase ).standard_normal((1, 4, 32, 32) )
A = torch.from_numpy(_lowercase ).to(device=_lowercase , dtype=_lowercase )
A = {
'prompt': 'A painting of a squirrel eating a burger',
'latents': latents,
'generator': generator,
'num_inference_steps': 50,
'guidance_scale': 6.0,
'output_type': 'numpy',
}
return inputs
def __a ( self : List[str] ):
A = LDMTextToImagePipeline.from_pretrained('CompVis/ldm-text2im-large-256' ).to(_lowercase )
pipe.set_progress_bar_config(disable=_lowercase )
A = self.get_inputs(_lowercase )
A = pipe(**_lowercase ).images[0]
A = load_numpy(
'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/ldm_text2img/ldm_large_256_ddim.npy' )
A = np.abs(expected_image - image ).max()
assert max_diff < 1e-3
| 690 | 1 |
"""simple docstring"""
import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCamelCase : int = logging.get_logger(__name__)
UpperCamelCase : Any = {
"google/owlvit-base-patch32": "https://huggingface.co/google/owlvit-base-patch32/resolve/main/config.json",
"google/owlvit-base-patch16": "https://huggingface.co/google/owlvit-base-patch16/resolve/main/config.json",
"google/owlvit-large-patch14": "https://huggingface.co/google/owlvit-large-patch14/resolve/main/config.json",
}
class lowerCamelCase__ ( UpperCAmelCase_ ):
lowerCAmelCase = """owlvit_text_model"""
def __init__( self : Union[str, Any] , _lowercase : Any=49_408 , _lowercase : Optional[Any]=512 , _lowercase : List[str]=2_048 , _lowercase : str=12 , _lowercase : List[Any]=8 , _lowercase : Dict=16 , _lowercase : Optional[Any]="quick_gelu" , _lowercase : Tuple=1e-5 , _lowercase : Optional[Any]=0.0 , _lowercase : Tuple=0.0_2 , _lowercase : Union[str, Any]=1.0 , _lowercase : Tuple=0 , _lowercase : Optional[int]=49_406 , _lowercase : int=49_407 , **_lowercase : Any , ):
super().__init__(pad_token_id=_lowercase , bos_token_id=_lowercase , eos_token_id=_lowercase , **_lowercase )
A = vocab_size
A = hidden_size
A = intermediate_size
A = num_hidden_layers
A = num_attention_heads
A = max_position_embeddings
A = hidden_act
A = layer_norm_eps
A = attention_dropout
A = initializer_range
A = initializer_factor
@classmethod
def __a ( cls : str , _lowercase : Union[str, os.PathLike] , **_lowercase : List[str] ):
cls._set_token_in_kwargs(_lowercase )
A , A = cls.get_config_dict(_lowercase , **_lowercase )
# get the text config dict if we are loading from OwlViTConfig
if config_dict.get('model_type' ) == "owlvit":
A = 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(_lowercase , **_lowercase )
class lowerCamelCase__ ( UpperCAmelCase_ ):
lowerCAmelCase = """owlvit_vision_model"""
def __init__( self : int , _lowercase : List[Any]=768 , _lowercase : Optional[Any]=3_072 , _lowercase : Optional[int]=12 , _lowercase : Optional[int]=12 , _lowercase : Any=3 , _lowercase : Any=768 , _lowercase : Tuple=32 , _lowercase : Dict="quick_gelu" , _lowercase : Tuple=1e-5 , _lowercase : Dict=0.0 , _lowercase : List[Any]=0.0_2 , _lowercase : Any=1.0 , **_lowercase : Any , ):
super().__init__(**_lowercase )
A = hidden_size
A = intermediate_size
A = num_hidden_layers
A = num_attention_heads
A = num_channels
A = image_size
A = patch_size
A = hidden_act
A = layer_norm_eps
A = attention_dropout
A = initializer_range
A = initializer_factor
@classmethod
def __a ( cls : List[Any] , _lowercase : Union[str, os.PathLike] , **_lowercase : Any ):
cls._set_token_in_kwargs(_lowercase )
A , A = cls.get_config_dict(_lowercase , **_lowercase )
# get the vision config dict if we are loading from OwlViTConfig
if config_dict.get('model_type' ) == "owlvit":
A = 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(_lowercase , **_lowercase )
class lowerCamelCase__ ( UpperCAmelCase_ ):
lowerCAmelCase = """owlvit"""
lowerCAmelCase = True
def __init__( self : int , _lowercase : List[Any]=None , _lowercase : Union[str, Any]=None , _lowercase : Optional[Any]=512 , _lowercase : Optional[Any]=2.6_5_9_2 , _lowercase : List[str]=True , **_lowercase : int , ):
super().__init__(**_lowercase )
if text_config is None:
A = {}
logger.info('text_config is None. Initializing the OwlViTTextConfig with default values.' )
if vision_config is None:
A = {}
logger.info('vision_config is None. initializing the OwlViTVisionConfig with default values.' )
A = OwlViTTextConfig(**_lowercase )
A = OwlViTVisionConfig(**_lowercase )
A = projection_dim
A = logit_scale_init_value
A = return_dict
A = 1.0
@classmethod
def __a ( cls : int , _lowercase : Union[str, os.PathLike] , **_lowercase : List[str] ):
cls._set_token_in_kwargs(_lowercase )
A , A = cls.get_config_dict(_lowercase , **_lowercase )
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(_lowercase , **_lowercase )
@classmethod
def __a ( cls : Any , _lowercase : Dict , _lowercase : Dict , **_lowercase : Any ):
A = {}
A = text_config
A = vision_config
return cls.from_dict(_lowercase , **_lowercase )
def __a ( self : Union[str, Any] ):
A = copy.deepcopy(self.__dict__ )
A = self.text_config.to_dict()
A = self.vision_config.to_dict()
A = self.__class__.model_type
return output
class lowerCamelCase__ ( UpperCAmelCase_ ):
@property
def __a ( self : Optional[int] ):
return OrderedDict(
[
('input_ids', {0: 'batch', 1: 'sequence'}),
('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
('attention_mask', {0: 'batch', 1: 'sequence'}),
] )
@property
def __a ( self : int ):
return OrderedDict(
[
('logits_per_image', {0: 'batch'}),
('logits_per_text', {0: 'batch'}),
('text_embeds', {0: 'batch'}),
('image_embeds', {0: 'batch'}),
] )
@property
def __a ( self : List[str] ):
return 1e-4
def __a ( self : List[Any] , _lowercase : "ProcessorMixin" , _lowercase : int = -1 , _lowercase : int = -1 , _lowercase : Optional["TensorType"] = None , ):
A = super().generate_dummy_inputs(
processor.tokenizer , batch_size=_lowercase , seq_length=_lowercase , framework=_lowercase )
A = super().generate_dummy_inputs(
processor.image_processor , batch_size=_lowercase , framework=_lowercase )
return {**text_input_dict, **image_input_dict}
@property
def __a ( self : Tuple ):
return 14
| 690 |
"""simple docstring"""
import os
import tempfile
import unittest
import numpy as np
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
from diffusers import FlaxDDIMScheduler, FlaxDiffusionPipeline, FlaxStableDiffusionPipeline
@require_flax
class lowerCamelCase__ ( unittest.TestCase ):
def __a ( self : Union[str, Any] ):
with tempfile.TemporaryDirectory() as tmpdirname:
# pipeline has Flax weights
A = FlaxDiffusionPipeline.from_pretrained(
'hf-internal-testing/tiny-stable-diffusion-pipe' , safety_checker=_lowercase , cache_dir=_lowercase )
A = [t[-1] for t in os.walk(os.path.join(_lowercase , os.listdir(_lowercase )[0] , 'snapshots' ) )]
A = [item for sublist in all_root_files for item in sublist]
# None of the downloaded files should be a PyTorch file even if we have some here:
# https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe/blob/main/unet/diffusion_pytorch_model.bin
assert not any(f.endswith('.bin' ) for f in files )
@slow
@require_flax
class lowerCamelCase__ ( unittest.TestCase ):
def __a ( self : Optional[Any] ):
A , A = FlaxStableDiffusionPipeline.from_pretrained(
'hf-internal-testing/tiny-stable-diffusion-pipe' , safety_checker=_lowercase )
A = (
'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
' field, close up, split lighting, cinematic'
)
A = jax.random.PRNGKey(0 )
A = 4
A = jax.device_count()
A = num_samples * [prompt]
A = pipeline.prepare_inputs(_lowercase )
# shard inputs and rng
A = replicate(_lowercase )
A = jax.random.split(_lowercase , _lowercase )
A = shard(_lowercase )
A = pipeline(_lowercase , _lowercase , _lowercase , _lowercase , jit=_lowercase ).images
assert images.shape == (num_samples, 1, 64, 64, 3)
if jax.device_count() == 8:
assert np.abs(np.abs(images[0, 0, :2, :2, -2:] , dtype=np.floataa ).sum() - 4.1_5_1_4_7_4_5 ) < 1e-3
assert np.abs(np.abs(_lowercase , dtype=np.floataa ).sum() - 4_9_9_4_7.8_7_5 ) < 5e-1
A = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:] ) ) )
assert len(_lowercase ) == num_samples
def __a ( self : Dict ):
A , A = FlaxStableDiffusionPipeline.from_pretrained(
'CompVis/stable-diffusion-v1-4' , revision='flax' , safety_checker=_lowercase )
A = (
'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
' field, close up, split lighting, cinematic'
)
A = jax.random.PRNGKey(0 )
A = 50
A = jax.device_count()
A = num_samples * [prompt]
A = pipeline.prepare_inputs(_lowercase )
# shard inputs and rng
A = replicate(_lowercase )
A = jax.random.split(_lowercase , _lowercase )
A = shard(_lowercase )
A = pipeline(_lowercase , _lowercase , _lowercase , _lowercase , jit=_lowercase ).images
assert images.shape == (num_samples, 1, 512, 512, 3)
if jax.device_count() == 8:
assert np.abs((np.abs(images[0, 0, :2, :2, -2:] , dtype=np.floataa ).sum() - 0.0_5_6_5_2_4_0_1) ) < 1e-3
assert np.abs((np.abs(_lowercase , dtype=np.floataa ).sum() - 2_3_8_3_8_0_8.2) ) < 5e-1
def __a ( self : List[str] ):
A , A = FlaxStableDiffusionPipeline.from_pretrained(
'CompVis/stable-diffusion-v1-4' , revision='bf16' , dtype=jnp.bfloataa , safety_checker=_lowercase )
A = (
'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
' field, close up, split lighting, cinematic'
)
A = jax.random.PRNGKey(0 )
A = 50
A = jax.device_count()
A = num_samples * [prompt]
A = pipeline.prepare_inputs(_lowercase )
# shard inputs and rng
A = replicate(_lowercase )
A = jax.random.split(_lowercase , _lowercase )
A = shard(_lowercase )
A = pipeline(_lowercase , _lowercase , _lowercase , _lowercase , jit=_lowercase ).images
assert images.shape == (num_samples, 1, 512, 512, 3)
if jax.device_count() == 8:
assert np.abs((np.abs(images[0, 0, :2, :2, -2:] , dtype=np.floataa ).sum() - 0.0_4_0_0_3_9_0_6) ) < 1e-3
assert np.abs((np.abs(_lowercase , dtype=np.floataa ).sum() - 2_3_7_3_5_1_6.7_5) ) < 5e-1
def __a ( self : str ):
A , A = FlaxStableDiffusionPipeline.from_pretrained(
'CompVis/stable-diffusion-v1-4' , revision='bf16' , dtype=jnp.bfloataa )
A = (
'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
' field, close up, split lighting, cinematic'
)
A = jax.random.PRNGKey(0 )
A = 50
A = jax.device_count()
A = num_samples * [prompt]
A = pipeline.prepare_inputs(_lowercase )
# shard inputs and rng
A = replicate(_lowercase )
A = jax.random.split(_lowercase , _lowercase )
A = shard(_lowercase )
A = pipeline(_lowercase , _lowercase , _lowercase , _lowercase , jit=_lowercase ).images
assert images.shape == (num_samples, 1, 512, 512, 3)
if jax.device_count() == 8:
assert np.abs((np.abs(images[0, 0, :2, :2, -2:] , dtype=np.floataa ).sum() - 0.0_4_0_0_3_9_0_6) ) < 1e-3
assert np.abs((np.abs(_lowercase , dtype=np.floataa ).sum() - 2_3_7_3_5_1_6.7_5) ) < 5e-1
def __a ( self : Any ):
A = FlaxDDIMScheduler(
beta_start=0.0_0_0_8_5 , beta_end=0.0_1_2 , beta_schedule='scaled_linear' , set_alpha_to_one=_lowercase , steps_offset=1 , )
A , A = FlaxStableDiffusionPipeline.from_pretrained(
'CompVis/stable-diffusion-v1-4' , revision='bf16' , dtype=jnp.bfloataa , scheduler=_lowercase , safety_checker=_lowercase , )
A = scheduler.create_state()
A = scheduler_state
A = (
'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
' field, close up, split lighting, cinematic'
)
A = jax.random.PRNGKey(0 )
A = 50
A = jax.device_count()
A = num_samples * [prompt]
A = pipeline.prepare_inputs(_lowercase )
# shard inputs and rng
A = replicate(_lowercase )
A = jax.random.split(_lowercase , _lowercase )
A = shard(_lowercase )
A = pipeline(_lowercase , _lowercase , _lowercase , _lowercase , jit=_lowercase ).images
assert images.shape == (num_samples, 1, 512, 512, 3)
if jax.device_count() == 8:
assert np.abs((np.abs(images[0, 0, :2, :2, -2:] , dtype=np.floataa ).sum() - 0.0_4_5_0_4_3_9_4_5) ) < 1e-3
assert np.abs((np.abs(_lowercase , dtype=np.floataa ).sum() - 2_3_4_7_6_9_3.5) ) < 5e-1
def __a ( self : List[str] ):
A = (
'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
' field, close up, split lighting, cinematic'
)
A = jax.device_count()
A = num_samples * [prompt]
A = jax.random.split(jax.random.PRNGKey(0 ) , _lowercase )
A , A = FlaxStableDiffusionPipeline.from_pretrained(
'CompVis/stable-diffusion-v1-4' , revision='bf16' , dtype=jnp.bfloataa , safety_checker=_lowercase , )
A = replicate(_lowercase )
A = pipeline.prepare_inputs(_lowercase )
A = shard(_lowercase )
A = pipeline(_lowercase , _lowercase , _lowercase , jit=_lowercase ).images
assert images.shape == (num_samples, 1, 512, 512, 3)
A = images[2, 0, 256, 10:17, 1]
# With memory efficient attention
A , A = FlaxStableDiffusionPipeline.from_pretrained(
'CompVis/stable-diffusion-v1-4' , revision='bf16' , dtype=jnp.bfloataa , safety_checker=_lowercase , use_memory_efficient_attention=_lowercase , )
A = replicate(_lowercase )
A = pipeline.prepare_inputs(_lowercase )
A = shard(_lowercase )
A = pipeline(_lowercase , _lowercase , _lowercase , jit=_lowercase ).images
assert images_eff.shape == (num_samples, 1, 512, 512, 3)
A = images[2, 0, 256, 10:17, 1]
# I checked the results visually and they are very similar. However, I saw that the max diff is `1` and the `sum`
# over the 8 images is exactly `256`, which is very suspicious. Testing a random slice for now.
assert abs(slice_eff - slice ).max() < 1e-2
| 690 | 1 |
"""simple docstring"""
def __snake_case ( UpperCamelCase__ ) -> str:
"""simple docstring"""
A = ''
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def __snake_case ( UpperCamelCase__ ) -> dict[str, str]:
"""simple docstring"""
A = [chr(i + 65 ) for i in range(26 )]
# Remove duplicate characters from key
A = remove_duplicates(key.upper() )
A = len(UpperCamelCase__ )
# First fill cipher with key characters
A = {alphabet[i]: char for i, char in enumerate(UpperCamelCase__ )}
# Then map remaining characters in alphabet to
# the alphabet from the beginning
for i in range(len(UpperCamelCase__ ) , 26 ):
A = alphabet[i - offset]
# Ensure we are not mapping letters to letters previously mapped
while char in key:
offset -= 1
A = alphabet[i - offset]
A = char
return cipher_alphabet
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ ) -> str:
"""simple docstring"""
return "".join(cipher_map.get(UpperCamelCase__ , UpperCamelCase__ ) for ch in message.upper() )
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ ) -> str:
"""simple docstring"""
A = {v: k for k, v in cipher_map.items()}
return "".join(rev_cipher_map.get(UpperCamelCase__ , UpperCamelCase__ ) for ch in message.upper() )
def __snake_case ( ) -> None:
"""simple docstring"""
A = input('Enter message to encode or decode: ' ).strip()
A = input('Enter keyword: ' ).strip()
A = input('Encipher or decipher? E/D:' ).strip()[0].lower()
try:
A = {'e': encipher, 'd': decipher}[option]
except KeyError:
raise KeyError('invalid input option' )
A = create_cipher_map(UpperCamelCase__ )
print(func(UpperCamelCase__ , UpperCamelCase__ ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 690 |
"""simple docstring"""
import os
import sys
UpperCamelCase : Optional[int] = os.path.join(os.path.dirname(__file__), "src")
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
AutoModelForSequenceClassification,
AutoTokenizer,
add_start_docstrings,
)
UpperCamelCase : Dict = [
"torch",
"numpy",
"tokenizers",
"filelock",
"requests",
"tqdm",
"regex",
"sentencepiece",
"sacremoses",
"importlib_metadata",
"huggingface_hub",
]
@add_start_docstrings(AutoConfig.__doc__ )
def __snake_case ( *UpperCamelCase__ , **UpperCamelCase__ ) -> Dict:
"""simple docstring"""
return AutoConfig.from_pretrained(*UpperCamelCase__ , **UpperCamelCase__ )
@add_start_docstrings(AutoTokenizer.__doc__ )
def __snake_case ( *UpperCamelCase__ , **UpperCamelCase__ ) -> List[str]:
"""simple docstring"""
return AutoTokenizer.from_pretrained(*UpperCamelCase__ , **UpperCamelCase__ )
@add_start_docstrings(AutoModel.__doc__ )
def __snake_case ( *UpperCamelCase__ , **UpperCamelCase__ ) -> str:
"""simple docstring"""
return AutoModel.from_pretrained(*UpperCamelCase__ , **UpperCamelCase__ )
@add_start_docstrings(AutoModelForCausalLM.__doc__ )
def __snake_case ( *UpperCamelCase__ , **UpperCamelCase__ ) -> List[Any]:
"""simple docstring"""
return AutoModelForCausalLM.from_pretrained(*UpperCamelCase__ , **UpperCamelCase__ )
@add_start_docstrings(AutoModelForMaskedLM.__doc__ )
def __snake_case ( *UpperCamelCase__ , **UpperCamelCase__ ) -> Optional[Any]:
"""simple docstring"""
return AutoModelForMaskedLM.from_pretrained(*UpperCamelCase__ , **UpperCamelCase__ )
@add_start_docstrings(AutoModelForSequenceClassification.__doc__ )
def __snake_case ( *UpperCamelCase__ , **UpperCamelCase__ ) -> List[str]:
"""simple docstring"""
return AutoModelForSequenceClassification.from_pretrained(*UpperCamelCase__ , **UpperCamelCase__ )
@add_start_docstrings(AutoModelForQuestionAnswering.__doc__ )
def __snake_case ( *UpperCamelCase__ , **UpperCamelCase__ ) -> int:
"""simple docstring"""
return AutoModelForQuestionAnswering.from_pretrained(*UpperCamelCase__ , **UpperCamelCase__ )
| 690 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
UpperCamelCase : List[str] = {
"configuration_convnext": ["CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ConvNextConfig", "ConvNextOnnxConfig"]
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase : List[str] = ["ConvNextFeatureExtractor"]
UpperCamelCase : str = ["ConvNextImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase : Optional[Any] = [
"CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
"ConvNextForImageClassification",
"ConvNextModel",
"ConvNextPreTrainedModel",
"ConvNextBackbone",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase : Tuple = [
"TFConvNextForImageClassification",
"TFConvNextModel",
"TFConvNextPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_convnext import CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvNextConfig, ConvNextOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_convnext import ConvNextFeatureExtractor
from .image_processing_convnext import ConvNextImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_convnext import (
CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
ConvNextBackbone,
ConvNextForImageClassification,
ConvNextModel,
ConvNextPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_convnext import TFConvNextForImageClassification, TFConvNextModel, TFConvNextPreTrainedModel
else:
import sys
UpperCamelCase : Tuple = _LazyModule(__name__, globals()["__file__"], _import_structure)
| 690 |
"""simple docstring"""
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging
if is_torch_available():
import torch
UpperCamelCase : List[str] = logging.get_logger(__name__)
class lowerCamelCase__ ( UpperCAmelCase_ ):
lowerCAmelCase = ["""pixel_values"""]
def __init__( self : Tuple , _lowercase : bool = True , _lowercase : Optional[Dict[str, int]] = None , _lowercase : PILImageResampling = PILImageResampling.BILINEAR , _lowercase : bool = True , _lowercase : Dict[str, int] = None , _lowercase : bool = True , _lowercase : Union[int, float] = 1 / 255 , _lowercase : bool = True , _lowercase : Optional[Union[float, List[float]]] = None , _lowercase : Optional[Union[float, List[float]]] = None , **_lowercase : List[str] , ):
super().__init__(**_lowercase )
A = size if size is not None else {'shortest_edge': 256}
A = get_size_dict(_lowercase , default_to_square=_lowercase )
A = crop_size if crop_size is not None else {'height': 224, 'width': 224}
A = get_size_dict(_lowercase , param_name='crop_size' )
A = do_resize
A = size
A = resample
A = do_center_crop
A = crop_size
A = do_rescale
A = rescale_factor
A = do_normalize
A = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
A = image_std if image_std is not None else IMAGENET_STANDARD_STD
def __a ( self : Any , _lowercase : np.ndarray , _lowercase : Dict[str, int] , _lowercase : PILImageResampling = PILImageResampling.BICUBIC , _lowercase : Optional[Union[str, ChannelDimension]] = None , **_lowercase : Tuple , ):
A = get_size_dict(_lowercase , default_to_square=_lowercase )
if "shortest_edge" not in size:
raise ValueError(f'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}' )
A = get_resize_output_image_size(_lowercase , size=size['shortest_edge'] , default_to_square=_lowercase )
return resize(_lowercase , size=_lowercase , resample=_lowercase , data_format=_lowercase , **_lowercase )
def __a ( self : List[Any] , _lowercase : np.ndarray , _lowercase : Dict[str, int] , _lowercase : Optional[Union[str, ChannelDimension]] = None , **_lowercase : Optional[int] , ):
A = get_size_dict(_lowercase )
if "height" not in size or "width" not in size:
raise ValueError(f'The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}' )
return center_crop(_lowercase , size=(size['height'], size['width']) , data_format=_lowercase , **_lowercase )
def __a ( self : int , _lowercase : np.ndarray , _lowercase : float , _lowercase : Optional[Union[str, ChannelDimension]] = None , **_lowercase : Tuple ):
return rescale(_lowercase , scale=_lowercase , data_format=_lowercase , **_lowercase )
def __a ( self : int , _lowercase : np.ndarray , _lowercase : Union[float, List[float]] , _lowercase : Union[float, List[float]] , _lowercase : Optional[Union[str, ChannelDimension]] = None , **_lowercase : str , ):
return normalize(_lowercase , mean=_lowercase , std=_lowercase , data_format=_lowercase , **_lowercase )
def __a ( self : Any , _lowercase : ImageInput , _lowercase : Optional[bool] = None , _lowercase : Dict[str, int] = None , _lowercase : PILImageResampling = None , _lowercase : bool = None , _lowercase : Dict[str, int] = None , _lowercase : Optional[bool] = None , _lowercase : Optional[float] = None , _lowercase : Optional[bool] = None , _lowercase : Optional[Union[float, List[float]]] = None , _lowercase : Optional[Union[float, List[float]]] = None , _lowercase : Optional[Union[str, TensorType]] = None , _lowercase : Union[str, ChannelDimension] = ChannelDimension.FIRST , **_lowercase : Any , ):
A = do_resize if do_resize is not None else self.do_resize
A = size if size is not None else self.size
A = get_size_dict(_lowercase , default_to_square=_lowercase )
A = resample if resample is not None else self.resample
A = do_center_crop if do_center_crop is not None else self.do_center_crop
A = crop_size if crop_size is not None else self.crop_size
A = get_size_dict(_lowercase , param_name='crop_size' )
A = do_rescale if do_rescale is not None else self.do_rescale
A = rescale_factor if rescale_factor is not None else self.rescale_factor
A = do_normalize if do_normalize is not None else self.do_normalize
A = image_mean if image_mean is not None else self.image_mean
A = image_std if image_std is not None else self.image_std
A = make_list_of_images(_lowercase )
if not valid_images(_lowercase ):
raise ValueError(
'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
'torch.Tensor, tf.Tensor or jax.ndarray.' )
if do_resize and size is None:
raise ValueError('Size must be specified if do_resize is True.' )
if do_center_crop and crop_size is None:
raise ValueError('Crop size must be specified if do_center_crop is True.' )
if do_rescale and rescale_factor is None:
raise ValueError('Rescale factor must be specified if do_rescale is True.' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('Image mean and std must be specified if do_normalize is True.' )
# All transformations expect numpy arrays.
A = [to_numpy_array(_lowercase ) for image in images]
if do_resize:
A = [self.resize(image=_lowercase , size=_lowercase , resample=_lowercase ) for image in images]
if do_center_crop:
A = [self.center_crop(image=_lowercase , size=_lowercase ) for image in images]
if do_rescale:
A = [self.rescale(image=_lowercase , scale=_lowercase ) for image in images]
if do_normalize:
A = [self.normalize(image=_lowercase , mean=_lowercase , std=_lowercase ) for image in images]
A = [to_channel_dimension_format(_lowercase , _lowercase ) for image in images]
A = {'pixel_values': images}
return BatchFeature(data=_lowercase , tensor_type=_lowercase )
def __a ( self : int , _lowercase : List[str] , _lowercase : List[Tuple] = None ):
A = outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(_lowercase ) != len(_lowercase ):
raise ValueError(
'Make sure that you pass in as many target sizes as the batch dimension of the logits' )
if is_torch_tensor(_lowercase ):
A = target_sizes.numpy()
A = []
for idx in range(len(_lowercase ) ):
A = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) , size=target_sizes[idx] , mode='bilinear' , align_corners=_lowercase )
A = resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(_lowercase )
else:
A = logits.argmax(dim=1 )
A = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation
| 690 | 1 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Callable
UpperCamelCase : int = list[list[float | int]]
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ ) -> Matrix:
"""simple docstring"""
A = len(UpperCamelCase__ )
A = [[0 for _ in range(size + 1 )] for _ in range(UpperCamelCase__ )]
A = 42
A = 42
A = 42
A = 42
A = 42
A = 42
for row in range(UpperCamelCase__ ):
for col in range(UpperCamelCase__ ):
A = matrix[row][col]
A = vector[row][0]
A = 0
A = 0
while row < size and col < size:
# pivoting
A = max((abs(augmented[rowa][col] ), rowa) for rowa in range(UpperCamelCase__ , UpperCamelCase__ ) )[
1
]
if augmented[pivot_row][col] == 0:
col += 1
continue
else:
A , A = augmented[pivot_row], augmented[row]
for rowa in range(row + 1 , UpperCamelCase__ ):
A = augmented[rowa][col] / augmented[row][col]
A = 0
for cola in range(col + 1 , size + 1 ):
augmented[rowa][cola] -= augmented[row][cola] * ratio
row += 1
col += 1
# back substitution
for col in range(1 , UpperCamelCase__ ):
for row in range(UpperCamelCase__ ):
A = augmented[row][col] / augmented[col][col]
for cola in range(UpperCamelCase__ , size + 1 ):
augmented[row][cola] -= augmented[col][cola] * ratio
# round to get rid of numbers like 2.000000000000004
return [
[round(augmented[row][size] / augmented[row][row] , 10 )] for row in range(UpperCamelCase__ )
]
def __snake_case ( UpperCamelCase__ ) -> Callable[[int], int]:
"""simple docstring"""
A = len(UpperCamelCase__ )
A = [[0 for _ in range(UpperCamelCase__ )] for _ in range(UpperCamelCase__ )]
A = [[0] for _ in range(UpperCamelCase__ )]
A = 42
A = 42
A = 42
A = 42
for x_val, y_val in enumerate(UpperCamelCase__ ):
for col in range(UpperCamelCase__ ):
A = (x_val + 1) ** (size - col - 1)
A = y_val
A = solve(UpperCamelCase__ , UpperCamelCase__ )
def interpolated_func(UpperCamelCase__ ) -> int:
return sum(
round(coeffs[x_val][0] ) * (var ** (size - x_val - 1))
for x_val in range(UpperCamelCase__ ) )
return interpolated_func
def __snake_case ( UpperCamelCase__ ) -> int:
"""simple docstring"""
return (
1
- variable
+ variable**2
- variable**3
+ variable**4
- variable**5
+ variable**6
- variable**7
+ variable**8
- variable**9
+ variable**10
)
def __snake_case ( UpperCamelCase__ = question_function , UpperCamelCase__ = 10 ) -> int:
"""simple docstring"""
A = [func(UpperCamelCase__ ) for x_val in range(1 , order + 1 )]
A = [
interpolate(data_points[:max_coeff] ) for max_coeff in range(1 , order + 1 )
]
A = 0
A = 42
A = 42
for poly in polynomials:
A = 1
while func(UpperCamelCase__ ) == poly(UpperCamelCase__ ):
x_val += 1
ret += poly(UpperCamelCase__ )
return ret
if __name__ == "__main__":
print(F"""{solution() = }""")
| 690 |
"""simple docstring"""
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def __snake_case ( UpperCamelCase__ = "laptop" ) -> DataFrame:
"""simple docstring"""
A = f'https://www.amazon.in/laptop/s?k={product}'
A = {
'User-Agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36\n (KHTML, like Gecko)Chrome/44.0.2403.157 Safari/537.36',
'Accept-Language': 'en-US, en;q=0.5',
}
A = BeautifulSoup(requests.get(UpperCamelCase__ , headers=UpperCamelCase__ ).text )
# Initialize a Pandas dataframe with the column titles
A = DataFrame(
columns=[
'Product Title',
'Product Link',
'Current Price of the product',
'Product Rating',
'MRP of the product',
'Discount',
] )
# Loop through each entry and store them in the dataframe
for item, _ in zip_longest(
soup.find_all(
'div' , attrs={'class': 's-result-item', 'data-component-type': 's-search-result'} , ) , soup.find_all('div' , attrs={'class': 'a-row a-size-base a-color-base'} ) , ):
try:
A = item.ha.text
A = 'https://www.amazon.in/' + item.ha.a['href']
A = item.find('span' , attrs={'class': 'a-offscreen'} ).text
try:
A = item.find('span' , attrs={'class': 'a-icon-alt'} ).text
except AttributeError:
A = 'Not available'
try:
A = (
'₹'
+ item.find(
'span' , attrs={'class': 'a-price a-text-price'} ).text.split('₹' )[1]
)
except AttributeError:
A = ''
try:
A = float(
(
(
float(product_mrp.strip('₹' ).replace(',' , '' ) )
- float(product_price.strip('₹' ).replace(',' , '' ) )
)
/ float(product_mrp.strip('₹' ).replace(',' , '' ) )
)
* 100 )
except ValueError:
A = float('nan' )
except AttributeError:
pass
A = [
product_title,
product_link,
product_price,
product_rating,
product_mrp,
discount,
]
A = ' '
A = ' '
data_frame.index += 1
return data_frame
if __name__ == "__main__":
UpperCamelCase : Any = "headphones"
get_amazon_product_data(product).to_csv(F"""Amazon Product Data for {product}.csv""")
| 690 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCamelCase : Dict = {
"configuration_nezha": ["NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP", "NezhaConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase : str = [
"NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST",
"NezhaForNextSentencePrediction",
"NezhaForMaskedLM",
"NezhaForPreTraining",
"NezhaForMultipleChoice",
"NezhaForQuestionAnswering",
"NezhaForSequenceClassification",
"NezhaForTokenClassification",
"NezhaModel",
"NezhaPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_nezha import (
NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST,
NezhaForMaskedLM,
NezhaForMultipleChoice,
NezhaForNextSentencePrediction,
NezhaForPreTraining,
NezhaForQuestionAnswering,
NezhaForSequenceClassification,
NezhaForTokenClassification,
NezhaModel,
NezhaPreTrainedModel,
)
else:
import sys
UpperCamelCase : str = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 690 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import DonutImageProcessor
class lowerCamelCase__ ( unittest.TestCase ):
def __init__( self : List[str] , _lowercase : Optional[Any] , _lowercase : int=7 , _lowercase : List[str]=3 , _lowercase : Tuple=18 , _lowercase : Dict=30 , _lowercase : Any=400 , _lowercase : int=True , _lowercase : List[Any]=None , _lowercase : Tuple=True , _lowercase : List[Any]=False , _lowercase : str=True , _lowercase : List[str]=True , _lowercase : int=[0.5, 0.5, 0.5] , _lowercase : Optional[int]=[0.5, 0.5, 0.5] , ):
A = parent
A = batch_size
A = num_channels
A = image_size
A = min_resolution
A = max_resolution
A = do_resize
A = size if size is not None else {'height': 18, 'width': 20}
A = do_thumbnail
A = do_align_axis
A = do_pad
A = do_normalize
A = image_mean
A = image_std
def __a ( self : Any ):
return {
"do_resize": self.do_resize,
"size": self.size,
"do_thumbnail": self.do_thumbnail,
"do_align_long_axis": self.do_align_axis,
"do_pad": self.do_pad,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
}
@require_torch
@require_vision
class lowerCamelCase__ ( UpperCAmelCase_ , unittest.TestCase ):
lowerCAmelCase = DonutImageProcessor if is_vision_available() else None
def __a ( self : List[str] ):
A = DonutImageProcessingTester(self )
@property
def __a ( self : int ):
return self.image_processor_tester.prepare_image_processor_dict()
def __a ( self : Union[str, Any] ):
A = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(_lowercase , 'do_resize' ) )
self.assertTrue(hasattr(_lowercase , 'size' ) )
self.assertTrue(hasattr(_lowercase , 'do_thumbnail' ) )
self.assertTrue(hasattr(_lowercase , 'do_align_long_axis' ) )
self.assertTrue(hasattr(_lowercase , 'do_pad' ) )
self.assertTrue(hasattr(_lowercase , 'do_normalize' ) )
self.assertTrue(hasattr(_lowercase , 'image_mean' ) )
self.assertTrue(hasattr(_lowercase , 'image_std' ) )
def __a ( self : int ):
A = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'height': 18, 'width': 20} )
A = self.image_processing_class.from_dict(self.image_processor_dict , size=42 )
self.assertEqual(image_processor.size , {'height': 42, 'width': 42} )
# Previous config had dimensions in (width, height) order
A = self.image_processing_class.from_dict(self.image_processor_dict , size=(42, 84) )
self.assertEqual(image_processor.size , {'height': 84, 'width': 42} )
def __a ( self : Any ):
pass
@is_flaky()
def __a ( self : int ):
# Initialize image_processing
A = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
A = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowercase )
for image in image_inputs:
self.assertIsInstance(_lowercase , Image.Image )
# Test not batched input
A = 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 = image_processing(_lowercase , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
) , )
@is_flaky()
def __a ( self : List[str] ):
# Initialize image_processing
A = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
A = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowercase , numpify=_lowercase )
for image in image_inputs:
self.assertIsInstance(_lowercase , np.ndarray )
# Test not batched input
A = 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 = image_processing(_lowercase , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
) , )
@is_flaky()
def __a ( self : List[Any] ):
# Initialize image_processing
A = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
A = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowercase , torchify=_lowercase )
for image in image_inputs:
self.assertIsInstance(_lowercase , torch.Tensor )
# Test not batched input
A = 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 = image_processing(_lowercase , 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'],
) , )
| 690 | 1 |
"""simple docstring"""
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER
sys.path.append(str(Path(__file__).parent.parent.parent.parent / "utils"))
from test_module.custom_configuration import CustomConfig # noqa E402
from test_module.custom_image_processing import CustomImageProcessor # noqa E402
class lowerCamelCase__ ( unittest.TestCase ):
def __a ( self : List[str] ):
A = 0
def __a ( self : int ):
A = AutoImageProcessor.from_pretrained('openai/clip-vit-base-patch32' )
self.assertIsInstance(_lowercase , _lowercase )
def __a ( self : str ):
with tempfile.TemporaryDirectory() as tmpdirname:
A = Path(_lowercase ) / 'preprocessor_config.json'
A = Path(_lowercase ) / 'config.json'
json.dump(
{'image_processor_type': 'CLIPImageProcessor', 'processor_class': 'CLIPProcessor'} , open(_lowercase , 'w' ) , )
json.dump({'model_type': 'clip'} , open(_lowercase , 'w' ) )
A = AutoImageProcessor.from_pretrained(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
def __a ( self : Optional[Any] ):
# Ensure we can load the image processor from the feature extractor config
with tempfile.TemporaryDirectory() as tmpdirname:
A = Path(_lowercase ) / 'preprocessor_config.json'
A = Path(_lowercase ) / 'config.json'
json.dump(
{'feature_extractor_type': 'CLIPFeatureExtractor', 'processor_class': 'CLIPProcessor'} , open(_lowercase , 'w' ) , )
json.dump({'model_type': 'clip'} , open(_lowercase , 'w' ) )
A = AutoImageProcessor.from_pretrained(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
def __a ( self : Dict ):
with tempfile.TemporaryDirectory() as tmpdirname:
A = CLIPConfig()
# Create a dummy config file with image_proceesor_type
A = Path(_lowercase ) / 'preprocessor_config.json'
A = Path(_lowercase ) / 'config.json'
json.dump(
{'image_processor_type': 'CLIPImageProcessor', 'processor_class': 'CLIPProcessor'} , open(_lowercase , 'w' ) , )
json.dump({'model_type': 'clip'} , open(_lowercase , 'w' ) )
# remove image_processor_type to make sure config.json alone is enough to load image processor locally
A = AutoImageProcessor.from_pretrained(_lowercase ).to_dict()
config_dict.pop('image_processor_type' )
A = CLIPImageProcessor(**_lowercase )
# save in new folder
model_config.save_pretrained(_lowercase )
config.save_pretrained(_lowercase )
A = AutoImageProcessor.from_pretrained(_lowercase )
# 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(_lowercase , _lowercase )
def __a ( self : Optional[int] ):
with tempfile.TemporaryDirectory() as tmpdirname:
A = Path(_lowercase ) / 'preprocessor_config.json'
json.dump(
{'image_processor_type': 'CLIPImageProcessor', 'processor_class': 'CLIPProcessor'} , open(_lowercase , 'w' ) , )
A = AutoImageProcessor.from_pretrained(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
def __a ( self : str ):
with self.assertRaisesRegex(
_lowercase , 'clip-base is not a local folder and is not a valid model identifier' ):
A = AutoImageProcessor.from_pretrained('clip-base' )
def __a ( self : Union[str, Any] ):
with self.assertRaisesRegex(
_lowercase , R'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)' ):
A = AutoImageProcessor.from_pretrained(_lowercase , revision='aaaaaa' )
def __a ( self : Any ):
with self.assertRaisesRegex(
_lowercase , 'hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.' , ):
A = AutoImageProcessor.from_pretrained('hf-internal-testing/config-no-model' )
def __a ( self : List[Any] ):
# If remote code is not set, we will time out when asking whether to load the model.
with self.assertRaises(_lowercase ):
A = AutoImageProcessor.from_pretrained('hf-internal-testing/test_dynamic_image_processor' )
# If remote code is disabled, we can't load this config.
with self.assertRaises(_lowercase ):
A = AutoImageProcessor.from_pretrained(
'hf-internal-testing/test_dynamic_image_processor' , trust_remote_code=_lowercase )
A = AutoImageProcessor.from_pretrained(
'hf-internal-testing/test_dynamic_image_processor' , trust_remote_code=_lowercase )
self.assertEqual(image_processor.__class__.__name__ , 'NewImageProcessor' )
# Test image processor can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(_lowercase )
A = AutoImageProcessor.from_pretrained(_lowercase , trust_remote_code=_lowercase )
self.assertEqual(reloaded_image_processor.__class__.__name__ , 'NewImageProcessor' )
def __a ( self : Optional[Any] ):
try:
AutoConfig.register('custom' , _lowercase )
AutoImageProcessor.register(_lowercase , _lowercase )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(_lowercase ):
AutoImageProcessor.register(_lowercase , _lowercase )
with tempfile.TemporaryDirectory() as tmpdirname:
A = Path(_lowercase ) / 'preprocessor_config.json'
A = Path(_lowercase ) / 'config.json'
json.dump(
{'feature_extractor_type': 'CLIPFeatureExtractor', 'processor_class': 'CLIPProcessor'} , open(_lowercase , 'w' ) , )
json.dump({'model_type': 'clip'} , open(_lowercase , 'w' ) )
A = CustomImageProcessor.from_pretrained(_lowercase )
# Now that the config is registered, it can be used as any other config with the auto-API
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(_lowercase )
A = AutoImageProcessor.from_pretrained(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
def __a ( self : Union[str, Any] ):
class lowerCamelCase__ ( UpperCAmelCase_ ):
lowerCAmelCase = True
try:
AutoConfig.register('custom' , _lowercase )
AutoImageProcessor.register(_lowercase , _lowercase )
# If remote code is not set, the default is to use local
A = AutoImageProcessor.from_pretrained('hf-internal-testing/test_dynamic_image_processor' )
self.assertEqual(image_processor.__class__.__name__ , 'NewImageProcessor' )
self.assertTrue(image_processor.is_local )
# If remote code is disabled, we load the local one.
A = AutoImageProcessor.from_pretrained(
'hf-internal-testing/test_dynamic_image_processor' , trust_remote_code=_lowercase )
self.assertEqual(image_processor.__class__.__name__ , 'NewImageProcessor' )
self.assertTrue(image_processor.is_local )
# If remote is enabled, we load from the Hub
A = AutoImageProcessor.from_pretrained(
'hf-internal-testing/test_dynamic_image_processor' , trust_remote_code=_lowercase )
self.assertEqual(image_processor.__class__.__name__ , 'NewImageProcessor' )
self.assertTrue(not hasattr(_lowercase , 'is_local' ) )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
| 690 |
"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class lowerCamelCase__ :
def __init__( self : Optional[Any] , _lowercase : int=2 , _lowercase : Optional[Any]=3 , _lowercase : Any=64 , _lowercase : Tuple=None ):
A = np.random.default_rng(_lowercase )
A = length
A = rng.normal(size=(length,) ).astype(np.floataa )
A = a * self.x + b + rng.normal(scale=0.1 , size=(length,) ).astype(np.floataa )
def __len__( self : str ):
return self.length
def __getitem__( self : List[str] , _lowercase : int ):
return {"x": self.x[i], "y": self.y[i]}
class lowerCamelCase__ ( torch.nn.Module ):
def __init__( self : Optional[int] , _lowercase : Any=0 , _lowercase : List[Any]=0 , _lowercase : Optional[int]=False ):
super().__init__()
A = torch.nn.Parameter(torch.tensor([2, 3] ).float() )
A = torch.nn.Parameter(torch.tensor([2, 3] ).float() )
A = True
def __a ( self : Optional[Any] , _lowercase : str=None ):
if self.first_batch:
print(f'Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}' )
A = False
return x * self.a[0] + self.b[0]
class lowerCamelCase__ ( torch.nn.Module ):
def __init__( self : Optional[Any] , _lowercase : Any=0 , _lowercase : List[str]=0 , _lowercase : str=False ):
super().__init__()
A = torch.nn.Parameter(torch.tensor(_lowercase ).float() )
A = torch.nn.Parameter(torch.tensor(_lowercase ).float() )
A = True
def __a ( self : int , _lowercase : Tuple=None ):
if self.first_batch:
print(f'Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}' )
A = False
return x * self.a + self.b
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ = 16 ) -> Optional[Any]:
"""simple docstring"""
from datasets import load_dataset
from transformers import AutoTokenizer
A = AutoTokenizer.from_pretrained('bert-base-cased' )
A = {'train': 'tests/test_samples/MRPC/train.csv', 'validation': 'tests/test_samples/MRPC/dev.csv'}
A = load_dataset('csv' , data_files=UpperCamelCase__ )
A = datasets['train'].unique('label' )
A = {v: i for i, v in enumerate(UpperCamelCase__ )}
def tokenize_function(UpperCamelCase__ ):
# max_length=None => use the model max length (it's actually the default)
A = tokenizer(
examples['sentence1'] , examples['sentence2'] , truncation=UpperCamelCase__ , max_length=UpperCamelCase__ , padding='max_length' )
if "label" in examples:
A = [label_to_id[l] for l in examples['label']]
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
A = datasets.map(
UpperCamelCase__ , batched=UpperCamelCase__ , remove_columns=['sentence1', 'sentence2', 'label'] , )
def collate_fn(UpperCamelCase__ ):
# On TPU it's best to pad everything to the same length or training will be very slow.
if accelerator.distributed_type == DistributedType.TPU:
return tokenizer.pad(UpperCamelCase__ , padding='max_length' , max_length=128 , return_tensors='pt' )
return tokenizer.pad(UpperCamelCase__ , padding='longest' , return_tensors='pt' )
# Instantiate dataloaders.
A = DataLoader(tokenized_datasets['train'] , shuffle=UpperCamelCase__ , collate_fn=UpperCamelCase__ , batch_size=2 )
A = DataLoader(tokenized_datasets['validation'] , shuffle=UpperCamelCase__ , collate_fn=UpperCamelCase__ , batch_size=1 )
return train_dataloader, eval_dataloader
| 690 | 1 |
"""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 __snake_case ( UpperCamelCase__ ) -> Tuple:
"""simple docstring"""
if is_torch_version('<' , '2.0.0' ) or not hasattr(UpperCamelCase__ , '_dynamo' ):
return False
return isinstance(UpperCamelCase__ , torch._dynamo.eval_frame.OptimizedModule )
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ = True ) -> str:
"""simple docstring"""
A = (torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)
A = is_compiled_module(UpperCamelCase__ )
if is_compiled:
A = model
A = model._orig_mod
if is_deepspeed_available():
options += (DeepSpeedEngine,)
while isinstance(UpperCamelCase__ , UpperCamelCase__ ):
A = model.module
if not keep_fpaa_wrapper:
A = getattr(UpperCamelCase__ , 'forward' )
A = model.__dict__.pop('_original_forward' , UpperCamelCase__ )
if original_forward is not None:
while hasattr(UpperCamelCase__ , '__wrapped__' ):
A = forward.__wrapped__
if forward == original_forward:
break
A = forward
if getattr(UpperCamelCase__ , '_converted_to_transformer_engine' , UpperCamelCase__ ):
convert_model(UpperCamelCase__ , to_transformer_engine=UpperCamelCase__ )
if is_compiled:
A = model
A = compiled_model
return model
def __snake_case ( ) -> int:
"""simple docstring"""
PartialState().wait_for_everyone()
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ ) -> Optional[int]:
"""simple docstring"""
if PartialState().distributed_type == DistributedType.TPU:
xm.save(UpperCamelCase__ , UpperCamelCase__ )
elif PartialState().local_process_index == 0:
torch.save(UpperCamelCase__ , UpperCamelCase__ )
@contextmanager
def __snake_case ( **UpperCamelCase__ ) -> Union[str, Any]:
"""simple docstring"""
for key, value in kwargs.items():
A = str(UpperCamelCase__ )
yield
for key in kwargs:
if key.upper() in os.environ:
del os.environ[key.upper()]
def __snake_case ( UpperCamelCase__ ) -> Tuple:
"""simple docstring"""
if not hasattr(UpperCamelCase__ , '__qualname__' ) and not hasattr(UpperCamelCase__ , '__name__' ):
A = getattr(UpperCamelCase__ , '__class__' , UpperCamelCase__ )
if hasattr(UpperCamelCase__ , '__qualname__' ):
return obj.__qualname__
if hasattr(UpperCamelCase__ , '__name__' ):
return obj.__name__
return str(UpperCamelCase__ )
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ ) -> Tuple:
"""simple docstring"""
for key, value in source.items():
if isinstance(UpperCamelCase__ , UpperCamelCase__ ):
A = destination.setdefault(UpperCamelCase__ , {} )
merge_dicts(UpperCamelCase__ , UpperCamelCase__ )
else:
A = value
return destination
def __snake_case ( UpperCamelCase__ = None ) -> bool:
"""simple docstring"""
if port is None:
A = 29500
with socket.socket(socket.AF_INET , socket.SOCK_STREAM ) as s:
return s.connect_ex(('localhost', port) ) == 0
| 690 |
"""simple docstring"""
from __future__ import annotations
def __snake_case ( UpperCamelCase__ ) -> list[int]: # This function is recursive
"""simple docstring"""
A = len(UpperCamelCase__ )
# If the array contains only one element, we return it (it's the stop condition of
# recursion)
if array_length <= 1:
return array
# Else
A = array[0]
A = False
A = 1
A = []
while not is_found and i < array_length:
if array[i] < pivot:
A = True
A = [element for element in array[i:] if element >= array[i]]
A = longest_subsequence(UpperCamelCase__ )
if len(UpperCamelCase__ ) > len(UpperCamelCase__ ):
A = temp_array
else:
i += 1
A = [element for element in array[1:] if element >= pivot]
A = [pivot, *longest_subsequence(UpperCamelCase__ )]
if len(UpperCamelCase__ ) > len(UpperCamelCase__ ):
return temp_array
else:
return longest_subseq
if __name__ == "__main__":
import doctest
doctest.testmod()
| 690 | 1 |
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
UpperCamelCase : List[str] = logging.get_logger(__name__)
def __snake_case ( UpperCamelCase__ ) -> List[int]:
"""simple docstring"""
if isinstance(UpperCamelCase__ , np.ndarray ):
return list(tensor.shape )
A = tf.shape(UpperCamelCase__ )
if tensor.shape == tf.TensorShape(UpperCamelCase__ ):
return dynamic
A = tensor.shape.as_list()
return [dynamic[i] if s is None else s for i, s in enumerate(UpperCamelCase__ )]
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ = None , UpperCamelCase__ = None ) -> tf.Tensor:
"""simple docstring"""
return tf.nn.softmax(logits=logits + 1E-9 , axis=UpperCamelCase__ , name=UpperCamelCase__ )
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__=1E-5 , UpperCamelCase__=-1 ) -> int:
"""simple docstring"""
if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(UpperCamelCase__ , UpperCamelCase__ ):
raise NotImplementedError('Only 1D weight and bias tensors are supported for now, with only a single axis.' )
# Get mean and variance on the axis to be normalized
A , A = tf.nn.moments(UpperCamelCase__ , axes=[axis] , keepdims=UpperCamelCase__ )
if axis != -1:
# Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
# on every dimension except axis
A = [1] * inputs.shape.rank
A = shape_list(UpperCamelCase__ )[axis]
A = tf.reshape(UpperCamelCase__ , UpperCamelCase__ )
A = tf.reshape(UpperCamelCase__ , UpperCamelCase__ )
# Compute layer normalization using the batch_normalization
# function.
A = tf.nn.batch_normalization(
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , offset=UpperCamelCase__ , scale=UpperCamelCase__ , variance_epsilon=UpperCamelCase__ , )
return outputs
def __snake_case ( UpperCamelCase__ , UpperCamelCase__=0 , UpperCamelCase__=-1 ) -> List[Any]:
"""simple docstring"""
if end_dim < 0:
end_dim += input.shape.rank
if start_dim < 0:
start_dim += input.shape.rank
if start_dim == end_dim:
return input
A = tf.shape(UpperCamelCase__ )
A = tf.math.reduce_prod(in_shape[start_dim : end_dim + 1] )
A = tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]] , axis=0 )
return tf.reshape(UpperCamelCase__ , UpperCamelCase__ )
def __snake_case ( UpperCamelCase__ ) -> tf.Tensor:
"""simple docstring"""
if not isinstance(UpperCamelCase__ , tf.Tensor ):
A = tf.convert_to_tensor(UpperCamelCase__ ) # Catches stray NumPy inputs
if encoder_attention_mask.shape.rank == 3:
A = encoder_attention_mask[:, None, :, :]
if encoder_attention_mask.shape.rank == 2:
A = encoder_attention_mask[:, None, None, :]
# T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
# Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
# /transformer/transformer_layers.py#L270
# encoder_extended_attention_mask = (encoder_extended_attention_mask ==
# encoder_extended_attention_mask.transpose(-1, -2))
A = (
tf.cast(1 , encoder_attention_mask.dtype ) - encoder_extended_attention_mask
) * encoder_extended_attention_mask.dtype.min
return encoder_extended_attention_mask
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = "input_ids" ) -> None:
"""simple docstring"""
tf.debugging.assert_less(
UpperCamelCase__ , tf.cast(UpperCamelCase__ , dtype=tensor.dtype ) , message=(
f'The maximum value of {tensor_name} ({tf.math.reduce_max(UpperCamelCase__ )}) must be smaller than the embedding '
f'layer\'s input dimension ({embed_dim}). The likely cause is some problem at tokenization time.'
