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| style_context
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import os
from typing import Dict, List, Tuple, TypeVar, Union
__lowerCAmelCase : Any = TypeVar("T")
__lowerCAmelCase : Dict = Union[List[T], Tuple[T, ...]]
__lowerCAmelCase : Optional[Any] = Union[T, List[T], Dict[str, T]]
__lowerCAmelCase : Tuple = Union[str, bytes, os.PathLike]
| 509 |
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def __UpperCAmelCase ( __a : bytes ,__a : int ) -> np.array:
"""simple docstring"""
_a : int = F"""{sampling_rate}"""
_a : str = '''1'''
_a : Optional[int] = '''f32le'''
_a : Optional[Any] = [
'''ffmpeg''',
'''-i''',
'''pipe:0''',
'''-ac''',
ac,
'''-ar''',
ar,
'''-f''',
format_for_conversion,
'''-hide_banner''',
'''-loglevel''',
'''quiet''',
'''pipe:1''',
]
try:
with subprocess.Popen(__a ,stdin=subprocess.PIPE ,stdout=subprocess.PIPE ) as ffmpeg_process:
_a : Any = ffmpeg_process.communicate(__a )
except FileNotFoundError as error:
raise ValueError('''ffmpeg was not found but is required to load audio files from filename''' ) from error
_a : Optional[Any] = output_stream[0]
_a : Optional[int] = np.frombuffer(__a ,np.floataa )
if audio.shape[0] == 0:
raise ValueError('''Malformed soundfile''' )
return audio
def __UpperCAmelCase ( __a : int ,__a : float ,__a : str = "f32le" ,) -> str:
"""simple docstring"""
_a : Dict = F"""{sampling_rate}"""
_a : Optional[Any] = '''1'''
if format_for_conversion == "s16le":
_a : Dict = 2
elif format_for_conversion == "f32le":
_a : Optional[Any] = 4
else:
raise ValueError(F"""Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`""" )
_a : Dict = platform.system()
if system == "Linux":
_a : Dict = '''alsa'''
_a : Union[str, Any] = '''default'''
elif system == "Darwin":
_a : Union[str, Any] = '''avfoundation'''
_a : List[str] = ''':0'''
elif system == "Windows":
_a : Optional[int] = '''dshow'''
_a : str = '''default'''
_a : Tuple = [
'''ffmpeg''',
'''-f''',
format_,
'''-i''',
input_,
'''-ac''',
ac,
'''-ar''',
ar,
'''-f''',
format_for_conversion,
'''-fflags''',
'''nobuffer''',
'''-hide_banner''',
'''-loglevel''',
'''quiet''',
'''pipe:1''',
]
_a : Any = int(round(sampling_rate * chunk_length_s ) ) * size_of_sample
_a : str = _ffmpeg_stream(__a ,__a )
for item in iterator:
yield item
def __UpperCAmelCase ( __a : int ,__a : float ,__a : Optional[int] = None ,__a : Optional[Union[Tuple[float, float], float]] = None ,__a : str = "f32le" ,) -> Optional[int]:
"""simple docstring"""
if stream_chunk_s is not None:
_a : Tuple = stream_chunk_s
else:
_a : Tuple = chunk_length_s
_a : Tuple = ffmpeg_microphone(__a ,__a ,format_for_conversion=__a )
if format_for_conversion == "s16le":
_a : Any = np.intaa
_a : Optional[int] = 2
elif format_for_conversion == "f32le":
_a : Dict = np.floataa
_a : List[Any] = 4
else:
raise ValueError(F"""Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`""" )
if stride_length_s is None:
_a : List[Any] = chunk_length_s / 6
_a : Optional[int] = int(round(sampling_rate * chunk_length_s ) ) * size_of_sample
if isinstance(__a ,(int, float) ):
_a : Optional[Any] = [stride_length_s, stride_length_s]
_a : Optional[Any] = int(round(sampling_rate * stride_length_s[0] ) ) * size_of_sample
_a : str = int(round(sampling_rate * stride_length_s[1] ) ) * size_of_sample
_a : Optional[Any] = datetime.datetime.now()
_a : Tuple = datetime.timedelta(seconds=__a )
for item in chunk_bytes_iter(__a ,__a ,stride=(stride_left, stride_right) ,stream=__a ):
# Put everything back in numpy scale
_a : Dict = np.frombuffer(item['''raw'''] ,dtype=__a )
_a : Dict = (
item['''stride'''][0] // size_of_sample,
item['''stride'''][1] // size_of_sample,
)
_a : str = sampling_rate
audio_time += delta
if datetime.datetime.now() > audio_time + 10 * delta:
# We're late !! SKIP
continue
yield item
def __UpperCAmelCase ( __a : Optional[int] ,__a : int ,__a : Tuple[int, int] ,__a : bool = False ) -> Optional[int]:
"""simple docstring"""
_a : Any = b''''''
_a , _a : List[str] = stride
if stride_left + stride_right >= chunk_len:
raise ValueError(
F"""Stride needs to be strictly smaller than chunk_len: ({stride_left}, {stride_right}) vs {chunk_len}""" )
_a : List[str] = 0
for raw in iterator:
acc += raw
if stream and len(__a ) < chunk_len:
_a : Dict = (_stride_left, 0)
yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
else:
while len(__a ) >= chunk_len:
# We are flushing the accumulator
_a : List[str] = (_stride_left, stride_right)
_a : List[Any] = {'''raw''': acc[:chunk_len], '''stride''': stride}
if stream:
_a : List[Any] = False
yield item
_a : Optional[Any] = stride_left
_a : Optional[Any] = acc[chunk_len - stride_left - stride_right :]
# Last chunk
if len(__a ) > stride_left:
_a : Optional[Any] = {'''raw''': acc, '''stride''': (_stride_left, 0)}
if stream:
_a : Dict = False
yield item
def __UpperCAmelCase ( __a : int ,__a : int ) -> Tuple:
"""simple docstring"""
_a : Dict = 2**24 # 16Mo
try:
with subprocess.Popen(__a ,stdout=subprocess.PIPE ,bufsize=__a ) as ffmpeg_process:
while True:
_a : int = ffmpeg_process.stdout.read(__a )
if raw == b"":
break
yield raw
except FileNotFoundError as error:
raise ValueError('''ffmpeg was not found but is required to stream audio files from filename''' ) from error
| 14 | 0 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, 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
if is_vision_available():
import PIL
SCREAMING_SNAKE_CASE :int = logging.get_logger(__name__)
class UpperCAmelCase ( __lowercase ):
'''simple docstring'''
snake_case_ = ["pixel_values"]
def __init__( self : Dict ,A : List[Any] = True ,A : str = None ,A : Any = PILImageResampling.BICUBIC ,A : Tuple = True ,A : List[Any] = 1 / 2_55 ,A : Optional[Any] = True ,A : Optional[Any] = None ,A : Optional[Any] = None ,A : Tuple = True ,**A : List[Any] ,):
super().__init__(**_a )
__A = size if size is not None else {'''height''': 3_84, '''width''': 3_84}
__A = get_size_dict(_a ,default_to_square=_a )
__A = do_resize
__A = size
__A = resample
__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 UpperCamelCase_ ( self : int ,A : Union[str, Any] ,A : List[str] ,A : str = PILImageResampling.BICUBIC ,A : Union[str, Any] = None ,**A : Union[str, Any] ,):
__A = get_size_dict(_a ,default_to_square=_a )
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 = (size['''height'''], size['''width'''])
return resize(_a ,size=_a ,resample=_a ,data_format=_a ,**_a )
def UpperCamelCase_ ( self : Union[str, Any] ,A : Union[str, Any] ,A : int ,A : List[str] = None ,**A : Optional[int] ,):
return rescale(_a ,scale=_a ,data_format=_a ,**_a )
def UpperCamelCase_ ( self : int ,A : int ,A : Any ,A : Optional[Any] ,A : Any = None ,**A : List[Any] ,):
return normalize(_a ,mean=_a ,std=_a ,data_format=_a ,**_a )
def UpperCamelCase_ ( self : Optional[Any] ,A : List[str] ,A : Optional[Any] = None ,A : int = None ,A : str = None ,A : List[str] = None ,A : Dict = None ,A : Optional[Any] = None ,A : List[Any] = None ,A : Dict = None ,A : str = None ,A : Any = None ,A : Tuple = ChannelDimension.FIRST ,**A : Optional[int] ,):
__A = do_resize if do_resize is not None else self.do_resize
__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 = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
__A = size if size is not None else self.size
__A = get_size_dict(_a ,default_to_square=_a )
__A = make_list_of_images(_a )
if not valid_images(_a ):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray." )
if do_resize and size is None 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." )
# PIL RGBA images are converted to RGB
if do_convert_rgb:
__A = [convert_to_rgb(_a ) for image in images]
# All transformations expect numpy arrays.
__A = [to_numpy_array(_a ) for image in images]
if do_resize:
__A = [self.resize(image=_a ,size=_a ,resample=_a ) for image in images]
if do_rescale:
__A = [self.rescale(image=_a ,scale=_a ) for image in images]
if do_normalize:
__A = [self.normalize(image=_a ,mean=_a ,std=_a ) for image in images]
__A = [to_channel_dimension_format(_a ,_a ) for image in images]
__A = BatchFeature(data={"pixel_values": images} ,tensor_type=_a )
return encoded_outputs
| 55 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, 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 UpperCAmelCase_ ( __lowercase , unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase__ : Union[str, Any] = KandinskyInpaintPipeline
UpperCAmelCase__ : Optional[int] = ["prompt", "image_embeds", "negative_image_embeds", "image", "mask_image"]
UpperCAmelCase__ : Optional[Any] = [
"prompt",
"negative_prompt",
"image_embeds",
"negative_image_embeds",
"image",
"mask_image",
]
UpperCAmelCase__ : Optional[int] = [
"generator",
"height",
"width",
"latents",
"guidance_scale",
"negative_prompt",
"num_inference_steps",
"return_dict",
"guidance_scale",
"num_images_per_prompt",
"output_type",
"return_dict",
]
UpperCAmelCase__ : Any = False
@property
def __lowercase ( self ) -> Optional[int]:
return 3_2
@property
def __lowercase ( self ) -> int:
return 3_2
@property
def __lowercase ( self ) -> List[str]:
return self.time_input_dim
@property
def __lowercase ( self ) -> List[str]:
return self.time_input_dim * 4
@property
def __lowercase ( self ) -> Optional[Any]:
return 1_0_0
@property
def __lowercase ( self ) -> Optional[Any]:
_a : Any = XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base''' )
return tokenizer
@property
def __lowercase ( self ) -> str:
torch.manual_seed(0 )
_a : List[Any] = MCLIPConfig(
numDims=self.cross_attention_dim , transformerDimensions=self.text_embedder_hidden_size , hidden_size=self.text_embedder_hidden_size , intermediate_size=3_7 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=1_0_0_5 , )
_a : Optional[int] = MultilingualCLIP(_a )
_a : Tuple = text_encoder.eval()
return text_encoder
@property
def __lowercase ( self ) -> str:
torch.manual_seed(0 )
_a : List[str] = {
'''in_channels''': 9,
# 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 : Dict = UNetaDConditionModel(**_a )
return model
@property
def __lowercase ( self ) -> Optional[int]:
return {
"block_out_channels": [3_2, 6_4],
"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": 1_2,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def __lowercase ( self ) -> Tuple:
torch.manual_seed(0 )
_a : List[Any] = VQModel(**self.dummy_movq_kwargs )
return model
def __lowercase ( self ) -> Any:
_a : List[Any] = self.dummy_text_encoder
_a : Optional[Any] = self.dummy_tokenizer
_a : Optional[Any] = self.dummy_unet
_a : Union[str, Any] = self.dummy_movq
_a : Tuple = DDIMScheduler(
num_train_timesteps=1_0_0_0 , beta_schedule='''linear''' , beta_start=0.0_0085 , beta_end=0.012 , clip_sample=_a , set_alpha_to_one=_a , steps_offset=1 , prediction_type='''epsilon''' , thresholding=_a , )
_a : str = {
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''unet''': unet,
'''scheduler''': scheduler,
'''movq''': movq,
}
return components
def __lowercase ( self , _a , _a=0 ) -> int:
_a : Union[str, Any] = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(_a ) ).to(_a )
_a : List[str] = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(seed + 1 ) ).to(_a )
# create init_image
_a : Tuple = floats_tensor((1, 3, 6_4, 6_4) , rng=random.Random(_a ) ).to(_a )
_a : Dict = image.cpu().permute(0 , 2 , 3 , 1 )[0]
_a : Optional[int] = Image.fromarray(np.uinta(_a ) ).convert('''RGB''' ).resize((2_5_6, 2_5_6) )
# create mask
_a : Union[str, Any] = np.ones((6_4, 6_4) , dtype=np.floataa )
_a : List[str] = 0
if str(_a ).startswith('''mps''' ):
_a : Tuple = torch.manual_seed(_a )
else:
_a : Any = torch.Generator(device=_a ).manual_seed(_a )
_a : Any = {
'''prompt''': '''horse''',
'''image''': init_image,
'''mask_image''': mask,
'''image_embeds''': image_embeds,
'''negative_image_embeds''': negative_image_embeds,
'''generator''': generator,
'''height''': 6_4,
'''width''': 6_4,
'''num_inference_steps''': 2,
'''guidance_scale''': 4.0,
'''output_type''': '''np''',
}
return inputs
def __lowercase ( self ) -> Optional[Any]:
_a : Optional[Any] = '''cpu'''
_a : List[Any] = self.get_dummy_components()
_a : Tuple = self.pipeline_class(**_a )
_a : int = pipe.to(_a )
pipe.set_progress_bar_config(disable=_a )
_a : Any = pipe(**self.get_dummy_inputs(_a ) )
_a : str = output.images
_a : Tuple = pipe(
**self.get_dummy_inputs(_a ) , return_dict=_a , )[0]
_a : Union[str, Any] = image[0, -3:, -3:, -1]
_a : Tuple = image_from_tuple[0, -3:, -3:, -1]
print(F"""image.shape {image.shape}""" )
assert image.shape == (1, 6_4, 6_4, 3)
_a : str = np.array(
[0.832_6919, 0.7379_0467, 0.2091_8581, 0.930_9612, 0.551_1791, 0.4371_3328, 0.551_3321, 0.4992_2934, 0.5949_7786] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
), F""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
), F""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""
def __lowercase ( self ) -> Dict:
super().test_inference_batch_single_identical(expected_max_diff=3e-3 )
@slow
@require_torch_gpu
class UpperCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
def __lowercase ( self ) -> str:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __lowercase ( self ) -> Union[str, Any]:
_a : Tuple = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy''' )
_a : str = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png''' )
_a : Tuple = np.ones((7_6_8, 7_6_8) , dtype=np.floataa )
_a : Any = 0
_a : Optional[Any] = '''a hat'''
_a : Optional[Any] = KandinskyPriorPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-1-prior''' , torch_dtype=torch.floataa )
pipe_prior.to(_a )
_a : Tuple = KandinskyInpaintPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-1-inpaint''' , torch_dtype=torch.floataa )
_a : Union[str, Any] = pipeline.to(_a )
pipeline.set_progress_bar_config(disable=_a )
_a : Union[str, Any] = torch.Generator(device='''cpu''' ).manual_seed(0 )
_a , _a : Dict = pipe_prior(
_a , generator=_a , num_inference_steps=5 , negative_prompt='''''' , ).to_tuple()
_a : Optional[int] = pipeline(
_a , image=_a , mask_image=_a , image_embeds=_a , negative_image_embeds=_a , generator=_a , num_inference_steps=1_0_0 , height=7_6_8 , width=7_6_8 , output_type='''np''' , )
_a : Optional[int] = output.images[0]
assert image.shape == (7_6_8, 7_6_8, 3)
assert_mean_pixel_difference(_a , _a )
| 14 | 0 |
__A : Tuple = [
(1_000, "M"),
(900, "CM"),
(500, "D"),
(400, "CD"),
(100, "C"),
(90, "XC"),
(50, "L"),
(40, "XL"),
(10, "X"),
(9, "IX"),
(5, "V"),
(4, "IV"),
(1, "I"),
]
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
_A = {'''I''': 1, '''V''': 5, '''X''': 10, '''L''': 50, '''C''': 100, '''D''': 500, '''M''': 1_000}
_A = 0
_A = 0
while place < len(__a ):
if (place + 1 < len(__a )) and (vals[roman[place]] < vals[roman[place + 1]]):
total += vals[roman[place + 1]] - vals[roman[place]]
place += 2
else:
total += vals[roman[place]]
place += 1
return total
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE ) -> str:
"""simple docstring"""
_A = []
for arabic, roman in ROMAN:
(_A) = divmod(__a , __a )
result.append(roman * factor )
if number == 0:
break
return "".join(__a )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 27 |
import argparse
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt
if __name__ == "__main__":
a__ = argparse.ArgumentParser()
parser.add_argument(
'''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
)
parser.add_argument(
'''--original_config_file''',
type=str,
required=True,
help='''The YAML config file corresponding to the original architecture.''',
)
parser.add_argument(
'''--num_in_channels''',
default=None,
type=int,
help='''The number of input channels. If `None` number of input channels will be automatically inferred.''',
)
parser.add_argument(
'''--image_size''',
default=512,
type=int,
help=(
'''The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2'''
''' Base. Use 768 for Stable Diffusion v2.'''
),
)
parser.add_argument(
'''--extract_ema''',
action='''store_true''',
help=(
'''Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights'''
''' or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield'''
''' higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.'''
),
)
parser.add_argument(
'''--upcast_attention''',
action='''store_true''',
help=(
'''Whether the attention computation should always be upcasted. This is necessary when running stable'''
''' diffusion 2.1.'''
),
)
parser.add_argument(
'''--from_safetensors''',
action='''store_true''',
help='''If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.''',
)
parser.add_argument(
'''--to_safetensors''',
action='''store_true''',
help='''Whether to store pipeline in safetensors format or not.''',
)
parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')
parser.add_argument('''--device''', type=str, help='''Device to use (e.g. cpu, cuda:0, cuda:1, etc.)''')
def __UpperCAmelCase ( __a : Any ) -> List[Any]:
"""simple docstring"""
if string == "True":
return True
elif string == "False":
return False
else:
raise ValueError(F"""could not parse string as bool {string}""" )
parser.add_argument(
'''--use_linear_projection''', help='''Override for use linear projection''', required=False, type=parse_bool
)
parser.add_argument('''--cross_attention_dim''', help='''Override for cross attention_dim''', required=False, type=int)
a__ = parser.parse_args()
a__ = download_controlnet_from_original_ckpt(
checkpoint_path=args.checkpoint_path,
original_config_file=args.original_config_file,
image_size=args.image_size,
extract_ema=args.extract_ema,
num_in_channels=args.num_in_channels,
upcast_attention=args.upcast_attention,
from_safetensors=args.from_safetensors,
device=args.device,
use_linear_projection=args.use_linear_projection,
cross_attention_dim=args.cross_attention_dim,
)
controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
| 14 | 0 |
'''simple docstring'''
import os
import unittest
from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class lowerCAmelCase ( __lowercase , unittest.TestCase):
__lowercase : Any = LxmertTokenizer
__lowercase : Optional[Any] = LxmertTokenizerFast
__lowercase : Any = True
__lowercase : Dict = True
def lowerCAmelCase ( self ) -> Union[str, Any]:
'''simple docstring'''
super().setUp()
__snake_case = [
'''[UNK]''',
'''[CLS]''',
'''[SEP]''',
'''want''',
'''##want''',
'''##ed''',
'''wa''',
'''un''',
'''runn''',
'''##ing''',
''',''',
'''low''',
'''lowest''',
]
__snake_case = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as vocab_writer:
vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens] ) )
def lowerCAmelCase ( self , __SCREAMING_SNAKE_CASE ) -> List[str]:
'''simple docstring'''
__snake_case = '''UNwant\u00E9d,running'''
__snake_case = '''unwanted, running'''
return input_text, output_text
def lowerCAmelCase ( self ) -> List[Any]:
'''simple docstring'''
__snake_case = self.tokenizer_class(self.vocab_file )
__snake_case = tokenizer.tokenize('''UNwant\u00E9d,running''' )
self.assertListEqual(_a , ['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing'''] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(_a ) , [7, 4, 5, 10, 8, 9] )
def lowerCAmelCase ( self ) -> List[Any]:
'''simple docstring'''
if not self.test_rust_tokenizer:
return
__snake_case = self.get_tokenizer()
__snake_case = self.get_rust_tokenizer()
__snake_case = '''I was born in 92000, and this is falsé.'''
__snake_case = tokenizer.tokenize(_a )
__snake_case = rust_tokenizer.tokenize(_a )
self.assertListEqual(_a , _a )
__snake_case = tokenizer.encode(_a , add_special_tokens=_a )
__snake_case = rust_tokenizer.encode(_a , add_special_tokens=_a )
self.assertListEqual(_a , _a )
__snake_case = self.get_rust_tokenizer()
__snake_case = tokenizer.encode(_a )
__snake_case = rust_tokenizer.encode(_a )
self.assertListEqual(_a , _a )
| 24 |
class UpperCAmelCase_ :
"""simple docstring"""
def __init__( self , _a , _a , _a ) -> List[str]:
_a : List[Any] = name
_a : List[str] = value
_a : List[str] = weight
def __repr__( self ) -> Optional[int]:
return F"""{self.__class__.__name__}({self.name}, {self.value}, {self.weight})"""
def __lowercase ( self ) -> List[Any]:
return self.value
def __lowercase ( self ) -> int:
return self.name
def __lowercase ( self ) -> Optional[int]:
return self.weight
def __lowercase ( self ) -> Optional[Any]:
return self.value / self.weight
def __UpperCAmelCase ( __a : Optional[int] ,__a : Tuple ,__a : List[str] ) -> List[str]:
"""simple docstring"""
_a : Optional[int] = []
for i in range(len(__a ) ):
menu.append(Things(name[i] ,value[i] ,weight[i] ) )
return menu
def __UpperCAmelCase ( __a : int ,__a : Union[str, Any] ,__a : int ) -> Union[str, Any]:
"""simple docstring"""
_a : Union[str, Any] = sorted(__a ,key=__a ,reverse=__a )
_a : Any = []
_a , _a : Optional[int] = 0.0, 0.0
for i in range(len(__a ) ):
if (total_cost + items_copy[i].get_weight()) <= max_cost:
result.append(items_copy[i] )
total_cost += items_copy[i].get_weight()
total_value += items_copy[i].get_value()
return (result, total_value)
def __UpperCAmelCase ( ) -> int:
"""simple docstring"""
if __name__ == "__main__":
import doctest
doctest.testmod()
| 14 | 0 |
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
_A = datasets.logging.get_logger(__name__)
_A = "\\n@InProceedings{moosavi2019minimum,\n author = { Nafise Sadat Moosavi, Leo Born, Massimo Poesio and Michael Strube},\n title = {Using Automatically Extracted Minimum Spans to Disentangle Coreference Evaluation from Boundary Detection},\n year = {2019},\n booktitle = {Proceedings of the 57th Annual Meeting of\n the Association for Computational Linguistics (Volume 1: Long Papers)},\n publisher = {Association for Computational Linguistics},\n address = {Florence, Italy},\n}\n\n@inproceedings{10.3115/1072399.1072405,\nauthor = {Vilain, Marc and Burger, John and Aberdeen, John and Connolly, Dennis and Hirschman, Lynette},\ntitle = {A Model-Theoretic Coreference Scoring Scheme},\nyear = {1995},\nisbn = {1558604022},\npublisher = {Association for Computational Linguistics},\naddress = {USA},\nurl = {https://doi.org/10.3115/1072399.1072405},\ndoi = {10.3115/1072399.1072405},\nbooktitle = {Proceedings of the 6th Conference on Message Understanding},\npages = {45–52},\nnumpages = {8},\nlocation = {Columbia, Maryland},\nseries = {MUC6 ’95}\n}\n\n@INPROCEEDINGS{Bagga98algorithmsfor,\n author = {Amit Bagga and Breck Baldwin},\n title = {Algorithms for Scoring Coreference Chains},\n booktitle = {In The First International Conference on Language Resources and Evaluation Workshop on Linguistics Coreference},\n year = {1998},\n pages = {563--566}\n}\n\n@INPROCEEDINGS{Luo05oncoreference,\n author = {Xiaoqiang Luo},\n title = {On coreference resolution performance metrics},\n booktitle = {In Proc. of HLT/EMNLP},\n year = {2005},\n pages = {25--32},\n publisher = {URL}\n}\n\n@inproceedings{moosavi-strube-2016-coreference,\n title = \"Which Coreference Evaluation Metric Do You Trust? A Proposal for a Link-based Entity Aware Metric\",\n author = \"Moosavi, Nafise Sadat and\n Strube, Michael\",\n booktitle = \"Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)\",\n month = aug,\n year = \"2016\",\n address = \"Berlin, Germany\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://www.aclweb.org/anthology/P16-1060\",\n doi = \"10.18653/v1/P16-1060\",\n pages = \"632--642\",\n}\n\n"
_A = "\\nCoVal is a coreference evaluation tool for the CoNLL and ARRAU datasets which\nimplements of the common evaluation metrics including MUC [Vilain et al, 1995],\nB-cubed [Bagga and Baldwin, 1998], CEAFe [Luo et al., 2005],\nLEA [Moosavi and Strube, 2016] and the averaged CoNLL score\n(the average of the F1 values of MUC, B-cubed and CEAFe)\n[Denis and Baldridge, 2009a; Pradhan et al., 2011].\n\nThis wrapper of CoVal currently only work with CoNLL line format:\nThe CoNLL format has one word per line with all the annotation for this word in column separated by spaces:\nColumn Type Description\n1 Document ID This is a variation on the document filename\n2 Part number Some files are divided into multiple parts numbered as 000, 001, 002, ... etc.\n3 Word number\n4 Word itself This is the token as segmented/tokenized in the Treebank. Initially the *_skel file contain the placeholder [WORD] which gets replaced by the actual token from the Treebank which is part of the OntoNotes release.\n5 Part-of-Speech\n6 Parse bit This is the bracketed structure broken before the first open parenthesis in the parse, and the word/part-of-speech leaf replaced with a *. The full parse can be created by substituting the asterix with the \"([pos] [word])\" string (or leaf) and concatenating the items in the rows of that column.\n7 Predicate lemma The predicate lemma is mentioned for the rows for which we have semantic role information. All other rows are marked with a \"-\"\n8 Predicate Frameset ID This is the PropBank frameset ID of the predicate in Column 7.\n9 Word sense This is the word sense of the word in Column 3.\n10 Speaker/Author This is the speaker or author name where available. Mostly in Broadcast Conversation and Web Log data.\n11 Named Entities These columns identifies the spans representing various named entities.\n12:N Predicate Arguments There is one column each of predicate argument structure information for the predicate mentioned in Column 7.\nN Coreference Coreference chain information encoded in a parenthesis structure.\nMore informations on the format can be found here (section \"*_conll File Format\"): http://www.conll.cemantix.org/2012/data.html\n\nDetails on the evaluation on CoNLL can be found here: https://github.com/ns-moosavi/coval/blob/master/conll/README.md\n\nCoVal code was written by @ns-moosavi.\nSome parts are borrowed from https://github.com/clarkkev/deep-coref/blob/master/evaluation.py\nThe test suite is taken from https://github.com/conll/reference-coreference-scorers/\nMention evaluation and the test suite are added by @andreasvc.\nParsing CoNLL files is developed by Leo Born.\n"
_A = "\nCalculates coreference evaluation metrics.\nArgs:\n predictions: list of sentences. Each sentence is a list of word predictions to score in the CoNLL format.\n Each prediction is a word with its annotations as a string made of columns joined with spaces.\n Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation)\n See the details on the format in the description of the metric.\n references: list of sentences. Each sentence is a list of word reference to score in the CoNLL format.\n Each reference is a word with its annotations as a string made of columns joined with spaces.\n Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation)\n See the details on the format in the description of the metric.\n keep_singletons: After extracting all mentions of key or system files,\n mentions whose corresponding coreference chain is of size one,\n are considered as singletons. The default evaluation mode will include\n singletons in evaluations if they are included in the key or the system files.\n By setting \'keep_singletons=False\', all singletons in the key and system files\n will be excluded from the evaluation.\n NP_only: Most of the recent coreference resolvers only resolve NP mentions and\n leave out the resolution of VPs. By setting the \'NP_only\' option, the scorer will only evaluate the resolution of NPs.\n min_span: By setting \'min_span\', the scorer reports the results based on automatically detected minimum spans.\n Minimum spans are determined using the MINA algorithm.\n\nReturns:\n \'mentions\': mentions\n \'muc\': MUC metric [Vilain et al, 1995]\n \'bcub\': B-cubed [Bagga and Baldwin, 1998]\n \'ceafe\': CEAFe [Luo et al., 2005]\n \'lea\': LEA [Moosavi and Strube, 2016]\n \'conll_score\': averaged CoNLL score (the average of the F1 values of MUC, B-cubed and CEAFe)\n\nExamples:\n\n >>> coval = datasets.load_metric(\'coval\')\n >>> words = [\'bc/cctv/00/cctv_0005 0 0 Thank VBP (TOP(S(VP* thank 01 1 Xu_li * (V*) * -\',\n ... \'bc/cctv/00/cctv_0005 0 1 you PRP (NP*) - - - Xu_li * (ARG1*) (ARG0*) (116)\',\n ... \'bc/cctv/00/cctv_0005 0 2 everyone NN (NP*) - - - Xu_li * (ARGM-DIS*) * (116)\',\n ... \'bc/cctv/00/cctv_0005 0 3 for IN (PP* - - - Xu_li * (ARG2* * -\',\n ... \'bc/cctv/00/cctv_0005 0 4 watching VBG (S(VP*)))) watch 01 1 Xu_li * *) (V*) -\',\n ... \'bc/cctv/00/cctv_0005 0 5 . . *)) - - - Xu_li * * * -\']\n >>> references = [words]\n >>> predictions = [words]\n >>> results = coval.compute(predictions=predictions, references=references)\n >>> print(results) # doctest:+ELLIPSIS\n {\'mentions/recall\': 1.0,[...] \'conll_score\': 100.0}\n"
def lowerCamelCase__ ( __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Tuple , __lowerCAmelCase : str=False , __lowerCAmelCase : Dict=False , __lowerCAmelCase : Any=True , __lowerCAmelCase : int=False , __lowerCAmelCase : Union[str, Any]="dummy_doc" ):
"""simple docstring"""
lowerCAmelCase_ = {doc: key_lines}
lowerCAmelCase_ = {doc: sys_lines}
lowerCAmelCase_ = {}
lowerCAmelCase_ = 0
lowerCAmelCase_ = 0
lowerCAmelCase_ = 0
lowerCAmelCase_ = 0
lowerCAmelCase_ = 0
lowerCAmelCase_ = 0
lowerCAmelCase_ = reader.get_doc_mentions(__a , key_doc_lines[doc] , __a )
key_singletons_num += singletons_num
if NP_only or min_span:
lowerCAmelCase_ = reader.set_annotated_parse_trees(__a , key_doc_lines[doc] , __a , __a )
lowerCAmelCase_ = reader.get_doc_mentions(__a , sys_doc_lines[doc] , __a )
sys_singletons_num += singletons_num
if NP_only or min_span:
lowerCAmelCase_ = reader.set_annotated_parse_trees(__a , key_doc_lines[doc] , __a , __a )
if remove_nested:
lowerCAmelCase_ = reader.remove_nested_coref_mentions(__a , __a )
key_nested_coref_num += nested_mentions
key_removed_nested_clusters += removed_clusters
lowerCAmelCase_ = reader.remove_nested_coref_mentions(__a , __a )
sys_nested_coref_num += nested_mentions
sys_removed_nested_clusters += removed_clusters
lowerCAmelCase_ = reader.get_mention_assignments(__a , __a )
lowerCAmelCase_ = reader.get_mention_assignments(__a , __a )
lowerCAmelCase_ = (key_clusters, sys_clusters, key_mention_sys_cluster, sys_mention_key_cluster)
if remove_nested:
logger.info(
"Number of removed nested coreferring mentions in the key "
F"""annotation: {key_nested_coref_num}; and system annotation: {sys_nested_coref_num}""" )
logger.info(
"Number of resulting singleton clusters in the key "
F"""annotation: {key_removed_nested_clusters}; and system annotation: {sys_removed_nested_clusters}""" )
if not keep_singletons:
logger.info(
F"""{key_singletons_num:d} and {sys_singletons_num:d} singletons are removed from the key and system """
"files, respectively" )
return doc_coref_infos
def lowerCamelCase__ ( __lowerCAmelCase : Any , __lowerCAmelCase : List[str] , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Any , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : List[str] , __lowerCAmelCase : int ):
"""simple docstring"""
lowerCAmelCase_ = get_coref_infos(__a , __a , __a , __a , __a , __a )
lowerCAmelCase_ = {}
lowerCAmelCase_ = 0
lowerCAmelCase_ = 0
for name, metric in metrics:
lowerCAmelCase_ = evaluator.evaluate_documents(__a , __a , beta=1 )
if name in ["muc", "bcub", "ceafe"]:
conll += fa
conll_subparts_num += 1
output_scores.update({F"""{name}/recall""": recall, F"""{name}/precision""": precision, F"""{name}/f1""": fa} )
logger.info(
name.ljust(10 ) , F"""Recall: {recall * 100:.2f}""" , F""" Precision: {precision * 100:.2f}""" , F""" F1: {fa * 100:.2f}""" , )
if conll_subparts_num == 3:
lowerCAmelCase_ = (conll / 3) * 100
logger.info(F"""CoNLL score: {conll:.2f}""" )
output_scores.update({"conll_score": conll} )
return output_scores
def lowerCamelCase__ ( __lowerCAmelCase : int ):
"""simple docstring"""
lowerCAmelCase_ = False
for line in key_lines:
if not line.startswith("#" ):
if len(line.split() ) > 6:
lowerCAmelCase_ = line.split()[5]
if not parse_col == "-":
lowerCAmelCase_ = True
break
else:
break
return has_gold_parse
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class _lowerCAmelCase ( datasets.Metric ):
def __a ( self ) -> List[Any]:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"predictions": datasets.Sequence(datasets.Value("string" ) ),
"references": datasets.Sequence(datasets.Value("string" ) ),
} ) , codebase_urls=["https://github.com/ns-moosavi/coval"] , reference_urls=[
"https://github.com/ns-moosavi/coval",
"https://www.aclweb.org/anthology/P16-1060",
"http://www.conll.cemantix.org/2012/data.html",
] , )
def __a ( self , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase=True , _UpperCamelCase=False , _UpperCamelCase=False , _UpperCamelCase=False ) -> Any:
lowerCAmelCase_ = [
('''mentions''', evaluator.mentions),
('''muc''', evaluator.muc),
('''bcub''', evaluator.b_cubed),
('''ceafe''', evaluator.ceafe),
('''lea''', evaluator.lea),
]
if min_span:
lowerCAmelCase_ = util.check_gold_parse_annotation(_a )
if not has_gold_parse:
raise NotImplementedError("References should have gold parse annotation to use \'min_span\'." )
# util.parse_key_file(key_file)
# key_file = key_file + ".parsed"
lowerCAmelCase_ = evaluate(
key_lines=_a , sys_lines=_a , metrics=_a , NP_only=_a , remove_nested=_a , keep_singletons=_a , min_span=_a , )
return score
| 290 |
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import SwiftFormerForImageClassification, SwiftFormerModel
from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class UpperCAmelCase_ :
"""simple docstring"""
def __init__( self , _a , _a=1_3 , _a=3 , _a=True , _a=True , _a=0.1 , _a=0.1 , _a=2_2_4 , _a=1_0_0_0 , _a=[3, 3, 6, 4] , _a=[4_8, 5_6, 1_1_2, 2_2_0] , ) -> Tuple:
_a : Dict = parent
_a : Optional[int] = batch_size
_a : Optional[Any] = num_channels
_a : Union[str, Any] = is_training
_a : Tuple = use_labels
_a : Dict = hidden_dropout_prob
_a : List[Any] = attention_probs_dropout_prob
_a : Dict = num_labels
_a : List[str] = image_size
_a : Dict = layer_depths
_a : str = embed_dims
def __lowercase ( self ) -> Optional[Any]:
_a : Dict = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_a : int = None
if self.use_labels:
_a : Optional[Any] = ids_tensor([self.batch_size] , self.num_labels )
_a : Dict = self.get_config()
return config, pixel_values, labels
def __lowercase ( self ) -> int:
return SwiftFormerConfig(
depths=self.layer_depths , embed_dims=self.embed_dims , mlp_ratio=4 , downsamples=[True, True, True, True] , hidden_act='''gelu''' , num_labels=self.num_labels , down_patch_size=3 , down_stride=2 , down_pad=1 , drop_rate=0.0 , drop_path_rate=0.0 , use_layer_scale=_a , layer_scale_init_value=1e-5 , )
def __lowercase ( self , _a , _a , _a ) -> str:
_a : List[Any] = SwiftFormerModel(config=_a )
model.to(_a )
model.eval()
_a : Optional[int] = model(_a )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.embed_dims[-1], 7, 7) )
def __lowercase ( self , _a , _a , _a ) -> Optional[Any]:
_a : List[str] = self.num_labels
_a : Optional[int] = SwiftFormerForImageClassification(_a )
model.to(_a )
model.eval()
_a : List[str] = model(_a , labels=_a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
_a : Union[str, Any] = SwiftFormerForImageClassification(_a )
model.to(_a )
model.eval()
_a : Dict = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_a : Optional[Any] = model(_a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __lowercase ( self ) -> Tuple:
((_a) , (_a) , (_a)) : Optional[int] = self.prepare_config_and_inputs()
_a : List[Any] = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class UpperCAmelCase_ ( __lowercase , __lowercase , unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase__ : List[str] = (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
UpperCAmelCase__ : Optional[int] = (
{"feature-extraction": SwiftFormerModel, "image-classification": SwiftFormerForImageClassification}
if is_torch_available()
else {}
)
UpperCAmelCase__ : Optional[Any] = False
UpperCAmelCase__ : str = False
UpperCAmelCase__ : Tuple = False
UpperCAmelCase__ : Tuple = False
UpperCAmelCase__ : str = False
def __lowercase ( self ) -> Optional[int]:
_a : Union[str, Any] = SwiftFormerModelTester(self )
_a : int = ConfigTester(
self , config_class=_a , has_text_modality=_a , hidden_size=3_7 , num_attention_heads=1_2 , num_hidden_layers=1_2 , )
def __lowercase ( self ) -> int:
self.config_tester.run_common_tests()
@unittest.skip(reason='''SwiftFormer does not use inputs_embeds''' )
def __lowercase ( self ) -> Union[str, Any]:
pass
def __lowercase ( self ) -> Dict:
_a , _a : int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_a : Any = model_class(_a )
_a : int = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(_a , nn.Linear ) )
def __lowercase ( self ) -> str:
_a , _a : str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_a : Optional[int] = model_class(_a )
_a : Tuple = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_a : Tuple = [*signature.parameters.keys()]
_a : List[str] = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , _a )
def __lowercase ( self ) -> int:
_a : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_a )
def __lowercase ( self ) -> Optional[int]:
_a : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_a )
@slow
def __lowercase ( self ) -> Optional[Any]:
for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_a : Any = SwiftFormerModel.from_pretrained(_a )
self.assertIsNotNone(_a )
@unittest.skip(reason='''SwiftFormer does not output attentions''' )
def __lowercase ( self ) -> List[Any]:
pass
def __lowercase ( self ) -> int:
def check_hidden_states_output(_a , _a , _a ):
_a : Optional[int] = model_class(_a )
model.to(_a )
model.eval()
with torch.no_grad():
_a : Union[str, Any] = model(**self._prepare_for_class(_a , _a ) )
_a : Optional[Any] = outputs.hidden_states
_a : Union[str, Any] = 8
self.assertEqual(len(_a ) , _a ) # TODO
# SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
# with the width and height being successively divided by 2, after every 2 blocks
for i in range(len(_a ) ):
self.assertEqual(
hidden_states[i].shape , torch.Size(
[
self.model_tester.batch_size,
self.model_tester.embed_dims[i // 2],
(self.model_tester.image_size // 4) // 2 ** (i // 2),
(self.model_tester.image_size // 4) // 2 ** (i // 2),
] ) , )
_a , _a : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_a : str = True
check_hidden_states_output(_a , _a , _a )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_a : List[str] = True
check_hidden_states_output(_a , _a , _a )
def __lowercase ( self ) -> str:
def _config_zero_init(_a ):
_a : List[Any] = copy.deepcopy(_a )
for key in configs_no_init.__dict__.keys():
if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
setattr(_a , _a , 1e-1_0 )
if isinstance(getattr(_a , _a , _a ) , _a ):
_a : int = _config_zero_init(getattr(_a , _a ) )
setattr(_a , _a , _a )
return configs_no_init
_a , _a : Any = self.model_tester.prepare_config_and_inputs_for_common()
_a : Dict = _config_zero_init(_a )
for model_class in self.all_model_classes:
_a : Dict = model_class(config=_a )
for name, param in model.named_parameters():
if param.requires_grad:
self.assertIn(
((param.data.mean() * 1e9) / 1e9).round().item() , [0.0, 1.0] , msg=F"""Parameter {name} of model {model_class} seems not properly initialized""" , )
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def __lowercase ( self ) -> Optional[Any]:
pass
def __UpperCAmelCase ( ) -> Optional[Any]:
"""simple docstring"""
_a : Optional[int] = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class UpperCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def __lowercase ( self ) -> str:
return ViTImageProcessor.from_pretrained('''MBZUAI/swiftformer-xs''' ) if is_vision_available() else None
@slow
def __lowercase ( self ) -> Dict:
_a : Any = SwiftFormerForImageClassification.from_pretrained('''MBZUAI/swiftformer-xs''' ).to(_a )
_a : Any = self.default_image_processor
_a : Any = prepare_img()
_a : Any = image_processor(images=_a , return_tensors='''pt''' ).to(_a )
# forward pass
with torch.no_grad():
_a : Optional[Any] = model(**_a )
# verify the logits
_a : List[str] = torch.Size((1, 1_0_0_0) )
self.assertEqual(outputs.logits.shape , _a )
_a : int = torch.tensor([[-2.1_7_0_3e0_0, 2.1_1_0_7e0_0, -2.0_8_1_1e0_0]] ).to(_a )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , _a , atol=1e-4 ) )
| 14 | 0 |
'''simple docstring'''
import shutil
import tempfile
import unittest
from unittest.mock import patch
from transformers import (
DefaultFlowCallback,
IntervalStrategy,
PrinterCallback,
ProgressCallback,
Trainer,
TrainerCallback,
TrainingArguments,
is_torch_available,
)
from transformers.testing_utils import require_torch
if is_torch_available():
from transformers.trainer import DEFAULT_CALLBACKS
from .test_trainer import RegressionDataset, RegressionModelConfig, RegressionPreTrainedModel
class _snake_case (__lowercase):
def __init__( self ):
UpperCAmelCase_ : Tuple = []
def UpperCamelCase__ ( self ,_snake_case ,_snake_case ,_snake_case ,**_snake_case ):
self.events.append("on_init_end" )
def UpperCamelCase__ ( self ,_snake_case ,_snake_case ,_snake_case ,**_snake_case ):
self.events.append("on_train_begin" )
def UpperCamelCase__ ( self ,_snake_case ,_snake_case ,_snake_case ,**_snake_case ):
self.events.append("on_train_end" )
def UpperCamelCase__ ( self ,_snake_case ,_snake_case ,_snake_case ,**_snake_case ):
self.events.append("on_epoch_begin" )
def UpperCamelCase__ ( self ,_snake_case ,_snake_case ,_snake_case ,**_snake_case ):
self.events.append("on_epoch_end" )
def UpperCamelCase__ ( self ,_snake_case ,_snake_case ,_snake_case ,**_snake_case ):
self.events.append("on_step_begin" )
def UpperCamelCase__ ( self ,_snake_case ,_snake_case ,_snake_case ,**_snake_case ):
self.events.append("on_step_end" )
def UpperCamelCase__ ( self ,_snake_case ,_snake_case ,_snake_case ,**_snake_case ):
self.events.append("on_evaluate" )
def UpperCamelCase__ ( self ,_snake_case ,_snake_case ,_snake_case ,**_snake_case ):
self.events.append("on_predict" )
def UpperCamelCase__ ( self ,_snake_case ,_snake_case ,_snake_case ,**_snake_case ):
self.events.append("on_save" )
def UpperCamelCase__ ( self ,_snake_case ,_snake_case ,_snake_case ,**_snake_case ):
self.events.append("on_log" )
def UpperCamelCase__ ( self ,_snake_case ,_snake_case ,_snake_case ,**_snake_case ):
self.events.append("on_prediction_step" )
@require_torch
class _snake_case (unittest.TestCase):
def UpperCamelCase__ ( self ):
UpperCAmelCase_ : str = tempfile.mkdtemp()
def UpperCamelCase__ ( self ):
shutil.rmtree(self.output_dir )
def UpperCamelCase__ ( self ,_snake_case=0 ,_snake_case=0 ,_snake_case=64 ,_snake_case=64 ,_snake_case=None ,_snake_case=False ,**_snake_case ):