) , )
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> Dict:
"""simple docstring"""
A = 64512
# Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
# because in that case even chunking the array would not make the saving
# possible.
A = [x for x in data if len(UpperCamelCase__ ) > HDF5_OBJECT_HEADER_LIMIT]
# Expecting this to never be true.
if bad_attributes:
raise RuntimeError(
'The following attributes cannot be saved to HDF5 file because '
f'they are larger than {HDF5_OBJECT_HEADER_LIMIT} '
f'bytes: {bad_attributes}' )
A = np.asarray(UpperCamelCase__ )
A = 1
A = np.array_split(UpperCamelCase__ , UpperCamelCase__ )
# This will never loop forever thanks to the test above.
while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data ):
num_chunks += 1
A = np.array_split(UpperCamelCase__ , UpperCamelCase__ )
if num_chunks > 1:
for chunk_id, chunk_data in enumerate(UpperCamelCase__ ):
A = chunk_data
else:
A = data
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ ) -> int:
"""simple docstring"""
if name in group.attrs:
A = [n.decode('utf8' ) if hasattr(UpperCamelCase__ , 'decode' ) else n for n in group.attrs[name]]
else:
A = []
A = 0
while "%s%d" % (name, chunk_id) in group.attrs:
data.extend(
[n.decode('utf8' ) if hasattr(UpperCamelCase__ , 'decode' ) else n for n in group.attrs['%s%d' % (name, chunk_id)]] )
chunk_id += 1
return data
def __snake_case ( UpperCamelCase__ ) -> int:
"""simple docstring"""
def _expand_single_ad_tensor(UpperCamelCase__ ):
if isinstance(UpperCamelCase__ , tf.Tensor ) and t.shape.rank == 1:
return tf.expand_dims(UpperCamelCase__ , axis=-1 )
return t
return tf.nest.map_structure(_expand_single_ad_tensor , UpperCamelCase__ )
| 690 |
"""simple docstring"""
from __future__ import annotations
import typing
from collections.abc import Iterable
import numpy as np
UpperCamelCase : Tuple = typing.Union[Iterable[float], Iterable[int], np.ndarray] # noqa: UP007
UpperCamelCase : Optional[int] = typing.Union[np.floataa, int, float] # noqa: UP007
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ ) -> VectorOut:
"""simple docstring"""
return np.sqrt(np.sum((np.asarray(UpperCamelCase__ ) - np.asarray(UpperCamelCase__ )) ** 2 ) )
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ ) -> VectorOut:
"""simple docstring"""
return sum((va - va) ** 2 for va, va in zip(UpperCamelCase__ , UpperCamelCase__ ) ) ** (1 / 2)
if __name__ == "__main__":
def __snake_case ( ) -> None:
"""simple docstring"""
from timeit import timeit
print('Without Numpy' )
print(
timeit(
'euclidean_distance_no_np([1, 2, 3], [4, 5, 6])' , number=10000 , globals=globals() , ) )
print('With Numpy' )
print(
timeit(
'euclidean_distance([1, 2, 3], [4, 5, 6])' , number=10000 , globals=globals() , ) )
benchmark()
| 690 | 1 |
"""simple docstring"""
import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel
from ...models.attention import BasicTransformerBlock
from ...utils import logging
UpperCamelCase : Tuple = logging.get_logger(__name__) # pylint: disable=invalid-name
class lowerCamelCase__ ( UpperCAmelCase_ ):
def __init__( self : List[str] , _lowercase : List[Any] , _lowercase : Optional[Any]=768 ):
super().__init__(_lowercase )
A = proj_size
A = CLIPVisionModel(_lowercase )
A = PaintByExampleMapper(_lowercase )
A = nn.LayerNorm(config.hidden_size )
A = nn.Linear(config.hidden_size , self.proj_size )
# uncondition for scaling
A = nn.Parameter(torch.randn((1, 1, self.proj_size) ) )
def __a ( self : List[str] , _lowercase : Any , _lowercase : Any=False ):
A = self.model(pixel_values=_lowercase )
A = clip_output.pooler_output
A = self.mapper(latent_states[:, None] )
A = self.final_layer_norm(_lowercase )
A = self.proj_out(_lowercase )
if return_uncond_vector:
return latent_states, self.uncond_vector
return latent_states
class lowerCamelCase__ ( nn.Module ):
def __init__( self : Dict , _lowercase : str ):
super().__init__()
A = (config.num_hidden_layers + 1) // 5
A = config.hidden_size
A = 1
A = nn.ModuleList(
[
BasicTransformerBlock(_lowercase , _lowercase , _lowercase , activation_fn='gelu' , attention_bias=_lowercase )
for _ in range(_lowercase )
] )
def __a ( self : str , _lowercase : Dict ):
for block in self.blocks:
A = block(_lowercase )
return hidden_states
| 690 |
"""simple docstring"""
import argparse
import os
import shutil
from pathlib import Path
import onnx
import torch
from packaging import version
from torch.onnx import export
from diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline, StableDiffusionPipeline
UpperCamelCase : List[Any] = version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__=False , ) -> Any:
"""simple docstring"""
output_path.parent.mkdir(parents=UpperCamelCase__ , exist_ok=UpperCamelCase__ )
# PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
# so we check the torch version for backwards compatibility
if is_torch_less_than_1_11:
export(
UpperCamelCase__ , UpperCamelCase__ , f=output_path.as_posix() , input_names=UpperCamelCase__ , output_names=UpperCamelCase__ , dynamic_axes=UpperCamelCase__ , do_constant_folding=UpperCamelCase__ , use_external_data_format=UpperCamelCase__ , enable_onnx_checker=UpperCamelCase__ , opset_version=UpperCamelCase__ , )
else:
export(
UpperCamelCase__ , UpperCamelCase__ , f=output_path.as_posix() , input_names=UpperCamelCase__ , output_names=UpperCamelCase__ , dynamic_axes=UpperCamelCase__ , do_constant_folding=UpperCamelCase__ , opset_version=UpperCamelCase__ , )
@torch.no_grad()
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = False ) -> str:
"""simple docstring"""
A = torch.floataa if fpaa else torch.floataa
if fpaa and torch.cuda.is_available():
A = 'cuda'
elif fpaa and not torch.cuda.is_available():
raise ValueError('`float16` model export is only supported on GPUs with CUDA' )
else:
A = 'cpu'
A = StableDiffusionPipeline.from_pretrained(UpperCamelCase__ , torch_dtype=UpperCamelCase__ ).to(UpperCamelCase__ )
A = Path(UpperCamelCase__ )
# TEXT ENCODER
A = pipeline.text_encoder.config.max_position_embeddings
A = pipeline.text_encoder.config.hidden_size
A = pipeline.tokenizer(
'A sample prompt' , padding='max_length' , max_length=pipeline.tokenizer.model_max_length , truncation=UpperCamelCase__ , return_tensors='pt' , )
onnx_export(
pipeline.text_encoder , model_args=(text_input.input_ids.to(device=UpperCamelCase__ , dtype=torch.intaa )) , output_path=output_path / 'text_encoder' / 'model.onnx' , ordered_input_names=['input_ids'] , output_names=['last_hidden_state', 'pooler_output'] , dynamic_axes={
'input_ids': {0: 'batch', 1: 'sequence'},
} , opset=UpperCamelCase__ , )
del pipeline.text_encoder
# UNET
A = pipeline.unet.config.in_channels
A = pipeline.unet.config.sample_size
A = output_path / 'unet' / 'model.onnx'
onnx_export(
pipeline.unet , model_args=(
torch.randn(2 , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ).to(device=UpperCamelCase__ , dtype=UpperCamelCase__ ),
torch.randn(2 ).to(device=UpperCamelCase__ , dtype=UpperCamelCase__ ),
torch.randn(2 , UpperCamelCase__ , UpperCamelCase__ ).to(device=UpperCamelCase__ , dtype=UpperCamelCase__ ),
False,
) , output_path=UpperCamelCase__ , ordered_input_names=['sample', 'timestep', 'encoder_hidden_states', 'return_dict'] , output_names=['out_sample'] , dynamic_axes={
'sample': {0: 'batch', 1: 'channels', 2: 'height', 3: 'width'},
'timestep': {0: 'batch'},
'encoder_hidden_states': {0: 'batch', 1: 'sequence'},
} , opset=UpperCamelCase__ , use_external_data_format=UpperCamelCase__ , )
A = str(unet_path.absolute().as_posix() )
A = os.path.dirname(UpperCamelCase__ )
A = onnx.load(UpperCamelCase__ )
# clean up existing tensor files
shutil.rmtree(UpperCamelCase__ )
os.mkdir(UpperCamelCase__ )
# collate external tensor files into one
onnx.save_model(
UpperCamelCase__ , UpperCamelCase__ , save_as_external_data=UpperCamelCase__ , all_tensors_to_one_file=UpperCamelCase__ , location='weights.pb' , convert_attribute=UpperCamelCase__ , )
del pipeline.unet
# VAE ENCODER
A = pipeline.vae
A = vae_encoder.config.in_channels
A = vae_encoder.config.sample_size
# need to get the raw tensor output (sample) from the encoder
A = lambda UpperCamelCase__ , UpperCamelCase__ : vae_encoder.encode(UpperCamelCase__ , UpperCamelCase__ )[0].sample()
onnx_export(
UpperCamelCase__ , model_args=(
torch.randn(1 , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ).to(device=UpperCamelCase__ , dtype=UpperCamelCase__ ),
False,
) , output_path=output_path / 'vae_encoder' / 'model.onnx' , ordered_input_names=['sample', 'return_dict'] , output_names=['latent_sample'] , dynamic_axes={
'sample': {0: 'batch', 1: 'channels', 2: 'height', 3: 'width'},
} , opset=UpperCamelCase__ , )
# VAE DECODER
A = pipeline.vae
A = vae_decoder.config.latent_channels
A = vae_decoder.config.out_channels
# forward only through the decoder part
A = vae_encoder.decode
onnx_export(
UpperCamelCase__ , model_args=(
torch.randn(1 , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ).to(device=UpperCamelCase__ , dtype=UpperCamelCase__ ),
False,
) , output_path=output_path / 'vae_decoder' / 'model.onnx' , ordered_input_names=['latent_sample', 'return_dict'] , output_names=['sample'] , dynamic_axes={
'latent_sample': {0: 'batch', 1: 'channels', 2: 'height', 3: 'width'},
} , opset=UpperCamelCase__ , )
del pipeline.vae
# SAFETY CHECKER
if pipeline.safety_checker is not None:
A = pipeline.safety_checker
A = safety_checker.config.vision_config.num_channels
A = safety_checker.config.vision_config.image_size
A = safety_checker.forward_onnx
onnx_export(
pipeline.safety_checker , model_args=(
torch.randn(
1 , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , ).to(device=UpperCamelCase__ , dtype=UpperCamelCase__ ),
torch.randn(1 , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ).to(device=UpperCamelCase__ , dtype=UpperCamelCase__ ),
) , output_path=output_path / 'safety_checker' / 'model.onnx' , ordered_input_names=['clip_input', 'images'] , output_names=['out_images', 'has_nsfw_concepts'] , dynamic_axes={
'clip_input': {0: 'batch', 1: 'channels', 2: 'height', 3: 'width'},
'images': {0: 'batch', 1: 'height', 2: 'width', 3: 'channels'},
} , opset=UpperCamelCase__ , )
del pipeline.safety_checker
A = OnnxRuntimeModel.from_pretrained(output_path / 'safety_checker' )
A = pipeline.feature_extractor
else:
A = None
A = None
A = OnnxStableDiffusionPipeline(
vae_encoder=OnnxRuntimeModel.from_pretrained(output_path / 'vae_encoder' ) , vae_decoder=OnnxRuntimeModel.from_pretrained(output_path / 'vae_decoder' ) , text_encoder=OnnxRuntimeModel.from_pretrained(output_path / 'text_encoder' ) , tokenizer=pipeline.tokenizer , unet=OnnxRuntimeModel.from_pretrained(output_path / 'unet' ) , scheduler=pipeline.scheduler , safety_checker=UpperCamelCase__ , feature_extractor=UpperCamelCase__ , requires_safety_checker=safety_checker is not None , )
onnx_pipeline.save_pretrained(UpperCamelCase__ )
print('ONNX pipeline saved to' , UpperCamelCase__ )
del pipeline
del onnx_pipeline
A = OnnxStableDiffusionPipeline.from_pretrained(UpperCamelCase__ , provider='CPUExecutionProvider' )
print('ONNX pipeline is loadable' )
if __name__ == "__main__":
UpperCamelCase : Optional[int] = argparse.ArgumentParser()
parser.add_argument(
"--model_path",
type=str,
required=True,
help="Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).",
)
parser.add_argument("--output_path", type=str, required=True, help="Path to the output model.")
parser.add_argument(
"--opset",
default=14,
type=int,
help="The version of the ONNX operator set to use.",
)
parser.add_argument("--fp16", action="store_true", default=False, help="Export the models in `float16` mode")
UpperCamelCase : str = parser.parse_args()
convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
| 690 | 1 |
"""simple docstring"""
import os
import sys
import unittest
UpperCamelCase : Tuple = 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
UpperCamelCase : List[str] = os.path.join(git_repo_path, "src", "diffusers")
class lowerCamelCase__ ( unittest.TestCase ):
def __a ( self : Dict ):
A = find_backend(' if not is_torch_available():' )
self.assertEqual(_lowercase , 'torch' )
# backend_with_underscore = find_backend(" if not is_tensorflow_text_available():")
# self.assertEqual(backend_with_underscore, "tensorflow_text")
A = find_backend(' if not (is_torch_available() and is_transformers_available()):' )
self.assertEqual(_lowercase , '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 = find_backend(
' if not (is_torch_available() and is_transformers_available() and is_onnx_available()):' )
self.assertEqual(_lowercase , 'torch_and_transformers_and_onnx' )
def __a ( self : List[str] ):
A = read_init()
# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
self.assertIn('torch' , _lowercase )
self.assertIn('torch_and_transformers' , _lowercase )
self.assertIn('flax_and_transformers' , _lowercase )
self.assertIn('torch_and_transformers_and_onnx' , _lowercase )
# 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 __a ( self : Dict ):
A = create_dummy_object('CONSTANT' , '\'torch\'' )
self.assertEqual(_lowercase , '\nCONSTANT = None\n' )
A = create_dummy_object('function' , '\'torch\'' )
self.assertEqual(
_lowercase , '\ndef function(*args, **kwargs):\n requires_backends(function, \'torch\')\n' )
A = '\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 = create_dummy_object('FakeClass' , '\'torch\'' )
self.assertEqual(_lowercase , _lowercase )
def __a ( self : int ):
A = '# 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 = create_dummy_files({'torch': ['CONSTANT', 'function', 'FakeClass']} )
self.assertEqual(dummy_files['torch'] , _lowercase )
| 690 |
"""simple docstring"""
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
UpperCamelCase : List[str] = Lock()
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> List[str]:
"""simple docstring"""
global process_lock
# we perform n swaps since after n swaps we know we are sorted
# we *could* stop early if we are sorted already, but it takes as long to
# find out we are sorted as it does to sort the list with this algorithm
for i in range(0 , 10 ):
if (i + position) % 2 == 0 and r_send is not None:
# send your value to your right neighbor
process_lock.acquire()
r_send[1].send(UpperCamelCase__ )
process_lock.release()
# receive your right neighbor's value
process_lock.acquire()
A = rr_cv[0].recv()
process_lock.release()
# take the lower value since you are on the left
A = min(UpperCamelCase__ , UpperCamelCase__ )
elif (i + position) % 2 != 0 and l_send is not None:
# send your value to your left neighbor
process_lock.acquire()
l_send[1].send(UpperCamelCase__ )
process_lock.release()
# receive your left neighbor's value
process_lock.acquire()
A = lr_cv[0].recv()
process_lock.release()
# take the higher value since you are on the right
A = max(UpperCamelCase__ , UpperCamelCase__ )
# after all swaps are performed, send the values back to main
result_pipe[1].send(UpperCamelCase__ )
def __snake_case ( UpperCamelCase__ ) -> List[str]:
"""simple docstring"""
A = []
A = []
# initialize the list of pipes where the values will be retrieved
for _ in arr:
result_pipe.append(Pipe() )
# creates the processes
# the first and last process only have one neighbor so they are made outside
# of the loop
A = Pipe()
A = Pipe()
process_array_.append(
Process(
target=UpperCamelCase__ , args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0]) , ) )
A = temp_rs
A = temp_rr
for i in range(1 , len(UpperCamelCase__ ) - 1 ):
A = Pipe()
A = Pipe()
process_array_.append(
Process(
target=UpperCamelCase__ , args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i]) , ) )
A = temp_rs
A = temp_rr
process_array_.append(
Process(
target=UpperCamelCase__ , args=(
len(UpperCamelCase__ ) - 1,
arr[len(UpperCamelCase__ ) - 1],
temp_ls,
None,
temp_lr,
None,
result_pipe[len(UpperCamelCase__ ) - 1],
) , ) )
# start the processes
for p in process_array_:
p.start()
# wait for the processes to end and write their values to the list
for p in range(0 , len(UpperCamelCase__ ) ):
A = result_pipe[p][0].recv()
process_array_[p].join()
return arr
def __snake_case ( ) -> Optional[Any]:
"""simple docstring"""
A = list(range(10 , 0 , -1 ) )
print('Initial List' )
print(*UpperCamelCase__ )
A = odd_even_transposition(UpperCamelCase__ )
print('Sorted List\n' )
print(*UpperCamelCase__ )
if __name__ == "__main__":
main()
| 690 | 1 |
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
CenterCrop,
Compose,
Normalize,
RandomHorizontalFlip,
RandomResizedCrop,
Resize,
ToTensor,
)
import transformers
from transformers import (
MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
AutoConfig,
AutoImageProcessor,
AutoModelForImageClassification,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
UpperCamelCase : Any = logging.getLogger(__name__)
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.31.0")
require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/image-classification/requirements.txt")
UpperCamelCase : Union[str, Any] = list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
UpperCamelCase : int = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
def __snake_case ( UpperCamelCase__ ) -> Optional[Any]:
"""simple docstring"""
with open(UpperCamelCase__ , 'rb' ) as f:
A = Image.open(UpperCamelCase__ )
return im.convert('RGB' )
@dataclass
class lowerCamelCase__ :
lowerCAmelCase = field(
default=UpperCAmelCase_ , metadata={
"""help""": """Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub)."""
} , )
lowerCAmelCase = field(
default=UpperCAmelCase_ , metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""} )
lowerCAmelCase = field(default=UpperCAmelCase_ , metadata={"""help""": """A folder containing the training data."""} )
lowerCAmelCase = field(default=UpperCAmelCase_ , metadata={"""help""": """A folder containing the validation data."""} )
lowerCAmelCase = field(
default=0.15 , metadata={"""help""": """Percent to split off of train for validation."""} )
lowerCAmelCase = field(
default=UpperCAmelCase_ , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of training examples to this """
"""value if set."""
)
} , )
lowerCAmelCase = field(
default=UpperCAmelCase_ , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of evaluation examples to this """
"""value if set."""
)
} , )
def __a ( self : List[Any] ):
if self.dataset_name is None and (self.train_dir is None and self.validation_dir is None):
raise ValueError(
'You must specify either a dataset name from the hub or a train and/or validation directory.' )
@dataclass
class lowerCamelCase__ :
lowerCAmelCase = field(
default="""google/vit-base-patch16-224-in21k""" , metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""} , )
lowerCAmelCase = field(
default=UpperCAmelCase_ , metadata={"""help""": """If training from scratch, pass a model type from the list: """ + """, """.join(UpperCAmelCase_ )} , )
lowerCAmelCase = field(
default=UpperCAmelCase_ , metadata={"""help""": """Pretrained config name or path if not the same as model_name"""} )
lowerCAmelCase = field(
default=UpperCAmelCase_ , metadata={"""help""": """Where do you want to store the pretrained models downloaded from s3"""} )
lowerCAmelCase = field(
default="""main""" , metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""} , )
lowerCAmelCase = field(default=UpperCAmelCase_ , metadata={"""help""": """Name or path of preprocessor config."""} )
lowerCAmelCase = field(
default=UpperCAmelCase_ , metadata={
"""help""": (
"""Will use the token generated when running `huggingface-cli login` (necessary to use this script """
"""with private models)."""
)
} , )
lowerCAmelCase = field(
default=UpperCAmelCase_ , metadata={"""help""": """Will enable to load a pretrained model whose head dimensions are different."""} , )
def __snake_case ( UpperCamelCase__ ) -> List[Any]:
"""simple docstring"""
A = torch.stack([example['pixel_values'] for example in examples] )
A = torch.tensor([example['labels'] for example in examples] )
return {"pixel_values": pixel_values, "labels": labels}
def __snake_case ( ) -> str:
"""simple docstring"""
A = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith('.json' ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
A , A , A = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
A , A , A = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry('run_image_classification' , UpperCamelCase__ , UpperCamelCase__ )
# Setup logging
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s' , datefmt='%m/%d/%Y %H:%M:%S' , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
A = training_args.get_process_log_level()
logger.setLevel(UpperCamelCase__ )
transformers.utils.logging.set_verbosity(UpperCamelCase__ )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
f'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'
+ f'distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}' )
logger.info(f'Training/evaluation parameters {training_args}' )
# Detecting last checkpoint.
A = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
A = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
f'Output directory ({training_args.output_dir}) already exists and is not empty. '
'Use --overwrite_output_dir to overcome.' )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
f'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.' )
# Set seed before initializing model.
set_seed(training_args.seed )
# Initialize our dataset and prepare it for the 'image-classification' task.
if data_args.dataset_name is not None:
A = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , cache_dir=model_args.cache_dir , task='image-classification' , use_auth_token=True if model_args.use_auth_token else None , )
else:
A = {}
if data_args.train_dir is not None:
A = os.path.join(data_args.train_dir , '**' )
if data_args.validation_dir is not None:
A = os.path.join(data_args.validation_dir , '**' )
A = load_dataset(
'imagefolder' , data_files=UpperCamelCase__ , cache_dir=model_args.cache_dir , task='image-classification' , )
# If we don't have a validation split, split off a percentage of train as validation.
A = None if 'validation' in dataset.keys() else data_args.train_val_split
if isinstance(data_args.train_val_split , UpperCamelCase__ ) and data_args.train_val_split > 0.0:
A = dataset['train'].train_test_split(data_args.train_val_split )
A = split['train']
A = split['test']
# Prepare label mappings.
# We'll include these in the model's config to get human readable labels in the Inference API.
A = dataset['train'].features['labels'].names
A , A = {}, {}
for i, label in enumerate(UpperCamelCase__ ):
A = str(UpperCamelCase__ )
A = label
# Load the accuracy metric from the datasets package
A = evaluate.load('accuracy' )
# Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
# predictions and label_ids field) and has to return a dictionary string to float.
def compute_metrics(UpperCamelCase__ ):
return metric.compute(predictions=np.argmax(p.predictions , axis=1 ) , references=p.label_ids )
A = AutoConfig.from_pretrained(
model_args.config_name or model_args.model_name_or_path , num_labels=len(UpperCamelCase__ ) , labelaid=UpperCamelCase__ , idalabel=UpperCamelCase__ , finetuning_task='image-classification' , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
A = AutoModelForImageClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool('.ckpt' in model_args.model_name_or_path ) , config=UpperCamelCase__ , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , ignore_mismatched_sizes=model_args.ignore_mismatched_sizes , )
A = AutoImageProcessor.from_pretrained(
model_args.image_processor_name or model_args.model_name_or_path , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# Define torchvision transforms to be applied to each image.
if "shortest_edge" in image_processor.size:
A = image_processor.size['shortest_edge']
else:
A = (image_processor.size['height'], image_processor.size['width'])
A = Normalize(mean=image_processor.image_mean , std=image_processor.image_std )
A = Compose(
[
RandomResizedCrop(UpperCamelCase__ ),
RandomHorizontalFlip(),
ToTensor(),
normalize,
] )
A = Compose(
[
Resize(UpperCamelCase__ ),
CenterCrop(UpperCamelCase__ ),
ToTensor(),
normalize,
] )
def train_transforms(UpperCamelCase__ ):
A = [
_train_transforms(pil_img.convert('RGB' ) ) for pil_img in example_batch['image']
]
return example_batch
def val_transforms(UpperCamelCase__ ):
A = [_val_transforms(pil_img.convert('RGB' ) ) for pil_img in example_batch['image']]
return example_batch
if training_args.do_train:
if "train" not in dataset:
raise ValueError('--do_train requires a train dataset' )
if data_args.max_train_samples is not None:
A = (
dataset['train'].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
)
# Set the training transforms
dataset["train"].set_transform(UpperCamelCase__ )
if training_args.do_eval:
if "validation" not in dataset:
raise ValueError('--do_eval requires a validation dataset' )
if data_args.max_eval_samples is not None:
A = (
dataset['validation'].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
)
# Set the validation transforms
dataset["validation"].set_transform(UpperCamelCase__ )
# Initalize our trainer
A = Trainer(
model=UpperCamelCase__ , args=UpperCamelCase__ , train_dataset=dataset['train'] if training_args.do_train else None , eval_dataset=dataset['validation'] if training_args.do_eval else None , compute_metrics=UpperCamelCase__ , tokenizer=UpperCamelCase__ , data_collator=UpperCamelCase__ , )
# Training
if training_args.do_train:
A = None
if training_args.resume_from_checkpoint is not None:
A = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
A = last_checkpoint
A = trainer.train(resume_from_checkpoint=UpperCamelCase__ )
trainer.save_model()
trainer.log_metrics('train' , train_result.metrics )
trainer.save_metrics('train' , train_result.metrics )
trainer.save_state()
# Evaluation
if training_args.do_eval:
A = trainer.evaluate()
trainer.log_metrics('eval' , UpperCamelCase__ )
trainer.save_metrics('eval' , UpperCamelCase__ )
# Write model card and (optionally) push to hub
A = {
'finetuned_from': model_args.model_name_or_path,
'tasks': 'image-classification',
'dataset': data_args.dataset_name,
'tags': ['image-classification', 'vision'],
}
if training_args.push_to_hub:
trainer.push_to_hub(**UpperCamelCase__ )
else:
trainer.create_model_card(**UpperCamelCase__ )
if __name__ == "__main__":
main()
| 690 |
"""simple docstring"""
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures
# Importing the dataset
UpperCamelCase : int = pd.read_csv(
"https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/"
"position_salaries.csv"
)
UpperCamelCase : List[Any] = dataset.iloc[:, 1:2].values
UpperCamelCase : Any = dataset.iloc[:, 2].values
UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase : str = train_test_split(X, y, test_size=0.2, random_state=0)
UpperCamelCase : List[str] = PolynomialFeatures(degree=4)
UpperCamelCase : Optional[int] = poly_reg.fit_transform(X)
UpperCamelCase : List[Any] = LinearRegression()
pol_reg.fit(X_poly, y)
def __snake_case ( ) -> Optional[int]:
"""simple docstring"""
plt.scatter(UpperCamelCase__ , UpperCamelCase__ , color='red' )
plt.plot(UpperCamelCase__ , pol_reg.predict(poly_reg.fit_transform(UpperCamelCase__ ) ) , color='blue' )
plt.title('Truth or Bluff (Linear Regression)' )
plt.xlabel('Position level' )
plt.ylabel('Salary' )
plt.show()
if __name__ == "__main__":
viz_polymonial()
# Predicting a new result with Polymonial Regression
pol_reg.predict(poly_reg.fit_transform([[5.5]]))
# output should be 132148.43750003
| 690 | 1 |
"""simple docstring"""
import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import (
SeqaSeqDataset,
calculate_bleu,
calculate_rouge,
chunks,
lmap,
load_json,
parse_numeric_n_bool_cl_kwargs,
save_json,
use_task_specific_params,
write_txt_file,
)
UpperCamelCase : Dict = getLogger(__name__)
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = 8 , UpperCamelCase__ = 1024 , UpperCamelCase__="val" , UpperCamelCase__=None , UpperCamelCase__=False , UpperCamelCase__="summarization" , UpperCamelCase__=None , UpperCamelCase__=1 , UpperCamelCase__ = None , UpperCamelCase__="" , **UpperCamelCase__ , ) -> Dict:
"""simple docstring"""
A = str(UpperCamelCase__ )
assert local_rank is not None
torch.distributed.init_process_group(backend='nccl' , rank=UpperCamelCase__ )
A = Path(UpperCamelCase__ )
A = save_dir.joinpath(f'rank_{local_rank}_output.json' )
torch.cuda.set_device(UpperCamelCase__ )
A = AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase__ ).cuda()
if fpaa:
A = model.half()
# determine if we need to increase num_beams
use_task_specific_params(UpperCamelCase__ , UpperCamelCase__ ) # update config with task specific params
A = generate_kwargs.pop('num_beams' , model.config.num_beams ) # AttributeError risk?
if num_return_sequences > num_beams:
A = num_return_sequences
A = AutoTokenizer.from_pretrained(UpperCamelCase__ )
logger.info(f'Inferred tokenizer type: {tokenizer.__class__}' ) # if this is wrong, check config.model_type.
if max_source_length is None:
A = tokenizer.model_max_length
if prefix is None:
A = prefix or getattr(model.config , 'prefix' , '' ) or ''
A = SeqaSeqDataset(
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , max_target_length=1024 , type_path=UpperCamelCase__ , n_obs=UpperCamelCase__ , prefix=UpperCamelCase__ , **UpperCamelCase__ , )
# I set shuffle=True for a more accurate progress bar.
# If all the longest samples are first, the prog bar estimate is too high at the beginning.
A = ds.make_sortish_sampler(UpperCamelCase__ , distributed=UpperCamelCase__ , add_extra_examples=UpperCamelCase__ , shuffle=UpperCamelCase__ )
A = DataLoader(UpperCamelCase__ , sampler=UpperCamelCase__ , batch_size=UpperCamelCase__ , collate_fn=ds.collate_fn )
A = []
for batch in tqdm(UpperCamelCase__ ):
A = model.generate(
input_ids=batch['input_ids'].to(model.device ) , attention_mask=batch['attention_mask'].to(model.device ) , num_return_sequences=UpperCamelCase__ , num_beams=UpperCamelCase__ , **UpperCamelCase__ , )
A = tokenizer.batch_decode(UpperCamelCase__ , skip_special_tokens=UpperCamelCase__ , clean_up_tokenization_spaces=UpperCamelCase__ )
A = batch['ids']
if num_return_sequences > 1:
A = chunks(UpperCamelCase__ , UpperCamelCase__ ) # batch size chunks, each of size num_return_seq
for i, pred in enumerate(UpperCamelCase__ ):
results.append({'pred': pred, 'id': ids[i].item()} )
save_json(UpperCamelCase__ , UpperCamelCase__ )
return results, sampler.num_replicas
def __snake_case ( ) -> Optional[int]:
"""simple docstring"""
A = argparse.ArgumentParser(
epilog='Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate' )
parser.add_argument('--data_dir' , type=UpperCamelCase__ , help='like cnn_dm/test.source' )
parser.add_argument(
'--model_name' , type=UpperCamelCase__ , help='like facebook/bart-large-cnn,t5-base, etc.' , default='sshleifer/distilbart-xsum-12-3' , )
parser.add_argument('--save_dir' , type=UpperCamelCase__ , help='where to save' , default='tmp_gen' )
parser.add_argument('--max_source_length' , type=UpperCamelCase__ , default=UpperCamelCase__ )
parser.add_argument(
'--type_path' , type=UpperCamelCase__ , default='test' , help='which subset to evaluate typically train/val/test' )
parser.add_argument('--task' , type=UpperCamelCase__ , default='summarization' , help='used for task_specific_params + metrics' )
parser.add_argument('--bs' , type=UpperCamelCase__ , default=8 , required=UpperCamelCase__ , help='batch size' )
parser.add_argument(
'--local_rank' , type=UpperCamelCase__ , default=-1 , required=UpperCamelCase__ , help='should be passed by distributed.launch' )
parser.add_argument(
'--n_obs' , type=UpperCamelCase__ , default=UpperCamelCase__ , required=UpperCamelCase__ , help='How many observations. Defaults to all.' )
parser.add_argument(
'--num_return_sequences' , type=UpperCamelCase__ , default=1 , required=UpperCamelCase__ , help='How many sequences to return' )
parser.add_argument(
'--sync_timeout' , type=UpperCamelCase__ , default=600 , required=UpperCamelCase__ , help='How long should master process wait for other processes to finish.' , )
parser.add_argument('--src_lang' , type=UpperCamelCase__ , default=UpperCamelCase__ , required=UpperCamelCase__ )
parser.add_argument('--tgt_lang' , type=UpperCamelCase__ , default=UpperCamelCase__ , required=UpperCamelCase__ )
parser.add_argument(
'--prefix' , type=UpperCamelCase__ , required=UpperCamelCase__ , default=UpperCamelCase__ , help='will be added to the begininng of src examples' )
parser.add_argument('--fp16' , action='store_true' )
parser.add_argument('--debug' , action='store_true' )
A = time.time()
A , A = parser.parse_known_args()
A = parse_numeric_n_bool_cl_kwargs(UpperCamelCase__ )
if generate_kwargs and args.local_rank <= 0:
print(f'parsed the following generate kwargs: {generate_kwargs}' )
A = Path(args.save_dir + '_tmp' )
Path(UpperCamelCase__ ).mkdir(exist_ok=UpperCamelCase__ ) # this handles locking.
A = list(json_save_dir.glob('rank_*.json' ) )
if intermediate_files:
raise ValueError(f'Found files at {json_save_dir} please move or remove them.' )
# In theory, a node could finish and save before another node hits this. If this happens, we can address later.
A = {}
if args.src_lang is not None:
A = args.src_lang
if args.tgt_lang is not None:
A = args.tgt_lang
Path(args.save_dir ).mkdir(exist_ok=UpperCamelCase__ )
A , A = eval_data_dir(
args.data_dir , UpperCamelCase__ , args.model_name , type_path=args.type_path , bs=args.bs , fpaa=args.fpaa , task=args.task , local_rank=args.local_rank , n_obs=args.n_obs , max_source_length=args.max_source_length , num_return_sequences=args.num_return_sequences , prefix=args.prefix , dataset_kwargs=UpperCamelCase__ , **UpperCamelCase__ , )
if args.local_rank <= 0:
A = Path(args.save_dir )
save_dir.mkdir(exist_ok=UpperCamelCase__ )
A = gather_results_from_each_node(UpperCamelCase__ , UpperCamelCase__ , args.sync_timeout )
A = combine_partial_results(UpperCamelCase__ )
if args.num_return_sequences > 1:
A = save_dir.joinpath('pseudolabel_results.json' )
print(f'Saving aggregated results at {save_path}, intermediate in {json_save_dir}/' )
save_json(UpperCamelCase__ , UpperCamelCase__ )
return
A = Path(args.data_dir ).joinpath(args.type_path + '.target' )
with open(UpperCamelCase__ ) as f:
A = [x.rstrip() for x in f.readlines()][: len(UpperCamelCase__ )]
# Calculate metrics, save metrics, and save _generations.txt
A = 'translation' in args.task
A = calculate_bleu if calc_bleu else calculate_rouge
A = 'bleu' if calc_bleu else 'rouge'
A = score_fn(UpperCamelCase__ , UpperCamelCase__ )
A = len(UpperCamelCase__ )
A = time.time() - start_time
A = round(runtime / metrics['n_obs'] , 4 )
A = num_replicas
# TODO(@stas00): add whatever metadata to metrics
A = save_dir.joinpath(f'{args.type_path}_{metric_name}.json' )
save_json(UpperCamelCase__ , UpperCamelCase__ , indent=UpperCamelCase__ )
print(UpperCamelCase__ )
write_txt_file(UpperCamelCase__ , save_dir.joinpath(f'{args.type_path}_generations.txt' ) )
if args.debug:
write_txt_file(UpperCamelCase__ , save_dir.joinpath(f'{args.type_path}.target' ) )
else:
shutil.rmtree(UpperCamelCase__ )
def __snake_case ( UpperCamelCase__ ) -> List:
"""simple docstring"""
A = []
for partial_result in partial_results:
records.extend(UpperCamelCase__ )
A = sorted(UpperCamelCase__ , key=lambda UpperCamelCase__ : x["id"] )
A = [x['pred'] for x in records]
return preds
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> List[Dict[str, List]]:
"""simple docstring"""
A = time.time()
logger.info('waiting for all nodes to finish' )
A = None
while (time.time() - start_wait) < timeout:
A = list(save_dir.glob('rank_*.json' ) )
if len(UpperCamelCase__ ) < num_replicas:
continue
try:
# make sure all json files are fully saved
A = lmap(UpperCamelCase__ , UpperCamelCase__ )
return json_data
except JSONDecodeError:
continue
else:
raise TimeoutError('Rank 0 gave up on waiting for other processes' )
# Unreachable
if __name__ == "__main__":
# Usage for MT:
run_generate()
| 690 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPENAI_CLIP_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
UpperCamelCase : Optional[Any] = logging.get_logger(__name__)
if is_vision_available():
import PIL
class lowerCamelCase__ ( UpperCAmelCase_ ):
lowerCAmelCase = ["""pixel_values"""]
def __init__( self : List[str] , _lowercase : bool = True , _lowercase : Dict[str, int] = None , _lowercase : PILImageResampling = PILImageResampling.BICUBIC , _lowercase : bool = True , _lowercase : Dict[str, int] = None , _lowercase : bool = True , _lowercase : Union[int, float] = 1 / 255 , _lowercase : bool = True , _lowercase : Optional[Union[float, List[float]]] = None , _lowercase : Optional[Union[float, List[float]]] = None , _lowercase : bool = True , **_lowercase : Tuple , ):
super().__init__(**_lowercase )
A = size if size is not None else {'shortest_edge': 224}
A = get_size_dict(_lowercase , default_to_square=_lowercase )
A = crop_size if crop_size is not None else {'height': 224, 'width': 224}
A = get_size_dict(_lowercase , default_to_square=_lowercase , param_name='crop_size' )
A = do_resize
A = size
A = resample
A = do_center_crop
A = crop_size
A = do_rescale
A = rescale_factor
A = do_normalize
A = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
A = image_std if image_std is not None else OPENAI_CLIP_STD
A = do_convert_rgb
def __a ( self : str , _lowercase : np.ndarray , _lowercase : Dict[str, int] , _lowercase : PILImageResampling = PILImageResampling.BICUBIC , _lowercase : Optional[Union[str, ChannelDimension]] = None , **_lowercase : List[str] , ):
A = get_size_dict(_lowercase , default_to_square=_lowercase )
if "shortest_edge" not in size:
raise ValueError(f'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}' )
A = get_resize_output_image_size(_lowercase , size=size['shortest_edge'] , default_to_square=_lowercase )
return resize(_lowercase , size=_lowercase , resample=_lowercase , data_format=_lowercase , **_lowercase )
def __a ( self : int , _lowercase : np.ndarray , _lowercase : Dict[str, int] , _lowercase : Optional[Union[str, ChannelDimension]] = None , **_lowercase : str , ):
A = get_size_dict(_lowercase )
if "height" not in size or "width" not in size:
raise ValueError(f'The `size` parameter must contain the keys (height, width). Got {size.keys()}' )
return center_crop(_lowercase , size=(size['height'], size['width']) , data_format=_lowercase , **_lowercase )
def __a ( self : List[str] , _lowercase : np.ndarray , _lowercase : Union[int, float] , _lowercase : Optional[Union[str, ChannelDimension]] = None , **_lowercase : List[str] , ):
return rescale(_lowercase , scale=_lowercase , data_format=_lowercase , **_lowercase )
def __a ( self : List[str] , _lowercase : np.ndarray , _lowercase : Union[float, List[float]] , _lowercase : Union[float, List[float]] , _lowercase : Optional[Union[str, ChannelDimension]] = None , **_lowercase : Union[str, Any] , ):
return normalize(_lowercase , mean=_lowercase , std=_lowercase , data_format=_lowercase , **_lowercase )
def __a ( self : Optional[int] , _lowercase : ImageInput , _lowercase : bool = None , _lowercase : Dict[str, int] = None , _lowercase : PILImageResampling = None , _lowercase : bool = None , _lowercase : int = None , _lowercase : bool = None , _lowercase : float = None , _lowercase : bool = None , _lowercase : Optional[Union[float, List[float]]] = None , _lowercase : Optional[Union[float, List[float]]] = None , _lowercase : bool = None , _lowercase : Optional[Union[str, TensorType]] = None , _lowercase : Optional[ChannelDimension] = ChannelDimension.FIRST , **_lowercase : int , ):
A = do_resize if do_resize is not None else self.do_resize
A = size if size is not None else self.size
A = get_size_dict(_lowercase , param_name='size' , default_to_square=_lowercase )
A = resample if resample is not None else self.resample
A = do_center_crop if do_center_crop is not None else self.do_center_crop
A = crop_size if crop_size is not None else self.crop_size
A = get_size_dict(_lowercase , param_name='crop_size' , default_to_square=_lowercase )
A = do_rescale if do_rescale is not None else self.do_rescale
A = rescale_factor if rescale_factor is not None else self.rescale_factor
A = do_normalize if do_normalize is not None else self.do_normalize
A = image_mean if image_mean is not None else self.image_mean
A = image_std if image_std is not None else self.image_std
A = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
A = make_list_of_images(_lowercase )
if not valid_images(_lowercase ):
raise ValueError(
'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
'torch.Tensor, tf.Tensor or jax.ndarray.' )
if do_resize and size is None:
raise ValueError('Size must be specified if do_resize is True.' )
if do_center_crop and crop_size is None:
raise ValueError('Crop size must be specified if do_center_crop is True.' )
if do_rescale and rescale_factor is None:
raise ValueError('Rescale factor must be specified if do_rescale is True.' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('Image mean and std must be specified if do_normalize is True.' )
# PIL RGBA images are converted to RGB
if do_convert_rgb:
A = [convert_to_rgb(_lowercase ) for image in images]
# All transformations expect numpy arrays.
A = [to_numpy_array(_lowercase ) for image in images]
if do_resize:
A = [self.resize(image=_lowercase , size=_lowercase , resample=_lowercase ) for image in images]
if do_center_crop:
A = [self.center_crop(image=_lowercase , size=_lowercase ) for image in images]
if do_rescale:
A = [self.rescale(image=_lowercase , scale=_lowercase ) for image in images]
if do_normalize:
A = [self.normalize(image=_lowercase , mean=_lowercase , std=_lowercase ) for image in images]
A = [to_channel_dimension_format(_lowercase , _lowercase ) for image in images]
A = {'pixel_values': images}
return BatchFeature(data=_lowercase , tensor_type=_lowercase )
| 690 | 1 |
"""simple docstring"""
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 lowerCamelCase__ ( unittest.TestCase ):
def __a ( self : Any ):
A = 'laion/clap-htsat-unfused'
A = tempfile.mkdtemp()
def __a ( self : Dict , **_lowercase : List[str] ):
return RobertaTokenizer.from_pretrained(self.checkpoint , **_lowercase )
def __a ( self : List[Any] , **_lowercase : int ):
return ClapFeatureExtractor.from_pretrained(self.checkpoint , **_lowercase )
def __a ( self : Dict ):
shutil.rmtree(self.tmpdirname )
def __a ( self : int ):
A = self.get_tokenizer()
A = self.get_feature_extractor()
A = ClapProcessor(tokenizer=_lowercase , feature_extractor=_lowercase )
processor.save_pretrained(self.tmpdirname )
A = ClapProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.tokenizer , _lowercase )
self.assertEqual(processor.feature_extractor.to_json_string() , feature_extractor.to_json_string() )
self.assertIsInstance(processor.feature_extractor , _lowercase )
def __a ( self : Optional[int] ):
A = ClapProcessor(tokenizer=self.get_tokenizer() , feature_extractor=self.get_feature_extractor() )
processor.save_pretrained(self.tmpdirname )
A = self.get_tokenizer(bos_token='(BOS)' , eos_token='(EOS)' )
A = self.get_feature_extractor(do_normalize=_lowercase , padding_value=1.0 )
A = ClapProcessor.from_pretrained(
self.tmpdirname , bos_token='(BOS)' , eos_token='(EOS)' , do_normalize=_lowercase , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , _lowercase )
self.assertEqual(processor.feature_extractor.to_json_string() , feature_extractor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.feature_extractor , _lowercase )
def __a ( self : Dict ):
A = self.get_feature_extractor()
A = self.get_tokenizer()
A = ClapProcessor(tokenizer=_lowercase , feature_extractor=_lowercase )
A = floats_list((3, 1_000) )
A = feature_extractor(_lowercase , return_tensors='np' )
A = processor(audios=_lowercase , 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 : List[Any] ):
A = self.get_feature_extractor()
A = self.get_tokenizer()
A = ClapProcessor(tokenizer=_lowercase , feature_extractor=_lowercase )
A = 'This is a test string'
A = processor(text=_lowercase )
A = tokenizer(_lowercase )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def __a ( self : Optional[int] ):
A = self.get_feature_extractor()
A = self.get_tokenizer()
A = ClapProcessor(tokenizer=_lowercase , feature_extractor=_lowercase )
A = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
A = processor.batch_decode(_lowercase )
A = tokenizer.batch_decode(_lowercase )
self.assertListEqual(_lowercase , _lowercase )
def __a ( self : List[Any] ):
A = self.get_feature_extractor()
A = self.get_tokenizer()
A = ClapProcessor(tokenizer=_lowercase , feature_extractor=_lowercase )
self.assertListEqual(
processor.model_input_names[2:] , feature_extractor.model_input_names , msg='`processor` and `feature_extractor` model input names do not match' , )
| 690 |
"""simple docstring"""
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class lowerCamelCase__ ( unittest.TestCase ):
def __a ( self : Union[str, Any] ):
A = torch.nn.Linear(10 , 10 )
A = torch.optim.SGD(model.parameters() , 0.1 )
A = Accelerator()
A = accelerator.prepare(_lowercase )
try:
pickle.loads(pickle.dumps(_lowercase ) )
except Exception as e:
self.fail(f'Accelerated optimizer pickling failed with {e}' )
AcceleratorState._reset_state()
| 690 | 1 |
"""simple docstring"""
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> List[Any]:
"""simple docstring"""
A = ('dense.weight', 'attention.self.query', 'attention.self.key', 'attention.self.value')
A = (
('layer.', 'layer_'),
('word_embeddings.weight', 'word_embeddings'),
('position_embeddings.weight', 'position_embeddings'),
('token_type_embeddings.weight', 'token_type_embeddings'),
('.', '/'),
('LayerNorm/weight', 'LayerNorm/gamma'),
('LayerNorm/bias', 'LayerNorm/beta'),
('weight', 'kernel'),
)
if not os.path.isdir(UpperCamelCase__ ):
os.makedirs(UpperCamelCase__ )
A = model.state_dict()
def to_tf_var_name(UpperCamelCase__ ):
for patt, repl in iter(UpperCamelCase__ ):
A = name.replace(UpperCamelCase__ , UpperCamelCase__ )
return f'bert/{name}'
def create_tf_var(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
A = tf.dtypes.as_dtype(tensor.dtype )
A = tf.get_variable(dtype=UpperCamelCase__ , shape=tensor.shape , name=UpperCamelCase__ , initializer=tf.zeros_initializer() )
session.run(tf.variables_initializer([tf_var] ) )
session.run(UpperCamelCase__ )
return tf_var
tf.reset_default_graph()
with tf.Session() as session:
for var_name in state_dict:
A = to_tf_var_name(UpperCamelCase__ )
A = state_dict[var_name].numpy()
if any(x in var_name for x in tensors_to_transpose ):
A = torch_tensor.T
A = create_tf_var(tensor=UpperCamelCase__ , name=UpperCamelCase__ , session=UpperCamelCase__ )
tf.keras.backend.set_value(UpperCamelCase__ , UpperCamelCase__ )
A = session.run(UpperCamelCase__ )
print(f'Successfully created {tf_name}: {np.allclose(UpperCamelCase__ , UpperCamelCase__ )}' )
A = tf.train.Saver(tf.trainable_variables() )
saver.save(UpperCamelCase__ , os.path.join(UpperCamelCase__ , model_name.replace('-' , '_' ) + '.ckpt' ) )
def __snake_case ( UpperCamelCase__=None ) -> List[Any]:
"""simple docstring"""
A = argparse.ArgumentParser()
parser.add_argument('--model_name' , type=UpperCamelCase__ , required=UpperCamelCase__ , help='model name e.g. bert-base-uncased' )
parser.add_argument(
'--cache_dir' , type=UpperCamelCase__ , default=UpperCamelCase__ , required=UpperCamelCase__ , help='Directory containing pytorch model' )
parser.add_argument('--pytorch_model_path' , type=UpperCamelCase__ , required=UpperCamelCase__ , help='/path/to/<pytorch-model-name>.bin' )
parser.add_argument('--tf_cache_dir' , type=UpperCamelCase__ , required=UpperCamelCase__ , help='Directory in which to save tensorflow model' )
A = parser.parse_args(UpperCamelCase__ )
A = BertModel.from_pretrained(
pretrained_model_name_or_path=args.model_name , state_dict=torch.load(args.pytorch_model_path ) , cache_dir=args.cache_dir , )
convert_pytorch_checkpoint_to_tf(model=UpperCamelCase__ , ckpt_dir=args.tf_cache_dir , model_name=args.model_name )
if __name__ == "__main__":
main()
| 690 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCamelCase : Optional[int] = logging.get_logger(__name__)
UpperCamelCase : Optional[Any] = {
"YituTech/conv-bert-base": "https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json",
"YituTech/conv-bert-medium-small": (
"https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json"
),
"YituTech/conv-bert-small": "https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json",
# See all ConvBERT models at https://huggingface.co/models?filter=convbert
}
class lowerCamelCase__ ( UpperCAmelCase_ ):
lowerCAmelCase = """convbert"""
def __init__( self : Optional[int] , _lowercase : List[Any]=30_522 , _lowercase : List[str]=768 , _lowercase : Optional[Any]=12 , _lowercase : Any=12 , _lowercase : str=3_072 , _lowercase : List[str]="gelu" , _lowercase : Dict=0.1 , _lowercase : Dict=0.1 , _lowercase : Any=512 , _lowercase : List[str]=2 , _lowercase : Tuple=0.0_2 , _lowercase : List[Any]=1e-12 , _lowercase : List[str]=1 , _lowercase : Tuple=0 , _lowercase : Any=2 , _lowercase : Union[str, Any]=768 , _lowercase : str=2 , _lowercase : Any=9 , _lowercase : Union[str, Any]=1 , _lowercase : Dict=None , **_lowercase : Union[str, Any] , ):
super().__init__(
pad_token_id=_lowercase , bos_token_id=_lowercase , eos_token_id=_lowercase , **_lowercase , )
A = vocab_size
A = hidden_size
A = num_hidden_layers
A = num_attention_heads
A = intermediate_size
A = hidden_act
A = hidden_dropout_prob
A = attention_probs_dropout_prob
A = max_position_embeddings
A = type_vocab_size
A = initializer_range
A = layer_norm_eps
A = embedding_size
A = head_ratio
A = conv_kernel_size
A = num_groups
A = classifier_dropout
class lowerCamelCase__ ( UpperCAmelCase_ ):
@property
def __a ( self : str ):
if self.task == "multiple-choice":
A = {0: 'batch', 1: 'choice', 2: 'sequence'}
else:
A = {0: 'batch', 1: 'sequence'}
return OrderedDict(
[
('input_ids', dynamic_axis),
('attention_mask', dynamic_axis),
('token_type_ids', dynamic_axis),
] )
| 690 | 1 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, 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 lowerCamelCase__ ( UpperCAmelCase_ , unittest.TestCase ):
lowerCAmelCase = KandinskyVaaPipeline
lowerCAmelCase = [
"""image_embeds""",
"""negative_image_embeds""",
]
lowerCAmelCase = ["""image_embeds""", """negative_image_embeds"""]
lowerCAmelCase = [
"""generator""",
"""height""",
"""width""",
"""latents""",
"""guidance_scale""",
"""num_inference_steps""",
"""return_dict""",
"""guidance_scale""",
"""num_images_per_prompt""",
"""output_type""",
"""return_dict""",
]
lowerCAmelCase = False
@property
def __a ( self : Optional[Any] ):
return 32
@property
def __a ( self : Optional[int] ):
return 32
@property
def __a ( self : Any ):
return self.time_input_dim
@property
def __a ( self : int ):
return self.time_input_dim * 4
@property
def __a ( self : Dict ):
return 100
@property
def __a ( self : int ):
torch.manual_seed(0 )
A = {
'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,
}
A = UNetaDConditionModel(**_lowercase )
return model
@property
def __a ( self : Any ):
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 __a ( self : Dict ):
torch.manual_seed(0 )
A = VQModel(**self.dummy_movq_kwargs )
return model
def __a ( self : str ):
A = self.dummy_unet
A = self.dummy_movq
A = DDIMScheduler(
num_train_timesteps=1_000 , beta_schedule='linear' , beta_start=0.0_0_0_8_5 , beta_end=0.0_1_2 , clip_sample=_lowercase , set_alpha_to_one=_lowercase , steps_offset=1 , prediction_type='epsilon' , thresholding=_lowercase , )
A = {
'unet': unet,
'scheduler': scheduler,
'movq': movq,
}
return components
def __a ( self : int , _lowercase : List[str] , _lowercase : str=0 ):
A = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(_lowercase ) ).to(_lowercase )
A = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(seed + 1 ) ).to(
_lowercase )
if str(_lowercase ).startswith('mps' ):
A = torch.manual_seed(_lowercase )
else:
A = torch.Generator(device=_lowercase ).manual_seed(_lowercase )
A = {
'image_embeds': image_embeds,
'negative_image_embeds': negative_image_embeds,
'generator': generator,
'height': 64,
'width': 64,
'guidance_scale': 4.0,
'num_inference_steps': 2,
'output_type': 'np',
}
return inputs
def __a ( self : str ):
A = 'cpu'
A = self.get_dummy_components()
A = self.pipeline_class(**_lowercase )
A = pipe.to(_lowercase )
pipe.set_progress_bar_config(disable=_lowercase )
A = pipe(**self.get_dummy_inputs(_lowercase ) )
A = output.images
A = pipe(
**self.get_dummy_inputs(_lowercase ) , return_dict=_lowercase , )[0]
A = image[0, -3:, -3:, -1]
A = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
A = np.array(
[0.6_2_3_7_9_7_6, 1.0, 0.3_6_4_4_1_3_3_2, 1.0, 0.7_0_6_3_9_6_3_4, 0.2_9_8_7_7_1_8_6, 0.8_5_6_5_2_1_2_5, 0.5_2_1_6_8_4_3, 0.5_4_4_5_4_0_4_6] )
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 lowerCamelCase__ ( unittest.TestCase ):
def __a ( self : List[Any] ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __a ( self : Optional[Any] ):
A = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/kandinskyv22/kandinskyv22_text2img_cat_fp16.npy' )
A = KandinskyVaaPriorPipeline.from_pretrained(
'kandinsky-community/kandinsky-2-2-prior' , torch_dtype=torch.floataa )
pipe_prior.to(_lowercase )
A = KandinskyVaaPipeline.from_pretrained(
'kandinsky-community/kandinsky-2-2-decoder' , torch_dtype=torch.floataa )
A = pipeline.to(_lowercase )
pipeline.set_progress_bar_config(disable=_lowercase )
A = 'red cat, 4k photo'
A = torch.Generator(device='cuda' ).manual_seed(0 )
A , A = pipe_prior(
_lowercase , generator=_lowercase , num_inference_steps=5 , negative_prompt='' , ).to_tuple()
A = torch.Generator(device='cuda' ).manual_seed(0 )
A = pipeline(
image_embeds=_lowercase , negative_image_embeds=_lowercase , generator=_lowercase , num_inference_steps=100 , output_type='np' , )
A = output.images[0]
assert image.shape == (512, 512, 3)
assert_mean_pixel_difference(_lowercase , _lowercase )
| 690 |
"""simple docstring"""
from .constants import (
MODEL_NAME,
OPTIMIZER_NAME,
RNG_STATE_NAME,
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
SCALER_NAME,
SCHEDULER_NAME,
TORCH_LAUNCH_PARAMS,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
)
from .dataclasses import (
BnbQuantizationConfig,
ComputeEnvironment,
CustomDtype,
DeepSpeedPlugin,
DistributedDataParallelKwargs,
DistributedType,
DynamoBackend,
FPaRecipeKwargs,
FullyShardedDataParallelPlugin,
GradientAccumulationPlugin,
GradScalerKwargs,
InitProcessGroupKwargs,
KwargsHandler,
LoggerType,
MegatronLMPlugin,
PrecisionType,
ProjectConfiguration,
RNGType,
SageMakerDistributedType,
TensorInformation,
TorchDynamoPlugin,
)
from .environment import get_int_from_env, parse_choice_from_env, parse_flag_from_env
from .imports import (
get_ccl_version,
is_abit_bnb_available,
is_abit_bnb_available,
is_aim_available,
is_bfaa_available,
is_bnb_available,
is_botoa_available,
is_ccl_available,
is_comet_ml_available,
is_datasets_available,
is_deepspeed_available,
is_fpa_available,
is_ipex_available,
is_megatron_lm_available,
is_mlflow_available,
is_mps_available,
is_npu_available,
is_rich_available,
is_safetensors_available,
is_sagemaker_available,
is_tensorboard_available,
is_tpu_available,
is_transformers_available,
is_wandb_available,
is_xpu_available,
)
from .modeling import (
check_device_map,
check_tied_parameters_in_config,
check_tied_parameters_on_same_device,
compute_module_sizes,
convert_file_size_to_int,
dtype_byte_size,
find_tied_parameters,
get_balanced_memory,
get_max_layer_size,
get_max_memory,
get_mixed_precision_context_manager,
id_tensor_storage,
infer_auto_device_map,
load_checkpoint_in_model,
load_offloaded_weights,
load_state_dict,
named_module_tensors,
retie_parameters,
set_module_tensor_to_device,
shard_checkpoint,
)
from .offload import (
OffloadedWeightsLoader,
PrefixedDataset,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_weight,
save_offload_index,
)
from .operations import (
broadcast,
broadcast_object_list,
concatenate,
convert_outputs_to_fpaa,
convert_to_fpaa,
find_batch_size,
find_device,
gather,
gather_object,
get_data_structure,
honor_type,
initialize_tensors,
is_namedtuple,
is_tensor_information,
is_torch_tensor,
listify,
pad_across_processes,
recursively_apply,
reduce,
send_to_device,
slice_tensors,
)
from .versions import compare_versions, is_torch_version
if is_deepspeed_available():
from .deepspeed import (
DeepSpeedEngineWrapper,
DeepSpeedOptimizerWrapper,
DeepSpeedSchedulerWrapper,
DummyOptim,
DummyScheduler,
HfDeepSpeedConfig,
)
from .bnb import has_abit_bnb_layers, load_and_quantize_model
from .fsdp_utils import load_fsdp_model, load_fsdp_optimizer, save_fsdp_model, save_fsdp_optimizer
from .launch import (
PrepareForLaunch,
_filter_args,
prepare_deepspeed_cmd_env,
prepare_multi_gpu_env,
prepare_sagemager_args_inputs,
prepare_simple_launcher_cmd_env,
prepare_tpu,
)
from .megatron_lm import (
AbstractTrainStep,
BertTrainStep,
GPTTrainStep,
MegatronEngine,
MegatronLMDummyDataLoader,
MegatronLMDummyScheduler,
MegatronLMOptimizerWrapper,
MegatronLMSchedulerWrapper,
TaTrainStep,
avg_losses_across_data_parallel_group,
gather_across_data_parallel_groups,
)
from .megatron_lm import initialize as megatron_lm_initialize
from .megatron_lm import prepare_data_loader as megatron_lm_prepare_data_loader
from .megatron_lm import prepare_model as megatron_lm_prepare_model
from .megatron_lm import prepare_optimizer as megatron_lm_prepare_optimizer
from .megatron_lm import prepare_scheduler as megatron_lm_prepare_scheduler
from .memory import find_executable_batch_size, release_memory
from .other import (
extract_model_from_parallel,
get_pretty_name,
is_port_in_use,
merge_dicts,
patch_environment,
save,
wait_for_everyone,
write_basic_config,
)
from .random import set_seed, synchronize_rng_state, synchronize_rng_states
from .torch_xla import install_xla
from .tqdm import tqdm
from .transformer_engine import convert_model, has_transformer_engine_layers
| 690 | 1 |
"""simple docstring"""
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...image_processing_utils import ImageProcessingMixin
from ...utils import CONFIG_NAME, IMAGE_PROCESSOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
CONFIG_MAPPING_NAMES,
AutoConfig,
model_type_to_module_name,
replace_list_option_in_docstrings,
)
UpperCamelCase : int = logging.get_logger(__name__)
UpperCamelCase : Tuple = OrderedDict(
[
("align", "EfficientNetImageProcessor"),
("beit", "BeitImageProcessor"),
("bit", "BitImageProcessor"),
("blip", "BlipImageProcessor"),
("blip-2", "BlipImageProcessor"),
("bridgetower", "BridgeTowerImageProcessor"),
("chinese_clip", "ChineseCLIPImageProcessor"),
("clip", "CLIPImageProcessor"),
("clipseg", "ViTImageProcessor"),
("conditional_detr", "ConditionalDetrImageProcessor"),
("convnext", "ConvNextImageProcessor"),
("convnextv2", "ConvNextImageProcessor"),
("cvt", "ConvNextImageProcessor"),
("data2vec-vision", "BeitImageProcessor"),
("deformable_detr", "DeformableDetrImageProcessor"),
("deit", "DeiTImageProcessor"),
("deta", "DetaImageProcessor"),
("detr", "DetrImageProcessor"),
("dinat", "ViTImageProcessor"),
("donut-swin", "DonutImageProcessor"),
("dpt", "DPTImageProcessor"),
("efficientformer", "EfficientFormerImageProcessor"),
("efficientnet", "EfficientNetImageProcessor"),
("flava", "FlavaImageProcessor"),
("focalnet", "BitImageProcessor"),
("git", "CLIPImageProcessor"),
("glpn", "GLPNImageProcessor"),
("groupvit", "CLIPImageProcessor"),
("imagegpt", "ImageGPTImageProcessor"),
("instructblip", "BlipImageProcessor"),
("layoutlmv2", "LayoutLMv2ImageProcessor"),
("layoutlmv3", "LayoutLMv3ImageProcessor"),
("levit", "LevitImageProcessor"),
("mask2former", "Mask2FormerImageProcessor"),
("maskformer", "MaskFormerImageProcessor"),
("mgp-str", "ViTImageProcessor"),
("mobilenet_v1", "MobileNetV1ImageProcessor"),
("mobilenet_v2", "MobileNetV2ImageProcessor"),
("mobilevit", "MobileViTImageProcessor"),
("mobilevit", "MobileViTImageProcessor"),
("mobilevitv2", "MobileViTImageProcessor"),
("nat", "ViTImageProcessor"),
("oneformer", "OneFormerImageProcessor"),
("owlvit", "OwlViTImageProcessor"),
("perceiver", "PerceiverImageProcessor"),
("pix2struct", "Pix2StructImageProcessor"),
("poolformer", "PoolFormerImageProcessor"),
("regnet", "ConvNextImageProcessor"),
("resnet", "ConvNextImageProcessor"),
("sam", "SamImageProcessor"),
("segformer", "SegformerImageProcessor"),
("swiftformer", "ViTImageProcessor"),
("swin", "ViTImageProcessor"),
("swin2sr", "Swin2SRImageProcessor"),
("swinv2", "ViTImageProcessor"),
("table-transformer", "DetrImageProcessor"),
("timesformer", "VideoMAEImageProcessor"),
("tvlt", "TvltImageProcessor"),
("upernet", "SegformerImageProcessor"),
("van", "ConvNextImageProcessor"),
("videomae", "VideoMAEImageProcessor"),
("vilt", "ViltImageProcessor"),
("vit", "ViTImageProcessor"),
("vit_hybrid", "ViTHybridImageProcessor"),
("vit_mae", "ViTImageProcessor"),
("vit_msn", "ViTImageProcessor"),
("xclip", "CLIPImageProcessor"),
("yolos", "YolosImageProcessor"),
]
)
UpperCamelCase : Tuple = _LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)
def __snake_case ( UpperCamelCase__ ) -> Any:
"""simple docstring"""
for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
if class_name in extractors:
A = model_type_to_module_name(UpperCamelCase__ )
A = importlib.import_module(f'.{module_name}' , 'transformers.models' )
try:
return getattr(UpperCamelCase__ , UpperCamelCase__ )
except AttributeError:
continue
for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
if getattr(UpperCamelCase__ , '__name__' , UpperCamelCase__ ) == class_name:
return extractor
# We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
# init and we return the proper dummy to get an appropriate error message.
A = importlib.import_module('transformers' )
if hasattr(UpperCamelCase__ , UpperCamelCase__ ):
return getattr(UpperCamelCase__ , UpperCamelCase__ )
return None
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ = None , UpperCamelCase__ = False , UpperCamelCase__ = False , UpperCamelCase__ = None , UpperCamelCase__ = None , UpperCamelCase__ = None , UpperCamelCase__ = False , **UpperCamelCase__ , ) -> str:
"""simple docstring"""
A = get_file_from_repo(
UpperCamelCase__ , UpperCamelCase__ , cache_dir=UpperCamelCase__ , force_download=UpperCamelCase__ , resume_download=UpperCamelCase__ , proxies=UpperCamelCase__ , use_auth_token=UpperCamelCase__ , revision=UpperCamelCase__ , local_files_only=UpperCamelCase__ , )
if resolved_config_file is None:
logger.info(
'Could not locate the image processor configuration file, will try to use the model config instead.' )
return {}
with open(UpperCamelCase__ , encoding='utf-8' ) as reader:
return json.load(UpperCamelCase__ )
class lowerCamelCase__ :
def __init__( self : Tuple ):
raise EnvironmentError(
'AutoImageProcessor is designed to be instantiated '
'using the `AutoImageProcessor.from_pretrained(pretrained_model_name_or_path)` method.' )
@classmethod
@replace_list_option_in_docstrings(_lowercase )
def __a ( cls : Dict , _lowercase : Any , **_lowercase : List[Any] ):
A = kwargs.pop('config' , _lowercase )
A = kwargs.pop('trust_remote_code' , _lowercase )
A = True
A , A = ImageProcessingMixin.get_image_processor_dict(_lowercase , **_lowercase )
A = config_dict.get('image_processor_type' , _lowercase )
A = None
if "AutoImageProcessor" in config_dict.get('auto_map' , {} ):
A = config_dict['auto_map']['AutoImageProcessor']
# If we still don't have the image processor class, check if we're loading from a previous feature extractor config
# and if so, infer the image processor class from there.
if image_processor_class is None and image_processor_auto_map is None:
A = config_dict.pop('feature_extractor_type' , _lowercase )
if feature_extractor_class is not None:
logger.warning(
'Could not find image processor class in the image processor config or the model config. Loading'
' based on pattern matching with the model\'s feature extractor configuration.' )
A = feature_extractor_class.replace('FeatureExtractor' , 'ImageProcessor' )
if "AutoFeatureExtractor" in config_dict.get('auto_map' , {} ):
A = config_dict['auto_map']['AutoFeatureExtractor']
A = feature_extractor_auto_map.replace('FeatureExtractor' , 'ImageProcessor' )
logger.warning(
'Could not find image processor auto map in the image processor config or the model config.'
' Loading based on pattern matching with the model\'s feature extractor configuration.' )
# If we don't find the image processor class in the image processor config, let's try the model config.
if image_processor_class is None and image_processor_auto_map is None:
if not isinstance(_lowercase , _lowercase ):
A = AutoConfig.from_pretrained(_lowercase , **_lowercase )
# It could be in `config.image_processor_type``
A = getattr(_lowercase , 'image_processor_type' , _lowercase )
if hasattr(_lowercase , 'auto_map' ) and "AutoImageProcessor" in config.auto_map:
A = config.auto_map['AutoImageProcessor']
if image_processor_class is not None:
A = image_processor_class_from_name(_lowercase )
A = image_processor_auto_map is not None
A = image_processor_class is not None or type(_lowercase ) in IMAGE_PROCESSOR_MAPPING
A = resolve_trust_remote_code(
_lowercase , _lowercase , _lowercase , _lowercase )
if has_remote_code and trust_remote_code:
A = get_class_from_dynamic_module(
_lowercase , _lowercase , **_lowercase )
A = kwargs.pop('code_revision' , _lowercase )
if os.path.isdir(_lowercase ):
image_processor_class.register_for_auto_class()
return image_processor_class.from_dict(_lowercase , **_lowercase )
elif image_processor_class is not None:
return image_processor_class.from_dict(_lowercase , **_lowercase )
# Last try: we use the IMAGE_PROCESSOR_MAPPING.
elif type(_lowercase ) in IMAGE_PROCESSOR_MAPPING:
A = IMAGE_PROCESSOR_MAPPING[type(_lowercase )]
return image_processor_class.from_dict(_lowercase , **_lowercase )
raise ValueError(
f'Unrecognized image processor in {pretrained_model_name_or_path}. Should have a '
f'`image_processor_type` key in its {IMAGE_PROCESSOR_NAME} of {CONFIG_NAME}, or one of the following '
f'`model_type` keys in its {CONFIG_NAME}: {", ".join(c for c in IMAGE_PROCESSOR_MAPPING_NAMES.keys() )}' )
@staticmethod
def __a ( _lowercase : Optional[int] , _lowercase : Dict ):
IMAGE_PROCESSOR_MAPPING.register(_lowercase , _lowercase )
| 690 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
def __snake_case ( UpperCamelCase__ ) -> int:
"""simple docstring"""
if not postfix_notation:
return 0
A = {'+', '-', '*', '/'}
A = []
for token in postfix_notation:
if token in operations:
A , A = stack.pop(), stack.pop()
if token == "+":
stack.append(a + b )
elif token == "-":
stack.append(a - b )
elif token == "*":
stack.append(a * b )
else:
if a * b < 0 and a % b != 0:
stack.append(a // b + 1 )
else:
stack.append(a // b )
else:
stack.append(int(UpperCamelCase__ ) )
return stack.pop()
if __name__ == "__main__":
import doctest
doctest.testmod()
| 690 | 1 |
"""simple docstring"""
def __snake_case ( UpperCamelCase__ ) -> bool:
"""simple docstring"""
A = (1 + 24 * n) ** 0.5
return ((1 + root) / 6) % 1 == 0
def __snake_case ( UpperCamelCase__ = 5000 ) -> int:
"""simple docstring"""
A = [(i * (3 * i - 1)) // 2 for i in range(1 , UpperCamelCase__ )]
for i, pentagonal_i in enumerate(UpperCamelCase__ ):
for j in range(UpperCamelCase__ , len(UpperCamelCase__ ) ):
A = pentagonal_nums[j]
A = pentagonal_i + pentagonal_j
A = pentagonal_j - pentagonal_i
if is_pentagonal(UpperCamelCase__ ) and is_pentagonal(UpperCamelCase__ ):
return b
return -1
if __name__ == "__main__":
print(F"""{solution() = }""")
| 690 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_xlnet import XLNetTokenizer
else:
UpperCamelCase : Any = None
UpperCamelCase : int = logging.get_logger(__name__)
UpperCamelCase : Union[str, Any] = {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}
UpperCamelCase : str = {
"vocab_file": {
"xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model",
"xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model",
},
"tokenizer_file": {
"xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json",
"xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json",
},
}
UpperCamelCase : Optional[int] = {
"xlnet-base-cased": None,
"xlnet-large-cased": None,
}
UpperCamelCase : str = "▁"
# Segments (not really needed)
UpperCamelCase : str = 0
UpperCamelCase : int = 1
UpperCamelCase : List[Any] = 2
UpperCamelCase : Union[str, Any] = 3
UpperCamelCase : Optional[Any] = 4
class lowerCamelCase__ ( UpperCAmelCase_ ):
lowerCAmelCase = VOCAB_FILES_NAMES
lowerCAmelCase = PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCAmelCase = """left"""
lowerCAmelCase = XLNetTokenizer
def __init__( self : Tuple , _lowercase : List[Any]=None , _lowercase : Any=None , _lowercase : int=False , _lowercase : Tuple=True , _lowercase : Union[str, Any]=False , _lowercase : int="<s>" , _lowercase : Optional[int]="</s>" , _lowercase : Dict="<unk>" , _lowercase : Optional[int]="<sep>" , _lowercase : int="<pad>" , _lowercase : Dict="<cls>" , _lowercase : str="<mask>" , _lowercase : List[str]=["<eop>", "<eod>"] , **_lowercase : Any , ):
# Mask token behave like a normal word, i.e. include the space before it
A = AddedToken(_lowercase , lstrip=_lowercase , rstrip=_lowercase ) if isinstance(_lowercase , _lowercase ) else mask_token
super().__init__(
vocab_file=_lowercase , tokenizer_file=_lowercase , do_lower_case=_lowercase , remove_space=_lowercase , keep_accents=_lowercase , bos_token=_lowercase , eos_token=_lowercase , unk_token=_lowercase , sep_token=_lowercase , pad_token=_lowercase , cls_token=_lowercase , mask_token=_lowercase , additional_special_tokens=_lowercase , **_lowercase , )
A = 3
A = do_lower_case
A = remove_space
A = keep_accents
A = vocab_file
A = False if not self.vocab_file else True
def __a ( self : List[Any] , _lowercase : List[int] , _lowercase : Optional[List[int]] = None ):
A = [self.sep_token_id]
A = [self.cls_token_id]
if token_ids_a is None:
return token_ids_a + sep + cls
return token_ids_a + sep + token_ids_a + sep + cls
def __a ( self : Tuple , _lowercase : List[int] , _lowercase : Optional[List[int]] = None ):
A = [self.sep_token_id]
A = [2]
if token_ids_a is None:
return len(token_ids_a + sep ) * [0] + cls_segment_id
return len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] + cls_segment_id
def __a ( self : Optional[Any] , _lowercase : str , _lowercase : Optional[str] = None ):
if not self.can_save_slow_tokenizer:
raise ValueError(
'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
'tokenizer.' )
if not os.path.isdir(_lowercase ):
logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
return
A = os.path.join(
_lowercase , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_lowercase ):
copyfile(self.vocab_file , _lowercase )
return (out_vocab_file,)
| 690 | 1 |
"""simple docstring"""
from typing import Any
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , ) -> list:
"""simple docstring"""
_validation(
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , )
# Creates data structures and fill initial step
A = {}
A = {}
for state in states_space:
A = observations_space[0]
A = (
initial_probabilities[state] * emission_probabilities[state][observation]
)
A = None
# Fills the data structure with the probabilities of
# different transitions and pointers to previous states
for o in range(1 , len(UpperCamelCase__ ) ):
A = observations_space[o]
A = observations_space[o - 1]
for state in states_space:
# Calculates the argmax for probability function
A = ''
A = -1
for k_state in states_space:
A = (
probabilities[(k_state, prior_observation)]
* transition_probabilities[k_state][state]
* emission_probabilities[state][observation]
)
if probability > max_probability:
A = probability
A = k_state
# Update probabilities and pointers dicts
A = (
probabilities[(arg_max, prior_observation)]
* transition_probabilities[arg_max][state]
* emission_probabilities[state][observation]
)
A = arg_max
# The final observation
A = observations_space[len(UpperCamelCase__ ) - 1]
# argmax for given final observation
A = ''
A = -1
for k_state in states_space:
A = probabilities[(k_state, final_observation)]
if probability > max_probability:
A = probability
A = k_state
A = arg_max
# Process pointers backwards
A = last_state
A = []
for o in range(len(UpperCamelCase__ ) - 1 , -1 , -1 ):
result.append(UpperCamelCase__ )
A = pointers[previous, observations_space[o]]
result.reverse()
return result
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , ) -> None:
"""simple docstring"""
_validate_not_empty(
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , )
_validate_lists(UpperCamelCase__ , UpperCamelCase__ )
_validate_dicts(
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , ) -> None:
"""simple docstring"""
if not all(
[
observations_space,
states_space,
initial_probabilities,
transition_probabilities,
emission_probabilities,
] ):
raise ValueError('There\'s an empty parameter' )
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ ) -> None:
"""simple docstring"""
_validate_list(UpperCamelCase__ , 'observations_space' )
_validate_list(UpperCamelCase__ , 'states_space' )
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ ) -> None:
"""simple docstring"""
if not isinstance(_object , UpperCamelCase__ ):
A = f'{var_name} must be a list'
raise ValueError(UpperCamelCase__ )
else:
for x in _object:
if not isinstance(UpperCamelCase__ , UpperCamelCase__ ):
A = f'{var_name} must be a list of strings'
raise ValueError(UpperCamelCase__ )
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , ) -> None:
"""simple docstring"""
_validate_dict(UpperCamelCase__ , 'initial_probabilities' , UpperCamelCase__ )
_validate_nested_dict(UpperCamelCase__ , 'transition_probabilities' )
_validate_nested_dict(UpperCamelCase__ , 'emission_probabilities' )
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ ) -> None:
"""simple docstring"""
_validate_dict(_object , UpperCamelCase__ , UpperCamelCase__ )
for x in _object.values():
_validate_dict(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = False ) -> None:
"""simple docstring"""
if not isinstance(_object , UpperCamelCase__ ):
A = f'{var_name} must be a dict'
raise ValueError(UpperCamelCase__ )
if not all(isinstance(UpperCamelCase__ , UpperCamelCase__ ) for x in _object ):
A = f'{var_name} all keys must be strings'
raise ValueError(UpperCamelCase__ )
if not all(isinstance(UpperCamelCase__ , UpperCamelCase__ ) for x in _object.values() ):
A = 'nested dictionary ' if nested else ''
A = f'{var_name} {nested_text}all values must be {value_type.__name__}'
raise ValueError(UpperCamelCase__ )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 690 |
"""simple docstring"""
from __future__ import annotations
UpperCamelCase : Any = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , ) -> tuple[list[list[int]], list[list[int]]]:
"""simple docstring"""
A = [
[0 for col in range(len(grid[0] ) )] for row in range(len(UpperCamelCase__ ) )
] # the reference grid
A = 1
A = [
[0 for col in range(len(grid[0] ) )] for row in range(len(UpperCamelCase__ ) )
] # the action grid
A = init[0]
A = init[1]
A = 0
A = g + heuristic[x][y] # cost from starting cell to destination cell
A = [[f, g, x, y]]
A = False # flag that is set when search is complete
A = False # flag set if we can't find expand
while not found and not resign:
if len(UpperCamelCase__ ) == 0:
raise ValueError('Algorithm is unable to find solution' )
else: # to choose the least costliest action so as to move closer to the goal
cell.sort()
cell.reverse()
A = cell.pop()
A = next_cell[2]
A = next_cell[3]
A = next_cell[1]
if x == goal[0] and y == goal[1]:
A = True
else:
for i in range(len(UpperCamelCase__ ) ): # to try out different valid actions
A = x + DIRECTIONS[i][0]
A = y + DIRECTIONS[i][1]
if xa >= 0 and xa < len(UpperCamelCase__ ) and ya >= 0 and ya < len(grid[0] ):
if closed[xa][ya] == 0 and grid[xa][ya] == 0:
A = g + cost
A = ga + heuristic[xa][ya]
cell.append([fa, ga, xa, ya] )
A = 1
A = i
A = []
A = goal[0]
A = goal[1]
invpath.append([x, y] ) # we get the reverse path from here
while x != init[0] or y != init[1]:
A = x - DIRECTIONS[action[x][y]][0]
A = y - DIRECTIONS[action[x][y]][1]
A = xa
A = ya
invpath.append([x, y] )
A = []
for i in range(len(UpperCamelCase__ ) ):
path.append(invpath[len(UpperCamelCase__ ) - 1 - i] )
return path, action
if __name__ == "__main__":
UpperCamelCase : Any = [
[0, 1, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 1, 0, 0, 0, 0],
[0, 1, 0, 0, 1, 0],
[0, 0, 0, 0, 1, 0],
]
UpperCamelCase : List[Any] = [0, 0]
# all coordinates are given in format [y,x]
UpperCamelCase : int = [len(grid) - 1, len(grid[0]) - 1]
UpperCamelCase : Tuple = 1
# the cost map which pushes the path closer to the goal
UpperCamelCase : Union[str, Any] = [[0 for row in range(len(grid[0]))] for col in range(len(grid))]
for i in range(len(grid)):
for j in range(len(grid[0])):
UpperCamelCase : List[str] = abs(i - goal[0]) + abs(j - goal[1])
if grid[i][j] == 1:
# added extra penalty in the heuristic map
UpperCamelCase : Dict = 99
UpperCamelCase , UpperCamelCase : Optional[Any] = search(grid, init, goal, cost, heuristic)
print("ACTION MAP")
for i in range(len(action)):
print(action[i])
for i in range(len(path)):
print(path[i])
| 690 | 1 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokenizer
UpperCamelCase : int = logging.get_logger(__name__)
UpperCamelCase : List[str] = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
UpperCamelCase : Optional[int] = {
"vocab_file": {
"google/realm-cc-news-pretrained-embedder": (
"https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/vocab.txt"
),
"google/realm-cc-news-pretrained-encoder": (
"https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/vocab.txt"
),
"google/realm-cc-news-pretrained-scorer": (
"https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/vocab.txt"
),
"google/realm-cc-news-pretrained-openqa": (
"https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/vocab.txt"
),
"google/realm-orqa-nq-openqa": "https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/vocab.txt",
"google/realm-orqa-nq-reader": "https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/vocab.txt",
"google/realm-orqa-wq-openqa": "https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/vocab.txt",
"google/realm-orqa-wq-reader": "https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/vocab.txt",
},
"tokenizer_file": {
"google/realm-cc-news-pretrained-embedder": (
"https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/tokenizer.jsont"
),
"google/realm-cc-news-pretrained-encoder": (
"https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/tokenizer.json"
),
"google/realm-cc-news-pretrained-scorer": (
"https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/tokenizer.json"
),
"google/realm-cc-news-pretrained-openqa": (
"https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/tokenizer.json"
),
"google/realm-orqa-nq-openqa": (
"https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/tokenizer.json"
),
"google/realm-orqa-nq-reader": (
"https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/tokenizer.json"
),
"google/realm-orqa-wq-openqa": (
"https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/tokenizer.json"
),
"google/realm-orqa-wq-reader": (
"https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/tokenizer.json"
),
},
}
UpperCamelCase : Optional[int] = {
"google/realm-cc-news-pretrained-embedder": 512,
"google/realm-cc-news-pretrained-encoder": 512,
"google/realm-cc-news-pretrained-scorer": 512,
"google/realm-cc-news-pretrained-openqa": 512,
"google/realm-orqa-nq-openqa": 512,
"google/realm-orqa-nq-reader": 512,
"google/realm-orqa-wq-openqa": 512,
"google/realm-orqa-wq-reader": 512,
}
UpperCamelCase : Tuple = {
"google/realm-cc-news-pretrained-embedder": {"do_lower_case": True},
"google/realm-cc-news-pretrained-encoder": {"do_lower_case": True},
"google/realm-cc-news-pretrained-scorer": {"do_lower_case": True},
"google/realm-cc-news-pretrained-openqa": {"do_lower_case": True},
"google/realm-orqa-nq-openqa": {"do_lower_case": True},
"google/realm-orqa-nq-reader": {"do_lower_case": True},
"google/realm-orqa-wq-openqa": {"do_lower_case": True},
"google/realm-orqa-wq-reader": {"do_lower_case": True},
}
class lowerCamelCase__ ( UpperCAmelCase_ ):
lowerCAmelCase = VOCAB_FILES_NAMES
lowerCAmelCase = PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase = PRETRAINED_INIT_CONFIGURATION
lowerCAmelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCAmelCase = RealmTokenizer
def __init__( self : Any , _lowercase : List[Any]=None , _lowercase : Any=None , _lowercase : Optional[Any]=True , _lowercase : Dict="[UNK]" , _lowercase : Optional[int]="[SEP]" , _lowercase : List[str]="[PAD]" , _lowercase : List[str]="[CLS]" , _lowercase : int="[MASK]" , _lowercase : List[Any]=True , _lowercase : Tuple=None , **_lowercase : Tuple , ):
super().__init__(
_lowercase , tokenizer_file=_lowercase , do_lower_case=_lowercase , unk_token=_lowercase , sep_token=_lowercase , pad_token=_lowercase , cls_token=_lowercase , mask_token=_lowercase , tokenize_chinese_chars=_lowercase , strip_accents=_lowercase , **_lowercase , )
A = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('lowercase' , _lowercase ) != do_lower_case
or normalizer_state.get('strip_accents' , _lowercase ) != strip_accents
or normalizer_state.get('handle_chinese_chars' , _lowercase ) != tokenize_chinese_chars
):
A = getattr(_lowercase , normalizer_state.pop('type' ) )
A = do_lower_case
A = strip_accents
A = tokenize_chinese_chars
A = normalizer_class(**_lowercase )
A = do_lower_case
def __a ( self : List[Any] , _lowercase : str , **_lowercase : str ):
A = PaddingStrategy.MAX_LENGTH
A = text
A = kwargs.pop('text_pair' , _lowercase )
A = kwargs.pop('return_tensors' , _lowercase )
A = {
'input_ids': [],
'attention_mask': [],
'token_type_ids': [],
}
for idx, candidate_text in enumerate(_lowercase ):
if batch_text_pair is not None:
A = batch_text_pair[idx]
else:
A = None
A = super().__call__(_lowercase , _lowercase , return_tensors=_lowercase , **_lowercase )
A = encoded_candidates.get('input_ids' )
A = encoded_candidates.get('attention_mask' )
A = encoded_candidates.get('token_type_ids' )
if encoded_input_ids is not None:
output_data["input_ids"].append(_lowercase )
if encoded_attention_mask is not None:
output_data["attention_mask"].append(_lowercase )
if encoded_token_type_ids is not None:
output_data["token_type_ids"].append(_lowercase )
A = {key: item for key, item in output_data.items() if len(_lowercase ) != 0}
return BatchEncoding(_lowercase , tensor_type=_lowercase )
def __a ( self : int , _lowercase : Tuple , _lowercase : Dict=None ):
A = [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 __a ( self : List[Any] , _lowercase : List[int] , _lowercase : Optional[List[int]] = None ):
A = [self.sep_token_id]
A = [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 __a ( self : Optional[int] , _lowercase : str , _lowercase : Optional[str] = None ):
A = self._tokenizer.model.save(_lowercase , name=_lowercase )
return tuple(_lowercase )
| 690 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCamelCase : Optional[int] = logging.get_logger(__name__)
UpperCamelCase : int = {"vocab_file": "sentencepiece.model"}
UpperCamelCase : Union[str, Any] = {
"vocab_file": {
"google/rembert": "https://huggingface.co/google/rembert/resolve/main/sentencepiece.model",
},
}
UpperCamelCase : Union[str, Any] = {
"google/rembert": 256,
}
class lowerCamelCase__ ( UpperCAmelCase_ ):
lowerCAmelCase = VOCAB_FILES_NAMES
lowerCAmelCase = PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__( self : Dict , _lowercase : Optional[Any] , _lowercase : Optional[Any]=False , _lowercase : Dict=True , _lowercase : List[str]=True , _lowercase : int="[CLS]" , _lowercase : str="[SEP]" , _lowercase : List[str]="[UNK]" , _lowercase : List[Any]="[SEP]" , _lowercase : Union[str, Any]="[PAD]" , _lowercase : List[str]="[CLS]" , _lowercase : Any="[MASK]" , **_lowercase : Optional[Any] , ):
super().__init__(
do_lower_case=_lowercase , remove_space=_lowercase , keep_accents=_lowercase , bos_token=_lowercase , eos_token=_lowercase , unk_token=_lowercase , sep_token=_lowercase , pad_token=_lowercase , cls_token=_lowercase , mask_token=_lowercase , **_lowercase , )
A = do_lower_case
A = remove_space
A = keep_accents
A = vocab_file
A = spm.SentencePieceProcessor()
self.sp_model.Load(_lowercase )
@property
def __a ( self : Tuple ):
return len(self.sp_model )
def __a ( self : List[str] ):
A = {self.convert_ids_to_tokens(_lowercase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self : Tuple ):
A = self.__dict__.copy()
A = None
return state
def __setstate__( self : List[str] , _lowercase : int ):
A = d
A = spm.SentencePieceProcessor()
self.sp_model.Load(self.vocab_file )
def __a ( self : Dict , _lowercase : Union[str, Any] , _lowercase : Dict=False ):
A = self.sp_model.EncodeAsPieces(_lowercase )
return pieces
def __a ( self : Dict , _lowercase : Tuple ):
return self.sp_model.PieceToId(_lowercase )
def __a ( self : str , _lowercase : Optional[int] ):
return self.sp_model.IdToPiece(_lowercase )
def __a ( self : Optional[int] , _lowercase : Optional[int] ):
A = self.sp_model.decode_pieces(_lowercase )
return out_string
def __a ( self : Optional[int] , _lowercase : List[int] , _lowercase : Optional[List[int]] = None ):
A = [self.sep_token_id]
A = [self.cls_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def __a ( self : Any , _lowercase : List[int] , _lowercase : Optional[List[int]] = None , _lowercase : bool = False ):
if already_has_special_tokens:
if token_ids_a is not None:
raise ValueError(
'You should not supply a second sequence if the provided sequence of '
'ids is already formatted with special tokens for the model.' )
return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]
if token_ids_a is not None:
return [1] + ([0] * len(_lowercase )) + [1] + ([0] * len(_lowercase )) + [1]
return [1] + ([0] * len(_lowercase )) + [1]
def __a ( self : str , _lowercase : List[int] , _lowercase : Optional[List[int]] = None ):
A = [self.sep_token_id]
A = [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 __a ( self : Optional[Any] , _lowercase : str , _lowercase : Optional[str] = None ):
if not os.path.isdir(_lowercase ):
logger.error('Vocabulary path ({}) should be a directory'.format(_lowercase ) )
return
A = os.path.join(
_lowercase , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_lowercase ):
copyfile(self.vocab_file , _lowercase )
return (out_vocab_file,)
| 690 | 1 |
"""simple docstring"""
import torch
from transformers import CamembertForMaskedLM, CamembertTokenizer
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__=5 ) -> int:
"""simple docstring"""
assert masked_input.count('<mask>' ) == 1
A = torch.tensor(tokenizer.encode(UpperCamelCase__ , add_special_tokens=UpperCamelCase__ ) ).unsqueeze(0 ) # Batch size 1
A = model(UpperCamelCase__ )[0] # The last hidden-state is the first element of the output tuple
A = (input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
A = logits[0, masked_index, :]
A = logits.softmax(dim=0 )
A , A = prob.topk(k=UpperCamelCase__ , dim=0 )
A = ' '.join(
[tokenizer.convert_ids_to_tokens(indices[i].item() ) for i in range(len(UpperCamelCase__ ) )] )
A = tokenizer.mask_token
A = []
for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(' ' ) ):
A = predicted_token_bpe.replace('\u2581' , ' ' )
if " {0}".format(UpperCamelCase__ ) in masked_input:
topk_filled_outputs.append(
(
masked_input.replace(' {0}'.format(UpperCamelCase__ ) , UpperCamelCase__ ),
values[index].item(),
predicted_token,
) )
else:
topk_filled_outputs.append(
(
masked_input.replace(UpperCamelCase__ , UpperCamelCase__ ),
values[index].item(),
predicted_token,
) )
return topk_filled_outputs
UpperCamelCase : List[str] = CamembertTokenizer.from_pretrained("camembert-base")
UpperCamelCase : Optional[int] = CamembertForMaskedLM.from_pretrained("camembert-base")
model.eval()
UpperCamelCase : int = "Le camembert est <mask> :)"
print(fill_mask(masked_input, model, tokenizer, topk=3))
| 690 |
"""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_mobilebert import MobileBertTokenizer
UpperCamelCase : str = logging.get_logger(__name__)
UpperCamelCase : List[str] = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
UpperCamelCase : List[Any] = {
"vocab_file": {"mobilebert-uncased": "https://huggingface.co/google/mobilebert-uncased/resolve/main/vocab.txt"},
"tokenizer_file": {
"mobilebert-uncased": "https://huggingface.co/google/mobilebert-uncased/resolve/main/tokenizer.json"
},
}
UpperCamelCase : Any = {"mobilebert-uncased": 512}
UpperCamelCase : Any = {}
class lowerCamelCase__ ( UpperCAmelCase_ ):
lowerCAmelCase = VOCAB_FILES_NAMES
lowerCAmelCase = PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase = PRETRAINED_INIT_CONFIGURATION
lowerCAmelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCAmelCase = MobileBertTokenizer
def __init__( self : Optional[int] , _lowercase : Optional[int]=None , _lowercase : Any=None , _lowercase : Optional[int]=True , _lowercase : int="[UNK]" , _lowercase : Dict="[SEP]" , _lowercase : Any="[PAD]" , _lowercase : str="[CLS]" , _lowercase : Union[str, Any]="[MASK]" , _lowercase : List[Any]=True , _lowercase : Any=None , **_lowercase : Optional[Any] , ):
super().__init__(
_lowercase , tokenizer_file=_lowercase , do_lower_case=_lowercase , unk_token=_lowercase , sep_token=_lowercase , pad_token=_lowercase , cls_token=_lowercase , mask_token=_lowercase , tokenize_chinese_chars=_lowercase , strip_accents=_lowercase , **_lowercase , )
A = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('lowercase' , _lowercase ) != do_lower_case
or normalizer_state.get('strip_accents' , _lowercase ) != strip_accents
or normalizer_state.get('handle_chinese_chars' , _lowercase ) != tokenize_chinese_chars
):
A = getattr(_lowercase , normalizer_state.pop('type' ) )
A = do_lower_case
A = strip_accents
A = tokenize_chinese_chars
A = normalizer_class(**_lowercase )
A = do_lower_case
def __a ( self : List[Any] , _lowercase : Tuple , _lowercase : Any=None ):
A = [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 __a ( self : Any , _lowercase : List[int] , _lowercase : Optional[List[int]] = None ):
A = [self.sep_token_id]
A = [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 __a ( self : Dict , _lowercase : str , _lowercase : Optional[str] = None ):
A = self._tokenizer.model.save(_lowercase , name=_lowercase )
return tuple(_lowercase )
| 690 | 1 |
"""simple docstring"""
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> List[Any]:
"""simple docstring"""
A = TaConfig.from_json_file(UpperCamelCase__ )
print(f'Building PyTorch model from configuration: {config}' )
A = TaForConditionalGeneration(UpperCamelCase__ )
# Load weights from tf checkpoint
load_tf_weights_in_ta(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
# Save pytorch-model
print(f'Save PyTorch model to {pytorch_dump_path}' )
model.save_pretrained(UpperCamelCase__ )
if __name__ == "__main__":
UpperCamelCase : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
)
parser.add_argument(
"--config_file",
default=None,
type=str,
required=True,
help=(
"The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture."