# disable_tqdm in TrainingArguments has a flaky default since it depends on the level of logging. We make sure
# its set to False since the tests later on depend on its value.
UpperCAmelCase_ : List[Any] = RegressionDataset(length=_a )
UpperCAmelCase_ : Any = RegressionDataset(length=_a )
UpperCAmelCase_ : List[Any] = RegressionModelConfig(a=_a ,b=_a )
UpperCAmelCase_ : str = RegressionPreTrainedModel(_a )
UpperCAmelCase_ : List[str] = TrainingArguments(self.output_dir ,disable_tqdm=_a ,report_to=[] ,**_a )
return Trainer(
_a ,_a ,train_dataset=_a ,eval_dataset=_a ,callbacks=_a ,)
def UpperCamelCase__ ( self ,_snake_case ,_snake_case ):
self.assertEqual(len(_a ) ,len(_a ) )
# Order doesn't matter
UpperCAmelCase_ : Optional[Any] = sorted(_a ,key=lambda _snake_case : cb.__name__ if isinstance(_a ,_a ) else cb.__class__.__name__ )
UpperCAmelCase_ : Union[str, Any] = sorted(_a ,key=lambda _snake_case : cb.__name__ if isinstance(_a ,_a ) else cb.__class__.__name__ )
for cba, cba in zip(_a ,_a ):
if isinstance(_a ,_a ) and isinstance(_a ,_a ):
self.assertEqual(_a ,_a )
elif isinstance(_a ,_a ) and not isinstance(_a ,_a ):
self.assertEqual(_a ,cba.__class__ )
elif not isinstance(_a ,_a ) and isinstance(_a ,_a ):
self.assertEqual(cba.__class__ ,_a )
else:
self.assertEqual(_a ,_a )
def UpperCamelCase__ ( self ,_snake_case ):
UpperCAmelCase_ : Optional[Any] = ['''on_init_end''', '''on_train_begin''']
UpperCAmelCase_ : str = 0
UpperCAmelCase_ : Dict = len(trainer.get_eval_dataloader() )
UpperCAmelCase_ : List[str] = ['''on_prediction_step'''] * len(trainer.get_eval_dataloader() ) + ['''on_log''', '''on_evaluate''']
for _ in range(trainer.state.num_train_epochs ):
expected_events.append("on_epoch_begin" )
for _ in range(_a ):
step += 1
expected_events += ["on_step_begin", "on_step_end"]
if step % trainer.args.logging_steps == 0:
expected_events.append("on_log" )
if trainer.args.evaluation_strategy == IntervalStrategy.STEPS and step % trainer.args.eval_steps == 0:
expected_events += evaluation_events.copy()
if step % trainer.args.save_steps == 0:
expected_events.append("on_save" )
expected_events.append("on_epoch_end" )
if trainer.args.evaluation_strategy == IntervalStrategy.EPOCH:
expected_events += evaluation_events.copy()
expected_events += ["on_log", "on_train_end"]
return expected_events
def UpperCamelCase__ ( self ):
UpperCAmelCase_ : Dict = self.get_trainer()
UpperCAmelCase_ : str = DEFAULT_CALLBACKS.copy() + [ProgressCallback]
self.check_callbacks_equality(trainer.callback_handler.callbacks ,_a )
# Callbacks passed at init are added to the default callbacks
UpperCAmelCase_ : List[Any] = self.get_trainer(callbacks=[MyTestTrainerCallback] )
expected_callbacks.append(_a )
self.check_callbacks_equality(trainer.callback_handler.callbacks ,_a )
# TrainingArguments.disable_tqdm controls if use ProgressCallback or PrinterCallback
UpperCAmelCase_ : List[str] = self.get_trainer(disable_tqdm=_a )
UpperCAmelCase_ : Optional[Any] = DEFAULT_CALLBACKS.copy() + [PrinterCallback]
self.check_callbacks_equality(trainer.callback_handler.callbacks ,_a )
def UpperCamelCase__ ( self ):
UpperCAmelCase_ : List[str] = DEFAULT_CALLBACKS.copy() + [ProgressCallback]
UpperCAmelCase_ : int = self.get_trainer()
# We can add, pop, or remove by class name
trainer.remove_callback(_a )
expected_callbacks.remove(_a )
self.check_callbacks_equality(trainer.callback_handler.callbacks ,_a )
UpperCAmelCase_ : str = self.get_trainer()
UpperCAmelCase_ : Union[str, Any] = trainer.pop_callback(_a )
self.assertEqual(cb.__class__ ,_a )
self.check_callbacks_equality(trainer.callback_handler.callbacks ,_a )
trainer.add_callback(_a )
expected_callbacks.insert(0 ,_a )
self.check_callbacks_equality(trainer.callback_handler.callbacks ,_a )
# We can also add, pop, or remove by instance
UpperCAmelCase_ : List[Any] = self.get_trainer()
UpperCAmelCase_ : Union[str, Any] = trainer.callback_handler.callbacks[0]
trainer.remove_callback(_a )
expected_callbacks.remove(_a )
self.check_callbacks_equality(trainer.callback_handler.callbacks ,_a )
UpperCAmelCase_ : Tuple = self.get_trainer()
UpperCAmelCase_ : List[Any] = trainer.callback_handler.callbacks[0]
UpperCAmelCase_ : Union[str, Any] = trainer.pop_callback(_a )
self.assertEqual(_a ,_a )
self.check_callbacks_equality(trainer.callback_handler.callbacks ,_a )
trainer.add_callback(_a )
expected_callbacks.insert(0 ,_a )
self.check_callbacks_equality(trainer.callback_handler.callbacks ,_a )
def UpperCamelCase__ ( self ):
import warnings
# XXX: for now ignore scatter_gather warnings in this test since it's not relevant to what's being tested
warnings.simplefilter(action="ignore" ,category=_a )
UpperCAmelCase_ : Union[str, Any] = self.get_trainer(callbacks=[MyTestTrainerCallback] )
trainer.train()
UpperCAmelCase_ : Dict = trainer.callback_handler.callbacks[-2].events
self.assertEqual(_a ,self.get_expected_events(_a ) )
# Independent log/save/eval
UpperCAmelCase_ : Optional[int] = self.get_trainer(callbacks=[MyTestTrainerCallback] ,logging_steps=5 )
trainer.train()
UpperCAmelCase_ : Any = trainer.callback_handler.callbacks[-2].events
self.assertEqual(_a ,self.get_expected_events(_a ) )
UpperCAmelCase_ : Optional[int] = self.get_trainer(callbacks=[MyTestTrainerCallback] ,save_steps=5 )
trainer.train()
UpperCAmelCase_ : Tuple = trainer.callback_handler.callbacks[-2].events
self.assertEqual(_a ,self.get_expected_events(_a ) )
UpperCAmelCase_ : Tuple = self.get_trainer(callbacks=[MyTestTrainerCallback] ,eval_steps=5 ,evaluation_strategy="steps" )
trainer.train()
UpperCAmelCase_ : Union[str, Any] = trainer.callback_handler.callbacks[-2].events
self.assertEqual(_a ,self.get_expected_events(_a ) )
UpperCAmelCase_ : Optional[Any] = self.get_trainer(callbacks=[MyTestTrainerCallback] ,evaluation_strategy="epoch" )
trainer.train()
UpperCAmelCase_ : Any = trainer.callback_handler.callbacks[-2].events
self.assertEqual(_a ,self.get_expected_events(_a ) )
# A bit of everything
UpperCAmelCase_ : Optional[Any] = self.get_trainer(
callbacks=[MyTestTrainerCallback] ,logging_steps=3 ,save_steps=10 ,eval_steps=5 ,evaluation_strategy="steps" ,)
trainer.train()
UpperCAmelCase_ : str = trainer.callback_handler.callbacks[-2].events
self.assertEqual(_a ,self.get_expected_events(_a ) )
# warning should be emitted for duplicated callbacks
with patch("transformers.trainer_callback.logger.warning" ) as warn_mock:
UpperCAmelCase_ : int = self.get_trainer(
callbacks=[MyTestTrainerCallback, MyTestTrainerCallback] ,)
assert str(_a ) in warn_mock.call_args[0][0]
| 71 |
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging
logging.set_verbosity_info()
a__ = logging.get_logger(__name__)
def __UpperCAmelCase ( __a : str ) -> List[Any]:
"""simple docstring"""
_a : Tuple = SwinConfig.from_pretrained(
'''microsoft/swin-tiny-patch4-window7-224''' ,out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4'''] )
_a : Dict = MaskFormerConfig(backbone_config=__a )
_a : Optional[Any] = '''huggingface/label-files'''
if "ade20k-full" in model_name:
# this should be ok
_a : Optional[Any] = 847
_a : List[Any] = '''maskformer-ade20k-full-id2label.json'''
elif "ade" in model_name:
# this should be ok
_a : Union[str, Any] = 150
_a : Any = '''ade20k-id2label.json'''
elif "coco-stuff" in model_name:
# this should be ok
_a : int = 171
_a : List[str] = '''maskformer-coco-stuff-id2label.json'''
elif "coco" in model_name:
# TODO
_a : Dict = 133
_a : Optional[Any] = '''coco-panoptic-id2label.json'''
elif "cityscapes" in model_name:
# this should be ok
_a : List[Any] = 19
_a : Optional[Any] = '''cityscapes-id2label.json'''
elif "vistas" in model_name:
# this should be ok
_a : List[Any] = 65
_a : Dict = '''mapillary-vistas-id2label.json'''
_a : Optional[int] = json.load(open(hf_hub_download(__a ,__a ,repo_type='''dataset''' ) ,'''r''' ) )
_a : Tuple = {int(__a ): v for k, v in idalabel.items()}
return config
def __UpperCAmelCase ( __a : Optional[Any] ) -> Tuple:
"""simple docstring"""
_a : Optional[Any] = []
# stem
# fmt: off
rename_keys.append(('''backbone.patch_embed.proj.weight''', '''model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight''') )
rename_keys.append(('''backbone.patch_embed.proj.bias''', '''model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias''') )
rename_keys.append(('''backbone.patch_embed.norm.weight''', '''model.pixel_level_module.encoder.model.embeddings.norm.weight''') )
rename_keys.append(('''backbone.patch_embed.norm.bias''', '''model.pixel_level_module.encoder.model.embeddings.norm.bias''') )
# stages
for i in range(len(config.backbone_config.depths ) ):
for j in range(config.backbone_config.depths[i] ):
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm1.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm1.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.relative_position_index""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.proj.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.proj.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm2.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm2.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc1.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc1.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc2.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc2.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias""") )
if i < 3:
rename_keys.append((F"""backbone.layers.{i}.downsample.reduction.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight""") )
rename_keys.append((F"""backbone.layers.{i}.downsample.norm.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight""") )
rename_keys.append((F"""backbone.layers.{i}.downsample.norm.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias""") )
rename_keys.append((F"""backbone.norm{i}.weight""", F"""model.pixel_level_module.encoder.hidden_states_norms.{i}.weight""") )
rename_keys.append((F"""backbone.norm{i}.bias""", F"""model.pixel_level_module.encoder.hidden_states_norms.{i}.bias""") )
# FPN
rename_keys.append(('''sem_seg_head.layer_4.weight''', '''model.pixel_level_module.decoder.fpn.stem.0.weight''') )
rename_keys.append(('''sem_seg_head.layer_4.norm.weight''', '''model.pixel_level_module.decoder.fpn.stem.1.weight''') )
rename_keys.append(('''sem_seg_head.layer_4.norm.bias''', '''model.pixel_level_module.decoder.fpn.stem.1.bias''') )
for source_index, target_index in zip(range(3 ,0 ,-1 ) ,range(0 ,3 ) ):
rename_keys.append((F"""sem_seg_head.adapter_{source_index}.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight""") )
rename_keys.append((F"""sem_seg_head.adapter_{source_index}.norm.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight""") )
rename_keys.append((F"""sem_seg_head.adapter_{source_index}.norm.bias""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias""") )
rename_keys.append((F"""sem_seg_head.layer_{source_index}.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight""") )
rename_keys.append((F"""sem_seg_head.layer_{source_index}.norm.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight""") )
rename_keys.append((F"""sem_seg_head.layer_{source_index}.norm.bias""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias""") )
rename_keys.append(('''sem_seg_head.mask_features.weight''', '''model.pixel_level_module.decoder.mask_projection.weight''') )
rename_keys.append(('''sem_seg_head.mask_features.bias''', '''model.pixel_level_module.decoder.mask_projection.bias''') )
# Transformer decoder
for idx in range(config.decoder_config.decoder_layers ):
# self-attention out projection
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight""", F"""model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias""", F"""model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias""") )
# cross-attention out projection
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias""") )
# MLP 1
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight""", F"""model.transformer_module.decoder.layers.{idx}.fc1.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias""", F"""model.transformer_module.decoder.layers.{idx}.fc1.bias""") )
# MLP 2
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight""", F"""model.transformer_module.decoder.layers.{idx}.fc2.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias""", F"""model.transformer_module.decoder.layers.{idx}.fc2.bias""") )
# layernorm 1 (self-attention layernorm)
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight""", F"""model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias""", F"""model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias""") )
# layernorm 2 (cross-attention layernorm)
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias""") )
# layernorm 3 (final layernorm)
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight""", F"""model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias""", F"""model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias""") )
rename_keys.append(('''sem_seg_head.predictor.transformer.decoder.norm.weight''', '''model.transformer_module.decoder.layernorm.weight''') )
rename_keys.append(('''sem_seg_head.predictor.transformer.decoder.norm.bias''', '''model.transformer_module.decoder.layernorm.bias''') )
# heads on top
rename_keys.append(('''sem_seg_head.predictor.query_embed.weight''', '''model.transformer_module.queries_embedder.weight''') )
rename_keys.append(('''sem_seg_head.predictor.input_proj.weight''', '''model.transformer_module.input_projection.weight''') )
rename_keys.append(('''sem_seg_head.predictor.input_proj.bias''', '''model.transformer_module.input_projection.bias''') )
rename_keys.append(('''sem_seg_head.predictor.class_embed.weight''', '''class_predictor.weight''') )
rename_keys.append(('''sem_seg_head.predictor.class_embed.bias''', '''class_predictor.bias''') )
for i in range(3 ):
rename_keys.append((F"""sem_seg_head.predictor.mask_embed.layers.{i}.weight""", F"""mask_embedder.{i}.0.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.mask_embed.layers.{i}.bias""", F"""mask_embedder.{i}.0.bias""") )
# fmt: on
return rename_keys
def __UpperCAmelCase ( __a : List[str] ,__a : List[Any] ,__a : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
_a : str = dct.pop(__a )
_a : str = val
def __UpperCAmelCase ( __a : List[Any] ,__a : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
_a : Union[str, Any] = [int(backbone_config.embed_dim * 2**i ) for i in range(len(backbone_config.depths ) )]
for i in range(len(backbone_config.depths ) ):
_a : Optional[Any] = num_features[i]
for j in range(backbone_config.depths[i] ):
# fmt: off
# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
_a : List[Any] = state_dict.pop(F"""backbone.layers.{i}.blocks.{j}.attn.qkv.weight""" )
_a : Optional[int] = state_dict.pop(F"""backbone.layers.{i}.blocks.{j}.attn.qkv.bias""" )
# next, add query, keys and values (in that order) to the state dict
_a : Optional[int] = in_proj_weight[:dim, :]
_a : List[Any] = in_proj_bias[: dim]
_a : Optional[int] = in_proj_weight[
dim : dim * 2, :
]
_a : Tuple = in_proj_bias[
dim : dim * 2
]
_a : int = in_proj_weight[
-dim :, :
]
_a : Optional[int] = in_proj_bias[-dim :]
# fmt: on
def __UpperCAmelCase ( __a : List[str] ,__a : List[Any] ) -> List[Any]:
"""simple docstring"""
_a : Optional[int] = config.decoder_config.hidden_size
for idx in range(config.decoder_config.decoder_layers ):
# read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
_a : Union[str, Any] = state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight""" )
_a : List[Any] = state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias""" )
# next, add query, keys and values (in that order) to the state dict
_a : Union[str, Any] = in_proj_weight[: hidden_size, :]
_a : List[Any] = in_proj_bias[:config.hidden_size]
_a : Dict = in_proj_weight[hidden_size : hidden_size * 2, :]
_a : Any = in_proj_bias[hidden_size : hidden_size * 2]
_a : Tuple = in_proj_weight[-hidden_size :, :]
_a : List[Any] = in_proj_bias[-hidden_size :]
# read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
_a : List[Any] = state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight""" )
_a : List[str] = state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias""" )
# next, add query, keys and values (in that order) to the state dict
_a : Optional[Any] = in_proj_weight[: hidden_size, :]
_a : Any = in_proj_bias[:config.hidden_size]
_a : List[str] = in_proj_weight[hidden_size : hidden_size * 2, :]
_a : Optional[Any] = in_proj_bias[hidden_size : hidden_size * 2]
_a : List[str] = in_proj_weight[-hidden_size :, :]
_a : int = in_proj_bias[-hidden_size :]
# fmt: on
def __UpperCAmelCase ( ) -> torch.Tensor:
"""simple docstring"""
_a : str = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
_a : Dict = Image.open(requests.get(__a ,stream=__a ).raw )
return im
@torch.no_grad()
def __UpperCAmelCase ( __a : str ,__a : str ,__a : str ,__a : bool = False ) -> Union[str, Any]:
"""simple docstring"""
_a : Optional[Any] = get_maskformer_config(__a )
# load original state_dict
with open(__a ,'''rb''' ) as f:
_a : str = pickle.load(__a )
_a : Union[str, Any] = data['''model''']
# for name, param in state_dict.items():
# print(name, param.shape)
# rename keys
_a : Any = create_rename_keys(__a )
for src, dest in rename_keys:
rename_key(__a ,__a ,__a )
read_in_swin_q_k_v(__a ,config.backbone_config )
read_in_decoder_q_k_v(__a ,__a )
# update to torch tensors
for key, value in state_dict.items():
_a : Optional[int] = torch.from_numpy(__a )
# load 🤗 model
_a : Dict = MaskFormerForInstanceSegmentation(__a )
model.eval()
for name, param in model.named_parameters():
print(__a ,param.shape )
_a , _a : Tuple = model.load_state_dict(__a ,strict=__a )
assert missing_keys == [
"model.pixel_level_module.encoder.model.layernorm.weight",
"model.pixel_level_module.encoder.model.layernorm.bias",
]
assert len(__a ) == 0, F"""Unexpected keys: {unexpected_keys}"""
# verify results
_a : Union[str, Any] = prepare_img()
if "vistas" in model_name:
_a : int = 65
elif "cityscapes" in model_name:
_a : Tuple = 65_535
else:
_a : str = 255
_a : Dict = True if '''ade''' in model_name else False
_a : Optional[Any] = MaskFormerImageProcessor(ignore_index=__a ,reduce_labels=__a )
_a : Optional[Any] = image_processor(__a ,return_tensors='''pt''' )
_a : int = model(**__a )
print('''Logits:''' ,outputs.class_queries_logits[0, :3, :3] )
if model_name == "maskformer-swin-tiny-ade":
_a : Union[str, Any] = torch.tensor(
[[3.63_53, -4.47_70, -2.60_65], [0.50_81, -4.23_94, -3.53_43], [2.19_09, -5.03_53, -1.93_23]] )
assert torch.allclose(outputs.class_queries_logits[0, :3, :3] ,__a ,atol=1E-4 )
print('''Looks ok!''' )
if pytorch_dump_folder_path is not None:
print(F"""Saving model and image processor to {pytorch_dump_folder_path}""" )
Path(__a ).mkdir(exist_ok=__a )
model.save_pretrained(__a )
image_processor.save_pretrained(__a )
if push_to_hub:
print('''Pushing model and image processor to the hub...''' )
model.push_to_hub(F"""nielsr/{model_name}""" )
image_processor.push_to_hub(F"""nielsr/{model_name}""" )
if __name__ == "__main__":
a__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--model_name''',
default='''maskformer-swin-tiny-ade''',
type=str,
help=('''Name of the MaskFormer model you\'d like to convert''',),
)
parser.add_argument(
'''--checkpoint_path''',
default='''/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl''',
type=str,
help='''Path to the original state dict (.pth file).''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
)
parser.add_argument(
'''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
)
a__ = parser.parse_args()
convert_maskformer_checkpoint(
args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
)
| 14 | 0 |
"""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 _a :
"""simple docstring"""
def __init__( self : Tuple , __UpperCamelCase : List[str] , __UpperCamelCase : int=9_9 , __UpperCamelCase : Tuple=1_3 , __UpperCamelCase : str=1_6 , __UpperCamelCase : int=7 , __UpperCamelCase : Tuple=True , __UpperCamelCase : Dict=True , __UpperCamelCase : Optional[int]=True , __UpperCamelCase : List[Any]=False , __UpperCamelCase : Tuple=True , __UpperCamelCase : Union[str, Any]=2 , __UpperCamelCase : List[Any]=3_2 , __UpperCamelCase : Any=4 , __UpperCamelCase : int=4 , __UpperCamelCase : str=3_0 , __UpperCamelCase : int=0 , __UpperCamelCase : Optional[Any]=1 , __UpperCamelCase : Optional[int]=2 , __UpperCamelCase : Any=None , )->Any:
_UpperCAmelCase = parent
_UpperCAmelCase = batch_size
_UpperCAmelCase = decoder_seq_length
# For common tests
_UpperCAmelCase = self.decoder_seq_length
_UpperCAmelCase = is_training
_UpperCAmelCase = use_attention_mask
_UpperCAmelCase = use_labels
_UpperCAmelCase = vocab_size
_UpperCAmelCase = d_model
_UpperCAmelCase = d_model
_UpperCAmelCase = decoder_layers
_UpperCAmelCase = decoder_layers
_UpperCAmelCase = decoder_ffn_dim
_UpperCAmelCase = decoder_attention_heads
_UpperCAmelCase = decoder_attention_heads
_UpperCAmelCase = eos_token_id
_UpperCAmelCase = bos_token_id
_UpperCAmelCase = pad_token_id
_UpperCAmelCase = decoder_start_token_id
_UpperCAmelCase = use_cache
_UpperCAmelCase = max_position_embeddings
_UpperCAmelCase = None
_UpperCAmelCase = decoder_seq_length
_UpperCAmelCase = 2
_UpperCAmelCase = 1
def lowercase__ ( self : Optional[Any] )->Dict:
_UpperCAmelCase = ids_tensor([self.batch_size, self.decoder_seq_length] , self.vocab_size )
_UpperCAmelCase = None
if self.use_attention_mask:
_UpperCAmelCase = ids_tensor([self.batch_size, self.decoder_seq_length] , vocab_size=2 )
_UpperCAmelCase = None
if self.use_labels:
_UpperCAmelCase = ids_tensor([self.batch_size, self.decoder_seq_length] , self.vocab_size )
_UpperCAmelCase = 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 lowercase__ ( self : int , __UpperCamelCase : List[str] , __UpperCamelCase : List[str] , __UpperCamelCase : int , __UpperCamelCase : str , )->List[Any]:
_UpperCAmelCase = True
_UpperCAmelCase = TrOCRDecoder(config=_a ).to(_a ).eval()
_UpperCAmelCase = input_ids[:2]
input_ids[input_ids == 0] += 1
# first forward pass
_UpperCAmelCase = model(_a , use_cache=_a )
_UpperCAmelCase = model(_a )
_UpperCAmelCase = model(_a , use_cache=_a )
self.parent.assertTrue(len(_a ) == len(_a ) )
self.parent.assertTrue(len(_a ) == len(_a ) + 1 )
_UpperCAmelCase = outputs['''past_key_values''']
# create hypothetical next token and extent to next_input_ids
_UpperCAmelCase = ids_tensor((2, 1) , config.vocab_size - 1 ) + 1
# append to next input_ids and
_UpperCAmelCase = torch.cat([input_ids, next_tokens] , dim=-1 )
_UpperCAmelCase = model(_a )['''last_hidden_state''']
_UpperCAmelCase = model(_a , past_key_values=_a )['''last_hidden_state''']
# select random slice
_UpperCAmelCase = ids_tensor((1,) , output_from_past.shape[-1] ).item()
_UpperCAmelCase = output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
_UpperCAmelCase = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
assert torch.allclose(_a , _a , atol=1e-3 )
def lowercase__ ( self : Optional[Any] )->Optional[Any]:
_UpperCAmelCase = self.prepare_config_and_inputs()
_UpperCAmelCase = config_and_inputs
_UpperCAmelCase = {'''input_ids''': input_ids, '''attention_mask''': attention_mask}
return config, inputs_dict
@require_torch
class _a ( __lowercase , __lowercase , __lowercase , unittest.TestCase):
"""simple docstring"""
UpperCamelCase__ = (TrOCRDecoder, TrOCRForCausalLM) if is_torch_available() else ()
UpperCamelCase__ = (TrOCRForCausalLM,) if is_torch_available() else ()
UpperCamelCase__ = {"text-generation": TrOCRForCausalLM} if is_torch_available() else {}
UpperCamelCase__ = True
UpperCamelCase__ = False
def lowercase__ ( self : str )->Tuple:
_UpperCAmelCase = TrOCRStandaloneDecoderModelTester(self , is_training=_a )
_UpperCAmelCase = ConfigTester(self , config_class=_a )
def lowercase__ ( self : Any )->Optional[Any]:
pass
def lowercase__ ( self : int )->Optional[Any]:
pass
def lowercase__ ( self : Any )->List[Any]:
pass
def lowercase__ ( self : Optional[Any] )->Union[str, Any]:
self.config_tester.run_common_tests()
def lowercase__ ( self : Optional[int] )->Union[str, Any]:
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past(*_a )
def lowercase__ ( self : Union[str, Any] )->List[str]:
return
@unittest.skip('''The model doesn\'t support left padding''' ) # and it's not used enough to be worth fixing :)
def lowercase__ ( self : List[Any] )->str:
pass
| 602 |
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
a__ = get_tests_dir('''fixtures/test_sentencepiece.model''')
@require_sentencepiece
class UpperCAmelCase_ ( __lowercase , unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase__ : List[str] = XLMProphetNetTokenizer
UpperCAmelCase__ : Optional[int] = False
UpperCAmelCase__ : List[Any] = True
def __lowercase ( self ) -> int:
super().setUp()
# We have a SentencePiece fixture for testing
_a : List[Any] = XLMProphetNetTokenizer(_a , keep_accents=_a )
tokenizer.save_pretrained(self.tmpdirname )
def __lowercase ( self ) -> Any:
_a : Tuple = '''[PAD]'''
_a : int = 0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(_a ) , _a )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(_a ) , _a )
def __lowercase ( self ) -> str:
_a : Any = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''[PAD]''' )
self.assertEqual(vocab_keys[1] , '''[CLS]''' )
self.assertEqual(vocab_keys[-1] , '''j''' )
self.assertEqual(len(_a ) , 1_0_1_2 )
def __lowercase ( self ) -> Union[str, Any]:
self.assertEqual(self.get_tokenizer().vocab_size , 1_0_1_2 )
def __lowercase ( self ) -> str:
_a : Tuple = XLMProphetNetTokenizer(_a , keep_accents=_a )
_a : Union[str, Any] = tokenizer.tokenize('''This is a test''' )
self.assertListEqual(_a , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(_a ) , [value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]] , )
_a : Optional[int] = tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
_a , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''9''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''é''',
'''.''',
] , )
_a : List[Any] = tokenizer.convert_tokens_to_ids(_a )
self.assertListEqual(
_a , [
value + tokenizer.fairseq_offset
for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, -9, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, -9, 4]
] , )
_a : List[str] = tokenizer.convert_ids_to_tokens(_a )
self.assertListEqual(
_a , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''[UNK]''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''[UNK]''',
'''.''',
] , )
@cached_property
def __lowercase ( self ) -> List[str]:
return XLMProphetNetTokenizer.from_pretrained('''microsoft/xprophetnet-large-wiki100-cased''' )
@slow
def __lowercase ( self ) -> Tuple:
_a : str = '''Hello World!'''
_a : Tuple = [3_5_3_8_9, 6_6_7_2, 4_9, 2]
self.assertListEqual(_a , self.big_tokenizer.encode(_a ) )
@slow
def __lowercase ( self ) -> str:
# fmt: off
_a : str = {'''input_ids''': [[1_1_0_7_3, 8_2_7_8_3, 1_8, 2_6, 8_2_7_8_3, 5_4_9, 5_1_5_4_0, 2_4_8, 1_7_2_0_9, 1_3_0_1, 2_1_7, 2_0, 2_1_5_1_8_6, 1_3_2_5, 1_4_7, 1_7_2_0_9, 1_3_0_1, 2_1_7, 2_0, 5_6_3_7_0, 5_3, 1_2_2_0_2_0, 2_0, 1_6_4_7_7, 2_7, 8_7_3_5_5, 4_5_4_8, 2_0, 4_7_2_8, 7_8_3_9_2, 1_7, 1_5_9_9_6_9, 1_8, 2_6, 2_4_4_9_1, 6_2_9, 1_5, 5_3_8, 2_2_7_0_4, 5_4_3_9, 1_5, 2_7_8_8, 2_4_4_9_1, 9_8_8_5, 1_5, 4_3_5_3_4, 6_0_5, 1_5, 8_1_4, 1_8_4_0_3, 3_3_2_0_0, 2_9, 1_5, 4_3_5_3_4, 2_4_4_5_8, 1_2_4_1_0, 1_1_1, 2_4_9_6_6, 8_3_6_6_9, 9_6_3_7, 1_4_4_0_6_8, 2_6, 8_5_0, 2_2_3_4_6, 2_7, 1_4_7, 2_4_9_6_6, 8_3_6_6_9, 8_3_4_9_0, 2_6, 3_9_1_1_3, 7_3_5, 2_7, 6_8_9, 6_5_6, 2_8_0_0, 1_3_3_9, 4_6_0_0, 5_3, 1_2_2_0_2_0, 1_1_5_7_8_5, 3_4, 8_1_6, 1_3_3_9, 4_6_8_8_7, 1_8, 1_4_7, 5_3_9_0_5, 1_9_5_1, 4_2_2_3_8, 4_1_1_7_0, 1_7_7_3_2, 8_3_4, 4_3_6, 1_5, 2_7_5_2_3, 9_8_7_3_3, 2_1_7, 1_4_7, 5_5_4_2, 4_9_8_1, 9_3_0, 1_7_3_4_7, 1_6, 2], [2_0_0_9_1, 6_2_9, 9_4, 8_2_7_8_6, 5_8, 4_9_0, 2_0, 1_5_2_8, 8_4, 5_3_9_0_5, 3_4_4, 8_0_5_9_2, 1_1_0_1_2_8, 1_8_8_2_2, 5_2_6_7, 1_3_0_6, 6_2, 1_5_2_5_3_7, 3_0_8, 7_9_9_7, 4_0_1, 1_2_4_4_2_7, 5_4_9, 3_5_4_4_2, 2_2_5, 1_0_9, 1_5_0_5_5, 2_5_7_4_8, 1_4_7, 7_1_1_9, 4_3_7_1_2, 3_4, 7_6_7, 1_3_5_3_6_6, 1_8, 1_6, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_9_2, 6_3_7_8_4, 1_1_9_4_6_6, 1_7, 1_4_7_8_0_8, 8_8_2_1_4, 1_8, 6_5_6, 8_1, 3_2, 3_2_9_6, 1_0_2_8_0, 1_6, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=_a , model_name='''microsoft/xprophetnet-large-wiki100-cased''' , revision='''1acad1643ddd54a44df6a1b797ada8373685d90e''' , )
| 14 | 0 |
"""simple docstring"""
def lowerCAmelCase_( lowercase_ : int ) -> int:
_lowerCamelCase = abs(__a )
_lowerCamelCase = 0
while n > 0:
res += n % 10
n //= 10
return res
def lowerCAmelCase_( lowercase_ : int ) -> int:
_lowerCamelCase = abs(__a )
return n if n < 10 else n % 10 + sum_of_digits(n // 10 )
def lowerCAmelCase_( lowercase_ : int ) -> int:
return sum(int(__a ) for c in str(abs(__a ) ) )
def lowerCAmelCase_( ) -> None:
from collections.abc import Callable
from timeit import timeit
def benchmark_a_function(lowercase_ : Callable , lowercase_ : int ) -> None:
_lowerCamelCase = F"""{func.__name__}({value})"""
_lowerCamelCase = timeit(F"""__main__.{call}""" , setup='''import __main__''' )
print(F"""{call:56} = {func(__a )} -- {timing:.4f} seconds""" )
for value in (26_21_44, 11_25_89_99_06_84_26_24, 1_26_76_50_60_02_28_22_94_01_49_67_03_20_53_76):
for func in (sum_of_digits, sum_of_digits_recursion, sum_of_digits_compact):
benchmark_a_function(__a , __a )
print()
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 661 |
import os
import unittest
from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class UpperCAmelCase_ ( __lowercase , unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase__ : Any = LxmertTokenizer
UpperCAmelCase__ : Optional[Any] = LxmertTokenizerFast
UpperCAmelCase__ : Any = True
UpperCAmelCase__ : Dict = True
def __lowercase ( self ) -> Union[str, Any]:
super().setUp()
_a : int = [
'''[UNK]''',
'''[CLS]''',
'''[SEP]''',
'''want''',
'''##want''',
'''##ed''',
'''wa''',
'''un''',
'''runn''',
'''##ing''',
''',''',
'''low''',
'''lowest''',
]
_a : Any = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as vocab_writer:
vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens] ) )
def __lowercase ( self , _a ) -> List[str]:
_a : Tuple = '''UNwant\u00E9d,running'''
_a : str = '''unwanted, running'''
return input_text, output_text
def __lowercase ( self ) -> List[Any]:
_a : str = self.tokenizer_class(self.vocab_file )
_a : str = tokenizer.tokenize('''UNwant\u00E9d,running''' )
self.assertListEqual(_a , ['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing'''] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(_a ) , [7, 4, 5, 1_0, 8, 9] )
def __lowercase ( self ) -> List[Any]:
if not self.test_rust_tokenizer:
return
_a : Optional[Any] = self.get_tokenizer()
_a : str = self.get_rust_tokenizer()
_a : Optional[Any] = '''I was born in 92000, and this is falsé.'''
_a : Optional[Any] = tokenizer.tokenize(_a )
_a : List[Any] = rust_tokenizer.tokenize(_a )
self.assertListEqual(_a , _a )
_a : List[Any] = tokenizer.encode(_a , add_special_tokens=_a )
_a : Any = rust_tokenizer.encode(_a , add_special_tokens=_a )
self.assertListEqual(_a , _a )
_a : Dict = self.get_rust_tokenizer()
_a : Optional[int] = tokenizer.encode(_a )
_a : Dict = rust_tokenizer.encode(_a )
self.assertListEqual(_a , _a )
| 14 | 0 |
from ...processing_utils import ProcessorMixin
class __A ( __lowercase ):
UpperCamelCase :str = "SpeechT5FeatureExtractor"
UpperCamelCase :Tuple = "SpeechT5Tokenizer"
def __init__(self , __magic_name__ , __magic_name__ ):
super().__init__(_a , _a )
def __call__(self , *__magic_name__ , **__magic_name__ ):
lowerCamelCase__ : Union[str, Any] = kwargs.pop("""audio""" , _a )
lowerCamelCase__ : Tuple = kwargs.pop("""text""" , _a )
lowerCamelCase__ : Union[str, Any] = kwargs.pop("""text_target""" , _a )
lowerCamelCase__ : Dict = kwargs.pop("""audio_target""" , _a )
lowerCamelCase__ : Union[str, Any] = kwargs.pop("""sampling_rate""" , _a )
if audio is not None and text is not None:
raise ValueError(
"""Cannot process both `audio` and `text` inputs. Did you mean `audio_target` or `text_target`?""" )
if audio_target is not None and text_target is not None:
raise ValueError(
"""Cannot process both `audio_target` and `text_target` inputs. Did you mean `audio` or `text`?""" )
if audio is None and audio_target is None and text is None and text_target is None:
raise ValueError(
"""You need to specify either an `audio`, `audio_target`, `text`, or `text_target` input to process.""" )
if audio is not None:
lowerCamelCase__ : int = self.feature_extractor(_a , *_a , sampling_rate=_a , **_a )
elif text is not None:
lowerCamelCase__ : Dict = self.tokenizer(_a , **_a )
else:
lowerCamelCase__ : Tuple = None
if audio_target is not None:
lowerCamelCase__ : Optional[int] = self.feature_extractor(audio_target=_a , *_a , sampling_rate=_a , **_a )
lowerCamelCase__ : str = targets['''input_values''']
elif text_target is not None:
lowerCamelCase__ : int = self.tokenizer(_a , **_a )
lowerCamelCase__ : Optional[Any] = targets['''input_ids''']
else:
lowerCamelCase__ : Any = None
if inputs is None:
return targets
if targets is not None:
lowerCamelCase__ : int = labels
lowerCamelCase__ : List[str] = targets.get("""attention_mask""" )
if decoder_attention_mask is not None:
lowerCamelCase__ : Optional[Any] = decoder_attention_mask
return inputs
def _snake_case (self , *__magic_name__ , **__magic_name__ ):
lowerCamelCase__ : str = kwargs.pop("""input_values""" , _a )
lowerCamelCase__ : Optional[Any] = kwargs.pop("""input_ids""" , _a )
lowerCamelCase__ : List[str] = kwargs.pop("""labels""" , _a )
if input_values is not None and input_ids is not None:
raise ValueError("""Cannot process both `input_values` and `input_ids` inputs.""" )
if input_values is None and input_ids is None and labels is None:
raise ValueError(
"""You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded.""" )
if input_values is not None:
lowerCamelCase__ : int = self.feature_extractor.pad(_a , *_a , **_a )
elif input_ids is not None:
lowerCamelCase__ : List[Any] = self.tokenizer.pad(_a , **_a )
else:
lowerCamelCase__ : Tuple = None
if labels is not None:
if "input_ids" in labels or (isinstance(_a , _a ) and "input_ids" in labels[0]):
lowerCamelCase__ : List[Any] = self.tokenizer.pad(_a , **_a )
lowerCamelCase__ : Any = targets['''input_ids''']
else:
lowerCamelCase__ : Union[str, Any] = self.feature_extractor.feature_size
lowerCamelCase__ : Any = self.feature_extractor.num_mel_bins
lowerCamelCase__ : Optional[int] = self.feature_extractor.pad(_a , *_a , **_a )
lowerCamelCase__ : Optional[int] = feature_size_hack
lowerCamelCase__ : Dict = targets['''input_values''']
else:
lowerCamelCase__ : Dict = None
if inputs is None:
return targets
if targets is not None:
lowerCamelCase__ : Dict = labels
lowerCamelCase__ : Union[str, Any] = targets.get("""attention_mask""" )
if decoder_attention_mask is not None:
lowerCamelCase__ : Any = decoder_attention_mask
return inputs
def _snake_case (self , *__magic_name__ , **__magic_name__ ):
return self.tokenizer.batch_decode(*_a , **_a )
def _snake_case (self , *__magic_name__ , **__magic_name__ ):
return self.tokenizer.decode(*_a , **_a )
| 157 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch
if is_vision_available():
from transformers import TvltImageProcessor
if is_speech_available():
from transformers import TvltFeatureExtractor
from transformers import TvltProcessor
@require_torch
class UpperCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
def __lowercase ( self ) -> int:
_a : Dict = '''ZinengTang/tvlt-base'''
_a : List[str] = tempfile.mkdtemp()
def __lowercase ( self , **_a ) -> int:
return TvltImageProcessor.from_pretrained(self.checkpoint , **_a )
def __lowercase ( self , **_a ) -> List[Any]:
return TvltFeatureExtractor.from_pretrained(self.checkpoint , **_a )
def __lowercase ( self ) -> Optional[int]:
shutil.rmtree(self.tmpdirname )
def __lowercase ( self ) -> Dict:
_a : Union[str, Any] = self.get_image_processor()
_a : Dict = self.get_feature_extractor()
_a : Optional[int] = TvltProcessor(image_processor=_a , feature_extractor=_a )
processor.save_pretrained(self.tmpdirname )
_a : Any = TvltProcessor.from_pretrained(self.tmpdirname )
self.assertIsInstance(processor.feature_extractor , _a )
self.assertIsInstance(processor.image_processor , _a )
def __lowercase ( self ) -> Any:
_a : Optional[Any] = self.get_image_processor()
_a : Dict = self.get_feature_extractor()
_a : Dict = TvltProcessor(image_processor=_a , feature_extractor=_a )
_a : Union[str, Any] = np.ones([1_2_0_0_0] )
_a : Dict = feature_extractor(_a , return_tensors='''np''' )
_a : Tuple = processor(audio=_a , return_tensors='''np''' )
for key in audio_dict.keys():
self.assertAlmostEqual(audio_dict[key].sum() , input_processor[key].sum() , delta=1e-2 )
def __lowercase ( self ) -> int:
_a : Optional[Any] = self.get_image_processor()
_a : Union[str, Any] = self.get_feature_extractor()
_a : Optional[Any] = TvltProcessor(image_processor=_a , feature_extractor=_a )
_a : List[Any] = np.ones([3, 2_2_4, 2_2_4] )
_a : int = image_processor(_a , return_tensors='''np''' )
_a : Optional[int] = processor(images=_a , return_tensors='''np''' )
for key in image_dict.keys():
self.assertAlmostEqual(image_dict[key].sum() , input_processor[key].sum() , delta=1e-2 )
def __lowercase ( self ) -> Union[str, Any]:
_a : int = self.get_image_processor()
_a : Union[str, Any] = self.get_feature_extractor()
_a : Any = TvltProcessor(image_processor=_a , feature_extractor=_a )
_a : List[str] = np.ones([1_2_0_0_0] )
_a : Optional[int] = np.ones([3, 2_2_4, 2_2_4] )
_a : int = processor(audio=_a , images=_a )
self.assertListEqual(list(inputs.keys() ) , ['''audio_values''', '''audio_mask''', '''pixel_values''', '''pixel_mask'''] )
# test if it raises when no input is passed
with pytest.raises(_a ):
processor()
def __lowercase ( self ) -> Union[str, Any]:
_a : str = self.get_image_processor()
_a : Union[str, Any] = self.get_feature_extractor()
_a : Dict = TvltProcessor(image_processor=_a , feature_extractor=_a )
self.assertListEqual(
processor.model_input_names , image_processor.model_input_names + feature_extractor.model_input_names , msg='''`processor` and `image_processor`+`feature_extractor` model input names do not match''' , )
| 14 | 0 |
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler
a_ = 16
a_ = 32
def a__ ( _UpperCamelCase : Accelerator ,_UpperCamelCase : int = 16 ,_UpperCamelCase : str = "bert-base-cased" ):
__lowerCamelCase = AutoTokenizer.from_pretrained(__a )
__lowerCamelCase = load_dataset('''glue''' ,'''mrpc''' )
def tokenize_function(_UpperCamelCase : Optional[Any] ):
# max_length=None => use the model max length (it's actually the default)
__lowerCamelCase = tokenizer(examples['''sentence1'''] ,examples['''sentence2'''] ,truncation=__a ,max_length=__a )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
__lowerCamelCase = datasets.map(
__a ,batched=__a ,remove_columns=['''idx''', '''sentence1''', '''sentence2'''] ,load_from_cache_file=__a )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
__lowerCamelCase = tokenized_datasets.rename_column('''label''' ,'''labels''' )
def collate_fn(_UpperCamelCase : List[str] ):
# On TPU it's best to pad everything to the same length or training will be very slow.
if accelerator.distributed_type == DistributedType.TPU:
return tokenizer.pad(__a ,padding='''max_length''' ,max_length=1_28 ,return_tensors='''pt''' )
return tokenizer.pad(__a ,padding='''longest''' ,return_tensors='''pt''' )
# Instantiate dataloaders.
__lowerCamelCase = DataLoader(
tokenized_datasets['''train'''] ,shuffle=__a ,collate_fn=__a ,batch_size=__a )
__lowerCamelCase = DataLoader(
tokenized_datasets['''validation'''] ,shuffle=__a ,collate_fn=__a ,batch_size=__a )
return train_dataloader, eval_dataloader
def a__ ( _UpperCamelCase : List[str] ,_UpperCamelCase : Union[str, Any] ,_UpperCamelCase : Any ,_UpperCamelCase : Optional[int] ):
model.eval()
__lowerCamelCase = 0
for step, batch in enumerate(__a ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
__lowerCamelCase = model(**__a )
__lowerCamelCase = outputs.logits.argmax(dim=-1 )
# It is slightly faster to call this once, than multiple times
__lowerCamelCase = accelerator.gather(
(predictions, batch['''labels''']) ) # If we are in a multiprocess environment, the last batch has duplicates
if accelerator.use_distributed:
if step == len(__a ) - 1:
__lowerCamelCase = predictions[: len(eval_dataloader.dataset ) - samples_seen]
__lowerCamelCase = references[: len(eval_dataloader.dataset ) - samples_seen]
else:
samples_seen += references.shape[0]
metric.add_batch(
predictions=__a ,references=__a ,)
__lowerCamelCase = metric.compute()
return eval_metric["accuracy"]
def a__ ( _UpperCamelCase : int ,_UpperCamelCase : Any ):
__lowerCamelCase = Accelerator()
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
__lowerCamelCase = config['''lr''']
__lowerCamelCase = int(config['''num_epochs'''] )
__lowerCamelCase = int(config['''seed'''] )
__lowerCamelCase = int(config['''batch_size'''] )
__lowerCamelCase = args.model_name_or_path
set_seed(__a )
__lowerCamelCase = get_dataloaders(__a ,__a ,__a )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
__lowerCamelCase = AutoModelForSequenceClassification.from_pretrained(__a ,return_dict=__a )
# Instantiate optimizer
__lowerCamelCase = (
AdamW
if accelerator.state.deepspeed_plugin is None
or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
else DummyOptim
)
__lowerCamelCase = optimizer_cls(params=model.parameters() ,lr=__a )
if accelerator.state.deepspeed_plugin is not None:
__lowerCamelCase = accelerator.state.deepspeed_plugin.deepspeed_config[
'''gradient_accumulation_steps'''
]
else:
__lowerCamelCase = 1
__lowerCamelCase = (len(__a ) * num_epochs) // gradient_accumulation_steps
# Instantiate scheduler
if (
accelerator.state.deepspeed_plugin is None
or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
):
__lowerCamelCase = get_linear_schedule_with_warmup(
optimizer=__a ,num_warmup_steps=0 ,num_training_steps=__a ,)
else:
__lowerCamelCase = DummyScheduler(__a ,total_num_steps=__a ,warmup_num_steps=0 )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
__lowerCamelCase = accelerator.prepare(
__a ,__a ,__a ,__a ,__a )
# We need to keep track of how many total steps we have iterated over
__lowerCamelCase = 0
# We also need to keep track of the stating epoch so files are named properly
__lowerCamelCase = 0
__lowerCamelCase = evaluate.load('''glue''' ,'''mrpc''' )
__lowerCamelCase = num_epochs
if args.partial_train_epoch is not None:
__lowerCamelCase = args.partial_train_epoch
if args.resume_from_checkpoint:
accelerator.load_state(args.resume_from_checkpoint )
__lowerCamelCase = args.resume_from_checkpoint.split('''epoch_''' )[1]
__lowerCamelCase = ''''''
for char in epoch_string:
if char.isdigit():
state_epoch_num += char
else:
break
__lowerCamelCase = int(__a ) + 1
__lowerCamelCase = evaluation_loop(__a ,__a ,__a ,__a )
accelerator.print('''resumed checkpoint performance:''' ,__a )
accelerator.print('''resumed checkpoint\'s scheduler\'s lr:''' ,lr_scheduler.get_lr()[0] )
accelerator.print('''resumed optimizers\'s lr:''' ,optimizer.param_groups[0]['''lr'''] )
with open(os.path.join(args.output_dir ,F"""state_{starting_epoch-1}.json""" ) ,'''r''' ) as f:
__lowerCamelCase = json.load(__a )
assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
assert (
resumed_state["lr"] == lr_scheduler.get_lr()[0]
), "Scheduler learning rate mismatch, loading from checkpoint failed"
assert (
resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
), "Optimizer learning rate mismatch, loading from checkpoint failed"
assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
return
# Now we train the model
__lowerCamelCase = {}
for epoch in range(__a ,__a ):
model.train()
for step, batch in enumerate(__a ):
__lowerCamelCase = model(**__a )
__lowerCamelCase = outputs.loss
__lowerCamelCase = loss / gradient_accumulation_steps
accelerator.backward(__a )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
overall_step += 1
__lowerCamelCase = F"""epoch_{epoch}"""
__lowerCamelCase = os.path.join(args.output_dir ,__a )
accelerator.save_state(__a )
__lowerCamelCase = evaluation_loop(__a ,__a ,__a ,__a )
__lowerCamelCase = accuracy
__lowerCamelCase = lr_scheduler.get_lr()[0]
__lowerCamelCase = optimizer.param_groups[0]['''lr''']
__lowerCamelCase = epoch
__lowerCamelCase = overall_step
accelerator.print(F"""epoch {epoch}:""" ,__a )
accelerator.wait_for_everyone()
if accelerator.is_main_process:
with open(os.path.join(args.output_dir ,F"""state_{epoch}.json""" ) ,'''w''' ) as f:
json.dump(__a ,__a )
def a__ ( ):
__lowerCamelCase = argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.''' )
parser.add_argument(
'''--model_name_or_path''' ,type=__a ,default='''bert-base-cased''' ,help='''Path to pretrained model or model identifier from huggingface.co/models.''' ,required=__a ,)
parser.add_argument(
'''--output_dir''' ,type=__a ,default='''.''' ,help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.''' ,)
parser.add_argument(
'''--resume_from_checkpoint''' ,type=__a ,default=__a ,help='''If the training should continue from a checkpoint folder.''' ,)
parser.add_argument(
'''--partial_train_epoch''' ,type=__a ,default=__a ,help='''If passed, the training will stop after this number of epochs.''' ,)
parser.add_argument(
'''--num_epochs''' ,type=__a ,default=2 ,help='''Number of train epochs.''' ,)
__lowerCamelCase = parser.parse_args()
__lowerCamelCase = {'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}
training_function(__a ,__a )
if __name__ == "__main__":
main()
| 175 |
def __UpperCAmelCase ( __a : str ) -> list:
"""simple docstring"""
if n_term == "":
return []
_a : list = []
for temp in range(int(__a ) ):
series.append(F"""1/{temp + 1}""" if series else '''1''' )
return series
if __name__ == "__main__":
a__ = input('''Enter the last number (nth term) of the Harmonic Series''')
print('''Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n''')
print(harmonic_series(nth_term))
| 14 | 0 |
'''simple docstring'''
import inspect
import unittest
import warnings
from math import ceil, floor
from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
MODEL_MAPPING,
LevitForImageClassification,
LevitForImageClassificationWithTeacher,
LevitModel,
)
from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import LevitImageProcessor
class a__ ( __lowercase ):
'''simple docstring'''
def lowerCAmelCase ( self : Any ) -> Tuple:
__A= self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(_a , 'hidden_sizes' ) )
self.parent.assertTrue(hasattr(_a , 'num_attention_heads' ) )
class a__ :
'''simple docstring'''
def __init__( self : Union[str, Any] , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Any=13 , lowerCAmelCase_ : str=64 , lowerCAmelCase_ : Any=3 , lowerCAmelCase_ : Optional[Any]=3 , lowerCAmelCase_ : Any=2 , lowerCAmelCase_ : Dict=1 , lowerCAmelCase_ : Optional[int]=16 , lowerCAmelCase_ : Tuple=[128, 256, 384] , lowerCAmelCase_ : Optional[Any]=[4, 6, 8] , lowerCAmelCase_ : Optional[Any]=[2, 3, 4] , lowerCAmelCase_ : Optional[Any]=[16, 16, 16] , lowerCAmelCase_ : Dict=0 , lowerCAmelCase_ : Any=[2, 2, 2] , lowerCAmelCase_ : Union[str, Any]=[2, 2, 2] , lowerCAmelCase_ : str=0.02 , lowerCAmelCase_ : Optional[Any]=True , lowerCAmelCase_ : str=True , lowerCAmelCase_ : Dict=2 , ) -> Tuple:
__A= parent
__A= batch_size
__A= image_size
__A= num_channels
__A= kernel_size
__A= stride
__A= padding
__A= hidden_sizes
__A= num_attention_heads
__A= depths
__A= key_dim
__A= drop_path_rate
__A= patch_size
__A= attention_ratio
__A= mlp_ratio
__A= initializer_range
__A= [
['''Subsample''', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
['''Subsample''', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
]
__A= is_training
__A= use_labels
__A= num_labels
__A= initializer_range
def lowerCAmelCase ( self : Optional[Any] ) -> List[str]:
__A= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__A= None
if self.use_labels:
__A= ids_tensor([self.batch_size] , self.num_labels )
__A= self.get_config()
return config, pixel_values, labels
def lowerCAmelCase ( self : Optional[Any] ) -> Optional[Any]:
return LevitConfig(
image_size=self.image_size , num_channels=self.num_channels , kernel_size=self.kernel_size , stride=self.stride , padding=self.padding , patch_size=self.patch_size , hidden_sizes=self.hidden_sizes , num_attention_heads=self.num_attention_heads , depths=self.depths , key_dim=self.key_dim , drop_path_rate=self.drop_path_rate , mlp_ratio=self.mlp_ratio , attention_ratio=self.attention_ratio , initializer_range=self.initializer_range , down_ops=self.down_ops , )
def lowerCAmelCase ( self : Tuple , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : str , lowerCAmelCase_ : List[Any] ) -> Optional[Any]:
__A= LevitModel(config=_a )
model.to(_a )
model.eval()
__A= model(_a )
__A= (self.image_size, self.image_size)
__A= image_size[0], image_size[1]
for _ in range(4 ):
__A= floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1 )
__A= floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1 )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, ceil(height / 4 ) * ceil(width / 4 ), self.hidden_sizes[-1]) , )
def lowerCAmelCase ( self : Optional[Any] , lowerCAmelCase_ : Dict , lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : str ) -> str:
__A= self.num_labels
__A= LevitForImageClassification(_a )
model.to(_a )
model.eval()
__A= model(_a , labels=_a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowerCAmelCase ( self : List[str] ) -> Dict:
__A= self.prepare_config_and_inputs()
__A= config_and_inputs
__A= {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class a__ ( __lowercase , __lowercase , unittest.TestCase ):
'''simple docstring'''
A : Tuple = (
(LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
if is_torch_available()
else ()
)
A : int = (
{
"feature-extraction": LevitModel,
"image-classification": (LevitForImageClassification, LevitForImageClassificationWithTeacher),
}
if is_torch_available()
else {}
)
A : Any = False
A : Union[str, Any] = False
A : Tuple = False
A : List[str] = False
A : List[Any] = False
def lowerCAmelCase ( self : List[str] ) -> Optional[int]:
__A= LevitModelTester(self )
__A= ConfigTester(self , config_class=_a , has_text_modality=_a , hidden_size=37 )
def lowerCAmelCase ( self : Tuple ) -> str:
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def lowerCAmelCase ( self : str ) -> int:
return
@unittest.skip(reason='Levit does not use inputs_embeds' )
def lowerCAmelCase ( self : Dict ) -> Optional[Any]:
pass
@unittest.skip(reason='Levit does not support input and output embeddings' )
def lowerCAmelCase ( self : Union[str, Any] ) -> int:
pass
@unittest.skip(reason='Levit does not output attentions' )
def lowerCAmelCase ( self : Optional[int] ) -> List[str]:
pass
def lowerCAmelCase ( self : str ) -> Tuple:
__A= self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__A= model_class(_a )
__A= inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__A= [*signature.parameters.keys()]
__A= ['''pixel_values''']
self.assertListEqual(arg_names[:1] , _a )
def lowerCAmelCase ( self : List[Any] ) -> Tuple:
def check_hidden_states_output(lowerCAmelCase_ : List[str] , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : int ):
__A= model_class(_a )
model.to(_a )
model.eval()
with torch.no_grad():
__A= model(**self._prepare_for_class(_a , _a ) )
__A= outputs.hidden_states
__A= len(self.model_tester.depths ) + 1
self.assertEqual(len(_a ) , _a )
__A= (self.model_tester.image_size, self.model_tester.image_size)
__A= image_size[0], image_size[1]
for _ in range(4 ):
__A= floor(
(
(height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
/ self.model_tester.stride
)
+ 1 )
__A= floor(
(
(width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
/ self.model_tester.stride
)
+ 1 )
# verify the first hidden states (first block)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [
height * width,
self.model_tester.hidden_sizes[0],
] , )
__A= self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__A= True
check_hidden_states_output(_a , _a , _a )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__A= True
check_hidden_states_output(_a , _a , _a )
@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
def lowerCAmelCase ( self : Optional[int] ) -> Tuple:
pass
def lowerCAmelCase ( self : str , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : int , lowerCAmelCase_ : str=False ) -> str:
__A= super()._prepare_for_class(_a , _a , return_labels=_a )
if return_labels:
if model_class.__name__ == "LevitForImageClassificationWithTeacher":
del inputs_dict["labels"]
return inputs_dict
def lowerCAmelCase ( self : Optional[int] ) -> Optional[int]:
__A= self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_a )
def lowerCAmelCase ( self : List[str] ) -> str:
__A= self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_a )
def lowerCAmelCase ( self : Union[str, Any] ) -> Tuple:
if not self.model_tester.is_training:
return
__A= self.model_tester.prepare_config_and_inputs_for_common()
__A= True
for model_class in self.all_model_classes:
# LevitForImageClassificationWithTeacher supports inference-only
if (
model_class in get_values(_a )
or model_class.__name__ == "LevitForImageClassificationWithTeacher"
):
continue
__A= model_class(_a )
model.to(_a )
model.train()
__A= self._prepare_for_class(_a , _a , return_labels=_a )
__A= model(**_a ).loss
loss.backward()
def lowerCAmelCase ( self : Optional[Any] ) -> Optional[int]:
__A= self.model_tester.prepare_config_and_inputs_for_common()
if not self.model_tester.is_training:
return
__A= False
__A= True
for model_class in self.all_model_classes:
if model_class in get_values(_a ) or not model_class.supports_gradient_checkpointing:
continue
# LevitForImageClassificationWithTeacher supports inference-only
if model_class.__name__ == "LevitForImageClassificationWithTeacher":
continue
__A= model_class(_a )
model.gradient_checkpointing_enable()
model.to(_a )
model.train()
__A= self._prepare_for_class(_a , _a , return_labels=_a )
__A= model(**_a ).loss
loss.backward()
def lowerCAmelCase ( self : Optional[Any] ) -> Optional[Any]:
__A= self.model_tester.prepare_config_and_inputs_for_common()
__A= [
{'''title''': '''multi_label_classification''', '''num_labels''': 2, '''dtype''': torch.float},
{'''title''': '''single_label_classification''', '''num_labels''': 1, '''dtype''': torch.long},
{'''title''': '''regression''', '''num_labels''': 1, '''dtype''': torch.float},
]
for model_class in self.all_model_classes:
if (
model_class
not in [
*get_values(_a ),
]
or model_class.__name__ == "LevitForImageClassificationWithTeacher"
):
continue
for problem_type in problem_types:
with self.subTest(msg=F"""Testing {model_class} with {problem_type["title"]}""" ):
__A= problem_type['''title''']
__A= problem_type['''num_labels''']
__A= model_class(_a )
model.to(_a )
model.train()
__A= self._prepare_for_class(_a , _a , return_labels=_a )
if problem_type["num_labels"] > 1:
__A= inputs['''labels'''].unsqueeze(1 ).repeat(1 , problem_type['num_labels'] )
__A= inputs['''labels'''].to(problem_type['dtype'] )
# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
# they have the same size." which is a symptom something in wrong for the regression problem.
# See https://github.com/huggingface/transformers/issues/11780
with warnings.catch_warnings(record=_a ) as warning_list:
__A= model(**_a ).loss
for w in warning_list:
if "Using a target size that is different to the input size" in str(w.message ):
raise ValueError(
F"""Something is going wrong in the regression problem: intercepted {w.message}""" )
loss.backward()
@slow
def lowerCAmelCase ( self : Union[str, Any] ) -> Any:
for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__A= LevitModel.from_pretrained(_a )
self.assertIsNotNone(_a )
def UpperCAmelCase__( ):
"""simple docstring"""
__A= Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_torch
@require_vision
class a__ ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def lowerCAmelCase ( self : List[Any] ) -> List[Any]:
return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
@slow
def lowerCAmelCase ( self : List[Any] ) -> Dict:
__A= LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(
_a )
__A= self.default_image_processor
__A= prepare_img()
__A= image_processor(images=_a , return_tensors='pt' ).to(_a )
# forward pass
with torch.no_grad():
__A= model(**_a )
# verify the logits
__A= torch.Size((1, 1_000) )
self.assertEqual(outputs.logits.shape , _a )
__A= torch.tensor([1.04_48, -0.37_45, -1.83_17] ).to(_a )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , _a , atol=1E-4 ) )
| 186 |
import argparse
import collections
import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def __UpperCAmelCase ( __a : List[Any] ,__a : Optional[Any] ,__a : Optional[int] ) -> Dict:
"""simple docstring"""
return params[F"""{prefix}/{prefix}/relpos_bias/rel_embedding"""][:, i, :]
def __UpperCAmelCase ( __a : List[Any] ,__a : Optional[int] ,__a : int ,__a : List[str]="attention" ) -> List[str]:
"""simple docstring"""
_a : str = np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/key/kernel"""][:, i, :, :] )
_a : Tuple = k_tmp.reshape(k_tmp.shape[0] ,k_tmp.shape[1] * k_tmp.shape[2] )
_a : Any = np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/out/kernel"""][:, i, :, :] )
_a : Dict = o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1] ,o_tmp.shape[2] )
_a : Union[str, Any] = np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/query/kernel"""][:, i, :, :] )
_a : Any = q_tmp.reshape(q_tmp.shape[0] ,q_tmp.shape[1] * q_tmp.shape[2] )
_a : Tuple = np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/value/kernel"""][:, i, :, :] )
_a : int = v_tmp.reshape(v_tmp.shape[0] ,v_tmp.shape[1] * v_tmp.shape[2] )
return k, o, q, v
def __UpperCAmelCase ( __a : Union[str, Any] ,__a : Union[str, Any] ,__a : List[Any] ,__a : Any=False ) -> Any:
"""simple docstring"""
if split_mlp_wi:
_a : Union[str, Any] = params[F"""{prefix}/{prefix}/mlp/wi_0/kernel"""][:, i, :]
_a : Union[str, Any] = params[F"""{prefix}/{prefix}/mlp/wi_1/kernel"""][:, i, :]
_a : List[str] = (wi_a, wi_a)
else:
_a : List[str] = params[F"""{prefix}/{prefix}/mlp/wi/kernel"""][:, i, :]
_a : Optional[int] = params[F"""{prefix}/{prefix}/mlp/wo/kernel"""][:, i, :]
return wi, wo
def __UpperCAmelCase ( __a : List[Any] ,__a : Optional[Any] ,__a : Union[str, Any] ,__a : str ) -> List[str]:
"""simple docstring"""
return params[F"""{prefix}/{prefix}/{layer_name}/scale"""][:, i]
def __UpperCAmelCase ( __a : dict ,*, __a : int ,__a : bool ,__a : bool = False ) -> Any:
"""simple docstring"""
_a : Dict = traverse_util.flatten_dict(variables['''target'''] )
_a : Any = {'''/'''.join(__a ): v for k, v in old.items()}
# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
_a : Optional[int] = '''encoder/encoder/mlp/wi_0/kernel''' in old
print('''Split MLP:''' ,__a )
_a : Tuple = collections.OrderedDict()
# Shared embeddings.
_a : Any = old['''token_embedder/embedding''']
# Encoder.
for i in range(__a ):
# Block i, layer 0 (Self Attention).
_a : Optional[Any] = tax_layer_norm_lookup(__a ,__a ,'''encoder''' ,'''pre_attention_layer_norm''' )
_a , _a , _a , _a : List[str] = tax_attention_lookup(__a ,__a ,'''encoder''' ,'''attention''' )
_a : List[str] = layer_norm
_a : Optional[Any] = k.T
_a : str = o.T
_a : List[Any] = q.T
_a : Tuple = v.T
# Block i, layer 1 (MLP).
_a : str = tax_layer_norm_lookup(__a ,__a ,'''encoder''' ,'''pre_mlp_layer_norm''' )
_a , _a : Any = tax_mlp_lookup(__a ,__a ,'''encoder''' ,__a )
_a : str = layer_norm
if split_mlp_wi:
_a : List[Any] = wi[0].T
_a : Any = wi[1].T
else:
_a : Any = wi.T
_a : Optional[Any] = wo.T
if scalable_attention:
# convert the rel_embedding of each layer
_a : Dict = tax_relpos_bias_lookup(
__a ,__a ,'''encoder''' ).T
_a : List[str] = old['''encoder/encoder_norm/scale''']
if not scalable_attention:
_a : List[Any] = tax_relpos_bias_lookup(
__a ,0 ,'''encoder''' ).T
_a : Optional[Any] = tax_relpos_bias_lookup(
__a ,0 ,'''decoder''' ).T
if not is_encoder_only:
# Decoder.
for i in range(__a ):
# Block i, layer 0 (Self Attention).
_a : Union[str, Any] = tax_layer_norm_lookup(__a ,__a ,'''decoder''' ,'''pre_self_attention_layer_norm''' )
_a , _a , _a , _a : Optional[Any] = tax_attention_lookup(__a ,__a ,'''decoder''' ,'''self_attention''' )
_a : Optional[Any] = layer_norm
_a : Dict = k.T
_a : str = o.T
_a : str = q.T
_a : List[str] = v.T
# Block i, layer 1 (Cross Attention).
_a : Any = tax_layer_norm_lookup(__a ,__a ,'''decoder''' ,'''pre_cross_attention_layer_norm''' )
_a , _a , _a , _a : str = tax_attention_lookup(__a ,__a ,'''decoder''' ,'''encoder_decoder_attention''' )
_a : Optional[Any] = layer_norm
_a : Optional[int] = k.T
_a : Dict = o.T
_a : str = q.T
_a : int = v.T
# Block i, layer 2 (MLP).
_a : Optional[int] = tax_layer_norm_lookup(__a ,__a ,'''decoder''' ,'''pre_mlp_layer_norm''' )
_a , _a : Tuple = tax_mlp_lookup(__a ,__a ,'''decoder''' ,__a )
_a : Optional[Any] = layer_norm
if split_mlp_wi:
_a : List[str] = wi[0].T
_a : List[Any] = wi[1].T
else:
_a : Dict = wi.T
_a : str = wo.T
if scalable_attention:
# convert the rel_embedding of each layer
_a : Tuple = tax_relpos_bias_lookup(__a ,__a ,'''decoder''' ).T
_a : Tuple = old['''decoder/decoder_norm/scale''']
# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
if "decoder/logits_dense/kernel" in old:
_a : Any = old['''decoder/logits_dense/kernel'''].T
return new
def __UpperCAmelCase ( __a : Dict ,__a : bool ) -> Tuple:
"""simple docstring"""
_a : Tuple = collections.OrderedDict([(k, torch.from_numpy(v.copy() )) for (k, v) in converted_params.items()] )
# Add what is missing.
if "encoder.embed_tokens.weight" not in state_dict:
_a : Any = state_dict['''shared.weight''']
if not is_encoder_only:
if "decoder.embed_tokens.weight" not in state_dict:
_a : Optional[int] = state_dict['''shared.weight''']
if "lm_head.weight" not in state_dict: # For old 1.0 models.
print('''Using shared word embeddings as lm_head.''' )
_a : str = state_dict['''shared.weight''']
return state_dict
def __UpperCAmelCase ( __a : List[str] ,__a : Union[str, Any] ,__a : Dict ,__a : Union[str, Any] ,__a : List[Any] ) -> int:
"""simple docstring"""
_a : List[str] = checkpoints.load_tax_checkpoint(__a )
_a : str = convert_tax_to_pytorch(
__a ,num_layers=config.num_layers ,is_encoder_only=__a ,scalable_attention=__a )
_a : str = make_state_dict(__a ,__a )
model.load_state_dict(__a ,strict=__a )
def __UpperCAmelCase ( __a : List[Any] ,__a : Any ,__a : Union[str, Any] ,__a : bool = False ,__a : bool = False ,) -> Optional[Any]:
"""simple docstring"""
_a : List[str] = MTaConfig.from_json_file(__a )
print(F"""Building PyTorch model from configuration: {config}""" )
# Non-v1.1 checkpoints could also use T5Model, but this works for all.
# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
if is_encoder_only:
_a : Any = UMTaEncoderModel(__a )
else:
_a : Tuple = UMTaForConditionalGeneration(__a )
# Load weights from tf checkpoint
load_tax_weights_in_ta(__a ,__a ,__a ,__a ,__a )
# Save pytorch-model
print(F"""Save PyTorch model to {pytorch_dump_path}""" )
model.save_pretrained(__a )
# Verify that we can load the checkpoint.
model.from_pretrained(__a )
print('''Done''' )
if __name__ == "__main__":
a__ = argparse.ArgumentParser(description='''Converts a native T5X checkpoint into a PyTorch checkpoint.''')