),
)
parser.add_argument(
"--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
UpperCamelCase : str = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
| 690 |
"""simple docstring"""
def __snake_case ( UpperCamelCase__ ) -> list[int]:
"""simple docstring"""
A = [0 for i in range(len(UpperCamelCase__ ) )]
# initialize interval's left pointer and right pointer
A , A = 0, 0
for i in range(1 , len(UpperCamelCase__ ) ):
# case when current index is inside the interval
if i <= right_pointer:
A = min(right_pointer - i + 1 , z_result[i - left_pointer] )
A = min_edge
while go_next(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
z_result[i] += 1
# if new index's result gives us more right interval,
# we've to update left_pointer and right_pointer
if i + z_result[i] - 1 > right_pointer:
A , A = i, i + z_result[i] - 1
return z_result
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> bool:
"""simple docstring"""
return i + z_result[i] < len(UpperCamelCase__ ) and s[z_result[i]] == s[i + z_result[i]]
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ ) -> int:
"""simple docstring"""
A = 0
# concatenate 'pattern' and 'input_str' and call z_function
# with concatenated string
A = z_function(pattern + input_str )
for val in z_result:
# if value is greater then length of the pattern string
# that means this index is starting position of substring
# which is equal to pattern string
if val >= len(UpperCamelCase__ ):
answer += 1
return answer
if __name__ == "__main__":
import doctest
doctest.testmod()
| 690 | 1 |
"""simple docstring"""
import argparse
import os
# New Code #
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.utils import find_executable_batch_size
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, 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)
#
# 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
#
########################################################################
UpperCamelCase : List[str] = 16
UpperCamelCase : Tuple = 32
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ = 16 ) -> Any:
"""simple docstring"""
A = AutoTokenizer.from_pretrained('bert-base-cased' )
A = load_dataset('glue' , 'mrpc' )
def tokenize_function(UpperCamelCase__ ):
# max_length=None => use the model max length (it's actually the default)
A = tokenizer(examples['sentence1'] , examples['sentence2'] , truncation=UpperCamelCase__ , max_length=UpperCamelCase__ )
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():
A = datasets.map(
UpperCamelCase__ , batched=UpperCamelCase__ , 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
A = tokenized_datasets.rename_column('label' , 'labels' )
def collate_fn(UpperCamelCase__ ):
# On TPU it's best to pad everything to the same length or training will be very slow.
A = 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":
A = 16
elif accelerator.mixed_precision != "no":
A = 8
else:
A = None
return tokenizer.pad(
UpperCamelCase__ , padding='longest' , max_length=UpperCamelCase__ , pad_to_multiple_of=UpperCamelCase__ , return_tensors='pt' , )
# Instantiate dataloaders.
A = DataLoader(
tokenized_datasets['train'] , shuffle=UpperCamelCase__ , collate_fn=UpperCamelCase__ , batch_size=UpperCamelCase__ )
A = DataLoader(
tokenized_datasets['validation'] , shuffle=UpperCamelCase__ , collate_fn=UpperCamelCase__ , batch_size=UpperCamelCase__ )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
UpperCamelCase : int = mocked_dataloaders # noqa: F811
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ ) -> List[Any]:
"""simple docstring"""
if os.environ.get('TESTING_MOCKED_DATALOADERS' , UpperCamelCase__ ) == "1":
A = 2
# Initialize accelerator
A = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
A = config['lr']
A = int(config['num_epochs'] )
A = int(config['seed'] )
A = int(config['batch_size'] )
A = evaluate.load('glue' , 'mrpc' )
# New Code #
# We now can define an inner training loop function. It should take a batch size as the only parameter,
# and build the dataloaders in there.
# It also gets our decorator
@find_executable_batch_size(starting_batch_size=UpperCamelCase__ )
def inner_training_loop(UpperCamelCase__ ):
# And now just move everything below under this function
# We need to bring in the Accelerator object from earlier
nonlocal accelerator
# And reset all of its attributes that could hold onto any memory:
accelerator.free_memory()
# Then we can declare the model, optimizer, and everything else:
set_seed(UpperCamelCase__ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
A = AutoModelForSequenceClassification.from_pretrained('bert-base-cased' , return_dict=UpperCamelCase__ )
# 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).
A = model.to(accelerator.device )
# Instantiate optimizer
A = AdamW(params=model.parameters() , lr=UpperCamelCase__ )
A , A = get_dataloaders(UpperCamelCase__ , UpperCamelCase__ )
# Instantiate scheduler
A = get_linear_schedule_with_warmup(
optimizer=UpperCamelCase__ , num_warmup_steps=100 , num_training_steps=(len(UpperCamelCase__ ) * num_epochs) , )
# 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.
A , A , A , A , A = accelerator.prepare(
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
# Now we train the model
for epoch in range(UpperCamelCase__ ):
model.train()
for step, batch in enumerate(UpperCamelCase__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
A = model(**UpperCamelCase__ )
A = outputs.loss
accelerator.backward(UpperCamelCase__ )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(UpperCamelCase__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
A = model(**UpperCamelCase__ )
A = outputs.logits.argmax(dim=-1 )
A , A = accelerator.gather_for_metrics((predictions, batch['labels']) )
metric.add_batch(
predictions=UpperCamelCase__ , references=UpperCamelCase__ , )
A = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(f'epoch {epoch}:' , UpperCamelCase__ )
# New Code #
# And call it at the end with no arguments
# Note: You could also refactor this outside of your training loop function
inner_training_loop()
def __snake_case ( ) -> Optional[Any]:
"""simple docstring"""
A = argparse.ArgumentParser(description='Simple example of training script.' )
parser.add_argument(
'--mixed_precision' , type=UpperCamelCase__ , default=UpperCamelCase__ , 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.' )
A = parser.parse_args()
A = {'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
training_function(UpperCamelCase__ , UpperCamelCase__ )
if __name__ == "__main__":
main()
| 690 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
enable_full_determinism,
load_numpy,
nightly,
require_torch_gpu,
slow,
torch_device,
)
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class lowerCamelCase__ ( UpperCAmelCase_ , unittest.TestCase ):
lowerCAmelCase = LDMTextToImagePipeline
lowerCAmelCase = TEXT_TO_IMAGE_PARAMS - {
"""negative_prompt""",
"""negative_prompt_embeds""",
"""cross_attention_kwargs""",
"""prompt_embeds""",
}
lowerCAmelCase = PipelineTesterMixin.required_optional_params - {
"""num_images_per_prompt""",
"""callback""",
"""callback_steps""",
}
lowerCAmelCase = TEXT_TO_IMAGE_BATCH_PARAMS
lowerCAmelCase = False
def __a ( self : Dict ):
torch.manual_seed(0 )
A = 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 , )
A = DDIMScheduler(
beta_start=0.0_0_0_8_5 , beta_end=0.0_1_2 , beta_schedule='scaled_linear' , clip_sample=_lowercase , set_alpha_to_one=_lowercase , )
torch.manual_seed(0 )
A = 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 )
A = 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=1_000 , )
A = CLIPTextModel(_lowercase )
A = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
A = {
'unet': unet,
'scheduler': scheduler,
'vqvae': vae,
'bert': text_encoder,
'tokenizer': tokenizer,
}
return components
def __a ( self : Union[str, Any] , _lowercase : Union[str, Any] , _lowercase : Union[str, Any]=0 ):
if str(_lowercase ).startswith('mps' ):
A = torch.manual_seed(_lowercase )
else:
A = torch.Generator(device=_lowercase ).manual_seed(_lowercase )
A = {
'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 __a ( self : Any ):
A = 'cpu' # ensure determinism for the device-dependent torch.Generator
A = self.get_dummy_components()
A = LDMTextToImagePipeline(**_lowercase )
pipe.to(_lowercase )
pipe.set_progress_bar_config(disable=_lowercase )
A = self.get_dummy_inputs(_lowercase )
A = pipe(**_lowercase ).images
A = image[0, -3:, -3:, -1]
assert image.shape == (1, 16, 16, 3)
A = np.array([0.6_1_0_1, 0.6_1_5_6, 0.5_6_2_2, 0.4_8_9_5, 0.6_6_6_1, 0.3_8_0_4, 0.5_7_4_8, 0.6_1_3_6, 0.5_0_1_4] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
@slow
@require_torch_gpu
class lowerCamelCase__ ( unittest.TestCase ):
def __a ( self : Optional[Any] ):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __a ( self : int , _lowercase : List[Any] , _lowercase : int=torch.floataa , _lowercase : int=0 ):
A = torch.manual_seed(_lowercase )
A = np.random.RandomState(_lowercase ).standard_normal((1, 4, 32, 32) )
A = torch.from_numpy(_lowercase ).to(device=_lowercase , dtype=_lowercase )
A = {
'prompt': 'A painting of a squirrel eating a burger',
'latents': latents,
'generator': generator,
'num_inference_steps': 3,
'guidance_scale': 6.0,
'output_type': 'numpy',
}
return inputs
def __a ( self : Union[str, Any] ):
A = LDMTextToImagePipeline.from_pretrained('CompVis/ldm-text2im-large-256' ).to(_lowercase )
pipe.set_progress_bar_config(disable=_lowercase )
A = self.get_inputs(_lowercase )
A = pipe(**_lowercase ).images
A = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 256, 256, 3)
A = np.array([0.5_1_8_2_5, 0.5_2_8_5_0, 0.5_2_5_4_3, 0.5_4_2_5_8, 0.5_2_3_0_4, 0.5_2_5_6_9, 0.5_4_3_6_3, 0.5_5_2_7_6, 0.5_6_8_7_8] )
A = np.abs(expected_slice - image_slice ).max()
assert max_diff < 1e-3
@nightly
@require_torch_gpu
class lowerCamelCase__ ( unittest.TestCase ):
def __a ( self : List[Any] ):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __a ( self : List[Any] , _lowercase : Optional[Any] , _lowercase : Tuple=torch.floataa , _lowercase : Optional[Any]=0 ):
A = torch.manual_seed(_lowercase )
A = np.random.RandomState(_lowercase ).standard_normal((1, 4, 32, 32) )
A = torch.from_numpy(_lowercase ).to(device=_lowercase , dtype=_lowercase )
A = {
'prompt': 'A painting of a squirrel eating a burger',
'latents': latents,
'generator': generator,
'num_inference_steps': 50,
'guidance_scale': 6.0,
'output_type': 'numpy',
}
return inputs
def __a ( self : List[str] ):
A = LDMTextToImagePipeline.from_pretrained('CompVis/ldm-text2im-large-256' ).to(_lowercase )
pipe.set_progress_bar_config(disable=_lowercase )
A = self.get_inputs(_lowercase )
A = pipe(**_lowercase ).images[0]
A = load_numpy(
'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/ldm_text2img/ldm_large_256_ddim.npy' )
A = np.abs(expected_image - image ).max()
assert max_diff < 1e-3
| 690 | 1 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
UpperCamelCase : Dict = logging.get_logger(__name__)
UpperCamelCase : Tuple = {
"post_extract_proj": "feature_projection.projection",
"encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
"self_attn.linear_k": "encoder.layers.*.self_attn.linear_k",
"self_attn.linear_v": "encoder.layers.*.self_attn.linear_v",
"self_attn.linear_q": "encoder.layers.*.self_attn.linear_q",
"self_attn.pos_bias_u": "encoder.layers.*.self_attn.pos_bias_u",
"self_attn.pos_bias_v": "encoder.layers.*.self_attn.pos_bias_v",
"self_attn.linear_out": "encoder.layers.*.self_attn.linear_out",
"self_attn.linear_pos": "encoder.layers.*.self_attn.linear_pos",
"self_attn.rotary_emb": "encoder.embed_positions",
"self_attn_layer_norm": "encoder.layers.*.self_attn_layer_norm",
"conv_module.pointwise_conv1": "encoder.layers.*.conv_module.pointwise_conv1",
"conv_module.pointwise_conv2": "encoder.layers.*.conv_module.pointwise_conv2",
"conv_module.depthwise_conv": "encoder.layers.*.conv_module.depthwise_conv",
"conv_module.batch_norm": "encoder.layers.*.conv_module.batch_norm",
"conv_module.layer_norm": "encoder.layers.*.conv_module.layer_norm",
"ffn1.w_1": "encoder.layers.*.ffn1.intermediate_dense",
"ffn1.w_2": "encoder.layers.*.ffn1.output_dense",
"ffn1.layer_norm": "encoder.layers.*.ffn1_layer_norm",
"ffn2.w_1": "encoder.layers.*.ffn2.intermediate_dense",
"ffn2.w_2": "encoder.layers.*.ffn2.output_dense",
"ffn2.layer_norm": "encoder.layers.*.ffn2_layer_norm",
"final_layer_norm": "encoder.layers.*.final_layer_norm",
"encoder.layer_norm": "encoder.layer_norm",
"w2v_model.layer_norm": "feature_projection.layer_norm",
"quantizer.weight_proj": "quantizer.weight_proj",
"quantizer.vars": "quantizer.codevectors",
"project_q": "project_q",
"final_proj": "project_hid",
"w2v_encoder.proj": "lm_head",
"mask_emb": "masked_spec_embed",
}
UpperCamelCase : Optional[int] = [
"lm_head",
"quantizer.weight_proj",
"quantizer.codevectors",
"project_q",
"project_hid",
]
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> Any:
"""simple docstring"""
for attribute in key.split('.' ):
A = getattr(UpperCamelCase__ , UpperCamelCase__ )
if weight_type is not None:
A = getattr(UpperCamelCase__ , UpperCamelCase__ ).shape
else:
A = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
f'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
f' {value.shape} for {full_name}' )
if weight_type == "weight":
A = value
elif weight_type == "weight_g":
A = value
elif weight_type == "weight_v":
A = value
elif weight_type == "bias":
A = value
elif weight_type == "running_mean":
A = value
elif weight_type == "running_var":
A = value
elif weight_type == "num_batches_tracked":
A = value
elif weight_type == "inv_freq":
A = value
else:
A = value
logger.info(f'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.' )
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> Any:
"""simple docstring"""
A = []
A = fairseq_model.state_dict()
A = hf_model.wavaveca_conformer.feature_extractor
for name, value in fairseq_dict.items():
A = False
if "conv_layers" in name:
load_conv_layer(
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , hf_model.config.feat_extract_norm == 'group' , )
A = True
else:
for key, mapped_key in MAPPING.items():
A = 'wav2vec2_conformer.' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split('w2v_model.' )[-1] == name.split('.' )[0]:
A = True
if "*" in mapped_key:
A = name.split(UpperCamelCase__ )[0].split('.' )[-2]
A = mapped_key.replace('*' , UpperCamelCase__ )
if "pos_bias_u" in name:
A = None
elif "pos_bias_v" in name:
A = None
elif "weight_g" in name:
A = 'weight_g'
elif "weight_v" in name:
A = 'weight_v'
elif "bias" in name:
A = 'bias'
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
A = 'weight'
elif "running_mean" in name:
A = 'running_mean'
elif "inv_freq" in name:
A = 'inv_freq'
elif "running_var" in name:
A = 'running_var'
elif "num_batches_tracked" in name:
A = 'num_batches_tracked'
else:
A = 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 __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> Dict:
"""simple docstring"""
A = full_name.split('conv_layers.' )[-1]
A = name.split('.' )
A = int(items[0] )
A = int(items[1] )
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
f'{full_name} has size {value.shape}, but'
f' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.' )
A = value
logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.' )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
f'{full_name} has size {value.shape}, but'
f' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.' )
A = value
logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.' )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
f'{full_name} has size {value.shape}, but'
f' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.' )
A = value
logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.' )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
f'{full_name} has size {value.shape}, but'
f' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.' )
A = value
logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.' )
else:
unused_weights.append(UpperCamelCase__ )
@torch.no_grad()
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__=None , UpperCamelCase__=None , UpperCamelCase__=True ) -> Union[str, Any]:
"""simple docstring"""
if config_path is not None:
A = WavaVecaConformerConfig.from_pretrained(UpperCamelCase__ , hidden_act='swish' )
else:
A = WavaVecaConformerConfig()
if "rope" in checkpoint_path:
A = 'rotary'
if is_finetuned:
if dict_path:
A = Dictionary.load(UpperCamelCase__ )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
A = target_dict.pad_index
A = target_dict.bos_index
A = target_dict.eos_index
A = len(target_dict.symbols )
A = 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__ )
A = target_dict.indices
# fairseq has the <pad> and <s> switched
A = 0
A = 1
with open(UpperCamelCase__ , 'w' , encoding='utf-8' ) as vocab_handle:
json.dump(UpperCamelCase__ , UpperCamelCase__ )
A = 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__ , )
A = True if config.feat_extract_norm == 'layer' else False
A = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=16000 , padding_value=0 , do_normalize=UpperCamelCase__ , return_attention_mask=UpperCamelCase__ , )
A = WavaVecaProcessor(feature_extractor=UpperCamelCase__ , tokenizer=UpperCamelCase__ )
processor.save_pretrained(UpperCamelCase__ )
A = WavaVecaConformerForCTC(UpperCamelCase__ )
else:
A = WavaVecaConformerForPreTraining(UpperCamelCase__ )
if is_finetuned:
A , A , A = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={'data': '/'.join(dict_path.split('/' )[:-1] )} )
else:
A = argparse.Namespace(task='audio_pretraining' )
A = fairseq.tasks.setup_task(UpperCamelCase__ )
A , A , A = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] , task=UpperCamelCase__ )
A = model[0].eval()
recursively_load_weights(UpperCamelCase__ , UpperCamelCase__ , not is_finetuned )
hf_wavavec.save_pretrained(UpperCamelCase__ )
if __name__ == "__main__":
UpperCamelCase : List[Any] = argparse.ArgumentParser()
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
parser.add_argument(
"--not_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
)
UpperCamelCase : List[Any] = parser.parse_args()
convert_wavaveca_conformer_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
)
| 690 |
"""simple docstring"""
import os
import tempfile
import unittest
import numpy as np
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
from diffusers import FlaxDDIMScheduler, FlaxDiffusionPipeline, FlaxStableDiffusionPipeline
@require_flax
class lowerCamelCase__ ( unittest.TestCase ):
def __a ( self : Union[str, Any] ):
with tempfile.TemporaryDirectory() as tmpdirname:
# pipeline has Flax weights
A = FlaxDiffusionPipeline.from_pretrained(
'hf-internal-testing/tiny-stable-diffusion-pipe' , safety_checker=_lowercase , cache_dir=_lowercase )
A = [t[-1] for t in os.walk(os.path.join(_lowercase , os.listdir(_lowercase )[0] , 'snapshots' ) )]
A = [item for sublist in all_root_files for item in sublist]
# None of the downloaded files should be a PyTorch file even if we have some here:
# https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe/blob/main/unet/diffusion_pytorch_model.bin
assert not any(f.endswith('.bin' ) for f in files )
@slow
@require_flax
class lowerCamelCase__ ( unittest.TestCase ):
def __a ( self : Optional[Any] ):
A , A = FlaxStableDiffusionPipeline.from_pretrained(
'hf-internal-testing/tiny-stable-diffusion-pipe' , safety_checker=_lowercase )
A = (
'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
' field, close up, split lighting, cinematic'
)
A = jax.random.PRNGKey(0 )
A = 4
A = jax.device_count()
A = num_samples * [prompt]
A = pipeline.prepare_inputs(_lowercase )
# shard inputs and rng
A = replicate(_lowercase )
A = jax.random.split(_lowercase , _lowercase )
A = shard(_lowercase )
A = pipeline(_lowercase , _lowercase , _lowercase , _lowercase , jit=_lowercase ).images
assert images.shape == (num_samples, 1, 64, 64, 3)
if jax.device_count() == 8:
assert np.abs(np.abs(images[0, 0, :2, :2, -2:] , dtype=np.floataa ).sum() - 4.1_5_1_4_7_4_5 ) < 1e-3
assert np.abs(np.abs(_lowercase , dtype=np.floataa ).sum() - 4_9_9_4_7.8_7_5 ) < 5e-1
A = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:] ) ) )
assert len(_lowercase ) == num_samples
def __a ( self : Dict ):
A , A = FlaxStableDiffusionPipeline.from_pretrained(
'CompVis/stable-diffusion-v1-4' , revision='flax' , safety_checker=_lowercase )
A = (
'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
' field, close up, split lighting, cinematic'
)
A = jax.random.PRNGKey(0 )
A = 50
A = jax.device_count()
A = num_samples * [prompt]
A = pipeline.prepare_inputs(_lowercase )
# shard inputs and rng
A = replicate(_lowercase )
A = jax.random.split(_lowercase , _lowercase )
A = shard(_lowercase )
A = pipeline(_lowercase , _lowercase , _lowercase , _lowercase , jit=_lowercase ).images
assert images.shape == (num_samples, 1, 512, 512, 3)
if jax.device_count() == 8:
assert np.abs((np.abs(images[0, 0, :2, :2, -2:] , dtype=np.floataa ).sum() - 0.0_5_6_5_2_4_0_1) ) < 1e-3
assert np.abs((np.abs(_lowercase , dtype=np.floataa ).sum() - 2_3_8_3_8_0_8.2) ) < 5e-1
def __a ( self : List[str] ):
A , A = FlaxStableDiffusionPipeline.from_pretrained(
'CompVis/stable-diffusion-v1-4' , revision='bf16' , dtype=jnp.bfloataa , safety_checker=_lowercase )
A = (
'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
' field, close up, split lighting, cinematic'
)
A = jax.random.PRNGKey(0 )
A = 50
A = jax.device_count()
A = num_samples * [prompt]
A = pipeline.prepare_inputs(_lowercase )
# shard inputs and rng
A = replicate(_lowercase )
A = jax.random.split(_lowercase , _lowercase )
A = shard(_lowercase )
A = pipeline(_lowercase , _lowercase , _lowercase , _lowercase , jit=_lowercase ).images
assert images.shape == (num_samples, 1, 512, 512, 3)
if jax.device_count() == 8:
assert np.abs((np.abs(images[0, 0, :2, :2, -2:] , dtype=np.floataa ).sum() - 0.0_4_0_0_3_9_0_6) ) < 1e-3
assert np.abs((np.abs(_lowercase , dtype=np.floataa ).sum() - 2_3_7_3_5_1_6.7_5) ) < 5e-1
def __a ( self : str ):
A , A = FlaxStableDiffusionPipeline.from_pretrained(
'CompVis/stable-diffusion-v1-4' , revision='bf16' , dtype=jnp.bfloataa )
A = (
'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
' field, close up, split lighting, cinematic'
)
A = jax.random.PRNGKey(0 )
A = 50
A = jax.device_count()
A = num_samples * [prompt]
A = pipeline.prepare_inputs(_lowercase )
# shard inputs and rng
A = replicate(_lowercase )
A = jax.random.split(_lowercase , _lowercase )
A = shard(_lowercase )
A = pipeline(_lowercase , _lowercase , _lowercase , _lowercase , jit=_lowercase ).images
assert images.shape == (num_samples, 1, 512, 512, 3)
if jax.device_count() == 8:
assert np.abs((np.abs(images[0, 0, :2, :2, -2:] , dtype=np.floataa ).sum() - 0.0_4_0_0_3_9_0_6) ) < 1e-3
assert np.abs((np.abs(_lowercase , dtype=np.floataa ).sum() - 2_3_7_3_5_1_6.7_5) ) < 5e-1
def __a ( self : Any ):
A = FlaxDDIMScheduler(
beta_start=0.0_0_0_8_5 , beta_end=0.0_1_2 , beta_schedule='scaled_linear' , set_alpha_to_one=_lowercase , steps_offset=1 , )
A , A = FlaxStableDiffusionPipeline.from_pretrained(
'CompVis/stable-diffusion-v1-4' , revision='bf16' , dtype=jnp.bfloataa , scheduler=_lowercase , safety_checker=_lowercase , )
A = scheduler.create_state()
A = scheduler_state
A = (
'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
' field, close up, split lighting, cinematic'
)
A = jax.random.PRNGKey(0 )
A = 50
A = jax.device_count()
A = num_samples * [prompt]
A = pipeline.prepare_inputs(_lowercase )
# shard inputs and rng
A = replicate(_lowercase )
A = jax.random.split(_lowercase , _lowercase )
A = shard(_lowercase )
A = pipeline(_lowercase , _lowercase , _lowercase , _lowercase , jit=_lowercase ).images
assert images.shape == (num_samples, 1, 512, 512, 3)
if jax.device_count() == 8:
assert np.abs((np.abs(images[0, 0, :2, :2, -2:] , dtype=np.floataa ).sum() - 0.0_4_5_0_4_3_9_4_5) ) < 1e-3
assert np.abs((np.abs(_lowercase , dtype=np.floataa ).sum() - 2_3_4_7_6_9_3.5) ) < 5e-1
def __a ( self : List[str] ):
A = (
'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
' field, close up, split lighting, cinematic'
)
A = jax.device_count()
A = num_samples * [prompt]
A = jax.random.split(jax.random.PRNGKey(0 ) , _lowercase )
A , A = FlaxStableDiffusionPipeline.from_pretrained(
'CompVis/stable-diffusion-v1-4' , revision='bf16' , dtype=jnp.bfloataa , safety_checker=_lowercase , )
A = replicate(_lowercase )
A = pipeline.prepare_inputs(_lowercase )
A = shard(_lowercase )
A = pipeline(_lowercase , _lowercase , _lowercase , jit=_lowercase ).images
assert images.shape == (num_samples, 1, 512, 512, 3)
A = images[2, 0, 256, 10:17, 1]
# With memory efficient attention
A , A = FlaxStableDiffusionPipeline.from_pretrained(
'CompVis/stable-diffusion-v1-4' , revision='bf16' , dtype=jnp.bfloataa , safety_checker=_lowercase , use_memory_efficient_attention=_lowercase , )
A = replicate(_lowercase )
A = pipeline.prepare_inputs(_lowercase )
A = shard(_lowercase )
A = pipeline(_lowercase , _lowercase , _lowercase , jit=_lowercase ).images
assert images_eff.shape == (num_samples, 1, 512, 512, 3)
A = images[2, 0, 256, 10:17, 1]
# I checked the results visually and they are very similar. However, I saw that the max diff is `1` and the `sum`
# over the 8 images is exactly `256`, which is very suspicious. Testing a random slice for now.
assert abs(slice_eff - slice ).max() < 1e-2
| 690 | 1 |
"""simple docstring"""
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 __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__=True , UpperCamelCase__="pt" ) -> Tuple:
"""simple docstring"""
A = {'add_prefix_space': True} if isinstance(UpperCamelCase__ , UpperCamelCase__ ) and not line.startswith(' ' ) else {}
A = padding_side
return tokenizer(
[line] , max_length=UpperCamelCase__ , padding='max_length' if pad_to_max_length else None , truncation=UpperCamelCase__ , return_tensors=UpperCamelCase__ , add_special_tokens=UpperCamelCase__ , **UpperCamelCase__ , )
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__=None , ) -> List[Any]:
"""simple docstring"""
A = input_ids.ne(UpperCamelCase__ ).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__ ( UpperCAmelCase_ ):
def __init__( self : str , _lowercase : Optional[int] , _lowercase : Tuple , _lowercase : Any , _lowercase : List[Any] , _lowercase : int="train" , _lowercase : str=None , _lowercase : Tuple=None , _lowercase : List[Any]=None , _lowercase : str="" , ):
super().__init__()
A = Path(_lowercase ).joinpath(type_path + '.source' )
A = Path(_lowercase ).joinpath(type_path + '.target' )
A = self.get_char_lens(self.src_file )
A = max_source_length
A = max_target_length
assert min(self.src_lens ) > 0, f'found empty line in {self.src_file}'
A = tokenizer
A = prefix
if n_obs is not None:
A = self.src_lens[:n_obs]
A = src_lang
A = tgt_lang
def __len__( self : Tuple ):
return len(self.src_lens )
def __getitem__( self : Any , _lowercase : str ):
A = index + 1 # linecache starts at 1
A = self.prefix + linecache.getline(str(self.src_file ) , _lowercase ).rstrip('\n' )
A = linecache.getline(str(self.tgt_file ) , _lowercase ).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 , _lowercase ):
source_line += self.tokenizer.eos_token
tgt_line += self.tokenizer.eos_token
# Pad source and target to the right
A = (
self.tokenizer.question_encoder if isinstance(self.tokenizer , _lowercase ) else self.tokenizer
)
A = self.tokenizer.generator if isinstance(self.tokenizer , _lowercase ) else self.tokenizer
A = encode_line(_lowercase , _lowercase , self.max_source_length , 'right' )
A = encode_line(_lowercase , _lowercase , self.max_target_length , 'right' )
A = source_inputs['input_ids'].squeeze()
A = target_inputs['input_ids'].squeeze()
A = source_inputs['attention_mask'].squeeze()
return {
"input_ids": source_ids,
"attention_mask": src_mask,
"decoder_input_ids": target_ids,
}
@staticmethod
def __a ( _lowercase : Any ):
return [len(_lowercase ) for x in Path(_lowercase ).open().readlines()]
def __a ( self : Optional[int] , _lowercase : int ):
A = torch.stack([x['input_ids'] for x in batch] )
A = torch.stack([x['attention_mask'] for x in batch] )
A = torch.stack([x['decoder_input_ids'] for x in batch] )
A = (
self.tokenizer.generator.pad_token_id
if isinstance(self.tokenizer , _lowercase )
else self.tokenizer.pad_token_id
)
A = (
self.tokenizer.question_encoder.pad_token_id
if isinstance(self.tokenizer , _lowercase )
else self.tokenizer.pad_token_id
)
A = trim_batch(_lowercase , _lowercase )
A , A = trim_batch(_lowercase , _lowercase , attention_mask=_lowercase )
A = {
'input_ids': source_ids,
'attention_mask': source_mask,
'decoder_input_ids': y,
}
return batch
UpperCamelCase : Union[str, Any] = getLogger(__name__)
def __snake_case ( UpperCamelCase__ ) -> List[Any]:
"""simple docstring"""
return list(itertools.chain.from_iterable(UpperCamelCase__ ) )
def __snake_case ( UpperCamelCase__ ) -> None:
"""simple docstring"""
A = get_git_info()
save_json(UpperCamelCase__ , os.path.join(UpperCamelCase__ , 'git_log.json' ) )
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__=4 , **UpperCamelCase__ ) -> Optional[int]:
"""simple docstring"""
with open(UpperCamelCase__ , 'w' ) as f:
json.dump(UpperCamelCase__ , UpperCamelCase__ , indent=UpperCamelCase__ , **UpperCamelCase__ )
def __snake_case ( UpperCamelCase__ ) -> List[str]:
"""simple docstring"""
with open(UpperCamelCase__ ) as f:
return json.load(UpperCamelCase__ )
def __snake_case ( ) -> int:
"""simple docstring"""
A = git.Repo(search_parent_directories=UpperCamelCase__ )
A = {
'repo_id': str(UpperCamelCase__ ),
'repo_sha': str(repo.head.object.hexsha ),
'repo_branch': str(repo.active_branch ),
'hostname': str(socket.gethostname() ),
}
return repo_infos
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ ) -> List:
"""simple docstring"""
return list(map(UpperCamelCase__ , UpperCamelCase__ ) )
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ ) -> Optional[Any]:
"""simple docstring"""
with open(UpperCamelCase__ , 'wb' ) as f:
return pickle.dump(UpperCamelCase__ , UpperCamelCase__ )
def __snake_case ( UpperCamelCase__ ) -> Optional[Any]:
"""simple docstring"""
def remove_articles(UpperCamelCase__ ):
return re.sub(r'\b(a|an|the)\b' , ' ' , UpperCamelCase__ )
def white_space_fix(UpperCamelCase__ ):
return " ".join(text.split() )
def remove_punc(UpperCamelCase__ ):
A = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(UpperCamelCase__ ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(UpperCamelCase__ ) ) ) )
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ ) -> str:
"""simple docstring"""
A = normalize_answer(UpperCamelCase__ ).split()
A = normalize_answer(UpperCamelCase__ ).split()
A = Counter(UpperCamelCase__ ) & Counter(UpperCamelCase__ )
A = sum(common.values() )
if num_same == 0:
return 0
A = 1.0 * num_same / len(UpperCamelCase__ )
A = 1.0 * num_same / len(UpperCamelCase__ )
A = (2 * precision * recall) / (precision + recall)
return fa
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ ) -> Dict:
"""simple docstring"""
return normalize_answer(UpperCamelCase__ ) == normalize_answer(UpperCamelCase__ )
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ ) -> Dict:
"""simple docstring"""
assert len(UpperCamelCase__ ) == len(UpperCamelCase__ )
A = 0
for hypo, pred in zip(UpperCamelCase__ , UpperCamelCase__ ):
em += exact_match_score(UpperCamelCase__ , UpperCamelCase__ )
if len(UpperCamelCase__ ) > 0:
em /= len(UpperCamelCase__ )
return {"em": em}
def __snake_case ( UpperCamelCase__ ) -> Any:
"""simple docstring"""
return model_prefix.startswith('rag' )
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> List[Any]:
"""simple docstring"""
A = {p: p for p in extra_params}
# T5 models don't have `dropout` param, they have `dropout_rate` instead
A = 'dropout_rate'
for p in extra_params:
if getattr(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
if not hasattr(UpperCamelCase__ , UpperCamelCase__ ) and not hasattr(UpperCamelCase__ , equivalent_param[p] ):
logger.info('config doesn\'t have a `{}` attribute'.format(UpperCamelCase__ ) )
delattr(UpperCamelCase__ , UpperCamelCase__ )
continue
A = p if hasattr(UpperCamelCase__ , UpperCamelCase__ ) else equivalent_param[p]
setattr(UpperCamelCase__ , UpperCamelCase__ , getattr(UpperCamelCase__ , UpperCamelCase__ ) )
delattr(UpperCamelCase__ , UpperCamelCase__ )
return hparams, config
| 690 |
"""simple docstring"""
import os
import sys
UpperCamelCase : Optional[int] = os.path.join(os.path.dirname(__file__), "src")
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
AutoModelForSequenceClassification,
AutoTokenizer,
add_start_docstrings,
)
UpperCamelCase : Dict = [
"torch",
"numpy",
"tokenizers",
"filelock",
"requests",
"tqdm",
"regex",
"sentencepiece",
"sacremoses",
"importlib_metadata",
"huggingface_hub",
]
@add_start_docstrings(AutoConfig.__doc__ )
def __snake_case ( *UpperCamelCase__ , **UpperCamelCase__ ) -> Dict:
"""simple docstring"""
return AutoConfig.from_pretrained(*UpperCamelCase__ , **UpperCamelCase__ )
@add_start_docstrings(AutoTokenizer.__doc__ )
def __snake_case ( *UpperCamelCase__ , **UpperCamelCase__ ) -> List[str]:
"""simple docstring"""
return AutoTokenizer.from_pretrained(*UpperCamelCase__ , **UpperCamelCase__ )
@add_start_docstrings(AutoModel.__doc__ )
def __snake_case ( *UpperCamelCase__ , **UpperCamelCase__ ) -> str:
"""simple docstring"""
return AutoModel.from_pretrained(*UpperCamelCase__ , **UpperCamelCase__ )
@add_start_docstrings(AutoModelForCausalLM.__doc__ )
def __snake_case ( *UpperCamelCase__ , **UpperCamelCase__ ) -> List[Any]:
"""simple docstring"""
return AutoModelForCausalLM.from_pretrained(*UpperCamelCase__ , **UpperCamelCase__ )
@add_start_docstrings(AutoModelForMaskedLM.__doc__ )
def __snake_case ( *UpperCamelCase__ , **UpperCamelCase__ ) -> Optional[Any]:
"""simple docstring"""
return AutoModelForMaskedLM.from_pretrained(*UpperCamelCase__ , **UpperCamelCase__ )
@add_start_docstrings(AutoModelForSequenceClassification.__doc__ )
def __snake_case ( *UpperCamelCase__ , **UpperCamelCase__ ) -> List[str]:
"""simple docstring"""
return AutoModelForSequenceClassification.from_pretrained(*UpperCamelCase__ , **UpperCamelCase__ )
@add_start_docstrings(AutoModelForQuestionAnswering.__doc__ )
def __snake_case ( *UpperCamelCase__ , **UpperCamelCase__ ) -> int:
"""simple docstring"""
return AutoModelForQuestionAnswering.from_pretrained(*UpperCamelCase__ , **UpperCamelCase__ )
| 690 | 1 |
"""simple docstring"""
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
UpperCamelCase : List[str] = Lock()
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> List[str]:
"""simple docstring"""
global process_lock
# we perform n swaps since after n swaps we know we are sorted
# we *could* stop early if we are sorted already, but it takes as long to
# find out we are sorted as it does to sort the list with this algorithm
for i in range(0 , 10 ):
if (i + position) % 2 == 0 and r_send is not None:
# send your value to your right neighbor
process_lock.acquire()
r_send[1].send(UpperCamelCase__ )
process_lock.release()
# receive your right neighbor's value
process_lock.acquire()
A = rr_cv[0].recv()
process_lock.release()
# take the lower value since you are on the left
A = min(UpperCamelCase__ , UpperCamelCase__ )
elif (i + position) % 2 != 0 and l_send is not None:
# send your value to your left neighbor
process_lock.acquire()
l_send[1].send(UpperCamelCase__ )
process_lock.release()
# receive your left neighbor's value
process_lock.acquire()
A = lr_cv[0].recv()
process_lock.release()
# take the higher value since you are on the right
A = max(UpperCamelCase__ , UpperCamelCase__ )
# after all swaps are performed, send the values back to main
result_pipe[1].send(UpperCamelCase__ )
def __snake_case ( UpperCamelCase__ ) -> List[str]:
"""simple docstring"""
A = []
A = []
# initialize the list of pipes where the values will be retrieved
for _ in arr:
result_pipe.append(Pipe() )
# creates the processes
# the first and last process only have one neighbor so they are made outside
# of the loop
A = Pipe()
A = Pipe()
process_array_.append(
Process(
target=UpperCamelCase__ , args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0]) , ) )
A = temp_rs
A = temp_rr
for i in range(1 , len(UpperCamelCase__ ) - 1 ):
A = Pipe()
A = Pipe()
process_array_.append(
Process(
target=UpperCamelCase__ , args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i]) , ) )
A = temp_rs
A = temp_rr
process_array_.append(
Process(
target=UpperCamelCase__ , args=(
len(UpperCamelCase__ ) - 1,
arr[len(UpperCamelCase__ ) - 1],
temp_ls,
None,
temp_lr,
None,
result_pipe[len(UpperCamelCase__ ) - 1],
) , ) )
# start the processes
for p in process_array_:
p.start()
# wait for the processes to end and write their values to the list
for p in range(0 , len(UpperCamelCase__ ) ):
A = result_pipe[p][0].recv()
process_array_[p].join()
return arr
def __snake_case ( ) -> Optional[Any]:
"""simple docstring"""
A = list(range(10 , 0 , -1 ) )
print('Initial List' )
print(*UpperCamelCase__ )
A = odd_even_transposition(UpperCamelCase__ )
print('Sorted List\n' )
print(*UpperCamelCase__ )
if __name__ == "__main__":
main()
| 690 |
"""simple docstring"""
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging
if is_torch_available():
import torch
UpperCamelCase : List[str] = logging.get_logger(__name__)
class lowerCamelCase__ ( UpperCAmelCase_ ):
lowerCAmelCase = ["""pixel_values"""]
def __init__( self : Tuple , _lowercase : bool = True , _lowercase : Optional[Dict[str, int]] = None , _lowercase : PILImageResampling = PILImageResampling.BILINEAR , _lowercase : bool = True , _lowercase : Dict[str, int] = None , _lowercase : bool = True , _lowercase : Union[int, float] = 1 / 255 , _lowercase : bool = True , _lowercase : Optional[Union[float, List[float]]] = None , _lowercase : Optional[Union[float, List[float]]] = None , **_lowercase : List[str] , ):
super().__init__(**_lowercase )
A = size if size is not None else {'shortest_edge': 256}
A = get_size_dict(_lowercase , default_to_square=_lowercase )
A = crop_size if crop_size is not None else {'height': 224, 'width': 224}
A = get_size_dict(_lowercase , param_name='crop_size' )
A = do_resize
A = size
A = resample
A = do_center_crop
A = crop_size
A = do_rescale
A = rescale_factor
A = do_normalize
A = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
A = image_std if image_std is not None else IMAGENET_STANDARD_STD
def __a ( self : Any , _lowercase : np.ndarray , _lowercase : Dict[str, int] , _lowercase : PILImageResampling = PILImageResampling.BICUBIC , _lowercase : Optional[Union[str, ChannelDimension]] = None , **_lowercase : Tuple , ):
A = get_size_dict(_lowercase , default_to_square=_lowercase )
if "shortest_edge" not in size:
raise ValueError(f'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}' )
A = get_resize_output_image_size(_lowercase , size=size['shortest_edge'] , default_to_square=_lowercase )
return resize(_lowercase , size=_lowercase , resample=_lowercase , data_format=_lowercase , **_lowercase )
def __a ( self : List[Any] , _lowercase : np.ndarray , _lowercase : Dict[str, int] , _lowercase : Optional[Union[str, ChannelDimension]] = None , **_lowercase : Optional[int] , ):
A = get_size_dict(_lowercase )
if "height" not in size or "width" not in size:
raise ValueError(f'The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}' )
return center_crop(_lowercase , size=(size['height'], size['width']) , data_format=_lowercase , **_lowercase )
def __a ( self : int , _lowercase : np.ndarray , _lowercase : float , _lowercase : Optional[Union[str, ChannelDimension]] = None , **_lowercase : Tuple ):
return rescale(_lowercase , scale=_lowercase , data_format=_lowercase , **_lowercase )
def __a ( self : int , _lowercase : np.ndarray , _lowercase : Union[float, List[float]] , _lowercase : Union[float, List[float]] , _lowercase : Optional[Union[str, ChannelDimension]] = None , **_lowercase : str , ):
return normalize(_lowercase , mean=_lowercase , std=_lowercase , data_format=_lowercase , **_lowercase )
def __a ( self : Any , _lowercase : ImageInput , _lowercase : Optional[bool] = None , _lowercase : Dict[str, int] = None , _lowercase : PILImageResampling = None , _lowercase : bool = None , _lowercase : Dict[str, int] = None , _lowercase : Optional[bool] = None , _lowercase : Optional[float] = None , _lowercase : Optional[bool] = None , _lowercase : Optional[Union[float, List[float]]] = None , _lowercase : Optional[Union[float, List[float]]] = None , _lowercase : Optional[Union[str, TensorType]] = None , _lowercase : Union[str, ChannelDimension] = ChannelDimension.FIRST , **_lowercase : Any , ):
A = do_resize if do_resize is not None else self.do_resize
A = size if size is not None else self.size
A = get_size_dict(_lowercase , default_to_square=_lowercase )
A = resample if resample is not None else self.resample
A = do_center_crop if do_center_crop is not None else self.do_center_crop
A = crop_size if crop_size is not None else self.crop_size
A = get_size_dict(_lowercase , param_name='crop_size' )
A = do_rescale if do_rescale is not None else self.do_rescale
A = rescale_factor if rescale_factor is not None else self.rescale_factor
A = do_normalize if do_normalize is not None else self.do_normalize
A = image_mean if image_mean is not None else self.image_mean
A = image_std if image_std is not None else self.image_std
A = make_list_of_images(_lowercase )
if not valid_images(_lowercase ):
raise ValueError(
'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
'torch.Tensor, tf.Tensor or jax.ndarray.' )
if do_resize and size is None:
raise ValueError('Size must be specified if do_resize is True.' )
if do_center_crop and crop_size is None:
raise ValueError('Crop size must be specified if do_center_crop is True.' )
if do_rescale and rescale_factor is None:
raise ValueError('Rescale factor must be specified if do_rescale is True.' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('Image mean and std must be specified if do_normalize is True.' )
# All transformations expect numpy arrays.
A = [to_numpy_array(_lowercase ) for image in images]
if do_resize:
A = [self.resize(image=_lowercase , size=_lowercase , resample=_lowercase ) for image in images]
if do_center_crop:
A = [self.center_crop(image=_lowercase , size=_lowercase ) for image in images]
if do_rescale:
A = [self.rescale(image=_lowercase , scale=_lowercase ) for image in images]
if do_normalize:
A = [self.normalize(image=_lowercase , mean=_lowercase , std=_lowercase ) for image in images]
A = [to_channel_dimension_format(_lowercase , _lowercase ) for image in images]
A = {'pixel_values': images}
return BatchFeature(data=_lowercase , tensor_type=_lowercase )
def __a ( self : int , _lowercase : List[str] , _lowercase : List[Tuple] = None ):
A = outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(_lowercase ) != len(_lowercase ):
raise ValueError(
'Make sure that you pass in as many target sizes as the batch dimension of the logits' )
if is_torch_tensor(_lowercase ):
A = target_sizes.numpy()
A = []
for idx in range(len(_lowercase ) ):
A = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) , size=target_sizes[idx] , mode='bilinear' , align_corners=_lowercase )
A = resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(_lowercase )
else:
A = logits.argmax(dim=1 )
A = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation
| 690 | 1 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
UpperCamelCase : Dict = TypeVar("T")
class lowerCamelCase__ ( Generic[T] ):
def __init__( self : int , _lowercase : T ):
A = data
A = None
def __str__( self : int ):
return f'{self.data}'
class lowerCamelCase__ ( Generic[T] ):
def __init__( self : str ):
A = None
def __iter__( self : Any ):
A = self.top
while node:
yield node.data
A = node.next
def __str__( self : Tuple ):
return "->".join([str(_lowercase ) for item in self] )
def __len__( self : Optional[int] ):
return len(tuple(iter(self ) ) )
def __a ( self : List[Any] ):
return self.top is None
def __a ( self : Dict , _lowercase : T ):
A = Node(_lowercase )
if not self.is_empty():
A = self.top
A = node
def __a ( self : Optional[Any] ):
if self.is_empty():
raise IndexError('pop from empty stack' )
assert isinstance(self.top , _lowercase )
A = self.top
A = self.top.next
return pop_node.data
def __a ( self : Optional[Any] ):
if self.is_empty():
raise IndexError('peek from empty stack' )
assert self.top is not None
return self.top.data
def __a ( self : Any ):
A = None
if __name__ == "__main__":
from doctest import testmod
testmod()
| 690 |
"""simple docstring"""
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def __snake_case ( UpperCamelCase__ = "laptop" ) -> DataFrame:
"""simple docstring"""
A = f'https://www.amazon.in/laptop/s?k={product}'
A = {
'User-Agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36\n (KHTML, like Gecko)Chrome/44.0.2403.157 Safari/537.36',
'Accept-Language': 'en-US, en;q=0.5',
}
A = BeautifulSoup(requests.get(UpperCamelCase__ , headers=UpperCamelCase__ ).text )
# Initialize a Pandas dataframe with the column titles
A = DataFrame(
columns=[
'Product Title',
'Product Link',
'Current Price of the product',
'Product Rating',
'MRP of the product',
'Discount',
] )
# Loop through each entry and store them in the dataframe
for item, _ in zip_longest(
soup.find_all(
'div' , attrs={'class': 's-result-item', 'data-component-type': 's-search-result'} , ) , soup.find_all('div' , attrs={'class': 'a-row a-size-base a-color-base'} ) , ):
try:
A = item.ha.text
A = 'https://www.amazon.in/' + item.ha.a['href']
A = item.find('span' , attrs={'class': 'a-offscreen'} ).text
try:
A = item.find('span' , attrs={'class': 'a-icon-alt'} ).text
except AttributeError:
A = 'Not available'
try:
A = (
'₹'
+ item.find(
'span' , attrs={'class': 'a-price a-text-price'} ).text.split('₹' )[1]
)
except AttributeError:
A = ''
try:
A = float(
(
(
float(product_mrp.strip('₹' ).replace(',' , '' ) )
- float(product_price.strip('₹' ).replace(',' , '' ) )
)
/ float(product_mrp.strip('₹' ).replace(',' , '' ) )
)
* 100 )
except ValueError:
A = float('nan' )
except AttributeError:
pass
A = [
product_title,
product_link,
product_price,
product_rating,
product_mrp,
discount,
]
A = ' '
A = ' '
data_frame.index += 1
return data_frame
if __name__ == "__main__":
UpperCamelCase : Any = "headphones"
get_amazon_product_data(product).to_csv(F"""Amazon Product Data for {product}.csv""")
| 690 | 1 |
"""simple docstring"""
def __snake_case ( UpperCamelCase__ ) -> int:
"""simple docstring"""
assert (
isinstance(UpperCamelCase__ , UpperCamelCase__ ) and number_of_steps > 0
), f'number_of_steps needs to be positive integer, your input {number_of_steps}'
if number_of_steps == 1:
return 1
A , A = 1, 1
for _ in range(number_of_steps - 1 ):
A , A = current + previous, current
return current
if __name__ == "__main__":
import doctest
doctest.testmod()
| 690 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import DonutImageProcessor
class lowerCamelCase__ ( unittest.TestCase ):
def __init__( self : List[str] , _lowercase : Optional[Any] , _lowercase : int=7 , _lowercase : List[str]=3 , _lowercase : Tuple=18 , _lowercase : Dict=30 , _lowercase : Any=400 , _lowercase : int=True , _lowercase : List[Any]=None , _lowercase : Tuple=True , _lowercase : List[Any]=False , _lowercase : str=True , _lowercase : List[str]=True , _lowercase : int=[0.5, 0.5, 0.5] , _lowercase : Optional[int]=[0.5, 0.5, 0.5] , ):
A = parent
A = batch_size
A = num_channels
A = image_size
A = min_resolution
A = max_resolution
A = do_resize
A = size if size is not None else {'height': 18, 'width': 20}
A = do_thumbnail
A = do_align_axis
A = do_pad
A = do_normalize
A = image_mean
A = image_std
def __a ( self : Any ):
return {
"do_resize": self.do_resize,
"size": self.size,
"do_thumbnail": self.do_thumbnail,
"do_align_long_axis": self.do_align_axis,
"do_pad": self.do_pad,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
}
@require_torch
@require_vision
class lowerCamelCase__ ( UpperCAmelCase_ , unittest.TestCase ):
lowerCAmelCase = DonutImageProcessor if is_vision_available() else None
def __a ( self : List[str] ):
A = DonutImageProcessingTester(self )
@property
def __a ( self : int ):
return self.image_processor_tester.prepare_image_processor_dict()
def __a ( self : Union[str, Any] ):
A = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(_lowercase , 'do_resize' ) )
self.assertTrue(hasattr(_lowercase , 'size' ) )
self.assertTrue(hasattr(_lowercase , 'do_thumbnail' ) )
self.assertTrue(hasattr(_lowercase , 'do_align_long_axis' ) )
self.assertTrue(hasattr(_lowercase , 'do_pad' ) )
self.assertTrue(hasattr(_lowercase , 'do_normalize' ) )
self.assertTrue(hasattr(_lowercase , 'image_mean' ) )
self.assertTrue(hasattr(_lowercase , 'image_std' ) )
def __a ( self : int ):
A = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'height': 18, 'width': 20} )
A = self.image_processing_class.from_dict(self.image_processor_dict , size=42 )
self.assertEqual(image_processor.size , {'height': 42, 'width': 42} )
# Previous config had dimensions in (width, height) order
A = self.image_processing_class.from_dict(self.image_processor_dict , size=(42, 84) )
self.assertEqual(image_processor.size , {'height': 84, 'width': 42} )
def __a ( self : Any ):
pass
@is_flaky()
def __a ( self : int ):
# Initialize image_processing
A = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
A = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowercase )
for image in image_inputs:
self.assertIsInstance(_lowercase , Image.Image )
# Test not batched input
A = 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 = image_processing(_lowercase , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
) , )
@is_flaky()
def __a ( self : List[str] ):
# Initialize image_processing
A = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
A = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowercase , numpify=_lowercase )
for image in image_inputs:
self.assertIsInstance(_lowercase , np.ndarray )
# Test not batched input
A = 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 = image_processing(_lowercase , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
) , )
@is_flaky()
def __a ( self : List[Any] ):
# Initialize image_processing
A = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
A = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowercase , torchify=_lowercase )
for image in image_inputs:
self.assertIsInstance(_lowercase , torch.Tensor )
# Test not batched input
A = 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 = image_processing(_lowercase , 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'],
) , )
| 690 | 1 |
"""simple docstring"""
import json
import logging
import os
import sys
from time import time
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, require_torch_tpu
logging.basicConfig(level=logging.DEBUG)
UpperCamelCase : str = logging.getLogger()
def __snake_case ( UpperCamelCase__ ) -> Tuple:
"""simple docstring"""
A = {}
A = os.path.join(UpperCamelCase__ , 'all_results.json' )
if os.path.exists(UpperCamelCase__ ):
with open(UpperCamelCase__ , 'r' ) as f:
A = json.load(UpperCamelCase__ )
else:
raise ValueError(f'can\'t find {path}' )
return results
UpperCamelCase : Union[str, Any] = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
@require_torch_tpu
class lowerCamelCase__ ( UpperCAmelCase_ ):
def __a ( self : Union[str, Any] ):
import xla_spawn
A = self.get_auto_remove_tmp_dir()
A = f'\n ./examples/pytorch/text-classification/run_glue.py\n --num_cores=8\n ./examples/pytorch/text-classification/run_glue.py\n --model_name_or_path distilbert-base-uncased\n --output_dir {tmp_dir}\n --overwrite_output_dir\n --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n --do_train\n --do_eval\n --debug tpu_metrics_debug\n --per_device_train_batch_size=2\n --per_device_eval_batch_size=1\n --learning_rate=1e-4\n --max_steps=10\n --warmup_steps=2\n --seed=42\n --max_seq_length=128\n '.split()
with patch.object(_lowercase , 'argv' , _lowercase ):
A = time()
xla_spawn.main()
A = time()
A = get_results(_lowercase )
self.assertGreaterEqual(result['eval_accuracy'] , 0.7_5 )
# Assert that the script takes less than 500 seconds to make sure it doesn't hang.
self.assertLess(end - start , 500 )
def __a ( self : List[Any] ):
import xla_spawn
A = '\n ./tests/test_trainer_tpu.py\n --num_cores=8\n ./tests/test_trainer_tpu.py\n '.split()
with patch.object(_lowercase , 'argv' , _lowercase ):
xla_spawn.main()
| 690 |
"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class lowerCamelCase__ :
def __init__( self : Optional[Any] , _lowercase : int=2 , _lowercase : Optional[Any]=3 , _lowercase : Any=64 , _lowercase : Tuple=None ):
A = np.random.default_rng(_lowercase )
A = length
A = rng.normal(size=(length,) ).astype(np.floataa )
A = a * self.x + b + rng.normal(scale=0.1 , size=(length,) ).astype(np.floataa )
def __len__( self : str ):
return self.length
def __getitem__( self : List[str] , _lowercase : int ):
return {"x": self.x[i], "y": self.y[i]}
class lowerCamelCase__ ( torch.nn.Module ):
def __init__( self : Optional[int] , _lowercase : Any=0 , _lowercase : List[Any]=0 , _lowercase : Optional[int]=False ):
super().__init__()
A = torch.nn.Parameter(torch.tensor([2, 3] ).float() )
A = torch.nn.Parameter(torch.tensor([2, 3] ).float() )
A = True
def __a ( self : Optional[Any] , _lowercase : str=None ):
if self.first_batch:
print(f'Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}' )
A = False
return x * self.a[0] + self.b[0]
class lowerCamelCase__ ( torch.nn.Module ):
def __init__( self : Optional[Any] , _lowercase : Any=0 , _lowercase : List[str]=0 , _lowercase : str=False ):
super().__init__()
A = torch.nn.Parameter(torch.tensor(_lowercase ).float() )
A = torch.nn.Parameter(torch.tensor(_lowercase ).float() )
A = True
def __a ( self : int , _lowercase : Tuple=None ):
if self.first_batch:
print(f'Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}' )
A = False
return x * self.a + self.b
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ = 16 ) -> Optional[Any]:
"""simple docstring"""
from datasets import load_dataset
from transformers import AutoTokenizer
A = AutoTokenizer.from_pretrained('bert-base-cased' )
A = {'train': 'tests/test_samples/MRPC/train.csv', 'validation': 'tests/test_samples/MRPC/dev.csv'}
A = load_dataset('csv' , data_files=UpperCamelCase__ )
A = datasets['train'].unique('label' )
A = {v: i for i, v in enumerate(UpperCamelCase__ )}
def tokenize_function(UpperCamelCase__ ):
# max_length=None => use the model max length (it's actually the default)
A = tokenizer(
examples['sentence1'] , examples['sentence2'] , truncation=UpperCamelCase__ , max_length=UpperCamelCase__ , padding='max_length' )
if "label" in examples:
A = [label_to_id[l] for l in examples['label']]
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
A = datasets.map(
UpperCamelCase__ , batched=UpperCamelCase__ , remove_columns=['sentence1', 'sentence2', 'label'] , )
def collate_fn(UpperCamelCase__ ):
# On TPU it's best to pad everything to the same length or training will be very slow.
if accelerator.distributed_type == DistributedType.TPU:
return tokenizer.pad(UpperCamelCase__ , padding='max_length' , max_length=128 , return_tensors='pt' )
return tokenizer.pad(UpperCamelCase__ , padding='longest' , return_tensors='pt' )