# Required parameters
parser.add_argument(
'''--t5x_checkpoint_path''', default=None, type=str, required=True, help='''Path to the T5X checkpoint.'''
)
parser.add_argument(
'''--config_file''',
default=None,
type=str,
required=True,
help='''The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.''',
)
parser.add_argument(
'''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
parser.add_argument(
'''--is_encoder_only''', action='''store_true''', help='''Check if the model is encoder-decoder model''', default=False
)
parser.add_argument(
'''--scalable_attention''',
action='''store_true''',
help='''Whether the model uses scaled attention (umt5 model)''',
default=False,
)
a__ = parser.parse_args()
convert_tax_checkpoint_to_pytorch(
args.tax_checkpoint_path,
args.config_file,
args.pytorch_dump_path,
args.is_encoder_only,
args.scalable_attention,
)
| 14 | 0 |
from ..utils import DummyObject, requires_backends
class __lowerCAmelCase ( metaclass=__lowercase ):
"""simple docstring"""
A__ : List[Any] = ["flax", "transformers"]
def __init__( self : Optional[Any] , *_snake_case : Optional[int] , **_snake_case : Tuple ):
requires_backends(self , ['''flax''', '''transformers'''] )
@classmethod
def snake_case_ ( cls : Any , *_snake_case : Any , **_snake_case : Optional[Any] ):
requires_backends(cls , ['''flax''', '''transformers'''] )
@classmethod
def snake_case_ ( cls : Optional[int] , *_snake_case : List[Any] , **_snake_case : Optional[int] ):
requires_backends(cls , ['''flax''', '''transformers'''] )
class __lowerCAmelCase ( metaclass=__lowercase ):
"""simple docstring"""
A__ : str = ["flax", "transformers"]
def __init__( self : List[Any] , *_snake_case : Any , **_snake_case : Tuple ):
requires_backends(self , ['''flax''', '''transformers'''] )
@classmethod
def snake_case_ ( cls : Union[str, Any] , *_snake_case : Tuple , **_snake_case : int ):
requires_backends(cls , ['''flax''', '''transformers'''] )
@classmethod
def snake_case_ ( cls : List[str] , *_snake_case : int , **_snake_case : Tuple ):
requires_backends(cls , ['''flax''', '''transformers'''] )
class __lowerCAmelCase ( metaclass=__lowercase ):
"""simple docstring"""
A__ : List[str] = ["flax", "transformers"]
def __init__( self : Optional[Any] , *_snake_case : Union[str, Any] , **_snake_case : Dict ):
requires_backends(self , ['''flax''', '''transformers'''] )
@classmethod
def snake_case_ ( cls : Tuple , *_snake_case : List[Any] , **_snake_case : Optional[int] ):
requires_backends(cls , ['''flax''', '''transformers'''] )
@classmethod
def snake_case_ ( cls : Any , *_snake_case : Tuple , **_snake_case : List[str] ):
requires_backends(cls , ['''flax''', '''transformers'''] )
class __lowerCAmelCase ( metaclass=__lowercase ):
"""simple docstring"""
A__ : List[Any] = ["flax", "transformers"]
def __init__( self : Optional[Any] , *_snake_case : Union[str, Any] , **_snake_case : Optional[Any] ):
requires_backends(self , ['''flax''', '''transformers'''] )
@classmethod
def snake_case_ ( cls : List[str] , *_snake_case : Dict , **_snake_case : Optional[int] ):
requires_backends(cls , ['''flax''', '''transformers'''] )
@classmethod
def snake_case_ ( cls : List[Any] , *_snake_case : Union[str, Any] , **_snake_case : str ):
requires_backends(cls , ['''flax''', '''transformers'''] )
| 509 |
import random
import sys
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap
a__ = '''Usage of script: script_name <size_of_canvas:int>'''
a__ = [0] * 100 + [1] * 10
random.shuffle(choice)
def __UpperCAmelCase ( __a : int ) -> list[list[bool]]:
"""simple docstring"""
_a : int = [[False for i in range(__a )] for j in range(__a )]
return canvas
def __UpperCAmelCase ( __a : list[list[bool]] ) -> None:
"""simple docstring"""
for i, row in enumerate(__a ):
for j, _ in enumerate(__a ):
_a : Optional[int] = bool(random.getrandbits(1 ) )
def __UpperCAmelCase ( __a : list[list[bool]] ) -> list[list[bool]]:
"""simple docstring"""
_a : Any = np.array(__a )
_a : Optional[int] = np.array(create_canvas(current_canvas.shape[0] ) )
for r, row in enumerate(__a ):
for c, pt in enumerate(__a ):
_a : Tuple = __judge_point(
__a ,current_canvas[r - 1 : r + 2, c - 1 : c + 2] )
_a : List[str] = next_gen_canvas
del next_gen_canvas # cleaning memory as we move on.
_a : list[list[bool]] = current_canvas.tolist()
return return_canvas
def __UpperCAmelCase ( __a : bool ,__a : list[list[bool]] ) -> bool:
"""simple docstring"""
_a : Optional[Any] = 0
_a : str = 0
# finding dead or alive neighbours count.
for i in neighbours:
for status in i:
if status:
alive += 1
else:
dead += 1
# handling duplicate entry for focus pt.
if pt:
alive -= 1
else:
dead -= 1
# running the rules of game here.
_a : Optional[int] = pt
if pt:
if alive < 2:
_a : Dict = False
elif alive == 2 or alive == 3:
_a : Optional[Any] = True
elif alive > 3:
_a : str = False
else:
if alive == 3:
_a : int = True
return state
if __name__ == "__main__":
if len(sys.argv) != 2:
raise Exception(usage_doc)
a__ = int(sys.argv[1])
# main working structure of this module.
a__ = create_canvas(canvas_size)
seed(c)
a__ , a__ = plt.subplots()
fig.show()
a__ = ListedColormap(['''w''', '''k'''])
try:
while True:
a__ = run(c)
ax.matshow(c, cmap=cmap)
fig.canvas.draw()
ax.cla()
except KeyboardInterrupt:
# do nothing.
pass
| 14 | 0 |
from __future__ import annotations
import unittest
from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers.models.xglm.modeling_tf_xglm import (
TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXGLMForCausalLM,
TFXGLMModel,
)
@require_tf
class UpperCAmelCase :
'''simple docstring'''
snake_case_ = XGLMConfig
snake_case_ = {}
snake_case_ = "gelu"
def __init__( self : str ,A : Optional[int] ,A : List[str]=14 ,A : List[str]=7 ,A : str=True ,A : int=True ,A : Optional[Any]=True ,A : List[Any]=99 ,A : Dict=32 ,A : Optional[int]=2 ,A : Any=4 ,A : Optional[Any]=37 ,A : Optional[Any]="gelu" ,A : List[Any]=0.1 ,A : Tuple=0.1 ,A : int=5_12 ,A : Optional[Any]=0.02 ,):
__A = parent
__A = batch_size
__A = seq_length
__A = is_training
__A = use_input_mask
__A = use_labels
__A = vocab_size
__A = d_model
__A = num_hidden_layers
__A = num_attention_heads
__A = ffn_dim
__A = activation_function
__A = activation_dropout
__A = attention_dropout
__A = max_position_embeddings
__A = initializer_range
__A = None
__A = 0
__A = 2
__A = 1
def UpperCamelCase_ ( self : Optional[int] ):
return XGLMConfig.from_pretrained("facebook/xglm-564M" )
def UpperCamelCase_ ( self : List[str] ):
__A = tf.clip_by_value(
ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size ) ,clip_value_min=0 ,clip_value_max=3 )
__A = None
if self.use_input_mask:
__A = random_attention_mask([self.batch_size, self.seq_length] )
__A = self.get_config()
__A = floats_tensor([self.num_hidden_layers, self.num_attention_heads] ,2 )
return (
config,
input_ids,
input_mask,
head_mask,
)
def UpperCamelCase_ ( self : List[str] ):
return XGLMConfig(
vocab_size=self.vocab_size ,d_model=self.hidden_size ,num_layers=self.num_hidden_layers ,attention_heads=self.num_attention_heads ,ffn_dim=self.ffn_dim ,activation_function=self.activation_function ,activation_dropout=self.activation_dropout ,attention_dropout=self.attention_dropout ,max_position_embeddings=self.max_position_embeddings ,initializer_range=self.initializer_range ,use_cache=_a ,bos_token_id=self.bos_token_id ,eos_token_id=self.eos_token_id ,pad_token_id=self.pad_token_id ,return_dict=_a ,)
def UpperCamelCase_ ( self : Dict ):
__A = self.prepare_config_and_inputs()
(
__A
) = config_and_inputs
__A = {
'''input_ids''': input_ids,
'''head_mask''': head_mask,
}
return config, inputs_dict
@require_tf
class UpperCAmelCase ( __lowercase , __lowercase , unittest.TestCase ):
'''simple docstring'''
snake_case_ = (TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
snake_case_ = (TFXGLMForCausalLM,) if is_tf_available() else ()
snake_case_ = (
{"feature-extraction": TFXGLMModel, "text-generation": TFXGLMForCausalLM} if is_tf_available() else {}
)
snake_case_ = False
snake_case_ = False
snake_case_ = False
def UpperCamelCase_ ( self : str ):
__A = TFXGLMModelTester(self )
__A = ConfigTester(self ,config_class=_a ,n_embd=37 )
def UpperCamelCase_ ( self : Tuple ):
self.config_tester.run_common_tests()
@slow
def UpperCamelCase_ ( self : Optional[Any] ):
for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__A = TFXGLMModel.from_pretrained(_a )
self.assertIsNotNone(_a )
@unittest.skip(reason="Currently, model embeddings are going to undergo a major refactor." )
def UpperCamelCase_ ( self : Union[str, Any] ):
super().test_resize_token_embeddings()
@require_tf
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
@slow
def UpperCamelCase_ ( self : Optional[Any] ,A : int=True ):
__A = TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M" )
__A = tf.convert_to_tensor([[2, 2_68, 98_65]] ,dtype=tf.intaa ) # The dog
# </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
# fmt: off
__A = [2, 2_68, 98_65, 67, 11, 19_88, 5_72_52, 98_65, 5, 9_84, 67, 19_88, 21_38_38, 16_58, 53, 7_04_46, 33, 66_57, 2_78, 15_81]
# fmt: on
__A = model.generate(_a ,do_sample=_a ,num_beams=1 )
if verify_outputs:
self.assertListEqual(output_ids[0].numpy().tolist() ,_a )
@slow
def UpperCamelCase_ ( self : Optional[int] ):
__A = XGLMTokenizer.from_pretrained("facebook/xglm-564M" )
__A = TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M" )
tf.random.set_seed(0 )
__A = tokenizer("Today is a nice day and" ,return_tensors="tf" )
__A = tokenized.input_ids
# forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
with tf.device(":/CPU:0" ):
__A = model.generate(_a ,do_sample=_a ,seed=[7, 0] )
__A = tokenizer.decode(output_ids[0] ,skip_special_tokens=_a )
__A = (
'''Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due'''
)
self.assertEqual(_a ,_a )
@slow
def UpperCamelCase_ ( self : Tuple ):
__A = TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M" )
__A = XGLMTokenizer.from_pretrained("facebook/xglm-564M" )
__A = '''left'''
# use different length sentences to test batching
__A = [
'''This is an extremelly long sentence that only exists to test the ability of the model to cope with '''
'''left-padding, such as in batched generation. The output for the sequence below should be the same '''
'''regardless of whether left padding is applied or not. When''',
'''Hello, my dog is a little''',
]
__A = tokenizer(_a ,return_tensors="tf" ,padding=_a )
__A = inputs['''input_ids''']
__A = model.generate(input_ids=_a ,attention_mask=inputs["attention_mask"] ,max_new_tokens=12 )
__A = tokenizer(sentences[0] ,return_tensors="tf" ).input_ids
__A = model.generate(input_ids=_a ,max_new_tokens=12 )
__A = tokenizer(sentences[1] ,return_tensors="tf" ).input_ids
__A = model.generate(input_ids=_a ,max_new_tokens=12 )
__A = tokenizer.batch_decode(_a ,skip_special_tokens=_a )
__A = tokenizer.decode(output_non_padded[0] ,skip_special_tokens=_a )
__A = tokenizer.decode(output_padded[0] ,skip_special_tokens=_a )
__A = [
'''This is an extremelly long sentence that only exists to test the ability of the model to cope with '''
'''left-padding, such as in batched generation. The output for the sequence below should be the same '''
'''regardless of whether left padding is applied or not. When left padding is applied, the sequence will be '''
'''a single''',
'''Hello, my dog is a little bit of a shy one, but he is very friendly''',
]
self.assertListEqual(_a ,_a )
self.assertListEqual(_a ,[non_padded_sentence, padded_sentence] )
| 55 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__ = logging.get_logger(__name__)
a__ = {
'''funnel-transformer/small''': '''https://huggingface.co/funnel-transformer/small/resolve/main/config.json''',
'''funnel-transformer/small-base''': '''https://huggingface.co/funnel-transformer/small-base/resolve/main/config.json''',
'''funnel-transformer/medium''': '''https://huggingface.co/funnel-transformer/medium/resolve/main/config.json''',
'''funnel-transformer/medium-base''': '''https://huggingface.co/funnel-transformer/medium-base/resolve/main/config.json''',
'''funnel-transformer/intermediate''': (
'''https://huggingface.co/funnel-transformer/intermediate/resolve/main/config.json'''
),
'''funnel-transformer/intermediate-base''': (
'''https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/config.json'''
),
'''funnel-transformer/large''': '''https://huggingface.co/funnel-transformer/large/resolve/main/config.json''',
'''funnel-transformer/large-base''': '''https://huggingface.co/funnel-transformer/large-base/resolve/main/config.json''',
'''funnel-transformer/xlarge''': '''https://huggingface.co/funnel-transformer/xlarge/resolve/main/config.json''',
'''funnel-transformer/xlarge-base''': '''https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/config.json''',
}
class UpperCAmelCase_ ( __lowercase ):
"""simple docstring"""
UpperCAmelCase__ : Optional[int] = "funnel"
UpperCAmelCase__ : Tuple = {
"hidden_size": "d_model",
"num_attention_heads": "n_head",
}
def __init__( self , _a=3_0_5_2_2 , _a=[4, 4, 4] , _a=None , _a=2 , _a=7_6_8 , _a=1_2 , _a=6_4 , _a=3_0_7_2 , _a="gelu_new" , _a=0.1 , _a=0.1 , _a=0.0 , _a=0.1 , _a=None , _a=1e-9 , _a="mean" , _a="relative_shift" , _a=True , _a=True , _a=True , **_a , ) -> List[Any]:
_a : Optional[int] = vocab_size
_a : Dict = block_sizes
_a : Optional[int] = [1] * len(_a ) if block_repeats is None else block_repeats
assert len(_a ) == len(
self.block_repeats ), "`block_sizes` and `block_repeats` should have the same length."
_a : int = num_decoder_layers
_a : List[str] = d_model
_a : Optional[Any] = n_head
_a : Tuple = d_head
_a : Dict = d_inner
_a : List[str] = hidden_act
_a : int = hidden_dropout
_a : Union[str, Any] = attention_dropout
_a : Tuple = activation_dropout
_a : Optional[Any] = initializer_range
_a : Dict = initializer_std
_a : Union[str, Any] = layer_norm_eps
assert pooling_type in [
"mean",
"max",
], F"""Got {pooling_type} for `pooling_type` but only 'mean' and 'max' are supported."""
_a : Any = pooling_type
assert attention_type in [
"relative_shift",
"factorized",
], F"""Got {attention_type} for `attention_type` but only 'relative_shift' and 'factorized' are supported."""
_a : Optional[Any] = attention_type
_a : int = separate_cls
_a : Tuple = truncate_seq
_a : List[Any] = pool_q_only
super().__init__(**_a )
@property
def __lowercase ( self ) -> Tuple:
return sum(self.block_sizes )
@num_hidden_layers.setter
def __lowercase ( self , _a ) -> List[str]:
raise NotImplementedError(
'''This model does not support the setting of `num_hidden_layers`. Please set `block_sizes`.''' )
@property
def __lowercase ( self ) -> Optional[int]:
return len(self.block_sizes )
@num_blocks.setter
def __lowercase ( self , _a ) -> Dict:
raise NotImplementedError('''This model does not support the setting of `num_blocks`. Please set `block_sizes`.''' )
| 14 | 0 |
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE ) -> Tuple:
"""simple docstring"""
_A = os.path.join(args.tf_model_dir , 'parameters.json' )
_A = json.loads(open(__a ).read() )
if not params:
raise ValueError(
F"It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file." )
if not args.output.endswith('.pt' ):
_A = args.output + '''.pt'''
_A = OrderedDict()
with tf.device('/CPU:0' ):
_A = tf.train.load_checkpoint(args.tf_model_dir )
_A = reader.get_variable_to_shape_map()
for key_name in shapes.keys():
_A = reader.get_tensor(__a ).astype(np.floataa )
if key_name.endswith('/adam_m' ) or key_name.endswith('/adam_v' ):
continue
if key_name.startswith('pasts/' ):
if key_name.startswith('pasts/mlp' ):
_A = int(key_name[9] )
elif key_name.startswith('pasts/out' ):
_A = 8
_A = '''model.sqout.%d.weight''' % (player * 2) # enter to nn.Sequencial with Tanh, so 2 at a time
_A = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
_A = torch.tensor(__a )
elif key_name.startswith('model/moe' ):
_A = int(key_name[9:].split('/' )[0] )
if key_name.endswith('/switch_gating/kernel' ):
_A = '''model.blocks.%d.feed_forward.mlp.router.classifier.weight''' % player
_A = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
_A = torch.tensor(__a )
elif key_name.endswith('/softmlp/kernel' ):
_A = '''model.blocks.%d.feed_forward.soft_bypass_mlp.weight''' % player
_A = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
_A = torch.tensor(__a )
elif key_name.endswith('/wo/kernel' ) or key_name.endswith('/wi/kernel' ):
_A = key_name[-9:-7]
for i in range(16 ):
_A = '''model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight''' % (player, i, nlayer)
_A = (
vnp[i].transpose([1, 0] ).copy()
) # In Mesh-Tensorflow, it is one array, so it is divided
_A = torch.tensor(__a )
elif key_name.startswith('model/mlp' ):
_A = int(key_name[9:].split('/' )[0] )
if key_name.endswith('/p1/kernel' ):
_A = '''model.blocks.%d.feed_forward.mlp.wi.weight''' % player
_A = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
_A = torch.tensor(__a )
elif key_name.endswith('/p1/bias' ):
_A = '''model.blocks.%d.feed_forward.mlp.wi.bias''' % player
_A = vnp.copy() # same because it is one dimensional
_A = torch.tensor(__a )
elif key_name.endswith('/p2/kernel' ):
_A = '''model.blocks.%d.feed_forward.mlp.wo.weight''' % player
_A = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
_A = torch.tensor(__a )
elif key_name.endswith('/p2/bias' ):
_A = '''model.blocks.%d.feed_forward.mlp.wo.bias''' % player
_A = vnp.copy() # same because it is one dimensional
_A = torch.tensor(__a )
elif key_name.startswith('model/ln' ):
_A = int(key_name[8:].split('/' )[0] )
if key_name.endswith('/b' ):
_A = '''model.blocks.%d.feed_forward.norm.bias''' % player
_A = vnp.copy() # same because it is one dimensional
_A = torch.tensor(__a )
elif key_name.endswith('/g' ):
_A = '''model.blocks.%d.feed_forward.norm.weight''' % player
_A = vnp.copy() # same because it is one dimensional
_A = torch.tensor(__a )
elif key_name.startswith('model/att' ):
_A = int(key_name[9:].split('/' )[0] )
if key_name.endswith('/qkv/kernel' ):
_A = vnp.copy() # Compute same dimension as Mesh-tensorflow using einsum
_A = state[:, 0, :, :]
_A = state[:, 1, :, :]
_A = state[:, 2, :, :]
_A = (
state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]] )
.transpose([1, 0] )
.copy()
) # Mesh-Tensorflow is a diagonal matrix
_A = (
state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]] )
.transpose([1, 0] )
.copy()
) # Mesh-Tensorflow is a diagonal matrix
_A = (
state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]] )
.transpose([1, 0] )
.copy()
) # Mesh-Tensorflow is a diagonal matrix
_A = '''model.blocks.%d.self_attn.self_attn.q_proj.weight''' % player
_A = torch.tensor(__a )
_A = '''model.blocks.%d.self_attn.self_attn.k_proj.weight''' % player
_A = torch.tensor(__a )
_A = '''model.blocks.%d.self_attn.self_attn.v_proj.weight''' % player
_A = torch.tensor(__a )
elif key_name.endswith('/o/kernel' ):
_A = '''model.blocks.%d.self_attn.self_attn.out_proj.weight''' % player
_A = (
vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]] ).transpose([1, 0] ).copy()
) # Mesh-Tensorflow is a diagonal matrix
_A = torch.tensor(__a )
elif key_name.startswith('model/an' ):
_A = int(key_name[8:].split('/' )[0] )
if key_name.endswith('/b' ):
_A = '''model.blocks.%d.self_attn.norm.bias''' % player
_A = vnp.copy() # same because it is one dimensional
_A = torch.tensor(__a )
elif key_name.endswith('/g' ):
_A = '''model.blocks.%d.self_attn.norm.weight''' % player
_A = vnp.copy() # same because it is one dimensional
_A = torch.tensor(__a )
elif (
key_name.startswith('model/wte' )
or key_name.startswith('model/wpe' )
or key_name.startswith('model/ete' )
):
_A = {'''wte''': '''embed_tokens''', '''wpe''': '''position_embeddings''', '''ete''': '''extra_position_embeddings'''}[
key_name[-3:]
]
_A = '''model.%s.weight''' % nlayer
_A = vnp.copy() # same in embedded
_A = torch.tensor(__a )
if key_name.startswith('model/wte' ):
_A = '''lm_head.weight'''
_A = vnp.copy() # same in embedded
_A = torch.tensor(__a )
elif key_name.startswith('model/wob' ):
_A = '''final_logits_bias'''
_A = vnp.copy() # same in embedded
_A = state.reshape((1, -1) )
_A = torch.tensor(__a )
elif key_name == "model/dense/kernel":
_A = '''model.last_project.weight'''
_A = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
_A = torch.tensor(__a )
elif key_name == "model/dense_1/bias":
_A = '''model.last_project.bias'''
_A = vnp.copy() # same because it is one dimensional
_A = torch.tensor(__a )
torch.save(__a , args.output )
if __name__ == "__main__":
__A : int = argparse.ArgumentParser(
description="model converter.", formatter_class=argparse.ArgumentDefaultsHelpFormatter
)
parser.add_argument("--tf_model_dir", metavar="PATH", type=str, required=True, help="import model")
parser.add_argument("--output", metavar="PATH", type=str, required=True, help="output model")
__A : List[str] = parser.parse_args()
convert_tf_gptsan_to_pt(args)
| 27 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a__ = logging.get_logger(__name__)
a__ = {
'''google/mobilenet_v1_1.0_224''': '''https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json''',
'''google/mobilenet_v1_0.75_192''': '''https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json''',
# See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}
class UpperCAmelCase_ ( __lowercase ):
"""simple docstring"""
UpperCAmelCase__ : int = "mobilenet_v1"
def __init__( self , _a=3 , _a=2_2_4 , _a=1.0 , _a=8 , _a="relu6" , _a=True , _a=0.999 , _a=0.02 , _a=0.001 , **_a , ) -> List[Any]:
super().__init__(**_a )
if depth_multiplier <= 0:
raise ValueError('''depth_multiplier must be greater than zero.''' )
_a : Tuple = num_channels
_a : str = image_size
_a : Tuple = depth_multiplier
_a : Any = min_depth
_a : int = hidden_act
_a : Optional[Any] = tf_padding
_a : str = classifier_dropout_prob
_a : Optional[int] = initializer_range
_a : Any = layer_norm_eps
class UpperCAmelCase_ ( __lowercase ):
"""simple docstring"""
UpperCAmelCase__ : str = version.parse("1.11" )
@property
def __lowercase ( self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict([('''pixel_values''', {0: '''batch'''})] )
@property
def __lowercase ( self ) -> Mapping[str, Mapping[int, str]]:
if self.task == "image-classification":
return OrderedDict([('''logits''', {0: '''batch'''})] )
else:
return OrderedDict([('''last_hidden_state''', {0: '''batch'''}), ('''pooler_output''', {0: '''batch'''})] )
@property
def __lowercase ( self ) -> float:
return 1e-4
| 14 | 0 |
'''simple docstring'''
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
UpperCAmelCase_ : List[Any] = TypeVar('''KEY''')
UpperCAmelCase_ : str = TypeVar('''VAL''')
@dataclass(frozen=__lowercase , slots=__lowercase)
class lowerCAmelCase ( Generic[KEY, VAL]):
__lowercase : KEY
__lowercase : VAL
class lowerCAmelCase ( _Item):
def __init__( self ) -> None:
'''simple docstring'''
super().__init__(_a , _a )
def __bool__( self ) -> bool:
'''simple docstring'''
return False
UpperCAmelCase_ : List[str] = _DeletedItem()
class lowerCAmelCase ( MutableMapping[KEY, VAL]):
def __init__( self , __SCREAMING_SNAKE_CASE = 8 , __SCREAMING_SNAKE_CASE = 0.75 ) -> None:
'''simple docstring'''
__snake_case = initial_block_size
__snake_case = [None] * initial_block_size
assert 0.0 < capacity_factor < 1.0
__snake_case = capacity_factor
__snake_case = 0
def lowerCAmelCase ( self , __SCREAMING_SNAKE_CASE ) -> int:
'''simple docstring'''
return hash(_a ) % len(self._buckets )
def lowerCAmelCase ( self , __SCREAMING_SNAKE_CASE ) -> int:
'''simple docstring'''
return (ind + 1) % len(self._buckets )
def lowerCAmelCase ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) -> bool:
'''simple docstring'''
__snake_case = self._buckets[ind]
if not stored:
__snake_case = _Item(_a , _a )
self._len += 1
return True
elif stored.key == key:
__snake_case = _Item(_a , _a )
return True
else:
return False
def lowerCAmelCase ( self ) -> bool:
'''simple docstring'''
__snake_case = len(self._buckets ) * self._capacity_factor
return len(self ) >= int(_a )
def lowerCAmelCase ( self ) -> bool:
'''simple docstring'''
if len(self._buckets ) <= self._initial_block_size:
return False
__snake_case = len(self._buckets ) * self._capacity_factor / 2
return len(self ) < limit
def lowerCAmelCase ( self , __SCREAMING_SNAKE_CASE ) -> None:
'''simple docstring'''
__snake_case = self._buckets
__snake_case = [None] * new_size
__snake_case = 0
for item in old_buckets:
if item:
self._add_item(item.key , item.val )
def lowerCAmelCase ( self ) -> None:
'''simple docstring'''
self._resize(len(self._buckets ) * 2 )
def lowerCAmelCase ( self ) -> None:
'''simple docstring'''
self._resize(len(self._buckets ) // 2 )
def lowerCAmelCase ( self , __SCREAMING_SNAKE_CASE ) -> Iterator[int]:
'''simple docstring'''
__snake_case = self._get_bucket_index(_a )
for _ in range(len(self._buckets ) ):
yield ind
__snake_case = self._get_next_ind(_a )
def lowerCAmelCase ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) -> None:
'''simple docstring'''
for ind in self._iterate_buckets(_a ):
if self._try_set(_a , _a , _a ):
break
def __setitem__( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) -> None:
'''simple docstring'''
if self._is_full():
self._size_up()
self._add_item(_a , _a )
def __delitem__( self , __SCREAMING_SNAKE_CASE ) -> None:
'''simple docstring'''
for ind in self._iterate_buckets(_a ):
__snake_case = self._buckets[ind]
if item is None:
raise KeyError(_a )
if item is _deleted:
continue
if item.key == key:
__snake_case = _deleted
self._len -= 1
break
if self._is_sparse():
self._size_down()
def __getitem__( self , __SCREAMING_SNAKE_CASE ) -> VAL:
'''simple docstring'''
for ind in self._iterate_buckets(_a ):
__snake_case = self._buckets[ind]
if item is None:
break
if item is _deleted:
continue
if item.key == key:
return item.val
raise KeyError(_a )
def __len__( self ) -> int:
'''simple docstring'''
return self._len
def __iter__( self ) -> Iterator[KEY]:
'''simple docstring'''
yield from (item.key for item in self._buckets if item)
def __repr__( self ) -> str:
'''simple docstring'''
__snake_case = ''' ,'''.join(
F'''{item.key}: {item.val}''' for item in self._buckets if item )
return F'''HashMap({val_string})'''
| 24 |
a__ = '''Input must be a string of 8 numbers plus letter'''
a__ = '''TRWAGMYFPDXBNJZSQVHLCKE'''
def __UpperCAmelCase ( __a : str ) -> bool:
"""simple docstring"""
if not isinstance(__a ,__a ):
_a : List[str] = F"""Expected string as input, found {type(__a ).__name__}"""
raise TypeError(__a )
_a : List[Any] = spanish_id.replace('''-''' ,'''''' ).upper()
if len(__a ) != 9:
raise ValueError(__a )
try:
_a : Any = int(spanish_id_clean[0:8] )
_a : str = spanish_id_clean[8]
except ValueError as ex:
raise ValueError(__a ) from ex
if letter.isdigit():
raise ValueError(__a )
return letter == LOOKUP_LETTERS[number % 23]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 14 | 0 |
_A = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"
def lowerCamelCase__ ( __lowerCAmelCase : bytes ):
"""simple docstring"""
if not isinstance(__a , __a ):
lowerCAmelCase_ = F"""a bytes-like object is required, not '{data.__class__.__name__}'"""
raise TypeError(__a )
lowerCAmelCase_ = ''''''.join(bin(__a )[2:].zfill(8 ) for byte in data )
lowerCAmelCase_ = len(__a ) % 6 != 0
if padding_needed:
# The padding that will be added later
lowerCAmelCase_ = b'''=''' * ((6 - len(__a ) % 6) // 2)
# Append binary_stream with arbitrary binary digits (0's by default) to make its
# length a multiple of 6.
binary_stream += "0" * (6 - len(__a ) % 6)
else:
lowerCAmelCase_ = b''''''
# Encode every 6 binary digits to their corresponding Base64 character
return (
"".join(
B64_CHARSET[int(binary_stream[index : index + 6] , 2 )]
for index in range(0 , len(__a ) , 6 ) ).encode()
+ padding
)
def lowerCamelCase__ ( __lowerCAmelCase : str ):
"""simple docstring"""
if not isinstance(__a , __a ) and not isinstance(__a , __a ):
lowerCAmelCase_ = (
'''argument should be a bytes-like object or ASCII string, '''
F"""not '{encoded_data.__class__.__name__}'"""
)
raise TypeError(__a )
# In case encoded_data is a bytes-like object, make sure it contains only
# ASCII characters so we convert it to a string object
if isinstance(__a , __a ):
try:
lowerCAmelCase_ = encoded_data.decode("utf-8" )
except UnicodeDecodeError:
raise ValueError("base64 encoded data should only contain ASCII characters" )
lowerCAmelCase_ = encoded_data.count("=" )
# Check if the encoded string contains non base64 characters
if padding:
assert all(
char in B64_CHARSET for char in encoded_data[:-padding] ), "Invalid base64 character(s) found."
else:
assert all(
char in B64_CHARSET for char in encoded_data ), "Invalid base64 character(s) found."
# Check the padding
assert len(__a ) % 4 == 0 and padding < 3, "Incorrect padding"
if padding:
# Remove padding if there is one
lowerCAmelCase_ = encoded_data[:-padding]
lowerCAmelCase_ = ''''''.join(
bin(B64_CHARSET.index(__a ) )[2:].zfill(6 ) for char in encoded_data )[: -padding * 2]
else:
lowerCAmelCase_ = ''''''.join(
bin(B64_CHARSET.index(__a ) )[2:].zfill(6 ) for char in encoded_data )
lowerCAmelCase_ = [
int(binary_stream[index : index + 8] , 2 )
for index in range(0 , len(__a ) , 8 )
]
return bytes(__a )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 290 |
from random import randint
from tempfile import TemporaryFile
import numpy as np
def __UpperCAmelCase ( __a : Optional[Any] ,__a : int ,__a : Any ) -> int:
"""simple docstring"""
_a : int = 0
if start < end:
_a : Tuple = randint(__a ,__a )
_a : Tuple = a[end]
_a : List[str] = a[pivot]
_a : Any = temp
_a , _a : Optional[int] = _in_place_partition(__a ,__a ,__a )
count += _in_place_quick_sort(__a ,__a ,p - 1 )
count += _in_place_quick_sort(__a ,p + 1 ,__a )
return count
def __UpperCAmelCase ( __a : List[Any] ,__a : Tuple ,__a : Dict ) -> Dict:
"""simple docstring"""
_a : Dict = 0
_a : Tuple = randint(__a ,__a )
_a : List[Any] = a[end]
_a : str = a[pivot]
_a : str = temp
_a : Dict = start - 1
for index in range(__a ,__a ):
count += 1
if a[index] < a[end]: # check if current val is less than pivot value
_a : int = new_pivot_index + 1
_a : Any = a[new_pivot_index]
_a : Optional[int] = a[index]
_a : str = temp
_a : Union[str, Any] = a[new_pivot_index + 1]
_a : Tuple = a[end]
_a : Any = temp
return new_pivot_index + 1, count
a__ = TemporaryFile()
a__ = 100 # 1000 elements are to be sorted
a__ , a__ = 0, 1 # mean and standard deviation
a__ = np.random.normal(mu, sigma, p)
np.save(outfile, X)
print('''The array is''')
print(X)
outfile.seek(0) # using the same array
a__ = np.load(outfile)
a__ = len(M) - 1
a__ = _in_place_quick_sort(M, 0, r)
print(
'''No of Comparisons for 100 elements selected from a standard normal distribution'''
'''is :'''
)
print(z)
| 14 | 0 |
'''simple docstring'''
import string
def a__ ( _SCREAMING_SNAKE_CASE : str ) -> None:
"""simple docstring"""
for key in range(len(string.ascii_uppercase ) ):
UpperCAmelCase_ : int = ''''''
for symbol in message:
if symbol in string.ascii_uppercase:
UpperCAmelCase_ : Union[str, Any] = string.ascii_uppercase.find(__a )
UpperCAmelCase_ : Optional[int] = num - key
if num < 0:
UpperCAmelCase_ : Any = num + len(string.ascii_uppercase )
UpperCAmelCase_ : Optional[int] = translated + string.ascii_uppercase[num]
else:
UpperCAmelCase_ : Optional[int] = translated + symbol
print(F'''Decryption using Key #{key}: {translated}''' )
def a__ ( ) -> None:
"""simple docstring"""
UpperCAmelCase_ : List[Any] = input("Encrypted message: " )
UpperCAmelCase_ : Union[str, Any] = message.upper()
decrypt(__a )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 71 |
import json
import os
import unittest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class UpperCAmelCase_ ( __lowercase , unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase__ : List[Any] = MgpstrTokenizer
UpperCAmelCase__ : int = False
UpperCAmelCase__ : Union[str, Any] = {}
UpperCAmelCase__ : List[Any] = False
def __lowercase ( self ) -> Any:
super().setUp()
# fmt: off
_a : Tuple = ['''[GO]''', '''[s]''', '''0''', '''1''', '''2''', '''3''', '''4''', '''5''', '''6''', '''7''', '''8''', '''9''', '''a''', '''b''', '''c''', '''d''', '''e''', '''f''', '''g''', '''h''', '''i''', '''j''', '''k''', '''l''', '''m''', '''n''', '''o''', '''p''', '''q''', '''r''', '''s''', '''t''', '''u''', '''v''', '''w''', '''x''', '''y''', '''z''']
# fmt: on
_a : Optional[int] = dict(zip(_a , range(len(_a ) ) ) )
_a : Any = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write(json.dumps(_a ) + '''\n''' )
def __lowercase ( self , **_a ) -> Dict:
return MgpstrTokenizer.from_pretrained(self.tmpdirname , **_a )
def __lowercase ( self , _a ) -> Tuple:
_a : List[str] = '''tester'''
_a : Optional[Any] = '''tester'''
return input_text, output_text
@unittest.skip('''MGP-STR always lower cases letters.''' )
def __lowercase ( self ) -> Any:
pass
def __lowercase ( self ) -> Any:
_a : Union[str, Any] = self.get_tokenizers(do_lower_case=_a )
for tokenizer in tokenizers:
with self.subTest(F"""{tokenizer.__class__.__name__}""" ):
_a : int = '''[SPECIAL_TOKEN]'''
tokenizer.add_special_tokens({'''cls_token''': special_token} )
_a : Tuple = tokenizer.encode([special_token] , add_special_tokens=_a )
self.assertEqual(len(_a ) , 1 )
_a : Tuple = tokenizer.decode(_a , skip_special_tokens=_a )
self.assertTrue(special_token not in decoded )
def __lowercase ( self ) -> Tuple:
_a : List[Any] = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(F"""{tokenizer.__class__.__name__}""" ):
_a , _a : int = self.get_input_output_texts(_a )
_a : List[str] = tokenizer.tokenize(_a )
_a : Optional[int] = tokenizer.convert_tokens_to_ids(_a )
_a : Tuple = tokenizer.encode(_a , add_special_tokens=_a )
self.assertListEqual(_a , _a )
_a : Optional[int] = tokenizer.convert_ids_to_tokens(_a )
self.assertNotEqual(len(_a ) , 0 )
_a : int = tokenizer.decode(_a )
self.assertIsInstance(_a , _a )
self.assertEqual(text_a.replace(''' ''' , '''''' ) , _a )
@unittest.skip('''MGP-STR tokenizer only handles one sequence.''' )
def __lowercase ( self ) -> List[str]:
pass
@unittest.skip('''inputs cannot be pretokenized in MgpstrTokenizer''' )
def __lowercase ( self ) -> Optional[Any]:
pass
| 14 | 0 |
"""simple docstring"""
from jiwer import compute_measures
import datasets
__A : Tuple = "\\n@inproceedings{inproceedings,\n author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n year = {2004},\n month = {01},\n pages = {},\n title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}\n}\n"
__A : List[str] = "\\nWord error rate (WER) is a common metric of the performance of an automatic speech recognition system.\n\nThe general difficulty of measuring performance lies in the fact that the recognized word sequence can have a different length from the reference word sequence (supposedly the correct one). The WER is derived from the Levenshtein distance, working at the word level instead of the phoneme level. The WER is a valuable tool for comparing different systems as well as for evaluating improvements within one system. This kind of measurement, however, provides no details on the nature of translation errors and further work is therefore required to identify the main source(s) of error and to focus any research effort.\n\nThis problem is solved by first aligning the recognized word sequence with the reference (spoken) word sequence using dynamic string alignment. Examination of this issue is seen through a theory called the power law that states the correlation between perplexity and word error rate.\n\nWord error rate can then be computed as:\n\nWER = (S + D + I) / N = (S + D + I) / (S + D + C)\n\nwhere\n\nS is the number of substitutions,\nD is the number of deletions,\nI is the number of insertions,\nC is the number of correct words,\nN is the number of words in the reference (N=S+D+C).\n\nThis value indicates the average number of errors per reference word. The lower the value, the better the\nperformance of the ASR system with a WER of 0 being a perfect score.\n"
__A : Any = "\nCompute WER score of transcribed segments against references.\n\nArgs:\n references: List of references for each speech input.\n predictions: List of transcriptions to score.\n concatenate_texts (bool, default=False): Whether to concatenate all input texts or compute WER iteratively.\n\nReturns:\n (float): the word error rate\n\nExamples:\n\n >>> predictions = [\"this is the prediction\", \"there is an other sample\"]\n >>> references = [\"this is the reference\", \"there is another one\"]\n >>> wer = datasets.load_metric(\"wer\")\n >>> wer_score = wer.compute(predictions=predictions, references=references)\n >>> print(wer_score)\n 0.5\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION)
class _a ( datasets.Metric):
"""simple docstring"""
def lowercase__ ( self : Tuple )->Optional[int]:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Value('''string''' , id='''sequence''' ),
'''references''': datasets.Value('''string''' , id='''sequence''' ),
} ) , codebase_urls=['''https://github.com/jitsi/jiwer/'''] , reference_urls=[
'''https://en.wikipedia.org/wiki/Word_error_rate''',
] , )
def lowercase__ ( self : Any , __UpperCamelCase : Optional[int]=None , __UpperCamelCase : Optional[Any]=None , __UpperCamelCase : List[Any]=False )->Optional[int]:
if concatenate_texts:
return compute_measures(_a , _a )["wer"]
else:
_UpperCAmelCase = 0
_UpperCAmelCase = 0
for prediction, reference in zip(_a , _a ):
_UpperCAmelCase = compute_measures(_a , _a )
incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
total += measures["substitutions"] + measures["deletions"] + measures["hits"]
return incorrect / total
| 602 |
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 UpperCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
def __lowercase ( self ) -> List[Any]:
_a : int = 0
def __lowercase ( self ) -> List[str]:
_a : Dict = AutoImageProcessor.from_pretrained('''openai/clip-vit-base-patch32''' )
self.assertIsInstance(_a , _a )
def __lowercase ( self ) -> Union[str, Any]:
with tempfile.TemporaryDirectory() as tmpdirname:
_a : Tuple = Path(_a ) / '''preprocessor_config.json'''
_a : Optional[Any] = Path(_a ) / '''config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(_a , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(_a , '''w''' ) )
_a : List[str] = AutoImageProcessor.from_pretrained(_a )
self.assertIsInstance(_a , _a )
def __lowercase ( self ) -> Optional[Any]:
# Ensure we can load the image processor from the feature extractor config
with tempfile.TemporaryDirectory() as tmpdirname:
_a : Optional[int] = Path(_a ) / '''preprocessor_config.json'''
_a : Any = Path(_a ) / '''config.json'''
json.dump(
{'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''} , open(_a , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(_a , '''w''' ) )
_a : Optional[Any] = AutoImageProcessor.from_pretrained(_a )
self.assertIsInstance(_a , _a )
def __lowercase ( self ) -> Optional[Any]:
with tempfile.TemporaryDirectory() as tmpdirname:
_a : Dict = CLIPConfig()
# Create a dummy config file with image_proceesor_type
_a : Tuple = Path(_a ) / '''preprocessor_config.json'''
_a : List[str] = Path(_a ) / '''config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(_a , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(_a , '''w''' ) )
# remove image_processor_type to make sure config.json alone is enough to load image processor locally
_a : Tuple = AutoImageProcessor.from_pretrained(_a ).to_dict()
config_dict.pop('''image_processor_type''' )
_a : Tuple = CLIPImageProcessor(**_a )
# save in new folder
model_config.save_pretrained(_a )
config.save_pretrained(_a )
_a : List[str] = AutoImageProcessor.from_pretrained(_a )
# make sure private variable is not incorrectly saved
_a : Optional[int] = json.loads(config.to_json_string() )
self.assertTrue('''_processor_class''' not in dict_as_saved )
self.assertIsInstance(_a , _a )
def __lowercase ( self ) -> Dict:
with tempfile.TemporaryDirectory() as tmpdirname:
_a : Optional[int] = Path(_a ) / '''preprocessor_config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(_a , '''w''' ) , )
_a : List[str] = AutoImageProcessor.from_pretrained(_a )
self.assertIsInstance(_a , _a )
def __lowercase ( self ) -> Any:
with self.assertRaisesRegex(
_a , '''clip-base is not a local folder and is not a valid model identifier''' ):
_a : Dict = AutoImageProcessor.from_pretrained('''clip-base''' )
def __lowercase ( self ) -> List[Any]:
with self.assertRaisesRegex(
_a , R'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)''' ):
_a : List[str] = AutoImageProcessor.from_pretrained(_a , revision='''aaaaaa''' )
def __lowercase ( self ) -> Dict:
with self.assertRaisesRegex(
_a , '''hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.''' , ):
_a : Optional[int] = AutoImageProcessor.from_pretrained('''hf-internal-testing/config-no-model''' )
def __lowercase ( self ) -> Union[str, Any]:
# If remote code is not set, we will time out when asking whether to load the model.
with self.assertRaises(_a ):
_a : str = AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor''' )
# If remote code is disabled, we can't load this config.
with self.assertRaises(_a ):
_a : Optional[Any] = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=_a )
_a : Union[str, Any] = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=_a )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
# Test image processor can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(_a )
_a : Optional[Any] = AutoImageProcessor.from_pretrained(_a , trust_remote_code=_a )
self.assertEqual(reloaded_image_processor.__class__.__name__ , '''NewImageProcessor''' )
def __lowercase ( self ) -> Dict:
try:
AutoConfig.register('''custom''' , _a )
AutoImageProcessor.register(_a , _a )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(_a ):
AutoImageProcessor.register(_a , _a )
with tempfile.TemporaryDirectory() as tmpdirname:
_a : int = Path(_a ) / '''preprocessor_config.json'''
_a : int = Path(_a ) / '''config.json'''
json.dump(
{'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''} , open(_a , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(_a , '''w''' ) )
_a : int = CustomImageProcessor.from_pretrained(_a )
# 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(_a )
_a : Optional[Any] = AutoImageProcessor.from_pretrained(_a )
self.assertIsInstance(_a , _a )
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 __lowercase ( self ) -> Union[str, Any]:
class UpperCAmelCase_ ( __lowercase ):
"""simple docstring"""
UpperCAmelCase__ : Tuple = True
try:
AutoConfig.register('''custom''' , _a )
AutoImageProcessor.register(_a , _a )
# If remote code is not set, the default is to use local
_a : str = 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 : int = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=_a )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
self.assertTrue(image_processor.is_local )
# If remote is enabled, we load from the Hub
_a : Dict = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=_a )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
self.assertTrue(not hasattr(_a , '''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]
| 14 | 0 |
"""simple docstring"""
class lowerCamelCase_:
'''simple docstring'''
def __init__( self , lowerCamelCase__ , lowerCamelCase__=None , lowerCamelCase__=None ):
_lowerCamelCase = data
_lowerCamelCase = previous
_lowerCamelCase = next_node
def __str__( self ):
return F"""{self.data}"""
def snake_case__ ( self ):
return self.data
def snake_case__ ( self ):
return self.next
def snake_case__ ( self ):
return self.previous
class lowerCamelCase_:
'''simple docstring'''
def __init__( self , lowerCamelCase__ ):
_lowerCamelCase = head
def __iter__( self ):
return self
def snake_case__ ( self ):
if not self.current:
raise StopIteration
else:
_lowerCamelCase = self.current.get_data()
_lowerCamelCase = self.current.get_next()
return value
class lowerCamelCase_:
'''simple docstring'''
def __init__( self ):
_lowerCamelCase = None # First node in list
_lowerCamelCase = None # Last node in list
def __str__( self ):
_lowerCamelCase = self.head
_lowerCamelCase = []
while current is not None:
nodes.append(current.get_data() )
_lowerCamelCase = current.get_next()
return " ".join(str(_a ) for node in nodes )
def __contains__( self , lowerCamelCase__ ):
_lowerCamelCase = self.head
while current:
if current.get_data() == value:
return True
_lowerCamelCase = current.get_next()
return False
def __iter__( self ):
return LinkedListIterator(self.head )
def snake_case__ ( self ):
if self.head:
return self.head.get_data()
return None
def snake_case__ ( self ):
if self.tail:
return self.tail.get_data()
return None
def snake_case__ ( self , lowerCamelCase__ ):
if self.head is None:
_lowerCamelCase = node
_lowerCamelCase = node
else:
self.insert_before_node(self.head , _a )
def snake_case__ ( self , lowerCamelCase__ ):
if self.head is None:
self.set_head(_a )
else:
self.insert_after_node(self.tail , _a )
def snake_case__ ( self , lowerCamelCase__ ):
_lowerCamelCase = Node(_a )
if self.head is None:
self.set_head(_a )
else:
self.set_tail(_a )
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ ):
_lowerCamelCase = node
_lowerCamelCase = node.previous
if node.get_previous() is None:
_lowerCamelCase = node_to_insert
else:
_lowerCamelCase = node_to_insert
_lowerCamelCase = node_to_insert
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ ):
_lowerCamelCase = node
_lowerCamelCase = node.next
if node.get_next() is None:
_lowerCamelCase = node_to_insert
else:
_lowerCamelCase = node_to_insert
_lowerCamelCase = node_to_insert
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ ):
_lowerCamelCase = 1
_lowerCamelCase = Node(_a )
_lowerCamelCase = self.head
while node:
if current_position == position:
self.insert_before_node(_a , _a )
return
current_position += 1
_lowerCamelCase = node.next
self.insert_after_node(self.tail , _a )
def snake_case__ ( self , lowerCamelCase__ ):
_lowerCamelCase = self.head
while node:
if node.get_data() == item:
return node
_lowerCamelCase = node.get_next()
raise Exception('''Node not found''' )
def snake_case__ ( self , lowerCamelCase__ ):
if (node := self.get_node(_a )) is not None:
if node == self.head:
_lowerCamelCase = self.head.get_next()
if node == self.tail:
_lowerCamelCase = self.tail.get_previous()
self.remove_node_pointers(_a )
@staticmethod
def snake_case__ ( lowerCamelCase__ ):
if node.get_next():
_lowerCamelCase = node.previous
if node.get_previous():
_lowerCamelCase = node.next
_lowerCamelCase = None
_lowerCamelCase = None
def snake_case__ ( self ):
return self.head is None
def lowerCAmelCase_( ) -> None:
pass
if __name__ == "__main__":
import doctest
doctest.testmod()
| 661 |
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class UpperCAmelCase_ :
"""simple docstring"""
UpperCAmelCase__ : float
UpperCAmelCase__ : TreeNode | None = None
UpperCAmelCase__ : TreeNode | None = None
def __UpperCAmelCase ( __a : TreeNode | None ) -> bool:
"""simple docstring"""
def is_valid_tree(__a : TreeNode | None ) -> bool:
if node is None:
return True
if not isinstance(__a ,__a ):
return False
try:
float(node.data )
except (TypeError, ValueError):
return False
return is_valid_tree(node.left ) and is_valid_tree(node.right )
if not is_valid_tree(__a ):
raise ValueError(
'''Each node should be type of TreeNode and data should be float.''' )
def is_binary_search_tree_recursive_check(
__a : TreeNode | None ,__a : float ,__a : float ) -> bool:
if node is None:
return True
return (
left_bound < node.data < right_bound
and is_binary_search_tree_recursive_check(node.left ,__a ,node.data )
and is_binary_search_tree_recursive_check(
node.right ,node.data ,__a )
)
return is_binary_search_tree_recursive_check(__a ,-float('''inf''' ) ,float('''inf''' ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 14 | 0 |
import argparse
import torch
from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging
logging.set_verbosity_info()
def _A (UpperCamelCase : Union[str, Any] , UpperCamelCase : List[Any] , UpperCamelCase : List[str] , UpperCamelCase : Tuple ) ->Dict:
'''simple docstring'''
lowerCamelCase__ : Optional[int] = FunnelConfig.from_json_file(__a )
print(f"Building PyTorch model from configuration: {config}" )
lowerCamelCase__ : Optional[Any] = FunnelBaseModel(__a ) if base_model else FunnelModel(__a )
# Load weights from tf checkpoint
load_tf_weights_in_funnel(__a , __a , __a )
# Save pytorch-model
print(f"Save PyTorch model to {pytorch_dump_path}" )
torch.save(model.state_dict() , __a )
if __name__ == "__main__":
_lowercase = 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 model. \nThis specifies the model architecture.''',
)
parser.add_argument(
'''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
parser.add_argument(
'''--base_model''', action='''store_true''', help='''Whether you want just the base model (no decoder) or not.'''
)
_lowercase = parser.parse_args()
convert_tf_checkpoint_to_pytorch(
args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model
)
| 157 |
from __future__ import annotations
import matplotlib.pyplot as plt # type: ignore
import numpy
# initial triangle of Koch snowflake
a__ = numpy.array([0, 0])
a__ = numpy.array([0.5, 0.8660254])
a__ = numpy.array([1, 0])
a__ = [VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]
def __UpperCAmelCase ( __a : list[numpy.ndarray] ,__a : int ) -> list[numpy.ndarray]:
"""simple docstring"""
_a : Tuple = initial_vectors
for _ in range(__a ):
_a : int = iteration_step(__a )
return vectors
def __UpperCAmelCase ( __a : list[numpy.ndarray] ) -> list[numpy.ndarray]:
"""simple docstring"""
_a : Tuple = []
for i, start_vector in enumerate(vectors[:-1] ):
_a : str = vectors[i + 1]
new_vectors.append(__a )
_a : Optional[int] = end_vector - start_vector
new_vectors.append(start_vector + difference_vector / 3 )
new_vectors.append(
start_vector + difference_vector / 3 + rotate(difference_vector / 3 ,60 ) )
new_vectors.append(start_vector + difference_vector * 2 / 3 )
new_vectors.append(vectors[-1] )
return new_vectors
def __UpperCAmelCase ( __a : numpy.ndarray ,__a : float ) -> numpy.ndarray:
"""simple docstring"""
_a : Tuple = numpy.radians(__a )
_a , _a : List[Any] = numpy.cos(__a ), numpy.sin(__a )
_a : Dict = numpy.array(((c, -s), (s, c)) )
return numpy.dot(__a ,__a )
def __UpperCAmelCase ( __a : list[numpy.ndarray] ) -> None:
"""simple docstring"""
_a : str = plt.gca()
axes.set_aspect('''equal''' )
# matplotlib.pyplot.plot takes a list of all x-coordinates and a list of all
# y-coordinates as inputs, which are constructed from the vector-list using
# zip()
_a , _a : Optional[int] = zip(*__a )
plt.plot(__a ,__a )
plt.show()
if __name__ == "__main__":
import doctest
doctest.testmod()
a__ = iterate(INITIAL_VECTORS, 5)
plot(processed_vectors)
| 14 | 0 |
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
a_ = logging.get_logger(__name__)
class __lowerCAmelCase ( __lowercase ):
def __init__( self , *__UpperCAmelCase , **__UpperCAmelCase ):
'''simple docstring'''
warnings.warn(
'''The class MobileViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
''' Please use MobileViTImageProcessor instead.''' , _a , )
super().__init__(*_a , **_a )
| 175 |
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def __UpperCAmelCase ( __a : Tuple ,__a : Dict ,__a : List[str] ,__a : Optional[Any] ,__a : Tuple ) -> Dict:
"""simple docstring"""
with open(__a ) as metadata_file:
_a : Optional[Any] = json.load(__a )
_a : List[Any] = LukeConfig(use_entity_aware_attention=__a ,**metadata['''model_config'''] )
# Load in the weights from the checkpoint_path
_a : Optional[Any] = torch.load(__a ,map_location='''cpu''' )['''module''']
# Load the entity vocab file
_a : Any = load_original_entity_vocab(__a )
# add an entry for [MASK2]
_a : Union[str, Any] = max(entity_vocab.values() ) + 1
config.entity_vocab_size += 1
_a : Dict = XLMRobertaTokenizer.from_pretrained(metadata['''model_config''']['''bert_model_name'''] )
# Add special tokens to the token vocabulary for downstream tasks
_a : Optional[int] = AddedToken('''<ent>''' ,lstrip=__a ,rstrip=__a )
_a : Tuple = AddedToken('''<ent2>''' ,lstrip=__a ,rstrip=__a )
tokenizer.add_special_tokens({'''additional_special_tokens''': [entity_token_a, entity_token_a]} )
config.vocab_size += 2
print(F"""Saving tokenizer to {pytorch_dump_folder_path}""" )
tokenizer.save_pretrained(__a )
with open(os.path.join(__a ,'''tokenizer_config.json''' ) ,'''r''' ) as f:
_a : List[str] = json.load(__a )
_a : Tuple = '''MLukeTokenizer'''
with open(os.path.join(__a ,'''tokenizer_config.json''' ) ,'''w''' ) as f:
json.dump(__a ,__a )
with open(os.path.join(__a ,MLukeTokenizer.vocab_files_names['''entity_vocab_file'''] ) ,'''w''' ) as f:
json.dump(__a ,__a )
_a : Optional[int] = MLukeTokenizer.from_pretrained(__a )
# Initialize the embeddings of the special tokens
_a : str = tokenizer.convert_tokens_to_ids(['''@'''] )[0]
_a : Tuple = tokenizer.convert_tokens_to_ids(['''#'''] )[0]
_a : Any = state_dict['''embeddings.word_embeddings.weight''']
_a : Optional[int] = word_emb[ent_init_index].unsqueeze(0 )
_a : Any = word_emb[enta_init_index].unsqueeze(0 )
_a : Union[str, Any] = torch.cat([word_emb, ent_emb, enta_emb] )
# add special tokens for 'entity_predictions.bias'
for bias_name in ["lm_head.decoder.bias", "lm_head.bias"]:
_a : Tuple = state_dict[bias_name]
_a : Optional[Any] = decoder_bias[ent_init_index].unsqueeze(0 )
_a : Optional[int] = decoder_bias[enta_init_index].unsqueeze(0 )
_a : Dict = torch.cat([decoder_bias, ent_decoder_bias, enta_decoder_bias] )
# Initialize the query layers of the entity-aware self-attention mechanism
for layer_index in range(config.num_hidden_layers ):
for matrix_name in ["query.weight", "query.bias"]:
_a : Tuple = F"""encoder.layer.{layer_index}.attention.self."""
_a : List[Any] = state_dict[prefix + matrix_name]
_a : Dict = state_dict[prefix + matrix_name]
_a : List[Any] = state_dict[prefix + matrix_name]
# Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
_a : Union[str, Any] = state_dict['''entity_embeddings.entity_embeddings.weight''']
_a : Optional[int] = entity_emb[entity_vocab['''[MASK]''']].unsqueeze(0 )
_a : Any = torch.cat([entity_emb, entity_mask_emb] )
# add [MASK2] for 'entity_predictions.bias'
_a : int = state_dict['''entity_predictions.bias''']
_a : int = entity_prediction_bias[entity_vocab['''[MASK]''']].unsqueeze(0 )
_a : Optional[Any] = torch.cat([entity_prediction_bias, entity_mask_bias] )
_a : Optional[int] = LukeForMaskedLM(config=__a ).eval()
state_dict.pop('''entity_predictions.decoder.weight''' )
state_dict.pop('''lm_head.decoder.weight''' )
state_dict.pop('''lm_head.decoder.bias''' )
_a : int = OrderedDict()
for key, value in state_dict.items():
if not (key.startswith('''lm_head''' ) or key.startswith('''entity_predictions''' )):
_a : Optional[int] = state_dict[key]
else:
_a : Tuple = state_dict[key]
_a , _a : int = model.load_state_dict(__a ,strict=__a )
if set(__a ) != {"luke.embeddings.position_ids"}:
raise ValueError(F"""Unexpected unexpected_keys: {unexpected_keys}""" )
if set(__a ) != {
"lm_head.decoder.weight",
"lm_head.decoder.bias",
"entity_predictions.decoder.weight",
}:
raise ValueError(F"""Unexpected missing_keys: {missing_keys}""" )
model.tie_weights()
assert (model.luke.embeddings.word_embeddings.weight == model.lm_head.decoder.weight).all()
assert (model.luke.entity_embeddings.entity_embeddings.weight == model.entity_predictions.decoder.weight).all()
# Check outputs
_a : Optional[int] = MLukeTokenizer.from_pretrained(__a ,task='''entity_classification''' )
_a : int = '''ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan).'''
_a : List[Any] = (0, 9)
_a : Tuple = tokenizer(__a ,entity_spans=[span] ,return_tensors='''pt''' )
_a : int = model(**__a )
# Verify word hidden states
if model_size == "large":
raise NotImplementedError
else: # base
_a : List[str] = torch.Size((1, 33, 768) )
_a : Union[str, Any] = torch.tensor([[0.08_92, 0.05_96, -0.28_19], [0.01_34, 0.11_99, 0.05_73], [-0.01_69, 0.09_27, 0.06_44]] )
if not (outputs.last_hidden_state.shape == expected_shape):
raise ValueError(
F"""Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}""" )
if not torch.allclose(outputs.last_hidden_state[0, :3, :3] ,__a ,atol=1E-4 ):
raise ValueError
# Verify entity hidden states
if model_size == "large":
raise NotImplementedError
else: # base
_a : str = torch.Size((1, 1, 768) )
_a : List[Any] = torch.tensor([[-0.14_82, 0.06_09, 0.03_22]] )
if not (outputs.entity_last_hidden_state.shape == expected_shape):
raise ValueError(
F"""Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is"""
F""" {expected_shape}""" )
if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3] ,__a ,atol=1E-4 ):
raise ValueError
# Verify masked word/entity prediction
_a : Optional[int] = MLukeTokenizer.from_pretrained(__a )
_a : Dict = '''Tokyo is the capital of <mask>.'''
_a : List[str] = (24, 30)
_a : Optional[int] = tokenizer(__a ,entity_spans=[span] ,return_tensors='''pt''' )
_a : Optional[Any] = model(**__a )
_a : Any = encoding['''input_ids'''][0].tolist()
_a : Optional[Any] = input_ids.index(tokenizer.convert_tokens_to_ids('''<mask>''' ) )
_a : Any = outputs.logits[0][mask_position_id].argmax(dim=-1 )
assert "Japan" == tokenizer.decode(__a )
_a : Any = outputs.entity_logits[0][0].argmax().item()
_a : Optional[Any] = [
entity for entity, entity_id in tokenizer.entity_vocab.items() if entity_id == predicted_entity_id
]
assert [e for e in multilingual_predicted_entities if e.startswith('''en:''' )][0] == "en:Japan"
# Finally, save our PyTorch model and tokenizer
print('''Saving PyTorch model to {}'''.format(__a ) )
model.save_pretrained(__a )
def __UpperCAmelCase ( __a : List[Any] ) -> int:
"""simple docstring"""
_a : Union[str, Any] = ['''[MASK]''', '''[PAD]''', '''[UNK]''']
_a : int = [json.loads(__a ) for line in open(__a )]
_a : List[Any] = {}
for entry in data:
_a : int = entry['''id''']
for entity_name, language in entry["entities"]:
if entity_name in SPECIAL_TOKENS:
_a : List[Any] = entity_id
break
_a : Dict = F"""{language}:{entity_name}"""
_a : int = entity_id
return new_mapping
if __name__ == "__main__":
a__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument('''--checkpoint_path''', type=str, help='''Path to a pytorch_model.bin file.''')
parser.add_argument(
'''--metadata_path''', default=None, type=str, help='''Path to a metadata.json file, defining the configuration.'''
)
parser.add_argument(
'''--entity_vocab_path''',
default=None,
type=str,
help='''Path to an entity_vocab.tsv file, containing the entity vocabulary.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to where to dump the output PyTorch model.'''
)
parser.add_argument(
'''--model_size''', default='''base''', type=str, choices=['''base''', '''large'''], help='''Size of the model to be converted.'''
)
a__ = parser.parse_args()
convert_luke_checkpoint(
args.checkpoint_path,
args.metadata_path,
args.entity_vocab_path,
args.pytorch_dump_folder_path,
args.model_size,
)
| 14 | 0 |
'''simple docstring'''
from __future__ import annotations
from decimal import Decimal
from numpy import array
def UpperCAmelCase__( _SCREAMING_SNAKE_CASE : list[list[float]] ):
"""simple docstring"""
__A= Decimal
# Check if the provided matrix has 2 rows and 2 columns
# since this implementation only works for 2x2 matrices
if len(__a ) == 2 and len(matrix[0] ) == 2 and len(matrix[1] ) == 2:
# Calculate the determinant of the matrix
__A= float(
d(matrix[0][0] ) * d(matrix[1][1] ) - d(matrix[1][0] ) * d(matrix[0][1] ) )
if determinant == 0:
raise ValueError('This matrix has no inverse.' )
# Creates a copy of the matrix with swapped positions of the elements
__A= [[0.0, 0.0], [0.0, 0.0]]
__A= matrix[1][1], matrix[0][0]
__A= -matrix[1][0], -matrix[0][1]
# Calculate the inverse of the matrix
return [
[(float(d(__a ) ) / determinant) or 0.0 for n in row] for row in swapped_matrix
]
elif (
len(__a ) == 3
and len(matrix[0] ) == 3
and len(matrix[1] ) == 3
and len(matrix[2] ) == 3
):
# Calculate the determinant of the matrix using Sarrus rule
__A= float(
(
(d(matrix[0][0] ) * d(matrix[1][1] ) * d(matrix[2][2] ))
+ (d(matrix[0][1] ) * d(matrix[1][2] ) * d(matrix[2][0] ))
+ (d(matrix[0][2] ) * d(matrix[1][0] ) * d(matrix[2][1] ))
)
- (
(d(matrix[0][2] ) * d(matrix[1][1] ) * d(matrix[2][0] ))
+ (d(matrix[0][1] ) * d(matrix[1][0] ) * d(matrix[2][2] ))
+ (d(matrix[0][0] ) * d(matrix[1][2] ) * d(matrix[2][1] ))
) )
if determinant == 0:
raise ValueError('This matrix has no inverse.' )
# Creating cofactor matrix
__A= [
[d(0.0 ), d(0.0 ), d(0.0 )],
[d(0.0 ), d(0.0 ), d(0.0 )],
[d(0.0 ), d(0.0 ), d(0.0 )],
]
__A= (d(matrix[1][1] ) * d(matrix[2][2] )) - (
d(matrix[1][2] ) * d(matrix[2][1] )
)
__A= -(
(d(matrix[1][0] ) * d(matrix[2][2] )) - (d(matrix[1][2] ) * d(matrix[2][0] ))
)
__A= (d(matrix[1][0] ) * d(matrix[2][1] )) - (
d(matrix[1][1] ) * d(matrix[2][0] )
)
__A= -(
(d(matrix[0][1] ) * d(matrix[2][2] )) - (d(matrix[0][2] ) * d(matrix[2][1] ))
)
__A= (d(matrix[0][0] ) * d(matrix[2][2] )) - (
d(matrix[0][2] ) * d(matrix[2][0] )
)
__A= -(
(d(matrix[0][0] ) * d(matrix[2][1] )) - (d(matrix[0][1] ) * d(matrix[2][0] ))
)
__A= (d(matrix[0][1] ) * d(matrix[1][2] )) - (
d(matrix[0][2] ) * d(matrix[1][1] )
)
__A= -(
(d(matrix[0][0] ) * d(matrix[1][2] )) - (d(matrix[0][2] ) * d(matrix[1][0] ))
)
__A= (d(matrix[0][0] ) * d(matrix[1][1] )) - (
d(matrix[0][1] ) * d(matrix[1][0] )
)
# Transpose the cofactor matrix (Adjoint matrix)
__A= array(__a )
for i in range(3 ):
for j in range(3 ):
__A= cofactor_matrix[j][i]
# Inverse of the matrix using the formula (1/determinant) * adjoint matrix
__A= array(__a )
for i in range(3 ):
for j in range(3 ):
inverse_matrix[i][j] /= d(__a )
# Calculate the inverse of the matrix
return [[float(d(__a ) ) or 0.0 for n in row] for row in inverse_matrix]
raise ValueError('Please provide a matrix of size 2x2 or 3x3.' )
| 186 |
from scipy.stats import spearmanr
import datasets
a__ = '''
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlations imply that as data in dataset x increases, so
does data in dataset y. Negative correlations imply that as x increases,
y decreases. Correlations of -1 or +1 imply an exact monotonic relationship.
Unlike the Pearson correlation, the Spearman correlation does not
assume that both datasets are normally distributed.
The p-value roughly indicates the probability of an uncorrelated system
producing datasets that have a Spearman correlation at least as extreme
as the one computed from these datasets. The p-values are not entirely
reliable but are probably reasonable for datasets larger than 500 or so.
'''
a__ = '''
Args:
predictions (`List[float]`): Predicted labels, as returned by a model.
references (`List[float]`): Ground truth labels.
return_pvalue (`bool`): If `True`, returns the p-value. If `False`, returns
only the spearmanr score. Defaults to `False`.
Returns:
spearmanr (`float`): Spearman correlation coefficient.
p-value (`float`): p-value. **Note**: is only returned if `return_pvalue=True` is input.
Examples:
Example 1:
>>> spearmanr_metric = datasets.load_metric("spearmanr")
>>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], predictions=[10, 9, 2.5, 6, 4])
>>> print(results)
{\'spearmanr\': -0.7}
Example 2:
>>> spearmanr_metric = datasets.load_metric("spearmanr")
>>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5],
... predictions=[10, 9, 2.5, 6, 4],
... return_pvalue=True)
>>> print(results[\'spearmanr\'])
-0.7
>>> print(round(results[\'spearmanr_pvalue\'], 2))
0.19
'''
a__ = R'''\
@book{kokoska2000crc,
title={CRC standard probability and statistics tables and formulae},
author={Kokoska, Stephen and Zwillinger, Daniel},
year={2000},
publisher={Crc Press}
}
@article{2020SciPy-NMeth,
author = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
Haberland, Matt and Reddy, Tyler and Cournapeau, David and
Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
Kern, Robert and Larson, Eric and Carey, C J and
Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and
{VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
Harris, Charles R. and Archibald, Anne M. and
Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and
{van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
title = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
Computing in Python}},
journal = {Nature Methods},
year = {2020},
volume = {17},
pages = {261--272},
adsurl = {https://rdcu.be/b08Wh},
doi = {10.1038/s41592-019-0686-2},
}
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class UpperCAmelCase_ ( datasets.Metric ):
"""simple docstring"""
def __lowercase ( self ) -> int:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Value('''float''' ),
'''references''': datasets.Value('''float''' ),
} ) , reference_urls=['''https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.spearmanr.html'''] , )
def __lowercase ( self , _a , _a , _a=False ) -> str:
_a : int = spearmanr(_a , _a )
if return_pvalue:
return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
else:
return {"spearmanr": results[0]}
| 14 | 0 |
import fire
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save
def UpperCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=1_024 , __lowerCAmelCase=1_024 , __lowerCAmelCase=False , **__lowerCAmelCase ) -> Optional[int]:
__lowercase : Tuple = AutoTokenizer.from_pretrained(__a )
__lowercase : int = SeqaSeqDataset(__a , __a , __a , __a , type_path='''train''' , **__a )
__lowercase : List[str] = tok.pad_token_id
def get_lens(__lowerCAmelCase ):
__lowercase : List[Any] = tqdm(
DataLoader(__a , batch_size=512 , num_workers=8 , shuffle=__a , collate_fn=ds.collate_fn ) , desc=str(ds.len_file ) , )
__lowercase : Optional[Any] = []
for batch in dl:
__lowercase : List[str] = batch['''input_ids'''].ne(__a ).sum(1 ).tolist()
__lowercase : Optional[Any] = batch['''labels'''].ne(__a ).sum(1 ).tolist()
if consider_target:
for src, tgt in zip(__a , __a ):
max_lens.append(max(__a , __a ) )
else:
max_lens.extend(__a )
return max_lens
__lowercase : List[str] = get_lens(__a )
__lowercase : Tuple = SeqaSeqDataset(__a , __a , __a , __a , type_path='''val''' , **__a )
__lowercase : Union[str, Any] = get_lens(__a )
pickle_save(__a , train_ds.len_file )
pickle_save(__a , val_ds.len_file )
if __name__ == "__main__":
fire.Fire(save_len_file)
| 509 |
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def __UpperCAmelCase ( __a : bytes ,__a : int ) -> np.array:
"""simple docstring"""
_a : int = F"""{sampling_rate}"""
_a : str = '''1'''
_a : Optional[int] = '''f32le'''
_a : Optional[Any] = [
'''ffmpeg''',
'''-i''',
'''pipe:0''',
'''-ac''',
ac,
'''-ar''',
ar,
'''-f''',
format_for_conversion,
'''-hide_banner''',
'''-loglevel''',
'''quiet''',
'''pipe:1''',
]
try:
with subprocess.Popen(__a ,stdin=subprocess.PIPE ,stdout=subprocess.PIPE ) as ffmpeg_process:
_a : Any = ffmpeg_process.communicate(__a )
except FileNotFoundError as error:
raise ValueError('''ffmpeg was not found but is required to load audio files from filename''' ) from error
_a : Optional[Any] = output_stream[0]
_a : Optional[int] = np.frombuffer(__a ,np.floataa )
if audio.shape[0] == 0:
raise ValueError('''Malformed soundfile''' )
return audio
def __UpperCAmelCase ( __a : int ,__a : float ,__a : str = "f32le" ,) -> str:
"""simple docstring"""
_a : Dict = F"""{sampling_rate}"""
_a : Optional[Any] = '''1'''
if format_for_conversion == "s16le":
_a : Dict = 2
elif format_for_conversion == "f32le":
_a : Optional[Any] = 4
else:
raise ValueError(F"""Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`""" )
_a : Dict = platform.system()
if system == "Linux":
_a : Dict = '''alsa'''
_a : Union[str, Any] = '''default'''
elif system == "Darwin":
_a : Union[str, Any] = '''avfoundation'''
_a : List[str] = ''':0'''
elif system == "Windows":
_a : Optional[int] = '''dshow'''
_a : str = '''default'''
_a : Tuple = [
'''ffmpeg''',
'''-f''',
format_,
'''-i''',
input_,
'''-ac''',
ac,
'''-ar''',
ar,
'''-f''',
format_for_conversion,
'''-fflags''',
'''nobuffer''',
'''-hide_banner''',
'''-loglevel''',
'''quiet''',
'''pipe:1''',
]
_a : Any = int(round(sampling_rate * chunk_length_s ) ) * size_of_sample
_a : str = _ffmpeg_stream(__a ,__a )
for item in iterator:
yield item
def __UpperCAmelCase ( __a : int ,__a : float ,__a : Optional[int] = None ,__a : Optional[Union[Tuple[float, float], float]] = None ,__a : str = "f32le" ,) -> Optional[int]:
"""simple docstring"""
if stream_chunk_s is not None:
_a : Tuple = stream_chunk_s
else:
_a : Tuple = chunk_length_s
_a : Tuple = ffmpeg_microphone(__a ,__a ,format_for_conversion=__a )
if format_for_conversion == "s16le":
_a : Any = np.intaa
_a : Optional[int] = 2
elif format_for_conversion == "f32le":
_a : Dict = np.floataa
_a : List[Any] = 4
else:
raise ValueError(F"""Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`""" )
if stride_length_s is None:
_a : List[Any] = chunk_length_s / 6
_a : Optional[int] = int(round(sampling_rate * chunk_length_s ) ) * size_of_sample
if isinstance(__a ,(int, float) ):
_a : Optional[Any] = [stride_length_s, stride_length_s]
_a : Optional[Any] = int(round(sampling_rate * stride_length_s[0] ) ) * size_of_sample
_a : str = int(round(sampling_rate * stride_length_s[1] ) ) * size_of_sample
_a : Optional[Any] = datetime.datetime.now()
_a : Tuple = datetime.timedelta(seconds=__a )
for item in chunk_bytes_iter(__a ,__a ,stride=(stride_left, stride_right) ,stream=__a ):
# Put everything back in numpy scale
_a : Dict = np.frombuffer(item['''raw'''] ,dtype=__a )
_a : Dict = (
item['''stride'''][0] // size_of_sample,
item['''stride'''][1] // size_of_sample,
)
_a : str = sampling_rate
audio_time += delta
if datetime.datetime.now() > audio_time + 10 * delta:
# We're late !! SKIP
continue
yield item
def __UpperCAmelCase ( __a : Optional[int] ,__a : int ,__a : Tuple[int, int] ,__a : bool = False ) -> Optional[int]:
"""simple docstring"""
_a : Any = b''''''
_a , _a : List[str] = stride
if stride_left + stride_right >= chunk_len:
raise ValueError(
F"""Stride needs to be strictly smaller than chunk_len: ({stride_left}, {stride_right}) vs {chunk_len}""" )
_a : List[str] = 0
for raw in iterator:
acc += raw
if stream and len(__a ) < chunk_len:
_a : Dict = (_stride_left, 0)
yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
else:
while len(__a ) >= chunk_len:
# We are flushing the accumulator
_a : List[str] = (_stride_left, stride_right)
_a : List[Any] = {'''raw''': acc[:chunk_len], '''stride''': stride}
if stream:
_a : List[Any] = False
yield item
_a : Optional[Any] = stride_left
_a : Optional[Any] = acc[chunk_len - stride_left - stride_right :]
# Last chunk
if len(__a ) > stride_left:
_a : Optional[Any] = {'''raw''': acc, '''stride''': (_stride_left, 0)}
if stream:
_a : Dict = False
yield item
def __UpperCAmelCase ( __a : int ,__a : int ) -> Tuple:
"""simple docstring"""
_a : Dict = 2**24 # 16Mo
try:
with subprocess.Popen(__a ,stdout=subprocess.PIPE ,bufsize=__a ) as ffmpeg_process:
while True:
_a : int = ffmpeg_process.stdout.read(__a )
if raw == b"":
break
yield raw
except FileNotFoundError as error:
raise ValueError('''ffmpeg was not found but is required to stream audio files from filename''' ) from error
| 14 | 0 |
from __future__ import annotations
def UpperCAmelCase ( a_ ) -> list:
"""simple docstring"""
if len(__a ) == 0:
return []
__A = min(__a ), max(__a )
__A = int(max_value - min_value ) + 1
__A = [[] for _ in range(__a )]
for i in my_list:
buckets[int(i - min_value )].append(__a )
return [v for bucket in buckets for v in sorted(__a )]
if __name__ == "__main__":
from doctest import testmod
testmod()
assert bucket_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
assert bucket_sort([0, 1, -10, 15, 2, -2]) == [-10, -2, 0, 1, 2, 15]
| 55 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, 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 UpperCAmelCase_ ( __lowercase , unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase__ : Union[str, Any] = KandinskyInpaintPipeline
UpperCAmelCase__ : Optional[int] = ["prompt", "image_embeds", "negative_image_embeds", "image", "mask_image"]
UpperCAmelCase__ : Optional[Any] = [
"prompt",
"negative_prompt",
"image_embeds",
"negative_image_embeds",
"image",
"mask_image",
]
UpperCAmelCase__ : Optional[int] = [
"generator",
"height",
"width",
"latents",
"guidance_scale",
"negative_prompt",
"num_inference_steps",
"return_dict",
"guidance_scale",
"num_images_per_prompt",
"output_type",
"return_dict",
]
UpperCAmelCase__ : Any = False
@property
def __lowercase ( self ) -> Optional[int]:
return 3_2
@property
def __lowercase ( self ) -> int:
return 3_2
@property
def __lowercase ( self ) -> List[str]:
return self.time_input_dim
@property
def __lowercase ( self ) -> List[str]:
return self.time_input_dim * 4
@property
def __lowercase ( self ) -> Optional[Any]:
return 1_0_0
@property
def __lowercase ( self ) -> Optional[Any]:
_a : Any = XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base''' )
return tokenizer
@property
def __lowercase ( self ) -> str:
torch.manual_seed(0 )
_a : List[Any] = MCLIPConfig(
numDims=self.cross_attention_dim , transformerDimensions=self.text_embedder_hidden_size , hidden_size=self.text_embedder_hidden_size , intermediate_size=3_7 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=1_0_0_5 , )
_a : Optional[int] = MultilingualCLIP(_a )
_a : Tuple = text_encoder.eval()
return text_encoder
@property
def __lowercase ( self ) -> str:
torch.manual_seed(0 )
_a : List[str] = {
'''in_channels''': 9,
# 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 : Dict = UNetaDConditionModel(**_a )
return model
@property
def __lowercase ( self ) -> Optional[int]:
return {
"block_out_channels": [3_2, 6_4],
"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": 1_2,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def __lowercase ( self ) -> Tuple:
torch.manual_seed(0 )
_a : List[Any] = VQModel(**self.dummy_movq_kwargs )
return model
def __lowercase ( self ) -> Any:
_a : List[Any] = self.dummy_text_encoder
_a : Optional[Any] = self.dummy_tokenizer
_a : Optional[Any] = self.dummy_unet
_a : Union[str, Any] = self.dummy_movq
_a : Tuple = DDIMScheduler(
num_train_timesteps=1_0_0_0 , beta_schedule='''linear''' , beta_start=0.0_0085 , beta_end=0.012 , clip_sample=_a , set_alpha_to_one=_a , steps_offset=1 , prediction_type='''epsilon''' , thresholding=_a , )
_a : str = {
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''unet''': unet,
'''scheduler''': scheduler,
'''movq''': movq,
}
return components
def __lowercase ( self , _a , _a=0 ) -> int:
_a : Union[str, Any] = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(_a ) ).to(_a )
_a : List[str] = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(seed + 1 ) ).to(_a )
# create init_image
_a : Tuple = floats_tensor((1, 3, 6_4, 6_4) , rng=random.Random(_a ) ).to(_a )
_a : Dict = image.cpu().permute(0 , 2 , 3 , 1 )[0]
_a : Optional[int] = Image.fromarray(np.uinta(_a ) ).convert('''RGB''' ).resize((2_5_6, 2_5_6) )
# create mask
_a : Union[str, Any] = np.ones((6_4, 6_4) , dtype=np.floataa )
_a : List[str] = 0
if str(_a ).startswith('''mps''' ):
_a : Tuple = torch.manual_seed(_a )
else:
_a : Any = torch.Generator(device=_a ).manual_seed(_a )
_a : Any = {
'''prompt''': '''horse''',
'''image''': init_image,
'''mask_image''': mask,
'''image_embeds''': image_embeds,
'''negative_image_embeds''': negative_image_embeds,
'''generator''': generator,
'''height''': 6_4,
'''width''': 6_4,
'''num_inference_steps''': 2,
'''guidance_scale''': 4.0,
'''output_type''': '''np''',
}
return inputs
def __lowercase ( self ) -> Optional[Any]:
_a : Optional[Any] = '''cpu'''
_a : List[Any] = self.get_dummy_components()
_a : Tuple = self.pipeline_class(**_a )
_a : int = pipe.to(_a )
pipe.set_progress_bar_config(disable=_a )
_a : Any = pipe(**self.get_dummy_inputs(_a ) )
_a : str = output.images
_a : Tuple = pipe(
**self.get_dummy_inputs(_a ) , return_dict=_a , )[0]
_a : Union[str, Any] = image[0, -3:, -3:, -1]
_a : Tuple = image_from_tuple[0, -3:, -3:, -1]
print(F"""image.shape {image.shape}""" )
assert image.shape == (1, 6_4, 6_4, 3)
_a : str = np.array(
[0.832_6919, 0.7379_0467, 0.2091_8581, 0.930_9612, 0.551_1791, 0.4371_3328, 0.551_3321, 0.4992_2934, 0.5949_7786] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
), F""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
), F""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""
def __lowercase ( self ) -> Dict:
super().test_inference_batch_single_identical(expected_max_diff=3e-3 )
@slow
@require_torch_gpu
class UpperCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
def __lowercase ( self ) -> str:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __lowercase ( self ) -> Union[str, Any]:
_a : Tuple = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy''' )
_a : str = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png''' )
_a : Tuple = np.ones((7_6_8, 7_6_8) , dtype=np.floataa )
_a : Any = 0
_a : Optional[Any] = '''a hat'''
_a : Optional[Any] = KandinskyPriorPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-1-prior''' , torch_dtype=torch.floataa )
pipe_prior.to(_a )
_a : Tuple = KandinskyInpaintPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-1-inpaint''' , torch_dtype=torch.floataa )
_a : Union[str, Any] = pipeline.to(_a )
pipeline.set_progress_bar_config(disable=_a )
_a : Union[str, Any] = torch.Generator(device='''cpu''' ).manual_seed(0 )
_a , _a : Dict = pipe_prior(
_a , generator=_a , num_inference_steps=5 , negative_prompt='''''' , ).to_tuple()
_a : Optional[int] = pipeline(
_a , image=_a , mask_image=_a , image_embeds=_a , negative_image_embeds=_a , generator=_a , num_inference_steps=1_0_0 , height=7_6_8 , width=7_6_8 , output_type='''np''' , )
_a : Optional[int] = output.images[0]
assert image.shape == (7_6_8, 7_6_8, 3)
assert_mean_pixel_difference(_a , _a )
| 14 | 0 |
import unittest
from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_rjieba
@require_tokenizers
class lowerCamelCase( __lowercase , unittest.TestCase ):
'''simple docstring'''
__magic_name__ = RoFormerTokenizer
__magic_name__ = RoFormerTokenizerFast
__magic_name__ = True
__magic_name__ = True
def lowerCAmelCase__ ( self ):
super().setUp()
def lowerCAmelCase__ ( self , **snake_case_ ):
return self.tokenizer_class.from_pretrained('junnyu/roformer_chinese_base' , **_a )
def lowerCAmelCase__ ( self , **snake_case_ ):
return self.rust_tokenizer_class.from_pretrained('junnyu/roformer_chinese_base' , **_a )
def lowerCAmelCase__ ( self ):
_A = '''永和服装饰品有限公司,今天天气非常好'''
_A = '''永和 服装 饰品 有限公司 , 今 天 天 气 非常 好'''
return input_text, output_text
def lowerCAmelCase__ ( self ):
_A = self.get_tokenizer()
_A = self.get_chinese_input_output_texts()
_A = tokenizer.tokenize(_a )
self.assertListEqual(_a , output_text.split() )
_A = tokens + [tokenizer.unk_token]
_A = [2_2943, 2_1332, 3_4431, 4_5904, 117, 306, 1231, 1231, 2653, 3_3994, 1266, 100]
self.assertListEqual(tokenizer.convert_tokens_to_ids(_a ) , _a )
def lowerCAmelCase__ ( self ):
_A = self.get_rust_tokenizer()
_A = self.get_chinese_input_output_texts()
_A = tokenizer.tokenize(_a )
self.assertListEqual(_a , output_text.split() )
_A = tokens + [tokenizer.unk_token]
_A = [2_2943, 2_1332, 3_4431, 4_5904, 117, 306, 1231, 1231, 2653, 3_3994, 1266, 100]
self.assertListEqual(tokenizer.convert_tokens_to_ids(_a ) , _a )
def lowerCAmelCase__ ( self ):
pass
def lowerCAmelCase__ ( self ):
pass
def lowerCAmelCase__ ( self ):
pass
| 27 |
import argparse
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt
if __name__ == "__main__":
a__ = argparse.ArgumentParser()
parser.add_argument(
'''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
)
parser.add_argument(
'''--original_config_file''',
type=str,
required=True,
help='''The YAML config file corresponding to the original architecture.''',
)
parser.add_argument(
'''--num_in_channels''',
default=None,
type=int,
help='''The number of input channels. If `None` number of input channels will be automatically inferred.''',
)
parser.add_argument(
'''--image_size''',
default=512,
type=int,
help=(
'''The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2'''
''' Base. Use 768 for Stable Diffusion v2.'''
),
)
parser.add_argument(
'''--extract_ema''',
action='''store_true''',
help=(
'''Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights'''
''' or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield'''
''' higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.'''
),
)
parser.add_argument(
'''--upcast_attention''',
action='''store_true''',
help=(
'''Whether the attention computation should always be upcasted. This is necessary when running stable'''
''' diffusion 2.1.'''
),
)
parser.add_argument(
'''--from_safetensors''',
action='''store_true''',
help='''If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.''',
)
parser.add_argument(
'''--to_safetensors''',
action='''store_true''',
help='''Whether to store pipeline in safetensors format or not.''',
)
parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')
parser.add_argument('''--device''', type=str, help='''Device to use (e.g. cpu, cuda:0, cuda:1, etc.)''')
def __UpperCAmelCase ( __a : Any ) -> List[Any]:
"""simple docstring"""
if string == "True":
return True
elif string == "False":
return False
else:
raise ValueError(F"""could not parse string as bool {string}""" )
parser.add_argument(
'''--use_linear_projection''', help='''Override for use linear projection''', required=False, type=parse_bool
)
parser.add_argument('''--cross_attention_dim''', help='''Override for cross attention_dim''', required=False, type=int)
a__ = parser.parse_args()
a__ = download_controlnet_from_original_ckpt(
checkpoint_path=args.checkpoint_path,
original_config_file=args.original_config_file,
image_size=args.image_size,
extract_ema=args.extract_ema,
num_in_channels=args.num_in_channels,
upcast_attention=args.upcast_attention,
from_safetensors=args.from_safetensors,
device=args.device,
use_linear_projection=args.use_linear_projection,
cross_attention_dim=args.cross_attention_dim,
)
controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
| 14 | 0 |
'''simple docstring'''
from typing import Optional, Tuple, Union
import torch
from einops import rearrange, reduce
from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput
UpperCAmelCase_ : Any = 8
def _UpperCamelCase (_lowerCamelCase : Union[str, Any] , _lowerCamelCase : Tuple=BITS )-> List[str]:
'''simple docstring'''
__snake_case = x.device
__snake_case = (x * 2_55).int().clamp(0 , 2_55 )
__snake_case = 2 ** torch.arange(bits - 1 , -1 , -1 , device=__a )
__snake_case = rearrange(__a , '''d -> d 1 1''' )
__snake_case = rearrange(__a , '''b c h w -> b c 1 h w''' )
__snake_case = ((x & mask) != 0).float()
__snake_case = rearrange(__a , '''b c d h w -> b (c d) h w''' )
__snake_case = bits * 2 - 1
return bits
def _UpperCamelCase (_lowerCamelCase : List[str] , _lowerCamelCase : List[str]=BITS )-> Optional[Any]:
'''simple docstring'''
__snake_case = x.device
__snake_case = (x > 0).int()
__snake_case = 2 ** torch.arange(bits - 1 , -1 , -1 , device=__a , dtype=torch.intaa )
__snake_case = rearrange(__a , '''d -> d 1 1''' )
__snake_case = rearrange(__a , '''b (c d) h w -> b c d h w''' , d=8 )
__snake_case = reduce(x * mask , '''b c d h w -> b c h w''' , '''sum''' )
return (dec / 2_55).clamp(0.0 , 1.0 )
def _UpperCamelCase (self : Optional[int] , _lowerCamelCase : torch.FloatTensor , _lowerCamelCase : int , _lowerCamelCase : torch.FloatTensor , _lowerCamelCase : float = 0.0 , _lowerCamelCase : bool = True , _lowerCamelCase : int=None , _lowerCamelCase : bool = True , )-> Union[DDIMSchedulerOutput, Tuple]:
'''simple docstring'''
if self.num_inference_steps is None:
raise ValueError(
'''Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler''' )
# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
# Ideally, read DDIM paper in-detail understanding
# Notation (<variable name> -> <name in paper>
# - pred_noise_t -> e_theta(x_t, t)
# - pred_original_sample -> f_theta(x_t, t) or x_0
# - std_dev_t -> sigma_t
# - eta -> η
# - pred_sample_direction -> "direction pointing to x_t"
# - pred_prev_sample -> "x_t-1"
# 1. get previous step value (=t-1)
__snake_case = timestep - self.config.num_train_timesteps // self.num_inference_steps
# 2. compute alphas, betas
__snake_case = self.alphas_cumprod[timestep]
__snake_case = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
__snake_case = 1 - alpha_prod_t
# 3. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
__snake_case = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
# 4. Clip "predicted x_0"
__snake_case = self.bit_scale
if self.config.clip_sample:
__snake_case = torch.clamp(__a , -scale , __a )
# 5. compute variance: "sigma_t(η)" -> see formula (16)
# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
__snake_case = self._get_variance(__a , __a )
__snake_case = eta * variance ** 0.5
if use_clipped_model_output:
# the model_output is always re-derived from the clipped x_0 in Glide
__snake_case = (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
# 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
__snake_case = (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output
# 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
__snake_case = alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction
if eta > 0:
# randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
__snake_case = model_output.device if torch.is_tensor(__a ) else '''cpu'''
__snake_case = torch.randn(model_output.shape , dtype=model_output.dtype , generator=__a ).to(__a )
__snake_case = self._get_variance(__a , __a ) ** 0.5 * eta * noise
__snake_case = prev_sample + variance
if not return_dict:
return (prev_sample,)
return DDIMSchedulerOutput(prev_sample=__a , pred_original_sample=__a )
def _UpperCamelCase (self : List[Any] , _lowerCamelCase : torch.FloatTensor , _lowerCamelCase : int , _lowerCamelCase : torch.FloatTensor , _lowerCamelCase : str="epsilon" , _lowerCamelCase : Optional[Any]=None , _lowerCamelCase : bool = True , )-> Union[DDPMSchedulerOutput, Tuple]:
'''simple docstring'''
__snake_case = timestep
if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
__snake_case = torch.split(__a , sample.shape[1] , dim=1 )
else:
__snake_case = None
# 1. compute alphas, betas
__snake_case = self.alphas_cumprod[t]
__snake_case = self.alphas_cumprod[t - 1] if t > 0 else self.one
__snake_case = 1 - alpha_prod_t
__snake_case = 1 - alpha_prod_t_prev
# 2. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
if prediction_type == "epsilon":
__snake_case = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
elif prediction_type == "sample":
__snake_case = model_output
else:
raise ValueError(f'''Unsupported prediction_type {prediction_type}.''' )
# 3. Clip "predicted x_0"
__snake_case = self.bit_scale
if self.config.clip_sample:
__snake_case = torch.clamp(__a , -scale , __a )
# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
__snake_case = (alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
__snake_case = self.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t
# 5. Compute predicted previous sample µ_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
__snake_case = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample
# 6. Add noise
__snake_case = 0
if t > 0:
__snake_case = torch.randn(
model_output.size() , dtype=model_output.dtype , layout=model_output.layout , generator=__a ).to(model_output.device )
__snake_case = (self._get_variance(__a , predicted_variance=__a ) ** 0.5) * noise
__snake_case = pred_prev_sample + variance
if not return_dict:
return (pred_prev_sample,)
return DDPMSchedulerOutput(prev_sample=__a , pred_original_sample=__a )
class lowerCAmelCase ( __lowercase):
def __init__( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = 1.0 , ) -> Optional[Any]:
'''simple docstring'''
super().__init__()
__snake_case = bit_scale
__snake_case = (
ddim_bit_scheduler_step if isinstance(_a , _a ) else ddpm_bit_scheduler_step
)
self.register_modules(unet=_a , scheduler=_a )
@torch.no_grad()
def __call__( self , __SCREAMING_SNAKE_CASE = 256 , __SCREAMING_SNAKE_CASE = 256 , __SCREAMING_SNAKE_CASE = 50 , __SCREAMING_SNAKE_CASE = None , __SCREAMING_SNAKE_CASE = 1 , __SCREAMING_SNAKE_CASE = "pil" , __SCREAMING_SNAKE_CASE = True , **__SCREAMING_SNAKE_CASE , ) -> Union[Tuple, ImagePipelineOutput]:
'''simple docstring'''
__snake_case = torch.randn(
(batch_size, self.unet.config.in_channels, height, width) , generator=_a , )
__snake_case = decimal_to_bits(_a ) * self.bit_scale
__snake_case = latents.to(self.device )
self.scheduler.set_timesteps(_a )
for t in self.progress_bar(self.scheduler.timesteps ):
# predict the noise residual
__snake_case = self.unet(_a , _a ).sample
# compute the previous noisy sample x_t -> x_t-1
__snake_case = self.scheduler.step(_a , _a , _a ).prev_sample
__snake_case = bits_to_decimal(_a )
if output_type == "pil":
__snake_case = self.numpy_to_pil(_a )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=_a )
| 24 |
class UpperCAmelCase_ :
"""simple docstring"""
def __init__( self , _a , _a , _a ) -> List[str]:
_a : List[Any] = name
_a : List[str] = value
_a : List[str] = weight
def __repr__( self ) -> Optional[int]:
return F"""{self.__class__.__name__}({self.name}, {self.value}, {self.weight})"""
def __lowercase ( self ) -> List[Any]:
return self.value
def __lowercase ( self ) -> int:
return self.name
def __lowercase ( self ) -> Optional[int]:
return self.weight
def __lowercase ( self ) -> Optional[Any]:
return self.value / self.weight
def __UpperCAmelCase ( __a : Optional[int] ,__a : Tuple ,__a : List[str] ) -> List[str]:
"""simple docstring"""
_a : Optional[int] = []
for i in range(len(__a ) ):
menu.append(Things(name[i] ,value[i] ,weight[i] ) )
return menu
def __UpperCAmelCase ( __a : int ,__a : Union[str, Any] ,__a : int ) -> Union[str, Any]:
"""simple docstring"""
_a : Union[str, Any] = sorted(__a ,key=__a ,reverse=__a )
_a : Any = []
_a , _a : Optional[int] = 0.0, 0.0
for i in range(len(__a ) ):
if (total_cost + items_copy[i].get_weight()) <= max_cost:
result.append(items_copy[i] )
total_cost += items_copy[i].get_weight()
total_value += items_copy[i].get_value()
return (result, total_value)
def __UpperCAmelCase ( ) -> int:
"""simple docstring"""
if __name__ == "__main__":
import doctest
doctest.testmod()
| 14 | 0 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
_A = logging.get_logger(__name__)
# TODO: upload to AWS
_A = {
"yjernite/retribert-base-uncased": (
"https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/config.json"
),
}
class _lowerCAmelCase ( __lowercase ):
_lowercase ="retribert"
def __init__( self , _UpperCamelCase=30_522 , _UpperCamelCase=768 , _UpperCamelCase=8 , _UpperCamelCase=12 , _UpperCamelCase=3_072 , _UpperCamelCase="gelu" , _UpperCamelCase=0.1 , _UpperCamelCase=0.1 , _UpperCamelCase=512 , _UpperCamelCase=2 , _UpperCamelCase=0.02 , _UpperCamelCase=1e-1_2 , _UpperCamelCase=True , _UpperCamelCase=128 , _UpperCamelCase=0 , **_UpperCamelCase , ) -> Optional[int]:
super().__init__(pad_token_id=_a , **_a )
lowerCAmelCase_ = vocab_size
lowerCAmelCase_ = hidden_size
lowerCAmelCase_ = num_hidden_layers
lowerCAmelCase_ = num_attention_heads
lowerCAmelCase_ = hidden_act
lowerCAmelCase_ = intermediate_size
lowerCAmelCase_ = hidden_dropout_prob
lowerCAmelCase_ = attention_probs_dropout_prob
lowerCAmelCase_ = max_position_embeddings
lowerCAmelCase_ = type_vocab_size
lowerCAmelCase_ = initializer_range
lowerCAmelCase_ = layer_norm_eps
lowerCAmelCase_ = share_encoders
lowerCAmelCase_ = projection_dim
| 290 |
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import SwiftFormerForImageClassification, SwiftFormerModel
from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class UpperCAmelCase_ :
"""simple docstring"""
def __init__( self , _a , _a=1_3 , _a=3 , _a=True , _a=True , _a=0.1 , _a=0.1 , _a=2_2_4 , _a=1_0_0_0 , _a=[3, 3, 6, 4] , _a=[4_8, 5_6, 1_1_2, 2_2_0] , ) -> Tuple:
_a : Dict = parent
_a : Optional[int] = batch_size
_a : Optional[Any] = num_channels
_a : Union[str, Any] = is_training
_a : Tuple = use_labels
_a : Dict = hidden_dropout_prob
_a : List[Any] = attention_probs_dropout_prob
_a : Dict = num_labels
_a : List[str] = image_size
_a : Dict = layer_depths
_a : str = embed_dims
def __lowercase ( self ) -> Optional[Any]:
_a : Dict = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_a : int = None
if self.use_labels:
_a : Optional[Any] = ids_tensor([self.batch_size] , self.num_labels )
_a : Dict = self.get_config()
return config, pixel_values, labels
def __lowercase ( self ) -> int:
return SwiftFormerConfig(
depths=self.layer_depths , embed_dims=self.embed_dims , mlp_ratio=4 , downsamples=[True, True, True, True] , hidden_act='''gelu''' , num_labels=self.num_labels , down_patch_size=3 , down_stride=2 , down_pad=1 , drop_rate=0.0 , drop_path_rate=0.0 , use_layer_scale=_a , layer_scale_init_value=1e-5 , )
def __lowercase ( self , _a , _a , _a ) -> str:
_a : List[Any] = SwiftFormerModel(config=_a )
model.to(_a )
model.eval()
_a : Optional[int] = model(_a )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.embed_dims[-1], 7, 7) )
def __lowercase ( self , _a , _a , _a ) -> Optional[Any]:
_a : List[str] = self.num_labels
_a : Optional[int] = SwiftFormerForImageClassification(_a )
model.to(_a )
model.eval()
_a : List[str] = model(_a , labels=_a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
_a : Union[str, Any] = SwiftFormerForImageClassification(_a )
model.to(_a )
model.eval()
_a : Dict = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_a : Optional[Any] = model(_a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __lowercase ( self ) -> Tuple:
((_a) , (_a) , (_a)) : Optional[int] = self.prepare_config_and_inputs()
_a : List[Any] = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class UpperCAmelCase_ ( __lowercase , __lowercase , unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase__ : List[str] = (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
UpperCAmelCase__ : Optional[int] = (
{"feature-extraction": SwiftFormerModel, "image-classification": SwiftFormerForImageClassification}
if is_torch_available()
else {}
)
UpperCAmelCase__ : Optional[Any] = False
UpperCAmelCase__ : str = False
UpperCAmelCase__ : Tuple = False
UpperCAmelCase__ : Tuple = False
UpperCAmelCase__ : str = False
def __lowercase ( self ) -> Optional[int]:
_a : Union[str, Any] = SwiftFormerModelTester(self )
_a : int = ConfigTester(
self , config_class=_a , has_text_modality=_a , hidden_size=3_7 , num_attention_heads=1_2 , num_hidden_layers=1_2 , )
def __lowercase ( self ) -> int:
self.config_tester.run_common_tests()
@unittest.skip(reason='''SwiftFormer does not use inputs_embeds''' )
def __lowercase ( self ) -> Union[str, Any]:
pass
def __lowercase ( self ) -> Dict:
_a , _a : int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_a : Any = model_class(_a )
_a : int = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(_a , nn.Linear ) )
def __lowercase ( self ) -> str:
_a , _a : str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_a : Optional[int] = model_class(_a )
_a : Tuple = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_a : Tuple = [*signature.parameters.keys()]
_a : List[str] = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , _a )
def __lowercase ( self ) -> int:
_a : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_a )
def __lowercase ( self ) -> Optional[int]:
_a : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_a )
@slow
def __lowercase ( self ) -> Optional[Any]:
for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_a : Any = SwiftFormerModel.from_pretrained(_a )
self.assertIsNotNone(_a )
@unittest.skip(reason='''SwiftFormer does not output attentions''' )
def __lowercase ( self ) -> List[Any]:
pass
def __lowercase ( self ) -> int:
def check_hidden_states_output(_a , _a , _a ):
_a : Optional[int] = model_class(_a )
model.to(_a )
model.eval()
with torch.no_grad():
_a : Union[str, Any] = model(**self._prepare_for_class(_a , _a ) )
_a : Optional[Any] = outputs.hidden_states
_a : Union[str, Any] = 8
self.assertEqual(len(_a ) , _a ) # TODO
# SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
# with the width and height being successively divided by 2, after every 2 blocks
for i in range(len(_a ) ):
self.assertEqual(
hidden_states[i].shape , torch.Size(
[
self.model_tester.batch_size,
self.model_tester.embed_dims[i // 2],
(self.model_tester.image_size // 4) // 2 ** (i // 2),
(self.model_tester.image_size // 4) // 2 ** (i // 2),
] ) , )
_a , _a : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_a : str = True
check_hidden_states_output(_a , _a , _a )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_a : List[str] = True
check_hidden_states_output(_a , _a , _a )
def __lowercase ( self ) -> str:
def _config_zero_init(_a ):
_a : List[Any] = copy.deepcopy(_a )
for key in configs_no_init.__dict__.keys():
if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
setattr(_a , _a , 1e-1_0 )
if isinstance(getattr(_a , _a , _a ) , _a ):
_a : int = _config_zero_init(getattr(_a , _a ) )
setattr(_a , _a , _a )
return configs_no_init
_a , _a : Any = self.model_tester.prepare_config_and_inputs_for_common()
_a : Dict = _config_zero_init(_a )
for model_class in self.all_model_classes:
_a : Dict = model_class(config=_a )
for name, param in model.named_parameters():
if param.requires_grad:
self.assertIn(
((param.data.mean() * 1e9) / 1e9).round().item() , [0.0, 1.0] , msg=F"""Parameter {name} of model {model_class} seems not properly initialized""" , )
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def __lowercase ( self ) -> Optional[Any]:
pass
def __UpperCAmelCase ( ) -> Optional[Any]:
"""simple docstring"""
_a : Optional[int] = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class UpperCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def __lowercase ( self ) -> str:
return ViTImageProcessor.from_pretrained('''MBZUAI/swiftformer-xs''' ) if is_vision_available() else None
@slow
def __lowercase ( self ) -> Dict:
_a : Any = SwiftFormerForImageClassification.from_pretrained('''MBZUAI/swiftformer-xs''' ).to(_a )
_a : Any = self.default_image_processor
_a : Any = prepare_img()
_a : Any = image_processor(images=_a , return_tensors='''pt''' ).to(_a )
# forward pass
with torch.no_grad():
_a : Optional[Any] = model(**_a )
# verify the logits
_a : List[str] = torch.Size((1, 1_0_0_0) )
self.assertEqual(outputs.logits.shape , _a )
_a : int = torch.tensor([[-2.1_7_0_3e0_0, 2.1_1_0_7e0_0, -2.0_8_1_1e0_0]] ).to(_a )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , _a , atol=1e-4 ) )
| 14 | 0 |
'''simple docstring'''
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def a__ ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : float = 1 / sqrt(2 ) ) -> IIRFilter:
"""simple docstring"""
UpperCAmelCase_ : List[str] = tau * frequency / samplerate
UpperCAmelCase_ : int = sin(__a )
UpperCAmelCase_ : str = cos(__a )
UpperCAmelCase_ : Optional[int] = _sin / (2 * q_factor)
UpperCAmelCase_ : List[Any] = (1 - _cos) / 2
UpperCAmelCase_ : Optional[int] = 1 - _cos
UpperCAmelCase_ : Union[str, Any] = 1 + alpha
UpperCAmelCase_ : Dict = -2 * _cos
UpperCAmelCase_ : Any = 1 - alpha
UpperCAmelCase_ : Tuple = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def a__ ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : float = 1 / sqrt(2 ) ) -> IIRFilter:
"""simple docstring"""
UpperCAmelCase_ : List[Any] = tau * frequency / samplerate
UpperCAmelCase_ : Union[str, Any] = sin(__a )
UpperCAmelCase_ : Optional[int] = cos(__a )
UpperCAmelCase_ : Optional[Any] = _sin / (2 * q_factor)
UpperCAmelCase_ : List[Any] = (1 + _cos) / 2
UpperCAmelCase_ : List[str] = -1 - _cos
UpperCAmelCase_ : Tuple = 1 + alpha
UpperCAmelCase_ : Optional[int] = -2 * _cos
UpperCAmelCase_ : Optional[Any] = 1 - alpha
UpperCAmelCase_ : Optional[int] = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def a__ ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : float = 1 / sqrt(2 ) ) -> IIRFilter:
"""simple docstring"""
UpperCAmelCase_ : List[Any] = tau * frequency / samplerate
UpperCAmelCase_ : Union[str, Any] = sin(__a )
UpperCAmelCase_ : Optional[int] = cos(__a )
UpperCAmelCase_ : str = _sin / (2 * q_factor)
UpperCAmelCase_ : Optional[Any] = _sin / 2
UpperCAmelCase_ : str = 0
UpperCAmelCase_ : List[str] = -ba
UpperCAmelCase_ : Tuple = 1 + alpha
UpperCAmelCase_ : Any = -2 * _cos
UpperCAmelCase_ : Any = 1 - alpha
UpperCAmelCase_ : List[str] = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def a__ ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : float = 1 / sqrt(2 ) ) -> IIRFilter:
"""simple docstring"""
UpperCAmelCase_ : List[str] = tau * frequency / samplerate
UpperCAmelCase_ : Any = sin(__a )
UpperCAmelCase_ : Optional[int] = cos(__a )
UpperCAmelCase_ : Any = _sin / (2 * q_factor)
UpperCAmelCase_ : int = 1 - alpha
UpperCAmelCase_ : List[str] = -2 * _cos
UpperCAmelCase_ : Optional[Any] = 1 + alpha
UpperCAmelCase_ : Optional[Any] = IIRFilter(2 )
filt.set_coefficients([ba, ba, ba] , [ba, ba, ba] )
return filt
def a__ ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : float , _SCREAMING_SNAKE_CASE : float = 1 / sqrt(2 ) , ) -> IIRFilter:
"""simple docstring"""
UpperCAmelCase_ : List[Any] = tau * frequency / samplerate
UpperCAmelCase_ : Optional[Any] = sin(__a )
UpperCAmelCase_ : Optional[Any] = cos(__a )
UpperCAmelCase_ : Optional[int] = _sin / (2 * q_factor)
UpperCAmelCase_ : Dict = 10 ** (gain_db / 40)
UpperCAmelCase_ : List[Any] = 1 + alpha * big_a
UpperCAmelCase_ : int = -2 * _cos
UpperCAmelCase_ : Dict = 1 - alpha * big_a
UpperCAmelCase_ : Optional[Any] = 1 + alpha / big_a
UpperCAmelCase_ : Dict = -2 * _cos
UpperCAmelCase_ : int = 1 - alpha / big_a
UpperCAmelCase_ : List[Any] = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def a__ ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : float , _SCREAMING_SNAKE_CASE : float = 1 / sqrt(2 ) , ) -> IIRFilter:
"""simple docstring"""
UpperCAmelCase_ : Optional[Any] = tau * frequency / samplerate
UpperCAmelCase_ : List[Any] = sin(__a )
UpperCAmelCase_ : Any = cos(__a )
UpperCAmelCase_ : str = _sin / (2 * q_factor)
UpperCAmelCase_ : Union[str, Any] = 10 ** (gain_db / 40)
UpperCAmelCase_ : Tuple = (big_a + 1) - (big_a - 1) * _cos
UpperCAmelCase_ : Tuple = (big_a + 1) + (big_a - 1) * _cos
UpperCAmelCase_ : Dict = (big_a - 1) - (big_a + 1) * _cos
UpperCAmelCase_ : Optional[Any] = (big_a - 1) + (big_a + 1) * _cos
UpperCAmelCase_ : Tuple = 2 * sqrt(__a ) * alpha
UpperCAmelCase_ : List[str] = big_a * (pmc + aaa)
UpperCAmelCase_ : List[str] = 2 * big_a * mpc
UpperCAmelCase_ : Union[str, Any] = big_a * (pmc - aaa)
UpperCAmelCase_ : Optional[Any] = ppmc + aaa
UpperCAmelCase_ : str = -2 * pmpc
UpperCAmelCase_ : str = ppmc - aaa
UpperCAmelCase_ : Union[str, Any] = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def a__ ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : float , _SCREAMING_SNAKE_CASE : float = 1 / sqrt(2 ) , ) -> IIRFilter:
"""simple docstring"""
UpperCAmelCase_ : int = tau * frequency / samplerate
UpperCAmelCase_ : Tuple = sin(__a )
UpperCAmelCase_ : Optional[int] = cos(__a )
UpperCAmelCase_ : Dict = _sin / (2 * q_factor)
UpperCAmelCase_ : Tuple = 10 ** (gain_db / 40)
UpperCAmelCase_ : Union[str, Any] = (big_a + 1) - (big_a - 1) * _cos
UpperCAmelCase_ : List[Any] = (big_a + 1) + (big_a - 1) * _cos
UpperCAmelCase_ : List[str] = (big_a - 1) - (big_a + 1) * _cos
UpperCAmelCase_ : Tuple = (big_a - 1) + (big_a + 1) * _cos
UpperCAmelCase_ : List[Any] = 2 * sqrt(__a ) * alpha
UpperCAmelCase_ : Any = big_a * (ppmc + aaa)
UpperCAmelCase_ : Dict = -2 * big_a * pmpc
UpperCAmelCase_ : Tuple = big_a * (ppmc - aaa)
UpperCAmelCase_ : List[str] = pmc + aaa
UpperCAmelCase_ : List[str] = 2 * mpc
UpperCAmelCase_ : Optional[Any] = pmc - aaa
UpperCAmelCase_ : List[str] = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
| 71 |
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging
logging.set_verbosity_info()
a__ = logging.get_logger(__name__)
def __UpperCAmelCase ( __a : str ) -> List[Any]:
"""simple docstring"""
_a : Tuple = SwinConfig.from_pretrained(
'''microsoft/swin-tiny-patch4-window7-224''' ,out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4'''] )
_a : Dict = MaskFormerConfig(backbone_config=__a )
_a : Optional[Any] = '''huggingface/label-files'''
if "ade20k-full" in model_name:
# this should be ok
_a : Optional[Any] = 847
_a : List[Any] = '''maskformer-ade20k-full-id2label.json'''
elif "ade" in model_name:
# this should be ok
_a : Union[str, Any] = 150
_a : Any = '''ade20k-id2label.json'''
elif "coco-stuff" in model_name:
# this should be ok
_a : int = 171
_a : List[str] = '''maskformer-coco-stuff-id2label.json'''
elif "coco" in model_name:
# TODO
_a : Dict = 133
_a : Optional[Any] = '''coco-panoptic-id2label.json'''
elif "cityscapes" in model_name:
# this should be ok
_a : List[Any] = 19
_a : Optional[Any] = '''cityscapes-id2label.json'''
elif "vistas" in model_name:
# this should be ok
_a : List[Any] = 65
_a : Dict = '''mapillary-vistas-id2label.json'''
_a : Optional[int] = json.load(open(hf_hub_download(__a ,__a ,repo_type='''dataset''' ) ,'''r''' ) )
_a : Tuple = {int(__a ): v for k, v in idalabel.items()}
return config
def __UpperCAmelCase ( __a : Optional[Any] ) -> Tuple:
"""simple docstring"""
_a : Optional[Any] = []
# stem
# fmt: off
rename_keys.append(('''backbone.patch_embed.proj.weight''', '''model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight''') )
rename_keys.append(('''backbone.patch_embed.proj.bias''', '''model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias''') )
rename_keys.append(('''backbone.patch_embed.norm.weight''', '''model.pixel_level_module.encoder.model.embeddings.norm.weight''') )
rename_keys.append(('''backbone.patch_embed.norm.bias''', '''model.pixel_level_module.encoder.model.embeddings.norm.bias''') )
# stages
for i in range(len(config.backbone_config.depths ) ):
for j in range(config.backbone_config.depths[i] ):
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm1.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm1.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.relative_position_index""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.proj.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.proj.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm2.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm2.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc1.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc1.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc2.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc2.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias""") )