# Instantiate dataloaders.
A = DataLoader(tokenized_datasets['train'] , shuffle=UpperCamelCase__ , collate_fn=UpperCamelCase__ , batch_size=2 )
A = DataLoader(tokenized_datasets['validation'] , shuffle=UpperCamelCase__ , collate_fn=UpperCamelCase__ , batch_size=1 )
return train_dataloader, eval_dataloader
| 690 | 1 |
"""simple docstring"""
import torch
from diffusers import DDIMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class lowerCamelCase__ ( UpperCAmelCase_ ):
lowerCAmelCase = (DDIMParallelScheduler,)
lowerCAmelCase = (("""eta""", 0.0), ("""num_inference_steps""", 50))
def __a ( self : Optional[Any] , **_lowercase : Optional[int] ):
A = {
'num_train_timesteps': 1_000,
'beta_start': 0.0_0_0_1,
'beta_end': 0.0_2,
'beta_schedule': 'linear',
'clip_sample': True,
}
config.update(**_lowercase )
return config
def __a ( self : List[Any] , **_lowercase : List[Any] ):
A = self.scheduler_classes[0]
A = self.get_scheduler_config(**_lowercase )
A = scheduler_class(**_lowercase )
A , A = 10, 0.0
A = self.dummy_model()
A = self.dummy_sample_deter
scheduler.set_timesteps(_lowercase )
for t in scheduler.timesteps:
A = model(_lowercase , _lowercase )
A = scheduler.step(_lowercase , _lowercase , _lowercase , _lowercase ).prev_sample
return sample
def __a ( self : Union[str, Any] ):
for timesteps in [100, 500, 1_000]:
self.check_over_configs(num_train_timesteps=_lowercase )
def __a ( self : str ):
for steps_offset in [0, 1]:
self.check_over_configs(steps_offset=_lowercase )
A = self.scheduler_classes[0]
A = self.get_scheduler_config(steps_offset=1 )
A = scheduler_class(**_lowercase )
scheduler.set_timesteps(5 )
assert torch.equal(scheduler.timesteps , torch.LongTensor([801, 601, 401, 201, 1] ) )
def __a ( self : int ):
for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1] , [0.0_0_2, 0.0_2, 0.2, 2] ):
self.check_over_configs(beta_start=_lowercase , beta_end=_lowercase )
def __a ( self : Union[str, Any] ):
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=_lowercase )
def __a ( self : List[str] ):
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=_lowercase )
def __a ( self : Tuple ):
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=_lowercase )
def __a ( self : int ):
for timestep_spacing in ["trailing", "leading"]:
self.check_over_configs(timestep_spacing=_lowercase )
def __a ( self : Tuple ):
for rescale_betas_zero_snr in [True, False]:
self.check_over_configs(rescale_betas_zero_snr=_lowercase )
def __a ( self : List[Any] ):
self.check_over_configs(thresholding=_lowercase )
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(
thresholding=_lowercase , prediction_type=_lowercase , sample_max_value=_lowercase , )
def __a ( self : Union[str, Any] ):
for t in [1, 10, 49]:
self.check_over_forward(time_step=_lowercase )
def __a ( self : List[Any] ):
for t, num_inference_steps in zip([1, 10, 50] , [10, 50, 500] ):
self.check_over_forward(time_step=_lowercase , num_inference_steps=_lowercase )
def __a ( self : int ):
for t, eta in zip([1, 10, 49] , [0.0, 0.5, 1.0] ):
self.check_over_forward(time_step=_lowercase , eta=_lowercase )
def __a ( self : List[str] ):
A = self.scheduler_classes[0]
A = self.get_scheduler_config()
A = scheduler_class(**_lowercase )
assert torch.sum(torch.abs(scheduler._get_variance(0 , 0 ) - 0.0 ) ) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(420 , 400 ) - 0.1_4_7_7_1 ) ) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(980 , 960 ) - 0.3_2_4_6_0 ) ) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(0 , 0 ) - 0.0 ) ) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(487 , 486 ) - 0.0_0_9_7_9 ) ) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(999 , 998 ) - 0.0_2 ) ) < 1e-5
def __a ( self : Any ):
A = self.scheduler_classes[0]
A = self.get_scheduler_config()
A = scheduler_class(**_lowercase )
A , A = 10, 0.0
scheduler.set_timesteps(_lowercase )
A = self.dummy_model()
A = self.dummy_sample_deter
A = self.dummy_sample_deter + 0.1
A = self.dummy_sample_deter - 0.1
A = samplea.shape[0]
A = torch.stack([samplea, samplea, samplea] , dim=0 )
A = torch.arange(_lowercase )[0:3, None].repeat(1 , _lowercase )
A = model(samples.flatten(0 , 1 ) , timesteps.flatten(0 , 1 ) )
A = scheduler.batch_step_no_noise(_lowercase , timesteps.flatten(0 , 1 ) , samples.flatten(0 , 1 ) , _lowercase )
A = torch.sum(torch.abs(_lowercase ) )
A = torch.mean(torch.abs(_lowercase ) )
assert abs(result_sum.item() - 1_1_4_7.7_9_0_4 ) < 1e-2
assert abs(result_mean.item() - 0.4_9_8_2 ) < 1e-3
def __a ( self : Optional[int] ):
A = self.full_loop()
A = torch.sum(torch.abs(_lowercase ) )
A = torch.mean(torch.abs(_lowercase ) )
assert abs(result_sum.item() - 1_7_2.0_0_6_7 ) < 1e-2
assert abs(result_mean.item() - 0.2_2_3_9_6_7 ) < 1e-3
def __a ( self : Optional[Any] ):
A = self.full_loop(prediction_type='v_prediction' )
A = torch.sum(torch.abs(_lowercase ) )
A = torch.mean(torch.abs(_lowercase ) )
assert abs(result_sum.item() - 5_2.5_3_0_2 ) < 1e-2
assert abs(result_mean.item() - 0.0_6_8_4 ) < 1e-3
def __a ( self : Any ):
# We specify different beta, so that the first alpha is 0.99
A = self.full_loop(set_alpha_to_one=_lowercase , beta_start=0.0_1 )
A = torch.sum(torch.abs(_lowercase ) )
A = torch.mean(torch.abs(_lowercase ) )
assert abs(result_sum.item() - 1_4_9.8_2_9_5 ) < 1e-2
assert abs(result_mean.item() - 0.1_9_5_1 ) < 1e-3
def __a ( self : Optional[Any] ):
# We specify different beta, so that the first alpha is 0.99
A = self.full_loop(set_alpha_to_one=_lowercase , beta_start=0.0_1 )
A = torch.sum(torch.abs(_lowercase ) )
A = torch.mean(torch.abs(_lowercase ) )
assert abs(result_sum.item() - 1_4_9.0_7_8_4 ) < 1e-2
assert abs(result_mean.item() - 0.1_9_4_1 ) < 1e-3
| 690 |
"""simple docstring"""
from __future__ import annotations
def __snake_case ( UpperCamelCase__ ) -> list[int]: # This function is recursive
"""simple docstring"""
A = len(UpperCamelCase__ )
# If the array contains only one element, we return it (it's the stop condition of
# recursion)
if array_length <= 1:
return array
# Else
A = array[0]
A = False
A = 1
A = []
while not is_found and i < array_length:
if array[i] < pivot:
A = True
A = [element for element in array[i:] if element >= array[i]]
A = longest_subsequence(UpperCamelCase__ )
if len(UpperCamelCase__ ) > len(UpperCamelCase__ ):
A = temp_array
else:
i += 1
A = [element for element in array[1:] if element >= pivot]
A = [pivot, *longest_subsequence(UpperCamelCase__ )]
if len(UpperCamelCase__ ) > len(UpperCamelCase__ ):
return temp_array
else:
return longest_subseq
if __name__ == "__main__":
import doctest
doctest.testmod()
| 690 | 1 |
"""simple docstring"""
from __future__ import annotations
UpperCamelCase : Union[str, Any] = []
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> bool:
"""simple docstring"""
for i in range(len(UpperCamelCase__ ) ):
if board[row][i] == 1:
return False
for i in range(len(UpperCamelCase__ ) ):
if board[i][column] == 1:
return False
for i, j in zip(range(UpperCamelCase__ , -1 , -1 ) , range(UpperCamelCase__ , -1 , -1 ) ):
if board[i][j] == 1:
return False
for i, j in zip(range(UpperCamelCase__ , -1 , -1 ) , range(UpperCamelCase__ , len(UpperCamelCase__ ) ) ):
if board[i][j] == 1:
return False
return True
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ ) -> bool:
"""simple docstring"""
if row >= len(UpperCamelCase__ ):
solution.append(UpperCamelCase__ )
printboard(UpperCamelCase__ )
print()
return True
for i in range(len(UpperCamelCase__ ) ):
if is_safe(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
A = 1
solve(UpperCamelCase__ , row + 1 )
A = 0
return False
def __snake_case ( UpperCamelCase__ ) -> None:
"""simple docstring"""
for i in range(len(UpperCamelCase__ ) ):
for j in range(len(UpperCamelCase__ ) ):
if board[i][j] == 1:
print('Q' , end=' ' )
else:
print('.' , end=' ' )
print()
# n=int(input("The no. of queens"))
UpperCamelCase : str = 8
UpperCamelCase : Any = [[0 for i in range(n)] for j in range(n)]
solve(board, 0)
print("The total no. of solutions are :", len(solution))
| 690 |
"""simple docstring"""
from __future__ import annotations
import typing
from collections.abc import Iterable
import numpy as np
UpperCamelCase : Tuple = typing.Union[Iterable[float], Iterable[int], np.ndarray] # noqa: UP007
UpperCamelCase : Optional[int] = typing.Union[np.floataa, int, float] # noqa: UP007
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ ) -> VectorOut:
"""simple docstring"""
return np.sqrt(np.sum((np.asarray(UpperCamelCase__ ) - np.asarray(UpperCamelCase__ )) ** 2 ) )
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ ) -> VectorOut:
"""simple docstring"""
return sum((va - va) ** 2 for va, va in zip(UpperCamelCase__ , UpperCamelCase__ ) ) ** (1 / 2)
if __name__ == "__main__":
def __snake_case ( ) -> None:
"""simple docstring"""
from timeit import timeit
print('Without Numpy' )
print(
timeit(
'euclidean_distance_no_np([1, 2, 3], [4, 5, 6])' , number=10000 , globals=globals() , ) )
print('With Numpy' )
print(
timeit(
'euclidean_distance([1, 2, 3], [4, 5, 6])' , number=10000 , globals=globals() , ) )
benchmark()
| 690 | 1 |
"""simple docstring"""
import torch
from diffusers import DDPMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class lowerCamelCase__ ( UpperCAmelCase_ ):
lowerCAmelCase = (DDPMParallelScheduler,)
def __a ( self : Optional[int] , **_lowercase : Any ):
A = {
'num_train_timesteps': 1_000,
'beta_start': 0.0_0_0_1,
'beta_end': 0.0_2,
'beta_schedule': 'linear',
'variance_type': 'fixed_small',
'clip_sample': True,
}
config.update(**_lowercase )
return config
def __a ( self : Optional[int] ):
for timesteps in [1, 5, 100, 1_000]:
self.check_over_configs(num_train_timesteps=_lowercase )
def __a ( self : Optional[Any] ):
for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1] , [0.0_0_2, 0.0_2, 0.2, 2] ):
self.check_over_configs(beta_start=_lowercase , beta_end=_lowercase )
def __a ( self : List[Any] ):
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=_lowercase )
def __a ( self : str ):
for variance in ["fixed_small", "fixed_large", "other"]:
self.check_over_configs(variance_type=_lowercase )
def __a ( self : Union[str, Any] ):
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=_lowercase )
def __a ( self : str ):
self.check_over_configs(thresholding=_lowercase )
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(
thresholding=_lowercase , prediction_type=_lowercase , sample_max_value=_lowercase , )
def __a ( self : Dict ):
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(prediction_type=_lowercase )
def __a ( self : List[str] ):
for t in [0, 500, 999]:
self.check_over_forward(time_step=_lowercase )
def __a ( self : Any ):
A = self.scheduler_classes[0]
A = self.get_scheduler_config()
A = scheduler_class(**_lowercase )
assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 0.0 ) ) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(487 ) - 0.0_0_9_7_9 ) ) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(999 ) - 0.0_2 ) ) < 1e-5
def __a ( self : Union[str, Any] ):
A = self.scheduler_classes[0]
A = self.get_scheduler_config()
A = scheduler_class(**_lowercase )
A = len(_lowercase )
A = self.dummy_model()
A = self.dummy_sample_deter
A = self.dummy_sample_deter + 0.1
A = self.dummy_sample_deter - 0.1
A = samplea.shape[0]
A = torch.stack([samplea, samplea, samplea] , dim=0 )
A = torch.arange(_lowercase )[0:3, None].repeat(1 , _lowercase )
A = model(samples.flatten(0 , 1 ) , timesteps.flatten(0 , 1 ) )
A = scheduler.batch_step_no_noise(_lowercase , timesteps.flatten(0 , 1 ) , samples.flatten(0 , 1 ) )
A = torch.sum(torch.abs(_lowercase ) )
A = torch.mean(torch.abs(_lowercase ) )
assert abs(result_sum.item() - 1_1_5_3.1_8_3_3 ) < 1e-2
assert abs(result_mean.item() - 0.5_0_0_5 ) < 1e-3
def __a ( self : Union[str, Any] ):
A = self.scheduler_classes[0]
A = self.get_scheduler_config()
A = scheduler_class(**_lowercase )
A = len(_lowercase )
A = self.dummy_model()
A = self.dummy_sample_deter
A = torch.manual_seed(0 )
for t in reversed(range(_lowercase ) ):
# 1. predict noise residual
A = model(_lowercase , _lowercase )
# 2. predict previous mean of sample x_t-1
A = scheduler.step(_lowercase , _lowercase , _lowercase , generator=_lowercase ).prev_sample
A = pred_prev_sample
A = torch.sum(torch.abs(_lowercase ) )
A = torch.mean(torch.abs(_lowercase ) )
assert abs(result_sum.item() - 2_5_8.9_6_0_6 ) < 1e-2
assert abs(result_mean.item() - 0.3_3_7_2 ) < 1e-3
def __a ( self : Dict ):
A = self.scheduler_classes[0]
A = self.get_scheduler_config(prediction_type='v_prediction' )
A = scheduler_class(**_lowercase )
A = len(_lowercase )
A = self.dummy_model()
A = self.dummy_sample_deter
A = torch.manual_seed(0 )
for t in reversed(range(_lowercase ) ):
# 1. predict noise residual
A = model(_lowercase , _lowercase )
# 2. predict previous mean of sample x_t-1
A = scheduler.step(_lowercase , _lowercase , _lowercase , generator=_lowercase ).prev_sample
A = pred_prev_sample
A = torch.sum(torch.abs(_lowercase ) )
A = torch.mean(torch.abs(_lowercase ) )
assert abs(result_sum.item() - 2_0_2.0_2_9_6 ) < 1e-2
assert abs(result_mean.item() - 0.2_6_3_1 ) < 1e-3
def __a ( self : List[str] ):
A = self.scheduler_classes[0]
A = self.get_scheduler_config()
A = scheduler_class(**_lowercase )
A = [100, 87, 50, 1, 0]
scheduler.set_timesteps(timesteps=_lowercase )
A = scheduler.timesteps
for i, timestep in enumerate(_lowercase ):
if i == len(_lowercase ) - 1:
A = -1
else:
A = timesteps[i + 1]
A = scheduler.previous_timestep(_lowercase )
A = prev_t.item()
self.assertEqual(_lowercase , _lowercase )
def __a ( self : Any ):
A = self.scheduler_classes[0]
A = self.get_scheduler_config()
A = scheduler_class(**_lowercase )
A = [100, 87, 50, 51, 0]
with self.assertRaises(_lowercase , msg='`custom_timesteps` must be in descending order.' ):
scheduler.set_timesteps(timesteps=_lowercase )
def __a ( self : Any ):
A = self.scheduler_classes[0]
A = self.get_scheduler_config()
A = scheduler_class(**_lowercase )
A = [100, 87, 50, 1, 0]
A = len(_lowercase )
with self.assertRaises(_lowercase , msg='Can only pass one of `num_inference_steps` or `custom_timesteps`.' ):
scheduler.set_timesteps(num_inference_steps=_lowercase , timesteps=_lowercase )
def __a ( self : List[str] ):
A = self.scheduler_classes[0]
A = self.get_scheduler_config()
A = scheduler_class(**_lowercase )
A = [scheduler.config.num_train_timesteps]
with self.assertRaises(
_lowercase , msg='`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}' , ):
scheduler.set_timesteps(timesteps=_lowercase )
| 690 |
"""simple docstring"""
import argparse
import os
import shutil
from pathlib import Path
import onnx
import torch
from packaging import version
from torch.onnx import export
from diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline, StableDiffusionPipeline
UpperCamelCase : List[Any] = version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__=False , ) -> Any:
"""simple docstring"""
output_path.parent.mkdir(parents=UpperCamelCase__ , exist_ok=UpperCamelCase__ )
# PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
# so we check the torch version for backwards compatibility
if is_torch_less_than_1_11:
export(
UpperCamelCase__ , UpperCamelCase__ , f=output_path.as_posix() , input_names=UpperCamelCase__ , output_names=UpperCamelCase__ , dynamic_axes=UpperCamelCase__ , do_constant_folding=UpperCamelCase__ , use_external_data_format=UpperCamelCase__ , enable_onnx_checker=UpperCamelCase__ , opset_version=UpperCamelCase__ , )
else:
export(
UpperCamelCase__ , UpperCamelCase__ , f=output_path.as_posix() , input_names=UpperCamelCase__ , output_names=UpperCamelCase__ , dynamic_axes=UpperCamelCase__ , do_constant_folding=UpperCamelCase__ , opset_version=UpperCamelCase__ , )
@torch.no_grad()
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = False ) -> str:
"""simple docstring"""
A = torch.floataa if fpaa else torch.floataa
if fpaa and torch.cuda.is_available():
A = 'cuda'
elif fpaa and not torch.cuda.is_available():
raise ValueError('`float16` model export is only supported on GPUs with CUDA' )
else:
A = 'cpu'
A = StableDiffusionPipeline.from_pretrained(UpperCamelCase__ , torch_dtype=UpperCamelCase__ ).to(UpperCamelCase__ )
A = Path(UpperCamelCase__ )
# TEXT ENCODER
A = pipeline.text_encoder.config.max_position_embeddings
A = pipeline.text_encoder.config.hidden_size
A = pipeline.tokenizer(
'A sample prompt' , padding='max_length' , max_length=pipeline.tokenizer.model_max_length , truncation=UpperCamelCase__ , return_tensors='pt' , )
onnx_export(
pipeline.text_encoder , model_args=(text_input.input_ids.to(device=UpperCamelCase__ , dtype=torch.intaa )) , output_path=output_path / 'text_encoder' / 'model.onnx' , ordered_input_names=['input_ids'] , output_names=['last_hidden_state', 'pooler_output'] , dynamic_axes={
'input_ids': {0: 'batch', 1: 'sequence'},
} , opset=UpperCamelCase__ , )
del pipeline.text_encoder
# UNET
A = pipeline.unet.config.in_channels
A = pipeline.unet.config.sample_size
A = output_path / 'unet' / 'model.onnx'
onnx_export(
pipeline.unet , model_args=(
torch.randn(2 , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ).to(device=UpperCamelCase__ , dtype=UpperCamelCase__ ),
torch.randn(2 ).to(device=UpperCamelCase__ , dtype=UpperCamelCase__ ),
torch.randn(2 , UpperCamelCase__ , UpperCamelCase__ ).to(device=UpperCamelCase__ , dtype=UpperCamelCase__ ),
False,
) , output_path=UpperCamelCase__ , ordered_input_names=['sample', 'timestep', 'encoder_hidden_states', 'return_dict'] , output_names=['out_sample'] , dynamic_axes={
'sample': {0: 'batch', 1: 'channels', 2: 'height', 3: 'width'},
'timestep': {0: 'batch'},
'encoder_hidden_states': {0: 'batch', 1: 'sequence'},
} , opset=UpperCamelCase__ , use_external_data_format=UpperCamelCase__ , )
A = str(unet_path.absolute().as_posix() )
A = os.path.dirname(UpperCamelCase__ )
A = onnx.load(UpperCamelCase__ )
# clean up existing tensor files
shutil.rmtree(UpperCamelCase__ )
os.mkdir(UpperCamelCase__ )
# collate external tensor files into one
onnx.save_model(
UpperCamelCase__ , UpperCamelCase__ , save_as_external_data=UpperCamelCase__ , all_tensors_to_one_file=UpperCamelCase__ , location='weights.pb' , convert_attribute=UpperCamelCase__ , )
del pipeline.unet
# VAE ENCODER
A = pipeline.vae
A = vae_encoder.config.in_channels
A = vae_encoder.config.sample_size
# need to get the raw tensor output (sample) from the encoder
A = lambda UpperCamelCase__ , UpperCamelCase__ : vae_encoder.encode(UpperCamelCase__ , UpperCamelCase__ )[0].sample()
onnx_export(
UpperCamelCase__ , model_args=(
torch.randn(1 , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ).to(device=UpperCamelCase__ , dtype=UpperCamelCase__ ),
False,
) , output_path=output_path / 'vae_encoder' / 'model.onnx' , ordered_input_names=['sample', 'return_dict'] , output_names=['latent_sample'] , dynamic_axes={
'sample': {0: 'batch', 1: 'channels', 2: 'height', 3: 'width'},
} , opset=UpperCamelCase__ , )
# VAE DECODER
A = pipeline.vae
A = vae_decoder.config.latent_channels
A = vae_decoder.config.out_channels
# forward only through the decoder part
A = vae_encoder.decode
onnx_export(
UpperCamelCase__ , model_args=(
torch.randn(1 , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ).to(device=UpperCamelCase__ , dtype=UpperCamelCase__ ),
False,
) , output_path=output_path / 'vae_decoder' / 'model.onnx' , ordered_input_names=['latent_sample', 'return_dict'] , output_names=['sample'] , dynamic_axes={
'latent_sample': {0: 'batch', 1: 'channels', 2: 'height', 3: 'width'},
} , opset=UpperCamelCase__ , )
del pipeline.vae
# SAFETY CHECKER
if pipeline.safety_checker is not None:
A = pipeline.safety_checker
A = safety_checker.config.vision_config.num_channels
A = safety_checker.config.vision_config.image_size
A = safety_checker.forward_onnx
onnx_export(
pipeline.safety_checker , model_args=(
torch.randn(
1 , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , ).to(device=UpperCamelCase__ , dtype=UpperCamelCase__ ),
torch.randn(1 , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ).to(device=UpperCamelCase__ , dtype=UpperCamelCase__ ),
) , output_path=output_path / 'safety_checker' / 'model.onnx' , ordered_input_names=['clip_input', 'images'] , output_names=['out_images', 'has_nsfw_concepts'] , dynamic_axes={
'clip_input': {0: 'batch', 1: 'channels', 2: 'height', 3: 'width'},
'images': {0: 'batch', 1: 'height', 2: 'width', 3: 'channels'},
} , opset=UpperCamelCase__ , )
del pipeline.safety_checker
A = OnnxRuntimeModel.from_pretrained(output_path / 'safety_checker' )
A = pipeline.feature_extractor
else:
A = None
A = None
A = OnnxStableDiffusionPipeline(
vae_encoder=OnnxRuntimeModel.from_pretrained(output_path / 'vae_encoder' ) , vae_decoder=OnnxRuntimeModel.from_pretrained(output_path / 'vae_decoder' ) , text_encoder=OnnxRuntimeModel.from_pretrained(output_path / 'text_encoder' ) , tokenizer=pipeline.tokenizer , unet=OnnxRuntimeModel.from_pretrained(output_path / 'unet' ) , scheduler=pipeline.scheduler , safety_checker=UpperCamelCase__ , feature_extractor=UpperCamelCase__ , requires_safety_checker=safety_checker is not None , )
onnx_pipeline.save_pretrained(UpperCamelCase__ )
print('ONNX pipeline saved to' , UpperCamelCase__ )
del pipeline
del onnx_pipeline
A = OnnxStableDiffusionPipeline.from_pretrained(UpperCamelCase__ , provider='CPUExecutionProvider' )
print('ONNX pipeline is loadable' )
if __name__ == "__main__":
UpperCamelCase : Optional[int] = argparse.ArgumentParser()
parser.add_argument(
"--model_path",
type=str,
required=True,
help="Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).",
)
parser.add_argument("--output_path", type=str, required=True, help="Path to the output model.")
parser.add_argument(
"--opset",
default=14,
type=int,
help="The version of the ONNX operator set to use.",
)
parser.add_argument("--fp16", action="store_true", default=False, help="Export the models in `float16` mode")
UpperCamelCase : str = parser.parse_args()
convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
| 690 | 1 |
"""simple docstring"""
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
UpperCamelCase : Optional[Any] = logging.get_logger(__name__)
def __snake_case ( UpperCamelCase__ ) -> Dict:
"""simple docstring"""
A = r'\w+[.]\d+'
A = re.findall(UpperCamelCase__ , UpperCamelCase__ )
for pat in pats:
A = key.replace(UpperCamelCase__ , '_'.join(pat.split('.' ) ) )
return key
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> List[Any]:
"""simple docstring"""
A = pt_tuple_key[:-1] + ('scale',)
if (
any('norm' in str_ for str_ in pt_tuple_key )
and (pt_tuple_key[-1] == "bias")
and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
):
A = pt_tuple_key[:-1] + ('scale',)
return renamed_pt_tuple_key, pt_tensor
elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
A = pt_tuple_key[:-1] + ('scale',)
return renamed_pt_tuple_key, pt_tensor
# embedding
if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
A = pt_tuple_key[:-1] + ('embedding',)
return renamed_pt_tuple_key, pt_tensor
# conv layer
A = pt_tuple_key[:-1] + ('kernel',)
if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
A = pt_tensor.transpose(2 , 3 , 1 , 0 )
return renamed_pt_tuple_key, pt_tensor
# linear layer
A = pt_tuple_key[:-1] + ('kernel',)
if pt_tuple_key[-1] == "weight":
A = pt_tensor.T
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm weight
A = pt_tuple_key[:-1] + ('weight',)
if pt_tuple_key[-1] == "gamma":
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm bias
A = pt_tuple_key[:-1] + ('bias',)
if pt_tuple_key[-1] == "beta":
return renamed_pt_tuple_key, pt_tensor
return pt_tuple_key, pt_tensor
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__=42 ) -> Optional[Any]:
"""simple docstring"""
A = {k: v.numpy() for k, v in pt_state_dict.items()}
# Step 2: Since the model is stateless, get random Flax params
A = flax_model.init_weights(PRNGKey(UpperCamelCase__ ) )
A = flatten_dict(UpperCamelCase__ )
A = {}
# Need to change some parameters name to match Flax names
for pt_key, pt_tensor in pt_state_dict.items():
A = rename_key(UpperCamelCase__ )
A = tuple(renamed_pt_key.split('.' ) )
# Correctly rename weight parameters
A , A = rename_key_and_reshape_tensor(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
if flax_key in random_flax_state_dict:
if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
raise ValueError(
f'PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '
f'{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.' )
# also add unexpected weight so that warning is thrown
A = jnp.asarray(UpperCamelCase__ )
return unflatten_dict(UpperCamelCase__ )
| 690 |
"""simple docstring"""
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
UpperCamelCase : List[str] = Lock()
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> List[str]:
"""simple docstring"""
global process_lock
# we perform n swaps since after n swaps we know we are sorted
# we *could* stop early if we are sorted already, but it takes as long to
# find out we are sorted as it does to sort the list with this algorithm
for i in range(0 , 10 ):
if (i + position) % 2 == 0 and r_send is not None:
# send your value to your right neighbor
process_lock.acquire()
r_send[1].send(UpperCamelCase__ )
process_lock.release()
# receive your right neighbor's value
process_lock.acquire()
A = rr_cv[0].recv()
process_lock.release()
# take the lower value since you are on the left
A = min(UpperCamelCase__ , UpperCamelCase__ )
elif (i + position) % 2 != 0 and l_send is not None:
# send your value to your left neighbor
process_lock.acquire()
l_send[1].send(UpperCamelCase__ )
process_lock.release()
# receive your left neighbor's value
process_lock.acquire()
A = lr_cv[0].recv()
process_lock.release()
# take the higher value since you are on the right
A = max(UpperCamelCase__ , UpperCamelCase__ )
# after all swaps are performed, send the values back to main
result_pipe[1].send(UpperCamelCase__ )
def __snake_case ( UpperCamelCase__ ) -> List[str]:
"""simple docstring"""
A = []
A = []
# initialize the list of pipes where the values will be retrieved
for _ in arr:
result_pipe.append(Pipe() )
# creates the processes
# the first and last process only have one neighbor so they are made outside
# of the loop
A = Pipe()
A = Pipe()
process_array_.append(
Process(
target=UpperCamelCase__ , args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0]) , ) )
A = temp_rs
A = temp_rr
for i in range(1 , len(UpperCamelCase__ ) - 1 ):
A = Pipe()
A = Pipe()
process_array_.append(
Process(
target=UpperCamelCase__ , args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i]) , ) )
A = temp_rs
A = temp_rr
process_array_.append(
Process(
target=UpperCamelCase__ , args=(
len(UpperCamelCase__ ) - 1,
arr[len(UpperCamelCase__ ) - 1],
temp_ls,
None,
temp_lr,
None,
result_pipe[len(UpperCamelCase__ ) - 1],
) , ) )
# start the processes
for p in process_array_:
p.start()
# wait for the processes to end and write their values to the list
for p in range(0 , len(UpperCamelCase__ ) ):
A = result_pipe[p][0].recv()
process_array_[p].join()
return arr
def __snake_case ( ) -> Optional[Any]:
"""simple docstring"""
A = list(range(10 , 0 , -1 ) )
print('Initial List' )
print(*UpperCamelCase__ )
A = odd_even_transposition(UpperCamelCase__ )
print('Sorted List\n' )
print(*UpperCamelCase__ )
if __name__ == "__main__":
main()
| 690 | 1 |
"""simple docstring"""
import re
from filelock import FileLock
try:
import nltk
UpperCamelCase : List[Any] = True
except (ImportError, ModuleNotFoundError):
UpperCamelCase : List[str] = False
if NLTK_AVAILABLE:
with FileLock(".lock") as lock:
nltk.download("punkt", quiet=True)
def __snake_case ( UpperCamelCase__ ) -> str:
"""simple docstring"""
re.sub('<n>' , '' , UpperCamelCase__ ) # remove pegasus newline char
assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
return "\n".join(nltk.sent_tokenize(UpperCamelCase__ ) )
| 690 |
"""simple docstring"""
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures
# Importing the dataset
UpperCamelCase : int = pd.read_csv(
"https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/"
"position_salaries.csv"
)
UpperCamelCase : List[Any] = dataset.iloc[:, 1:2].values
UpperCamelCase : Any = dataset.iloc[:, 2].values
UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase : str = train_test_split(X, y, test_size=0.2, random_state=0)
UpperCamelCase : List[str] = PolynomialFeatures(degree=4)
UpperCamelCase : Optional[int] = poly_reg.fit_transform(X)
UpperCamelCase : List[Any] = LinearRegression()
pol_reg.fit(X_poly, y)
def __snake_case ( ) -> Optional[int]:
"""simple docstring"""
plt.scatter(UpperCamelCase__ , UpperCamelCase__ , color='red' )
plt.plot(UpperCamelCase__ , pol_reg.predict(poly_reg.fit_transform(UpperCamelCase__ ) ) , color='blue' )
plt.title('Truth or Bluff (Linear Regression)' )
plt.xlabel('Position level' )
plt.ylabel('Salary' )
plt.show()
if __name__ == "__main__":
viz_polymonial()
# Predicting a new result with Polymonial Regression
pol_reg.predict(poly_reg.fit_transform([[5.5]]))
# output should be 132148.43750003
| 690 | 1 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFAutoModelForSeqaSeqLM, TFMBartForConditionalGeneration, TFMBartModel
@require_tf
class lowerCamelCase__ :
lowerCAmelCase = MBartConfig
lowerCAmelCase = {}
lowerCAmelCase = """gelu"""
def __init__( self : Union[str, Any] , _lowercase : Union[str, Any] , _lowercase : List[str]=13 , _lowercase : str=7 , _lowercase : Optional[Any]=True , _lowercase : Tuple=False , _lowercase : Union[str, Any]=99 , _lowercase : str=32 , _lowercase : List[str]=2 , _lowercase : Optional[int]=4 , _lowercase : Dict=37 , _lowercase : Dict=0.1 , _lowercase : str=0.1 , _lowercase : Optional[Any]=20 , _lowercase : Optional[int]=2 , _lowercase : str=1 , _lowercase : Optional[int]=0 , ):
A = parent
A = batch_size
A = seq_length
A = is_training
A = use_labels
A = vocab_size
A = hidden_size
A = num_hidden_layers
A = num_attention_heads
A = intermediate_size
A = hidden_dropout_prob
A = attention_probs_dropout_prob
A = max_position_embeddings
A = eos_token_id
A = pad_token_id
A = bos_token_id
def __a ( self : Tuple ):
A = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
A = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
A = tf.concat([input_ids, eos_tensor] , axis=1 )
A = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
A = self.config_cls(
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 , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , **self.config_updates , )
A = prepare_mbart_inputs_dict(_lowercase , _lowercase , _lowercase )
return config, inputs_dict
def __a ( self : Optional[int] , _lowercase : Tuple , _lowercase : int ):
A = TFMBartModel(config=_lowercase ).get_decoder()
A = inputs_dict['input_ids']
A = input_ids[:1, :]
A = inputs_dict['attention_mask'][:1, :]
A = inputs_dict['head_mask']
A = 1
# first forward pass
A = model(_lowercase , attention_mask=_lowercase , head_mask=_lowercase , use_cache=_lowercase )
A , A = outputs.to_tuple()
A = past_key_values[1]
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__=None , UpperCamelCase__=None , UpperCamelCase__=None , UpperCamelCase__=None , UpperCamelCase__=None , ) -> Tuple:
"""simple docstring"""
if attention_mask is None:
A = tf.cast(tf.math.not_equal(UpperCamelCase__ , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
A = tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ) , tf.inta ),
] , axis=-1 , )
if head_mask is None:
A = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
A = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
A = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
@require_tf
class lowerCamelCase__ ( UpperCAmelCase_ , UpperCAmelCase_ , unittest.TestCase ):
lowerCAmelCase = (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
lowerCAmelCase = (TFMBartForConditionalGeneration,) if is_tf_available() else ()
lowerCAmelCase = (
{
"""conversational""": TFMBartForConditionalGeneration,
"""feature-extraction""": TFMBartModel,
"""summarization""": TFMBartForConditionalGeneration,
"""text2text-generation""": TFMBartForConditionalGeneration,
"""translation""": TFMBartForConditionalGeneration,
}
if is_tf_available()
else {}
)
lowerCAmelCase = True
lowerCAmelCase = False
lowerCAmelCase = False
def __a ( self : int , _lowercase : str , _lowercase : Dict , _lowercase : Tuple , _lowercase : Dict , _lowercase : List[str] ):
if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
# Exception encountered when calling layer '...'
return True
return False
def __a ( self : int ):
A = TFMBartModelTester(self )
A = ConfigTester(self , config_class=_lowercase )
def __a ( self : List[Any] ):
self.config_tester.run_common_tests()
def __a ( self : List[Any] ):
A = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*_lowercase )
@require_sentencepiece
@require_tokenizers
@require_tf
class lowerCamelCase__ ( unittest.TestCase ):
lowerCAmelCase = [
""" UN Chief Says There Is No Military Solution in Syria""",
]
lowerCAmelCase = [
"""Şeful ONU declară că nu există o soluţie militară în Siria""",
]
lowerCAmelCase = """facebook/mbart-large-en-ro"""
@cached_property
def __a ( self : List[Any] ):
return AutoTokenizer.from_pretrained(self.model_name )
@cached_property
def __a ( self : Optional[int] ):
A = TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
return model
def __a ( self : str , **_lowercase : Union[str, Any] ):
A = self.translate_src_text(**_lowercase )
self.assertListEqual(self.expected_text , _lowercase )
def __a ( self : List[str] , **_lowercase : Tuple ):
A = self.tokenizer(self.src_text , **_lowercase , return_tensors='tf' )
A = self.model.generate(
model_inputs.input_ids , attention_mask=model_inputs.attention_mask , num_beams=2 )
A = self.tokenizer.batch_decode(_lowercase , skip_special_tokens=_lowercase )
return generated_words
@slow
def __a ( self : List[Any] ):
self._assert_generated_batch_equal_expected()
| 690 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPENAI_CLIP_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
UpperCamelCase : Optional[Any] = logging.get_logger(__name__)
if is_vision_available():
import PIL
class lowerCamelCase__ ( UpperCAmelCase_ ):
lowerCAmelCase = ["""pixel_values"""]
def __init__( self : List[str] , _lowercase : bool = True , _lowercase : Dict[str, int] = None , _lowercase : PILImageResampling = PILImageResampling.BICUBIC , _lowercase : bool = True , _lowercase : Dict[str, int] = None , _lowercase : bool = True , _lowercase : Union[int, float] = 1 / 255 , _lowercase : bool = True , _lowercase : Optional[Union[float, List[float]]] = None , _lowercase : Optional[Union[float, List[float]]] = None , _lowercase : bool = True , **_lowercase : Tuple , ):
super().__init__(**_lowercase )
A = size if size is not None else {'shortest_edge': 224}
A = get_size_dict(_lowercase , default_to_square=_lowercase )
A = crop_size if crop_size is not None else {'height': 224, 'width': 224}
A = get_size_dict(_lowercase , default_to_square=_lowercase , param_name='crop_size' )
A = do_resize
A = size
A = resample
A = do_center_crop
A = crop_size
A = do_rescale
A = rescale_factor
A = do_normalize
A = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
A = image_std if image_std is not None else OPENAI_CLIP_STD
A = do_convert_rgb
def __a ( self : str , _lowercase : np.ndarray , _lowercase : Dict[str, int] , _lowercase : PILImageResampling = PILImageResampling.BICUBIC , _lowercase : Optional[Union[str, ChannelDimension]] = None , **_lowercase : List[str] , ):
A = get_size_dict(_lowercase , default_to_square=_lowercase )
if "shortest_edge" not in size:
raise ValueError(f'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}' )
A = get_resize_output_image_size(_lowercase , size=size['shortest_edge'] , default_to_square=_lowercase )
return resize(_lowercase , size=_lowercase , resample=_lowercase , data_format=_lowercase , **_lowercase )
def __a ( self : int , _lowercase : np.ndarray , _lowercase : Dict[str, int] , _lowercase : Optional[Union[str, ChannelDimension]] = None , **_lowercase : str , ):
A = get_size_dict(_lowercase )
if "height" not in size or "width" not in size:
raise ValueError(f'The `size` parameter must contain the keys (height, width). Got {size.keys()}' )
return center_crop(_lowercase , size=(size['height'], size['width']) , data_format=_lowercase , **_lowercase )
def __a ( self : List[str] , _lowercase : np.ndarray , _lowercase : Union[int, float] , _lowercase : Optional[Union[str, ChannelDimension]] = None , **_lowercase : List[str] , ):
return rescale(_lowercase , scale=_lowercase , data_format=_lowercase , **_lowercase )
def __a ( self : List[str] , _lowercase : np.ndarray , _lowercase : Union[float, List[float]] , _lowercase : Union[float, List[float]] , _lowercase : Optional[Union[str, ChannelDimension]] = None , **_lowercase : Union[str, Any] , ):
return normalize(_lowercase , mean=_lowercase , std=_lowercase , data_format=_lowercase , **_lowercase )
def __a ( self : Optional[int] , _lowercase : ImageInput , _lowercase : bool = None , _lowercase : Dict[str, int] = None , _lowercase : PILImageResampling = None , _lowercase : bool = None , _lowercase : int = None , _lowercase : bool = None , _lowercase : float = None , _lowercase : bool = None , _lowercase : Optional[Union[float, List[float]]] = None , _lowercase : Optional[Union[float, List[float]]] = None , _lowercase : bool = None , _lowercase : Optional[Union[str, TensorType]] = None , _lowercase : Optional[ChannelDimension] = ChannelDimension.FIRST , **_lowercase : int , ):
A = do_resize if do_resize is not None else self.do_resize
A = size if size is not None else self.size
A = get_size_dict(_lowercase , param_name='size' , default_to_square=_lowercase )
A = resample if resample is not None else self.resample
A = do_center_crop if do_center_crop is not None else self.do_center_crop
A = crop_size if crop_size is not None else self.crop_size
A = get_size_dict(_lowercase , param_name='crop_size' , default_to_square=_lowercase )
A = do_rescale if do_rescale is not None else self.do_rescale
A = rescale_factor if rescale_factor is not None else self.rescale_factor
A = do_normalize if do_normalize is not None else self.do_normalize
A = image_mean if image_mean is not None else self.image_mean
A = image_std if image_std is not None else self.image_std
A = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
A = make_list_of_images(_lowercase )
if not valid_images(_lowercase ):
raise ValueError(
'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
'torch.Tensor, tf.Tensor or jax.ndarray.' )
if do_resize and size is None:
raise ValueError('Size must be specified if do_resize is True.' )
if do_center_crop and crop_size is None:
raise ValueError('Crop size must be specified if do_center_crop is True.' )
if do_rescale and rescale_factor is None:
raise ValueError('Rescale factor must be specified if do_rescale is True.' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('Image mean and std must be specified if do_normalize is True.' )
# PIL RGBA images are converted to RGB
if do_convert_rgb:
A = [convert_to_rgb(_lowercase ) for image in images]
# All transformations expect numpy arrays.
A = [to_numpy_array(_lowercase ) for image in images]
if do_resize:
A = [self.resize(image=_lowercase , size=_lowercase , resample=_lowercase ) for image in images]
if do_center_crop:
A = [self.center_crop(image=_lowercase , size=_lowercase ) for image in images]
if do_rescale:
A = [self.rescale(image=_lowercase , scale=_lowercase ) for image in images]
if do_normalize:
A = [self.normalize(image=_lowercase , mean=_lowercase , std=_lowercase ) for image in images]
A = [to_channel_dimension_format(_lowercase , _lowercase ) for image in images]
A = {'pixel_values': images}
return BatchFeature(data=_lowercase , tensor_type=_lowercase )
| 690 | 1 |
"""simple docstring"""
import unittest
from datasets import load_dataset
from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow
@is_pipeline_test
@require_torch
class lowerCamelCase__ ( unittest.TestCase ):
@require_torch
def __a ( self : List[str] ):
A = pipeline(
task='zero-shot-audio-classification' , model='hf-internal-testing/tiny-clap-htsat-unfused' )
A = load_dataset('ashraq/esc50' )
A = dataset['train']['audio'][-1]['array']
A = audio_classifier(_lowercase , candidate_labels=['Sound of a dog', 'Sound of vaccum cleaner'] )
self.assertEqual(
nested_simplify(_lowercase ) , [{'score': 0.5_0_1, 'label': 'Sound of a dog'}, {'score': 0.4_9_9, 'label': 'Sound of vaccum cleaner'}] , )
@unittest.skip('No models are available in TF' )
def __a ( self : Union[str, Any] ):
pass
@slow
@require_torch
def __a ( self : Any ):
A = pipeline(
task='zero-shot-audio-classification' , model='laion/clap-htsat-unfused' , )
# This is an audio of a dog
A = load_dataset('ashraq/esc50' )
A = dataset['train']['audio'][-1]['array']
A = audio_classifier(_lowercase , candidate_labels=['Sound of a dog', 'Sound of vaccum cleaner'] )
self.assertEqual(
nested_simplify(_lowercase ) , [
{'score': 0.9_9_9, 'label': 'Sound of a dog'},
{'score': 0.0_0_1, 'label': 'Sound of vaccum cleaner'},
] , )
A = audio_classifier([audio] * 5 , candidate_labels=['Sound of a dog', 'Sound of vaccum cleaner'] )
self.assertEqual(
nested_simplify(_lowercase ) , [
[
{'score': 0.9_9_9, 'label': 'Sound of a dog'},
{'score': 0.0_0_1, 'label': 'Sound of vaccum cleaner'},
],
]
* 5 , )
A = audio_classifier(
[audio] * 5 , candidate_labels=['Sound of a dog', 'Sound of vaccum cleaner'] , batch_size=5 )
self.assertEqual(
nested_simplify(_lowercase ) , [
[
{'score': 0.9_9_9, 'label': 'Sound of a dog'},
{'score': 0.0_0_1, 'label': 'Sound of vaccum cleaner'},
],
]
* 5 , )
@unittest.skip('No models are available in TF' )
def __a ( self : Dict ):
pass
| 690 |
"""simple docstring"""
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class lowerCamelCase__ ( unittest.TestCase ):
def __a ( self : Union[str, Any] ):
A = torch.nn.Linear(10 , 10 )
A = torch.optim.SGD(model.parameters() , 0.1 )
A = Accelerator()
A = accelerator.prepare(_lowercase )
try:
pickle.loads(pickle.dumps(_lowercase ) )
except Exception as e:
self.fail(f'Accelerated optimizer pickling failed with {e}' )
AcceleratorState._reset_state()
| 690 | 1 |
"""simple docstring"""
import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device
from ..test_pipelines_common import to_np
class lowerCamelCase__ :
def __a ( self : Union[str, Any] ):
torch.manual_seed(0 )
A = TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5' )
torch.manual_seed(0 )
A = AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-t5' )
torch.manual_seed(0 )
A = UNetaDConditionModel(
sample_size=32 , layers_per_block=1 , block_out_channels=[32, 64] , down_block_types=[
'ResnetDownsampleBlock2D',
'SimpleCrossAttnDownBlock2D',
] , mid_block_type='UNetMidBlock2DSimpleCrossAttn' , up_block_types=['SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'] , in_channels=3 , out_channels=6 , cross_attention_dim=32 , encoder_hid_dim=32 , attention_head_dim=8 , addition_embed_type='text' , addition_embed_type_num_heads=2 , cross_attention_norm='group_norm' , resnet_time_scale_shift='scale_shift' , act_fn='gelu' , )
unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
torch.manual_seed(0 )
A = DDPMScheduler(
num_train_timesteps=1_000 , beta_schedule='squaredcos_cap_v2' , beta_start=0.0_0_0_1 , beta_end=0.0_2 , thresholding=_lowercase , dynamic_thresholding_ratio=0.9_5 , sample_max_value=1.0 , prediction_type='epsilon' , variance_type='learned_range' , )
torch.manual_seed(0 )
A = IFWatermarker()
return {
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"unet": unet,
"scheduler": scheduler,
"watermarker": watermarker,
"safety_checker": None,
"feature_extractor": None,
}
def __a ( self : Dict ):
torch.manual_seed(0 )
A = TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5' )
torch.manual_seed(0 )
A = AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-t5' )
torch.manual_seed(0 )
A = UNetaDConditionModel(
sample_size=32 , layers_per_block=[1, 2] , block_out_channels=[32, 64] , down_block_types=[
'ResnetDownsampleBlock2D',
'SimpleCrossAttnDownBlock2D',
] , mid_block_type='UNetMidBlock2DSimpleCrossAttn' , up_block_types=['SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'] , in_channels=6 , out_channels=6 , cross_attention_dim=32 , encoder_hid_dim=32 , attention_head_dim=8 , addition_embed_type='text' , addition_embed_type_num_heads=2 , cross_attention_norm='group_norm' , resnet_time_scale_shift='scale_shift' , act_fn='gelu' , class_embed_type='timestep' , mid_block_scale_factor=1.4_1_4 , time_embedding_act_fn='gelu' , time_embedding_dim=32 , )
unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
torch.manual_seed(0 )
A = DDPMScheduler(
num_train_timesteps=1_000 , beta_schedule='squaredcos_cap_v2' , beta_start=0.0_0_0_1 , beta_end=0.0_2 , thresholding=_lowercase , dynamic_thresholding_ratio=0.9_5 , sample_max_value=1.0 , prediction_type='epsilon' , variance_type='learned_range' , )
torch.manual_seed(0 )
A = DDPMScheduler(
num_train_timesteps=1_000 , beta_schedule='squaredcos_cap_v2' , beta_start=0.0_0_0_1 , beta_end=0.0_2 , )
torch.manual_seed(0 )
A = IFWatermarker()
return {
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"unet": unet,
"scheduler": scheduler,
"image_noising_scheduler": image_noising_scheduler,
"watermarker": watermarker,
"safety_checker": None,
"feature_extractor": None,
}
def __a ( self : str ):
A = self.get_dummy_components()
A = self.pipeline_class(**_lowercase )
pipe.to(_lowercase )
pipe.set_progress_bar_config(disable=_lowercase )
A = self.get_dummy_inputs(_lowercase )
A = inputs['prompt']
A = inputs['generator']
A = inputs['num_inference_steps']
A = inputs['output_type']
if "image" in inputs:
A = inputs['image']
else:
A = None
if "mask_image" in inputs:
A = inputs['mask_image']
else:
A = None
if "original_image" in inputs:
A = inputs['original_image']
else:
A = None
A , A = pipe.encode_prompt(_lowercase )
# inputs with prompt converted to embeddings
A = {
'prompt_embeds': prompt_embeds,
'negative_prompt_embeds': negative_prompt_embeds,
'generator': generator,
'num_inference_steps': num_inference_steps,
'output_type': output_type,
}
if image is not None:
A = image
if mask_image is not None:
A = mask_image
if original_image is not None:
A = original_image
# set all optional components to None
for optional_component in pipe._optional_components:
setattr(_lowercase , _lowercase , _lowercase )
A = pipe(**_lowercase )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(_lowercase )
A = self.pipeline_class.from_pretrained(_lowercase )
pipe_loaded.to(_lowercase )
pipe_loaded.set_progress_bar_config(disable=_lowercase )
pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
for optional_component in pipe._optional_components:
self.assertTrue(
getattr(_lowercase , _lowercase ) is None , f'`{optional_component}` did not stay set to None after loading.' , )
A = self.get_dummy_inputs(_lowercase )
A = inputs['generator']
A = inputs['num_inference_steps']
A = inputs['output_type']
# inputs with prompt converted to embeddings
A = {
'prompt_embeds': prompt_embeds,
'negative_prompt_embeds': negative_prompt_embeds,
'generator': generator,
'num_inference_steps': num_inference_steps,
'output_type': output_type,
}
if image is not None:
A = image
if mask_image is not None:
A = mask_image
if original_image is not None:
A = original_image
A = pipe_loaded(**_lowercase )[0]
A = np.abs(to_np(_lowercase ) - to_np(_lowercase ) ).max()
self.assertLess(_lowercase , 1e-4 )
def __a ( self : Dict ):
A = self.get_dummy_components()
A = self.pipeline_class(**_lowercase )
pipe.to(_lowercase )
pipe.set_progress_bar_config(disable=_lowercase )
A = self.get_dummy_inputs(_lowercase )
A = pipe(**_lowercase )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(_lowercase )
A = self.pipeline_class.from_pretrained(_lowercase )
pipe_loaded.to(_lowercase )
pipe_loaded.set_progress_bar_config(disable=_lowercase )
pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
A = self.get_dummy_inputs(_lowercase )
A = pipe_loaded(**_lowercase )[0]
A = np.abs(to_np(_lowercase ) - to_np(_lowercase ) ).max()
self.assertLess(_lowercase , 1e-4 )
| 690 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCamelCase : Optional[int] = logging.get_logger(__name__)
UpperCamelCase : Optional[Any] = {
"YituTech/conv-bert-base": "https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json",
"YituTech/conv-bert-medium-small": (
"https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json"
),
"YituTech/conv-bert-small": "https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json",
# See all ConvBERT models at https://huggingface.co/models?filter=convbert
}
class lowerCamelCase__ ( UpperCAmelCase_ ):
lowerCAmelCase = """convbert"""
def __init__( self : Optional[int] , _lowercase : List[Any]=30_522 , _lowercase : List[str]=768 , _lowercase : Optional[Any]=12 , _lowercase : Any=12 , _lowercase : str=3_072 , _lowercase : List[str]="gelu" , _lowercase : Dict=0.1 , _lowercase : Dict=0.1 , _lowercase : Any=512 , _lowercase : List[str]=2 , _lowercase : Tuple=0.0_2 , _lowercase : List[Any]=1e-12 , _lowercase : List[str]=1 , _lowercase : Tuple=0 , _lowercase : Any=2 , _lowercase : Union[str, Any]=768 , _lowercase : str=2 , _lowercase : Any=9 , _lowercase : Union[str, Any]=1 , _lowercase : Dict=None , **_lowercase : Union[str, Any] , ):
super().__init__(
pad_token_id=_lowercase , bos_token_id=_lowercase , eos_token_id=_lowercase , **_lowercase , )
A = vocab_size
A = hidden_size
A = num_hidden_layers
A = num_attention_heads
A = intermediate_size
A = hidden_act
A = hidden_dropout_prob
A = attention_probs_dropout_prob
A = max_position_embeddings
A = type_vocab_size
A = initializer_range
A = layer_norm_eps
A = embedding_size
A = head_ratio
A = conv_kernel_size
A = num_groups
A = classifier_dropout
class lowerCamelCase__ ( UpperCAmelCase_ ):
@property
def __a ( self : str ):
if self.task == "multiple-choice":
A = {0: 'batch', 1: 'choice', 2: 'sequence'}
else:
A = {0: 'batch', 1: 'sequence'}
return OrderedDict(
[
('input_ids', dynamic_axis),
('attention_mask', dynamic_axis),
('token_type_ids', dynamic_axis),
] )
| 690 | 1 |
"""simple docstring"""
import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field
import torch
from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging
UpperCamelCase : Optional[Any] = logging.get_logger(__name__)
def __snake_case ( ) -> Any:
"""simple docstring"""
A = os.getenv('SM_HP_MP_PARAMETERS' , '{}' )
try:
# Parse it and check the field "partitions" is included, it is required for model parallel.
A = json.loads(UpperCamelCase__ )
if "partitions" not in smp_options:
return False
except json.JSONDecodeError:
return False
# Get the sagemaker specific framework parameters from mpi_options variable.
A = os.getenv('SM_FRAMEWORK_PARAMS' , '{}' )
try:
# Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
A = json.loads(UpperCamelCase__ )
if not mpi_options.get('sagemaker_mpi_enabled' , UpperCamelCase__ ):
return False
except json.JSONDecodeError:
return False
# Lastly, check if the `smdistributed` module is present.
return importlib.util.find_spec('smdistributed' ) is not None
if is_sagemaker_model_parallel_available():
import smdistributed.modelparallel.torch as smp
smp.init()
@dataclass
class lowerCamelCase__ ( UpperCAmelCase_ ):
lowerCAmelCase = field(
default="""""" , metadata={"""help""": """Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer"""} , )
def __a ( self : List[str] ):
super().__post_init__()
warnings.warn(
'`SageMakerTrainingArguments` is deprecated and will be removed in v5 of Transformers. You can use '
'`TrainingArguments` instead.' , _lowercase , )
@cached_property
def __a ( self : str ):
logger.info('PyTorch: setting up devices' )
if torch.distributed.is_available() and torch.distributed.is_initialized() and self.local_rank == -1:
logger.warning(
'torch.distributed process group is initialized, but local_rank == -1. '
'In order to use Torch DDP, launch your script with `python -m torch.distributed.launch' )
if self.no_cuda:
A = torch.device('cpu' )
A = 0
elif is_sagemaker_model_parallel_available():
A = smp.local_rank()
A = torch.device('cuda' , _lowercase )
A = 1
elif is_sagemaker_dp_enabled():
import smdistributed.dataparallel.torch.torch_smddp # noqa: F401
torch.distributed.init_process_group(backend='smddp' , timeout=self.ddp_timeout_delta )
A = int(os.getenv('SMDATAPARALLEL_LOCAL_RANK' ) )
A = torch.device('cuda' , self.local_rank )
A = 1
elif self.local_rank == -1:
# if n_gpu is > 1 we'll use nn.DataParallel.
# If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
# Explicitly set CUDA to the first (index 0) CUDA device, otherwise `set_device` will
# trigger an error that a device index is missing. Index 0 takes into account the
# GPUs available in the environment, so `CUDA_VISIBLE_DEVICES=1,2` with `cuda:0`
# will use the first GPU in that env, i.e. GPU#1
A = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu' )
# Sometimes the line in the postinit has not been run before we end up here, so just checking we're not at
# the default value.
A = torch.cuda.device_count()
else:
# Here, we'll use torch.distributed.
# Initializes the distributed backend which will take care of synchronizing nodes/GPUs
if not torch.distributed.is_initialized():
torch.distributed.init_process_group(backend='nccl' , timeout=self.ddp_timeout_delta )
A = torch.device('cuda' , self.local_rank )
A = 1
if device.type == "cuda":
torch.cuda.set_device(_lowercase )
return device
@property
def __a ( self : Optional[int] ):
if is_sagemaker_model_parallel_available():
return smp.dp_size()
return super().world_size
@property
def __a ( self : Optional[Any] ):
return not is_sagemaker_model_parallel_available()
@property
def __a ( self : List[str] ):
return False
| 690 |
"""simple docstring"""
from .constants import (
MODEL_NAME,
OPTIMIZER_NAME,
RNG_STATE_NAME,
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
SCALER_NAME,
SCHEDULER_NAME,
TORCH_LAUNCH_PARAMS,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
)
from .dataclasses import (
BnbQuantizationConfig,
ComputeEnvironment,
CustomDtype,
DeepSpeedPlugin,
DistributedDataParallelKwargs,
DistributedType,
DynamoBackend,
FPaRecipeKwargs,
FullyShardedDataParallelPlugin,
GradientAccumulationPlugin,
GradScalerKwargs,
InitProcessGroupKwargs,
KwargsHandler,
LoggerType,
MegatronLMPlugin,
PrecisionType,
ProjectConfiguration,
RNGType,
SageMakerDistributedType,
TensorInformation,
TorchDynamoPlugin,
)
from .environment import get_int_from_env, parse_choice_from_env, parse_flag_from_env
from .imports import (
get_ccl_version,
is_abit_bnb_available,
is_abit_bnb_available,
is_aim_available,
is_bfaa_available,
is_bnb_available,
is_botoa_available,
is_ccl_available,
is_comet_ml_available,
is_datasets_available,
is_deepspeed_available,
is_fpa_available,
is_ipex_available,
is_megatron_lm_available,
is_mlflow_available,
is_mps_available,
is_npu_available,
is_rich_available,
is_safetensors_available,
is_sagemaker_available,
is_tensorboard_available,
is_tpu_available,
is_transformers_available,
is_wandb_available,
is_xpu_available,
)
from .modeling import (
check_device_map,
check_tied_parameters_in_config,
check_tied_parameters_on_same_device,
compute_module_sizes,
convert_file_size_to_int,
dtype_byte_size,
find_tied_parameters,
get_balanced_memory,
get_max_layer_size,
get_max_memory,
get_mixed_precision_context_manager,
id_tensor_storage,
infer_auto_device_map,
load_checkpoint_in_model,
load_offloaded_weights,
load_state_dict,
named_module_tensors,
retie_parameters,
set_module_tensor_to_device,
shard_checkpoint,
)
from .offload import (
OffloadedWeightsLoader,
PrefixedDataset,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_weight,
save_offload_index,
)
from .operations import (
broadcast,
broadcast_object_list,
concatenate,
convert_outputs_to_fpaa,
convert_to_fpaa,
find_batch_size,
find_device,
gather,
gather_object,
get_data_structure,
honor_type,
initialize_tensors,
is_namedtuple,
is_tensor_information,
is_torch_tensor,
listify,
pad_across_processes,
recursively_apply,
reduce,
send_to_device,
slice_tensors,
)
from .versions import compare_versions, is_torch_version
if is_deepspeed_available():
from .deepspeed import (
DeepSpeedEngineWrapper,
DeepSpeedOptimizerWrapper,
DeepSpeedSchedulerWrapper,
DummyOptim,
DummyScheduler,
HfDeepSpeedConfig,
)
from .bnb import has_abit_bnb_layers, load_and_quantize_model
from .fsdp_utils import load_fsdp_model, load_fsdp_optimizer, save_fsdp_model, save_fsdp_optimizer
from .launch import (
PrepareForLaunch,
_filter_args,
prepare_deepspeed_cmd_env,
prepare_multi_gpu_env,
prepare_sagemager_args_inputs,
prepare_simple_launcher_cmd_env,
prepare_tpu,
)
from .megatron_lm import (
AbstractTrainStep,
BertTrainStep,
GPTTrainStep,
MegatronEngine,
MegatronLMDummyDataLoader,
MegatronLMDummyScheduler,
MegatronLMOptimizerWrapper,
MegatronLMSchedulerWrapper,
TaTrainStep,
avg_losses_across_data_parallel_group,
gather_across_data_parallel_groups,
)
from .megatron_lm import initialize as megatron_lm_initialize
from .megatron_lm import prepare_data_loader as megatron_lm_prepare_data_loader
from .megatron_lm import prepare_model as megatron_lm_prepare_model
from .megatron_lm import prepare_optimizer as megatron_lm_prepare_optimizer
from .megatron_lm import prepare_scheduler as megatron_lm_prepare_scheduler
from .memory import find_executable_batch_size, release_memory
from .other import (
extract_model_from_parallel,
get_pretty_name,
is_port_in_use,
merge_dicts,
patch_environment,
save,
wait_for_everyone,
write_basic_config,
)
from .random import set_seed, synchronize_rng_state, synchronize_rng_states
from .torch_xla import install_xla
from .tqdm import tqdm
from .transformer_engine import convert_model, has_transformer_engine_layers
| 690 | 1 |
"""simple docstring"""
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def __snake_case ( UpperCamelCase__ ) -> Optional[int]:
"""simple docstring"""
A = filter(lambda UpperCamelCase__ : p.requires_grad , model.parameters() )
A = sum([np.prod(p.size() ) for p in model_parameters] )
return params
UpperCamelCase : str = logging.getLogger(__name__)
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ ) -> Union[str, Any]:
"""simple docstring"""
if metric == "rouge2":
A = '{val_avg_rouge2:.4f}-{step_count}'
elif metric == "bleu":
A = '{val_avg_bleu:.4f}-{step_count}'
elif metric == "em":
A = '{val_avg_em:.4f}-{step_count}'
elif metric == "loss":
A = '{val_avg_loss:.4f}-{step_count}'
else:
raise NotImplementedError(
f'seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this'
' function.' )
A = ModelCheckpoint(
dirpath=UpperCamelCase__ , filename=UpperCamelCase__ , monitor=f'val_{metric}' , mode='max' , save_top_k=1 , every_n_epochs=1 , )
return checkpoint_callback
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ ) -> str:
"""simple docstring"""
return EarlyStopping(
monitor=f'val_{metric}' , mode='min' if 'loss' in metric else 'max' , patience=UpperCamelCase__ , verbose=UpperCamelCase__ , )
class lowerCamelCase__ ( pl.Callback ):
def __a ( self : Optional[Any] , _lowercase : Optional[int] , _lowercase : Any ):
A = {f'lr_group_{i}': param['lr'] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups )}
pl_module.logger.log_metrics(_lowercase )
@rank_zero_only
def __a ( self : Tuple , _lowercase : pl.Trainer , _lowercase : pl.LightningModule , _lowercase : str , _lowercase : Any=True ):
logger.info(f'***** {type_path} results at step {trainer.global_step:05d} *****' )
A = trainer.callback_metrics
trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ['log', 'progress_bar', 'preds']} )
# Log results
A = Path(pl_module.hparams.output_dir )
if type_path == "test":
A = od / 'test_results.txt'
A = od / 'test_generations.txt'
else:
# this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
# If people want this it will be easy enough to add back.
A = od / f'{type_path}_results/{trainer.global_step:05d}.txt'
A = od / f'{type_path}_generations/{trainer.global_step:05d}.txt'
results_file.parent.mkdir(exist_ok=_lowercase )
generations_file.parent.mkdir(exist_ok=_lowercase )
with open(_lowercase , 'a+' ) as writer:
for key in sorted(_lowercase ):
if key in ["log", "progress_bar", "preds"]:
continue
A = metrics[key]
if isinstance(_lowercase , torch.Tensor ):
A = val.item()
A = f'{key}: {val:.6f}\n'
writer.write(_lowercase )
if not save_generations:
return
if "preds" in metrics:
A = '\n'.join(metrics['preds'] )
generations_file.open('w+' ).write(_lowercase )
@rank_zero_only
def __a ( self : Any , _lowercase : Union[str, Any] , _lowercase : List[str] ):
try:
A = pl_module.model.model.num_parameters()
except AttributeError:
A = pl_module.model.num_parameters()
A = count_trainable_parameters(_lowercase )
# mp stands for million parameters
trainer.logger.log_metrics({'n_params': npars, 'mp': npars / 1e6, 'grad_mp': n_trainable_pars / 1e6} )
@rank_zero_only
def __a ( self : List[Any] , _lowercase : pl.Trainer , _lowercase : pl.LightningModule ):
save_json(pl_module.metrics , pl_module.metrics_save_path )
return self._write_logs(_lowercase , _lowercase , 'test' )
@rank_zero_only
def __a ( self : Tuple , _lowercase : pl.Trainer , _lowercase : Optional[int] ):
save_json(pl_module.metrics , pl_module.metrics_save_path )
# Uncommenting this will save val generations
# return self._write_logs(trainer, pl_module, "valid")
| 690 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
def __snake_case ( UpperCamelCase__ ) -> int:
"""simple docstring"""
if not postfix_notation:
return 0
A = {'+', '-', '*', '/'}
A = []
for token in postfix_notation:
if token in operations:
A , A = stack.pop(), stack.pop()
if token == "+":
stack.append(a + b )
elif token == "-":
stack.append(a - b )
elif token == "*":
stack.append(a * b )
else:
if a * b < 0 and a % b != 0:
stack.append(a // b + 1 )
else:
stack.append(a // b )
else:
stack.append(int(UpperCamelCase__ ) )
return stack.pop()
if __name__ == "__main__":
import doctest
doctest.testmod()
| 690 | 1 |
"""simple docstring"""
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation
def __snake_case ( UpperCamelCase__ ) -> List[str]:
"""simple docstring"""
A = 384
A = 7
if "tiny" in model_name:
A = 96
A = (2, 2, 6, 2)
A = (3, 6, 12, 24)
elif "small" in model_name:
A = 96
A = (2, 2, 18, 2)
A = (3, 6, 12, 24)
elif "base" in model_name:
A = 128
A = (2, 2, 18, 2)
A = (4, 8, 16, 32)
A = 12
A = 512
elif "large" in model_name:
A = 192
A = (2, 2, 18, 2)
A = (6, 12, 24, 48)
A = 12
A = 768
# set label information
A = 150
A = 'huggingface/label-files'
A = 'ade20k-id2label.json'
A = json.load(open(hf_hub_download(UpperCamelCase__ , UpperCamelCase__ , repo_type='dataset' ) , 'r' ) )
A = {int(UpperCamelCase__ ): v for k, v in idalabel.items()}
A = {v: k for k, v in idalabel.items()}
A = SwinConfig(
embed_dim=UpperCamelCase__ , depths=UpperCamelCase__ , num_heads=UpperCamelCase__ , window_size=UpperCamelCase__ , out_features=['stage1', 'stage2', 'stage3', 'stage4'] , )
A = UperNetConfig(
backbone_config=UpperCamelCase__ , auxiliary_in_channels=UpperCamelCase__ , num_labels=UpperCamelCase__ , idalabel=UpperCamelCase__ , labelaid=UpperCamelCase__ , )
return config
def __snake_case ( UpperCamelCase__ ) -> Any:
"""simple docstring"""
A = []
# fmt: off
# stem
rename_keys.append(('backbone.patch_embed.projection.weight', 'backbone.embeddings.patch_embeddings.projection.weight') )
rename_keys.append(('backbone.patch_embed.projection.bias', 'backbone.embeddings.patch_embeddings.projection.bias') )
rename_keys.append(('backbone.patch_embed.norm.weight', 'backbone.embeddings.norm.weight') )
rename_keys.append(('backbone.patch_embed.norm.bias', 'backbone.embeddings.norm.bias') )
# stages
for i in range(len(config.backbone_config.depths ) ):
for j in range(config.backbone_config.depths[i] ):
rename_keys.append((f'backbone.stages.{i}.blocks.{j}.norm1.weight', f'backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.weight') )
rename_keys.append((f'backbone.stages.{i}.blocks.{j}.norm1.bias', f'backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.bias') )
rename_keys.append((f'backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_bias_table', f'backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table') )
rename_keys.append((f'backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_index', f'backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index') )
rename_keys.append((f'backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.weight', f'backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight') )
rename_keys.append((f'backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.bias', f'backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias') )
rename_keys.append((f'backbone.stages.{i}.blocks.{j}.norm2.weight', f'backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.weight') )
rename_keys.append((f'backbone.stages.{i}.blocks.{j}.norm2.bias', f'backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.bias') )
rename_keys.append((f'backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.weight', f'backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight') )
rename_keys.append((f'backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.bias', f'backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias') )
rename_keys.append((f'backbone.stages.{i}.blocks.{j}.ffn.layers.1.weight', f'backbone.encoder.layers.{i}.blocks.{j}.output.dense.weight') )
rename_keys.append((f'backbone.stages.{i}.blocks.{j}.ffn.layers.1.bias', f'backbone.encoder.layers.{i}.blocks.{j}.output.dense.bias') )
if i < 3:
rename_keys.append((f'backbone.stages.{i}.downsample.reduction.weight', f'backbone.encoder.layers.{i}.downsample.reduction.weight') )
rename_keys.append((f'backbone.stages.{i}.downsample.norm.weight', f'backbone.encoder.layers.{i}.downsample.norm.weight') )
rename_keys.append((f'backbone.stages.{i}.downsample.norm.bias', f'backbone.encoder.layers.{i}.downsample.norm.bias') )
rename_keys.append((f'backbone.norm{i}.weight', f'backbone.hidden_states_norms.stage{i+1}.weight') )
rename_keys.append((f'backbone.norm{i}.bias', f'backbone.hidden_states_norms.stage{i+1}.bias') )
# decode head
rename_keys.extend(
[
('decode_head.conv_seg.weight', 'decode_head.classifier.weight'),
('decode_head.conv_seg.bias', 'decode_head.classifier.bias'),
('auxiliary_head.conv_seg.weight', 'auxiliary_head.classifier.weight'),
('auxiliary_head.conv_seg.bias', 'auxiliary_head.classifier.bias'),
] )
# fmt: on
return rename_keys
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> Optional[int]:
"""simple docstring"""
A = dct.pop(UpperCamelCase__ )
A = val
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ ) -> List[str]:
"""simple docstring"""
A = [int(backbone_config.embed_dim * 2**i ) for i in range(len(backbone_config.depths ) )]
for i in range(len(backbone_config.depths ) ):
A = num_features[i]
for j in range(backbone_config.depths[i] ):
# fmt: off
# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
A = state_dict.pop(f'backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight' )
A = state_dict.pop(f'backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.bias' )
# next, add query, keys and values (in that order) to the state dict
A = in_proj_weight[:dim, :]
A = in_proj_bias[: dim]
A = in_proj_weight[
dim : dim * 2, :
]
A = in_proj_bias[
dim : dim * 2
]
A = in_proj_weight[
-dim :, :
]
A = in_proj_bias[-dim :]
# fmt: on
def __snake_case ( UpperCamelCase__ ) -> Optional[int]:
"""simple docstring"""
A , A = x.shape
A = x.reshape(UpperCamelCase__ , 4 , in_channel // 4 )
A = x[:, [0, 2, 1, 3], :].transpose(1 , 2 ).reshape(UpperCamelCase__ , UpperCamelCase__ )
return x
def __snake_case ( UpperCamelCase__ ) -> Optional[int]:
"""simple docstring"""
A , A = x.shape
A = x.reshape(UpperCamelCase__ , in_channel // 4 , 4 )
A = x[:, :, [0, 2, 1, 3]].transpose(1 , 2 ).reshape(UpperCamelCase__ , UpperCamelCase__ )
return x
def __snake_case ( UpperCamelCase__ ) -> int:
"""simple docstring"""
A = x.shape[0]
A = x.reshape(4 , in_channel // 4 )
A = x[[0, 2, 1, 3], :].transpose(0 , 1 ).reshape(UpperCamelCase__ )
return x
def __snake_case ( UpperCamelCase__ ) -> int:
"""simple docstring"""
A = x.shape[0]
A = x.reshape(in_channel // 4 , 4 )
A = x[:, [0, 2, 1, 3]].transpose(0 , 1 ).reshape(UpperCamelCase__ )
return x
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> Any:
"""simple docstring"""
A = {
'upernet-swin-tiny': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210531_112542-e380ad3e.pth',
'upernet-swin-small': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192015-ee2fff1c.pth',
'upernet-swin-base': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K_20210531_125459-429057bf.pth',
'upernet-swin-large': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k_20220318_091743-9ba68901.pth',
}
A = model_name_to_url[model_name]
A = torch.hub.load_state_dict_from_url(UpperCamelCase__ , map_location='cpu' , file_name=UpperCamelCase__ )[
'state_dict'
]
for name, param in state_dict.items():
print(UpperCamelCase__ , param.shape )
A = get_upernet_config(UpperCamelCase__ )
A = UperNetForSemanticSegmentation(UpperCamelCase__ )
model.eval()
# replace "bn" => "batch_norm"
for key in state_dict.copy().keys():
A = state_dict.pop(UpperCamelCase__ )
if "bn" in key:
A = key.replace('bn' , 'batch_norm' )
A = val
# rename keys
A = create_rename_keys(UpperCamelCase__ )
for src, dest in rename_keys:
rename_key(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
read_in_q_k_v(UpperCamelCase__ , config.backbone_config )
# fix downsample parameters
for key, value in state_dict.items():
if "downsample" in key:
if "reduction" in key:
A = reverse_correct_unfold_reduction_order(UpperCamelCase__ )
if "norm" in key:
A = reverse_correct_unfold_norm_order(UpperCamelCase__ )
model.load_state_dict(UpperCamelCase__ )
# verify on image
A = 'https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg'
A = Image.open(requests.get(UpperCamelCase__ , stream=UpperCamelCase__ ).raw ).convert('RGB' )
A = SegformerImageProcessor()
A = processor(UpperCamelCase__ , return_tensors='pt' ).pixel_values
with torch.no_grad():
A = model(UpperCamelCase__ )
A = outputs.logits
print(logits.shape )
print('First values of logits:' , logits[0, 0, :3, :3] )
# assert values
if model_name == "upernet-swin-tiny":
A = torch.tensor(
[[-7.5_9_5_8, -7.5_9_5_8, -7.4_3_0_2], [-7.5_9_5_8, -7.5_9_5_8, -7.4_3_0_2], [-7.4_7_9_7, -7.4_7_9_7, -7.3_0_6_8]] )
elif model_name == "upernet-swin-small":
A = torch.tensor(
[[-7.1_9_2_1, -7.1_9_2_1, -6.9_5_3_2], [-7.1_9_2_1, -7.1_9_2_1, -6.9_5_3_2], [-7.0_9_0_8, -7.0_9_0_8, -6.8_5_3_4]] )
elif model_name == "upernet-swin-base":
A = torch.tensor(
[[-6.5_8_5_1, -6.5_8_5_1, -6.4_3_3_0], [-6.5_8_5_1, -6.5_8_5_1, -6.4_3_3_0], [-6.4_7_6_3, -6.4_7_6_3, -6.3_2_5_4]] )
elif model_name == "upernet-swin-large":
A = torch.tensor(
[[-7.5_2_9_7, -7.5_2_9_7, -7.3_8_0_2], [-7.5_2_9_7, -7.5_2_9_7, -7.3_8_0_2], [-7.4_0_4_4, -7.4_0_4_4, -7.2_5_8_6]] )
print('Logits:' , outputs.logits[0, 0, :3, :3] )
assert torch.allclose(outputs.logits[0, 0, :3, :3] , UpperCamelCase__ , atol=1E-4 )
print('Looks ok!' )
if pytorch_dump_folder_path is not None:
print(f'Saving model {model_name} to {pytorch_dump_folder_path}' )
model.save_pretrained(UpperCamelCase__ )
print(f'Saving processor to {pytorch_dump_folder_path}' )
processor.save_pretrained(UpperCamelCase__ )
if push_to_hub:
print(f'Pushing model and processor for {model_name} to hub' )
model.push_to_hub(f'openmmlab/{model_name}' )
processor.push_to_hub(f'openmmlab/{model_name}' )
if __name__ == "__main__":
UpperCamelCase : Optional[int] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default="upernet-swin-tiny",
type=str,
choices=[F"""upernet-swin-{size}""" for size in ["tiny", "small", "base", "large"]],
help="Name of the Swin + UperNet model you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
)
parser.add_argument(
"--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
)
UpperCamelCase : Any = parser.parse_args()
convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 690 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_xlnet import XLNetTokenizer
else:
UpperCamelCase : Any = None
UpperCamelCase : int = logging.get_logger(__name__)
UpperCamelCase : Union[str, Any] = {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}
UpperCamelCase : str = {
"vocab_file": {
"xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model",
"xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model",
},
"tokenizer_file": {
"xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json",
"xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json",
},
}
UpperCamelCase : Optional[int] = {
"xlnet-base-cased": None,
"xlnet-large-cased": None,
}
UpperCamelCase : str = "▁"
# Segments (not really needed)
UpperCamelCase : str = 0
UpperCamelCase : int = 1
UpperCamelCase : List[Any] = 2
UpperCamelCase : Union[str, Any] = 3
UpperCamelCase : Optional[Any] = 4
class lowerCamelCase__ ( UpperCAmelCase_ ):
lowerCAmelCase = VOCAB_FILES_NAMES
lowerCAmelCase = PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCAmelCase = """left"""
lowerCAmelCase = XLNetTokenizer
def __init__( self : Tuple , _lowercase : List[Any]=None , _lowercase : Any=None , _lowercase : int=False , _lowercase : Tuple=True , _lowercase : Union[str, Any]=False , _lowercase : int="<s>" , _lowercase : Optional[int]="</s>" , _lowercase : Dict="<unk>" , _lowercase : Optional[int]="<sep>" , _lowercase : int="<pad>" , _lowercase : Dict="<cls>" , _lowercase : str="<mask>" , _lowercase : List[str]=["<eop>", "<eod>"] , **_lowercase : Any , ):
# Mask token behave like a normal word, i.e. include the space before it
A = AddedToken(_lowercase , lstrip=_lowercase , rstrip=_lowercase ) if isinstance(_lowercase , _lowercase ) else mask_token
super().__init__(
vocab_file=_lowercase , tokenizer_file=_lowercase , do_lower_case=_lowercase , remove_space=_lowercase , keep_accents=_lowercase , bos_token=_lowercase , eos_token=_lowercase , unk_token=_lowercase , sep_token=_lowercase , pad_token=_lowercase , cls_token=_lowercase , mask_token=_lowercase , additional_special_tokens=_lowercase , **_lowercase , )
A = 3
A = do_lower_case
A = remove_space
A = keep_accents
A = vocab_file
A = False if not self.vocab_file else True
def __a ( self : List[Any] , _lowercase : List[int] , _lowercase : Optional[List[int]] = None ):
A = [self.sep_token_id]
A = [self.cls_token_id]
if token_ids_a is None:
return token_ids_a + sep + cls
return token_ids_a + sep + token_ids_a + sep + cls
def __a ( self : Tuple , _lowercase : List[int] , _lowercase : Optional[List[int]] = None ):
A = [self.sep_token_id]
A = [2]
if token_ids_a is None:
return len(token_ids_a + sep ) * [0] + cls_segment_id
return len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] + cls_segment_id
def __a ( self : Optional[Any] , _lowercase : str , _lowercase : Optional[str] = None ):
if not self.can_save_slow_tokenizer:
raise ValueError(
'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
'tokenizer.' )
if not os.path.isdir(_lowercase ):
logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
return
A = os.path.join(
_lowercase , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_lowercase ):
copyfile(self.vocab_file , _lowercase )
return (out_vocab_file,)
| 690 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
UpperCamelCase : Optional[int] = {
"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:
UpperCamelCase : List[Any] = ["OwlViTFeatureExtractor"]
UpperCamelCase : int = ["OwlViTImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase : List[str] = [
"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
UpperCamelCase : Tuple = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 690 |
"""simple docstring"""
from __future__ import annotations
UpperCamelCase : Any = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , ) -> tuple[list[list[int]], list[list[int]]]:
"""simple docstring"""
A = [
[0 for col in range(len(grid[0] ) )] for row in range(len(UpperCamelCase__ ) )
] # the reference grid
A = 1
A = [
[0 for col in range(len(grid[0] ) )] for row in range(len(UpperCamelCase__ ) )
] # the action grid
A = init[0]
A = init[1]
A = 0
A = g + heuristic[x][y] # cost from starting cell to destination cell
A = [[f, g, x, y]]
A = False # flag that is set when search is complete
A = False # flag set if we can't find expand
while not found and not resign:
if len(UpperCamelCase__ ) == 0:
raise ValueError('Algorithm is unable to find solution' )
else: # to choose the least costliest action so as to move closer to the goal
cell.sort()
cell.reverse()
A = cell.pop()
A = next_cell[2]
A = next_cell[3]
A = next_cell[1]
if x == goal[0] and y == goal[1]:
A = True
else:
for i in range(len(UpperCamelCase__ ) ): # to try out different valid actions
A = x + DIRECTIONS[i][0]
A = y + DIRECTIONS[i][1]
if xa >= 0 and xa < len(UpperCamelCase__ ) and ya >= 0 and ya < len(grid[0] ):
if closed[xa][ya] == 0 and grid[xa][ya] == 0:
A = g + cost
A = ga + heuristic[xa][ya]
cell.append([fa, ga, xa, ya] )
A = 1
A = i
A = []
A = goal[0]
A = goal[1]
invpath.append([x, y] ) # we get the reverse path from here
while x != init[0] or y != init[1]:
A = x - DIRECTIONS[action[x][y]][0]
A = y - DIRECTIONS[action[x][y]][1]
A = xa
A = ya
invpath.append([x, y] )
A = []
for i in range(len(UpperCamelCase__ ) ):
path.append(invpath[len(UpperCamelCase__ ) - 1 - i] )
return path, action
if __name__ == "__main__":
UpperCamelCase : Any = [
[0, 1, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 1, 0, 0, 0, 0],
[0, 1, 0, 0, 1, 0],
[0, 0, 0, 0, 1, 0],
]
UpperCamelCase : List[Any] = [0, 0]
# all coordinates are given in format [y,x]
UpperCamelCase : int = [len(grid) - 1, len(grid[0]) - 1]
UpperCamelCase : Tuple = 1
# the cost map which pushes the path closer to the goal
UpperCamelCase : Union[str, Any] = [[0 for row in range(len(grid[0]))] for col in range(len(grid))]
for i in range(len(grid)):
for j in range(len(grid[0])):
UpperCamelCase : List[str] = abs(i - goal[0]) + abs(j - goal[1])
if grid[i][j] == 1:
# added extra penalty in the heuristic map
UpperCamelCase : Dict = 99
UpperCamelCase , UpperCamelCase : Optional[Any] = search(grid, init, goal, cost, heuristic)
print("ACTION MAP")
for i in range(len(action)):
print(action[i])
for i in range(len(path)):
print(path[i])
| 690 | 1 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase : Tuple = logging.get_logger(__name__)
UpperCamelCase : Optional[Any] = {
"facebook/wav2vec2-base-960h": "https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json",
# See all Wav2Vec2 models at https://huggingface.co/models?filter=wav2vec2
}
class lowerCamelCase__ ( UpperCAmelCase_ ):
lowerCAmelCase = """wav2vec2"""
def __init__( self : int , _lowercase : Any=32 , _lowercase : List[str]=768 , _lowercase : int=12 , _lowercase : Optional[Any]=12 , _lowercase : Any=3_072 , _lowercase : Any="gelu" , _lowercase : str=0.1 , _lowercase : Any=0.1 , _lowercase : List[Any]=0.1 , _lowercase : Union[str, Any]=0.0 , _lowercase : Optional[Any]=0.0 , _lowercase : str=0.1 , _lowercase : Tuple=0.1 , _lowercase : List[Any]=0.0_2 , _lowercase : Union[str, Any]=1e-5 , _lowercase : List[Any]="group" , _lowercase : Optional[int]="gelu" , _lowercase : Dict=(512, 512, 512, 512, 512, 512, 512) , _lowercase : str=(5, 2, 2, 2, 2, 2, 2) , _lowercase : Optional[int]=(10, 3, 3, 3, 3, 2, 2) , _lowercase : Union[str, Any]=False , _lowercase : List[Any]=128 , _lowercase : Optional[int]=16 , _lowercase : Any=False , _lowercase : Tuple=True , _lowercase : Union[str, Any]=0.0_5 , _lowercase : Optional[Any]=10 , _lowercase : List[str]=2 , _lowercase : Tuple=0.0 , _lowercase : List[str]=10 , _lowercase : int=0 , _lowercase : int=320 , _lowercase : Optional[Any]=2 , _lowercase : Optional[int]=0.1 , _lowercase : Dict=100 , _lowercase : Dict=256 , _lowercase : int=256 , _lowercase : Optional[Any]=0.1 , _lowercase : List[Any]="sum" , _lowercase : Tuple=False , _lowercase : Tuple=False , _lowercase : str=256 , _lowercase : Union[str, Any]=(512, 512, 512, 512, 1_500) , _lowercase : Any=(5, 3, 3, 1, 1) , _lowercase : List[Any]=(1, 2, 3, 1, 1) , _lowercase : str=512 , _lowercase : Any=0 , _lowercase : int=1 , _lowercase : Optional[Any]=2 , _lowercase : List[str]=False , _lowercase : Any=3 , _lowercase : Tuple=2 , _lowercase : Optional[int]=3 , _lowercase : Any=None , _lowercase : List[Any]=None , **_lowercase : Any , ):
super().__init__(**_lowercase , pad_token_id=_lowercase , bos_token_id=_lowercase , eos_token_id=_lowercase )
A = hidden_size
A = feat_extract_norm
A = feat_extract_activation
A = list(_lowercase )
A = list(_lowercase )
A = list(_lowercase )
A = conv_bias
A = num_conv_pos_embeddings
A = num_conv_pos_embedding_groups
A = len(self.conv_dim )
A = num_hidden_layers
A = intermediate_size
A = hidden_act
A = num_attention_heads
A = hidden_dropout
A = attention_dropout
A = activation_dropout
A = feat_proj_dropout
A = final_dropout
A = layerdrop
A = layer_norm_eps
A = initializer_range
A = vocab_size
A = do_stable_layer_norm
A = use_weighted_layer_sum
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
'Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='
' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='
f' {len(self.conv_dim )}`, `len(config.conv_stride) = {len(self.conv_stride )}`,'
f' `len(config.conv_kernel) = {len(self.conv_kernel )}`.' )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
A = apply_spec_augment
A = mask_time_prob
A = mask_time_length
A = mask_time_min_masks
A = mask_feature_prob
A = mask_feature_length
A = mask_feature_min_masks
# parameters for pretraining with codevector quantized representations
A = num_codevectors_per_group
A = num_codevector_groups
A = contrastive_logits_temperature
A = feat_quantizer_dropout
A = num_negatives
A = codevector_dim
A = proj_codevector_dim
A = diversity_loss_weight
# ctc loss
A = ctc_loss_reduction
A = ctc_zero_infinity
# adapter
A = add_adapter
A = adapter_kernel_size
A = adapter_stride
A = num_adapter_layers
A = output_hidden_size or hidden_size
A = adapter_attn_dim
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
A = classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
A = list(_lowercase )
A = list(_lowercase )
A = list(_lowercase )
A = xvector_output_dim
@property
def __a ( self : int ):
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 690 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCamelCase : Optional[int] = logging.get_logger(__name__)
UpperCamelCase : int = {"vocab_file": "sentencepiece.model"}
UpperCamelCase : Union[str, Any] = {
"vocab_file": {
"google/rembert": "https://huggingface.co/google/rembert/resolve/main/sentencepiece.model",
},
}
UpperCamelCase : Union[str, Any] = {
"google/rembert": 256,
}
class lowerCamelCase__ ( UpperCAmelCase_ ):
lowerCAmelCase = VOCAB_FILES_NAMES
lowerCAmelCase = PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__( self : Dict , _lowercase : Optional[Any] , _lowercase : Optional[Any]=False , _lowercase : Dict=True , _lowercase : List[str]=True , _lowercase : int="[CLS]" , _lowercase : str="[SEP]" , _lowercase : List[str]="[UNK]" , _lowercase : List[Any]="[SEP]" , _lowercase : Union[str, Any]="[PAD]" , _lowercase : List[str]="[CLS]" , _lowercase : Any="[MASK]" , **_lowercase : Optional[Any] , ):
super().__init__(
do_lower_case=_lowercase , remove_space=_lowercase , keep_accents=_lowercase , bos_token=_lowercase , eos_token=_lowercase , unk_token=_lowercase , sep_token=_lowercase , pad_token=_lowercase , cls_token=_lowercase , mask_token=_lowercase , **_lowercase , )
A = do_lower_case
A = remove_space
A = keep_accents
A = vocab_file
A = spm.SentencePieceProcessor()
self.sp_model.Load(_lowercase )
@property
def __a ( self : Tuple ):
return len(self.sp_model )
def __a ( self : List[str] ):
A = {self.convert_ids_to_tokens(_lowercase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self : Tuple ):
A = self.__dict__.copy()
A = None
return state
def __setstate__( self : List[str] , _lowercase : int ):
A = d
A = spm.SentencePieceProcessor()
self.sp_model.Load(self.vocab_file )
def __a ( self : Dict , _lowercase : Union[str, Any] , _lowercase : Dict=False ):
A = self.sp_model.EncodeAsPieces(_lowercase )
return pieces
def __a ( self : Dict , _lowercase : Tuple ):
return self.sp_model.PieceToId(_lowercase )
def __a ( self : str , _lowercase : Optional[int] ):
return self.sp_model.IdToPiece(_lowercase )
def __a ( self : Optional[int] , _lowercase : Optional[int] ):
A = self.sp_model.decode_pieces(_lowercase )
return out_string
def __a ( self : Optional[int] , _lowercase : List[int] , _lowercase : Optional[List[int]] = None ):
A = [self.sep_token_id]
A = [self.cls_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def __a ( self : Any , _lowercase : List[int] , _lowercase : Optional[List[int]] = None , _lowercase : bool = False ):
if already_has_special_tokens:
if token_ids_a is not None:
raise ValueError(
'You should not supply a second sequence if the provided sequence of '
'ids is already formatted with special tokens for the model.' )
return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]
if token_ids_a is not None:
return [1] + ([0] * len(_lowercase )) + [1] + ([0] * len(_lowercase )) + [1]
return [1] + ([0] * len(_lowercase )) + [1]
def __a ( self : str , _lowercase : List[int] , _lowercase : Optional[List[int]] = None ):
A = [self.sep_token_id]
A = [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 __a ( self : Optional[Any] , _lowercase : str , _lowercase : Optional[str] = None ):
if not os.path.isdir(_lowercase ):
logger.error('Vocabulary path ({}) should be a directory'.format(_lowercase ) )
return
A = os.path.join(
_lowercase , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_lowercase ):
copyfile(self.vocab_file , _lowercase )
return (out_vocab_file,)
| 690 | 1 |
"""simple docstring"""
import argparse
import os
import shutil
from pathlib import Path
import onnx
import torch
from packaging import version
from torch.onnx import export
from diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline, StableDiffusionPipeline
UpperCamelCase : List[Any] = version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__=False , ) -> Any:
"""simple docstring"""
output_path.parent.mkdir(parents=UpperCamelCase__ , exist_ok=UpperCamelCase__ )
# PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
# so we check the torch version for backwards compatibility
if is_torch_less_than_1_11:
export(
UpperCamelCase__ , UpperCamelCase__ , f=output_path.as_posix() , input_names=UpperCamelCase__ , output_names=UpperCamelCase__ , dynamic_axes=UpperCamelCase__ , do_constant_folding=UpperCamelCase__ , use_external_data_format=UpperCamelCase__ , enable_onnx_checker=UpperCamelCase__ , opset_version=UpperCamelCase__ , )
else:
export(
UpperCamelCase__ , UpperCamelCase__ , f=output_path.as_posix() , input_names=UpperCamelCase__ , output_names=UpperCamelCase__ , dynamic_axes=UpperCamelCase__ , do_constant_folding=UpperCamelCase__ , opset_version=UpperCamelCase__ , )
@torch.no_grad()
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = False ) -> str:
"""simple docstring"""
A = torch.floataa if fpaa else torch.floataa
if fpaa and torch.cuda.is_available():
A = 'cuda'
elif fpaa and not torch.cuda.is_available():
raise ValueError('`float16` model export is only supported on GPUs with CUDA' )
else:
A = 'cpu'
A = StableDiffusionPipeline.from_pretrained(UpperCamelCase__ , torch_dtype=UpperCamelCase__ ).to(UpperCamelCase__ )
A = Path(UpperCamelCase__ )
# TEXT ENCODER
A = pipeline.text_encoder.config.max_position_embeddings
A = pipeline.text_encoder.config.hidden_size
A = pipeline.tokenizer(
'A sample prompt' , padding='max_length' , max_length=pipeline.tokenizer.model_max_length , truncation=UpperCamelCase__ , return_tensors='pt' , )
onnx_export(
pipeline.text_encoder , model_args=(text_input.input_ids.to(device=UpperCamelCase__ , dtype=torch.intaa )) , output_path=output_path / 'text_encoder' / 'model.onnx' , ordered_input_names=['input_ids'] , output_names=['last_hidden_state', 'pooler_output'] , dynamic_axes={
'input_ids': {0: 'batch', 1: 'sequence'},
} , opset=UpperCamelCase__ , )
del pipeline.text_encoder
# UNET
A = pipeline.unet.config.in_channels
A = pipeline.unet.config.sample_size
A = output_path / 'unet' / 'model.onnx'
onnx_export(
pipeline.unet , model_args=(
torch.randn(2 , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ).to(device=UpperCamelCase__ , dtype=UpperCamelCase__ ),
torch.randn(2 ).to(device=UpperCamelCase__ , dtype=UpperCamelCase__ ),
torch.randn(2 , UpperCamelCase__ , UpperCamelCase__ ).to(device=UpperCamelCase__ , dtype=UpperCamelCase__ ),
False,
) , output_path=UpperCamelCase__ , ordered_input_names=['sample', 'timestep', 'encoder_hidden_states', 'return_dict'] , output_names=['out_sample'] , dynamic_axes={
'sample': {0: 'batch', 1: 'channels', 2: 'height', 3: 'width'},
'timestep': {0: 'batch'},
'encoder_hidden_states': {0: 'batch', 1: 'sequence'},
} , opset=UpperCamelCase__ , use_external_data_format=UpperCamelCase__ , )
A = str(unet_path.absolute().as_posix() )
A = os.path.dirname(UpperCamelCase__ )
A = onnx.load(UpperCamelCase__ )
# clean up existing tensor files
shutil.rmtree(UpperCamelCase__ )
os.mkdir(UpperCamelCase__ )
# collate external tensor files into one
onnx.save_model(
UpperCamelCase__ , UpperCamelCase__ , save_as_external_data=UpperCamelCase__ , all_tensors_to_one_file=UpperCamelCase__ , location='weights.pb' , convert_attribute=UpperCamelCase__ , )
del pipeline.unet
# VAE ENCODER
A = pipeline.vae
A = vae_encoder.config.in_channels
A = vae_encoder.config.sample_size
# need to get the raw tensor output (sample) from the encoder
A = lambda UpperCamelCase__ , UpperCamelCase__ : vae_encoder.encode(UpperCamelCase__ , UpperCamelCase__ )[0].sample()
onnx_export(
UpperCamelCase__ , model_args=(
torch.randn(1 , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ).to(device=UpperCamelCase__ , dtype=UpperCamelCase__ ),
False,
) , output_path=output_path / 'vae_encoder' / 'model.onnx' , ordered_input_names=['sample', 'return_dict'] , output_names=['latent_sample'] , dynamic_axes={
'sample': {0: 'batch', 1: 'channels', 2: 'height', 3: 'width'},
} , opset=UpperCamelCase__ , )
# VAE DECODER
A = pipeline.vae
A = vae_decoder.config.latent_channels
A = vae_decoder.config.out_channels
# forward only through the decoder part
A = vae_encoder.decode
onnx_export(
UpperCamelCase__ , model_args=(
torch.randn(1 , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ).to(device=UpperCamelCase__ , dtype=UpperCamelCase__ ),
False,
) , output_path=output_path / 'vae_decoder' / 'model.onnx' , ordered_input_names=['latent_sample', 'return_dict'] , output_names=['sample'] , dynamic_axes={
'latent_sample': {0: 'batch', 1: 'channels', 2: 'height', 3: 'width'},
} , opset=UpperCamelCase__ , )
del pipeline.vae
# SAFETY CHECKER
if pipeline.safety_checker is not None:
A = pipeline.safety_checker
A = safety_checker.config.vision_config.num_channels
A = safety_checker.config.vision_config.image_size
A = safety_checker.forward_onnx
onnx_export(
pipeline.safety_checker , model_args=(
torch.randn(
1 , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , ).to(device=UpperCamelCase__ , dtype=UpperCamelCase__ ),
torch.randn(1 , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ).to(device=UpperCamelCase__ , dtype=UpperCamelCase__ ),
) , output_path=output_path / 'safety_checker' / 'model.onnx' , ordered_input_names=['clip_input', 'images'] , output_names=['out_images', 'has_nsfw_concepts'] , dynamic_axes={
'clip_input': {0: 'batch', 1: 'channels', 2: 'height', 3: 'width'},
'images': {0: 'batch', 1: 'height', 2: 'width', 3: 'channels'},
} , opset=UpperCamelCase__ , )
del pipeline.safety_checker
A = OnnxRuntimeModel.from_pretrained(output_path / 'safety_checker' )
A = pipeline.feature_extractor
else:
A = None
A = None
A = OnnxStableDiffusionPipeline(
vae_encoder=OnnxRuntimeModel.from_pretrained(output_path / 'vae_encoder' ) , vae_decoder=OnnxRuntimeModel.from_pretrained(output_path / 'vae_decoder' ) , text_encoder=OnnxRuntimeModel.from_pretrained(output_path / 'text_encoder' ) , tokenizer=pipeline.tokenizer , unet=OnnxRuntimeModel.from_pretrained(output_path / 'unet' ) , scheduler=pipeline.scheduler , safety_checker=UpperCamelCase__ , feature_extractor=UpperCamelCase__ , requires_safety_checker=safety_checker is not None , )
onnx_pipeline.save_pretrained(UpperCamelCase__ )
print('ONNX pipeline saved to' , UpperCamelCase__ )
del pipeline
del onnx_pipeline
A = OnnxStableDiffusionPipeline.from_pretrained(UpperCamelCase__ , provider='CPUExecutionProvider' )
print('ONNX pipeline is loadable' )
if __name__ == "__main__":
UpperCamelCase : Optional[int] = argparse.ArgumentParser()
parser.add_argument(
"--model_path",
type=str,
required=True,
help="Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).",
)
parser.add_argument("--output_path", type=str, required=True, help="Path to the output model.")
parser.add_argument(
"--opset",
default=14,
type=int,
help="The version of the ONNX operator set to use.",
)
parser.add_argument("--fp16", action="store_true", default=False, help="Export the models in `float16` mode")
UpperCamelCase : str = parser.parse_args()
convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
| 690 |
"""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_mobilebert import MobileBertTokenizer
UpperCamelCase : str = logging.get_logger(__name__)
UpperCamelCase : List[str] = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
UpperCamelCase : List[Any] = {
"vocab_file": {"mobilebert-uncased": "https://huggingface.co/google/mobilebert-uncased/resolve/main/vocab.txt"},
"tokenizer_file": {
"mobilebert-uncased": "https://huggingface.co/google/mobilebert-uncased/resolve/main/tokenizer.json"
},
}
UpperCamelCase : Any = {"mobilebert-uncased": 512}
UpperCamelCase : Any = {}
class lowerCamelCase__ ( UpperCAmelCase_ ):
lowerCAmelCase = VOCAB_FILES_NAMES
lowerCAmelCase = PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase = PRETRAINED_INIT_CONFIGURATION
lowerCAmelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCAmelCase = MobileBertTokenizer
def __init__( self : Optional[int] , _lowercase : Optional[int]=None , _lowercase : Any=None , _lowercase : Optional[int]=True , _lowercase : int="[UNK]" , _lowercase : Dict="[SEP]" , _lowercase : Any="[PAD]" , _lowercase : str="[CLS]" , _lowercase : Union[str, Any]="[MASK]" , _lowercase : List[Any]=True , _lowercase : Any=None , **_lowercase : Optional[Any] , ):
super().__init__(
_lowercase , tokenizer_file=_lowercase , do_lower_case=_lowercase , unk_token=_lowercase , sep_token=_lowercase , pad_token=_lowercase , cls_token=_lowercase , mask_token=_lowercase , tokenize_chinese_chars=_lowercase , strip_accents=_lowercase , **_lowercase , )
A = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('lowercase' , _lowercase ) != do_lower_case
or normalizer_state.get('strip_accents' , _lowercase ) != strip_accents
or normalizer_state.get('handle_chinese_chars' , _lowercase ) != tokenize_chinese_chars
):
A = getattr(_lowercase , normalizer_state.pop('type' ) )
A = do_lower_case
A = strip_accents
A = tokenize_chinese_chars
A = normalizer_class(**_lowercase )
A = do_lower_case
def __a ( self : List[Any] , _lowercase : Tuple , _lowercase : Any=None ):
A = [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 __a ( self : Any , _lowercase : List[int] , _lowercase : Optional[List[int]] = None ):
A = [self.sep_token_id]
A = [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 __a ( self : Dict , _lowercase : str , _lowercase : Optional[str] = None ):
A = self._tokenizer.model.save(_lowercase , name=_lowercase )
return tuple(_lowercase )
| 690 | 1 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool
class lowerCamelCase__ ( UpperCAmelCase_ ):
lowerCAmelCase = """facebook/bart-large-mnli"""
lowerCAmelCase = (
"""This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which """
"""should be the text to classify, and `labels`, which should be the list of labels to use for classification. """
"""It returns the most likely label in the list of provided `labels` for the input text."""