if i < 3:
rename_keys.append((F"""backbone.layers.{i}.downsample.reduction.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight""") )
rename_keys.append((F"""backbone.layers.{i}.downsample.norm.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight""") )
rename_keys.append((F"""backbone.layers.{i}.downsample.norm.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias""") )
rename_keys.append((F"""backbone.norm{i}.weight""", F"""model.pixel_level_module.encoder.hidden_states_norms.{i}.weight""") )
rename_keys.append((F"""backbone.norm{i}.bias""", F"""model.pixel_level_module.encoder.hidden_states_norms.{i}.bias""") )
# FPN
rename_keys.append(('''sem_seg_head.layer_4.weight''', '''model.pixel_level_module.decoder.fpn.stem.0.weight''') )
rename_keys.append(('''sem_seg_head.layer_4.norm.weight''', '''model.pixel_level_module.decoder.fpn.stem.1.weight''') )
rename_keys.append(('''sem_seg_head.layer_4.norm.bias''', '''model.pixel_level_module.decoder.fpn.stem.1.bias''') )
for source_index, target_index in zip(range(3 ,0 ,-1 ) ,range(0 ,3 ) ):
rename_keys.append((F"""sem_seg_head.adapter_{source_index}.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight""") )
rename_keys.append((F"""sem_seg_head.adapter_{source_index}.norm.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight""") )
rename_keys.append((F"""sem_seg_head.adapter_{source_index}.norm.bias""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias""") )
rename_keys.append((F"""sem_seg_head.layer_{source_index}.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight""") )
rename_keys.append((F"""sem_seg_head.layer_{source_index}.norm.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight""") )
rename_keys.append((F"""sem_seg_head.layer_{source_index}.norm.bias""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias""") )
rename_keys.append(('''sem_seg_head.mask_features.weight''', '''model.pixel_level_module.decoder.mask_projection.weight''') )
rename_keys.append(('''sem_seg_head.mask_features.bias''', '''model.pixel_level_module.decoder.mask_projection.bias''') )
# Transformer decoder
for idx in range(config.decoder_config.decoder_layers ):
# self-attention out projection
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight""", F"""model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias""", F"""model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias""") )
# cross-attention out projection
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias""") )
# MLP 1
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight""", F"""model.transformer_module.decoder.layers.{idx}.fc1.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias""", F"""model.transformer_module.decoder.layers.{idx}.fc1.bias""") )
# MLP 2
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight""", F"""model.transformer_module.decoder.layers.{idx}.fc2.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias""", F"""model.transformer_module.decoder.layers.{idx}.fc2.bias""") )
# layernorm 1 (self-attention layernorm)
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight""", F"""model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias""", F"""model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias""") )
# layernorm 2 (cross-attention layernorm)
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias""") )
# layernorm 3 (final layernorm)
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight""", F"""model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias""", F"""model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias""") )
rename_keys.append(('''sem_seg_head.predictor.transformer.decoder.norm.weight''', '''model.transformer_module.decoder.layernorm.weight''') )
rename_keys.append(('''sem_seg_head.predictor.transformer.decoder.norm.bias''', '''model.transformer_module.decoder.layernorm.bias''') )
# heads on top
rename_keys.append(('''sem_seg_head.predictor.query_embed.weight''', '''model.transformer_module.queries_embedder.weight''') )
rename_keys.append(('''sem_seg_head.predictor.input_proj.weight''', '''model.transformer_module.input_projection.weight''') )
rename_keys.append(('''sem_seg_head.predictor.input_proj.bias''', '''model.transformer_module.input_projection.bias''') )
rename_keys.append(('''sem_seg_head.predictor.class_embed.weight''', '''class_predictor.weight''') )
rename_keys.append(('''sem_seg_head.predictor.class_embed.bias''', '''class_predictor.bias''') )
for i in range(3 ):
rename_keys.append((F"""sem_seg_head.predictor.mask_embed.layers.{i}.weight""", F"""mask_embedder.{i}.0.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.mask_embed.layers.{i}.bias""", F"""mask_embedder.{i}.0.bias""") )
# fmt: on
return rename_keys
def __UpperCAmelCase ( __a : List[str] ,__a : List[Any] ,__a : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
_a : str = dct.pop(__a )
_a : str = val
def __UpperCAmelCase ( __a : List[Any] ,__a : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
_a : Union[str, Any] = [int(backbone_config.embed_dim * 2**i ) for i in range(len(backbone_config.depths ) )]
for i in range(len(backbone_config.depths ) ):
_a : Optional[Any] = num_features[i]
for j in range(backbone_config.depths[i] ):
# fmt: off
# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
_a : List[Any] = state_dict.pop(F"""backbone.layers.{i}.blocks.{j}.attn.qkv.weight""" )
_a : Optional[int] = state_dict.pop(F"""backbone.layers.{i}.blocks.{j}.attn.qkv.bias""" )
# next, add query, keys and values (in that order) to the state dict
_a : Optional[int] = in_proj_weight[:dim, :]
_a : List[Any] = in_proj_bias[: dim]
_a : Optional[int] = in_proj_weight[
dim : dim * 2, :
]
_a : Tuple = in_proj_bias[
dim : dim * 2
]
_a : int = in_proj_weight[
-dim :, :
]
_a : Optional[int] = in_proj_bias[-dim :]
# fmt: on
def __UpperCAmelCase ( __a : List[str] ,__a : List[Any] ) -> List[Any]:
"""simple docstring"""
_a : Optional[int] = config.decoder_config.hidden_size
for idx in range(config.decoder_config.decoder_layers ):
# read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
_a : Union[str, Any] = state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight""" )
_a : List[Any] = state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias""" )
# next, add query, keys and values (in that order) to the state dict
_a : Union[str, Any] = in_proj_weight[: hidden_size, :]
_a : List[Any] = in_proj_bias[:config.hidden_size]
_a : Dict = in_proj_weight[hidden_size : hidden_size * 2, :]
_a : Any = in_proj_bias[hidden_size : hidden_size * 2]
_a : Tuple = in_proj_weight[-hidden_size :, :]
_a : List[Any] = in_proj_bias[-hidden_size :]
# read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
_a : List[Any] = state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight""" )
_a : List[str] = state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias""" )
# next, add query, keys and values (in that order) to the state dict
_a : Optional[Any] = in_proj_weight[: hidden_size, :]
_a : Any = in_proj_bias[:config.hidden_size]
_a : List[str] = in_proj_weight[hidden_size : hidden_size * 2, :]
_a : Optional[Any] = in_proj_bias[hidden_size : hidden_size * 2]
_a : List[str] = in_proj_weight[-hidden_size :, :]
_a : int = in_proj_bias[-hidden_size :]
# fmt: on
def __UpperCAmelCase ( ) -> torch.Tensor:
"""simple docstring"""
_a : str = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
_a : Dict = Image.open(requests.get(__a ,stream=__a ).raw )
return im
@torch.no_grad()
def __UpperCAmelCase ( __a : str ,__a : str ,__a : str ,__a : bool = False ) -> Union[str, Any]:
"""simple docstring"""
_a : Optional[Any] = get_maskformer_config(__a )
# load original state_dict
with open(__a ,'''rb''' ) as f:
_a : str = pickle.load(__a )
_a : Union[str, Any] = data['''model''']
# for name, param in state_dict.items():
# print(name, param.shape)
# rename keys
_a : Any = create_rename_keys(__a )
for src, dest in rename_keys:
rename_key(__a ,__a ,__a )
read_in_swin_q_k_v(__a ,config.backbone_config )
read_in_decoder_q_k_v(__a ,__a )
# update to torch tensors
for key, value in state_dict.items():
_a : Optional[int] = torch.from_numpy(__a )
# load 🤗 model
_a : Dict = MaskFormerForInstanceSegmentation(__a )
model.eval()
for name, param in model.named_parameters():
print(__a ,param.shape )
_a , _a : Tuple = model.load_state_dict(__a ,strict=__a )
assert missing_keys == [
"model.pixel_level_module.encoder.model.layernorm.weight",
"model.pixel_level_module.encoder.model.layernorm.bias",
]
assert len(__a ) == 0, F"""Unexpected keys: {unexpected_keys}"""
# verify results
_a : Union[str, Any] = prepare_img()
if "vistas" in model_name:
_a : int = 65
elif "cityscapes" in model_name:
_a : Tuple = 65_535
else:
_a : str = 255
_a : Dict = True if '''ade''' in model_name else False
_a : Optional[Any] = MaskFormerImageProcessor(ignore_index=__a ,reduce_labels=__a )
_a : Optional[Any] = image_processor(__a ,return_tensors='''pt''' )
_a : int = model(**__a )
print('''Logits:''' ,outputs.class_queries_logits[0, :3, :3] )
if model_name == "maskformer-swin-tiny-ade":
_a : Union[str, Any] = torch.tensor(
[[3.63_53, -4.47_70, -2.60_65], [0.50_81, -4.23_94, -3.53_43], [2.19_09, -5.03_53, -1.93_23]] )
assert torch.allclose(outputs.class_queries_logits[0, :3, :3] ,__a ,atol=1E-4 )
print('''Looks ok!''' )
if pytorch_dump_folder_path is not None:
print(F"""Saving model and image processor to {pytorch_dump_folder_path}""" )
Path(__a ).mkdir(exist_ok=__a )
model.save_pretrained(__a )
image_processor.save_pretrained(__a )
if push_to_hub:
print('''Pushing model and image processor to the hub...''' )
model.push_to_hub(F"""nielsr/{model_name}""" )
image_processor.push_to_hub(F"""nielsr/{model_name}""" )
if __name__ == "__main__":
a__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--model_name''',
default='''maskformer-swin-tiny-ade''',
type=str,
help=('''Name of the MaskFormer model you\'d like to convert''',),
)
parser.add_argument(
'''--checkpoint_path''',
default='''/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl''',
type=str,
help='''Path to the original state dict (.pth file).''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
)
parser.add_argument(
'''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
)
a__ = parser.parse_args()
convert_maskformer_checkpoint(
args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
)
| 14 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : int = logging.get_logger(__name__)
__A : Union[str, Any] = {
"facebook/nllb-moe-54B": "https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json",
}
class _a ( __lowercase):
"""simple docstring"""
UpperCamelCase__ = "nllb-moe"
UpperCamelCase__ = ["past_key_values"]
UpperCamelCase__ = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
def __init__( self : Any , __UpperCamelCase : Any=1_2_8_1_1_2 , __UpperCamelCase : Union[str, Any]=1_0_2_4 , __UpperCamelCase : List[str]=1_2 , __UpperCamelCase : int=4_0_9_6 , __UpperCamelCase : int=1_6 , __UpperCamelCase : Optional[int]=1_2 , __UpperCamelCase : int=4_0_9_6 , __UpperCamelCase : List[str]=1_6 , __UpperCamelCase : Union[str, Any]=0.0_5 , __UpperCamelCase : Tuple=0.0_5 , __UpperCamelCase : Optional[int]=True , __UpperCamelCase : Dict=True , __UpperCamelCase : Optional[int]="relu" , __UpperCamelCase : Any=1_0_2_4 , __UpperCamelCase : List[str]=0.1 , __UpperCamelCase : str=0.1 , __UpperCamelCase : str=0.0 , __UpperCamelCase : Optional[Any]=0.0_2 , __UpperCamelCase : Tuple=2 , __UpperCamelCase : str=True , __UpperCamelCase : Any=False , __UpperCamelCase : Dict="float32" , __UpperCamelCase : List[str]=False , __UpperCamelCase : Optional[Any]=1_2_8 , __UpperCamelCase : Optional[int]=6_4 , __UpperCamelCase : List[str]=4 , __UpperCamelCase : List[str]=4 , __UpperCamelCase : Optional[Any]=0.0_0_1 , __UpperCamelCase : Optional[int]=0.0_0_1 , __UpperCamelCase : Dict="all" , __UpperCamelCase : List[str]=False , __UpperCamelCase : str=False , __UpperCamelCase : Optional[int]=1.0 , __UpperCamelCase : List[str]=0.2 , __UpperCamelCase : Tuple=1 , __UpperCamelCase : int=0 , __UpperCamelCase : List[str]=2 , __UpperCamelCase : str=False , **__UpperCamelCase : Tuple , )->str:
_UpperCAmelCase = vocab_size
_UpperCAmelCase = max_position_embeddings
_UpperCAmelCase = d_model
_UpperCAmelCase = encoder_ffn_dim
_UpperCAmelCase = encoder_layers
_UpperCAmelCase = encoder_attention_heads
_UpperCAmelCase = decoder_ffn_dim
_UpperCAmelCase = decoder_layers
_UpperCAmelCase = decoder_attention_heads
_UpperCAmelCase = dropout
_UpperCAmelCase = attention_dropout
_UpperCAmelCase = activation_dropout
_UpperCAmelCase = activation_function
_UpperCAmelCase = init_std
_UpperCAmelCase = encoder_layerdrop
_UpperCAmelCase = decoder_layerdrop
_UpperCAmelCase = use_cache
_UpperCAmelCase = encoder_layers
_UpperCAmelCase = scale_embedding # scale factor will be sqrt(d_model) if True
_UpperCAmelCase = router_z_loss_coef
_UpperCAmelCase = router_aux_loss_coef
_UpperCAmelCase = decoder_sparse_step
_UpperCAmelCase = encoder_sparse_step
_UpperCAmelCase = num_experts
_UpperCAmelCase = expert_capacity
_UpperCAmelCase = router_bias
if router_dtype not in ["float32", "float16", "bfloat16"]:
raise ValueError(F'`router_dtype` must be one of \'float32\', \'float16\' or \'bfloat16\', got {router_dtype}' )
_UpperCAmelCase = router_dtype
_UpperCAmelCase = router_ignore_padding_tokens
_UpperCAmelCase = batch_prioritized_routing
_UpperCAmelCase = second_expert_policy
_UpperCAmelCase = normalize_router_prob_before_dropping
_UpperCAmelCase = moe_eval_capacity_token_fraction
_UpperCAmelCase = moe_token_dropout
_UpperCAmelCase = output_router_logits
super().__init__(
pad_token_id=_a , bos_token_id=_a , eos_token_id=_a , is_encoder_decoder=_a , decoder_start_token_id=_a , **_a , )
| 602 |
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
a__ = get_tests_dir('''fixtures/test_sentencepiece.model''')
@require_sentencepiece
class UpperCAmelCase_ ( __lowercase , unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase__ : List[str] = XLMProphetNetTokenizer
UpperCAmelCase__ : Optional[int] = False
UpperCAmelCase__ : List[Any] = True
def __lowercase ( self ) -> int:
super().setUp()
# We have a SentencePiece fixture for testing
_a : List[Any] = XLMProphetNetTokenizer(_a , keep_accents=_a )
tokenizer.save_pretrained(self.tmpdirname )
def __lowercase ( self ) -> Any:
_a : Tuple = '''[PAD]'''
_a : int = 0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(_a ) , _a )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(_a ) , _a )
def __lowercase ( self ) -> str:
_a : Any = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''[PAD]''' )
self.assertEqual(vocab_keys[1] , '''[CLS]''' )
self.assertEqual(vocab_keys[-1] , '''j''' )
self.assertEqual(len(_a ) , 1_0_1_2 )
def __lowercase ( self ) -> Union[str, Any]:
self.assertEqual(self.get_tokenizer().vocab_size , 1_0_1_2 )
def __lowercase ( self ) -> str:
_a : Tuple = XLMProphetNetTokenizer(_a , keep_accents=_a )
_a : Union[str, Any] = tokenizer.tokenize('''This is a test''' )
self.assertListEqual(_a , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(_a ) , [value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]] , )
_a : Optional[int] = tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
_a , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''9''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''é''',
'''.''',
] , )
_a : List[Any] = tokenizer.convert_tokens_to_ids(_a )
self.assertListEqual(
_a , [
value + tokenizer.fairseq_offset
for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, -9, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, -9, 4]
] , )
_a : List[str] = tokenizer.convert_ids_to_tokens(_a )
self.assertListEqual(
_a , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''[UNK]''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''[UNK]''',
'''.''',
] , )
@cached_property
def __lowercase ( self ) -> List[str]:
return XLMProphetNetTokenizer.from_pretrained('''microsoft/xprophetnet-large-wiki100-cased''' )
@slow
def __lowercase ( self ) -> Tuple:
_a : str = '''Hello World!'''
_a : Tuple = [3_5_3_8_9, 6_6_7_2, 4_9, 2]
self.assertListEqual(_a , self.big_tokenizer.encode(_a ) )
@slow
def __lowercase ( self ) -> str:
# fmt: off
_a : str = {'''input_ids''': [[1_1_0_7_3, 8_2_7_8_3, 1_8, 2_6, 8_2_7_8_3, 5_4_9, 5_1_5_4_0, 2_4_8, 1_7_2_0_9, 1_3_0_1, 2_1_7, 2_0, 2_1_5_1_8_6, 1_3_2_5, 1_4_7, 1_7_2_0_9, 1_3_0_1, 2_1_7, 2_0, 5_6_3_7_0, 5_3, 1_2_2_0_2_0, 2_0, 1_6_4_7_7, 2_7, 8_7_3_5_5, 4_5_4_8, 2_0, 4_7_2_8, 7_8_3_9_2, 1_7, 1_5_9_9_6_9, 1_8, 2_6, 2_4_4_9_1, 6_2_9, 1_5, 5_3_8, 2_2_7_0_4, 5_4_3_9, 1_5, 2_7_8_8, 2_4_4_9_1, 9_8_8_5, 1_5, 4_3_5_3_4, 6_0_5, 1_5, 8_1_4, 1_8_4_0_3, 3_3_2_0_0, 2_9, 1_5, 4_3_5_3_4, 2_4_4_5_8, 1_2_4_1_0, 1_1_1, 2_4_9_6_6, 8_3_6_6_9, 9_6_3_7, 1_4_4_0_6_8, 2_6, 8_5_0, 2_2_3_4_6, 2_7, 1_4_7, 2_4_9_6_6, 8_3_6_6_9, 8_3_4_9_0, 2_6, 3_9_1_1_3, 7_3_5, 2_7, 6_8_9, 6_5_6, 2_8_0_0, 1_3_3_9, 4_6_0_0, 5_3, 1_2_2_0_2_0, 1_1_5_7_8_5, 3_4, 8_1_6, 1_3_3_9, 4_6_8_8_7, 1_8, 1_4_7, 5_3_9_0_5, 1_9_5_1, 4_2_2_3_8, 4_1_1_7_0, 1_7_7_3_2, 8_3_4, 4_3_6, 1_5, 2_7_5_2_3, 9_8_7_3_3, 2_1_7, 1_4_7, 5_5_4_2, 4_9_8_1, 9_3_0, 1_7_3_4_7, 1_6, 2], [2_0_0_9_1, 6_2_9, 9_4, 8_2_7_8_6, 5_8, 4_9_0, 2_0, 1_5_2_8, 8_4, 5_3_9_0_5, 3_4_4, 8_0_5_9_2, 1_1_0_1_2_8, 1_8_8_2_2, 5_2_6_7, 1_3_0_6, 6_2, 1_5_2_5_3_7, 3_0_8, 7_9_9_7, 4_0_1, 1_2_4_4_2_7, 5_4_9, 3_5_4_4_2, 2_2_5, 1_0_9, 1_5_0_5_5, 2_5_7_4_8, 1_4_7, 7_1_1_9, 4_3_7_1_2, 3_4, 7_6_7, 1_3_5_3_6_6, 1_8, 1_6, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_9_2, 6_3_7_8_4, 1_1_9_4_6_6, 1_7, 1_4_7_8_0_8, 8_8_2_1_4, 1_8, 6_5_6, 8_1, 3_2, 3_2_9_6, 1_0_2_8_0, 1_6, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=_a , model_name='''microsoft/xprophetnet-large-wiki100-cased''' , revision='''1acad1643ddd54a44df6a1b797ada8373685d90e''' , )
| 14 | 0 |
"""simple docstring"""
from __future__ import annotations
import matplotlib.pyplot as plt # type: ignore
import numpy
# initial triangle of Koch snowflake
__SCREAMING_SNAKE_CASE : Tuple = numpy.array([0, 0])
__SCREAMING_SNAKE_CASE : List[str] = numpy.array([0.5, 0.866_0254])
__SCREAMING_SNAKE_CASE : Tuple = numpy.array([1, 0])
__SCREAMING_SNAKE_CASE : str = [VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]
def lowerCAmelCase_( lowercase_ : list[numpy.ndarray] , lowercase_ : int ) -> list[numpy.ndarray]:
_lowerCamelCase = initial_vectors
for _ in range(__a ):
_lowerCamelCase = iteration_step(__a )
return vectors
def lowerCAmelCase_( lowercase_ : list[numpy.ndarray] ) -> list[numpy.ndarray]:
_lowerCamelCase = []
for i, start_vector in enumerate(vectors[:-1] ):
_lowerCamelCase = vectors[i + 1]
new_vectors.append(__a )
_lowerCamelCase = end_vector - start_vector
new_vectors.append(start_vector + difference_vector / 3 )
new_vectors.append(
start_vector + difference_vector / 3 + rotate(difference_vector / 3 , 60 ) )
new_vectors.append(start_vector + difference_vector * 2 / 3 )
new_vectors.append(vectors[-1] )
return new_vectors
def lowerCAmelCase_( lowercase_ : numpy.ndarray , lowercase_ : float ) -> numpy.ndarray:
_lowerCamelCase = numpy.radians(__a )
_lowerCamelCase = numpy.cos(__a ), numpy.sin(__a )
_lowerCamelCase = numpy.array(((c, -s), (s, c)) )
return numpy.dot(__a , __a )
def lowerCAmelCase_( lowercase_ : list[numpy.ndarray] ) -> None:
_lowerCamelCase = plt.gca()
axes.set_aspect('''equal''' )
# matplotlib.pyplot.plot takes a list of all x-coordinates and a list of all
# y-coordinates as inputs, which are constructed from the vector-list using
# zip()
_lowerCamelCase = zip(*__a )
plt.plot(__a , __a )
plt.show()
if __name__ == "__main__":
import doctest
doctest.testmod()
__SCREAMING_SNAKE_CASE : Tuple = iterate(INITIAL_VECTORS, 5)
plot(processed_vectors)
| 661 |
import os
import unittest
from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class UpperCAmelCase_ ( __lowercase , unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase__ : Any = LxmertTokenizer
UpperCAmelCase__ : Optional[Any] = LxmertTokenizerFast
UpperCAmelCase__ : Any = True
UpperCAmelCase__ : Dict = True
def __lowercase ( self ) -> Union[str, Any]:
super().setUp()
_a : int = [
'''[UNK]''',
'''[CLS]''',
'''[SEP]''',
'''want''',
'''##want''',
'''##ed''',
'''wa''',
'''un''',
'''runn''',
'''##ing''',
''',''',
'''low''',
'''lowest''',
]
_a : Any = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as vocab_writer:
vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens] ) )
def __lowercase ( self , _a ) -> List[str]:
_a : Tuple = '''UNwant\u00E9d,running'''
_a : str = '''unwanted, running'''
return input_text, output_text
def __lowercase ( self ) -> List[Any]:
_a : str = self.tokenizer_class(self.vocab_file )
_a : str = tokenizer.tokenize('''UNwant\u00E9d,running''' )
self.assertListEqual(_a , ['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing'''] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(_a ) , [7, 4, 5, 1_0, 8, 9] )
def __lowercase ( self ) -> List[Any]:
if not self.test_rust_tokenizer:
return
_a : Optional[Any] = self.get_tokenizer()
_a : str = self.get_rust_tokenizer()
_a : Optional[Any] = '''I was born in 92000, and this is falsé.'''
_a : Optional[Any] = tokenizer.tokenize(_a )
_a : List[Any] = rust_tokenizer.tokenize(_a )
self.assertListEqual(_a , _a )
_a : List[Any] = tokenizer.encode(_a , add_special_tokens=_a )
_a : Any = rust_tokenizer.encode(_a , add_special_tokens=_a )
self.assertListEqual(_a , _a )
_a : Dict = self.get_rust_tokenizer()
_a : Optional[int] = tokenizer.encode(_a )
_a : Dict = rust_tokenizer.encode(_a )
self.assertListEqual(_a , _a )
| 14 | 0 |
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class __A ( unittest.TestCase ):
def _snake_case (self ):
lowerCamelCase__ : Union[str, Any] = '''ylacombe/bark-small'''
lowerCamelCase__ : Dict = tempfile.mkdtemp()
lowerCamelCase__ : int = '''en_speaker_1'''
lowerCamelCase__ : Dict = '''This is a test string'''
lowerCamelCase__ : int = '''speaker_embeddings_path.json'''
lowerCamelCase__ : List[Any] = '''speaker_embeddings'''
def _snake_case (self , **__magic_name__ ):
return AutoTokenizer.from_pretrained(self.checkpoint , **_a )
def _snake_case (self ):
shutil.rmtree(self.tmpdirname )
def _snake_case (self ):
lowerCamelCase__ : int = self.get_tokenizer()
lowerCamelCase__ : Optional[Any] = BarkProcessor(tokenizer=_a )
processor.save_pretrained(self.tmpdirname )
lowerCamelCase__ : Optional[Any] = BarkProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
@slow
def _snake_case (self ):
lowerCamelCase__ : str = BarkProcessor.from_pretrained(
pretrained_processor_name_or_path=self.checkpoint , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , )
processor.save_pretrained(
self.tmpdirname , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , speaker_embeddings_directory=self.speaker_embeddings_directory , )
lowerCamelCase__ : int = self.get_tokenizer(bos_token="""(BOS)""" , eos_token="""(EOS)""" )
lowerCamelCase__ : int = BarkProcessor.from_pretrained(
self.tmpdirname , self.speaker_embeddings_dict_path , bos_token="""(BOS)""" , eos_token="""(EOS)""" , )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
def _snake_case (self ):
lowerCamelCase__ : List[Any] = BarkProcessor.from_pretrained(
pretrained_processor_name_or_path=self.checkpoint , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , )
lowerCamelCase__ : Optional[int] = 35
lowerCamelCase__ : str = 2
lowerCamelCase__ : Dict = 8
lowerCamelCase__ : Any = {
'''semantic_prompt''': np.ones(_a ),
'''coarse_prompt''': np.ones((nb_codebooks_coarse, seq_len) ),
'''fine_prompt''': np.ones((nb_codebooks_total, seq_len) ),
}
# test providing already loaded voice_preset
lowerCamelCase__ : int = processor(text=self.input_string , voice_preset=_a )
lowerCamelCase__ : Optional[Any] = inputs['''history_prompt''']
for key in voice_preset:
self.assertListEqual(voice_preset[key].tolist() , processed_voice_preset.get(_a , np.array([] ) ).tolist() )
# test loading voice preset from npz file
lowerCamelCase__ : str = os.path.join(self.tmpdirname , """file.npz""" )
np.savez(_a , **_a )
lowerCamelCase__ : List[str] = processor(text=self.input_string , voice_preset=_a )
lowerCamelCase__ : List[str] = inputs['''history_prompt''']
for key in voice_preset:
self.assertListEqual(voice_preset[key].tolist() , processed_voice_preset.get(_a , np.array([] ) ).tolist() )
# test loading voice preset from the hub
lowerCamelCase__ : Dict = processor(text=self.input_string , voice_preset=self.voice_preset )
def _snake_case (self ):
lowerCamelCase__ : int = self.get_tokenizer()
lowerCamelCase__ : Any = BarkProcessor(tokenizer=_a )
lowerCamelCase__ : int = processor(text=self.input_string )
lowerCamelCase__ : Optional[Any] = tokenizer(
self.input_string , padding="""max_length""" , max_length=256 , add_special_tokens=_a , return_attention_mask=_a , return_token_type_ids=_a , )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key].squeeze().tolist() )
| 157 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch
if is_vision_available():
from transformers import TvltImageProcessor
if is_speech_available():
from transformers import TvltFeatureExtractor
from transformers import TvltProcessor
@require_torch
class UpperCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
def __lowercase ( self ) -> int:
_a : Dict = '''ZinengTang/tvlt-base'''
_a : List[str] = tempfile.mkdtemp()
def __lowercase ( self , **_a ) -> int:
return TvltImageProcessor.from_pretrained(self.checkpoint , **_a )
def __lowercase ( self , **_a ) -> List[Any]:
return TvltFeatureExtractor.from_pretrained(self.checkpoint , **_a )
def __lowercase ( self ) -> Optional[int]:
shutil.rmtree(self.tmpdirname )
def __lowercase ( self ) -> Dict:
_a : Union[str, Any] = self.get_image_processor()
_a : Dict = self.get_feature_extractor()
_a : Optional[int] = TvltProcessor(image_processor=_a , feature_extractor=_a )
processor.save_pretrained(self.tmpdirname )
_a : Any = TvltProcessor.from_pretrained(self.tmpdirname )
self.assertIsInstance(processor.feature_extractor , _a )
self.assertIsInstance(processor.image_processor , _a )
def __lowercase ( self ) -> Any:
_a : Optional[Any] = self.get_image_processor()
_a : Dict = self.get_feature_extractor()
_a : Dict = TvltProcessor(image_processor=_a , feature_extractor=_a )
_a : Union[str, Any] = np.ones([1_2_0_0_0] )
_a : Dict = feature_extractor(_a , return_tensors='''np''' )
_a : Tuple = processor(audio=_a , return_tensors='''np''' )
for key in audio_dict.keys():
self.assertAlmostEqual(audio_dict[key].sum() , input_processor[key].sum() , delta=1e-2 )
def __lowercase ( self ) -> int:
_a : Optional[Any] = self.get_image_processor()
_a : Union[str, Any] = self.get_feature_extractor()
_a : Optional[Any] = TvltProcessor(image_processor=_a , feature_extractor=_a )
_a : List[Any] = np.ones([3, 2_2_4, 2_2_4] )
_a : int = image_processor(_a , return_tensors='''np''' )
_a : Optional[int] = processor(images=_a , return_tensors='''np''' )
for key in image_dict.keys():
self.assertAlmostEqual(image_dict[key].sum() , input_processor[key].sum() , delta=1e-2 )
def __lowercase ( self ) -> Union[str, Any]:
_a : int = self.get_image_processor()
_a : Union[str, Any] = self.get_feature_extractor()
_a : Any = TvltProcessor(image_processor=_a , feature_extractor=_a )
_a : List[str] = np.ones([1_2_0_0_0] )
_a : Optional[int] = np.ones([3, 2_2_4, 2_2_4] )
_a : int = processor(audio=_a , images=_a )
self.assertListEqual(list(inputs.keys() ) , ['''audio_values''', '''audio_mask''', '''pixel_values''', '''pixel_mask'''] )
# test if it raises when no input is passed
with pytest.raises(_a ):
processor()
def __lowercase ( self ) -> Union[str, Any]:
_a : str = self.get_image_processor()
_a : Union[str, Any] = self.get_feature_extractor()
_a : Dict = TvltProcessor(image_processor=_a , feature_extractor=_a )
self.assertListEqual(
processor.model_input_names , image_processor.model_input_names + feature_extractor.model_input_names , msg='''`processor` and `image_processor`+`feature_extractor` model input names do not match''' , )
| 14 | 0 |
from __future__ import annotations
import math
def a__ ( _UpperCamelCase : int ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 ,int(math.sqrt(__a ) + 1 ) ,6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
a_ = [num for num in range(3, 100_001, 2) if not is_prime(num)]
def a__ ( _UpperCamelCase : int ):
if not isinstance(__a ,__a ):
raise ValueError('''n must be an integer''' )
if n <= 0:
raise ValueError('''n must be >= 0''' )
__lowerCamelCase = []
for num in range(len(__a ) ):
__lowerCamelCase = 0
while 2 * i * i <= odd_composites[num]:
__lowerCamelCase = odd_composites[num] - 2 * i * i
if is_prime(__a ):
break
i += 1
else:
list_nums.append(odd_composites[num] )
if len(__a ) == n:
return list_nums
return []
def a__ ( ):
return compute_nums(1 )[0]
if __name__ == "__main__":
print(f"{solution() = }")
| 175 |
def __UpperCAmelCase ( __a : str ) -> list:
"""simple docstring"""
if n_term == "":
return []
_a : list = []
for temp in range(int(__a ) ):
series.append(F"""1/{temp + 1}""" if series else '''1''' )
return series
if __name__ == "__main__":
a__ = input('''Enter the last number (nth term) of the Harmonic Series''')
print('''Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n''')
print(harmonic_series(nth_term))
| 14 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
'''EleutherAI/gpt-neox-20b''': '''https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json''',
# See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}
class a__ ( __lowercase ):
'''simple docstring'''
A : Tuple = "gpt_neox"
def __init__( self : str , lowerCAmelCase_ : Optional[int]=50_432 , lowerCAmelCase_ : Optional[int]=6_144 , lowerCAmelCase_ : List[str]=44 , lowerCAmelCase_ : str=64 , lowerCAmelCase_ : List[str]=24_576 , lowerCAmelCase_ : List[str]="gelu" , lowerCAmelCase_ : List[str]=0.25 , lowerCAmelCase_ : Dict=10_000 , lowerCAmelCase_ : Optional[Any]=0.0 , lowerCAmelCase_ : Dict=0.0 , lowerCAmelCase_ : Union[str, Any]=0.1 , lowerCAmelCase_ : Any=2_048 , lowerCAmelCase_ : str=0.02 , lowerCAmelCase_ : Optional[Any]=1E-5 , lowerCAmelCase_ : str=True , lowerCAmelCase_ : Union[str, Any]=0 , lowerCAmelCase_ : Dict=2 , lowerCAmelCase_ : Optional[int]=False , lowerCAmelCase_ : List[Any]=True , lowerCAmelCase_ : List[Any]=None , **lowerCAmelCase_ : List[str] , ) -> str:
super().__init__(bos_token_id=_a , eos_token_id=_a , **_a )
__A= vocab_size
__A= max_position_embeddings
__A= hidden_size
__A= num_hidden_layers
__A= num_attention_heads
__A= intermediate_size
__A= hidden_act
__A= rotary_pct
__A= rotary_emb_base
__A= attention_dropout
__A= hidden_dropout
__A= classifier_dropout
__A= initializer_range
__A= layer_norm_eps
__A= use_cache
__A= tie_word_embeddings
__A= use_parallel_residual
__A= rope_scaling
self._rope_scaling_validation()
if self.hidden_size % self.num_attention_heads != 0:
raise ValueError(
'The hidden size is not divisble by the number of attention heads! Make sure to update them!' )
def lowerCAmelCase ( self : List[Any] ) -> List[str]:
if self.rope_scaling is None:
return
if not isinstance(self.rope_scaling , _a ) or len(self.rope_scaling ) != 2:
raise ValueError(
'`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '
F"""got {self.rope_scaling}""" )
__A= self.rope_scaling.get('type' , _a )
__A= self.rope_scaling.get('factor' , _a )
if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
raise ValueError(
F"""`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}""" )
if rope_scaling_factor is None or not isinstance(_a , _a ) or rope_scaling_factor <= 1.0:
raise ValueError(F"""`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}""" )
| 186 |
import argparse
import collections
import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def __UpperCAmelCase ( __a : List[Any] ,__a : Optional[Any] ,__a : Optional[int] ) -> Dict:
"""simple docstring"""
return params[F"""{prefix}/{prefix}/relpos_bias/rel_embedding"""][:, i, :]
def __UpperCAmelCase ( __a : List[Any] ,__a : Optional[int] ,__a : int ,__a : List[str]="attention" ) -> List[str]:
"""simple docstring"""
_a : str = np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/key/kernel"""][:, i, :, :] )
_a : Tuple = k_tmp.reshape(k_tmp.shape[0] ,k_tmp.shape[1] * k_tmp.shape[2] )
_a : Any = np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/out/kernel"""][:, i, :, :] )
_a : Dict = o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1] ,o_tmp.shape[2] )
_a : Union[str, Any] = np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/query/kernel"""][:, i, :, :] )
_a : Any = q_tmp.reshape(q_tmp.shape[0] ,q_tmp.shape[1] * q_tmp.shape[2] )
_a : Tuple = np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/value/kernel"""][:, i, :, :] )
_a : int = v_tmp.reshape(v_tmp.shape[0] ,v_tmp.shape[1] * v_tmp.shape[2] )
return k, o, q, v
def __UpperCAmelCase ( __a : Union[str, Any] ,__a : Union[str, Any] ,__a : List[Any] ,__a : Any=False ) -> Any:
"""simple docstring"""
if split_mlp_wi:
_a : Union[str, Any] = params[F"""{prefix}/{prefix}/mlp/wi_0/kernel"""][:, i, :]
_a : Union[str, Any] = params[F"""{prefix}/{prefix}/mlp/wi_1/kernel"""][:, i, :]
_a : List[str] = (wi_a, wi_a)
else:
_a : List[str] = params[F"""{prefix}/{prefix}/mlp/wi/kernel"""][:, i, :]
_a : Optional[int] = params[F"""{prefix}/{prefix}/mlp/wo/kernel"""][:, i, :]
return wi, wo
def __UpperCAmelCase ( __a : List[Any] ,__a : Optional[Any] ,__a : Union[str, Any] ,__a : str ) -> List[str]:
"""simple docstring"""
return params[F"""{prefix}/{prefix}/{layer_name}/scale"""][:, i]
def __UpperCAmelCase ( __a : dict ,*, __a : int ,__a : bool ,__a : bool = False ) -> Any:
"""simple docstring"""
_a : Dict = traverse_util.flatten_dict(variables['''target'''] )
_a : Any = {'''/'''.join(__a ): v for k, v in old.items()}
# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
_a : Optional[int] = '''encoder/encoder/mlp/wi_0/kernel''' in old
print('''Split MLP:''' ,__a )
_a : Tuple = collections.OrderedDict()
# Shared embeddings.
_a : Any = old['''token_embedder/embedding''']
# Encoder.
for i in range(__a ):
# Block i, layer 0 (Self Attention).
_a : Optional[Any] = tax_layer_norm_lookup(__a ,__a ,'''encoder''' ,'''pre_attention_layer_norm''' )
_a , _a , _a , _a : List[str] = tax_attention_lookup(__a ,__a ,'''encoder''' ,'''attention''' )
_a : List[str] = layer_norm
_a : Optional[Any] = k.T
_a : str = o.T
_a : List[Any] = q.T
_a : Tuple = v.T
# Block i, layer 1 (MLP).
_a : str = tax_layer_norm_lookup(__a ,__a ,'''encoder''' ,'''pre_mlp_layer_norm''' )
_a , _a : Any = tax_mlp_lookup(__a ,__a ,'''encoder''' ,__a )
_a : str = layer_norm
if split_mlp_wi:
_a : List[Any] = wi[0].T
_a : Any = wi[1].T
else:
_a : Any = wi.T
_a : Optional[Any] = wo.T
if scalable_attention:
# convert the rel_embedding of each layer
_a : Dict = tax_relpos_bias_lookup(
__a ,__a ,'''encoder''' ).T
_a : List[str] = old['''encoder/encoder_norm/scale''']
if not scalable_attention:
_a : List[Any] = tax_relpos_bias_lookup(
__a ,0 ,'''encoder''' ).T
_a : Optional[Any] = tax_relpos_bias_lookup(
__a ,0 ,'''decoder''' ).T
if not is_encoder_only:
# Decoder.
for i in range(__a ):
# Block i, layer 0 (Self Attention).
_a : Union[str, Any] = tax_layer_norm_lookup(__a ,__a ,'''decoder''' ,'''pre_self_attention_layer_norm''' )
_a , _a , _a , _a : Optional[Any] = tax_attention_lookup(__a ,__a ,'''decoder''' ,'''self_attention''' )
_a : Optional[Any] = layer_norm
_a : Dict = k.T
_a : str = o.T
_a : str = q.T
_a : List[str] = v.T
# Block i, layer 1 (Cross Attention).
_a : Any = tax_layer_norm_lookup(__a ,__a ,'''decoder''' ,'''pre_cross_attention_layer_norm''' )
_a , _a , _a , _a : str = tax_attention_lookup(__a ,__a ,'''decoder''' ,'''encoder_decoder_attention''' )
_a : Optional[Any] = layer_norm
_a : Optional[int] = k.T
_a : Dict = o.T
_a : str = q.T
_a : int = v.T
# Block i, layer 2 (MLP).
_a : Optional[int] = tax_layer_norm_lookup(__a ,__a ,'''decoder''' ,'''pre_mlp_layer_norm''' )
_a , _a : Tuple = tax_mlp_lookup(__a ,__a ,'''decoder''' ,__a )
_a : Optional[Any] = layer_norm
if split_mlp_wi:
_a : List[str] = wi[0].T
_a : List[Any] = wi[1].T
else:
_a : Dict = wi.T
_a : str = wo.T
if scalable_attention:
# convert the rel_embedding of each layer
_a : Tuple = tax_relpos_bias_lookup(__a ,__a ,'''decoder''' ).T
_a : Tuple = old['''decoder/decoder_norm/scale''']
# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
if "decoder/logits_dense/kernel" in old:
_a : Any = old['''decoder/logits_dense/kernel'''].T
return new
def __UpperCAmelCase ( __a : Dict ,__a : bool ) -> Tuple:
"""simple docstring"""
_a : Tuple = collections.OrderedDict([(k, torch.from_numpy(v.copy() )) for (k, v) in converted_params.items()] )
# Add what is missing.
if "encoder.embed_tokens.weight" not in state_dict:
_a : Any = state_dict['''shared.weight''']
if not is_encoder_only:
if "decoder.embed_tokens.weight" not in state_dict:
_a : Optional[int] = state_dict['''shared.weight''']
if "lm_head.weight" not in state_dict: # For old 1.0 models.
print('''Using shared word embeddings as lm_head.''' )
_a : str = state_dict['''shared.weight''']
return state_dict
def __UpperCAmelCase ( __a : List[str] ,__a : Union[str, Any] ,__a : Dict ,__a : Union[str, Any] ,__a : List[Any] ) -> int:
"""simple docstring"""
_a : List[str] = checkpoints.load_tax_checkpoint(__a )
_a : str = convert_tax_to_pytorch(
__a ,num_layers=config.num_layers ,is_encoder_only=__a ,scalable_attention=__a )
_a : str = make_state_dict(__a ,__a )
model.load_state_dict(__a ,strict=__a )
def __UpperCAmelCase ( __a : List[Any] ,__a : Any ,__a : Union[str, Any] ,__a : bool = False ,__a : bool = False ,) -> Optional[Any]:
"""simple docstring"""
_a : List[str] = MTaConfig.from_json_file(__a )
print(F"""Building PyTorch model from configuration: {config}""" )
# Non-v1.1 checkpoints could also use T5Model, but this works for all.
# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
if is_encoder_only:
_a : Any = UMTaEncoderModel(__a )
else:
_a : Tuple = UMTaForConditionalGeneration(__a )
# Load weights from tf checkpoint
load_tax_weights_in_ta(__a ,__a ,__a ,__a ,__a )
# Save pytorch-model
print(F"""Save PyTorch model to {pytorch_dump_path}""" )
model.save_pretrained(__a )
# Verify that we can load the checkpoint.
model.from_pretrained(__a )
print('''Done''' )
if __name__ == "__main__":
a__ = argparse.ArgumentParser(description='''Converts a native T5X checkpoint into a PyTorch checkpoint.''')
# Required parameters
parser.add_argument(
'''--t5x_checkpoint_path''', default=None, type=str, required=True, help='''Path to the T5X checkpoint.'''
)
parser.add_argument(
'''--config_file''',
default=None,
type=str,
required=True,
help='''The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.''',
)
parser.add_argument(
'''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
parser.add_argument(
'''--is_encoder_only''', action='''store_true''', help='''Check if the model is encoder-decoder model''', default=False
)
parser.add_argument(
'''--scalable_attention''',
action='''store_true''',
help='''Whether the model uses scaled attention (umt5 model)''',
default=False,
)
a__ = parser.parse_args()
convert_tax_checkpoint_to_pytorch(
args.tax_checkpoint_path,
args.config_file,
args.pytorch_dump_path,
args.is_encoder_only,
args.scalable_attention,
)
| 14 | 0 |
def UpperCAmelCase_ ( __lowerCAmelCase ) -> str:
__lowercase : List[str] = int(__a )
if decimal in (0, 1): # Exit cases for the recursion
return str(__a )
__lowercase : List[Any] = divmod(__a , 2 )
return binary_recursive(__a ) + str(__a )
def UpperCAmelCase_ ( __lowerCAmelCase ) -> str:
__lowercase : Union[str, Any] = str(__a ).strip()
if not number:
raise ValueError('''No input value was provided''' )
__lowercase : List[Any] = '''-''' if number.startswith('''-''' ) else ''''''
__lowercase : Union[str, Any] = number.lstrip('''-''' )
if not number.isnumeric():
raise ValueError('''Input value is not an integer''' )
return F'{negative}0b{binary_recursive(int(__a ) )}'
if __name__ == "__main__":
from doctest import testmod
testmod()
| 509 |
import random
import sys
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap
a__ = '''Usage of script: script_name <size_of_canvas:int>'''
a__ = [0] * 100 + [1] * 10
random.shuffle(choice)
def __UpperCAmelCase ( __a : int ) -> list[list[bool]]:
"""simple docstring"""
_a : int = [[False for i in range(__a )] for j in range(__a )]
return canvas
def __UpperCAmelCase ( __a : list[list[bool]] ) -> None:
"""simple docstring"""
for i, row in enumerate(__a ):
for j, _ in enumerate(__a ):
_a : Optional[int] = bool(random.getrandbits(1 ) )
def __UpperCAmelCase ( __a : list[list[bool]] ) -> list[list[bool]]:
"""simple docstring"""
_a : Any = np.array(__a )
_a : Optional[int] = np.array(create_canvas(current_canvas.shape[0] ) )
for r, row in enumerate(__a ):
for c, pt in enumerate(__a ):
_a : Tuple = __judge_point(
__a ,current_canvas[r - 1 : r + 2, c - 1 : c + 2] )
_a : List[str] = next_gen_canvas
del next_gen_canvas # cleaning memory as we move on.
_a : list[list[bool]] = current_canvas.tolist()
return return_canvas
def __UpperCAmelCase ( __a : bool ,__a : list[list[bool]] ) -> bool:
"""simple docstring"""
_a : Optional[Any] = 0
_a : str = 0
# finding dead or alive neighbours count.
for i in neighbours:
for status in i:
if status:
alive += 1
else:
dead += 1
# handling duplicate entry for focus pt.
if pt:
alive -= 1
else:
dead -= 1
# running the rules of game here.
_a : Optional[int] = pt
if pt:
if alive < 2:
_a : Dict = False
elif alive == 2 or alive == 3:
_a : Optional[Any] = True
elif alive > 3:
_a : str = False
else:
if alive == 3:
_a : int = True
return state
if __name__ == "__main__":
if len(sys.argv) != 2:
raise Exception(usage_doc)
a__ = int(sys.argv[1])
# main working structure of this module.
a__ = create_canvas(canvas_size)
seed(c)
a__ , a__ = plt.subplots()
fig.show()
a__ = ListedColormap(['''w''', '''k'''])
try:
while True:
a__ = run(c)
ax.matshow(c, cmap=cmap)
fig.canvas.draw()
ax.cla()
except KeyboardInterrupt:
# do nothing.
pass
| 14 | 0 |
import argparse
import os
import re
import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor
from accelerate import Accelerator
def UpperCAmelCase ( a_ ) -> str:
"""simple docstring"""
__A = fname.split(os.path.sep )[-1]
return re.search(r"^(.*)_\d+\.jpg$" , __a ).groups()[0]
class UpperCAmelCase ( __lowercase ):
'''simple docstring'''
def __init__( self : Union[str, Any] ,A : str ,A : List[Any]=None ,A : List[str]=None ):
__A = file_names
__A = image_transform
__A = label_to_id
def __len__( self : Union[str, Any] ):
return len(self.file_names )
def __getitem__( self : Tuple ,A : Any ):
__A = self.file_names[idx]
__A = PIL.Image.open(_a )
__A = raw_image.convert("RGB" )
if self.image_transform is not None:
__A = self.image_transform(_a )
__A = extract_label(_a )
if self.label_to_id is not None:
__A = self.label_to_id[label]
return {"image": image, "label": label}
def UpperCAmelCase ( a_ , a_ ) -> Union[str, Any]:
"""simple docstring"""
if args.with_tracking:
__A = Accelerator(
cpu=args.cpu , mixed_precision=args.mixed_precision , log_with="all" , project_dir=args.project_dir )
else:
__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 = config['''image_size''']
if not isinstance(__a , (list, tuple) ):
__A = (image_size, image_size)
# Parse out whether we are saving every epoch or after a certain number of batches
if hasattr(args.checkpointing_steps , "isdigit" ):
if args.checkpointing_steps == "epoch":
__A = args.checkpointing_steps
elif args.checkpointing_steps.isdigit():
__A = int(args.checkpointing_steps )
else:
raise ValueError(
F'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.''' )
else:
__A = None
# We need to initialize the trackers we use, and also store our configuration
if args.with_tracking:
__A = os.path.split(__a )[-1].split("." )[0]
accelerator.init_trackers(__a , __a )
# Grab all the image filenames
__A = [os.path.join(args.data_dir , __a ) for fname in os.listdir(args.data_dir ) if fname.endswith(".jpg" )]
# Build the label correspondences
__A = [extract_label(__a ) for fname in file_names]
__A = list(set(__a ) )
id_to_label.sort()
__A = {lbl: i for i, lbl in enumerate(__a )}
# Set the seed before splitting the data.
np.random.seed(__a )
torch.manual_seed(__a )
torch.cuda.manual_seed_all(__a )
# Split our filenames between train and validation
__A = np.random.permutation(len(__a ) )
__A = int(0.8 * len(__a ) )
__A = random_perm[:cut]
__A = random_perm[cut:]
# For training we use a simple RandomResizedCrop
__A = Compose([RandomResizedCrop(__a , scale=(0.5, 1.0) ), ToTensor()] )
__A = PetsDataset(
[file_names[i] for i in train_split] , image_transform=__a , label_to_id=__a )
# For evaluation, we use a deterministic Resize
__A = Compose([Resize(__a ), ToTensor()] )
__A = PetsDataset([file_names[i] for i in eval_split] , image_transform=__a , label_to_id=__a )
# Instantiate dataloaders.
__A = DataLoader(__a , shuffle=__a , batch_size=__a , num_workers=4 )
__A = DataLoader(__a , shuffle=__a , batch_size=__a , num_workers=4 )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
__A = create_model("resnet50d" , pretrained=__a , num_classes=len(__a ) )
# 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 )
# Freezing the base model
for param in model.parameters():
__A = False
for param in model.get_classifier().parameters():
__A = True
# We normalize the batches of images to be a bit faster.
__A = torch.tensor(model.default_cfg["mean"] )[None, :, None, None].to(accelerator.device )
__A = torch.tensor(model.default_cfg["std"] )[None, :, None, None].to(accelerator.device )
# Instantiate optimizer
__A = torch.optim.Adam(params=model.parameters() , lr=lr / 2_5 )
# Instantiate learning rate scheduler
__A = OneCycleLR(optimizer=__a , max_lr=__a , epochs=__a , steps_per_epoch=len(__a ) )
# 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 = accelerator.prepare(
__a , __a , __a , __a , __a )
# We need to keep track of how many total steps we have iterated over
__A = 0
# We also need to keep track of the starting epoch so files are named properly
__A = 0
# Potentially load in the weights and states from a previous save
if args.resume_from_checkpoint:
if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
accelerator.print(F'''Resumed from checkpoint: {args.resume_from_checkpoint}''' )
accelerator.load_state(args.resume_from_checkpoint )
__A = os.path.basename(args.resume_from_checkpoint )
else:
# Get the most recent checkpoint
__A = [f.name for f in os.scandir(os.getcwd() ) if f.is_dir()]
dirs.sort(key=os.path.getctime )
__A = dirs[-1] # Sorts folders by date modified, most recent checkpoint is the last
# Extract `epoch_{i}` or `step_{i}`
__A = os.path.splitext(__a )[0]
if "epoch" in training_difference:
__A = int(training_difference.replace("epoch_" , "" ) ) + 1
__A = None
else:
__A = int(training_difference.replace("step_" , "" ) )
__A = resume_step // len(__a )
resume_step -= starting_epoch * len(__a )
# Now we train the model
for epoch in range(__a , __a ):
model.train()
if args.with_tracking:
__A = 0
if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
# We need to skip steps until we reach the resumed step
__A = accelerator.skip_first_batches(__a , __a )
overall_step += resume_step
else:
# After the first iteration though, we need to go back to the original dataloader
__A = train_dataloader
for batch in active_dataloader:
# We could avoid this line since we set the accelerator with `device_placement=True`.
__A = {k: v.to(accelerator.device ) for k, v in batch.items()}
__A = (batch['''image'''] - mean) / std
__A = model(__a )
__A = torch.nn.functional.cross_entropy(__a , batch["label"] )
# We keep track of the loss at each epoch
if args.with_tracking:
total_loss += loss.detach().float()
accelerator.backward(__a )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
overall_step += 1
if isinstance(__a , __a ):
__A = F'''step_{overall_step}'''
if overall_step % checkpointing_steps == 0:
if args.output_dir is not None:
__A = os.path.join(args.output_dir , __a )
accelerator.save_state(__a )
model.eval()
__A = 0
__A = 0
for step, batch in enumerate(__a ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
__A = {k: v.to(accelerator.device ) for k, v in batch.items()}
__A = (batch['''image'''] - mean) / std
with torch.no_grad():
__A = model(__a )
__A = outputs.argmax(dim=-1 )
__A = accelerator.gather_for_metrics((predictions, batch["label"]) )
__A = predictions == references
num_elems += accurate_preds.shape[0]
accurate += accurate_preds.long().sum()
__A = accurate.item() / num_elems
# Use accelerator.print to print only on the main process.
accelerator.print(F'''epoch {epoch}: {1_0_0 * eval_metric:.2f}''' )
if args.with_tracking:
accelerator.log(
{
"accuracy": 1_0_0 * eval_metric,
"train_loss": total_loss.item() / len(__a ),
"epoch": epoch,
} , step=__a , )
if checkpointing_steps == "epoch":
__A = F'''epoch_{epoch}'''
if args.output_dir is not None:
__A = os.path.join(args.output_dir , __a )
accelerator.save_state(__a )
if args.with_tracking:
accelerator.end_training()
def UpperCAmelCase ( ) -> List[Any]:
"""simple docstring"""
__A = argparse.ArgumentParser(description="Simple example of training script." )
parser.add_argument("--data_dir" , required=__a , help="The data folder on disk." )
parser.add_argument("--fp16" , action="store_true" , help="If passed, will use FP16 training." )
parser.add_argument(
"--mixed_precision" , type=__a , default=__a , 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." )
parser.add_argument(
"--checkpointing_steps" , type=__a , default=__a , help="Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch." , )
parser.add_argument(
"--output_dir" , type=__a , default="." , help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory." , )
parser.add_argument(
"--resume_from_checkpoint" , type=__a , default=__a , help="If the training should continue from a checkpoint folder." , )
parser.add_argument(
"--with_tracking" , action="store_true" , help="Whether to load in all available experiment trackers from the environment and use them for logging." , )
parser.add_argument(
"--project_dir" , type=__a , default="logs" , help="Location on where to store experiment tracking logs` and relevent project information" , )
__A = parser.parse_args()
__A = {'''lr''': 3E-2, '''num_epochs''': 3, '''seed''': 4_2, '''batch_size''': 6_4, '''image_size''': 2_2_4}
training_function(__a , __a )
if __name__ == "__main__":
main()
| 55 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__ = logging.get_logger(__name__)
a__ = {
'''funnel-transformer/small''': '''https://huggingface.co/funnel-transformer/small/resolve/main/config.json''',
'''funnel-transformer/small-base''': '''https://huggingface.co/funnel-transformer/small-base/resolve/main/config.json''',
'''funnel-transformer/medium''': '''https://huggingface.co/funnel-transformer/medium/resolve/main/config.json''',
'''funnel-transformer/medium-base''': '''https://huggingface.co/funnel-transformer/medium-base/resolve/main/config.json''',
'''funnel-transformer/intermediate''': (
'''https://huggingface.co/funnel-transformer/intermediate/resolve/main/config.json'''
),
'''funnel-transformer/intermediate-base''': (
'''https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/config.json'''
),
'''funnel-transformer/large''': '''https://huggingface.co/funnel-transformer/large/resolve/main/config.json''',
'''funnel-transformer/large-base''': '''https://huggingface.co/funnel-transformer/large-base/resolve/main/config.json''',
'''funnel-transformer/xlarge''': '''https://huggingface.co/funnel-transformer/xlarge/resolve/main/config.json''',
'''funnel-transformer/xlarge-base''': '''https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/config.json''',
}
class UpperCAmelCase_ ( __lowercase ):
"""simple docstring"""
UpperCAmelCase__ : Optional[int] = "funnel"
UpperCAmelCase__ : Tuple = {
"hidden_size": "d_model",
"num_attention_heads": "n_head",
}
def __init__( self , _a=3_0_5_2_2 , _a=[4, 4, 4] , _a=None , _a=2 , _a=7_6_8 , _a=1_2 , _a=6_4 , _a=3_0_7_2 , _a="gelu_new" , _a=0.1 , _a=0.1 , _a=0.0 , _a=0.1 , _a=None , _a=1e-9 , _a="mean" , _a="relative_shift" , _a=True , _a=True , _a=True , **_a , ) -> List[Any]:
_a : Optional[int] = vocab_size
_a : Dict = block_sizes
_a : Optional[int] = [1] * len(_a ) if block_repeats is None else block_repeats
assert len(_a ) == len(
self.block_repeats ), "`block_sizes` and `block_repeats` should have the same length."
_a : int = num_decoder_layers
_a : List[str] = d_model
_a : Optional[Any] = n_head
_a : Tuple = d_head
_a : Dict = d_inner
_a : List[str] = hidden_act
_a : int = hidden_dropout
_a : Union[str, Any] = attention_dropout
_a : Tuple = activation_dropout
_a : Optional[Any] = initializer_range
_a : Dict = initializer_std
_a : Union[str, Any] = layer_norm_eps
assert pooling_type in [
"mean",
"max",
], F"""Got {pooling_type} for `pooling_type` but only 'mean' and 'max' are supported."""
_a : Any = pooling_type
assert attention_type in [
"relative_shift",
"factorized",
], F"""Got {attention_type} for `attention_type` but only 'relative_shift' and 'factorized' are supported."""
_a : Optional[Any] = attention_type
_a : int = separate_cls
_a : Tuple = truncate_seq
_a : List[Any] = pool_q_only
super().__init__(**_a )
@property
def __lowercase ( self ) -> Tuple:
return sum(self.block_sizes )
@num_hidden_layers.setter
def __lowercase ( self , _a ) -> List[str]:
raise NotImplementedError(
'''This model does not support the setting of `num_hidden_layers`. Please set `block_sizes`.''' )
@property
def __lowercase ( self ) -> Optional[int]:
return len(self.block_sizes )
@num_blocks.setter
def __lowercase ( self , _a ) -> Dict:
raise NotImplementedError('''This model does not support the setting of `num_blocks`. Please set `block_sizes`.''' )
| 14 | 0 |
import heapq
import sys
import numpy as np
__A : Tuple = tuple[int, int]
class lowerCamelCase:
'''simple docstring'''
def __init__( self ):
_A = []
_A = set()
def lowerCAmelCase__ ( self ):
if not self.empty():
return self.elements[0][0]
else:
return float('inf' )
def lowerCAmelCase__ ( self ):
return len(self.elements ) == 0
def lowerCAmelCase__ ( self , snake_case_ , snake_case_ ):
if item not in self.set:
heapq.heappush(self.elements , (priority, item) )
self.set.add(_a )
else:
# update
# print("update", item)
_A = []
(_A) = heapq.heappop(self.elements )
while x != item:
temp.append((pri, x) )
(_A) = heapq.heappop(self.elements )
temp.append((priority, item) )
for pro, xxx in temp:
heapq.heappush(self.elements , (pro, xxx) )
def lowerCAmelCase__ ( self , snake_case_ ):
if item in self.set:
self.set.remove(_a )
_A = []
(_A) = heapq.heappop(self.elements )
while x != item:
temp.append((pro, x) )
(_A) = heapq.heappop(self.elements )
for prito, yyy in temp:
heapq.heappush(self.elements , (prito, yyy) )
def lowerCAmelCase__ ( self ):
return self.elements[0][1]
def lowerCAmelCase__ ( self ):
(_A) = heapq.heappop(self.elements )
self.set.remove(_a )
return (priority, item)
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Optional[Any]:
"""simple docstring"""
_A = np.array(__a )
_A = np.array(__a )
return np.linalg.norm(a - b )
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> List[Any]:
"""simple docstring"""
return consistent_heuristic(__a , __a ) // t
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> str:
"""simple docstring"""
return abs(p[0] - goal[0] ) + abs(p[1] - goal[1] )
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Optional[int]:
"""simple docstring"""
_A = g_function[start] + Wa * heuristics[i](__a , __a )
return ans
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Optional[Any]:
"""simple docstring"""
_A = np.chararray((n, n) )
for i in range(__a ):
for j in range(__a ):
_A = '''*'''
for i in range(__a ):
for j in range(__a ):
if (j, (n - 1) - i) in blocks:
_A = '''#'''
_A = '''-'''
_A = back_pointer[goal]
while x != start:
(_A) = x
# print(x)
_A = '''-'''
_A = back_pointer[x]
_A = '''-'''
for i in range(__a ):
for j in range(__a ):
if (i, j) == (0, n - 1):
print(grid[i][j] , end=' ' )
print('<-- End position' , end=' ' )
else:
print(grid[i][j] , end=' ' )
print()
print('^' )
print('Start position' )
print()
print('# is an obstacle' )
print('- is the path taken by algorithm' )
print('PATH TAKEN BY THE ALGORITHM IS:-' )
_A = back_pointer[goal]
while x != start:
print(__a , end=' ' )
_A = back_pointer[x]
print(__a )
sys.exit()
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE ) -> List[str]:
"""simple docstring"""
if p[0] < 0 or p[0] > n - 1:
return False
if p[1] < 0 or p[1] > n - 1:
return False
return True
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , ) -> Optional[Any]:
"""simple docstring"""
for itera in range(__a ):
open_list[itera].remove_element(__a )
# print("s", s)
# print("j", j)
(_A) = s
_A = (x - 1, y)
_A = (x + 1, y)
_A = (x, y + 1)
_A = (x, y - 1)
for neighbours in [left, right, up, down]:
if neighbours not in blocks:
if valid(__a ) and neighbours not in visited:
# print("neighbour", neighbours)
visited.add(__a )
_A = -1
_A = float('inf' )
if valid(__a ) and g_function[neighbours] > g_function[s] + 1:
_A = g_function[s] + 1
_A = s
if neighbours not in close_list_anchor:
open_list[0].put(__a , key(__a , 0 , __a , __a ) )
if neighbours not in close_list_inad:
for var in range(1 , __a ):
if key(__a , __a , __a , __a ) <= Wa * key(
__a , 0 , __a , __a ):
open_list[j].put(
__a , key(__a , __a , __a , __a ) )
def __lowerCAmelCase( ) -> List[str]:
"""simple docstring"""
_A = []
for x in range(1 , 5 ):
for y in range(1 , 6 ):
some_list.append((x, y) )
for x in range(15 , 20 ):
some_list.append((x, 17) )
for x in range(10 , 19 ):
for y in range(1 , 15 ):
some_list.append((x, y) )
# L block
for x in range(1 , 4 ):
for y in range(12 , 19 ):
some_list.append((x, y) )
for x in range(3 , 13 ):
for y in range(16 , 19 ):
some_list.append((x, y) )
return some_list
__A : List[str] = {0: consistent_heuristic, 1: heuristic_a, 2: heuristic_a}
__A : Optional[int] = [
(0, 1),
(1, 1),
(2, 1),
(3, 1),
(4, 1),
(5, 1),
(6, 1),
(7, 1),
(8, 1),
(9, 1),
(10, 1),
(11, 1),
(12, 1),
(13, 1),
(14, 1),
(15, 1),
(16, 1),
(17, 1),
(18, 1),
(19, 1),
]
__A : Tuple = make_common_ground()
__A : Any = blocks_blk
# hyper parameters
__A : List[str] = 1
__A : Tuple = 1
__A : Optional[Any] = 20
__A : Optional[int] = 3 # one consistent and two other inconsistent
# start and end destination
__A : Dict = (0, 0)
__A : Tuple = (n - 1, n - 1)
__A : Union[str, Any] = 1
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> List[Any]:
"""simple docstring"""
_A = {start: 0, goal: float('inf' )}
_A = {start: -1, goal: -1}
_A = []
_A = set()
for i in range(__a ):
open_list.append(PriorityQueue() )
open_list[i].put(__a , key(__a , __a , __a , __a ) )
_A = []
_A = []
while open_list[0].minkey() < float('inf' ):
for i in range(1 , __a ):
# print(open_list[0].minkey(), open_list[i].minkey())
if open_list[i].minkey() <= Wa * open_list[0].minkey():
global t
t += 1
if g_function[goal] <= open_list[i].minkey():
if g_function[goal] < float('inf' ):
do_something(__a , __a , __a )
else:
_A = open_list[i].top_show()
visited.add(__a )
expand_state(
__a , __a , __a , __a , __a , __a , __a , __a , )
close_list_inad.append(__a )
else:
if g_function[goal] <= open_list[0].minkey():
if g_function[goal] < float('inf' ):
do_something(__a , __a , __a )
else:
_A = open_list[0].top_show()
visited.add(__a )
expand_state(
__a , 0 , __a , __a , __a , __a , __a , __a , )
close_list_anchor.append(__a )
print('No path found to goal' )
print()
for i in range(n - 1 , -1 , -1 ):
for j in range(__a ):
if (j, i) in blocks:
print('#' , end=' ' )
elif (j, i) in back_pointer:
if (j, i) == (n - 1, n - 1):
print('*' , end=' ' )
else:
print('-' , end=' ' )
else:
print('*' , end=' ' )
if (j, i) == (n - 1, n - 1):
print('<-- End position' , end=' ' )
print()
print('^' )
print('Start position' )
print()
print('# is an obstacle' )
print('- is the path taken by algorithm' )
if __name__ == "__main__":
multi_a_star(start, goal, n_heuristic)
| 27 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a__ = logging.get_logger(__name__)
a__ = {
'''google/mobilenet_v1_1.0_224''': '''https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json''',
'''google/mobilenet_v1_0.75_192''': '''https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json''',
# See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}
class UpperCAmelCase_ ( __lowercase ):
"""simple docstring"""
UpperCAmelCase__ : int = "mobilenet_v1"
def __init__( self , _a=3 , _a=2_2_4 , _a=1.0 , _a=8 , _a="relu6" , _a=True , _a=0.999 , _a=0.02 , _a=0.001 , **_a , ) -> List[Any]:
super().__init__(**_a )
if depth_multiplier <= 0:
raise ValueError('''depth_multiplier must be greater than zero.''' )
_a : Tuple = num_channels
_a : str = image_size
_a : Tuple = depth_multiplier
_a : Any = min_depth
_a : int = hidden_act
_a : Optional[Any] = tf_padding
_a : str = classifier_dropout_prob
_a : Optional[int] = initializer_range
_a : Any = layer_norm_eps
class UpperCAmelCase_ ( __lowercase ):
"""simple docstring"""
UpperCAmelCase__ : str = version.parse("1.11" )
@property
def __lowercase ( self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict([('''pixel_values''', {0: '''batch'''})] )
@property
def __lowercase ( self ) -> Mapping[str, Mapping[int, str]]:
if self.task == "image-classification":
return OrderedDict([('''logits''', {0: '''batch'''})] )
else:
return OrderedDict([('''last_hidden_state''', {0: '''batch'''}), ('''pooler_output''', {0: '''batch'''})] )
@property
def __lowercase ( self ) -> float:
return 1e-4
| 14 | 0 |
'''simple docstring'''
UpperCAmelCase_ : str = '''Input must be a string of 8 numbers plus letter'''
UpperCAmelCase_ : str = '''TRWAGMYFPDXBNJZSQVHLCKE'''
def _UpperCamelCase (_lowerCamelCase : str )-> bool:
'''simple docstring'''
if not isinstance(__a , __a ):
__snake_case = f'''Expected string as input, found {type(__a ).__name__}'''
raise TypeError(__a )
__snake_case = spanish_id.replace('''-''' , '''''' ).upper()
if len(__a ) != 9:
raise ValueError(__a )
try:
__snake_case = int(spanish_id_clean[0:8] )
__snake_case = spanish_id_clean[8]
except ValueError as ex:
raise ValueError(__a ) from ex
if letter.isdigit():
raise ValueError(__a )
return letter == LOOKUP_LETTERS[number % 23]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 24 |
a__ = '''Input must be a string of 8 numbers plus letter'''
a__ = '''TRWAGMYFPDXBNJZSQVHLCKE'''
def __UpperCAmelCase ( __a : str ) -> bool:
"""simple docstring"""
if not isinstance(__a ,__a ):
_a : List[str] = F"""Expected string as input, found {type(__a ).__name__}"""
raise TypeError(__a )
_a : List[Any] = spanish_id.replace('''-''' ,'''''' ).upper()
if len(__a ) != 9:
raise ValueError(__a )
try:
_a : Any = int(spanish_id_clean[0:8] )
_a : str = spanish_id_clean[8]
except ValueError as ex:
raise ValueError(__a ) from ex
if letter.isdigit():
raise ValueError(__a )
return letter == LOOKUP_LETTERS[number % 23]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 14 | 0 |
import argparse
import torch
from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
_A = logging.get_logger(__name__)
_A = [
["attention", "attn"],
["encoder_attention", "encoder_attn"],
["q_lin", "q_proj"],
["k_lin", "k_proj"],
["v_lin", "v_proj"],
["out_lin", "out_proj"],
["norm_embeddings", "layernorm_embedding"],
["position_embeddings", "embed_positions"],
["embeddings", "embed_tokens"],
["ffn.lin", "fc"],
]
def lowerCamelCase__ ( __lowerCAmelCase : Any ):
"""simple docstring"""
if k == "embeddings.weight":
return "shared.weight"
for parlai_name, hf_name in PATTERNS:
lowerCAmelCase_ = k.replace(__a , __a )
if k.startswith("encoder" ):
lowerCAmelCase_ = k.replace(".attn" , ".self_attn" )
lowerCAmelCase_ = k.replace("norm1" , "self_attn_layer_norm" )
lowerCAmelCase_ = k.replace("norm2" , "final_layer_norm" )
elif k.startswith("decoder" ):
lowerCAmelCase_ = k.replace("norm1" , "self_attn_layer_norm" )
lowerCAmelCase_ = k.replace("norm2" , "encoder_attn_layer_norm" )
lowerCAmelCase_ = k.replace("norm3" , "final_layer_norm" )
return k
def lowerCamelCase__ ( __lowerCAmelCase : Union[str, Any] ):
"""simple docstring"""
lowerCAmelCase_ = [
'''model.encoder.layernorm_embedding.weight''',
'''model.encoder.layernorm_embedding.bias''',
'''model.decoder.layernorm_embedding.weight''',
'''model.decoder.layernorm_embedding.bias''',
]
for k in keys:
lowerCAmelCase_ = sd.pop(__a )
lowerCAmelCase_ = k.replace("layernorm_embedding" , "layer_norm" )
assert new_k not in sd
lowerCAmelCase_ = v
_A = ["START"]
@torch.no_grad()
def lowerCamelCase__ ( __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : str , __lowerCAmelCase : Union[str, Any] ):
"""simple docstring"""
lowerCAmelCase_ = torch.load(__a , map_location="cpu" )
lowerCAmelCase_ = model['''model''']
lowerCAmelCase_ = BlenderbotConfig.from_json_file(__a )
lowerCAmelCase_ = BlenderbotForConditionalGeneration(__a )
lowerCAmelCase_ = m.model.state_dict().keys()
lowerCAmelCase_ = []
lowerCAmelCase_ = {}
for k, v in sd.items():
if k in IGNORE_KEYS:
continue
lowerCAmelCase_ = rename_state_dict_key(__a )
if new_k not in valid_keys:
failures.append([k, new_k] )
else:
lowerCAmelCase_ = v
if cfg.normalize_before: # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
rename_layernorm_keys(__a )
m.model.load_state_dict(__a , strict=__a )
m.half()
m.save_pretrained(__a )
if __name__ == "__main__":
_A = argparse.ArgumentParser()
# Required parameters
parser.add_argument("--src_path", type=str, help="like blenderbot-model.bin")
parser.add_argument("--save_dir", default="hf_blenderbot", type=str, help="Where to save converted model.")
parser.add_argument(
"--hf_config_json", default="blenderbot-3b-config.json", type=str, help="Path to config to use"
)
_A = parser.parse_args()
convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)
| 290 |
from random import randint
from tempfile import TemporaryFile
import numpy as np
def __UpperCAmelCase ( __a : Optional[Any] ,__a : int ,__a : Any ) -> int:
"""simple docstring"""
_a : int = 0
if start < end:
_a : Tuple = randint(__a ,__a )
_a : Tuple = a[end]
_a : List[str] = a[pivot]
_a : Any = temp
_a , _a : Optional[int] = _in_place_partition(__a ,__a ,__a )
count += _in_place_quick_sort(__a ,__a ,p - 1 )
count += _in_place_quick_sort(__a ,p + 1 ,__a )
return count
def __UpperCAmelCase ( __a : List[Any] ,__a : Tuple ,__a : Dict ) -> Dict:
"""simple docstring"""
_a : Dict = 0
_a : Tuple = randint(__a ,__a )
_a : List[Any] = a[end]
_a : str = a[pivot]
_a : str = temp
_a : Dict = start - 1
for index in range(__a ,__a ):
count += 1
if a[index] < a[end]: # check if current val is less than pivot value
_a : int = new_pivot_index + 1
_a : Any = a[new_pivot_index]
_a : Optional[int] = a[index]
_a : str = temp
_a : Union[str, Any] = a[new_pivot_index + 1]
_a : Tuple = a[end]
_a : Any = temp
return new_pivot_index + 1, count
a__ = TemporaryFile()
a__ = 100 # 1000 elements are to be sorted
a__ , a__ = 0, 1 # mean and standard deviation
a__ = np.random.normal(mu, sigma, p)
np.save(outfile, X)
print('''The array is''')
print(X)
outfile.seek(0) # using the same array
a__ = np.load(outfile)
a__ = len(M) - 1
a__ = _in_place_quick_sort(M, 0, r)
print(
'''No of Comparisons for 100 elements selected from a standard normal distribution'''
'''is :'''
)
print(z)
| 14 | 0 |
'''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 _snake_case (unittest.TestCase):
def UpperCamelCase__ ( self ):
UpperCAmelCase_ : int = 0
def UpperCamelCase__ ( self ):
UpperCAmelCase_ : Dict = AutoImageProcessor.from_pretrained("openai/clip-vit-base-patch32" )
self.assertIsInstance(_a ,_a )
def UpperCamelCase__ ( self ):
with tempfile.TemporaryDirectory() as tmpdirname:
UpperCAmelCase_ : Tuple = Path(_a ) / '''preprocessor_config.json'''
UpperCAmelCase_ : Optional[Any] = Path(_a ) / '''config.json'''
json.dump(
{"image_processor_type": "CLIPImageProcessor", "processor_class": "CLIPProcessor"} ,open(_a ,"w" ) ,)
json.dump({"model_type": "clip"} ,open(_a ,"w" ) )
UpperCAmelCase_ : List[str] = AutoImageProcessor.from_pretrained(_a )
self.assertIsInstance(_a ,_a )
def UpperCamelCase__ ( self ):
# Ensure we can load the image processor from the feature extractor config
with tempfile.TemporaryDirectory() as tmpdirname:
UpperCAmelCase_ : Optional[int] = Path(_a ) / '''preprocessor_config.json'''
UpperCAmelCase_ : Any = Path(_a ) / '''config.json'''
json.dump(
{"feature_extractor_type": "CLIPFeatureExtractor", "processor_class": "CLIPProcessor"} ,open(_a ,"w" ) ,)
json.dump({"model_type": "clip"} ,open(_a ,"w" ) )
UpperCAmelCase_ : Optional[Any] = AutoImageProcessor.from_pretrained(_a )
self.assertIsInstance(_a ,_a )
def UpperCamelCase__ ( self ):
with tempfile.TemporaryDirectory() as tmpdirname:
UpperCAmelCase_ : Dict = CLIPConfig()
# Create a dummy config file with image_proceesor_type
UpperCAmelCase_ : Tuple = Path(_a ) / '''preprocessor_config.json'''
UpperCAmelCase_ : List[str] = Path(_a ) / '''config.json'''
json.dump(
{"image_processor_type": "CLIPImageProcessor", "processor_class": "CLIPProcessor"} ,open(_a ,"w" ) ,)
json.dump({"model_type": "clip"} ,open(_a ,"w" ) )
# remove image_processor_type to make sure config.json alone is enough to load image processor locally
UpperCAmelCase_ : Tuple = AutoImageProcessor.from_pretrained(_a ).to_dict()
config_dict.pop("image_processor_type" )
UpperCAmelCase_ : Tuple = CLIPImageProcessor(**_a )
# save in new folder
model_config.save_pretrained(_a )
config.save_pretrained(_a )
UpperCAmelCase_ : List[str] = AutoImageProcessor.from_pretrained(_a )
# make sure private variable is not incorrectly saved
UpperCAmelCase_ : Optional[int] = json.loads(config.to_json_string() )
self.assertTrue("_processor_class" not in dict_as_saved )
self.assertIsInstance(_a ,_a )
def UpperCamelCase__ ( self ):
with tempfile.TemporaryDirectory() as tmpdirname:
UpperCAmelCase_ : Optional[int] = Path(_a ) / '''preprocessor_config.json'''
json.dump(
{"image_processor_type": "CLIPImageProcessor", "processor_class": "CLIPProcessor"} ,open(_a ,"w" ) ,)
UpperCAmelCase_ : List[str] = AutoImageProcessor.from_pretrained(_a )
self.assertIsInstance(_a ,_a )
def UpperCamelCase__ ( self ):
with self.assertRaisesRegex(
_a ,"clip-base is not a local folder and is not a valid model identifier" ):
UpperCAmelCase_ : Dict = AutoImageProcessor.from_pretrained("clip-base" )
def UpperCamelCase__ ( self ):
with self.assertRaisesRegex(
_a ,R"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)" ):
UpperCAmelCase_ : List[str] = AutoImageProcessor.from_pretrained(_a ,revision="aaaaaa" )
def UpperCamelCase__ ( self ):
with self.assertRaisesRegex(
_a ,"hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json." ,):
UpperCAmelCase_ : Optional[int] = AutoImageProcessor.from_pretrained("hf-internal-testing/config-no-model" )
def UpperCamelCase__ ( self ):
# If remote code is not set, we will time out when asking whether to load the model.
with self.assertRaises(_a ):
UpperCAmelCase_ : str = AutoImageProcessor.from_pretrained("hf-internal-testing/test_dynamic_image_processor" )
# If remote code is disabled, we can't load this config.
with self.assertRaises(_a ):
UpperCAmelCase_ : Optional[Any] = AutoImageProcessor.from_pretrained(
"hf-internal-testing/test_dynamic_image_processor" ,trust_remote_code=_a )
UpperCAmelCase_ : Union[str, Any] = AutoImageProcessor.from_pretrained(
"hf-internal-testing/test_dynamic_image_processor" ,trust_remote_code=_a )
self.assertEqual(image_processor.__class__.__name__ ,"NewImageProcessor" )
# Test image processor can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(_a )
UpperCAmelCase_ : Optional[Any] = AutoImageProcessor.from_pretrained(_a ,trust_remote_code=_a )
self.assertEqual(reloaded_image_processor.__class__.__name__ ,"NewImageProcessor" )
def UpperCamelCase__ ( self ):
try:
AutoConfig.register("custom" ,_a )
AutoImageProcessor.register(_a ,_a )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(_a ):
AutoImageProcessor.register(_a ,_a )
with tempfile.TemporaryDirectory() as tmpdirname:
UpperCAmelCase_ : int = Path(_a ) / '''preprocessor_config.json'''
UpperCAmelCase_ : int = Path(_a ) / '''config.json'''
json.dump(
{"feature_extractor_type": "CLIPFeatureExtractor", "processor_class": "CLIPProcessor"} ,open(_a ,"w" ) ,)
json.dump({"model_type": "clip"} ,open(_a ,"w" ) )
UpperCAmelCase_ : int = CustomImageProcessor.from_pretrained(_a )
# 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(_a )
UpperCAmelCase_ : Optional[Any] = AutoImageProcessor.from_pretrained(_a )
self.assertIsInstance(_a ,_a )
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 UpperCamelCase__ ( self ):
class _snake_case (__lowercase):
__A : Tuple =True
try:
AutoConfig.register("custom" ,_a )
AutoImageProcessor.register(_a ,_a )
# If remote code is not set, the default is to use local
UpperCAmelCase_ : str = 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.
UpperCAmelCase_ : int = AutoImageProcessor.from_pretrained(
"hf-internal-testing/test_dynamic_image_processor" ,trust_remote_code=_a )
self.assertEqual(image_processor.__class__.__name__ ,"NewImageProcessor" )
self.assertTrue(image_processor.is_local )
# If remote is enabled, we load from the Hub
UpperCAmelCase_ : Dict = AutoImageProcessor.from_pretrained(
"hf-internal-testing/test_dynamic_image_processor" ,trust_remote_code=_a )
self.assertEqual(image_processor.__class__.__name__ ,"NewImageProcessor" )
self.assertTrue(not hasattr(_a ,"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]
| 71 |
import json
import os
import unittest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class UpperCAmelCase_ ( __lowercase , unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase__ : List[Any] = MgpstrTokenizer
UpperCAmelCase__ : int = False
UpperCAmelCase__ : Union[str, Any] = {}
UpperCAmelCase__ : List[Any] = False
def __lowercase ( self ) -> Any:
super().setUp()
# fmt: off
_a : Tuple = ['''[GO]''', '''[s]''', '''0''', '''1''', '''2''', '''3''', '''4''', '''5''', '''6''', '''7''', '''8''', '''9''', '''a''', '''b''', '''c''', '''d''', '''e''', '''f''', '''g''', '''h''', '''i''', '''j''', '''k''', '''l''', '''m''', '''n''', '''o''', '''p''', '''q''', '''r''', '''s''', '''t''', '''u''', '''v''', '''w''', '''x''', '''y''', '''z''']
# fmt: on
_a : Optional[int] = dict(zip(_a , range(len(_a ) ) ) )
_a : Any = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write(json.dumps(_a ) + '''\n''' )
def __lowercase ( self , **_a ) -> Dict:
return MgpstrTokenizer.from_pretrained(self.tmpdirname , **_a )
def __lowercase ( self , _a ) -> Tuple:
_a : List[str] = '''tester'''
_a : Optional[Any] = '''tester'''
return input_text, output_text
@unittest.skip('''MGP-STR always lower cases letters.''' )
def __lowercase ( self ) -> Any:
pass
def __lowercase ( self ) -> Any:
_a : Union[str, Any] = self.get_tokenizers(do_lower_case=_a )
for tokenizer in tokenizers:
with self.subTest(F"""{tokenizer.__class__.__name__}""" ):
_a : int = '''[SPECIAL_TOKEN]'''
tokenizer.add_special_tokens({'''cls_token''': special_token} )
_a : Tuple = tokenizer.encode([special_token] , add_special_tokens=_a )
self.assertEqual(len(_a ) , 1 )
_a : Tuple = tokenizer.decode(_a , skip_special_tokens=_a )
self.assertTrue(special_token not in decoded )
def __lowercase ( self ) -> Tuple:
_a : List[Any] = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(F"""{tokenizer.__class__.__name__}""" ):
_a , _a : int = self.get_input_output_texts(_a )
_a : List[str] = tokenizer.tokenize(_a )
_a : Optional[int] = tokenizer.convert_tokens_to_ids(_a )
_a : Tuple = tokenizer.encode(_a , add_special_tokens=_a )
self.assertListEqual(_a , _a )
_a : Optional[int] = tokenizer.convert_ids_to_tokens(_a )
self.assertNotEqual(len(_a ) , 0 )
_a : int = tokenizer.decode(_a )
self.assertIsInstance(_a , _a )
self.assertEqual(text_a.replace(''' ''' , '''''' ) , _a )
@unittest.skip('''MGP-STR tokenizer only handles one sequence.''' )
def __lowercase ( self ) -> List[str]:
pass
@unittest.skip('''inputs cannot be pretokenized in MgpstrTokenizer''' )
def __lowercase ( self ) -> Optional[Any]:
pass
| 14 | 0 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=__lowercase)
class _a ( __lowercase):
"""simple docstring"""
UpperCamelCase__ = field(default="""language-modeling""" , metadata={"""include_in_asdict_even_if_is_default""": True})
UpperCamelCase__ = Features({"""text""": Value("""string""")})
UpperCamelCase__ = Features({})
UpperCamelCase__ = "text"
@property
def lowercase__ ( self : List[str] )->Dict[str, str]:
return {self.text_column: "text"}
| 602 |
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 UpperCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
def __lowercase ( self ) -> List[Any]:
_a : int = 0
def __lowercase ( self ) -> List[str]:
_a : Dict = AutoImageProcessor.from_pretrained('''openai/clip-vit-base-patch32''' )
self.assertIsInstance(_a , _a )
def __lowercase ( self ) -> Union[str, Any]:
with tempfile.TemporaryDirectory() as tmpdirname:
_a : Tuple = Path(_a ) / '''preprocessor_config.json'''
_a : Optional[Any] = Path(_a ) / '''config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(_a , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(_a , '''w''' ) )
_a : List[str] = AutoImageProcessor.from_pretrained(_a )
self.assertIsInstance(_a , _a )
def __lowercase ( self ) -> Optional[Any]:
# Ensure we can load the image processor from the feature extractor config
with tempfile.TemporaryDirectory() as tmpdirname:
_a : Optional[int] = Path(_a ) / '''preprocessor_config.json'''
_a : Any = Path(_a ) / '''config.json'''
json.dump(
{'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''} , open(_a , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(_a , '''w''' ) )
_a : Optional[Any] = AutoImageProcessor.from_pretrained(_a )
self.assertIsInstance(_a , _a )
def __lowercase ( self ) -> Optional[Any]:
with tempfile.TemporaryDirectory() as tmpdirname:
_a : Dict = CLIPConfig()
# Create a dummy config file with image_proceesor_type
_a : Tuple = Path(_a ) / '''preprocessor_config.json'''
_a : List[str] = Path(_a ) / '''config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(_a , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(_a , '''w''' ) )
# remove image_processor_type to make sure config.json alone is enough to load image processor locally
_a : Tuple = AutoImageProcessor.from_pretrained(_a ).to_dict()
config_dict.pop('''image_processor_type''' )
_a : Tuple = CLIPImageProcessor(**_a )
# save in new folder
model_config.save_pretrained(_a )
config.save_pretrained(_a )
_a : List[str] = AutoImageProcessor.from_pretrained(_a )
# make sure private variable is not incorrectly saved
_a : Optional[int] = json.loads(config.to_json_string() )
self.assertTrue('''_processor_class''' not in dict_as_saved )
self.assertIsInstance(_a , _a )
def __lowercase ( self ) -> Dict:
with tempfile.TemporaryDirectory() as tmpdirname:
_a : Optional[int] = Path(_a ) / '''preprocessor_config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(_a , '''w''' ) , )
_a : List[str] = AutoImageProcessor.from_pretrained(_a )
self.assertIsInstance(_a , _a )
def __lowercase ( self ) -> Any:
with self.assertRaisesRegex(
_a , '''clip-base is not a local folder and is not a valid model identifier''' ):
_a : Dict = AutoImageProcessor.from_pretrained('''clip-base''' )
def __lowercase ( self ) -> List[Any]:
with self.assertRaisesRegex(
_a , R'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)''' ):
_a : List[str] = AutoImageProcessor.from_pretrained(_a , revision='''aaaaaa''' )
def __lowercase ( self ) -> Dict:
with self.assertRaisesRegex(
_a , '''hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.''' , ):
_a : Optional[int] = AutoImageProcessor.from_pretrained('''hf-internal-testing/config-no-model''' )
def __lowercase ( self ) -> Union[str, Any]:
# If remote code is not set, we will time out when asking whether to load the model.
with self.assertRaises(_a ):
_a : str = AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor''' )
# If remote code is disabled, we can't load this config.
with self.assertRaises(_a ):
_a : Optional[Any] = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=_a )
_a : Union[str, Any] = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=_a )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
# Test image processor can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(_a )
_a : Optional[Any] = AutoImageProcessor.from_pretrained(_a , trust_remote_code=_a )
self.assertEqual(reloaded_image_processor.__class__.__name__ , '''NewImageProcessor''' )
def __lowercase ( self ) -> Dict:
try:
AutoConfig.register('''custom''' , _a )
AutoImageProcessor.register(_a , _a )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(_a ):
AutoImageProcessor.register(_a , _a )
with tempfile.TemporaryDirectory() as tmpdirname:
_a : int = Path(_a ) / '''preprocessor_config.json'''
_a : int = Path(_a ) / '''config.json'''
json.dump(
{'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''} , open(_a , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(_a , '''w''' ) )
_a : int = CustomImageProcessor.from_pretrained(_a )
# 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(_a )
_a : Optional[Any] = AutoImageProcessor.from_pretrained(_a )
self.assertIsInstance(_a , _a )
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 __lowercase ( self ) -> Union[str, Any]:
class UpperCAmelCase_ ( __lowercase ):
"""simple docstring"""
UpperCAmelCase__ : Tuple = True
try:
AutoConfig.register('''custom''' , _a )
AutoImageProcessor.register(_a , _a )
# If remote code is not set, the default is to use local
_a : str = 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 : int = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=_a )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
self.assertTrue(image_processor.is_local )
# If remote is enabled, we load from the Hub
_a : Dict = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=_a )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
self.assertTrue(not hasattr(_a , '''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]
| 14 | 0 |
"""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
__SCREAMING_SNAKE_CASE : List[Any] = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : List[Any] = {
'''google/mobilenet_v1_1.0_224''': '''https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json''',
'''google/mobilenet_v1_0.75_192''': '''https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json''',
# See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}
class lowerCamelCase_( __lowercase ):
'''simple docstring'''
lowercase__ : int = "mobilenet_v1"
def __init__( self , lowerCamelCase__=3 , lowerCamelCase__=2_2_4 , lowerCamelCase__=1.0 , lowerCamelCase__=8 , lowerCamelCase__="relu6" , lowerCamelCase__=True , lowerCamelCase__=0.9_9_9 , lowerCamelCase__=0.0_2 , lowerCamelCase__=0.0_0_1 , **lowerCamelCase__ , ):
super().__init__(**_a )
if depth_multiplier <= 0:
raise ValueError('''depth_multiplier must be greater than zero.''' )
_lowerCamelCase = num_channels
_lowerCamelCase = image_size
_lowerCamelCase = depth_multiplier
_lowerCamelCase = min_depth
_lowerCamelCase = hidden_act
_lowerCamelCase = tf_padding
_lowerCamelCase = classifier_dropout_prob
_lowerCamelCase = initializer_range
_lowerCamelCase = layer_norm_eps
class lowerCamelCase_( __lowercase ):
'''simple docstring'''
lowercase__ : str = version.parse('1.11' )
@property
def snake_case__ ( self ):
return OrderedDict([('''pixel_values''', {0: '''batch'''})] )
@property
def snake_case__ ( self ):
if self.task == "image-classification":
return OrderedDict([('''logits''', {0: '''batch'''})] )
else:
return OrderedDict([('''last_hidden_state''', {0: '''batch'''}), ('''pooler_output''', {0: '''batch'''})] )
@property
def snake_case__ ( self ):
return 1e-4
| 661 |
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class UpperCAmelCase_ :
"""simple docstring"""
UpperCAmelCase__ : float
UpperCAmelCase__ : TreeNode | None = None
UpperCAmelCase__ : TreeNode | None = None
def __UpperCAmelCase ( __a : TreeNode | None ) -> bool:
"""simple docstring"""
def is_valid_tree(__a : TreeNode | None ) -> bool:
if node is None:
return True
if not isinstance(__a ,__a ):
return False
try:
float(node.data )
except (TypeError, ValueError):
return False
return is_valid_tree(node.left ) and is_valid_tree(node.right )
if not is_valid_tree(__a ):
raise ValueError(
'''Each node should be type of TreeNode and data should be float.''' )
def is_binary_search_tree_recursive_check(
__a : TreeNode | None ,__a : float ,__a : float ) -> bool:
if node is None:
return True
return (
left_bound < node.data < right_bound
and is_binary_search_tree_recursive_check(node.left ,__a ,node.data )
and is_binary_search_tree_recursive_check(
node.right ,node.data ,__a )
)
return is_binary_search_tree_recursive_check(__a ,-float('''inf''' ) ,float('''inf''' ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 14 | 0 |
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging
logging.set_verbosity_info()
_lowercase = logging.get_logger(__name__)
def _A (UpperCamelCase : str ) ->List[Any]:
'''simple docstring'''
lowerCamelCase__ : Tuple = SwinConfig.from_pretrained(
"""microsoft/swin-tiny-patch4-window7-224""" , out_features=["""stage1""", """stage2""", """stage3""", """stage4"""] )
lowerCamelCase__ : Dict = MaskFormerConfig(backbone_config=__a )
lowerCamelCase__ : Optional[Any] = '''huggingface/label-files'''
if "ade20k-full" in model_name:
# this should be ok
lowerCamelCase__ : Optional[Any] = 847
lowerCamelCase__ : List[Any] = '''maskformer-ade20k-full-id2label.json'''
elif "ade" in model_name:
# this should be ok
lowerCamelCase__ : Union[str, Any] = 150
lowerCamelCase__ : Any = '''ade20k-id2label.json'''
elif "coco-stuff" in model_name:
# this should be ok
lowerCamelCase__ : int = 171
lowerCamelCase__ : List[str] = '''maskformer-coco-stuff-id2label.json'''
elif "coco" in model_name:
# TODO
lowerCamelCase__ : Dict = 133
lowerCamelCase__ : Optional[Any] = '''coco-panoptic-id2label.json'''
elif "cityscapes" in model_name:
# this should be ok
lowerCamelCase__ : List[Any] = 19
lowerCamelCase__ : Optional[Any] = '''cityscapes-id2label.json'''
elif "vistas" in model_name:
# this should be ok
lowerCamelCase__ : List[Any] = 65
lowerCamelCase__ : Dict = '''mapillary-vistas-id2label.json'''
lowerCamelCase__ : Optional[int] = json.load(open(hf_hub_download(__a , __a , repo_type="""dataset""" ) , """r""" ) )
lowerCamelCase__ : Tuple = {int(__a ): v for k, v in idalabel.items()}
return config
def _A (UpperCamelCase : Optional[Any] ) ->Tuple:
'''simple docstring'''
lowerCamelCase__ : Optional[Any] = []
# stem
# fmt: off
rename_keys.append(("""backbone.patch_embed.proj.weight""", """model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight""") )
rename_keys.append(("""backbone.patch_embed.proj.bias""", """model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias""") )
rename_keys.append(("""backbone.patch_embed.norm.weight""", """model.pixel_level_module.encoder.model.embeddings.norm.weight""") )
rename_keys.append(("""backbone.patch_embed.norm.bias""", """model.pixel_level_module.encoder.model.embeddings.norm.bias""") )
# stages
for i in range(len(config.backbone_config.depths ) ):
for j in range(config.backbone_config.depths[i] ):
rename_keys.append((f"backbone.layers.{i}.blocks.{j}.norm1.weight", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight") )
rename_keys.append((f"backbone.layers.{i}.blocks.{j}.norm1.bias", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias") )
rename_keys.append((f"backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table") )
rename_keys.append((f"backbone.layers.{i}.blocks.{j}.attn.relative_position_index", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index") )
rename_keys.append((f"backbone.layers.{i}.blocks.{j}.attn.proj.weight", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight") )
rename_keys.append((f"backbone.layers.{i}.blocks.{j}.attn.proj.bias", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias") )
rename_keys.append((f"backbone.layers.{i}.blocks.{j}.norm2.weight", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight") )
rename_keys.append((f"backbone.layers.{i}.blocks.{j}.norm2.bias", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias") )
rename_keys.append((f"backbone.layers.{i}.blocks.{j}.mlp.fc1.weight", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight") )
rename_keys.append((f"backbone.layers.{i}.blocks.{j}.mlp.fc1.bias", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias") )
rename_keys.append((f"backbone.layers.{i}.blocks.{j}.mlp.fc2.weight", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight") )
rename_keys.append((f"backbone.layers.{i}.blocks.{j}.mlp.fc2.bias", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias") )
if i < 3:
rename_keys.append((f"backbone.layers.{i}.downsample.reduction.weight", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight") )
rename_keys.append((f"backbone.layers.{i}.downsample.norm.weight", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight") )
rename_keys.append((f"backbone.layers.{i}.downsample.norm.bias", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias") )
rename_keys.append((f"backbone.norm{i}.weight", f"model.pixel_level_module.encoder.hidden_states_norms.{i}.weight") )
rename_keys.append((f"backbone.norm{i}.bias", f"model.pixel_level_module.encoder.hidden_states_norms.{i}.bias") )
# FPN
rename_keys.append(("""sem_seg_head.layer_4.weight""", """model.pixel_level_module.decoder.fpn.stem.0.weight""") )
rename_keys.append(("""sem_seg_head.layer_4.norm.weight""", """model.pixel_level_module.decoder.fpn.stem.1.weight""") )
rename_keys.append(("""sem_seg_head.layer_4.norm.bias""", """model.pixel_level_module.decoder.fpn.stem.1.bias""") )
for source_index, target_index in zip(range(3 , 0 , -1 ) , range(0 , 3 ) ):
rename_keys.append((f"sem_seg_head.adapter_{source_index}.weight", f"model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight") )
rename_keys.append((f"sem_seg_head.adapter_{source_index}.norm.weight", f"model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight") )
rename_keys.append((f"sem_seg_head.adapter_{source_index}.norm.bias", f"model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias") )
rename_keys.append((f"sem_seg_head.layer_{source_index}.weight", f"model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight") )
rename_keys.append((f"sem_seg_head.layer_{source_index}.norm.weight", f"model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight") )
rename_keys.append((f"sem_seg_head.layer_{source_index}.norm.bias", f"model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias") )
rename_keys.append(("""sem_seg_head.mask_features.weight""", """model.pixel_level_module.decoder.mask_projection.weight""") )
rename_keys.append(("""sem_seg_head.mask_features.bias""", """model.pixel_level_module.decoder.mask_projection.bias""") )
# Transformer decoder
for idx in range(config.decoder_config.decoder_layers ):
# self-attention out projection
rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight", f"model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight") )
rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias", f"model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias") )
# cross-attention out projection
rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight", f"model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight") )
rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias", f"model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias") )
# MLP 1
rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight", f"model.transformer_module.decoder.layers.{idx}.fc1.weight") )
rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias", f"model.transformer_module.decoder.layers.{idx}.fc1.bias") )
# MLP 2
rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight", f"model.transformer_module.decoder.layers.{idx}.fc2.weight") )
rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias", f"model.transformer_module.decoder.layers.{idx}.fc2.bias") )
# layernorm 1 (self-attention layernorm)
rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight", f"model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight") )
rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias", f"model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias") )
# layernorm 2 (cross-attention layernorm)
rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight", f"model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight") )
rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias", f"model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias") )
# layernorm 3 (final layernorm)
rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight", f"model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight") )
rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias", f"model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias") )
rename_keys.append(("""sem_seg_head.predictor.transformer.decoder.norm.weight""", """model.transformer_module.decoder.layernorm.weight""") )
rename_keys.append(("""sem_seg_head.predictor.transformer.decoder.norm.bias""", """model.transformer_module.decoder.layernorm.bias""") )
# heads on top
rename_keys.append(("""sem_seg_head.predictor.query_embed.weight""", """model.transformer_module.queries_embedder.weight""") )
rename_keys.append(("""sem_seg_head.predictor.input_proj.weight""", """model.transformer_module.input_projection.weight""") )
rename_keys.append(("""sem_seg_head.predictor.input_proj.bias""", """model.transformer_module.input_projection.bias""") )
rename_keys.append(("""sem_seg_head.predictor.class_embed.weight""", """class_predictor.weight""") )
rename_keys.append(("""sem_seg_head.predictor.class_embed.bias""", """class_predictor.bias""") )
for i in range(3 ):
rename_keys.append((f"sem_seg_head.predictor.mask_embed.layers.{i}.weight", f"mask_embedder.{i}.0.weight") )
rename_keys.append((f"sem_seg_head.predictor.mask_embed.layers.{i}.bias", f"mask_embedder.{i}.0.bias") )
# fmt: on
return rename_keys
def _A (UpperCamelCase : List[str] , UpperCamelCase : List[Any] , UpperCamelCase : Optional[Any] ) ->Union[str, Any]:
'''simple docstring'''
lowerCamelCase__ : str = dct.pop(__a )
lowerCamelCase__ : str = val
def _A (UpperCamelCase : List[Any] , UpperCamelCase : Union[str, Any] ) ->Union[str, Any]:
'''simple docstring'''
lowerCamelCase__ : Union[str, Any] = [int(backbone_config.embed_dim * 2**i ) for i in range(len(backbone_config.depths ) )]
for i in range(len(backbone_config.depths ) ):
lowerCamelCase__ : Optional[Any] = num_features[i]
for j in range(backbone_config.depths[i] ):
# fmt: off
# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
lowerCamelCase__ : List[Any] = state_dict.pop(f"backbone.layers.{i}.blocks.{j}.attn.qkv.weight" )
lowerCamelCase__ : Optional[int] = state_dict.pop(f"backbone.layers.{i}.blocks.{j}.attn.qkv.bias" )
# next, add query, keys and values (in that order) to the state dict
lowerCamelCase__ : Optional[int] = in_proj_weight[:dim, :]
lowerCamelCase__ : List[Any] = in_proj_bias[: dim]
lowerCamelCase__ : Optional[int] = in_proj_weight[
dim : dim * 2, :
]
lowerCamelCase__ : Tuple = in_proj_bias[
dim : dim * 2
]
lowerCamelCase__ : int = in_proj_weight[
-dim :, :
]
lowerCamelCase__ : Optional[int] = in_proj_bias[-dim :]
# fmt: on
def _A (UpperCamelCase : List[str] , UpperCamelCase : List[Any] ) ->List[Any]:
'''simple docstring'''
lowerCamelCase__ : Optional[int] = config.decoder_config.hidden_size
for idx in range(config.decoder_config.decoder_layers ):
# read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
lowerCamelCase__ : Union[str, Any] = state_dict.pop(f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight" )
lowerCamelCase__ : List[Any] = state_dict.pop(f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias" )
# next, add query, keys and values (in that order) to the state dict
lowerCamelCase__ : Union[str, Any] = in_proj_weight[: hidden_size, :]
lowerCamelCase__ : List[Any] = in_proj_bias[:config.hidden_size]
lowerCamelCase__ : Dict = in_proj_weight[hidden_size : hidden_size * 2, :]
lowerCamelCase__ : Any = in_proj_bias[hidden_size : hidden_size * 2]
lowerCamelCase__ : Tuple = in_proj_weight[-hidden_size :, :]
lowerCamelCase__ : List[Any] = in_proj_bias[-hidden_size :]
# read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
lowerCamelCase__ : List[Any] = state_dict.pop(f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight" )
lowerCamelCase__ : List[str] = state_dict.pop(f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias" )
# next, add query, keys and values (in that order) to the state dict
lowerCamelCase__ : Optional[Any] = in_proj_weight[: hidden_size, :]
lowerCamelCase__ : Any = in_proj_bias[:config.hidden_size]
lowerCamelCase__ : List[str] = in_proj_weight[hidden_size : hidden_size * 2, :]
lowerCamelCase__ : Optional[Any] = in_proj_bias[hidden_size : hidden_size * 2]
lowerCamelCase__ : List[str] = in_proj_weight[-hidden_size :, :]
lowerCamelCase__ : int = in_proj_bias[-hidden_size :]
# fmt: on
def _A () ->torch.Tensor:
'''simple docstring'''
lowerCamelCase__ : str = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
lowerCamelCase__ : Dict = Image.open(requests.get(__a , stream=__a ).raw )
return im
@torch.no_grad()
def _A (UpperCamelCase : str , UpperCamelCase : str , UpperCamelCase : str , UpperCamelCase : bool = False ) ->Union[str, Any]:
'''simple docstring'''
lowerCamelCase__ : Optional[Any] = get_maskformer_config(__a )
# load original state_dict
with open(__a , """rb""" ) as f:
lowerCamelCase__ : str = pickle.load(__a )
lowerCamelCase__ : Union[str, Any] = data['''model''']
# for name, param in state_dict.items():
# print(name, param.shape)
# rename keys
lowerCamelCase__ : Any = create_rename_keys(__a )
for src, dest in rename_keys:
rename_key(__a , __a , __a )
read_in_swin_q_k_v(__a , config.backbone_config )
read_in_decoder_q_k_v(__a , __a )
# update to torch tensors
for key, value in state_dict.items():
lowerCamelCase__ : Optional[int] = torch.from_numpy(__a )
# load 🤗 model
lowerCamelCase__ : Dict = MaskFormerForInstanceSegmentation(__a )
model.eval()
for name, param in model.named_parameters():
print(__a , param.shape )
lowerCamelCase__ : Tuple = model.load_state_dict(__a , strict=__a )
assert missing_keys == [
"model.pixel_level_module.encoder.model.layernorm.weight",
"model.pixel_level_module.encoder.model.layernorm.bias",
]
assert len(__a ) == 0, f"Unexpected keys: {unexpected_keys}"
# verify results
lowerCamelCase__ : Union[str, Any] = prepare_img()
if "vistas" in model_name:
lowerCamelCase__ : int = 65
elif "cityscapes" in model_name:
lowerCamelCase__ : Tuple = 65535
else:
lowerCamelCase__ : str = 255
lowerCamelCase__ : Dict = True if '''ade''' in model_name else False
lowerCamelCase__ : Optional[Any] = MaskFormerImageProcessor(ignore_index=__a , reduce_labels=__a )
lowerCamelCase__ : Optional[Any] = image_processor(__a , return_tensors="""pt""" )
lowerCamelCase__ : int = model(**__a )
print("""Logits:""" , outputs.class_queries_logits[0, :3, :3] )
if model_name == "maskformer-swin-tiny-ade":
lowerCamelCase__ : Union[str, Any] = torch.tensor(
[[3.6_353, -4.4_770, -2.6_065], [0.5_081, -4.2_394, -3.5_343], [2.1_909, -5.0_353, -1.9_323]] )
assert torch.allclose(outputs.class_queries_logits[0, :3, :3] , __a , atol=1E-4 )
print("""Looks ok!""" )
if pytorch_dump_folder_path is not None:
print(f"Saving model and image processor to {pytorch_dump_folder_path}" )
Path(__a ).mkdir(exist_ok=__a )
model.save_pretrained(__a )
image_processor.save_pretrained(__a )
if push_to_hub:
print("""Pushing model and image processor to the hub...""" )
model.push_to_hub(f"nielsr/{model_name}" )
image_processor.push_to_hub(f"nielsr/{model_name}" )
if __name__ == "__main__":
_lowercase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--model_name''',
default='''maskformer-swin-tiny-ade''',
type=str,
help=('''Name of the MaskFormer model you\'d like to convert''',),
)
parser.add_argument(
'''--checkpoint_path''',
default='''/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl''',
type=str,
help='''Path to the original state dict (.pth file).''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
)
parser.add_argument(
'''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
)
_lowercase = parser.parse_args()
convert_maskformer_checkpoint(
args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
)
| 157 |
from __future__ import annotations
import matplotlib.pyplot as plt # type: ignore
import numpy
# initial triangle of Koch snowflake
a__ = numpy.array([0, 0])
a__ = numpy.array([0.5, 0.8660254])
a__ = numpy.array([1, 0])
a__ = [VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]
def __UpperCAmelCase ( __a : list[numpy.ndarray] ,__a : int ) -> list[numpy.ndarray]:
"""simple docstring"""
_a : Tuple = initial_vectors
for _ in range(__a ):
_a : int = iteration_step(__a )
return vectors
def __UpperCAmelCase ( __a : list[numpy.ndarray] ) -> list[numpy.ndarray]:
"""simple docstring"""
_a : Tuple = []
for i, start_vector in enumerate(vectors[:-1] ):
_a : str = vectors[i + 1]
new_vectors.append(__a )
_a : Optional[int] = end_vector - start_vector
new_vectors.append(start_vector + difference_vector / 3 )
new_vectors.append(
start_vector + difference_vector / 3 + rotate(difference_vector / 3 ,60 ) )
new_vectors.append(start_vector + difference_vector * 2 / 3 )
new_vectors.append(vectors[-1] )
return new_vectors
def __UpperCAmelCase ( __a : numpy.ndarray ,__a : float ) -> numpy.ndarray:
"""simple docstring"""
_a : Tuple = numpy.radians(__a )
_a , _a : List[Any] = numpy.cos(__a ), numpy.sin(__a )
_a : Dict = numpy.array(((c, -s), (s, c)) )
return numpy.dot(__a ,__a )
def __UpperCAmelCase ( __a : list[numpy.ndarray] ) -> None:
"""simple docstring"""
_a : str = plt.gca()
axes.set_aspect('''equal''' )
# matplotlib.pyplot.plot takes a list of all x-coordinates and a list of all
# y-coordinates as inputs, which are constructed from the vector-list using
# zip()
_a , _a : Optional[int] = zip(*__a )
plt.plot(__a ,__a )
plt.show()
if __name__ == "__main__":
import doctest
doctest.testmod()
a__ = iterate(INITIAL_VECTORS, 5)
plot(processed_vectors)
| 14 | 0 |
import argparse
import collections
import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def a__ ( _UpperCamelCase : List[Any] ,_UpperCamelCase : Optional[Any] ,_UpperCamelCase : Optional[int] ):
return params[F"""{prefix}/{prefix}/relpos_bias/rel_embedding"""][:, i, :]
def a__ ( _UpperCamelCase : List[Any] ,_UpperCamelCase : Optional[int] ,_UpperCamelCase : int ,_UpperCamelCase : List[str]="attention" ):
__lowerCamelCase = np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/key/kernel"""][:, i, :, :] )
__lowerCamelCase = k_tmp.reshape(k_tmp.shape[0] ,k_tmp.shape[1] * k_tmp.shape[2] )
__lowerCamelCase = np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/out/kernel"""][:, i, :, :] )
__lowerCamelCase = o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1] ,o_tmp.shape[2] )
__lowerCamelCase = np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/query/kernel"""][:, i, :, :] )
__lowerCamelCase = q_tmp.reshape(q_tmp.shape[0] ,q_tmp.shape[1] * q_tmp.shape[2] )
__lowerCamelCase = np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/value/kernel"""][:, i, :, :] )
__lowerCamelCase = v_tmp.reshape(v_tmp.shape[0] ,v_tmp.shape[1] * v_tmp.shape[2] )
return k, o, q, v
def a__ ( _UpperCamelCase : Union[str, Any] ,_UpperCamelCase : Union[str, Any] ,_UpperCamelCase : List[Any] ,_UpperCamelCase : Any=False ):
if split_mlp_wi:
__lowerCamelCase = params[F"""{prefix}/{prefix}/mlp/wi_0/kernel"""][:, i, :]
__lowerCamelCase = params[F"""{prefix}/{prefix}/mlp/wi_1/kernel"""][:, i, :]
__lowerCamelCase = (wi_a, wi_a)
else:
__lowerCamelCase = params[F"""{prefix}/{prefix}/mlp/wi/kernel"""][:, i, :]
__lowerCamelCase = params[F"""{prefix}/{prefix}/mlp/wo/kernel"""][:, i, :]
return wi, wo
def a__ ( _UpperCamelCase : List[Any] ,_UpperCamelCase : Optional[Any] ,_UpperCamelCase : Union[str, Any] ,_UpperCamelCase : str ):
return params[F"""{prefix}/{prefix}/{layer_name}/scale"""][:, i]
def a__ ( _UpperCamelCase : dict ,*, _UpperCamelCase : int ,_UpperCamelCase : bool ,_UpperCamelCase : bool = False ):
__lowerCamelCase = traverse_util.flatten_dict(variables['''target'''] )
__lowerCamelCase = {'''/'''.join(__a ): v for k, v in old.items()}
# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
__lowerCamelCase = '''encoder/encoder/mlp/wi_0/kernel''' in old
print('''Split MLP:''' ,__a )
__lowerCamelCase = collections.OrderedDict()
# Shared embeddings.
__lowerCamelCase = old['''token_embedder/embedding''']
# Encoder.
for i in range(__a ):
# Block i, layer 0 (Self Attention).
__lowerCamelCase = tax_layer_norm_lookup(__a ,__a ,'''encoder''' ,'''pre_attention_layer_norm''' )
__lowerCamelCase = tax_attention_lookup(__a ,__a ,'''encoder''' ,'''attention''' )
__lowerCamelCase = layer_norm
__lowerCamelCase = k.T
__lowerCamelCase = o.T
__lowerCamelCase = q.T
__lowerCamelCase = v.T
# Block i, layer 1 (MLP).
__lowerCamelCase = tax_layer_norm_lookup(__a ,__a ,'''encoder''' ,'''pre_mlp_layer_norm''' )
__lowerCamelCase = tax_mlp_lookup(__a ,__a ,'''encoder''' ,__a )
__lowerCamelCase = layer_norm
if split_mlp_wi:
__lowerCamelCase = wi[0].T
__lowerCamelCase = wi[1].T
else:
__lowerCamelCase = wi.T
__lowerCamelCase = wo.T
if scalable_attention:
# convert the rel_embedding of each layer
__lowerCamelCase = tax_relpos_bias_lookup(
__a ,__a ,'''encoder''' ).T
__lowerCamelCase = old['''encoder/encoder_norm/scale''']
if not scalable_attention:
__lowerCamelCase = tax_relpos_bias_lookup(
__a ,0 ,'''encoder''' ).T
__lowerCamelCase = tax_relpos_bias_lookup(
__a ,0 ,'''decoder''' ).T
if not is_encoder_only:
# Decoder.
for i in range(__a ):
# Block i, layer 0 (Self Attention).
__lowerCamelCase = tax_layer_norm_lookup(__a ,__a ,'''decoder''' ,'''pre_self_attention_layer_norm''' )
__lowerCamelCase = tax_attention_lookup(__a ,__a ,'''decoder''' ,'''self_attention''' )
__lowerCamelCase = layer_norm
__lowerCamelCase = k.T
__lowerCamelCase = o.T
__lowerCamelCase = q.T
__lowerCamelCase = v.T
# Block i, layer 1 (Cross Attention).
__lowerCamelCase = tax_layer_norm_lookup(__a ,__a ,'''decoder''' ,'''pre_cross_attention_layer_norm''' )
__lowerCamelCase = tax_attention_lookup(__a ,__a ,'''decoder''' ,'''encoder_decoder_attention''' )
__lowerCamelCase = layer_norm
__lowerCamelCase = k.T
__lowerCamelCase = o.T
__lowerCamelCase = q.T
__lowerCamelCase = v.T
# Block i, layer 2 (MLP).
__lowerCamelCase = tax_layer_norm_lookup(__a ,__a ,'''decoder''' ,'''pre_mlp_layer_norm''' )
__lowerCamelCase = tax_mlp_lookup(__a ,__a ,'''decoder''' ,__a )
__lowerCamelCase = layer_norm
if split_mlp_wi:
__lowerCamelCase = wi[0].T
__lowerCamelCase = wi[1].T
else:
__lowerCamelCase = wi.T
__lowerCamelCase = wo.T
if scalable_attention:
# convert the rel_embedding of each layer
__lowerCamelCase = tax_relpos_bias_lookup(__a ,__a ,'''decoder''' ).T
__lowerCamelCase = old['''decoder/decoder_norm/scale''']
# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
if "decoder/logits_dense/kernel" in old:
__lowerCamelCase = old['''decoder/logits_dense/kernel'''].T
return new
def a__ ( _UpperCamelCase : Dict ,_UpperCamelCase : bool ):
__lowerCamelCase = collections.OrderedDict([(k, torch.from_numpy(v.copy() )) for (k, v) in converted_params.items()] )
# Add what is missing.
if "encoder.embed_tokens.weight" not in state_dict:
__lowerCamelCase = state_dict['''shared.weight''']
if not is_encoder_only:
if "decoder.embed_tokens.weight" not in state_dict:
__lowerCamelCase = state_dict['''shared.weight''']
if "lm_head.weight" not in state_dict: # For old 1.0 models.