)
lowerCAmelCase = """text_classifier"""
lowerCAmelCase = AutoTokenizer
lowerCAmelCase = AutoModelForSequenceClassification
lowerCAmelCase = ["""text""", ["""text"""]]
lowerCAmelCase = ["""text"""]
def __a ( self : Union[str, Any] ):
super().setup()
A = self.model.config
A = -1
for idx, label in config.idalabel.items():
if label.lower().startswith('entail' ):
A = int(_lowercase )
if self.entailment_id == -1:
raise ValueError('Could not determine the entailment ID from the model config, please pass it at init.' )
def __a ( self : Optional[Any] , _lowercase : Optional[int] , _lowercase : List[Any] ):
A = labels
return self.pre_processor(
[text] * len(_lowercase ) , [f'This example is {label}' for label in labels] , return_tensors='pt' , padding='max_length' , )
def __a ( self : Optional[int] , _lowercase : List[Any] ):
A = outputs.logits
A = torch.argmax(logits[:, 2] ).item()
return self._labels[label_id]
| 690 |
"""simple docstring"""
def __snake_case ( UpperCamelCase__ ) -> list[int]:
"""simple docstring"""
A = [0 for i in range(len(UpperCamelCase__ ) )]
# initialize interval's left pointer and right pointer
A , A = 0, 0
for i in range(1 , len(UpperCamelCase__ ) ):
# case when current index is inside the interval
if i <= right_pointer:
A = min(right_pointer - i + 1 , z_result[i - left_pointer] )
A = min_edge
while go_next(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
z_result[i] += 1
# if new index's result gives us more right interval,
# we've to update left_pointer and right_pointer
if i + z_result[i] - 1 > right_pointer:
A , A = i, i + z_result[i] - 1
return z_result
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> bool:
"""simple docstring"""
return i + z_result[i] < len(UpperCamelCase__ ) and s[z_result[i]] == s[i + z_result[i]]
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ ) -> int:
"""simple docstring"""
A = 0
# concatenate 'pattern' and 'input_str' and call z_function
# with concatenated string
A = z_function(pattern + input_str )
for val in z_result:
# if value is greater then length of the pattern string
# that means this index is starting position of substring
# which is equal to pattern string
if val >= len(UpperCamelCase__ ):
answer += 1
return answer
if __name__ == "__main__":
import doctest
doctest.testmod()
| 690 | 1 |
"""simple docstring"""
from __future__ import annotations
from PIL import Image
# Define glider example
UpperCamelCase : Optional[int] = [
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
]
# Define blinker example
UpperCamelCase : Any = [[0, 1, 0], [0, 1, 0], [0, 1, 0]]
def __snake_case ( UpperCamelCase__ ) -> list[list[int]]:
"""simple docstring"""
A = []
for i in range(len(UpperCamelCase__ ) ):
A = []
for j in range(len(cells[i] ) ):
# Get the number of live neighbours
A = 0
if i > 0 and j > 0:
neighbour_count += cells[i - 1][j - 1]
if i > 0:
neighbour_count += cells[i - 1][j]
if i > 0 and j < len(cells[i] ) - 1:
neighbour_count += cells[i - 1][j + 1]
if j > 0:
neighbour_count += cells[i][j - 1]
if j < len(cells[i] ) - 1:
neighbour_count += cells[i][j + 1]
if i < len(UpperCamelCase__ ) - 1 and j > 0:
neighbour_count += cells[i + 1][j - 1]
if i < len(UpperCamelCase__ ) - 1:
neighbour_count += cells[i + 1][j]
if i < len(UpperCamelCase__ ) - 1 and j < len(cells[i] ) - 1:
neighbour_count += cells[i + 1][j + 1]
# Rules of the game of life (excerpt from Wikipedia):
# 1. Any live cell with two or three live neighbours survives.
# 2. Any dead cell with three live neighbours becomes a live cell.
# 3. All other live cells die in the next generation.
# Similarly, all other dead cells stay dead.
A = cells[i][j] == 1
if (
(alive and 2 <= neighbour_count <= 3)
or not alive
and neighbour_count == 3
):
next_generation_row.append(1 )
else:
next_generation_row.append(0 )
next_generation.append(UpperCamelCase__ )
return next_generation
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ ) -> list[Image.Image]:
"""simple docstring"""
A = []
for _ in range(UpperCamelCase__ ):
# Create output image
A = Image.new('RGB' , (len(cells[0] ), len(UpperCamelCase__ )) )
A = img.load()
# Save cells to image
for x in range(len(UpperCamelCase__ ) ):
for y in range(len(cells[0] ) ):
A = 255 - cells[y][x] * 255
A = (colour, colour, colour)
# Save image
images.append(UpperCamelCase__ )
A = new_generation(UpperCamelCase__ )
return images
if __name__ == "__main__":
UpperCamelCase : Tuple = generate_images(GLIDER, 16)
images[0].save("out.gif", save_all=True, append_images=images[1:])
| 690 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
enable_full_determinism,
load_numpy,
nightly,
require_torch_gpu,
slow,
torch_device,
)
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class lowerCamelCase__ ( UpperCAmelCase_ , unittest.TestCase ):
lowerCAmelCase = LDMTextToImagePipeline
lowerCAmelCase = TEXT_TO_IMAGE_PARAMS - {
"""negative_prompt""",
"""negative_prompt_embeds""",
"""cross_attention_kwargs""",
"""prompt_embeds""",
}
lowerCAmelCase = PipelineTesterMixin.required_optional_params - {
"""num_images_per_prompt""",
"""callback""",
"""callback_steps""",
}
lowerCAmelCase = TEXT_TO_IMAGE_BATCH_PARAMS
lowerCAmelCase = False
def __a ( self : Dict ):
torch.manual_seed(0 )
A = 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 , )
A = DDIMScheduler(
beta_start=0.0_0_0_8_5 , beta_end=0.0_1_2 , beta_schedule='scaled_linear' , clip_sample=_lowercase , set_alpha_to_one=_lowercase , )
torch.manual_seed(0 )
A = 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 )
A = 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=1_000 , )
A = CLIPTextModel(_lowercase )
A = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
A = {
'unet': unet,
'scheduler': scheduler,
'vqvae': vae,
'bert': text_encoder,
'tokenizer': tokenizer,
}
return components
def __a ( self : Union[str, Any] , _lowercase : Union[str, Any] , _lowercase : Union[str, Any]=0 ):
if str(_lowercase ).startswith('mps' ):
A = torch.manual_seed(_lowercase )
else:
A = torch.Generator(device=_lowercase ).manual_seed(_lowercase )
A = {
'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 __a ( self : Any ):
A = 'cpu' # ensure determinism for the device-dependent torch.Generator
A = self.get_dummy_components()
A = LDMTextToImagePipeline(**_lowercase )
pipe.to(_lowercase )
pipe.set_progress_bar_config(disable=_lowercase )
A = self.get_dummy_inputs(_lowercase )
A = pipe(**_lowercase ).images
A = image[0, -3:, -3:, -1]
assert image.shape == (1, 16, 16, 3)
A = np.array([0.6_1_0_1, 0.6_1_5_6, 0.5_6_2_2, 0.4_8_9_5, 0.6_6_6_1, 0.3_8_0_4, 0.5_7_4_8, 0.6_1_3_6, 0.5_0_1_4] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
@slow
@require_torch_gpu
class lowerCamelCase__ ( unittest.TestCase ):
def __a ( self : Optional[Any] ):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __a ( self : int , _lowercase : List[Any] , _lowercase : int=torch.floataa , _lowercase : int=0 ):
A = torch.manual_seed(_lowercase )
A = np.random.RandomState(_lowercase ).standard_normal((1, 4, 32, 32) )
A = torch.from_numpy(_lowercase ).to(device=_lowercase , dtype=_lowercase )
A = {
'prompt': 'A painting of a squirrel eating a burger',
'latents': latents,
'generator': generator,
'num_inference_steps': 3,
'guidance_scale': 6.0,
'output_type': 'numpy',
}
return inputs
def __a ( self : Union[str, Any] ):
A = LDMTextToImagePipeline.from_pretrained('CompVis/ldm-text2im-large-256' ).to(_lowercase )
pipe.set_progress_bar_config(disable=_lowercase )
A = self.get_inputs(_lowercase )
A = pipe(**_lowercase ).images
A = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 256, 256, 3)
A = np.array([0.5_1_8_2_5, 0.5_2_8_5_0, 0.5_2_5_4_3, 0.5_4_2_5_8, 0.5_2_3_0_4, 0.5_2_5_6_9, 0.5_4_3_6_3, 0.5_5_2_7_6, 0.5_6_8_7_8] )
A = np.abs(expected_slice - image_slice ).max()
assert max_diff < 1e-3
@nightly
@require_torch_gpu
class lowerCamelCase__ ( unittest.TestCase ):
def __a ( self : List[Any] ):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __a ( self : List[Any] , _lowercase : Optional[Any] , _lowercase : Tuple=torch.floataa , _lowercase : Optional[Any]=0 ):
A = torch.manual_seed(_lowercase )
A = np.random.RandomState(_lowercase ).standard_normal((1, 4, 32, 32) )
A = torch.from_numpy(_lowercase ).to(device=_lowercase , dtype=_lowercase )
A = {
'prompt': 'A painting of a squirrel eating a burger',
'latents': latents,
'generator': generator,
'num_inference_steps': 50,
'guidance_scale': 6.0,
'output_type': 'numpy',
}
return inputs
def __a ( self : List[str] ):
A = LDMTextToImagePipeline.from_pretrained('CompVis/ldm-text2im-large-256' ).to(_lowercase )
pipe.set_progress_bar_config(disable=_lowercase )
A = self.get_inputs(_lowercase )
A = pipe(**_lowercase ).images[0]
A = load_numpy(
'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/ldm_text2img/ldm_large_256_ddim.npy' )
A = np.abs(expected_image - image ).max()
assert max_diff < 1e-3
| 690 | 1 |
"""simple docstring"""
from timeit import timeit
def __snake_case ( UpperCamelCase__ ) -> int:
"""simple docstring"""
if number < 0:
raise ValueError('the value of input must not be negative' )
A = 0
while number:
number &= number - 1
result += 1
return result
def __snake_case ( UpperCamelCase__ ) -> int:
"""simple docstring"""
if number < 0:
raise ValueError('the value of input must not be negative' )
A = 0
while number:
if number % 2 == 1:
result += 1
number >>= 1
return result
def __snake_case ( ) -> None:
"""simple docstring"""
def do_benchmark(UpperCamelCase__ ) -> None:
A = 'import __main__ as z'
print(f'Benchmark when {number = }:' )
print(f'{get_set_bits_count_using_modulo_operator(UpperCamelCase__ ) = }' )
A = 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__ ) = }' )
A = 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()
| 690 |
"""simple docstring"""
import os
import tempfile
import unittest
import numpy as np
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
from diffusers import FlaxDDIMScheduler, FlaxDiffusionPipeline, FlaxStableDiffusionPipeline
@require_flax
class lowerCamelCase__ ( unittest.TestCase ):
def __a ( self : Union[str, Any] ):
with tempfile.TemporaryDirectory() as tmpdirname:
# pipeline has Flax weights
A = FlaxDiffusionPipeline.from_pretrained(
'hf-internal-testing/tiny-stable-diffusion-pipe' , safety_checker=_lowercase , cache_dir=_lowercase )
A = [t[-1] for t in os.walk(os.path.join(_lowercase , os.listdir(_lowercase )[0] , 'snapshots' ) )]
A = [item for sublist in all_root_files for item in sublist]
# None of the downloaded files should be a PyTorch file even if we have some here:
# https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe/blob/main/unet/diffusion_pytorch_model.bin
assert not any(f.endswith('.bin' ) for f in files )
@slow
@require_flax
class lowerCamelCase__ ( unittest.TestCase ):
def __a ( self : Optional[Any] ):
A , A = FlaxStableDiffusionPipeline.from_pretrained(
'hf-internal-testing/tiny-stable-diffusion-pipe' , safety_checker=_lowercase )
A = (
'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
' field, close up, split lighting, cinematic'
)
A = jax.random.PRNGKey(0 )
A = 4
A = jax.device_count()
A = num_samples * [prompt]
A = pipeline.prepare_inputs(_lowercase )
# shard inputs and rng
A = replicate(_lowercase )
A = jax.random.split(_lowercase , _lowercase )
A = shard(_lowercase )
A = pipeline(_lowercase , _lowercase , _lowercase , _lowercase , jit=_lowercase ).images
assert images.shape == (num_samples, 1, 64, 64, 3)
if jax.device_count() == 8:
assert np.abs(np.abs(images[0, 0, :2, :2, -2:] , dtype=np.floataa ).sum() - 4.1_5_1_4_7_4_5 ) < 1e-3
assert np.abs(np.abs(_lowercase , dtype=np.floataa ).sum() - 4_9_9_4_7.8_7_5 ) < 5e-1
A = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:] ) ) )
assert len(_lowercase ) == num_samples
def __a ( self : Dict ):
A , A = FlaxStableDiffusionPipeline.from_pretrained(
'CompVis/stable-diffusion-v1-4' , revision='flax' , safety_checker=_lowercase )
A = (
'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
' field, close up, split lighting, cinematic'
)
A = jax.random.PRNGKey(0 )
A = 50
A = jax.device_count()
A = num_samples * [prompt]
A = pipeline.prepare_inputs(_lowercase )
# shard inputs and rng
A = replicate(_lowercase )
A = jax.random.split(_lowercase , _lowercase )
A = shard(_lowercase )
A = pipeline(_lowercase , _lowercase , _lowercase , _lowercase , jit=_lowercase ).images
assert images.shape == (num_samples, 1, 512, 512, 3)
if jax.device_count() == 8:
assert np.abs((np.abs(images[0, 0, :2, :2, -2:] , dtype=np.floataa ).sum() - 0.0_5_6_5_2_4_0_1) ) < 1e-3
assert np.abs((np.abs(_lowercase , dtype=np.floataa ).sum() - 2_3_8_3_8_0_8.2) ) < 5e-1
def __a ( self : List[str] ):
A , A = FlaxStableDiffusionPipeline.from_pretrained(
'CompVis/stable-diffusion-v1-4' , revision='bf16' , dtype=jnp.bfloataa , safety_checker=_lowercase )
A = (
'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
' field, close up, split lighting, cinematic'
)
A = jax.random.PRNGKey(0 )
A = 50
A = jax.device_count()
A = num_samples * [prompt]
A = pipeline.prepare_inputs(_lowercase )
# shard inputs and rng
A = replicate(_lowercase )
A = jax.random.split(_lowercase , _lowercase )
A = shard(_lowercase )
A = pipeline(_lowercase , _lowercase , _lowercase , _lowercase , jit=_lowercase ).images
assert images.shape == (num_samples, 1, 512, 512, 3)
if jax.device_count() == 8:
assert np.abs((np.abs(images[0, 0, :2, :2, -2:] , dtype=np.floataa ).sum() - 0.0_4_0_0_3_9_0_6) ) < 1e-3
assert np.abs((np.abs(_lowercase , dtype=np.floataa ).sum() - 2_3_7_3_5_1_6.7_5) ) < 5e-1
def __a ( self : str ):
A , A = FlaxStableDiffusionPipeline.from_pretrained(
'CompVis/stable-diffusion-v1-4' , revision='bf16' , dtype=jnp.bfloataa )
A = (
'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
' field, close up, split lighting, cinematic'
)
A = jax.random.PRNGKey(0 )
A = 50
A = jax.device_count()
A = num_samples * [prompt]
A = pipeline.prepare_inputs(_lowercase )
# shard inputs and rng
A = replicate(_lowercase )
A = jax.random.split(_lowercase , _lowercase )
A = shard(_lowercase )
A = pipeline(_lowercase , _lowercase , _lowercase , _lowercase , jit=_lowercase ).images
assert images.shape == (num_samples, 1, 512, 512, 3)
if jax.device_count() == 8:
assert np.abs((np.abs(images[0, 0, :2, :2, -2:] , dtype=np.floataa ).sum() - 0.0_4_0_0_3_9_0_6) ) < 1e-3
assert np.abs((np.abs(_lowercase , dtype=np.floataa ).sum() - 2_3_7_3_5_1_6.7_5) ) < 5e-1
def __a ( self : Any ):
A = FlaxDDIMScheduler(
beta_start=0.0_0_0_8_5 , beta_end=0.0_1_2 , beta_schedule='scaled_linear' , set_alpha_to_one=_lowercase , steps_offset=1 , )
A , A = FlaxStableDiffusionPipeline.from_pretrained(
'CompVis/stable-diffusion-v1-4' , revision='bf16' , dtype=jnp.bfloataa , scheduler=_lowercase , safety_checker=_lowercase , )
A = scheduler.create_state()
A = scheduler_state
A = (
'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
' field, close up, split lighting, cinematic'
)
A = jax.random.PRNGKey(0 )
A = 50
A = jax.device_count()
A = num_samples * [prompt]
A = pipeline.prepare_inputs(_lowercase )
# shard inputs and rng
A = replicate(_lowercase )
A = jax.random.split(_lowercase , _lowercase )
A = shard(_lowercase )
A = pipeline(_lowercase , _lowercase , _lowercase , _lowercase , jit=_lowercase ).images
assert images.shape == (num_samples, 1, 512, 512, 3)
if jax.device_count() == 8:
assert np.abs((np.abs(images[0, 0, :2, :2, -2:] , dtype=np.floataa ).sum() - 0.0_4_5_0_4_3_9_4_5) ) < 1e-3
assert np.abs((np.abs(_lowercase , dtype=np.floataa ).sum() - 2_3_4_7_6_9_3.5) ) < 5e-1
def __a ( self : List[str] ):
A = (
'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
' field, close up, split lighting, cinematic'
)
A = jax.device_count()
A = num_samples * [prompt]
A = jax.random.split(jax.random.PRNGKey(0 ) , _lowercase )
A , A = FlaxStableDiffusionPipeline.from_pretrained(
'CompVis/stable-diffusion-v1-4' , revision='bf16' , dtype=jnp.bfloataa , safety_checker=_lowercase , )
A = replicate(_lowercase )
A = pipeline.prepare_inputs(_lowercase )
A = shard(_lowercase )
A = pipeline(_lowercase , _lowercase , _lowercase , jit=_lowercase ).images
assert images.shape == (num_samples, 1, 512, 512, 3)
A = images[2, 0, 256, 10:17, 1]
# With memory efficient attention
A , A = FlaxStableDiffusionPipeline.from_pretrained(
'CompVis/stable-diffusion-v1-4' , revision='bf16' , dtype=jnp.bfloataa , safety_checker=_lowercase , use_memory_efficient_attention=_lowercase , )
A = replicate(_lowercase )
A = pipeline.prepare_inputs(_lowercase )
A = shard(_lowercase )
A = pipeline(_lowercase , _lowercase , _lowercase , jit=_lowercase ).images
assert images_eff.shape == (num_samples, 1, 512, 512, 3)
A = images[2, 0, 256, 10:17, 1]
# I checked the results visually and they are very similar. However, I saw that the max diff is `1` and the `sum`
# over the 8 images is exactly `256`, which is very suspicious. Testing a random slice for now.
assert abs(slice_eff - slice ).max() < 1e-2
| 690 | 1 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCamelCase : List[str] = logging.get_logger(__name__)
UpperCamelCase : Optional[Any] = {"vocab_file": "spiece.model"}
UpperCamelCase : Optional[Any] = {
"vocab_file": {
"bert_for_seq_generation": (
"https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model"
),
}
}
UpperCamelCase : int = {"bert_for_seq_generation": 512}
class lowerCamelCase__ ( UpperCAmelCase_ ):
lowerCAmelCase = VOCAB_FILES_NAMES
lowerCAmelCase = PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCAmelCase = []
lowerCAmelCase = ["""input_ids""", """attention_mask"""]
def __init__( self : Dict , _lowercase : Union[str, Any] , _lowercase : Dict="<s>" , _lowercase : Optional[int]="</s>" , _lowercase : Tuple="<unk>" , _lowercase : Tuple="<pad>" , _lowercase : Optional[Any]="<::::>" , _lowercase : Optional[Dict[str, Any]] = None , **_lowercase : Any , ):
A = {} if sp_model_kwargs is None else sp_model_kwargs
# Add extra_ids to the special token list
super().__init__(
bos_token=_lowercase , eos_token=_lowercase , unk_token=_lowercase , pad_token=_lowercase , sep_token=_lowercase , sp_model_kwargs=self.sp_model_kwargs , **_lowercase , )
A = vocab_file
A = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(_lowercase )
@property
def __a ( self : str ):
return self.sp_model.get_piece_size()
def __a ( self : str ):
A = {self.convert_ids_to_tokens(_lowercase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self : str ):
A = self.__dict__.copy()
A = None
return state
def __setstate__( self : Tuple , _lowercase : int ):
A = d
# for backward compatibility
if not hasattr(self , 'sp_model_kwargs' ):
A = {}
A = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def __a ( self : Optional[int] , _lowercase : str ):
return self.sp_model.encode(_lowercase , out_type=_lowercase )
def __a ( self : Optional[Any] , _lowercase : str ):
return self.sp_model.piece_to_id(_lowercase )
def __a ( self : Union[str, Any] , _lowercase : Optional[Any] ):
A = self.sp_model.IdToPiece(_lowercase )
return token
def __a ( self : str , _lowercase : Optional[Any] ):
A = []
A = ''
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
out_string += self.sp_model.decode(_lowercase ) + token
A = []
else:
current_sub_tokens.append(_lowercase )
out_string += self.sp_model.decode(_lowercase )
return out_string.strip()
def __a ( self : Tuple , _lowercase : str , _lowercase : Optional[str] = None ):
if not os.path.isdir(_lowercase ):
logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
return
A = os.path.join(
_lowercase , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_lowercase ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , _lowercase )
elif not os.path.isfile(self.vocab_file ):
with open(_lowercase , 'wb' ) as fi:
A = self.sp_model.serialized_model_proto()
fi.write(_lowercase )
return (out_vocab_file,)
| 690 |
"""simple docstring"""
import os
import sys
UpperCamelCase : Optional[int] = os.path.join(os.path.dirname(__file__), "src")
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
AutoModelForSequenceClassification,
AutoTokenizer,
add_start_docstrings,
)
UpperCamelCase : Dict = [
"torch",
"numpy",
"tokenizers",
"filelock",
"requests",
"tqdm",
"regex",
"sentencepiece",
"sacremoses",
"importlib_metadata",
"huggingface_hub",
]
@add_start_docstrings(AutoConfig.__doc__ )
def __snake_case ( *UpperCamelCase__ , **UpperCamelCase__ ) -> Dict:
"""simple docstring"""
return AutoConfig.from_pretrained(*UpperCamelCase__ , **UpperCamelCase__ )
@add_start_docstrings(AutoTokenizer.__doc__ )
def __snake_case ( *UpperCamelCase__ , **UpperCamelCase__ ) -> List[str]:
"""simple docstring"""
return AutoTokenizer.from_pretrained(*UpperCamelCase__ , **UpperCamelCase__ )
@add_start_docstrings(AutoModel.__doc__ )
def __snake_case ( *UpperCamelCase__ , **UpperCamelCase__ ) -> str:
"""simple docstring"""
return AutoModel.from_pretrained(*UpperCamelCase__ , **UpperCamelCase__ )
@add_start_docstrings(AutoModelForCausalLM.__doc__ )
def __snake_case ( *UpperCamelCase__ , **UpperCamelCase__ ) -> List[Any]:
"""simple docstring"""
return AutoModelForCausalLM.from_pretrained(*UpperCamelCase__ , **UpperCamelCase__ )
@add_start_docstrings(AutoModelForMaskedLM.__doc__ )
def __snake_case ( *UpperCamelCase__ , **UpperCamelCase__ ) -> Optional[Any]:
"""simple docstring"""
return AutoModelForMaskedLM.from_pretrained(*UpperCamelCase__ , **UpperCamelCase__ )
@add_start_docstrings(AutoModelForSequenceClassification.__doc__ )
def __snake_case ( *UpperCamelCase__ , **UpperCamelCase__ ) -> List[str]:
"""simple docstring"""
return AutoModelForSequenceClassification.from_pretrained(*UpperCamelCase__ , **UpperCamelCase__ )
@add_start_docstrings(AutoModelForQuestionAnswering.__doc__ )
def __snake_case ( *UpperCamelCase__ , **UpperCamelCase__ ) -> int:
"""simple docstring"""
return AutoModelForQuestionAnswering.from_pretrained(*UpperCamelCase__ , **UpperCamelCase__ )
| 690 | 1 |
"""simple docstring"""
import math
UpperCamelCase : Optional[Any] = 10
UpperCamelCase : str = 7
UpperCamelCase : Dict = BALLS_PER_COLOUR * NUM_COLOURS
def __snake_case ( UpperCamelCase__ = 20 ) -> str:
"""simple docstring"""
A = math.comb(UpperCamelCase__ , UpperCamelCase__ )
A = math.comb(NUM_BALLS - BALLS_PER_COLOUR , UpperCamelCase__ )
A = NUM_COLOURS * (1 - missing_colour / total)
return f'{result:.9f}'
if __name__ == "__main__":
print(solution(20))
| 690 |
"""simple docstring"""
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging
if is_torch_available():
import torch
UpperCamelCase : List[str] = logging.get_logger(__name__)
class lowerCamelCase__ ( UpperCAmelCase_ ):
lowerCAmelCase = ["""pixel_values"""]
def __init__( self : Tuple , _lowercase : bool = True , _lowercase : Optional[Dict[str, int]] = None , _lowercase : PILImageResampling = PILImageResampling.BILINEAR , _lowercase : bool = True , _lowercase : Dict[str, int] = None , _lowercase : bool = True , _lowercase : Union[int, float] = 1 / 255 , _lowercase : bool = True , _lowercase : Optional[Union[float, List[float]]] = None , _lowercase : Optional[Union[float, List[float]]] = None , **_lowercase : List[str] , ):
super().__init__(**_lowercase )
A = size if size is not None else {'shortest_edge': 256}
A = get_size_dict(_lowercase , default_to_square=_lowercase )
A = crop_size if crop_size is not None else {'height': 224, 'width': 224}
A = get_size_dict(_lowercase , param_name='crop_size' )
A = do_resize
A = size
A = resample
A = do_center_crop
A = crop_size
A = do_rescale
A = rescale_factor
A = do_normalize
A = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
A = image_std if image_std is not None else IMAGENET_STANDARD_STD
def __a ( self : Any , _lowercase : np.ndarray , _lowercase : Dict[str, int] , _lowercase : PILImageResampling = PILImageResampling.BICUBIC , _lowercase : Optional[Union[str, ChannelDimension]] = None , **_lowercase : Tuple , ):
A = get_size_dict(_lowercase , default_to_square=_lowercase )
if "shortest_edge" not in size:
raise ValueError(f'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}' )
A = get_resize_output_image_size(_lowercase , size=size['shortest_edge'] , default_to_square=_lowercase )
return resize(_lowercase , size=_lowercase , resample=_lowercase , data_format=_lowercase , **_lowercase )
def __a ( self : List[Any] , _lowercase : np.ndarray , _lowercase : Dict[str, int] , _lowercase : Optional[Union[str, ChannelDimension]] = None , **_lowercase : Optional[int] , ):
A = get_size_dict(_lowercase )
if "height" not in size or "width" not in size:
raise ValueError(f'The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}' )
return center_crop(_lowercase , size=(size['height'], size['width']) , data_format=_lowercase , **_lowercase )
def __a ( self : int , _lowercase : np.ndarray , _lowercase : float , _lowercase : Optional[Union[str, ChannelDimension]] = None , **_lowercase : Tuple ):
return rescale(_lowercase , scale=_lowercase , data_format=_lowercase , **_lowercase )
def __a ( self : int , _lowercase : np.ndarray , _lowercase : Union[float, List[float]] , _lowercase : Union[float, List[float]] , _lowercase : Optional[Union[str, ChannelDimension]] = None , **_lowercase : str , ):
return normalize(_lowercase , mean=_lowercase , std=_lowercase , data_format=_lowercase , **_lowercase )
def __a ( self : Any , _lowercase : ImageInput , _lowercase : Optional[bool] = None , _lowercase : Dict[str, int] = None , _lowercase : PILImageResampling = None , _lowercase : bool = None , _lowercase : Dict[str, int] = None , _lowercase : Optional[bool] = None , _lowercase : Optional[float] = None , _lowercase : Optional[bool] = None , _lowercase : Optional[Union[float, List[float]]] = None , _lowercase : Optional[Union[float, List[float]]] = None , _lowercase : Optional[Union[str, TensorType]] = None , _lowercase : Union[str, ChannelDimension] = ChannelDimension.FIRST , **_lowercase : Any , ):
A = do_resize if do_resize is not None else self.do_resize
A = size if size is not None else self.size
A = get_size_dict(_lowercase , default_to_square=_lowercase )
A = resample if resample is not None else self.resample
A = do_center_crop if do_center_crop is not None else self.do_center_crop
A = crop_size if crop_size is not None else self.crop_size
A = get_size_dict(_lowercase , param_name='crop_size' )
A = do_rescale if do_rescale is not None else self.do_rescale
A = rescale_factor if rescale_factor is not None else self.rescale_factor
A = do_normalize if do_normalize is not None else self.do_normalize
A = image_mean if image_mean is not None else self.image_mean
A = image_std if image_std is not None else self.image_std
A = make_list_of_images(_lowercase )
if not valid_images(_lowercase ):
raise ValueError(
'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
'torch.Tensor, tf.Tensor or jax.ndarray.' )
if do_resize and size is None:
raise ValueError('Size must be specified if do_resize is True.' )
if do_center_crop and crop_size is None:
raise ValueError('Crop size must be specified if do_center_crop is True.' )
if do_rescale and rescale_factor is None:
raise ValueError('Rescale factor must be specified if do_rescale is True.' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('Image mean and std must be specified if do_normalize is True.' )
# All transformations expect numpy arrays.
A = [to_numpy_array(_lowercase ) for image in images]
if do_resize:
A = [self.resize(image=_lowercase , size=_lowercase , resample=_lowercase ) for image in images]
if do_center_crop:
A = [self.center_crop(image=_lowercase , size=_lowercase ) for image in images]
if do_rescale:
A = [self.rescale(image=_lowercase , scale=_lowercase ) for image in images]
if do_normalize:
A = [self.normalize(image=_lowercase , mean=_lowercase , std=_lowercase ) for image in images]
A = [to_channel_dimension_format(_lowercase , _lowercase ) for image in images]
A = {'pixel_values': images}
return BatchFeature(data=_lowercase , tensor_type=_lowercase )
def __a ( self : int , _lowercase : List[str] , _lowercase : List[Tuple] = None ):
A = outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(_lowercase ) != len(_lowercase ):
raise ValueError(
'Make sure that you pass in as many target sizes as the batch dimension of the logits' )
if is_torch_tensor(_lowercase ):
A = target_sizes.numpy()
A = []
for idx in range(len(_lowercase ) ):
A = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) , size=target_sizes[idx] , mode='bilinear' , align_corners=_lowercase )
A = resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(_lowercase )
else:
A = logits.argmax(dim=1 )
A = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation
| 690 | 1 |
"""simple docstring"""
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm
import transformers
from transformers import AutoConfig, set_seed
from transformers.trainer_utils import IntervalStrategy
UpperCamelCase : Union[str, Any] = logging.getLogger(__name__)
UpperCamelCase : Tuple = "pytorch_model.bin"
@dataclasses.dataclass
class lowerCamelCase__ :
lowerCAmelCase = dataclasses.field(
metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models."""} )
lowerCAmelCase = dataclasses.field(
default=UpperCAmelCase_ , metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co."""} , )
@dataclasses.dataclass
class lowerCamelCase__ :
lowerCAmelCase = dataclasses.field(metadata={"""help""": """A csv or a json file containing the training data."""} )
lowerCAmelCase = dataclasses.field(metadata={"""help""": """A csv or a json file containing the data to predict on."""} )
lowerCAmelCase = dataclasses.field(
default=UpperCAmelCase_ , metadata={"""help""": """A csv or a json file containing the validation data."""} )
lowerCAmelCase = dataclasses.field(
default=UpperCAmelCase_ , metadata={"""help""": """The name of the task to train on."""} , )
lowerCAmelCase = dataclasses.field(
default=UpperCAmelCase_ , metadata={"""help""": """The list of labels for the task."""} )
@dataclasses.dataclass
class lowerCamelCase__ :
lowerCAmelCase = dataclasses.field(
metadata={"""help""": """The output directory where the model predictions and checkpoints will be written."""} )
lowerCAmelCase = dataclasses.field(
default="""accuracy""" , metadata={"""help""": """The evaluation metric used for the task."""} )
lowerCAmelCase = dataclasses.field(
default="""no""" , metadata={
"""help""": """The evaluation strategy to adopt during training. Possible values are: [\"no\", \"step\", \"epoch]"""
} , )
lowerCAmelCase = dataclasses.field(
default=10 , metadata={"""help""": """Number of evaluation calls with no improvement after which training will be stopped."""} , )
lowerCAmelCase = dataclasses.field(
default=0.0 , metadata={
"""help""": """How much the specified evaluation metric must improve to satisfy early stopping conditions."""
} , )
lowerCAmelCase = dataclasses.field(
default=UpperCAmelCase_ , metadata={"""help""": """Whether to filter the pseudo-labeled data based on the confidence score."""} , )
lowerCAmelCase = dataclasses.field(
default=UpperCAmelCase_ , metadata={"""help""": """Whether to filter the pseudo-labeled data based on the validation performance."""} , )
lowerCAmelCase = dataclasses.field(
default=UpperCAmelCase_ , metadata={"""help""": """Whether to fine-tune on labeled data after pseudo training."""} , )
lowerCAmelCase = dataclasses.field(
default=0.0 , metadata={"""help""": """Confidence threshold for pseudo-labeled data filtering."""} , )
lowerCAmelCase = dataclasses.field(
default=100 , metadata={"""help""": """Number of evaluation calls with no improvement after which training will be stopped."""} , )
lowerCAmelCase = dataclasses.field(
default=UpperCAmelCase_ , metadata={"""help""": """Random seed for initialization."""} , )
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> Optional[Any]:
"""simple docstring"""
A = datasets.concatenate_datasets([infer_input, infer_output] , axis=1 )
if args.do_filter_by_confidence:
A = dataset.filter(lambda UpperCamelCase__ : example["probability"] > args.confidence_threshold )
if args.do_filter_by_val_performance:
assert eval_result >= 0.0 and eval_result <= 1.0
A = int(eval_result * len(UpperCamelCase__ ) )
print(UpperCamelCase__ )
A = dataset.sort('probability' , reverse=UpperCamelCase__ )
A = dataset.select(range(UpperCamelCase__ ) )
A = dataset.remove_columns(['label', 'probability'] )
A = dataset.rename_column('prediction' , 'label' )
A = dataset.map(lambda UpperCamelCase__ : {"label": idalabel[example["label"]]} )
A = dataset.shuffle(seed=args.seed )
A = os.path.join(UpperCamelCase__ , f'train_pseudo.{args.data_file_extension}' )
if args.data_file_extension == "csv":
dataset.to_csv(UpperCamelCase__ , index=UpperCamelCase__ )
else:
dataset.to_json(UpperCamelCase__ )
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , **UpperCamelCase__ ) -> Any:
"""simple docstring"""
A = Accelerator()
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s' , datefmt='%m/%d/%Y %H:%M:%S' , level=logging.INFO , )
logger.info(accelerator.state )
# Setup logging, we only want one process per machine to log things on the
# screen. accelerator.is_local_main_process is only True for one process per
# machine.
logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR )
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_info()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
A = STModelArguments(model_name_or_path=UpperCamelCase__ )
A = STDataArguments(train_file=UpperCamelCase__ , infer_file=UpperCamelCase__ )
A = STTrainingArguments(output_dir=UpperCamelCase__ )
A = argparse.Namespace()
for arg_class in (model_args, data_args, training_args):
for key, value in vars(UpperCamelCase__ ).items():
setattr(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
for key, value in kwargs.items():
if hasattr(UpperCamelCase__ , UpperCamelCase__ ):
setattr(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
# Sanity checks
A = {}
A = None
# You need to provide the training data and the data to predict on
assert args.train_file is not None
assert args.infer_file is not None
A = args.train_file
A = args.infer_file
if args.evaluation_strategy != IntervalStrategy.NO.value:
assert args.eval_file is not None
A = args.eval_file
for key in data_files:
A = data_files[key].split('.' )[-1]
assert extension in ["csv", "json"], f'`{key}_file` should be a csv or a json file.'
if args.data_file_extension is None:
A = extension
else:
assert extension == args.data_file_extension, f'`{key}_file` should be a {args.data_file_extension} file`.'
assert (
args.eval_metric in datasets.list_metrics()
), f'{args.eval_metric} not in the list of supported metrics {datasets.list_metrics()}.'
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed )
logger.info('Creating the initial data directory for self-training...' )
A = f'{args.output_dir}/self-train_iter-{{}}'.format
A = data_dir_format(0 )
if accelerator.is_main_process:
if args.output_dir is not None:
os.makedirs(args.output_dir , exist_ok=UpperCamelCase__ )
os.makedirs(UpperCamelCase__ , exist_ok=UpperCamelCase__ )
accelerator.wait_for_everyone()
A = None
A = None
A = 0
A = False
# Show the progress bar
A = tqdm(range(args.max_selftrain_iterations ) , disable=not accelerator.is_local_main_process )
# Self-train
for iteration in range(0 , int(args.max_selftrain_iterations ) ):
A = data_dir_format(UpperCamelCase__ )
assert os.path.exists(UpperCamelCase__ )
# Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
# iteration > 0
A = os.path.join(UpperCamelCase__ , 'stage-1' )
A = {
'accelerator': accelerator,
'model_name_or_path': args.model_name_or_path,
'cache_dir': args.cache_dir,
'do_train': True,
'train_file': data_files['train'] if iteration == 0 else data_files['train_pseudo'],
'do_eval': True if args.eval_file is not None else False,
'eval_file': data_files['eval'],
'do_predict': True,
'infer_file': data_files['infer'],
'task_name': args.task_name,
'label_list': args.label_list,
'output_dir': current_output_dir,
'eval_metric': args.eval_metric,
'evaluation_strategy': args.evaluation_strategy,
'early_stopping_patience': args.early_stopping_patience,
'early_stopping_threshold': args.early_stopping_threshold,
'seed': args.seed,
}
# Add additional training arguments
for key, value in kwargs.items():
if key not in arguments_dict and not hasattr(UpperCamelCase__ , UpperCamelCase__ ):
arguments_dict.update({key: value} )
A = os.path.join(UpperCamelCase__ , 'best-checkpoint' , UpperCamelCase__ )
if os.path.exists(UpperCamelCase__ ):
logger.info(
'Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 1.' , UpperCamelCase__ , UpperCamelCase__ , )
else:
logger.info('***** Running self-training: iteration: %d, stage: 1 *****' , UpperCamelCase__ )
finetune(**UpperCamelCase__ )
accelerator.wait_for_everyone()
assert os.path.exists(UpperCamelCase__ )
logger.info('Self-training job completed: iteration: %d, stage: 1.' , UpperCamelCase__ )
if iteration > 0 and args.finetune_on_labeled_data:
# Stage 2 (optional): fine-tuning on the original labeled data
A = os.path.join(UpperCamelCase__ , 'best-checkpoint' )
A = os.path.join(UpperCamelCase__ , 'stage-2' )
# Update arguments_dict
A = model_path
A = data_files['train']
A = current_output_dir
A = os.path.join(UpperCamelCase__ , 'best-checkpoint' , UpperCamelCase__ )
if os.path.exists(UpperCamelCase__ ):
logger.info(
'Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 2.' , UpperCamelCase__ , UpperCamelCase__ , )
else:
logger.info('***** Running self-training: iteration: %d, stage: 2 *****' , UpperCamelCase__ )
finetune(**UpperCamelCase__ )
accelerator.wait_for_everyone()
assert os.path.exists(UpperCamelCase__ )
logger.info('Self-training job completed: iteration: %d, stage: 2.' , UpperCamelCase__ )
A = iteration
A = data_dir_format(iteration + 1 )
A = AutoConfig.from_pretrained(os.path.join(UpperCamelCase__ , 'best-checkpoint' ) )
A = config.idalabel
A = os.path.join(UpperCamelCase__ , 'eval_results_best-checkpoint.json' )
A = os.path.join(UpperCamelCase__ , 'test_results_best-checkpoint.json' )
assert os.path.exists(UpperCamelCase__ )
with open(UpperCamelCase__ , 'r' ) as f:
A = float(json.load(UpperCamelCase__ )[args.eval_metric] )
A = os.path.join(UpperCamelCase__ , 'infer_output_best-checkpoint.csv' )
assert os.path.exists(UpperCamelCase__ )
# Loading the dataset from local csv or json files.
A = load_dataset(args.data_file_extension , data_files={'data': data_files['infer']} )['data']
A = load_dataset('csv' , data_files={'data': infer_output_file} )['data']
if accelerator.is_main_process:
os.makedirs(UpperCamelCase__ , exist_ok=UpperCamelCase__ )
shutil.copy(UpperCamelCase__ , os.path.join(UpperCamelCase__ , f'eval_results_iter-{iteration}.json' ) )
if os.path.exists(UpperCamelCase__ ):
shutil.copy(UpperCamelCase__ , os.path.join(UpperCamelCase__ , f'test_results_iter-{iteration}.json' ) )
create_pseudo_labeled_data(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
accelerator.wait_for_everyone()
A = os.path.join(UpperCamelCase__ , f'train_pseudo.{args.data_file_extension}' )
if args.evaluation_strategy != IntervalStrategy.NO.value:
A = eval_result
if best_iteration is None:
A = new_iteration
A = new_eval_result
else:
if new_eval_result - best_eval_result > args.early_stopping_threshold:
A = new_iteration
A = new_eval_result
A = 0
else:
if new_eval_result == best_eval_result:
A = new_iteration
A = new_eval_result
early_stopping_patience_counter += 1
if early_stopping_patience_counter >= args.early_stopping_patience:
A = True
progress_bar.update(1 )
if should_training_stop:
break
if best_iteration is not None:
# Save the best iteration
logger.info('Best iteration: %d' , UpperCamelCase__ )
logger.info('Best evaluation result: %s = %f' , args.eval_metric , UpperCamelCase__ )
accelerator.wait_for_everyone()
if accelerator.is_main_process:
shutil.copy(
os.path.join(UpperCamelCase__ , f'eval_results_iter-{iteration}.json' ) , os.path.join(UpperCamelCase__ , 'eval_results_best-iteration.json' ) , )
else:
# Assume that the last iteration is the best
logger.info('Best iteration: %d' , args.max_selftrain_iterations - 1 )
logger.info('Best evaluation result: %s = %f' , args.eval_metric , UpperCamelCase__ )
accelerator.wait_for_everyone()
if accelerator.is_main_process:
shutil.copy(
os.path.join(UpperCamelCase__ , f'eval_results_iter-{args.max_selftrain_iterations - 1}.json' ) , os.path.join(UpperCamelCase__ , 'eval_results_best-iteration.json' ) , )
| 690 |
"""simple docstring"""
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def __snake_case ( UpperCamelCase__ = "laptop" ) -> DataFrame:
"""simple docstring"""
A = f'https://www.amazon.in/laptop/s?k={product}'
A = {
'User-Agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36\n (KHTML, like Gecko)Chrome/44.0.2403.157 Safari/537.36',
'Accept-Language': 'en-US, en;q=0.5',
}
A = BeautifulSoup(requests.get(UpperCamelCase__ , headers=UpperCamelCase__ ).text )
# Initialize a Pandas dataframe with the column titles
A = DataFrame(
columns=[
'Product Title',
'Product Link',
'Current Price of the product',
'Product Rating',
'MRP of the product',
'Discount',
] )
# Loop through each entry and store them in the dataframe
for item, _ in zip_longest(
soup.find_all(
'div' , attrs={'class': 's-result-item', 'data-component-type': 's-search-result'} , ) , soup.find_all('div' , attrs={'class': 'a-row a-size-base a-color-base'} ) , ):
try:
A = item.ha.text
A = 'https://www.amazon.in/' + item.ha.a['href']
A = item.find('span' , attrs={'class': 'a-offscreen'} ).text
try:
A = item.find('span' , attrs={'class': 'a-icon-alt'} ).text
except AttributeError:
A = 'Not available'
try:
A = (
'₹'
+ item.find(
'span' , attrs={'class': 'a-price a-text-price'} ).text.split('₹' )[1]
)
except AttributeError:
A = ''
try:
A = float(
(
(
float(product_mrp.strip('₹' ).replace(',' , '' ) )
- float(product_price.strip('₹' ).replace(',' , '' ) )
)
/ float(product_mrp.strip('₹' ).replace(',' , '' ) )
)
* 100 )
except ValueError:
A = float('nan' )
except AttributeError:
pass
A = [
product_title,
product_link,
product_price,
product_rating,
product_mrp,
discount,
]
A = ' '
A = ' '
data_frame.index += 1
return data_frame
if __name__ == "__main__":
UpperCamelCase : Any = "headphones"
get_amazon_product_data(product).to_csv(F"""Amazon Product Data for {product}.csv""")
| 690 | 1 |
"""simple docstring"""
from __future__ import annotations
def __snake_case ( UpperCamelCase__ ) -> list[int]: # This function is recursive
"""simple docstring"""
A = len(UpperCamelCase__ )
# If the array contains only one element, we return it (it's the stop condition of
# recursion)
if array_length <= 1:
return array
# Else
A = array[0]
A = False
A = 1
A = []
while not is_found and i < array_length:
if array[i] < pivot:
A = True
A = [element for element in array[i:] if element >= array[i]]
A = longest_subsequence(UpperCamelCase__ )
if len(UpperCamelCase__ ) > len(UpperCamelCase__ ):
A = temp_array
else:
i += 1
A = [element for element in array[1:] if element >= pivot]
A = [pivot, *longest_subsequence(UpperCamelCase__ )]
if len(UpperCamelCase__ ) > len(UpperCamelCase__ ):
return temp_array
else:
return longest_subseq
if __name__ == "__main__":
import doctest
doctest.testmod()
| 690 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import DonutImageProcessor
class lowerCamelCase__ ( unittest.TestCase ):
def __init__( self : List[str] , _lowercase : Optional[Any] , _lowercase : int=7 , _lowercase : List[str]=3 , _lowercase : Tuple=18 , _lowercase : Dict=30 , _lowercase : Any=400 , _lowercase : int=True , _lowercase : List[Any]=None , _lowercase : Tuple=True , _lowercase : List[Any]=False , _lowercase : str=True , _lowercase : List[str]=True , _lowercase : int=[0.5, 0.5, 0.5] , _lowercase : Optional[int]=[0.5, 0.5, 0.5] , ):
A = parent
A = batch_size
A = num_channels
A = image_size
A = min_resolution
A = max_resolution
A = do_resize
A = size if size is not None else {'height': 18, 'width': 20}
A = do_thumbnail
A = do_align_axis
A = do_pad
A = do_normalize
A = image_mean
A = image_std
def __a ( self : Any ):
return {
"do_resize": self.do_resize,
"size": self.size,
"do_thumbnail": self.do_thumbnail,
"do_align_long_axis": self.do_align_axis,
"do_pad": self.do_pad,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
}
@require_torch
@require_vision
class lowerCamelCase__ ( UpperCAmelCase_ , unittest.TestCase ):
lowerCAmelCase = DonutImageProcessor if is_vision_available() else None
def __a ( self : List[str] ):
A = DonutImageProcessingTester(self )
@property
def __a ( self : int ):
return self.image_processor_tester.prepare_image_processor_dict()
def __a ( self : Union[str, Any] ):
A = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(_lowercase , 'do_resize' ) )
self.assertTrue(hasattr(_lowercase , 'size' ) )
self.assertTrue(hasattr(_lowercase , 'do_thumbnail' ) )
self.assertTrue(hasattr(_lowercase , 'do_align_long_axis' ) )
self.assertTrue(hasattr(_lowercase , 'do_pad' ) )
self.assertTrue(hasattr(_lowercase , 'do_normalize' ) )
self.assertTrue(hasattr(_lowercase , 'image_mean' ) )
self.assertTrue(hasattr(_lowercase , 'image_std' ) )
def __a ( self : int ):
A = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'height': 18, 'width': 20} )
A = self.image_processing_class.from_dict(self.image_processor_dict , size=42 )
self.assertEqual(image_processor.size , {'height': 42, 'width': 42} )
# Previous config had dimensions in (width, height) order
A = self.image_processing_class.from_dict(self.image_processor_dict , size=(42, 84) )
self.assertEqual(image_processor.size , {'height': 84, 'width': 42} )
def __a ( self : Any ):
pass
@is_flaky()
def __a ( self : int ):
# Initialize image_processing
A = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
A = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowercase )
for image in image_inputs:
self.assertIsInstance(_lowercase , Image.Image )
# Test not batched input
A = 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 = image_processing(_lowercase , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
) , )
@is_flaky()
def __a ( self : List[str] ):
# Initialize image_processing
A = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
A = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowercase , numpify=_lowercase )
for image in image_inputs:
self.assertIsInstance(_lowercase , np.ndarray )
# Test not batched input
A = 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 = image_processing(_lowercase , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
) , )
@is_flaky()
def __a ( self : List[Any] ):
# Initialize image_processing
A = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
A = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowercase , torchify=_lowercase )
for image in image_inputs:
self.assertIsInstance(_lowercase , torch.Tensor )
# Test not batched input
A = 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 = image_processing(_lowercase , 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'],
) , )
| 690 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCamelCase : Optional[Any] = {
"configuration_transfo_xl": ["TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP", "TransfoXLConfig"],
"tokenization_transfo_xl": ["TransfoXLCorpus", "TransfoXLTokenizer"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase : Any = [
"TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST",
"AdaptiveEmbedding",
"TransfoXLForSequenceClassification",
"TransfoXLLMHeadModel",
"TransfoXLModel",
"TransfoXLPreTrainedModel",
"load_tf_weights_in_transfo_xl",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase : Optional[Any] = [
"TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFAdaptiveEmbedding",
"TFTransfoXLForSequenceClassification",
"TFTransfoXLLMHeadModel",
"TFTransfoXLMainLayer",
"TFTransfoXLModel",
"TFTransfoXLPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_transfo_xl import (
TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
AdaptiveEmbedding,
TransfoXLForSequenceClassification,
TransfoXLLMHeadModel,
TransfoXLModel,
TransfoXLPreTrainedModel,
load_tf_weights_in_transfo_xl,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_transfo_xl import (
TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
TFAdaptiveEmbedding,
TFTransfoXLForSequenceClassification,
TFTransfoXLLMHeadModel,
TFTransfoXLMainLayer,
TFTransfoXLModel,
TFTransfoXLPreTrainedModel,
)
else:
import sys
UpperCamelCase : str = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 690 |
"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class lowerCamelCase__ :
def __init__( self : Optional[Any] , _lowercase : int=2 , _lowercase : Optional[Any]=3 , _lowercase : Any=64 , _lowercase : Tuple=None ):
A = np.random.default_rng(_lowercase )
A = length
A = rng.normal(size=(length,) ).astype(np.floataa )
A = a * self.x + b + rng.normal(scale=0.1 , size=(length,) ).astype(np.floataa )
def __len__( self : str ):
return self.length
def __getitem__( self : List[str] , _lowercase : int ):
return {"x": self.x[i], "y": self.y[i]}
class lowerCamelCase__ ( torch.nn.Module ):
def __init__( self : Optional[int] , _lowercase : Any=0 , _lowercase : List[Any]=0 , _lowercase : Optional[int]=False ):
super().__init__()
A = torch.nn.Parameter(torch.tensor([2, 3] ).float() )
A = torch.nn.Parameter(torch.tensor([2, 3] ).float() )
A = True
def __a ( self : Optional[Any] , _lowercase : str=None ):
if self.first_batch:
print(f'Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}' )
A = False
return x * self.a[0] + self.b[0]
class lowerCamelCase__ ( torch.nn.Module ):
def __init__( self : Optional[Any] , _lowercase : Any=0 , _lowercase : List[str]=0 , _lowercase : str=False ):
super().__init__()
A = torch.nn.Parameter(torch.tensor(_lowercase ).float() )
A = torch.nn.Parameter(torch.tensor(_lowercase ).float() )
A = True
def __a ( self : int , _lowercase : Tuple=None ):
if self.first_batch:
print(f'Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}' )
A = False
return x * self.a + self.b
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ = 16 ) -> Optional[Any]:
"""simple docstring"""
from datasets import load_dataset
from transformers import AutoTokenizer
A = AutoTokenizer.from_pretrained('bert-base-cased' )
A = {'train': 'tests/test_samples/MRPC/train.csv', 'validation': 'tests/test_samples/MRPC/dev.csv'}
A = load_dataset('csv' , data_files=UpperCamelCase__ )
A = datasets['train'].unique('label' )
A = {v: i for i, v in enumerate(UpperCamelCase__ )}
def tokenize_function(UpperCamelCase__ ):
# max_length=None => use the model max length (it's actually the default)
A = tokenizer(
examples['sentence1'] , examples['sentence2'] , truncation=UpperCamelCase__ , max_length=UpperCamelCase__ , padding='max_length' )
if "label" in examples:
A = [label_to_id[l] for l in examples['label']]
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
A = datasets.map(
UpperCamelCase__ , batched=UpperCamelCase__ , remove_columns=['sentence1', 'sentence2', 'label'] , )
def collate_fn(UpperCamelCase__ ):
# On TPU it's best to pad everything to the same length or training will be very slow.
if accelerator.distributed_type == DistributedType.TPU:
return tokenizer.pad(UpperCamelCase__ , padding='max_length' , max_length=128 , return_tensors='pt' )
return tokenizer.pad(UpperCamelCase__ , padding='longest' , return_tensors='pt' )
# Instantiate dataloaders.
A = DataLoader(tokenized_datasets['train'] , shuffle=UpperCamelCase__ , collate_fn=UpperCamelCase__ , batch_size=2 )
A = DataLoader(tokenized_datasets['validation'] , shuffle=UpperCamelCase__ , collate_fn=UpperCamelCase__ , batch_size=1 )
return train_dataloader, eval_dataloader
| 690 | 1 |
"""simple docstring"""
# Copyright (c) 2021-, NVIDIA CORPORATION. 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.
####################################################################################################