print('''Using shared word embeddings as lm_head.''' )
__lowerCamelCase = state_dict['''shared.weight''']
return state_dict
def a__ ( _UpperCamelCase : List[str] ,_UpperCamelCase : Union[str, Any] ,_UpperCamelCase : Dict ,_UpperCamelCase : Union[str, Any] ,_UpperCamelCase : List[Any] ):
__lowerCamelCase = checkpoints.load_tax_checkpoint(__a )
__lowerCamelCase = convert_tax_to_pytorch(
__a ,num_layers=config.num_layers ,is_encoder_only=__a ,scalable_attention=__a )
__lowerCamelCase = make_state_dict(__a ,__a )
model.load_state_dict(__a ,strict=__a )
def a__ ( _UpperCamelCase : List[Any] ,_UpperCamelCase : Any ,_UpperCamelCase : Union[str, Any] ,_UpperCamelCase : bool = False ,_UpperCamelCase : bool = False ,):
__lowerCamelCase = MTaConfig.from_json_file(__a )
print(F"""Building PyTorch model from configuration: {config}""" )
# Non-v1.1 checkpoints could also use T5Model, but this works for all.
# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
if is_encoder_only:
__lowerCamelCase = UMTaEncoderModel(__a )
else:
__lowerCamelCase = UMTaForConditionalGeneration(__a )
# Load weights from tf checkpoint
load_tax_weights_in_ta(__a ,__a ,__a ,__a ,__a )
# Save pytorch-model
print(F"""Save PyTorch model to {pytorch_dump_path}""" )
model.save_pretrained(__a )
# Verify that we can load the checkpoint.
model.from_pretrained(__a )
print('''Done''' )
if __name__ == "__main__":
a_ = argparse.ArgumentParser(description="""Converts a native T5X checkpoint into a PyTorch checkpoint.""")
# Required parameters
parser.add_argument(
"""--t5x_checkpoint_path""", default=None, type=str, required=True, help="""Path to the T5X checkpoint."""
)
parser.add_argument(
"""--config_file""",
default=None,
type=str,
required=True,
help="""The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.""",
)
parser.add_argument(
"""--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
)
parser.add_argument(
"""--is_encoder_only""", action="""store_true""", help="""Check if the model is encoder-decoder model""", default=False
)
parser.add_argument(
"""--scalable_attention""",
action="""store_true""",
help="""Whether the model uses scaled attention (umt5 model)""",
default=False,
)
a_ = parser.parse_args()
convert_tax_checkpoint_to_pytorch(
args.tax_checkpoint_path,
args.config_file,
args.pytorch_dump_path,
args.is_encoder_only,
args.scalable_attention,
)
| 175 |
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def __UpperCAmelCase ( __a : Tuple ,__a : Dict ,__a : List[str] ,__a : Optional[Any] ,__a : Tuple ) -> Dict:
"""simple docstring"""
with open(__a ) as metadata_file:
_a : Optional[Any] = json.load(__a )
_a : List[Any] = LukeConfig(use_entity_aware_attention=__a ,**metadata['''model_config'''] )
# Load in the weights from the checkpoint_path
_a : Optional[Any] = torch.load(__a ,map_location='''cpu''' )['''module''']
# Load the entity vocab file
_a : Any = load_original_entity_vocab(__a )
# add an entry for [MASK2]
_a : Union[str, Any] = max(entity_vocab.values() ) + 1
config.entity_vocab_size += 1
_a : Dict = XLMRobertaTokenizer.from_pretrained(metadata['''model_config''']['''bert_model_name'''] )
# Add special tokens to the token vocabulary for downstream tasks
_a : Optional[int] = AddedToken('''<ent>''' ,lstrip=__a ,rstrip=__a )
_a : Tuple = AddedToken('''<ent2>''' ,lstrip=__a ,rstrip=__a )
tokenizer.add_special_tokens({'''additional_special_tokens''': [entity_token_a, entity_token_a]} )
config.vocab_size += 2
print(F"""Saving tokenizer to {pytorch_dump_folder_path}""" )
tokenizer.save_pretrained(__a )
with open(os.path.join(__a ,'''tokenizer_config.json''' ) ,'''r''' ) as f:
_a : List[str] = json.load(__a )
_a : Tuple = '''MLukeTokenizer'''
with open(os.path.join(__a ,'''tokenizer_config.json''' ) ,'''w''' ) as f:
json.dump(__a ,__a )
with open(os.path.join(__a ,MLukeTokenizer.vocab_files_names['''entity_vocab_file'''] ) ,'''w''' ) as f:
json.dump(__a ,__a )
_a : Optional[int] = MLukeTokenizer.from_pretrained(__a )
# Initialize the embeddings of the special tokens
_a : str = tokenizer.convert_tokens_to_ids(['''@'''] )[0]
_a : Tuple = tokenizer.convert_tokens_to_ids(['''#'''] )[0]
_a : Any = state_dict['''embeddings.word_embeddings.weight''']
_a : Optional[int] = word_emb[ent_init_index].unsqueeze(0 )
_a : Any = word_emb[enta_init_index].unsqueeze(0 )
_a : Union[str, Any] = torch.cat([word_emb, ent_emb, enta_emb] )
# add special tokens for 'entity_predictions.bias'
for bias_name in ["lm_head.decoder.bias", "lm_head.bias"]:
_a : Tuple = state_dict[bias_name]
_a : Optional[Any] = decoder_bias[ent_init_index].unsqueeze(0 )
_a : Optional[int] = decoder_bias[enta_init_index].unsqueeze(0 )
_a : Dict = torch.cat([decoder_bias, ent_decoder_bias, enta_decoder_bias] )
# Initialize the query layers of the entity-aware self-attention mechanism
for layer_index in range(config.num_hidden_layers ):
for matrix_name in ["query.weight", "query.bias"]:
_a : Tuple = F"""encoder.layer.{layer_index}.attention.self."""
_a : List[Any] = state_dict[prefix + matrix_name]
_a : Dict = state_dict[prefix + matrix_name]
_a : List[Any] = state_dict[prefix + matrix_name]
# Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
_a : Union[str, Any] = state_dict['''entity_embeddings.entity_embeddings.weight''']
_a : Optional[int] = entity_emb[entity_vocab['''[MASK]''']].unsqueeze(0 )
_a : Any = torch.cat([entity_emb, entity_mask_emb] )
# add [MASK2] for 'entity_predictions.bias'
_a : int = state_dict['''entity_predictions.bias''']
_a : int = entity_prediction_bias[entity_vocab['''[MASK]''']].unsqueeze(0 )
_a : Optional[Any] = torch.cat([entity_prediction_bias, entity_mask_bias] )
_a : Optional[int] = LukeForMaskedLM(config=__a ).eval()
state_dict.pop('''entity_predictions.decoder.weight''' )
state_dict.pop('''lm_head.decoder.weight''' )
state_dict.pop('''lm_head.decoder.bias''' )
_a : int = OrderedDict()
for key, value in state_dict.items():
if not (key.startswith('''lm_head''' ) or key.startswith('''entity_predictions''' )):
_a : Optional[int] = state_dict[key]
else:
_a : Tuple = state_dict[key]
_a , _a : int = model.load_state_dict(__a ,strict=__a )
if set(__a ) != {"luke.embeddings.position_ids"}:
raise ValueError(F"""Unexpected unexpected_keys: {unexpected_keys}""" )
if set(__a ) != {
"lm_head.decoder.weight",
"lm_head.decoder.bias",
"entity_predictions.decoder.weight",
}:
raise ValueError(F"""Unexpected missing_keys: {missing_keys}""" )
model.tie_weights()
assert (model.luke.embeddings.word_embeddings.weight == model.lm_head.decoder.weight).all()
assert (model.luke.entity_embeddings.entity_embeddings.weight == model.entity_predictions.decoder.weight).all()
# Check outputs
_a : Optional[int] = MLukeTokenizer.from_pretrained(__a ,task='''entity_classification''' )
_a : int = '''ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan).'''
_a : List[Any] = (0, 9)
_a : Tuple = tokenizer(__a ,entity_spans=[span] ,return_tensors='''pt''' )
_a : int = model(**__a )
# Verify word hidden states
if model_size == "large":
raise NotImplementedError
else: # base
_a : List[str] = torch.Size((1, 33, 768) )
_a : Union[str, Any] = torch.tensor([[0.08_92, 0.05_96, -0.28_19], [0.01_34, 0.11_99, 0.05_73], [-0.01_69, 0.09_27, 0.06_44]] )
if not (outputs.last_hidden_state.shape == expected_shape):
raise ValueError(
F"""Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}""" )
if not torch.allclose(outputs.last_hidden_state[0, :3, :3] ,__a ,atol=1E-4 ):
raise ValueError
# Verify entity hidden states
if model_size == "large":
raise NotImplementedError
else: # base
_a : str = torch.Size((1, 1, 768) )
_a : List[Any] = torch.tensor([[-0.14_82, 0.06_09, 0.03_22]] )
if not (outputs.entity_last_hidden_state.shape == expected_shape):
raise ValueError(
F"""Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is"""
F""" {expected_shape}""" )
if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3] ,__a ,atol=1E-4 ):
raise ValueError
# Verify masked word/entity prediction
_a : Optional[int] = MLukeTokenizer.from_pretrained(__a )
_a : Dict = '''Tokyo is the capital of <mask>.'''
_a : List[str] = (24, 30)
_a : Optional[int] = tokenizer(__a ,entity_spans=[span] ,return_tensors='''pt''' )
_a : Optional[Any] = model(**__a )
_a : Any = encoding['''input_ids'''][0].tolist()
_a : Optional[Any] = input_ids.index(tokenizer.convert_tokens_to_ids('''<mask>''' ) )
_a : Any = outputs.logits[0][mask_position_id].argmax(dim=-1 )
assert "Japan" == tokenizer.decode(__a )
_a : Any = outputs.entity_logits[0][0].argmax().item()
_a : Optional[Any] = [
entity for entity, entity_id in tokenizer.entity_vocab.items() if entity_id == predicted_entity_id
]
assert [e for e in multilingual_predicted_entities if e.startswith('''en:''' )][0] == "en:Japan"
# Finally, save our PyTorch model and tokenizer
print('''Saving PyTorch model to {}'''.format(__a ) )
model.save_pretrained(__a )
def __UpperCAmelCase ( __a : List[Any] ) -> int:
"""simple docstring"""
_a : Union[str, Any] = ['''[MASK]''', '''[PAD]''', '''[UNK]''']
_a : int = [json.loads(__a ) for line in open(__a )]
_a : List[Any] = {}
for entry in data:
_a : int = entry['''id''']
for entity_name, language in entry["entities"]:
if entity_name in SPECIAL_TOKENS:
_a : List[Any] = entity_id
break
_a : Dict = F"""{language}:{entity_name}"""
_a : int = entity_id
return new_mapping
if __name__ == "__main__":
a__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument('''--checkpoint_path''', type=str, help='''Path to a pytorch_model.bin file.''')
parser.add_argument(
'''--metadata_path''', default=None, type=str, help='''Path to a metadata.json file, defining the configuration.'''
)
parser.add_argument(
'''--entity_vocab_path''',
default=None,
type=str,
help='''Path to an entity_vocab.tsv file, containing the entity vocabulary.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to where to dump the output PyTorch model.'''
)
parser.add_argument(
'''--model_size''', default='''base''', type=str, choices=['''base''', '''large'''], help='''Size of the model to be converted.'''
)
a__ = parser.parse_args()
convert_luke_checkpoint(
args.checkpoint_path,
args.metadata_path,
args.entity_vocab_path,
args.pytorch_dump_folder_path,
args.model_size,
)
| 14 | 0 |
'''simple docstring'''
def UpperCAmelCase__( _SCREAMING_SNAKE_CASE : int = 1000 ):
"""simple docstring"""
__A= 1, 1
__A= []
for i in range(1,n + 1 ):
__A= prev_numerator + 2 * prev_denominator
__A= prev_numerator + prev_denominator
if len(str(__a ) ) > len(str(__a ) ):
result.append(__a )
__A= numerator
__A= denominator
return len(__a )
if __name__ == "__main__":
print(F"""{solution() = }""")
| 186 |
from scipy.stats import spearmanr
import datasets
a__ = '''
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlations imply that as data in dataset x increases, so
does data in dataset y. Negative correlations imply that as x increases,
y decreases. Correlations of -1 or +1 imply an exact monotonic relationship.
Unlike the Pearson correlation, the Spearman correlation does not
assume that both datasets are normally distributed.
The p-value roughly indicates the probability of an uncorrelated system
producing datasets that have a Spearman correlation at least as extreme
as the one computed from these datasets. The p-values are not entirely
reliable but are probably reasonable for datasets larger than 500 or so.
'''
a__ = '''
Args:
predictions (`List[float]`): Predicted labels, as returned by a model.
references (`List[float]`): Ground truth labels.
return_pvalue (`bool`): If `True`, returns the p-value. If `False`, returns
only the spearmanr score. Defaults to `False`.
Returns:
spearmanr (`float`): Spearman correlation coefficient.
p-value (`float`): p-value. **Note**: is only returned if `return_pvalue=True` is input.
Examples:
Example 1:
>>> spearmanr_metric = datasets.load_metric("spearmanr")
>>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], predictions=[10, 9, 2.5, 6, 4])
>>> print(results)
{\'spearmanr\': -0.7}
Example 2:
>>> spearmanr_metric = datasets.load_metric("spearmanr")
>>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5],
... predictions=[10, 9, 2.5, 6, 4],
... return_pvalue=True)
>>> print(results[\'spearmanr\'])
-0.7
>>> print(round(results[\'spearmanr_pvalue\'], 2))
0.19
'''
a__ = R'''\
@book{kokoska2000crc,
title={CRC standard probability and statistics tables and formulae},
author={Kokoska, Stephen and Zwillinger, Daniel},
year={2000},
publisher={Crc Press}
}
@article{2020SciPy-NMeth,
author = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
Haberland, Matt and Reddy, Tyler and Cournapeau, David and
Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
Kern, Robert and Larson, Eric and Carey, C J and
Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and
{VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
Harris, Charles R. and Archibald, Anne M. and
Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and
{van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
title = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
Computing in Python}},
journal = {Nature Methods},
year = {2020},
volume = {17},
pages = {261--272},
adsurl = {https://rdcu.be/b08Wh},
doi = {10.1038/s41592-019-0686-2},
}
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class UpperCAmelCase_ ( datasets.Metric ):
"""simple docstring"""
def __lowercase ( self ) -> int:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Value('''float''' ),
'''references''': datasets.Value('''float''' ),
} ) , reference_urls=['''https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.spearmanr.html'''] , )
def __lowercase ( self , _a , _a , _a=False ) -> str:
_a : int = spearmanr(_a , _a )
if return_pvalue:
return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
else:
return {"spearmanr": results[0]}
| 14 | 0 |
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_head,
ftp_get,
ftp_head,
get_from_cache,
http_get,
http_head,
)
__lowerCAmelCase : Union[str, Any] = "\\n Text data.\n Second line of data."
__lowerCAmelCase : Optional[int] = "file"
@pytest.fixture(scope='''session''' )
def UpperCAmelCase_ ( __lowerCAmelCase ) -> Union[str, Any]:
__lowercase : int = tmp_path_factory.mktemp('''data''' ) / (FILE_PATH + '''.zstd''')
__lowercase : Optional[int] = bytes(__a , '''utf-8''' )
with zstd.open(__a , '''wb''' ) as f:
f.write(__a )
return path
@pytest.fixture
def UpperCAmelCase_ ( __lowerCAmelCase ) -> str:
with open(os.path.join(tmpfs.local_root_dir , __a ) , '''w''' ) as f:
f.write(__a )
return FILE_PATH
@pytest.mark.parametrize('''compression_format''' , ['''gzip''', '''xz''', '''zstd'''] )
def UpperCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> str:
__lowercase : Any = {'''gzip''': gz_file, '''xz''': xz_file, '''zstd''': zstd_path}
__lowercase : Union[str, Any] = input_paths[compression_format]
__lowercase : Dict = tmp_path / '''cache'''
__lowercase : List[str] = DownloadConfig(cache_dir=__a , extract_compressed_file=__a )
__lowercase : str = cached_path(__a , download_config=__a )
with open(__a ) as f:
__lowercase : List[Any] = f.read()
with open(__a ) as f:
__lowercase : Any = f.read()
assert extracted_file_content == expected_file_content
@pytest.mark.parametrize('''default_extracted''' , [True, False] )
@pytest.mark.parametrize('''default_cache_dir''' , [True, False] )
def UpperCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> int:
__lowercase : Union[str, Any] = '''custom_cache'''
__lowercase : Union[str, Any] = '''custom_extracted_dir'''
__lowercase : Union[str, Any] = tmp_path / '''custom_extracted_path'''
if default_extracted:
__lowercase : List[Any] = ('''downloads''' if default_cache_dir else custom_cache_dir, '''extracted''')
else:
monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_DIR''' , __a )
monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_PATH''' , str(__a ) )
__lowercase : Tuple = custom_extracted_path.parts[-2:] if default_cache_dir else (custom_cache_dir, custom_extracted_dir)
__lowercase : Dict = xz_file
__lowercase : int = (
DownloadConfig(extract_compressed_file=__a )
if default_cache_dir
else DownloadConfig(cache_dir=tmp_path / custom_cache_dir , extract_compressed_file=__a )
)
__lowercase : int = cached_path(__a , download_config=__a )
assert Path(__a ).parent.parts[-2:] == expected
def UpperCAmelCase_ ( __lowerCAmelCase ) -> Union[str, Any]:
__lowercase : Optional[Any] = str(Path(__a ).resolve() )
assert cached_path(__a ) == text_file
# relative path
__lowercase : Optional[int] = str(Path(__a ).resolve().relative_to(Path(os.getcwd() ) ) )
assert cached_path(__a ) == text_file
def UpperCAmelCase_ ( __lowerCAmelCase ) -> Dict:
__lowercase : Union[str, Any] = str(tmp_path.resolve() / '''__missing_file__.txt''' )
with pytest.raises(__a ):
cached_path(__a )
# relative path
__lowercase : Tuple = '''./__missing_file__.txt'''
with pytest.raises(__a ):
cached_path(__a )
def UpperCAmelCase_ ( __lowerCAmelCase ) -> List[str]:
__lowercase : Tuple = get_from_cache(F'tmp://{tmpfs_file}' )
with open(__a ) as f:
__lowercase : List[Any] = f.read()
assert output_file_content == FILE_CONTENT
@patch('''datasets.config.HF_DATASETS_OFFLINE''' , __a )
def UpperCAmelCase_ ( ) -> Union[str, Any]:
with pytest.raises(__a ):
cached_path('''https://huggingface.co''' )
@patch('''datasets.config.HF_DATASETS_OFFLINE''' , __a )
def UpperCAmelCase_ ( __lowerCAmelCase ) -> int:
__lowercase : Optional[int] = tmp_path_factory.mktemp('''data''' ) / '''file.html'''
with pytest.raises(__a ):
http_get('''https://huggingface.co''' , temp_file=__a )
with pytest.raises(__a ):
http_head('''https://huggingface.co''' )
@patch('''datasets.config.HF_DATASETS_OFFLINE''' , __a )
def UpperCAmelCase_ ( __lowerCAmelCase ) -> Tuple:
__lowercase : Optional[Any] = tmp_path_factory.mktemp('''data''' ) / '''file.html'''
with pytest.raises(__a ):
ftp_get('''ftp://huggingface.co''' , temp_file=__a )
with pytest.raises(__a ):
ftp_head('''ftp://huggingface.co''' )
@patch('''datasets.config.HF_DATASETS_OFFLINE''' , __a )
def UpperCAmelCase_ ( __lowerCAmelCase ) -> Dict:
__lowercase : Tuple = tmp_path_factory.mktemp('''data''' ) / '''file.html'''
with pytest.raises(__a ):
fsspec_get('''s3://huggingface.co''' , temp_file=__a )
with pytest.raises(__a ):
fsspec_head('''s3://huggingface.co''' )
| 509 |
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def __UpperCAmelCase ( __a : bytes ,__a : int ) -> np.array:
"""simple docstring"""
_a : int = F"""{sampling_rate}"""
_a : str = '''1'''
_a : Optional[int] = '''f32le'''
_a : Optional[Any] = [
'''ffmpeg''',
'''-i''',
'''pipe:0''',
'''-ac''',
ac,
'''-ar''',
ar,
'''-f''',
format_for_conversion,
'''-hide_banner''',
'''-loglevel''',
'''quiet''',
'''pipe:1''',
]
try:
with subprocess.Popen(__a ,stdin=subprocess.PIPE ,stdout=subprocess.PIPE ) as ffmpeg_process:
_a : Any = ffmpeg_process.communicate(__a )
except FileNotFoundError as error:
raise ValueError('''ffmpeg was not found but is required to load audio files from filename''' ) from error
_a : Optional[Any] = output_stream[0]
_a : Optional[int] = np.frombuffer(__a ,np.floataa )
if audio.shape[0] == 0:
raise ValueError('''Malformed soundfile''' )
return audio
def __UpperCAmelCase ( __a : int ,__a : float ,__a : str = "f32le" ,) -> str:
"""simple docstring"""
_a : Dict = F"""{sampling_rate}"""
_a : Optional[Any] = '''1'''
if format_for_conversion == "s16le":
_a : Dict = 2
elif format_for_conversion == "f32le":
_a : Optional[Any] = 4
else:
raise ValueError(F"""Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`""" )
_a : Dict = platform.system()
if system == "Linux":
_a : Dict = '''alsa'''
_a : Union[str, Any] = '''default'''
elif system == "Darwin":
_a : Union[str, Any] = '''avfoundation'''
_a : List[str] = ''':0'''
elif system == "Windows":
_a : Optional[int] = '''dshow'''
_a : str = '''default'''
_a : Tuple = [
'''ffmpeg''',
'''-f''',
format_,
'''-i''',
input_,
'''-ac''',
ac,
'''-ar''',
ar,
'''-f''',
format_for_conversion,
'''-fflags''',
'''nobuffer''',
'''-hide_banner''',
'''-loglevel''',
'''quiet''',
'''pipe:1''',
]
_a : Any = int(round(sampling_rate * chunk_length_s ) ) * size_of_sample
_a : str = _ffmpeg_stream(__a ,__a )
for item in iterator:
yield item
def __UpperCAmelCase ( __a : int ,__a : float ,__a : Optional[int] = None ,__a : Optional[Union[Tuple[float, float], float]] = None ,__a : str = "f32le" ,) -> Optional[int]:
"""simple docstring"""
if stream_chunk_s is not None:
_a : Tuple = stream_chunk_s
else:
_a : Tuple = chunk_length_s
_a : Tuple = ffmpeg_microphone(__a ,__a ,format_for_conversion=__a )
if format_for_conversion == "s16le":
_a : Any = np.intaa
_a : Optional[int] = 2
elif format_for_conversion == "f32le":
_a : Dict = np.floataa
_a : List[Any] = 4
else:
raise ValueError(F"""Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`""" )
if stride_length_s is None:
_a : List[Any] = chunk_length_s / 6
_a : Optional[int] = int(round(sampling_rate * chunk_length_s ) ) * size_of_sample
if isinstance(__a ,(int, float) ):
_a : Optional[Any] = [stride_length_s, stride_length_s]
_a : Optional[Any] = int(round(sampling_rate * stride_length_s[0] ) ) * size_of_sample
_a : str = int(round(sampling_rate * stride_length_s[1] ) ) * size_of_sample
_a : Optional[Any] = datetime.datetime.now()
_a : Tuple = datetime.timedelta(seconds=__a )
for item in chunk_bytes_iter(__a ,__a ,stride=(stride_left, stride_right) ,stream=__a ):
# Put everything back in numpy scale
_a : Dict = np.frombuffer(item['''raw'''] ,dtype=__a )
_a : Dict = (
item['''stride'''][0] // size_of_sample,
item['''stride'''][1] // size_of_sample,
)
_a : str = sampling_rate
audio_time += delta
if datetime.datetime.now() > audio_time + 10 * delta:
# We're late !! SKIP
continue
yield item
def __UpperCAmelCase ( __a : Optional[int] ,__a : int ,__a : Tuple[int, int] ,__a : bool = False ) -> Optional[int]:
"""simple docstring"""
_a : Any = b''''''
_a , _a : List[str] = stride
if stride_left + stride_right >= chunk_len:
raise ValueError(
F"""Stride needs to be strictly smaller than chunk_len: ({stride_left}, {stride_right}) vs {chunk_len}""" )
_a : List[str] = 0
for raw in iterator:
acc += raw
if stream and len(__a ) < chunk_len:
_a : Dict = (_stride_left, 0)
yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
else:
while len(__a ) >= chunk_len:
# We are flushing the accumulator
_a : List[str] = (_stride_left, stride_right)
_a : List[Any] = {'''raw''': acc[:chunk_len], '''stride''': stride}
if stream:
_a : List[Any] = False
yield item
_a : Optional[Any] = stride_left
_a : Optional[Any] = acc[chunk_len - stride_left - stride_right :]
# Last chunk
if len(__a ) > stride_left:
_a : Optional[Any] = {'''raw''': acc, '''stride''': (_stride_left, 0)}
if stream:
_a : Dict = False
yield item
def __UpperCAmelCase ( __a : int ,__a : int ) -> Tuple:
"""simple docstring"""
_a : Dict = 2**24 # 16Mo
try:
with subprocess.Popen(__a ,stdout=subprocess.PIPE ,bufsize=__a ) as ffmpeg_process:
while True:
_a : int = ffmpeg_process.stdout.read(__a )
if raw == b"":
break
yield raw
except FileNotFoundError as error:
raise ValueError('''ffmpeg was not found but is required to stream audio files from filename''' ) from error
| 14 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow
from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class UpperCAmelCase ( __lowercase , __lowercase , __lowercase , unittest.TestCase ):
'''simple docstring'''
snake_case_ = StableDiffusionInpaintPipeline
snake_case_ = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
snake_case_ = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
snake_case_ = frozenset(
[] ) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
snake_case_ = frozenset([] )
def UpperCamelCase_ ( self : List[Any] ):
torch.manual_seed(0 )
__A = UNetaDConditionModel(
block_out_channels=(32, 64) ,layers_per_block=2 ,sample_size=32 ,in_channels=9 ,out_channels=4 ,down_block_types=("DownBlock2D", "CrossAttnDownBlock2D") ,up_block_types=("CrossAttnUpBlock2D", "UpBlock2D") ,cross_attention_dim=32 ,attention_head_dim=(2, 4) ,use_linear_projection=_a ,)
__A = PNDMScheduler(skip_prk_steps=_a )
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 ,sample_size=1_28 ,)
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=10_00 ,hidden_act="gelu" ,projection_dim=5_12 ,)
__A = CLIPTextModel(_a )
__A = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
__A = {
'''unet''': unet,
'''scheduler''': scheduler,
'''vae''': vae,
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''safety_checker''': None,
'''feature_extractor''': None,
}
return components
def UpperCamelCase_ ( self : Optional[Any] ,A : int ,A : str=0 ):
# TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
__A = floats_tensor((1, 3, 32, 32) ,rng=random.Random(_a ) ).to(_a )
__A = image.cpu().permute(0 ,2 ,3 ,1 )[0]
__A = Image.fromarray(np.uinta(_a ) ).convert("RGB" ).resize((64, 64) )
__A = Image.fromarray(np.uinta(image + 4 ) ).convert("RGB" ).resize((64, 64) )
if str(_a ).startswith("mps" ):
__A = torch.manual_seed(_a )
else:
__A = torch.Generator(device=_a ).manual_seed(_a )
__A = {
'''prompt''': '''A painting of a squirrel eating a burger''',
'''image''': init_image,
'''mask_image''': mask_image,
'''generator''': generator,
'''num_inference_steps''': 2,
'''guidance_scale''': 6.0,
'''output_type''': '''numpy''',
}
return inputs
def UpperCamelCase_ ( self : Dict ):
__A = '''cpu''' # ensure determinism for the device-dependent torch.Generator
__A = self.get_dummy_components()
__A = StableDiffusionInpaintPipeline(**_a )
__A = sd_pipe.to(_a )
sd_pipe.set_progress_bar_config(disable=_a )
__A = self.get_dummy_inputs(_a )
__A = sd_pipe(**_a ).images
__A = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
__A = np.array([0.47_27, 0.57_35, 0.39_41, 0.54_46, 0.59_26, 0.43_94, 0.50_62, 0.46_54, 0.44_76] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def UpperCamelCase_ ( self : str ):
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
@slow
@require_torch_gpu
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase_ ( self : int ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCamelCase_ ( self : str ):
__A = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/sd2-inpaint/init_image.png" )
__A = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png" )
__A = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"
"/yellow_cat_sitting_on_a_park_bench.npy" )
__A = '''stabilityai/stable-diffusion-2-inpainting'''
__A = StableDiffusionInpaintPipeline.from_pretrained(_a ,safety_checker=_a )
pipe.to(_a )
pipe.set_progress_bar_config(disable=_a )
pipe.enable_attention_slicing()
__A = '''Face of a yellow cat, high resolution, sitting on a park bench'''
__A = torch.manual_seed(0 )
__A = pipe(
prompt=_a ,image=_a ,mask_image=_a ,generator=_a ,output_type="np" ,)
__A = output.images[0]
assert image.shape == (5_12, 5_12, 3)
assert np.abs(expected_image - image ).max() < 9E-3
def UpperCamelCase_ ( self : Any ):
__A = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/sd2-inpaint/init_image.png" )
__A = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png" )
__A = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"
"/yellow_cat_sitting_on_a_park_bench_fp16.npy" )
__A = '''stabilityai/stable-diffusion-2-inpainting'''
__A = StableDiffusionInpaintPipeline.from_pretrained(
_a ,torch_dtype=torch.floataa ,safety_checker=_a ,)
pipe.to(_a )
pipe.set_progress_bar_config(disable=_a )
pipe.enable_attention_slicing()
__A = '''Face of a yellow cat, high resolution, sitting on a park bench'''
__A = torch.manual_seed(0 )
__A = pipe(
prompt=_a ,image=_a ,mask_image=_a ,generator=_a ,output_type="np" ,)
__A = output.images[0]
assert image.shape == (5_12, 5_12, 3)
assert np.abs(expected_image - image ).max() < 5E-1
def UpperCamelCase_ ( self : Optional[int] ):
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
__A = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/sd2-inpaint/init_image.png" )
__A = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png" )
__A = '''stabilityai/stable-diffusion-2-inpainting'''
__A = PNDMScheduler.from_pretrained(_a ,subfolder="scheduler" )
__A = StableDiffusionInpaintPipeline.from_pretrained(
_a ,safety_checker=_a ,scheduler=_a ,torch_dtype=torch.floataa ,)
pipe.to(_a )
pipe.set_progress_bar_config(disable=_a )
pipe.enable_attention_slicing(1 )
pipe.enable_sequential_cpu_offload()
__A = '''Face of a yellow cat, high resolution, sitting on a park bench'''
__A = torch.manual_seed(0 )
__A = pipe(
prompt=_a ,image=_a ,mask_image=_a ,generator=_a ,num_inference_steps=2 ,output_type="np" ,)
__A = torch.cuda.max_memory_allocated()
# make sure that less than 2.65 GB is allocated
assert mem_bytes < 2.65 * 10**9
| 55 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, 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 UpperCAmelCase_ ( __lowercase , unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase__ : Union[str, Any] = KandinskyInpaintPipeline
UpperCAmelCase__ : Optional[int] = ["prompt", "image_embeds", "negative_image_embeds", "image", "mask_image"]
UpperCAmelCase__ : Optional[Any] = [
"prompt",
"negative_prompt",
"image_embeds",
"negative_image_embeds",
"image",
"mask_image",
]
UpperCAmelCase__ : Optional[int] = [
"generator",
"height",
"width",
"latents",
"guidance_scale",
"negative_prompt",
"num_inference_steps",
"return_dict",
"guidance_scale",
"num_images_per_prompt",
"output_type",
"return_dict",
]
UpperCAmelCase__ : Any = False
@property
def __lowercase ( self ) -> Optional[int]:
return 3_2
@property
def __lowercase ( self ) -> int:
return 3_2
@property
def __lowercase ( self ) -> List[str]:
return self.time_input_dim
@property
def __lowercase ( self ) -> List[str]:
return self.time_input_dim * 4
@property
def __lowercase ( self ) -> Optional[Any]:
return 1_0_0
@property
def __lowercase ( self ) -> Optional[Any]:
_a : Any = XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base''' )
return tokenizer
@property
def __lowercase ( self ) -> str:
torch.manual_seed(0 )
_a : List[Any] = MCLIPConfig(
numDims=self.cross_attention_dim , transformerDimensions=self.text_embedder_hidden_size , hidden_size=self.text_embedder_hidden_size , intermediate_size=3_7 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=1_0_0_5 , )
_a : Optional[int] = MultilingualCLIP(_a )
_a : Tuple = text_encoder.eval()
return text_encoder
@property
def __lowercase ( self ) -> str:
torch.manual_seed(0 )
_a : List[str] = {
'''in_channels''': 9,
# 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 : Dict = UNetaDConditionModel(**_a )
return model
@property
def __lowercase ( self ) -> Optional[int]:
return {
"block_out_channels": [3_2, 6_4],
"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": 1_2,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def __lowercase ( self ) -> Tuple:
torch.manual_seed(0 )
_a : List[Any] = VQModel(**self.dummy_movq_kwargs )
return model
def __lowercase ( self ) -> Any:
_a : List[Any] = self.dummy_text_encoder
_a : Optional[Any] = self.dummy_tokenizer
_a : Optional[Any] = self.dummy_unet
_a : Union[str, Any] = self.dummy_movq
_a : Tuple = DDIMScheduler(
num_train_timesteps=1_0_0_0 , beta_schedule='''linear''' , beta_start=0.0_0085 , beta_end=0.012 , clip_sample=_a , set_alpha_to_one=_a , steps_offset=1 , prediction_type='''epsilon''' , thresholding=_a , )
_a : str = {
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''unet''': unet,
'''scheduler''': scheduler,
'''movq''': movq,
}
return components
def __lowercase ( self , _a , _a=0 ) -> int:
_a : Union[str, Any] = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(_a ) ).to(_a )
_a : List[str] = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(seed + 1 ) ).to(_a )
# create init_image
_a : Tuple = floats_tensor((1, 3, 6_4, 6_4) , rng=random.Random(_a ) ).to(_a )
_a : Dict = image.cpu().permute(0 , 2 , 3 , 1 )[0]
_a : Optional[int] = Image.fromarray(np.uinta(_a ) ).convert('''RGB''' ).resize((2_5_6, 2_5_6) )
# create mask
_a : Union[str, Any] = np.ones((6_4, 6_4) , dtype=np.floataa )
_a : List[str] = 0
if str(_a ).startswith('''mps''' ):
_a : Tuple = torch.manual_seed(_a )
else:
_a : Any = torch.Generator(device=_a ).manual_seed(_a )
_a : Any = {
'''prompt''': '''horse''',
'''image''': init_image,
'''mask_image''': mask,
'''image_embeds''': image_embeds,
'''negative_image_embeds''': negative_image_embeds,
'''generator''': generator,
'''height''': 6_4,
'''width''': 6_4,
'''num_inference_steps''': 2,
'''guidance_scale''': 4.0,
'''output_type''': '''np''',
}
return inputs
def __lowercase ( self ) -> Optional[Any]:
_a : Optional[Any] = '''cpu'''
_a : List[Any] = self.get_dummy_components()
_a : Tuple = self.pipeline_class(**_a )
_a : int = pipe.to(_a )
pipe.set_progress_bar_config(disable=_a )
_a : Any = pipe(**self.get_dummy_inputs(_a ) )
_a : str = output.images
_a : Tuple = pipe(
**self.get_dummy_inputs(_a ) , return_dict=_a , )[0]
_a : Union[str, Any] = image[0, -3:, -3:, -1]
_a : Tuple = image_from_tuple[0, -3:, -3:, -1]
print(F"""image.shape {image.shape}""" )
assert image.shape == (1, 6_4, 6_4, 3)
_a : str = np.array(
[0.832_6919, 0.7379_0467, 0.2091_8581, 0.930_9612, 0.551_1791, 0.4371_3328, 0.551_3321, 0.4992_2934, 0.5949_7786] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
), F""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
), F""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""
def __lowercase ( self ) -> Dict:
super().test_inference_batch_single_identical(expected_max_diff=3e-3 )
@slow
@require_torch_gpu
class UpperCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
def __lowercase ( self ) -> str:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __lowercase ( self ) -> Union[str, Any]:
_a : Tuple = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy''' )
_a : str = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png''' )
_a : Tuple = np.ones((7_6_8, 7_6_8) , dtype=np.floataa )
_a : Any = 0
_a : Optional[Any] = '''a hat'''
_a : Optional[Any] = KandinskyPriorPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-1-prior''' , torch_dtype=torch.floataa )
pipe_prior.to(_a )
_a : Tuple = KandinskyInpaintPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-1-inpaint''' , torch_dtype=torch.floataa )
_a : Union[str, Any] = pipeline.to(_a )
pipeline.set_progress_bar_config(disable=_a )
_a : Union[str, Any] = torch.Generator(device='''cpu''' ).manual_seed(0 )
_a , _a : Dict = pipe_prior(
_a , generator=_a , num_inference_steps=5 , negative_prompt='''''' , ).to_tuple()
_a : Optional[int] = pipeline(
_a , image=_a , mask_image=_a , image_embeds=_a , negative_image_embeds=_a , generator=_a , num_inference_steps=1_0_0 , height=7_6_8 , width=7_6_8 , output_type='''np''' , )
_a : Optional[int] = output.images[0]
assert image.shape == (7_6_8, 7_6_8, 3)
assert_mean_pixel_difference(_a , _a )
| 14 | 0 |
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:
'''simple docstring'''
def lowerCAmelCase__ ( self ):
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=1000 , beta_schedule='squaredcos_cap_v2' , beta_start=0.0001 , beta_end=0.02 , thresholding=_a , dynamic_thresholding_ratio=0.95 , 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 lowerCAmelCase__ ( self ):
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=1.414 , 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=1000 , beta_schedule='squaredcos_cap_v2' , beta_start=0.0001 , beta_end=0.02 , thresholding=_a , dynamic_thresholding_ratio=0.95 , sample_max_value=1.0 , prediction_type='epsilon' , variance_type='learned_range' , )
torch.manual_seed(0 )
_A = DDPMScheduler(
num_train_timesteps=1000 , beta_schedule='squaredcos_cap_v2' , beta_start=0.0001 , beta_end=0.02 , )
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 lowerCAmelCase__ ( self ):
_A = self.get_dummy_components()
_A = self.pipeline_class(**_a )
pipe.to(_a )
pipe.set_progress_bar_config(disable=_a )
_A = self.get_dummy_inputs(_a )
_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 = pipe.encode_prompt(_a )
# 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(_a , _a , _a )
_A = pipe(**_a )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(_a )
_A = self.pipeline_class.from_pretrained(_a )
pipe_loaded.to(_a )
pipe_loaded.set_progress_bar_config(disable=_a )
pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
for optional_component in pipe._optional_components:
self.assertTrue(
getattr(_a , _a ) is None , F"`{optional_component}` did not stay set to None after loading." , )
_A = self.get_dummy_inputs(_a )
_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(**_a )[0]
_A = np.abs(to_np(_a ) - to_np(_a ) ).max()
self.assertLess(_a , 1E-4 )
def lowerCAmelCase__ ( self ):
_A = self.get_dummy_components()
_A = self.pipeline_class(**_a )
pipe.to(_a )
pipe.set_progress_bar_config(disable=_a )
_A = self.get_dummy_inputs(_a )
_A = pipe(**_a )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(_a )
_A = self.pipeline_class.from_pretrained(_a )
pipe_loaded.to(_a )
pipe_loaded.set_progress_bar_config(disable=_a )
pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
_A = self.get_dummy_inputs(_a )
_A = pipe_loaded(**_a )[0]
_A = np.abs(to_np(_a ) - to_np(_a ) ).max()
self.assertLess(_a , 1E-4 )
| 27 |
import argparse
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt
if __name__ == "__main__":
a__ = argparse.ArgumentParser()
parser.add_argument(
'''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
)
parser.add_argument(
'''--original_config_file''',
type=str,
required=True,
help='''The YAML config file corresponding to the original architecture.''',
)
parser.add_argument(
'''--num_in_channels''',
default=None,
type=int,
help='''The number of input channels. If `None` number of input channels will be automatically inferred.''',
)
parser.add_argument(
'''--image_size''',
default=512,
type=int,
help=(
'''The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2'''
''' Base. Use 768 for Stable Diffusion v2.'''
),
)
parser.add_argument(
'''--extract_ema''',
action='''store_true''',
help=(
'''Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights'''
''' or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield'''
''' higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.'''
),
)
parser.add_argument(
'''--upcast_attention''',
action='''store_true''',
help=(
'''Whether the attention computation should always be upcasted. This is necessary when running stable'''
''' diffusion 2.1.'''