#
# Note: If when running this conversion script you're getting an exception:
# ModuleNotFoundError: No module named 'megatron.model.enums'
# you need to tell python where to find the clone of Megatron-LM, e.g.:
#
# cd /tmp
# git clone https://github.com/NVIDIA/Megatron-LM
# PYTHONPATH=/tmp/Megatron-LM python src/transformers/models/megatron_gpt2/convert_megatron_gpt2_checkpoint.py ...
#
# if you already have it cloned elsewhere, simply adjust the path to the existing path
#
# If the training was done using a Megatron-LM fork, e.g.,
# https://github.com/microsoft/Megatron-DeepSpeed/ then chances are that you need to have that one
# in your path, i.e., /path/to/Megatron-DeepSpeed/
#
import argparse
import os
import re
import zipfile
import torch
from transformers import AutoTokenizer, GPTaConfig
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__=0 ) -> Any:
"""simple docstring"""
if name is None:
A = None
else:
A = '.' * max(0 , spaces - 2 ) + '# {:' + str(50 - spaces ) + 's}'
A = fmt.format(UpperCamelCase__ )
# Print and recurse (if needed).
if isinstance(UpperCamelCase__ , UpperCamelCase__ ):
if msg is not None:
print(UpperCamelCase__ )
for k in val.keys():
recursive_print(UpperCamelCase__ , val[k] , spaces + 2 )
elif isinstance(UpperCamelCase__ , torch.Tensor ):
print(UpperCamelCase__ , ':' , val.size() )
else:
print(UpperCamelCase__ , ':' , UpperCamelCase__ )
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> str:
"""simple docstring"""
A = param.size()
if checkpoint_version == 1.0:
# version 1.0 stores [num_heads * hidden_size * num_splits, :]
A = (num_heads, hidden_size, num_splits) + input_shape[1:]
A = param.view(*UpperCamelCase__ )
A = param.transpose(0 , 2 )
A = param.transpose(1 , 2 ).contiguous()
elif checkpoint_version >= 2.0:
# other versions store [num_heads * num_splits * hidden_size, :]
A = (num_heads, num_splits, hidden_size) + input_shape[1:]
A = param.view(*UpperCamelCase__ )
A = param.transpose(0 , 1 ).contiguous()
A = param.view(*UpperCamelCase__ )
return param
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> Dict:
"""simple docstring"""
A = {}
# old versions did not store training args
A = input_state_dict.get('args' , UpperCamelCase__ )
if ds_args is not None:
# do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
# from pprint import pprint
# pprint(vars(ds_args))
A = ds_args.padded_vocab_size
A = ds_args.max_position_embeddings
A = ds_args.hidden_size
A = ds_args.num_layers
A = ds_args.num_attention_heads
A = ds_args.ffn_hidden_size
# pprint(config)
# The number of heads.
A = config.n_head
# The hidden_size per head.
A = config.n_embd // config.n_head
# Megatron-LM checkpoint version
if "checkpoint_version" in input_state_dict.keys():
A = input_state_dict['checkpoint_version']
else:
A = 0.0
# The model.
A = input_state_dict['model']
# The language model.
A = model['language_model']
# The embeddings.
A = lm['embedding']
# The word embeddings.
A = embeddings['word_embeddings']['weight']
# Truncate the embedding table to vocab_size rows.
A = word_embeddings[: config.vocab_size, :]
A = word_embeddings
# The position embeddings.
A = embeddings['position_embeddings']['weight']
# Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
A = pos_embeddings.size(0 )
if n_positions != config.n_positions:
raise ValueError(
f'pos_embeddings.max_sequence_length={n_positions} and config.n_positions={config.n_positions} don\'t match' )
# Store the position embeddings.
A = pos_embeddings
# The transformer.
A = lm['transformer'] if 'transformer' in lm.keys() else lm['encoder']
# The regex to extract layer names.
A = re.compile(r'layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)' )
# The simple map of names for "automated" rules.
A = {
'attention.dense': '.attn.c_proj.',
'self_attention.dense': '.attn.c_proj.',
'mlp.dense_h_to_4h': '.mlp.c_fc.',
'mlp.dense_4h_to_h': '.mlp.c_proj.',
}
# Extract the layers.
for key, val in transformer.items():
# Match the name.
A = layer_re.match(UpperCamelCase__ )
# Stop if that's not a layer
if m is None:
break
# The index of the layer.
A = int(m.group(1 ) )
# The name of the operation.
A = m.group(2 )
# Is it a weight or a bias?
A = m.group(3 )
# The name of the layer.
A = f'transformer.h.{layer_idx}'
# For layernorm(s), simply store the layer norm.
if op_name.endswith('layernorm' ):
A = 'ln_1' if op_name.startswith('input' ) else 'ln_2'
A = val
# Transpose the QKV matrix.
elif (
op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
) and weight_or_bias == "weight":
# Insert a tensor of 1x1xDxD bias.
A = torch.tril(torch.ones((n_positions, n_positions) , dtype=torch.floataa ) ).view(
1 , 1 , UpperCamelCase__ , UpperCamelCase__ )
A = causal_mask
# Insert a "dummy" tensor for masked_bias.
A = torch.tensor(-1E4 , dtype=torch.floataa )
A = masked_bias
A = fix_query_key_value_ordering(UpperCamelCase__ , UpperCamelCase__ , 3 , UpperCamelCase__ , UpperCamelCase__ )
# Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
A = out_val.transpose(0 , 1 ).contiguous()
# Store.
A = out_val
# Transpose the bias.
elif (
op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
) and weight_or_bias == "bias":
A = fix_query_key_value_ordering(UpperCamelCase__ , UpperCamelCase__ , 3 , UpperCamelCase__ , UpperCamelCase__ )
# Store. No change of shape.
A = out_val
# Transpose the weights.
elif weight_or_bias == "weight":
A = megatron_to_transformers[op_name]
A = val.transpose(0 , 1 )
# Copy the bias.
elif weight_or_bias == "bias":
A = megatron_to_transformers[op_name]
A = val
# DEBUG.
assert config.n_layer == layer_idx + 1
# The final layernorm.
A = transformer['final_layernorm.weight']
A = transformer['final_layernorm.bias']
# For LM head, transformers' wants the matrix to weight embeddings.
A = word_embeddings
# It should be done!
return output_state_dict
def __snake_case ( ) -> Union[str, Any]:
"""simple docstring"""
A = argparse.ArgumentParser()
parser.add_argument('--print-checkpoint-structure' , action='store_true' )
parser.add_argument(
'path_to_checkpoint' , type=UpperCamelCase__ , help='Path to the checkpoint file (.zip archive or direct .pt file)' , )
parser.add_argument(
'--config_file' , default='' , type=UpperCamelCase__ , help='An optional config json file describing the pre-trained model.' , )
A = parser.parse_args()
# Extract the basename.
A = os.path.dirname(args.path_to_checkpoint )
# Load the model.
# the .zip is very optional, let's keep it for backward compatibility
print(f'Extracting PyTorch state dictionary from {args.path_to_checkpoint}' )
if args.path_to_checkpoint.endswith('.zip' ):
with zipfile.ZipFile(args.path_to_checkpoint , 'r' ) as checkpoint:
with checkpoint.open('release/mp_rank_00/model_optim_rng.pt' ) as pytorch_dict:
A = torch.load(UpperCamelCase__ , map_location='cpu' )
else:
A = torch.load(args.path_to_checkpoint , map_location='cpu' )
A = input_state_dict.get('args' , UpperCamelCase__ )
# Read the config, or default to the model released by NVIDIA.
if args.config_file == "":
if ds_args is not None:
if ds_args.bias_gelu_fusion:
A = 'gelu_fast'
elif ds_args.openai_gelu:
A = 'gelu_new'
else:
A = 'gelu'
else:
# in the very early days this used to be "gelu_new"
A = 'gelu_new'
# Spell out all parameters in case the defaults change.
A = GPTaConfig(
vocab_size=50257 , n_positions=1024 , n_embd=1024 , n_layer=24 , n_head=16 , n_inner=4096 , activation_function=UpperCamelCase__ , resid_pdrop=0.1 , embd_pdrop=0.1 , attn_pdrop=0.1 , layer_norm_epsilon=1E-5 , initializer_range=0.0_2 , summary_type='cls_index' , summary_use_proj=UpperCamelCase__ , summary_activation=UpperCamelCase__ , summary_proj_to_labels=UpperCamelCase__ , summary_first_dropout=0.1 , scale_attn_weights=UpperCamelCase__ , use_cache=UpperCamelCase__ , bos_token_id=50256 , eos_token_id=50256 , )
else:
A = GPTaConfig.from_json_file(args.config_file )
A = ['GPT2LMHeadModel']
# Convert.
print('Converting' )
A = convert_megatron_checkpoint(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
# Print the structure of converted state dict.
if args.print_checkpoint_structure:
recursive_print(UpperCamelCase__ , UpperCamelCase__ )
# Add tokenizer class info to config
# see https://github.com/huggingface/transformers/issues/13906)
if ds_args is not None:
A = ds_args.tokenizer_type
if tokenizer_type == "GPT2BPETokenizer":
A = 'gpt2'
elif tokenizer_type == "PretrainedFromHF":
A = ds_args.tokenizer_name_or_path
else:
raise ValueError(f'Unrecognized tokenizer_type {tokenizer_type}' )
else:
A = 'gpt2'
A = AutoTokenizer.from_pretrained(UpperCamelCase__ )
A = type(UpperCamelCase__ ).__name__
A = tokenizer_class
# Store the config to file.
print('Saving config' )
config.save_pretrained(UpperCamelCase__ )
# Save tokenizer based on args
print(f'Adding {tokenizer_class} tokenizer files' )
tokenizer.save_pretrained(UpperCamelCase__ )
# Store the state_dict to file.
A = os.path.join(UpperCamelCase__ , 'pytorch_model.bin' )
print(f'Saving checkpoint to "{output_checkpoint_file}"' )
torch.save(UpperCamelCase__ , UpperCamelCase__ )
####################################################################################################
if __name__ == "__main__":
main()
####################################################################################################
| 690 |
"""simple docstring"""
from __future__ import annotations
def __snake_case ( UpperCamelCase__ ) -> list[int]: # This function is recursive
"""simple docstring"""
A = len(UpperCamelCase__ )
# If the array contains only one element, we return it (it's the stop condition of
# recursion)
if array_length <= 1:
return array
# Else
A = array[0]
A = False
A = 1
A = []
while not is_found and i < array_length:
if array[i] < pivot:
A = True
A = [element for element in array[i:] if element >= array[i]]
A = longest_subsequence(UpperCamelCase__ )
if len(UpperCamelCase__ ) > len(UpperCamelCase__ ):
A = temp_array
else:
i += 1
A = [element for element in array[1:] if element >= pivot]
A = [pivot, *longest_subsequence(UpperCamelCase__ )]
if len(UpperCamelCase__ ) > len(UpperCamelCase__ ):
return temp_array
else:
return longest_subseq
if __name__ == "__main__":
import doctest
doctest.testmod()
| 690 | 1 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class lowerCamelCase__ ( UpperCAmelCase_ , unittest.TestCase ):
lowerCAmelCase = KandinskyImgaImgPipeline
lowerCAmelCase = ["""prompt""", """image_embeds""", """negative_image_embeds""", """image"""]
lowerCAmelCase = [
"""prompt""",
"""negative_prompt""",
"""image_embeds""",
"""negative_image_embeds""",
"""image""",
]
lowerCAmelCase = [
"""generator""",
"""height""",
"""width""",
"""strength""",
"""guidance_scale""",
"""negative_prompt""",
"""num_inference_steps""",
"""return_dict""",
"""guidance_scale""",
"""num_images_per_prompt""",
"""output_type""",
"""return_dict""",
]
lowerCAmelCase = False
@property
def __a ( self : Union[str, Any] ):
return 32
@property
def __a ( self : int ):
return 32
@property
def __a ( self : List[Any] ):
return self.time_input_dim
@property
def __a ( self : List[str] ):
return self.time_input_dim * 4
@property
def __a ( self : Any ):
return 100
@property
def __a ( self : int ):
A = XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base' )
return tokenizer
@property
def __a ( self : Optional[int] ):
torch.manual_seed(0 )
A = MCLIPConfig(
numDims=self.cross_attention_dim , transformerDimensions=self.text_embedder_hidden_size , hidden_size=self.text_embedder_hidden_size , intermediate_size=37 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=1_005 , )
A = MultilingualCLIP(_lowercase )
A = text_encoder.eval()
return text_encoder
@property
def __a ( self : List[str] ):
torch.manual_seed(0 )
A = {
'in_channels': 4,
# Out channels is double in channels because predicts mean and variance
'out_channels': 8,
'addition_embed_type': 'text_image',
'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
'layers_per_block': 1,
'encoder_hid_dim': self.text_embedder_hidden_size,
'encoder_hid_dim_type': 'text_image_proj',
'cross_attention_dim': self.cross_attention_dim,
'attention_head_dim': 4,
'resnet_time_scale_shift': 'scale_shift',
'class_embed_type': None,
}
A = UNetaDConditionModel(**_lowercase )
return model
@property
def __a ( self : str ):
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 __a ( self : Optional[Any] ):
torch.manual_seed(0 )
A = VQModel(**self.dummy_movq_kwargs )
return model
def __a ( self : str ):
A = self.dummy_text_encoder
A = self.dummy_tokenizer
A = self.dummy_unet
A = self.dummy_movq
A = {
'num_train_timesteps': 1_000,
'beta_schedule': 'linear',
'beta_start': 0.0_0_0_8_5,
'beta_end': 0.0_1_2,
'clip_sample': False,
'set_alpha_to_one': False,
'steps_offset': 0,
'prediction_type': 'epsilon',
'thresholding': False,
}
A = DDIMScheduler(**_lowercase )
A = {
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'unet': unet,
'scheduler': scheduler,
'movq': movq,
}
return components
def __a ( self : str , _lowercase : List[str] , _lowercase : str=0 ):
A = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(_lowercase ) ).to(_lowercase )
A = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(seed + 1 ) ).to(_lowercase )
# create init_image
A = floats_tensor((1, 3, 64, 64) , rng=random.Random(_lowercase ) ).to(_lowercase )
A = image.cpu().permute(0 , 2 , 3 , 1 )[0]
A = Image.fromarray(np.uinta(_lowercase ) ).convert('RGB' ).resize((256, 256) )
if str(_lowercase ).startswith('mps' ):
A = torch.manual_seed(_lowercase )
else:
A = torch.Generator(device=_lowercase ).manual_seed(_lowercase )
A = {
'prompt': 'horse',
'image': init_image,
'image_embeds': image_embeds,
'negative_image_embeds': negative_image_embeds,
'generator': generator,
'height': 64,
'width': 64,
'num_inference_steps': 10,
'guidance_scale': 7.0,
'strength': 0.2,
'output_type': 'np',
}
return inputs
def __a ( self : List[str] ):
A = 'cpu'
A = self.get_dummy_components()
A = self.pipeline_class(**_lowercase )
A = pipe.to(_lowercase )
pipe.set_progress_bar_config(disable=_lowercase )
A = pipe(**self.get_dummy_inputs(_lowercase ) )
A = output.images
A = pipe(
**self.get_dummy_inputs(_lowercase ) , return_dict=_lowercase , )[0]
A = image[0, -3:, -3:, -1]
A = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
A = np.array(
[0.6_1_4_7_4_9_4_3, 0.6_0_7_3_5_3_9, 0.4_3_3_0_8_5_4_4, 0.5_9_2_8_2_6_9, 0.4_7_4_9_3_5_9_5, 0.4_6_7_5_5_9_7_3, 0.4_6_1_3_8_3_8, 0.4_5_3_6_8_7_9_7, 0.5_0_1_1_9_2_3_3] )
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 lowerCamelCase__ ( unittest.TestCase ):
def __a ( self : Dict ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __a ( self : Dict ):
A = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/kandinsky/kandinsky_img2img_frog.npy' )
A = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png' )
A = 'A red cartoon frog, 4k'
A = KandinskyPriorPipeline.from_pretrained(
'kandinsky-community/kandinsky-2-1-prior' , torch_dtype=torch.floataa )
pipe_prior.to(_lowercase )
A = KandinskyImgaImgPipeline.from_pretrained(
'kandinsky-community/kandinsky-2-1' , torch_dtype=torch.floataa )
A = pipeline.to(_lowercase )
pipeline.set_progress_bar_config(disable=_lowercase )
A = torch.Generator(device='cpu' ).manual_seed(0 )
A , A = pipe_prior(
_lowercase , generator=_lowercase , num_inference_steps=5 , negative_prompt='' , ).to_tuple()
A = pipeline(
_lowercase , image=_lowercase , image_embeds=_lowercase , negative_image_embeds=_lowercase , generator=_lowercase , num_inference_steps=100 , height=768 , width=768 , strength=0.2 , output_type='np' , )
A = output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(_lowercase , _lowercase )
| 690 |
"""simple docstring"""
from __future__ import annotations
import typing
from collections.abc import Iterable
import numpy as np
UpperCamelCase : Tuple = typing.Union[Iterable[float], Iterable[int], np.ndarray] # noqa: UP007
UpperCamelCase : Optional[int] = typing.Union[np.floataa, int, float] # noqa: UP007
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ ) -> VectorOut:
"""simple docstring"""
return np.sqrt(np.sum((np.asarray(UpperCamelCase__ ) - np.asarray(UpperCamelCase__ )) ** 2 ) )
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ ) -> VectorOut:
"""simple docstring"""
return sum((va - va) ** 2 for va, va in zip(UpperCamelCase__ , UpperCamelCase__ ) ) ** (1 / 2)
if __name__ == "__main__":
def __snake_case ( ) -> None:
"""simple docstring"""
from timeit import timeit
print('Without Numpy' )
print(
timeit(
'euclidean_distance_no_np([1, 2, 3], [4, 5, 6])' , number=10000 , globals=globals() , ) )
print('With Numpy' )
print(
timeit(
'euclidean_distance([1, 2, 3], [4, 5, 6])' , number=10000 , globals=globals() , ) )
benchmark()
| 690 | 1 |
"""simple docstring"""
import string
from math import logaa
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ ) -> int:
"""simple docstring"""
A = document.translate(
str.maketrans('' , '' , string.punctuation ) ).replace('\n' , '' )
A = document_without_punctuation.split(' ' ) # word tokenization
return len([word for word in tokenize_document if word.lower() == term.lower()] )
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ ) -> tuple[int, int]:
"""simple docstring"""
A = corpus.lower().translate(
str.maketrans('' , '' , string.punctuation ) ) # strip all punctuation and replace it with ''
A = corpus_without_punctuation.split('\n' )
A = term.lower()
return (len([doc for doc in docs if term in doc] ), len(UpperCamelCase__ ))
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__=False ) -> float:
"""simple docstring"""
if smoothing:
if n == 0:
raise ValueError('log10(0) is undefined.' )
return round(1 + logaa(n / (1 + df) ) , 3 )
if df == 0:
raise ZeroDivisionError('df must be > 0' )
elif n == 0:
raise ValueError('log10(0) is undefined.' )
return round(logaa(n / df ) , 3 )
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ ) -> float:
"""simple docstring"""
return round(tf * idf , 3 )
| 690 |
"""simple docstring"""
import argparse
import os
import shutil
from pathlib import Path
import onnx
import torch
from packaging import version
from torch.onnx import export
from diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline, StableDiffusionPipeline
UpperCamelCase : List[Any] = version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__=False , ) -> Any:
"""simple docstring"""
output_path.parent.mkdir(parents=UpperCamelCase__ , exist_ok=UpperCamelCase__ )
# PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
# so we check the torch version for backwards compatibility
if is_torch_less_than_1_11:
export(
UpperCamelCase__ , UpperCamelCase__ , f=output_path.as_posix() , input_names=UpperCamelCase__ , output_names=UpperCamelCase__ , dynamic_axes=UpperCamelCase__ , do_constant_folding=UpperCamelCase__ , use_external_data_format=UpperCamelCase__ , enable_onnx_checker=UpperCamelCase__ , opset_version=UpperCamelCase__ , )
else:
export(
UpperCamelCase__ , UpperCamelCase__ , f=output_path.as_posix() , input_names=UpperCamelCase__ , output_names=UpperCamelCase__ , dynamic_axes=UpperCamelCase__ , do_constant_folding=UpperCamelCase__ , opset_version=UpperCamelCase__ , )
@torch.no_grad()
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = False ) -> str:
"""simple docstring"""
A = torch.floataa if fpaa else torch.floataa
if fpaa and torch.cuda.is_available():
A = 'cuda'
elif fpaa and not torch.cuda.is_available():
raise ValueError('`float16` model export is only supported on GPUs with CUDA' )
else:
A = 'cpu'
A = StableDiffusionPipeline.from_pretrained(UpperCamelCase__ , torch_dtype=UpperCamelCase__ ).to(UpperCamelCase__ )
A = Path(UpperCamelCase__ )
# TEXT ENCODER
A = pipeline.text_encoder.config.max_position_embeddings
A = pipeline.text_encoder.config.hidden_size
A = pipeline.tokenizer(
'A sample prompt' , padding='max_length' , max_length=pipeline.tokenizer.model_max_length , truncation=UpperCamelCase__ , return_tensors='pt' , )
onnx_export(
pipeline.text_encoder , model_args=(text_input.input_ids.to(device=UpperCamelCase__ , dtype=torch.intaa )) , output_path=output_path / 'text_encoder' / 'model.onnx' , ordered_input_names=['input_ids'] , output_names=['last_hidden_state', 'pooler_output'] , dynamic_axes={
'input_ids': {0: 'batch', 1: 'sequence'},
} , opset=UpperCamelCase__ , )
del pipeline.text_encoder
# UNET
A = pipeline.unet.config.in_channels
A = pipeline.unet.config.sample_size
A = output_path / 'unet' / 'model.onnx'
onnx_export(
pipeline.unet , model_args=(
torch.randn(2 , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ).to(device=UpperCamelCase__ , dtype=UpperCamelCase__ ),
torch.randn(2 ).to(device=UpperCamelCase__ , dtype=UpperCamelCase__ ),
torch.randn(2 , UpperCamelCase__ , UpperCamelCase__ ).to(device=UpperCamelCase__ , dtype=UpperCamelCase__ ),
False,
) , output_path=UpperCamelCase__ , ordered_input_names=['sample', 'timestep', 'encoder_hidden_states', 'return_dict'] , output_names=['out_sample'] , dynamic_axes={
'sample': {0: 'batch', 1: 'channels', 2: 'height', 3: 'width'},
'timestep': {0: 'batch'},
'encoder_hidden_states': {0: 'batch', 1: 'sequence'},
} , opset=UpperCamelCase__ , use_external_data_format=UpperCamelCase__ , )
A = str(unet_path.absolute().as_posix() )
A = os.path.dirname(UpperCamelCase__ )
A = onnx.load(UpperCamelCase__ )
# clean up existing tensor files
shutil.rmtree(UpperCamelCase__ )
os.mkdir(UpperCamelCase__ )
# collate external tensor files into one
onnx.save_model(
UpperCamelCase__ , UpperCamelCase__ , save_as_external_data=UpperCamelCase__ , all_tensors_to_one_file=UpperCamelCase__ , location='weights.pb' , convert_attribute=UpperCamelCase__ , )
del pipeline.unet
# VAE ENCODER
A = pipeline.vae
A = vae_encoder.config.in_channels
A = vae_encoder.config.sample_size
# need to get the raw tensor output (sample) from the encoder
A = lambda UpperCamelCase__ , UpperCamelCase__ : vae_encoder.encode(UpperCamelCase__ , UpperCamelCase__ )[0].sample()
onnx_export(
UpperCamelCase__ , model_args=(
torch.randn(1 , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ).to(device=UpperCamelCase__ , dtype=UpperCamelCase__ ),
False,
) , output_path=output_path / 'vae_encoder' / 'model.onnx' , ordered_input_names=['sample', 'return_dict'] , output_names=['latent_sample'] , dynamic_axes={
'sample': {0: 'batch', 1: 'channels', 2: 'height', 3: 'width'},
} , opset=UpperCamelCase__ , )
# VAE DECODER
A = pipeline.vae
A = vae_decoder.config.latent_channels
A = vae_decoder.config.out_channels
# forward only through the decoder part
A = vae_encoder.decode
onnx_export(
UpperCamelCase__ , model_args=(
torch.randn(1 , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ).to(device=UpperCamelCase__ , dtype=UpperCamelCase__ ),
False,
) , output_path=output_path / 'vae_decoder' / 'model.onnx' , ordered_input_names=['latent_sample', 'return_dict'] , output_names=['sample'] , dynamic_axes={
'latent_sample': {0: 'batch', 1: 'channels', 2: 'height', 3: 'width'},
} , opset=UpperCamelCase__ , )
del pipeline.vae
# SAFETY CHECKER
if pipeline.safety_checker is not None:
A = pipeline.safety_checker
A = safety_checker.config.vision_config.num_channels
A = safety_checker.config.vision_config.image_size
A = safety_checker.forward_onnx
onnx_export(
pipeline.safety_checker , model_args=(
torch.randn(
1 , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , ).to(device=UpperCamelCase__ , dtype=UpperCamelCase__ ),
torch.randn(1 , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ).to(device=UpperCamelCase__ , dtype=UpperCamelCase__ ),
) , output_path=output_path / 'safety_checker' / 'model.onnx' , ordered_input_names=['clip_input', 'images'] , output_names=['out_images', 'has_nsfw_concepts'] , dynamic_axes={
'clip_input': {0: 'batch', 1: 'channels', 2: 'height', 3: 'width'},
'images': {0: 'batch', 1: 'height', 2: 'width', 3: 'channels'},
} , opset=UpperCamelCase__ , )
del pipeline.safety_checker
A = OnnxRuntimeModel.from_pretrained(output_path / 'safety_checker' )
A = pipeline.feature_extractor
else:
A = None
A = None
A = OnnxStableDiffusionPipeline(
vae_encoder=OnnxRuntimeModel.from_pretrained(output_path / 'vae_encoder' ) , vae_decoder=OnnxRuntimeModel.from_pretrained(output_path / 'vae_decoder' ) , text_encoder=OnnxRuntimeModel.from_pretrained(output_path / 'text_encoder' ) , tokenizer=pipeline.tokenizer , unet=OnnxRuntimeModel.from_pretrained(output_path / 'unet' ) , scheduler=pipeline.scheduler , safety_checker=UpperCamelCase__ , feature_extractor=UpperCamelCase__ , requires_safety_checker=safety_checker is not None , )
onnx_pipeline.save_pretrained(UpperCamelCase__ )
print('ONNX pipeline saved to' , UpperCamelCase__ )
del pipeline
del onnx_pipeline
A = OnnxStableDiffusionPipeline.from_pretrained(UpperCamelCase__ , provider='CPUExecutionProvider' )
print('ONNX pipeline is loadable' )
if __name__ == "__main__":
UpperCamelCase : Optional[int] = argparse.ArgumentParser()
parser.add_argument(
"--model_path",
type=str,
required=True,
help="Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).",
)
parser.add_argument("--output_path", type=str, required=True, help="Path to the output model.")
parser.add_argument(
"--opset",
default=14,
type=int,
help="The version of the ONNX operator set to use.",
)
parser.add_argument("--fp16", action="store_true", default=False, help="Export the models in `float16` mode")
UpperCamelCase : str = parser.parse_args()
convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
| 690 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
UpperCamelCase : List[Any] = logging.get_logger(__name__)
UpperCamelCase : Tuple = {
"microsoft/swin-tiny-patch4-window7-224": (
"https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json"
),
# See all Swin models at https://huggingface.co/models?filter=swin
}
class lowerCamelCase__ ( UpperCAmelCase_ , UpperCAmelCase_ ):
lowerCAmelCase = """swin"""
lowerCAmelCase = {
"""num_attention_heads""": """num_heads""",
"""num_hidden_layers""": """num_layers""",
}
def __init__( self : Dict , _lowercase : Dict=224 , _lowercase : Dict=4 , _lowercase : Dict=3 , _lowercase : Optional[Any]=96 , _lowercase : Optional[Any]=[2, 2, 6, 2] , _lowercase : Tuple=[3, 6, 12, 24] , _lowercase : Optional[Any]=7 , _lowercase : List[Any]=4.0 , _lowercase : int=True , _lowercase : Tuple=0.0 , _lowercase : Dict=0.0 , _lowercase : str=0.1 , _lowercase : Union[str, Any]="gelu" , _lowercase : Any=False , _lowercase : Tuple=0.0_2 , _lowercase : Optional[int]=1e-5 , _lowercase : int=32 , _lowercase : Any=None , _lowercase : List[str]=None , **_lowercase : Tuple , ):
super().__init__(**_lowercase )
A = image_size
A = patch_size
A = num_channels
A = embed_dim
A = depths
A = len(_lowercase )
A = num_heads
A = window_size
A = mlp_ratio
A = qkv_bias
A = hidden_dropout_prob
A = attention_probs_dropout_prob
A = drop_path_rate
A = hidden_act
A = use_absolute_embeddings
A = layer_norm_eps
A = initializer_range
A = encoder_stride
# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
A = int(embed_dim * 2 ** (len(_lowercase ) - 1) )
A = ['stem'] + [f'stage{idx}' for idx in range(1 , len(_lowercase ) + 1 )]
A , A = get_aligned_output_features_output_indices(
out_features=_lowercase , out_indices=_lowercase , stage_names=self.stage_names )
class lowerCamelCase__ ( UpperCAmelCase_ ):
lowerCAmelCase = version.parse("""1.11""" )
@property
def __a ( self : str ):
return OrderedDict(
[
('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
] )
@property
def __a ( self : List[Any] ):
return 1e-4
| 690 |
"""simple docstring"""
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
UpperCamelCase : List[str] = Lock()
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> List[str]:
"""simple docstring"""
global process_lock
# we perform n swaps since after n swaps we know we are sorted
# we *could* stop early if we are sorted already, but it takes as long to
# find out we are sorted as it does to sort the list with this algorithm
for i in range(0 , 10 ):
if (i + position) % 2 == 0 and r_send is not None:
# send your value to your right neighbor
process_lock.acquire()
r_send[1].send(UpperCamelCase__ )
process_lock.release()
# receive your right neighbor's value
process_lock.acquire()
A = rr_cv[0].recv()
process_lock.release()
# take the lower value since you are on the left
A = min(UpperCamelCase__ , UpperCamelCase__ )
elif (i + position) % 2 != 0 and l_send is not None:
# send your value to your left neighbor
process_lock.acquire()
l_send[1].send(UpperCamelCase__ )
process_lock.release()
# receive your left neighbor's value
process_lock.acquire()
A = lr_cv[0].recv()
process_lock.release()
# take the higher value since you are on the right
A = max(UpperCamelCase__ , UpperCamelCase__ )
# after all swaps are performed, send the values back to main
result_pipe[1].send(UpperCamelCase__ )
def __snake_case ( UpperCamelCase__ ) -> List[str]:
"""simple docstring"""
A = []
A = []
# initialize the list of pipes where the values will be retrieved
for _ in arr:
result_pipe.append(Pipe() )
# creates the processes
# the first and last process only have one neighbor so they are made outside
# of the loop
A = Pipe()
A = Pipe()
process_array_.append(
Process(
target=UpperCamelCase__ , args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0]) , ) )
A = temp_rs
A = temp_rr
for i in range(1 , len(UpperCamelCase__ ) - 1 ):
A = Pipe()
A = Pipe()
process_array_.append(
Process(
target=UpperCamelCase__ , args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i]) , ) )
A = temp_rs
A = temp_rr
process_array_.append(
Process(
target=UpperCamelCase__ , args=(
len(UpperCamelCase__ ) - 1,
arr[len(UpperCamelCase__ ) - 1],
temp_ls,
None,
temp_lr,
None,
result_pipe[len(UpperCamelCase__ ) - 1],
) , ) )
# start the processes
for p in process_array_:
p.start()
# wait for the processes to end and write their values to the list
for p in range(0 , len(UpperCamelCase__ ) ):
A = result_pipe[p][0].recv()
process_array_[p].join()
return arr
def __snake_case ( ) -> Optional[Any]:
"""simple docstring"""
A = list(range(10 , 0 , -1 ) )
print('Initial List' )
print(*UpperCamelCase__ )
A = odd_even_transposition(UpperCamelCase__ )
print('Sorted List\n' )
print(*UpperCamelCase__ )
if __name__ == "__main__":
main()
| 690 | 1 |
"""simple docstring"""
import unittest
from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
UpperCamelCase : Optional[int] = get_tests_dir("fixtures/test_sentencepiece.model")
@require_sentencepiece
@require_tokenizers
class lowerCamelCase__ ( UpperCAmelCase_ , unittest.TestCase ):
lowerCAmelCase = ReformerTokenizer
lowerCAmelCase = ReformerTokenizerFast
lowerCAmelCase = True
lowerCAmelCase = False
lowerCAmelCase = True
def __a ( self : Any ):
super().setUp()
A = ReformerTokenizer(_lowercase , keep_accents=_lowercase )
tokenizer.save_pretrained(self.tmpdirname )
def __a ( self : List[Any] ):
A = '<s>'
A = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(_lowercase ) , _lowercase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(_lowercase ) , _lowercase )
def __a ( self : Optional[Any] ):
A = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '<unk>' )
self.assertEqual(vocab_keys[1] , '<s>' )
self.assertEqual(vocab_keys[-1] , 'j' )
self.assertEqual(len(_lowercase ) , 1_000 )
def __a ( self : Optional[Any] ):
self.assertEqual(self.get_tokenizer().vocab_size , 1_000 )
def __a ( self : Optional[Any] ):
if not self.test_rust_tokenizer:
return
A = self.get_tokenizer()
A = self.get_rust_tokenizer()
A = 'I was born in 92000, and this is falsé.'
A = tokenizer.tokenize(_lowercase )
A = rust_tokenizer.tokenize(_lowercase )
self.assertListEqual(_lowercase , _lowercase )
A = tokenizer.encode(_lowercase , add_special_tokens=_lowercase )
A = rust_tokenizer.encode(_lowercase , add_special_tokens=_lowercase )
self.assertListEqual(_lowercase , _lowercase )
A = self.get_rust_tokenizer()
A = tokenizer.encode(_lowercase )
A = rust_tokenizer.encode(_lowercase )
self.assertListEqual(_lowercase , _lowercase )
def __a ( self : Union[str, Any] , _lowercase : Union[str, Any]=15 ):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})' ):
A = self.rust_tokenizer_class.from_pretrained(_lowercase , **_lowercase )
# Simple input
A = 'This is a simple input'
A = ['This is a simple input 1', 'This is a simple input 2']
A = ('This is a simple input', 'This is a pair')
A = [
('This is a simple input 1', 'This is a simple input 2'),
('This is a simple pair 1', 'This is a simple pair 2'),
]
# Simple input tests
self.assertRaises(_lowercase , tokenizer_r.encode , _lowercase , max_length=_lowercase , padding='max_length' )
# Simple input
self.assertRaises(_lowercase , tokenizer_r.encode_plus , _lowercase , max_length=_lowercase , padding='max_length' )
# Simple input
self.assertRaises(
_lowercase , tokenizer_r.batch_encode_plus , _lowercase , max_length=_lowercase , padding='max_length' , )
# Pair input
self.assertRaises(_lowercase , tokenizer_r.encode , _lowercase , max_length=_lowercase , padding='max_length' )
# Pair input
self.assertRaises(_lowercase , tokenizer_r.encode_plus , _lowercase , max_length=_lowercase , padding='max_length' )
# Pair input
self.assertRaises(
_lowercase , tokenizer_r.batch_encode_plus , _lowercase , max_length=_lowercase , padding='max_length' , )
def __a ( self : List[str] ):
pass
def __a ( self : List[Any] ):
A = ReformerTokenizer(_lowercase , keep_accents=_lowercase )
A = tokenizer.tokenize('This is a test' )
self.assertListEqual(_lowercase , ['▁This', '▁is', '▁a', '▁t', 'est'] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(_lowercase ) , [285, 46, 10, 170, 382] , )
A = tokenizer.tokenize('I was born in 92000, and this is falsé.' )
self.assertListEqual(
_lowercase , [
SPIECE_UNDERLINE + 'I',
SPIECE_UNDERLINE + 'was',
SPIECE_UNDERLINE + 'b',
'or',
'n',
SPIECE_UNDERLINE + 'in',
SPIECE_UNDERLINE + '',
'9',
'2',
'0',
'0',
'0',
',',
SPIECE_UNDERLINE + 'and',
SPIECE_UNDERLINE + 'this',
SPIECE_UNDERLINE + 'is',
SPIECE_UNDERLINE + 'f',
'al',
's',
'é',
'.',
] , )
A = tokenizer.convert_tokens_to_ids(_lowercase )
self.assertListEqual(
_lowercase , [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4] , )
A = tokenizer.convert_ids_to_tokens(_lowercase )
self.assertListEqual(
_lowercase , [
SPIECE_UNDERLINE + 'I',
SPIECE_UNDERLINE + 'was',
SPIECE_UNDERLINE + 'b',
'or',
'n',
SPIECE_UNDERLINE + 'in',
SPIECE_UNDERLINE + '',
'<unk>',
'2',
'0',
'0',
'0',
',',
SPIECE_UNDERLINE + 'and',
SPIECE_UNDERLINE + 'this',
SPIECE_UNDERLINE + 'is',
SPIECE_UNDERLINE + 'f',
'al',
's',
'<unk>',
'.',
] , )
@cached_property
def __a ( self : Optional[int] ):
return ReformerTokenizer.from_pretrained('google/reformer-crime-and-punishment' )
@slow
def __a ( self : Any ):
A = 'Hello World!'
A = [126, 32, 262, 152, 38, 72, 287]
self.assertListEqual(_lowercase , self.big_tokenizer.encode(_lowercase ) )
@slow
def __a ( self : Tuple ):
A = (
'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'
)
A = [
108,
265,
24,
111,
4,
258,
156,
35,
28,
275,
3,
259,
297,
260,
84,
4,
35,
110,
44,
8,
259,
91,
268,
21,
11,
209,
274,
109,
266,
277,
117,
86,
93,
315,
258,
278,
258,
277,
258,
0,
258,
288,
258,
319,
258,
0,
258,
0,
258,
0,
258,
0,
258,
287,
258,
315,
258,
289,
258,
278,
99,
269,
266,
262,
8,
259,
241,
4,
217,
230,
268,
266,
55,
168,
106,
75,
193,
266,
223,
27,
49,
26,
282,
25,
264,
299,
19,
26,
0,
258,
277,
117,
86,
93,
176,
183,
270,
11,
262,
42,
61,
265,
]
self.assertListEqual(_lowercase , self.big_tokenizer.encode(_lowercase ) )
@require_torch
@slow
def __a ( self : List[str] ):
import torch
from transformers import ReformerConfig, ReformerModel
# Build sequence
A = list(self.big_tokenizer.get_vocab().keys() )[:10]
A = ' '.join(_lowercase )
A = self.big_tokenizer.encode_plus(_lowercase , return_tensors='pt' )
A = self.big_tokenizer.batch_encode_plus([sequence, sequence] , return_tensors='pt' )
A = ReformerConfig()
# The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
A = encoded_sequence['input_ids'].shape
A = ReformerModel(_lowercase )
# Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size
with torch.no_grad():
model(**_lowercase )
model(**_lowercase )
@slow
def __a ( self : int ):
# fmt: off
A = {'input_ids': [[108, 265, 24, 111, 4, 258, 156, 7, 51, 279, 58, 7, 76, 25, 69, 278], [140, 243, 264, 134, 17, 267, 77, 263, 22, 262, 297, 258, 304, 177, 279, 266, 14, 89, 13, 35, 261, 299, 272, 137, 275, 278]], '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]]} # noqa: E501
# fmt: on
# This tokenizer does not know some characters like ")".
# That is the reason why we use very simple texts here.
# Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064
A = [
'This is a very simple sentence.',
'The quick brown fox jumps over the lazy dog.',
]
self.tokenizer_integration_test_util(
expected_encoding=_lowercase , model_name='google/reformer-crime-and-punishment' , revision='0e6c3decb8211d49bf881013425dc8b0448b3f5a' , padding=_lowercase , sequences=_lowercase , )
| 690 |
"""simple docstring"""
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures
# Importing the dataset
UpperCamelCase : int = pd.read_csv(
"https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/"
"position_salaries.csv"
)
UpperCamelCase : List[Any] = dataset.iloc[:, 1:2].values
UpperCamelCase : Any = dataset.iloc[:, 2].values
UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase : str = train_test_split(X, y, test_size=0.2, random_state=0)
UpperCamelCase : List[str] = PolynomialFeatures(degree=4)
UpperCamelCase : Optional[int] = poly_reg.fit_transform(X)
UpperCamelCase : List[Any] = LinearRegression()
pol_reg.fit(X_poly, y)
def __snake_case ( ) -> Optional[int]:
"""simple docstring"""
plt.scatter(UpperCamelCase__ , UpperCamelCase__ , color='red' )
plt.plot(UpperCamelCase__ , pol_reg.predict(poly_reg.fit_transform(UpperCamelCase__ ) ) , color='blue' )
plt.title('Truth or Bluff (Linear Regression)' )
plt.xlabel('Position level' )
plt.ylabel('Salary' )
plt.show()
if __name__ == "__main__":
viz_polymonial()
# Predicting a new result with Polymonial Regression
pol_reg.predict(poly_reg.fit_transform([[5.5]]))
# output should be 132148.43750003
| 690 | 1 |
"""simple docstring"""
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__ , UpperCamelCase__=10 ) -> List[str]:
"""simple docstring"""
A = []
for _ in range(UpperCamelCase__ ):
lrs.append(scheduler.get_lr()[0] )
scheduler.step()
return lrs
def __snake_case ( UpperCamelCase__ , UpperCamelCase__=10 ) -> Optional[Any]:
"""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 lowerCamelCase__ ( unittest.TestCase ):
def __a ( self : Optional[int] , _lowercase : Dict , _lowercase : Dict , _lowercase : Any ):
self.assertEqual(len(_lowercase ) , len(_lowercase ) )
for a, b in zip(_lowercase , _lowercase ):
self.assertAlmostEqual(_lowercase , _lowercase , delta=_lowercase )
def __a ( self : Tuple ):
A = torch.tensor([0.1, -0.2, -0.1] , requires_grad=_lowercase )
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(_lowercase , _lowercase )
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=_lowercase )
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=_lowercase , weight_decay=0.0 , relative_step=_lowercase , scale_parameter=_lowercase , warmup_init=_lowercase , )
for _ in range(1_000 ):
A = criterion(_lowercase , _lowercase )
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 lowerCamelCase__ ( unittest.TestCase ):
lowerCAmelCase = nn.Linear(50 , 50 ) if is_torch_available() else None
lowerCAmelCase = AdamW(m.parameters() , lr=10.0 ) if is_torch_available() else None
lowerCAmelCase = 10
def __a ( self : int , _lowercase : Optional[Any] , _lowercase : Optional[int] , _lowercase : str , _lowercase : Optional[int]=None ):
self.assertEqual(len(_lowercase ) , len(_lowercase ) )
for a, b in zip(_lowercase , _lowercase ):
self.assertAlmostEqual(_lowercase , _lowercase , delta=_lowercase , msg=_lowercase )
def __a ( self : Optional[Any] ):
A = {'num_warmup_steps': 2, 'num_training_steps': 10}
# schedulers doct format
# function: (sched_args_dict, expected_learning_rates)
A = {
get_constant_schedule: ({}, [1_0.0] * self.num_steps),
get_constant_schedule_with_warmup: (
{'num_warmup_steps': 4},
[0.0, 2.5, 5.0, 7.5, 1_0.0, 1_0.0, 1_0.0, 1_0.0, 1_0.0, 1_0.0],
),
get_linear_schedule_with_warmup: (
{**common_kwargs},
[0.0, 5.0, 1_0.0, 8.7_5, 7.5, 6.2_5, 5.0, 3.7_5, 2.5, 1.2_5],
),
get_cosine_schedule_with_warmup: (
{**common_kwargs},
[0.0, 5.0, 1_0.0, 9.6_1, 8.5_3, 6.9_1, 5.0, 3.0_8, 1.4_6, 0.3_8],
),
get_cosine_with_hard_restarts_schedule_with_warmup: (
{**common_kwargs, 'num_cycles': 2},
[0.0, 5.0, 1_0.0, 8.5_3, 5.0, 1.4_6, 1_0.0, 8.5_3, 5.0, 1.4_6],
),
get_polynomial_decay_schedule_with_warmup: (
{**common_kwargs, 'power': 2.0, 'lr_end': 1e-7},
[0.0, 5.0, 1_0.0, 7.6_5_6, 5.6_2_5, 3.9_0_6, 2.5, 1.4_0_6, 0.6_2_5, 0.1_5_6],
),
get_inverse_sqrt_schedule: (
{'num_warmup_steps': 2},
[0.0, 5.0, 1_0.0, 8.1_6_5, 7.0_7_1, 6.3_2_5, 5.7_7_4, 5.3_4_5, 5.0, 4.7_1_4],
),
}
for scheduler_func, data in scheds.items():
A , A = data
A = scheduler_func(self.optimizer , **_lowercase )
self.assertEqual(len([scheduler.get_lr()[0]] ) , 1 )
A = unwrap_schedule(_lowercase , self.num_steps )
self.assertListAlmostEqual(
_lowercase , _lowercase , tol=1e-2 , msg=f'failed for {scheduler_func} in normal scheduler' , )
A = scheduler_func(self.optimizer , **_lowercase )
if scheduler_func.__name__ != "get_constant_schedule":
LambdaScheduleWrapper.wrap_scheduler(_lowercase ) # wrap to test picklability of the schedule
A = unwrap_and_save_reload_schedule(_lowercase , self.num_steps )
self.assertListEqual(_lowercase , _lowercase , msg=f'failed for {scheduler_func} in save and reload' )
class lowerCamelCase__ :
def __init__( self : Union[str, Any] , _lowercase : List[Any] ):
A = fn
def __call__( self : Tuple , *_lowercase : Tuple , **_lowercase : Tuple ):
return self.fn(*_lowercase , **_lowercase )
@classmethod
def __a ( self : Any , _lowercase : List[Any] ):
A = list(map(self , scheduler.lr_lambdas ) )
| 690 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPENAI_CLIP_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
UpperCamelCase : Optional[Any] = logging.get_logger(__name__)
if is_vision_available():
import PIL
class lowerCamelCase__ ( UpperCAmelCase_ ):
lowerCAmelCase = ["""pixel_values"""]
def __init__( self : List[str] , _lowercase : bool = True , _lowercase : Dict[str, int] = None , _lowercase : PILImageResampling = PILImageResampling.BICUBIC , _lowercase : bool = True , _lowercase : Dict[str, int] = None , _lowercase : bool = True , _lowercase : Union[int, float] = 1 / 255 , _lowercase : bool = True , _lowercase : Optional[Union[float, List[float]]] = None , _lowercase : Optional[Union[float, List[float]]] = None , _lowercase : bool = True , **_lowercase : Tuple , ):
super().__init__(**_lowercase )
A = size if size is not None else {'shortest_edge': 224}
A = get_size_dict(_lowercase , default_to_square=_lowercase )
A = crop_size if crop_size is not None else {'height': 224, 'width': 224}
A = get_size_dict(_lowercase , default_to_square=_lowercase , param_name='crop_size' )
A = do_resize
A = size
A = resample
A = do_center_crop
A = crop_size
A = do_rescale
A = rescale_factor
A = do_normalize
A = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
A = image_std if image_std is not None else OPENAI_CLIP_STD
A = do_convert_rgb
def __a ( self : str , _lowercase : np.ndarray , _lowercase : Dict[str, int] , _lowercase : PILImageResampling = PILImageResampling.BICUBIC , _lowercase : Optional[Union[str, ChannelDimension]] = None , **_lowercase : List[str] , ):
A = get_size_dict(_lowercase , default_to_square=_lowercase )
if "shortest_edge" not in size:
raise ValueError(f'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}' )
A = get_resize_output_image_size(_lowercase , size=size['shortest_edge'] , default_to_square=_lowercase )
return resize(_lowercase , size=_lowercase , resample=_lowercase , data_format=_lowercase , **_lowercase )
def __a ( self : int , _lowercase : np.ndarray , _lowercase : Dict[str, int] , _lowercase : Optional[Union[str, ChannelDimension]] = None , **_lowercase : str , ):
A = get_size_dict(_lowercase )
if "height" not in size or "width" not in size:
raise ValueError(f'The `size` parameter must contain the keys (height, width). Got {size.keys()}' )
return center_crop(_lowercase , size=(size['height'], size['width']) , data_format=_lowercase , **_lowercase )
def __a ( self : List[str] , _lowercase : np.ndarray , _lowercase : Union[int, float] , _lowercase : Optional[Union[str, ChannelDimension]] = None , **_lowercase : List[str] , ):
return rescale(_lowercase , scale=_lowercase , data_format=_lowercase , **_lowercase )
def __a ( self : List[str] , _lowercase : np.ndarray , _lowercase : Union[float, List[float]] , _lowercase : Union[float, List[float]] , _lowercase : Optional[Union[str, ChannelDimension]] = None , **_lowercase : Union[str, Any] , ):
return normalize(_lowercase , mean=_lowercase , std=_lowercase , data_format=_lowercase , **_lowercase )
def __a ( self : Optional[int] , _lowercase : ImageInput , _lowercase : bool = None , _lowercase : Dict[str, int] = None , _lowercase : PILImageResampling = None , _lowercase : bool = None , _lowercase : int = None , _lowercase : bool = None , _lowercase : float = None , _lowercase : bool = None , _lowercase : Optional[Union[float, List[float]]] = None , _lowercase : Optional[Union[float, List[float]]] = None , _lowercase : bool = None , _lowercase : Optional[Union[str, TensorType]] = None , _lowercase : Optional[ChannelDimension] = ChannelDimension.FIRST , **_lowercase : int , ):
A = do_resize if do_resize is not None else self.do_resize
A = size if size is not None else self.size
A = get_size_dict(_lowercase , param_name='size' , default_to_square=_lowercase )
A = resample if resample is not None else self.resample
A = do_center_crop if do_center_crop is not None else self.do_center_crop
A = crop_size if crop_size is not None else self.crop_size
A = get_size_dict(_lowercase , param_name='crop_size' , default_to_square=_lowercase )
A = do_rescale if do_rescale is not None else self.do_rescale
A = rescale_factor if rescale_factor is not None else self.rescale_factor
A = do_normalize if do_normalize is not None else self.do_normalize
A = image_mean if image_mean is not None else self.image_mean
A = image_std if image_std is not None else self.image_std
A = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
A = make_list_of_images(_lowercase )
if not valid_images(_lowercase ):
raise ValueError(
'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
'torch.Tensor, tf.Tensor or jax.ndarray.' )
if do_resize and size is None:
raise ValueError('Size must be specified if do_resize is True.' )
if do_center_crop and crop_size is None:
raise ValueError('Crop size must be specified if do_center_crop is True.' )
if do_rescale and rescale_factor is None:
raise ValueError('Rescale factor must be specified if do_rescale is True.' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('Image mean and std must be specified if do_normalize is True.' )
# PIL RGBA images are converted to RGB
if do_convert_rgb:
A = [convert_to_rgb(_lowercase ) for image in images]
# All transformations expect numpy arrays.
A = [to_numpy_array(_lowercase ) for image in images]
if do_resize:
A = [self.resize(image=_lowercase , size=_lowercase , resample=_lowercase ) for image in images]
if do_center_crop:
A = [self.center_crop(image=_lowercase , size=_lowercase ) for image in images]
if do_rescale:
A = [self.rescale(image=_lowercase , scale=_lowercase ) for image in images]
if do_normalize:
A = [self.normalize(image=_lowercase , mean=_lowercase , std=_lowercase ) for image in images]
A = [to_channel_dimension_format(_lowercase , _lowercase ) for image in images]
A = {'pixel_values': images}
return BatchFeature(data=_lowercase , tensor_type=_lowercase )
| 690 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
UpperCamelCase : Union[str, Any] = {"configuration_speech_encoder_decoder": ["SpeechEncoderDecoderConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase : List[str] = ["SpeechEncoderDecoderModel"]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase : List[str] = ["FlaxSpeechEncoderDecoderModel"]
if TYPE_CHECKING:
from .configuration_speech_encoder_decoder import SpeechEncoderDecoderConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_speech_encoder_decoder import SpeechEncoderDecoderModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_speech_encoder_decoder import FlaxSpeechEncoderDecoderModel
else:
import sys
UpperCamelCase : Optional[Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 690 |
"""simple docstring"""
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class lowerCamelCase__ ( unittest.TestCase ):
def __a ( self : Union[str, Any] ):
A = torch.nn.Linear(10 , 10 )
A = torch.optim.SGD(model.parameters() , 0.1 )
A = Accelerator()
A = accelerator.prepare(_lowercase )
try:
pickle.loads(pickle.dumps(_lowercase ) )
except Exception as e:
self.fail(f'Accelerated optimizer pickling failed with {e}' )
AcceleratorState._reset_state()
| 690 | 1 |
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