),
)
parser.add_argument(
'''--from_safetensors''',
action='''store_true''',
help='''If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.''',
)
parser.add_argument(
'''--to_safetensors''',
action='''store_true''',
help='''Whether to store pipeline in safetensors format or not.''',
)
parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')
parser.add_argument('''--device''', type=str, help='''Device to use (e.g. cpu, cuda:0, cuda:1, etc.)''')
def __UpperCAmelCase ( __a : Any ) -> List[Any]:
"""simple docstring"""
if string == "True":
return True
elif string == "False":
return False
else:
raise ValueError(F"""could not parse string as bool {string}""" )
parser.add_argument(
'''--use_linear_projection''', help='''Override for use linear projection''', required=False, type=parse_bool
)
parser.add_argument('''--cross_attention_dim''', help='''Override for cross attention_dim''', required=False, type=int)
a__ = parser.parse_args()
a__ = download_controlnet_from_original_ckpt(
checkpoint_path=args.checkpoint_path,
original_config_file=args.original_config_file,
image_size=args.image_size,
extract_ema=args.extract_ema,
num_in_channels=args.num_in_channels,
upcast_attention=args.upcast_attention,
from_safetensors=args.from_safetensors,
device=args.device,
use_linear_projection=args.use_linear_projection,
cross_attention_dim=args.cross_attention_dim,
)
controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
| 14 | 0 |
'''simple docstring'''
from __future__ import annotations
def _UpperCamelCase (_lowerCamelCase : int | str )-> bool:
'''simple docstring'''
__snake_case = str(__a )
return n == n[::-1]
def _UpperCamelCase (_lowerCamelCase : int = 1_00_00_00 )-> Optional[Any]:
'''simple docstring'''
__snake_case = 0
for i in range(1 , __a ):
if is_palindrome(__a ) and is_palindrome(bin(__a ).split('''b''' )[1] ):
total += i
return total
if __name__ == "__main__":
print(solution(int(str(input().strip()))))
| 24 |
class UpperCAmelCase_ :
"""simple docstring"""
def __init__( self , _a , _a , _a ) -> List[str]:
_a : List[Any] = name
_a : List[str] = value
_a : List[str] = weight
def __repr__( self ) -> Optional[int]:
return F"""{self.__class__.__name__}({self.name}, {self.value}, {self.weight})"""
def __lowercase ( self ) -> List[Any]:
return self.value
def __lowercase ( self ) -> int:
return self.name
def __lowercase ( self ) -> Optional[int]:
return self.weight
def __lowercase ( self ) -> Optional[Any]:
return self.value / self.weight
def __UpperCAmelCase ( __a : Optional[int] ,__a : Tuple ,__a : List[str] ) -> List[str]:
"""simple docstring"""
_a : Optional[int] = []
for i in range(len(__a ) ):
menu.append(Things(name[i] ,value[i] ,weight[i] ) )
return menu
def __UpperCAmelCase ( __a : int ,__a : Union[str, Any] ,__a : int ) -> Union[str, Any]:
"""simple docstring"""
_a : Union[str, Any] = sorted(__a ,key=__a ,reverse=__a )
_a : Any = []
_a , _a : Optional[int] = 0.0, 0.0
for i in range(len(__a ) ):
if (total_cost + items_copy[i].get_weight()) <= max_cost:
result.append(items_copy[i] )
total_cost += items_copy[i].get_weight()
total_value += items_copy[i].get_value()
return (result, total_value)
def __UpperCAmelCase ( ) -> int:
"""simple docstring"""
if __name__ == "__main__":
import doctest
doctest.testmod()
| 14 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_A = {
"configuration_swinv2": ["SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Swinv2Config"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_A = [
"SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST",
"Swinv2ForImageClassification",
"Swinv2ForMaskedImageModeling",
"Swinv2Model",
"Swinv2PreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_swinva import (
SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
SwinvaForImageClassification,
SwinvaForMaskedImageModeling,
SwinvaModel,
SwinvaPreTrainedModel,
)
else:
import sys
_A = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 290 |
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import SwiftFormerForImageClassification, SwiftFormerModel
from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class UpperCAmelCase_ :
"""simple docstring"""
def __init__( self , _a , _a=1_3 , _a=3 , _a=True , _a=True , _a=0.1 , _a=0.1 , _a=2_2_4 , _a=1_0_0_0 , _a=[3, 3, 6, 4] , _a=[4_8, 5_6, 1_1_2, 2_2_0] , ) -> Tuple:
_a : Dict = parent
_a : Optional[int] = batch_size
_a : Optional[Any] = num_channels
_a : Union[str, Any] = is_training
_a : Tuple = use_labels
_a : Dict = hidden_dropout_prob
_a : List[Any] = attention_probs_dropout_prob
_a : Dict = num_labels
_a : List[str] = image_size
_a : Dict = layer_depths
_a : str = embed_dims
def __lowercase ( self ) -> Optional[Any]:
_a : Dict = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_a : int = None
if self.use_labels:
_a : Optional[Any] = ids_tensor([self.batch_size] , self.num_labels )
_a : Dict = self.get_config()
return config, pixel_values, labels
def __lowercase ( self ) -> int:
return SwiftFormerConfig(
depths=self.layer_depths , embed_dims=self.embed_dims , mlp_ratio=4 , downsamples=[True, True, True, True] , hidden_act='''gelu''' , num_labels=self.num_labels , down_patch_size=3 , down_stride=2 , down_pad=1 , drop_rate=0.0 , drop_path_rate=0.0 , use_layer_scale=_a , layer_scale_init_value=1e-5 , )
def __lowercase ( self , _a , _a , _a ) -> str:
_a : List[Any] = SwiftFormerModel(config=_a )
model.to(_a )
model.eval()
_a : Optional[int] = model(_a )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.embed_dims[-1], 7, 7) )
def __lowercase ( self , _a , _a , _a ) -> Optional[Any]:
_a : List[str] = self.num_labels
_a : Optional[int] = SwiftFormerForImageClassification(_a )
model.to(_a )
model.eval()
_a : List[str] = model(_a , labels=_a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
_a : Union[str, Any] = SwiftFormerForImageClassification(_a )
model.to(_a )
model.eval()
_a : Dict = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_a : Optional[Any] = model(_a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __lowercase ( self ) -> Tuple:
((_a) , (_a) , (_a)) : Optional[int] = self.prepare_config_and_inputs()
_a : List[Any] = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class UpperCAmelCase_ ( __lowercase , __lowercase , unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase__ : List[str] = (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
UpperCAmelCase__ : Optional[int] = (
{"feature-extraction": SwiftFormerModel, "image-classification": SwiftFormerForImageClassification}
if is_torch_available()
else {}
)
UpperCAmelCase__ : Optional[Any] = False
UpperCAmelCase__ : str = False
UpperCAmelCase__ : Tuple = False
UpperCAmelCase__ : Tuple = False
UpperCAmelCase__ : str = False
def __lowercase ( self ) -> Optional[int]:
_a : Union[str, Any] = SwiftFormerModelTester(self )
_a : int = ConfigTester(
self , config_class=_a , has_text_modality=_a , hidden_size=3_7 , num_attention_heads=1_2 , num_hidden_layers=1_2 , )
def __lowercase ( self ) -> int:
self.config_tester.run_common_tests()
@unittest.skip(reason='''SwiftFormer does not use inputs_embeds''' )
def __lowercase ( self ) -> Union[str, Any]:
pass
def __lowercase ( self ) -> Dict:
_a , _a : int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_a : Any = model_class(_a )
_a : int = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(_a , nn.Linear ) )
def __lowercase ( self ) -> str:
_a , _a : str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_a : Optional[int] = model_class(_a )
_a : Tuple = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_a : Tuple = [*signature.parameters.keys()]
_a : List[str] = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , _a )
def __lowercase ( self ) -> int:
_a : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_a )
def __lowercase ( self ) -> Optional[int]:
_a : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_a )
@slow
def __lowercase ( self ) -> Optional[Any]:
for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_a : Any = SwiftFormerModel.from_pretrained(_a )
self.assertIsNotNone(_a )
@unittest.skip(reason='''SwiftFormer does not output attentions''' )
def __lowercase ( self ) -> List[Any]:
pass
def __lowercase ( self ) -> int:
def check_hidden_states_output(_a , _a , _a ):
_a : Optional[int] = model_class(_a )
model.to(_a )
model.eval()
with torch.no_grad():
_a : Union[str, Any] = model(**self._prepare_for_class(_a , _a ) )
_a : Optional[Any] = outputs.hidden_states
_a : Union[str, Any] = 8
self.assertEqual(len(_a ) , _a ) # TODO
# SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
# with the width and height being successively divided by 2, after every 2 blocks
for i in range(len(_a ) ):
self.assertEqual(
hidden_states[i].shape , torch.Size(
[
self.model_tester.batch_size,
self.model_tester.embed_dims[i // 2],
(self.model_tester.image_size // 4) // 2 ** (i // 2),
(self.model_tester.image_size // 4) // 2 ** (i // 2),
] ) , )
_a , _a : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_a : str = True
check_hidden_states_output(_a , _a , _a )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_a : List[str] = True
check_hidden_states_output(_a , _a , _a )
def __lowercase ( self ) -> str:
def _config_zero_init(_a ):
_a : List[Any] = copy.deepcopy(_a )
for key in configs_no_init.__dict__.keys():
if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
setattr(_a , _a , 1e-1_0 )
if isinstance(getattr(_a , _a , _a ) , _a ):
_a : int = _config_zero_init(getattr(_a , _a ) )
setattr(_a , _a , _a )
return configs_no_init
_a , _a : Any = self.model_tester.prepare_config_and_inputs_for_common()
_a : Dict = _config_zero_init(_a )
for model_class in self.all_model_classes:
_a : Dict = model_class(config=_a )
for name, param in model.named_parameters():
if param.requires_grad:
self.assertIn(
((param.data.mean() * 1e9) / 1e9).round().item() , [0.0, 1.0] , msg=F"""Parameter {name} of model {model_class} seems not properly initialized""" , )
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def __lowercase ( self ) -> Optional[Any]:
pass
def __UpperCAmelCase ( ) -> Optional[Any]:
"""simple docstring"""
_a : Optional[int] = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class UpperCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def __lowercase ( self ) -> str:
return ViTImageProcessor.from_pretrained('''MBZUAI/swiftformer-xs''' ) if is_vision_available() else None
@slow
def __lowercase ( self ) -> Dict:
_a : Any = SwiftFormerForImageClassification.from_pretrained('''MBZUAI/swiftformer-xs''' ).to(_a )
_a : Any = self.default_image_processor
_a : Any = prepare_img()
_a : Any = image_processor(images=_a , return_tensors='''pt''' ).to(_a )
# forward pass
with torch.no_grad():
_a : Optional[Any] = model(**_a )
# verify the logits
_a : List[str] = torch.Size((1, 1_0_0_0) )
self.assertEqual(outputs.logits.shape , _a )
_a : int = torch.tensor([[-2.1_7_0_3e0_0, 2.1_1_0_7e0_0, -2.0_8_1_1e0_0]] ).to(_a )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , _a , atol=1e-4 ) )
| 14 | 0 |
'''simple docstring'''
from decimal import Decimal, getcontext
from math import ceil, factorial
def a__ ( _SCREAMING_SNAKE_CASE : int ) -> str:
"""simple docstring"""
if not isinstance(__a , __a ):
raise TypeError("Undefined for non-integers" )
elif precision < 1:
raise ValueError("Undefined for non-natural numbers" )
UpperCAmelCase_ : Union[str, Any] = precision
UpperCAmelCase_ : str = ceil(precision / 14 )
UpperCAmelCase_ : Dict = 42_68_80 * Decimal(1_00_05 ).sqrt()
UpperCAmelCase_ : Optional[Any] = 1
UpperCAmelCase_ : Optional[Any] = 13_59_14_09
UpperCAmelCase_ : List[Any] = Decimal(__a )
for k in range(1 , __a ):
UpperCAmelCase_ : Optional[Any] = factorial(6 * k ) // (factorial(3 * k ) * factorial(__a ) ** 3)
linear_term += 5_45_14_01_34
exponential_term *= -26_25_37_41_26_40_76_80_00
partial_sum += Decimal(multinomial_term * linear_term ) / exponential_term
return str(constant_term / partial_sum )[:-1]
if __name__ == "__main__":
_lowerCamelCase = 50
print(f"""The first {n} digits of pi is: {pi(n)}""")
| 71 |
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging
logging.set_verbosity_info()
a__ = logging.get_logger(__name__)
def __UpperCAmelCase ( __a : str ) -> List[Any]:
"""simple docstring"""
_a : Tuple = SwinConfig.from_pretrained(
'''microsoft/swin-tiny-patch4-window7-224''' ,out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4'''] )
_a : Dict = MaskFormerConfig(backbone_config=__a )
_a : Optional[Any] = '''huggingface/label-files'''
if "ade20k-full" in model_name:
# this should be ok
_a : Optional[Any] = 847
_a : List[Any] = '''maskformer-ade20k-full-id2label.json'''
elif "ade" in model_name:
# this should be ok
_a : Union[str, Any] = 150
_a : Any = '''ade20k-id2label.json'''
elif "coco-stuff" in model_name:
# this should be ok
_a : int = 171
_a : List[str] = '''maskformer-coco-stuff-id2label.json'''
elif "coco" in model_name:
# TODO
_a : Dict = 133
_a : Optional[Any] = '''coco-panoptic-id2label.json'''
elif "cityscapes" in model_name:
# this should be ok
_a : List[Any] = 19
_a : Optional[Any] = '''cityscapes-id2label.json'''
elif "vistas" in model_name:
# this should be ok
_a : List[Any] = 65
_a : Dict = '''mapillary-vistas-id2label.json'''
_a : Optional[int] = json.load(open(hf_hub_download(__a ,__a ,repo_type='''dataset''' ) ,'''r''' ) )
_a : Tuple = {int(__a ): v for k, v in idalabel.items()}
return config
def __UpperCAmelCase ( __a : Optional[Any] ) -> Tuple:
"""simple docstring"""
_a : Optional[Any] = []
# stem
# fmt: off
rename_keys.append(('''backbone.patch_embed.proj.weight''', '''model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight''') )
rename_keys.append(('''backbone.patch_embed.proj.bias''', '''model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias''') )
rename_keys.append(('''backbone.patch_embed.norm.weight''', '''model.pixel_level_module.encoder.model.embeddings.norm.weight''') )
rename_keys.append(('''backbone.patch_embed.norm.bias''', '''model.pixel_level_module.encoder.model.embeddings.norm.bias''') )
# stages
for i in range(len(config.backbone_config.depths ) ):
for j in range(config.backbone_config.depths[i] ):
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm1.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm1.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.relative_position_index""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.proj.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.proj.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm2.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm2.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc1.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc1.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc2.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc2.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias""") )
if i < 3:
rename_keys.append((F"""backbone.layers.{i}.downsample.reduction.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight""") )
rename_keys.append((F"""backbone.layers.{i}.downsample.norm.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight""") )
rename_keys.append((F"""backbone.layers.{i}.downsample.norm.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias""") )
rename_keys.append((F"""backbone.norm{i}.weight""", F"""model.pixel_level_module.encoder.hidden_states_norms.{i}.weight""") )
rename_keys.append((F"""backbone.norm{i}.bias""", F"""model.pixel_level_module.encoder.hidden_states_norms.{i}.bias""") )
# FPN
rename_keys.append(('''sem_seg_head.layer_4.weight''', '''model.pixel_level_module.decoder.fpn.stem.0.weight''') )
rename_keys.append(('''sem_seg_head.layer_4.norm.weight''', '''model.pixel_level_module.decoder.fpn.stem.1.weight''') )
rename_keys.append(('''sem_seg_head.layer_4.norm.bias''', '''model.pixel_level_module.decoder.fpn.stem.1.bias''') )
for source_index, target_index in zip(range(3 ,0 ,-1 ) ,range(0 ,3 ) ):
rename_keys.append((F"""sem_seg_head.adapter_{source_index}.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight""") )
rename_keys.append((F"""sem_seg_head.adapter_{source_index}.norm.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight""") )
rename_keys.append((F"""sem_seg_head.adapter_{source_index}.norm.bias""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias""") )
rename_keys.append((F"""sem_seg_head.layer_{source_index}.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight""") )
rename_keys.append((F"""sem_seg_head.layer_{source_index}.norm.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight""") )
rename_keys.append((F"""sem_seg_head.layer_{source_index}.norm.bias""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias""") )
rename_keys.append(('''sem_seg_head.mask_features.weight''', '''model.pixel_level_module.decoder.mask_projection.weight''') )
rename_keys.append(('''sem_seg_head.mask_features.bias''', '''model.pixel_level_module.decoder.mask_projection.bias''') )
# Transformer decoder
for idx in range(config.decoder_config.decoder_layers ):
# self-attention out projection
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight""", F"""model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias""", F"""model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias""") )
# cross-attention out projection
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias""") )
# MLP 1
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight""", F"""model.transformer_module.decoder.layers.{idx}.fc1.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias""", F"""model.transformer_module.decoder.layers.{idx}.fc1.bias""") )
# MLP 2
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight""", F"""model.transformer_module.decoder.layers.{idx}.fc2.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias""", F"""model.transformer_module.decoder.layers.{idx}.fc2.bias""") )
# layernorm 1 (self-attention layernorm)
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight""", F"""model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias""", F"""model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias""") )
# layernorm 2 (cross-attention layernorm)
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias""") )
# layernorm 3 (final layernorm)
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight""", F"""model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias""", F"""model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias""") )
rename_keys.append(('''sem_seg_head.predictor.transformer.decoder.norm.weight''', '''model.transformer_module.decoder.layernorm.weight''') )
rename_keys.append(('''sem_seg_head.predictor.transformer.decoder.norm.bias''', '''model.transformer_module.decoder.layernorm.bias''') )
# heads on top
rename_keys.append(('''sem_seg_head.predictor.query_embed.weight''', '''model.transformer_module.queries_embedder.weight''') )
rename_keys.append(('''sem_seg_head.predictor.input_proj.weight''', '''model.transformer_module.input_projection.weight''') )
rename_keys.append(('''sem_seg_head.predictor.input_proj.bias''', '''model.transformer_module.input_projection.bias''') )
rename_keys.append(('''sem_seg_head.predictor.class_embed.weight''', '''class_predictor.weight''') )
rename_keys.append(('''sem_seg_head.predictor.class_embed.bias''', '''class_predictor.bias''') )
for i in range(3 ):
rename_keys.append((F"""sem_seg_head.predictor.mask_embed.layers.{i}.weight""", F"""mask_embedder.{i}.0.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.mask_embed.layers.{i}.bias""", F"""mask_embedder.{i}.0.bias""") )
# fmt: on
return rename_keys
def __UpperCAmelCase ( __a : List[str] ,__a : List[Any] ,__a : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
_a : str = dct.pop(__a )
_a : str = val
def __UpperCAmelCase ( __a : List[Any] ,__a : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
_a : Union[str, Any] = [int(backbone_config.embed_dim * 2**i ) for i in range(len(backbone_config.depths ) )]
for i in range(len(backbone_config.depths ) ):
_a : Optional[Any] = num_features[i]
for j in range(backbone_config.depths[i] ):
# fmt: off
# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
_a : List[Any] = state_dict.pop(F"""backbone.layers.{i}.blocks.{j}.attn.qkv.weight""" )
_a : Optional[int] = state_dict.pop(F"""backbone.layers.{i}.blocks.{j}.attn.qkv.bias""" )
# next, add query, keys and values (in that order) to the state dict
_a : Optional[int] = in_proj_weight[:dim, :]
_a : List[Any] = in_proj_bias[: dim]
_a : Optional[int] = in_proj_weight[
dim : dim * 2, :
]
_a : Tuple = in_proj_bias[
dim : dim * 2
]
_a : int = in_proj_weight[
-dim :, :
]
_a : Optional[int] = in_proj_bias[-dim :]
# fmt: on
def __UpperCAmelCase ( __a : List[str] ,__a : List[Any] ) -> List[Any]:
"""simple docstring"""
_a : Optional[int] = config.decoder_config.hidden_size
for idx in range(config.decoder_config.decoder_layers ):
# read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
_a : Union[str, Any] = state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight""" )
_a : List[Any] = state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias""" )
# next, add query, keys and values (in that order) to the state dict
_a : Union[str, Any] = in_proj_weight[: hidden_size, :]
_a : List[Any] = in_proj_bias[:config.hidden_size]
_a : Dict = in_proj_weight[hidden_size : hidden_size * 2, :]
_a : Any = in_proj_bias[hidden_size : hidden_size * 2]
_a : Tuple = in_proj_weight[-hidden_size :, :]
_a : List[Any] = in_proj_bias[-hidden_size :]
# read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
_a : List[Any] = state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight""" )
_a : List[str] = state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias""" )
# next, add query, keys and values (in that order) to the state dict
_a : Optional[Any] = in_proj_weight[: hidden_size, :]
_a : Any = in_proj_bias[:config.hidden_size]
_a : List[str] = in_proj_weight[hidden_size : hidden_size * 2, :]
_a : Optional[Any] = in_proj_bias[hidden_size : hidden_size * 2]
_a : List[str] = in_proj_weight[-hidden_size :, :]
_a : int = in_proj_bias[-hidden_size :]
# fmt: on
def __UpperCAmelCase ( ) -> torch.Tensor:
"""simple docstring"""
_a : str = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
_a : Dict = Image.open(requests.get(__a ,stream=__a ).raw )
return im
@torch.no_grad()
def __UpperCAmelCase ( __a : str ,__a : str ,__a : str ,__a : bool = False ) -> Union[str, Any]:
"""simple docstring"""
_a : Optional[Any] = get_maskformer_config(__a )
# load original state_dict
with open(__a ,'''rb''' ) as f:
_a : str = pickle.load(__a )
_a : Union[str, Any] = data['''model''']
# for name, param in state_dict.items():
# print(name, param.shape)
# rename keys
_a : Any = create_rename_keys(__a )
for src, dest in rename_keys:
rename_key(__a ,__a ,__a )
read_in_swin_q_k_v(__a ,config.backbone_config )
read_in_decoder_q_k_v(__a ,__a )
# update to torch tensors
for key, value in state_dict.items():
_a : Optional[int] = torch.from_numpy(__a )
# load 🤗 model
_a : Dict = MaskFormerForInstanceSegmentation(__a )
model.eval()
for name, param in model.named_parameters():
print(__a ,param.shape )
_a , _a : Tuple = model.load_state_dict(__a ,strict=__a )
assert missing_keys == [
"model.pixel_level_module.encoder.model.layernorm.weight",
"model.pixel_level_module.encoder.model.layernorm.bias",
]
assert len(__a ) == 0, F"""Unexpected keys: {unexpected_keys}"""
# verify results
_a : Union[str, Any] = prepare_img()
if "vistas" in model_name:
_a : int = 65
elif "cityscapes" in model_name:
_a : Tuple = 65_535
else:
_a : str = 255
_a : Dict = True if '''ade''' in model_name else False
_a : Optional[Any] = MaskFormerImageProcessor(ignore_index=__a ,reduce_labels=__a )
_a : Optional[Any] = image_processor(__a ,return_tensors='''pt''' )
_a : int = model(**__a )
print('''Logits:''' ,outputs.class_queries_logits[0, :3, :3] )
if model_name == "maskformer-swin-tiny-ade":
_a : Union[str, Any] = torch.tensor(
[[3.63_53, -4.47_70, -2.60_65], [0.50_81, -4.23_94, -3.53_43], [2.19_09, -5.03_53, -1.93_23]] )
assert torch.allclose(outputs.class_queries_logits[0, :3, :3] ,__a ,atol=1E-4 )
print('''Looks ok!''' )
if pytorch_dump_folder_path is not None:
print(F"""Saving model and image processor to {pytorch_dump_folder_path}""" )
Path(__a ).mkdir(exist_ok=__a )
model.save_pretrained(__a )
image_processor.save_pretrained(__a )
if push_to_hub:
print('''Pushing model and image processor to the hub...''' )
model.push_to_hub(F"""nielsr/{model_name}""" )
image_processor.push_to_hub(F"""nielsr/{model_name}""" )
if __name__ == "__main__":
a__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--model_name''',
default='''maskformer-swin-tiny-ade''',
type=str,
help=('''Name of the MaskFormer model you\'d like to convert''',),
)
parser.add_argument(
'''--checkpoint_path''',
default='''/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl''',
type=str,
help='''Path to the original state dict (.pth file).''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
)
parser.add_argument(
'''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
)
a__ = parser.parse_args()
convert_maskformer_checkpoint(
args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
)
| 14 | 0 |
"""simple docstring"""
import os
import random
import sys
from . import cryptomath_module as cryptoMath # noqa: N812
from . import rabin_miller as rabinMiller # noqa: N812
def lowercase ( ):
'''simple docstring'''
print('''Making key files...''' )
make_key_files('''rsa''' , 1024 )
print('''Key files generation successful.''' )
def lowercase ( _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
print('''Generating prime p...''' )
_UpperCAmelCase = rabinMiller.generate_large_prime(__a )
print('''Generating prime q...''' )
_UpperCAmelCase = rabinMiller.generate_large_prime(__a )
_UpperCAmelCase = p * q
print('''Generating e that is relatively prime to (p - 1) * (q - 1)...''' )
while True:
_UpperCAmelCase = random.randrange(2 ** (key_size - 1) , 2 ** (key_size) )
if cryptoMath.gcd(__a , (p - 1) * (q - 1) ) == 1:
break
print('''Calculating d that is mod inverse of e...''' )
_UpperCAmelCase = cryptoMath.find_mod_inverse(__a , (p - 1) * (q - 1) )
_UpperCAmelCase = (n, e)
_UpperCAmelCase = (n, d)
return (public_key, private_key)
def lowercase ( _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
if os.path.exists(f'{name}_pubkey.txt' ) or os.path.exists(f'{name}_privkey.txt' ):
print('''\nWARNING:''' )
print(
f'\"{name}_pubkey.txt\" or \"{name}_privkey.txt\" already exists. \n'
'''Use a different name or delete these files and re-run this program.''' )
sys.exit()
_UpperCAmelCase = generate_key(__a )
print(f'\nWriting public key to file {name}_pubkey.txt...' )
with open(f'{name}_pubkey.txt' , '''w''' ) as out_file:
out_file.write(f'{key_size},{public_key[0]},{public_key[1]}' )
print(f'Writing private key to file {name}_privkey.txt...' )
with open(f'{name}_privkey.txt' , '''w''' ) as out_file:
out_file.write(f'{key_size},{private_key[0]},{private_key[1]}' )
if __name__ == "__main__":
main()
| 602 |
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
a__ = get_tests_dir('''fixtures/test_sentencepiece.model''')
@require_sentencepiece
class UpperCAmelCase_ ( __lowercase , unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase__ : List[str] = XLMProphetNetTokenizer
UpperCAmelCase__ : Optional[int] = False
UpperCAmelCase__ : List[Any] = True
def __lowercase ( self ) -> int:
super().setUp()
# We have a SentencePiece fixture for testing
_a : List[Any] = XLMProphetNetTokenizer(_a , keep_accents=_a )
tokenizer.save_pretrained(self.tmpdirname )
def __lowercase ( self ) -> Any:
_a : Tuple = '''[PAD]'''
_a : int = 0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(_a ) , _a )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(_a ) , _a )
def __lowercase ( self ) -> str:
_a : Any = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''[PAD]''' )
self.assertEqual(vocab_keys[1] , '''[CLS]''' )
self.assertEqual(vocab_keys[-1] , '''j''' )
self.assertEqual(len(_a ) , 1_0_1_2 )
def __lowercase ( self ) -> Union[str, Any]:
self.assertEqual(self.get_tokenizer().vocab_size , 1_0_1_2 )
def __lowercase ( self ) -> str:
_a : Tuple = XLMProphetNetTokenizer(_a , keep_accents=_a )
_a : Union[str, Any] = tokenizer.tokenize('''This is a test''' )
self.assertListEqual(_a , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(_a ) , [value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]] , )
_a : Optional[int] = tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
_a , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''9''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''é''',
'''.''',
] , )
_a : List[Any] = tokenizer.convert_tokens_to_ids(_a )
self.assertListEqual(
_a , [
value + tokenizer.fairseq_offset
for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, -9, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, -9, 4]
] , )
_a : List[str] = tokenizer.convert_ids_to_tokens(_a )
self.assertListEqual(
_a , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''[UNK]''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''[UNK]''',
'''.''',
] , )
@cached_property
def __lowercase ( self ) -> List[str]:
return XLMProphetNetTokenizer.from_pretrained('''microsoft/xprophetnet-large-wiki100-cased''' )
@slow
def __lowercase ( self ) -> Tuple:
_a : str = '''Hello World!'''
_a : Tuple = [3_5_3_8_9, 6_6_7_2, 4_9, 2]
self.assertListEqual(_a , self.big_tokenizer.encode(_a ) )
@slow
def __lowercase ( self ) -> str:
# fmt: off
_a : str = {'''input_ids''': [[1_1_0_7_3, 8_2_7_8_3, 1_8, 2_6, 8_2_7_8_3, 5_4_9, 5_1_5_4_0, 2_4_8, 1_7_2_0_9, 1_3_0_1, 2_1_7, 2_0, 2_1_5_1_8_6, 1_3_2_5, 1_4_7, 1_7_2_0_9, 1_3_0_1, 2_1_7, 2_0, 5_6_3_7_0, 5_3, 1_2_2_0_2_0, 2_0, 1_6_4_7_7, 2_7, 8_7_3_5_5, 4_5_4_8, 2_0, 4_7_2_8, 7_8_3_9_2, 1_7, 1_5_9_9_6_9, 1_8, 2_6, 2_4_4_9_1, 6_2_9, 1_5, 5_3_8, 2_2_7_0_4, 5_4_3_9, 1_5, 2_7_8_8, 2_4_4_9_1, 9_8_8_5, 1_5, 4_3_5_3_4, 6_0_5, 1_5, 8_1_4, 1_8_4_0_3, 3_3_2_0_0, 2_9, 1_5, 4_3_5_3_4, 2_4_4_5_8, 1_2_4_1_0, 1_1_1, 2_4_9_6_6, 8_3_6_6_9, 9_6_3_7, 1_4_4_0_6_8, 2_6, 8_5_0, 2_2_3_4_6, 2_7, 1_4_7, 2_4_9_6_6, 8_3_6_6_9, 8_3_4_9_0, 2_6, 3_9_1_1_3, 7_3_5, 2_7, 6_8_9, 6_5_6, 2_8_0_0, 1_3_3_9, 4_6_0_0, 5_3, 1_2_2_0_2_0, 1_1_5_7_8_5, 3_4, 8_1_6, 1_3_3_9, 4_6_8_8_7, 1_8, 1_4_7, 5_3_9_0_5, 1_9_5_1, 4_2_2_3_8, 4_1_1_7_0, 1_7_7_3_2, 8_3_4, 4_3_6, 1_5, 2_7_5_2_3, 9_8_7_3_3, 2_1_7, 1_4_7, 5_5_4_2, 4_9_8_1, 9_3_0, 1_7_3_4_7, 1_6, 2], [2_0_0_9_1, 6_2_9, 9_4, 8_2_7_8_6, 5_8, 4_9_0, 2_0, 1_5_2_8, 8_4, 5_3_9_0_5, 3_4_4, 8_0_5_9_2, 1_1_0_1_2_8, 1_8_8_2_2, 5_2_6_7, 1_3_0_6, 6_2, 1_5_2_5_3_7, 3_0_8, 7_9_9_7, 4_0_1, 1_2_4_4_2_7, 5_4_9, 3_5_4_4_2, 2_2_5, 1_0_9, 1_5_0_5_5, 2_5_7_4_8, 1_4_7, 7_1_1_9, 4_3_7_1_2, 3_4, 7_6_7, 1_3_5_3_6_6, 1_8, 1_6, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_9_2, 6_3_7_8_4, 1_1_9_4_6_6, 1_7, 1_4_7_8_0_8, 8_8_2_1_4, 1_8, 6_5_6, 8_1, 3_2, 3_2_9_6, 1_0_2_8_0, 1_6, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=_a , model_name='''microsoft/xprophetnet-large-wiki100-cased''' , revision='''1acad1643ddd54a44df6a1b797ada8373685d90e''' , )
| 14 | 0 |
"""simple docstring"""
def lowerCAmelCase_( lowercase_ : list[int] , lowercase_ : list[int] ) -> None:
_lowerCamelCase = len(__a )
print('''The following activities are selected:''' )
# The first activity is always selected
_lowerCamelCase = 0
print(__a , end=''',''' )
# Consider rest of the activities
for j in range(__a ):
# If this activity has start time greater than
# or equal to the finish time of previously
# selected activity, then select it
if start[j] >= finish[i]:
print(__a , end=''',''' )
_lowerCamelCase = j
if __name__ == "__main__":
import doctest
doctest.testmod()
__SCREAMING_SNAKE_CASE : List[str] = [1, 3, 0, 5, 8, 5]
__SCREAMING_SNAKE_CASE : Optional[int] = [2, 4, 6, 7, 9, 9]
print_max_activities(start, finish)
| 661 |
import os
import unittest
from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class UpperCAmelCase_ ( __lowercase , unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase__ : Any = LxmertTokenizer
UpperCAmelCase__ : Optional[Any] = LxmertTokenizerFast
UpperCAmelCase__ : Any = True
UpperCAmelCase__ : Dict = True
def __lowercase ( self ) -> Union[str, Any]:
super().setUp()
_a : int = [
'''[UNK]''',
'''[CLS]''',
'''[SEP]''',
'''want''',
'''##want''',
'''##ed''',
'''wa''',
'''un''',
'''runn''',
'''##ing''',
''',''',
'''low''',
'''lowest''',
]
_a : Any = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as vocab_writer:
vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens] ) )
def __lowercase ( self , _a ) -> List[str]:
_a : Tuple = '''UNwant\u00E9d,running'''
_a : str = '''unwanted, running'''
return input_text, output_text
def __lowercase ( self ) -> List[Any]:
_a : str = self.tokenizer_class(self.vocab_file )
_a : str = tokenizer.tokenize('''UNwant\u00E9d,running''' )
self.assertListEqual(_a , ['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing'''] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(_a ) , [7, 4, 5, 1_0, 8, 9] )
def __lowercase ( self ) -> List[Any]:
if not self.test_rust_tokenizer:
return
_a : Optional[Any] = self.get_tokenizer()
_a : str = self.get_rust_tokenizer()
_a : Optional[Any] = '''I was born in 92000, and this is falsé.'''
_a : Optional[Any] = tokenizer.tokenize(_a )
_a : List[Any] = rust_tokenizer.tokenize(_a )
self.assertListEqual(_a , _a )
_a : List[Any] = tokenizer.encode(_a , add_special_tokens=_a )
_a : Any = rust_tokenizer.encode(_a , add_special_tokens=_a )
self.assertListEqual(_a , _a )
_a : Dict = self.get_rust_tokenizer()
_a : Optional[int] = tokenizer.encode(_a )
_a : Dict = rust_tokenizer.encode(_a )
self.assertListEqual(_a , _a )
| 14 | 0 |
import math
from collections.abc import Iterator
from itertools import takewhile
def _A (UpperCamelCase : int ) ->bool:
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(__a ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def _A () ->Iterator[int]:
'''simple docstring'''
lowerCamelCase__ : Dict = 2
while True:
if is_prime(__a ):
yield num
num += 1
def _A (UpperCamelCase : int = 2000000 ) ->int:
'''simple docstring'''
return sum(takewhile(lambda UpperCamelCase : x < n , prime_generator() ) )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 157 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch
if is_vision_available():
from transformers import TvltImageProcessor
if is_speech_available():
from transformers import TvltFeatureExtractor
from transformers import TvltProcessor
@require_torch
class UpperCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
def __lowercase ( self ) -> int:
_a : Dict = '''ZinengTang/tvlt-base'''
_a : List[str] = tempfile.mkdtemp()
def __lowercase ( self , **_a ) -> int:
return TvltImageProcessor.from_pretrained(self.checkpoint , **_a )
def __lowercase ( self , **_a ) -> List[Any]:
return TvltFeatureExtractor.from_pretrained(self.checkpoint , **_a )
def __lowercase ( self ) -> Optional[int]:
shutil.rmtree(self.tmpdirname )
def __lowercase ( self ) -> Dict:
_a : Union[str, Any] = self.get_image_processor()
_a : Dict = self.get_feature_extractor()
_a : Optional[int] = TvltProcessor(image_processor=_a , feature_extractor=_a )
processor.save_pretrained(self.tmpdirname )
_a : Any = TvltProcessor.from_pretrained(self.tmpdirname )
self.assertIsInstance(processor.feature_extractor , _a )
self.assertIsInstance(processor.image_processor , _a )
def __lowercase ( self ) -> Any:
_a : Optional[Any] = self.get_image_processor()
_a : Dict = self.get_feature_extractor()
_a : Dict = TvltProcessor(image_processor=_a , feature_extractor=_a )
_a : Union[str, Any] = np.ones([1_2_0_0_0] )
_a : Dict = feature_extractor(_a , return_tensors='''np''' )
_a : Tuple = processor(audio=_a , return_tensors='''np''' )
for key in audio_dict.keys():
self.assertAlmostEqual(audio_dict[key].sum() , input_processor[key].sum() , delta=1e-2 )
def __lowercase ( self ) -> int:
_a : Optional[Any] = self.get_image_processor()
_a : Union[str, Any] = self.get_feature_extractor()
_a : Optional[Any] = TvltProcessor(image_processor=_a , feature_extractor=_a )
_a : List[Any] = np.ones([3, 2_2_4, 2_2_4] )
_a : int = image_processor(_a , return_tensors='''np''' )
_a : Optional[int] = processor(images=_a , return_tensors='''np''' )
for key in image_dict.keys():
self.assertAlmostEqual(image_dict[key].sum() , input_processor[key].sum() , delta=1e-2 )
def __lowercase ( self ) -> Union[str, Any]:
_a : int = self.get_image_processor()
_a : Union[str, Any] = self.get_feature_extractor()
_a : Any = TvltProcessor(image_processor=_a , feature_extractor=_a )
_a : List[str] = np.ones([1_2_0_0_0] )
_a : Optional[int] = np.ones([3, 2_2_4, 2_2_4] )
_a : int = processor(audio=_a , images=_a )
self.assertListEqual(list(inputs.keys() ) , ['''audio_values''', '''audio_mask''', '''pixel_values''', '''pixel_mask'''] )
# test if it raises when no input is passed
with pytest.raises(_a ):
processor()
def __lowercase ( self ) -> Union[str, Any]:
_a : str = self.get_image_processor()
_a : Union[str, Any] = self.get_feature_extractor()
_a : Dict = TvltProcessor(image_processor=_a , feature_extractor=_a )
self.assertListEqual(
processor.model_input_names , image_processor.model_input_names + feature_extractor.model_input_names , msg='''`processor` and `image_processor`+`feature_extractor` model input names do not match''' , )
| 14 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
"""funnel-transformer/small""": """https://huggingface.co/funnel-transformer/small/resolve/main/config.json""",
"""funnel-transformer/small-base""": """https://huggingface.co/funnel-transformer/small-base/resolve/main/config.json""",
"""funnel-transformer/medium""": """https://huggingface.co/funnel-transformer/medium/resolve/main/config.json""",
"""funnel-transformer/medium-base""": """https://huggingface.co/funnel-transformer/medium-base/resolve/main/config.json""",
"""funnel-transformer/intermediate""": (
"""https://huggingface.co/funnel-transformer/intermediate/resolve/main/config.json"""
),
"""funnel-transformer/intermediate-base""": (
"""https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/config.json"""
),
"""funnel-transformer/large""": """https://huggingface.co/funnel-transformer/large/resolve/main/config.json""",
"""funnel-transformer/large-base""": """https://huggingface.co/funnel-transformer/large-base/resolve/main/config.json""",
"""funnel-transformer/xlarge""": """https://huggingface.co/funnel-transformer/xlarge/resolve/main/config.json""",
"""funnel-transformer/xlarge-base""": """https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/config.json""",
}
class __lowerCAmelCase ( __lowercase ):
lowerCAmelCase__ = "funnel"
lowerCAmelCase__ = {
"hidden_size": "d_model",
"num_attention_heads": "n_head",
}
def __init__( self , __UpperCAmelCase=30522 , __UpperCAmelCase=[4, 4, 4] , __UpperCAmelCase=None , __UpperCAmelCase=2 , __UpperCAmelCase=768 , __UpperCAmelCase=12 , __UpperCAmelCase=64 , __UpperCAmelCase=3072 , __UpperCAmelCase="gelu_new" , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.0 , __UpperCAmelCase=0.1 , __UpperCAmelCase=None , __UpperCAmelCase=1E-9 , __UpperCAmelCase="mean" , __UpperCAmelCase="relative_shift" , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=True , **__UpperCAmelCase , ):
'''simple docstring'''
__lowerCamelCase = vocab_size
__lowerCamelCase = block_sizes
__lowerCamelCase = [1] * len(_a ) if block_repeats is None else block_repeats
assert len(_a ) == len(
self.block_repeats ), "`block_sizes` and `block_repeats` should have the same length."
__lowerCamelCase = num_decoder_layers
__lowerCamelCase = d_model
__lowerCamelCase = n_head
__lowerCamelCase = d_head
__lowerCamelCase = d_inner
__lowerCamelCase = hidden_act
__lowerCamelCase = hidden_dropout
__lowerCamelCase = attention_dropout
__lowerCamelCase = activation_dropout
__lowerCamelCase = initializer_range
__lowerCamelCase = initializer_std
__lowerCamelCase = layer_norm_eps
assert pooling_type in [
"mean",
"max",
], F"""Got {pooling_type} for `pooling_type` but only 'mean' and 'max' are supported."""
__lowerCamelCase = pooling_type
assert attention_type in [
"relative_shift",
"factorized",
], F"""Got {attention_type} for `attention_type` but only 'relative_shift' and 'factorized' are supported."""
__lowerCamelCase = attention_type
__lowerCamelCase = separate_cls
__lowerCamelCase = truncate_seq
__lowerCamelCase = pool_q_only
super().__init__(**_a )
@property
def lowerCamelCase ( self ):
'''simple docstring'''
return sum(self.block_sizes )
@num_hidden_layers.setter
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
raise NotImplementedError(
'''This model does not support the setting of `num_hidden_layers`. Please set `block_sizes`.''' )
@property
def lowerCamelCase ( self ):
'''simple docstring'''
return len(self.block_sizes )
@num_blocks.setter
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
raise NotImplementedError('''This model does not support the setting of `num_blocks`. Please set `block_sizes`.''' )
| 175 |
def __UpperCAmelCase ( __a : str ) -> list:
"""simple docstring"""
if n_term == "":
return []
_a : list = []
for temp in range(int(__a ) ):
series.append(F"""1/{temp + 1}""" if series else '''1''' )
return series
if __name__ == "__main__":
a__ = input('''Enter the last number (nth term) of the Harmonic Series''')
print('''Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n''')
print(harmonic_series(nth_term))
| 14 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
'''sayakpaul/vit-msn-base''': '''https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json''',
# See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}
class a__ ( __lowercase ):
'''simple docstring'''
A : Optional[int] = "vit_msn"
def __init__( self : Any , lowerCAmelCase_ : Union[str, Any]=768 , lowerCAmelCase_ : str=12 , lowerCAmelCase_ : Tuple=12 , lowerCAmelCase_ : Dict=3_072 , lowerCAmelCase_ : Optional[int]="gelu" , lowerCAmelCase_ : Any=0.0 , lowerCAmelCase_ : Tuple=0.0 , lowerCAmelCase_ : Optional[int]=0.02 , lowerCAmelCase_ : Optional[Any]=1E-06 , lowerCAmelCase_ : Optional[int]=224 , lowerCAmelCase_ : int=16 , lowerCAmelCase_ : int=3 , lowerCAmelCase_ : List[Any]=True , **lowerCAmelCase_ : Union[str, Any] , ) -> int:
super().__init__(**_a )
__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= initializer_range
__A= layer_norm_eps
__A= image_size
__A= patch_size
__A= num_channels
__A= qkv_bias
| 186 |
import argparse
import collections
import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def __UpperCAmelCase ( __a : List[Any] ,__a : Optional[Any] ,__a : Optional[int] ) -> Dict:
"""simple docstring"""
return params[F"""{prefix}/{prefix}/relpos_bias/rel_embedding"""][:, i, :]
def __UpperCAmelCase ( __a : List[Any] ,__a : Optional[int] ,__a : int ,__a : List[str]="attention" ) -> List[str]:
"""simple docstring"""
_a : str = np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/key/kernel"""][:, i, :, :] )
_a : Tuple = k_tmp.reshape(k_tmp.shape[0] ,k_tmp.shape[1] * k_tmp.shape[2] )
_a : Any = np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/out/kernel"""][:, i, :, :] )
_a : Dict = o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1] ,o_tmp.shape[2] )
_a : Union[str, Any] = np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/query/kernel"""][:, i, :, :] )
_a : Any = q_tmp.reshape(q_tmp.shape[0] ,q_tmp.shape[1] * q_tmp.shape[2] )
_a : Tuple = np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/value/kernel"""][:, i, :, :] )
_a : int = v_tmp.reshape(v_tmp.shape[0] ,v_tmp.shape[1] * v_tmp.shape[2] )
return k, o, q, v
def __UpperCAmelCase ( __a : Union[str, Any] ,__a : Union[str, Any] ,__a : List[Any] ,__a : Any=False ) -> Any:
"""simple docstring"""
if split_mlp_wi:
_a : Union[str, Any] = params[F"""{prefix}/{prefix}/mlp/wi_0/kernel"""][:, i, :]
_a : Union[str, Any] = params[F"""{prefix}/{prefix}/mlp/wi_1/kernel"""][:, i, :]
_a : List[str] = (wi_a, wi_a)
else:
_a : List[str] = params[F"""{prefix}/{prefix}/mlp/wi/kernel"""][:, i, :]
_a : Optional[int] = params[F"""{prefix}/{prefix}/mlp/wo/kernel"""][:, i, :]
return wi, wo
def __UpperCAmelCase ( __a : List[Any] ,__a : Optional[Any] ,__a : Union[str, Any] ,__a : str ) -> List[str]:
"""simple docstring"""
return params[F"""{prefix}/{prefix}/{layer_name}/scale"""][:, i]
def __UpperCAmelCase ( __a : dict ,*, __a : int ,__a : bool ,__a : bool = False ) -> Any:
"""simple docstring"""
_a : Dict = traverse_util.flatten_dict(variables['''target'''] )
_a : Any = {'''/'''.join(__a ): v for k, v in old.items()}
# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
_a : Optional[int] = '''encoder/encoder/mlp/wi_0/kernel''' in old
print('''Split MLP:''' ,__a )
_a : Tuple = collections.OrderedDict()
# Shared embeddings.
_a : Any = old['''token_embedder/embedding''']
# Encoder.
for i in range(__a ):
# Block i, layer 0 (Self Attention).
_a : Optional[Any] = tax_layer_norm_lookup(__a ,__a ,'''encoder''' ,'''pre_attention_layer_norm''' )
_a , _a , _a , _a : List[str] = tax_attention_lookup(__a ,__a ,'''encoder''' ,'''attention''' )
_a : List[str] = layer_norm
_a : Optional[Any] = k.T
_a : str = o.T
_a : List[Any] = q.T
_a : Tuple = v.T
# Block i, layer 1 (MLP).
_a : str = tax_layer_norm_lookup(__a ,__a ,'''encoder''' ,'''pre_mlp_layer_norm''' )
_a , _a : Any = tax_mlp_lookup(__a ,__a ,'''encoder''' ,__a )
_a : str = layer_norm
if split_mlp_wi:
_a : List[Any] = wi[0].T
_a : Any = wi[1].T
else:
_a : Any = wi.T
_a : Optional[Any] = wo.T
if scalable_attention:
# convert the rel_embedding of each layer
_a : Dict = tax_relpos_bias_lookup(
__a ,__a ,'''encoder''' ).T
_a : List[str] = old['''encoder/encoder_norm/scale''']
if not scalable_attention:
_a : List[Any] = tax_relpos_bias_lookup(
__a ,0 ,'''encoder''' ).T
_a : Optional[Any] = tax_relpos_bias_lookup(
__a ,0 ,'''decoder''' ).T
if not is_encoder_only:
# Decoder.
for i in range(__a ):
# Block i, layer 0 (Self Attention).
_a : Union[str, Any] = tax_layer_norm_lookup(__a ,__a ,'''decoder''' ,'''pre_self_attention_layer_norm''' )
_a , _a , _a , _a : Optional[Any] = tax_attention_lookup(__a ,__a ,'''decoder''' ,'''self_attention''' )
_a : Optional[Any] = layer_norm
_a : Dict = k.T
_a : str = o.T
_a : str = q.T
_a : List[str] = v.T
# Block i, layer 1 (Cross Attention).
_a : Any = tax_layer_norm_lookup(__a ,__a ,'''decoder''' ,'''pre_cross_attention_layer_norm''' )
_a , _a , _a , _a : str = tax_attention_lookup(__a ,__a ,'''decoder''' ,'''encoder_decoder_attention''' )
_a : Optional[Any] = layer_norm
_a : Optional[int] = k.T
_a : Dict = o.T
_a : str = q.T
_a : int = v.T
# Block i, layer 2 (MLP).
_a : Optional[int] = tax_layer_norm_lookup(__a ,__a ,'''decoder''' ,'''pre_mlp_layer_norm''' )
_a , _a : Tuple = tax_mlp_lookup(__a ,__a ,'''decoder''' ,__a )
_a : Optional[Any] = layer_norm
if split_mlp_wi:
_a : List[str] = wi[0].T
_a : List[Any] = wi[1].T
else:
_a : Dict = wi.T
_a : str = wo.T
if scalable_attention:
# convert the rel_embedding of each layer
_a : Tuple = tax_relpos_bias_lookup(__a ,__a ,'''decoder''' ).T
_a : Tuple = old['''decoder/decoder_norm/scale''']
# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
if "decoder/logits_dense/kernel" in old:
_a : Any = old['''decoder/logits_dense/kernel'''].T
return new
def __UpperCAmelCase ( __a : Dict ,__a : bool ) -> Tuple:
"""simple docstring"""
_a : Tuple = collections.OrderedDict([(k, torch.from_numpy(v.copy() )) for (k, v) in converted_params.items()] )
# Add what is missing.
if "encoder.embed_tokens.weight" not in state_dict:
_a : Any = state_dict['''shared.weight''']
if not is_encoder_only:
if "decoder.embed_tokens.weight" not in state_dict:
_a : Optional[int] = state_dict['''shared.weight''']
if "lm_head.weight" not in state_dict: # For old 1.0 models.
print('''Using shared word embeddings as lm_head.''' )
_a : str = state_dict['''shared.weight''']
return state_dict
def __UpperCAmelCase ( __a : List[str] ,__a : Union[str, Any] ,__a : Dict ,__a : Union[str, Any] ,__a : List[Any] ) -> int:
"""simple docstring"""
_a : List[str] = checkpoints.load_tax_checkpoint(__a )
_a : str = convert_tax_to_pytorch(
__a ,num_layers=config.num_layers ,is_encoder_only=__a ,scalable_attention=__a )
_a : str = make_state_dict(__a ,__a )
model.load_state_dict(__a ,strict=__a )
def __UpperCAmelCase ( __a : List[Any] ,__a : Any ,__a : Union[str, Any] ,__a : bool = False ,__a : bool = False ,) -> Optional[Any]:
"""simple docstring"""
_a : List[str] = MTaConfig.from_json_file(__a )
print(F"""Building PyTorch model from configuration: {config}""" )
# Non-v1.1 checkpoints could also use T5Model, but this works for all.
# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
if is_encoder_only:
_a : Any = UMTaEncoderModel(__a )
else:
_a : Tuple = UMTaForConditionalGeneration(__a )
# Load weights from tf checkpoint
load_tax_weights_in_ta(__a ,__a ,__a ,__a ,__a )
# Save pytorch-model
print(F"""Save PyTorch model to {pytorch_dump_path}""" )
model.save_pretrained(__a )
# Verify that we can load the checkpoint.
model.from_pretrained(__a )
print('''Done''' )
if __name__ == "__main__":
a__ = argparse.ArgumentParser(description='''Converts a native T5X checkpoint into a PyTorch checkpoint.''')
# Required parameters
parser.add_argument(
'''--t5x_checkpoint_path''', default=None, type=str, required=True, help='''Path to the T5X checkpoint.'''
)
parser.add_argument(
'''--config_file''',
default=None,
type=str,
required=True,
help='''The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.''',
)
parser.add_argument(
'''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
parser.add_argument(
'''--is_encoder_only''', action='''store_true''', help='''Check if the model is encoder-decoder model''', default=False
)
parser.add_argument(
'''--scalable_attention''',
action='''store_true''',
help='''Whether the model uses scaled attention (umt5 model)''',
default=False,
)
a__ = parser.parse_args()
convert_tax_checkpoint_to_pytorch(
args.tax_checkpoint_path,
args.config_file,
args.pytorch_dump_path,
args.is_encoder_only,
args.scalable_attention,
)
| 14 | 0 |
from .imports import is_rich_available
if is_rich_available():
from rich.traceback import install
install(show_locals=False)
else:
raise ModuleNotFoundError("To use the rich extension, install rich with `pip install rich`")
| 509 |
import random
import sys
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap
a__ = '''Usage of script: script_name <size_of_canvas:int>'''
a__ = [0] * 100 + [1] * 10
random.shuffle(choice)
def __UpperCAmelCase ( __a : int ) -> list[list[bool]]:
"""simple docstring"""
_a : int = [[False for i in range(__a )] for j in range(__a )]
return canvas
def __UpperCAmelCase ( __a : list[list[bool]] ) -> None:
"""simple docstring"""
for i, row in enumerate(__a ):
for j, _ in enumerate(__a ):
_a : Optional[int] = bool(random.getrandbits(1 ) )
def __UpperCAmelCase ( __a : list[list[bool]] ) -> list[list[bool]]:
"""simple docstring"""
_a : Any = np.array(__a )
_a : Optional[int] = np.array(create_canvas(current_canvas.shape[0] ) )
for r, row in enumerate(__a ):
for c, pt in enumerate(__a ):
_a : Tuple = __judge_point(
__a ,current_canvas[r - 1 : r + 2, c - 1 : c + 2] )
_a : List[str] = next_gen_canvas
del next_gen_canvas # cleaning memory as we move on.
_a : list[list[bool]] = current_canvas.tolist()
return return_canvas
def __UpperCAmelCase ( __a : bool ,__a : list[list[bool]] ) -> bool:
"""simple docstring"""
_a : Optional[Any] = 0
_a : str = 0
# finding dead or alive neighbours count.
for i in neighbours:
for status in i:
if status:
alive += 1
else:
dead += 1
# handling duplicate entry for focus pt.
if pt:
alive -= 1
else:
dead -= 1
# running the rules of game here.
_a : Optional[int] = pt
if pt:
if alive < 2:
_a : Dict = False
elif alive == 2 or alive == 3:
_a : Optional[Any] = True
elif alive > 3:
_a : str = False
else:
if alive == 3:
_a : int = True
return state
if __name__ == "__main__":
if len(sys.argv) != 2:
raise Exception(usage_doc)
a__ = int(sys.argv[1])
# main working structure of this module.
a__ = create_canvas(canvas_size)
seed(c)
a__ , a__ = plt.subplots()
fig.show()
a__ = ListedColormap(['''w''', '''k'''])
try:
while True:
a__ = run(c)
ax.matshow(c, cmap=cmap)
fig.canvas.draw()
ax.cla()
except KeyboardInterrupt:
# do nothing.
pass
| 14 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE :int = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE :Dict = {
'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 UpperCAmelCase ( __lowercase ):
'''simple docstring'''
snake_case_ = "convbert"
def __init__( self : Any ,A : List[str]=3_05_22 ,A : List[str]=7_68 ,A : List[str]=12 ,A : List[str]=12 ,A : Optional[Any]=30_72 ,A : Dict="gelu" ,A : Dict=0.1 ,A : Union[str, Any]=0.1 ,A : str=5_12 ,A : Dict=2 ,A : str=0.02 ,A : Any=1E-12 ,A : Union[str, Any]=1 ,A : Optional[int]=0 ,A : Optional[Any]=2 ,A : Optional[int]=7_68 ,A : Dict=2 ,A : Tuple=9 ,A : int=1 ,A : List[str]=None ,**A : List[str] ,):
super().__init__(
pad_token_id=_a ,bos_token_id=_a ,eos_token_id=_a ,**_a ,)
__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 UpperCAmelCase ( __lowercase ):
'''simple docstring'''
@property
def UpperCamelCase_ ( self : int ):
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),
] )
| 55 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__ = logging.get_logger(__name__)
a__ = {
'''funnel-transformer/small''': '''https://huggingface.co/funnel-transformer/small/resolve/main/config.json''',
'''funnel-transformer/small-base''': '''https://huggingface.co/funnel-transformer/small-base/resolve/main/config.json''',
'''funnel-transformer/medium''': '''https://huggingface.co/funnel-transformer/medium/resolve/main/config.json''',
'''funnel-transformer/medium-base''': '''https://huggingface.co/funnel-transformer/medium-base/resolve/main/config.json''',
'''funnel-transformer/intermediate''': (
'''https://huggingface.co/funnel-transformer/intermediate/resolve/main/config.json'''
),
'''funnel-transformer/intermediate-base''': (
'''https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/config.json'''
),
'''funnel-transformer/large''': '''https://huggingface.co/funnel-transformer/large/resolve/main/config.json''',
'''funnel-transformer/large-base''': '''https://huggingface.co/funnel-transformer/large-base/resolve/main/config.json''',
'''funnel-transformer/xlarge''': '''https://huggingface.co/funnel-transformer/xlarge/resolve/main/config.json''',
'''funnel-transformer/xlarge-base''': '''https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/config.json''',
}
class UpperCAmelCase_ ( __lowercase ):
"""simple docstring"""
UpperCAmelCase__ : Optional[int] = "funnel"
UpperCAmelCase__ : Tuple = {
"hidden_size": "d_model",
"num_attention_heads": "n_head",
}
def __init__( self , _a=3_0_5_2_2 , _a=[4, 4, 4] , _a=None , _a=2 , _a=7_6_8 , _a=1_2 , _a=6_4 , _a=3_0_7_2 , _a="gelu_new" , _a=0.1 , _a=0.1 , _a=0.0 , _a=0.1 , _a=None , _a=1e-9 , _a="mean" , _a="relative_shift" , _a=True , _a=True , _a=True , **_a , ) -> List[Any]:
_a : Optional[int] = vocab_size
_a : Dict = block_sizes
_a : Optional[int] = [1] * len(_a ) if block_repeats is None else block_repeats
assert len(_a ) == len(
self.block_repeats ), "`block_sizes` and `block_repeats` should have the same length."
_a : int = num_decoder_layers
_a : List[str] = d_model
_a : Optional[Any] = n_head
_a : Tuple = d_head
_a : Dict = d_inner
_a : List[str] = hidden_act
_a : int = hidden_dropout
_a : Union[str, Any] = attention_dropout
_a : Tuple = activation_dropout
_a : Optional[Any] = initializer_range
_a : Dict = initializer_std
_a : Union[str, Any] = layer_norm_eps
assert pooling_type in [
"mean",
"max",
], F"""Got {pooling_type} for `pooling_type` but only 'mean' and 'max' are supported."""
_a : Any = pooling_type
assert attention_type in [
"relative_shift",
"factorized",
], F"""Got {attention_type} for `attention_type` but only 'relative_shift' and 'factorized' are supported."""
_a : Optional[Any] = attention_type
_a : int = separate_cls
_a : Tuple = truncate_seq
_a : List[Any] = pool_q_only
super().__init__(**_a )
@property
def __lowercase ( self ) -> Tuple:
return sum(self.block_sizes )
@num_hidden_layers.setter
def __lowercase ( self , _a ) -> List[str]:
raise NotImplementedError(
'''This model does not support the setting of `num_hidden_layers`. Please set `block_sizes`.''' )
@property
def __lowercase ( self ) -> Optional[int]:
return len(self.block_sizes )
@num_blocks.setter
def __lowercase ( self , _a ) -> Dict:
raise NotImplementedError('''This model does not support the setting of `num_blocks`. Please set `block_sizes`.''' )
| 14 | 0 |
import random
import sys
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap
__A : int = "Usage of script: script_name <size_of_canvas:int>"
__A : List[str] = [0] * 100 + [1] * 10
random.shuffle(choice)
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE ) -> list[list[bool]]:
"""simple docstring"""
_A = [[False for i in range(__a )] for j in range(__a )]
return canvas
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE ) -> None:
"""simple docstring"""
for i, row in enumerate(__a ):
for j, _ in enumerate(__a ):
_A = bool(random.getrandbits(1 ) )
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE ) -> list[list[bool]]:
"""simple docstring"""
_A = np.array(__a )
_A = np.array(create_canvas(current_canvas.shape[0] ) )
for r, row in enumerate(__a ):
for c, pt in enumerate(__a ):
_A = __judge_point(
__a , current_canvas[r - 1 : r + 2, c - 1 : c + 2] )
_A = next_gen_canvas
del next_gen_canvas # cleaning memory as we move on.
_A = current_canvas.tolist()
return return_canvas
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> bool:
"""simple docstring"""
_A = 0
_A = 0
# finding dead or alive neighbours count.
for i in neighbours:
for status in i:
if status:
alive += 1
else:
dead += 1
# handling duplicate entry for focus pt.
if pt:
alive -= 1
else:
dead -= 1
# running the rules of game here.
_A = pt
if pt:
if alive < 2:
_A = False
elif alive == 2 or alive == 3:
_A = True
elif alive > 3:
_A = False
else:
if alive == 3:
_A = True
return state
if __name__ == "__main__":
if len(sys.argv) != 2:
raise Exception(usage_doc)
__A : Dict = int(sys.argv[1])
# main working structure of this module.
__A : Optional[int] = create_canvas(canvas_size)
seed(c)
__A , __A : Tuple = plt.subplots()
fig.show()
__A : Any = ListedColormap(["w", "k"])
try:
while True:
__A : Any = run(c)
ax.matshow(c, cmap=cmap)
fig.canvas.draw()
ax.cla()
except KeyboardInterrupt:
# do nothing.
pass
| 27 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a__ = logging.get_logger(__name__)
a__ = {
'''google/mobilenet_v1_1.0_224''': '''https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json''',
'''google/mobilenet_v1_0.75_192''': '''https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json''',
# See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}
class UpperCAmelCase_ ( __lowercase ):
"""simple docstring"""
UpperCAmelCase__ : int = "mobilenet_v1"
def __init__( self , _a=3 , _a=2_2_4 , _a=1.0 , _a=8 , _a="relu6" , _a=True , _a=0.999 , _a=0.02 , _a=0.001 , **_a , ) -> List[Any]:
super().__init__(**_a )
if depth_multiplier <= 0:
raise ValueError('''depth_multiplier must be greater than zero.''' )
_a : Tuple = num_channels
_a : str = image_size
_a : Tuple = depth_multiplier
_a : Any = min_depth
_a : int = hidden_act
_a : Optional[Any] = tf_padding
_a : str = classifier_dropout_prob
_a : Optional[int] = initializer_range
_a : Any = layer_norm_eps
class UpperCAmelCase_ ( __lowercase ):
"""simple docstring"""
UpperCAmelCase__ : str = version.parse("1.11" )
@property
def __lowercase ( self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict([('''pixel_values''', {0: '''batch'''})] )
@property
def __lowercase ( self ) -> Mapping[str, Mapping[int, str]]:
if self.task == "image-classification":
return OrderedDict([('''logits''', {0: '''batch'''})] )
else:
return OrderedDict([('''last_hidden_state''', {0: '''batch'''}), ('''pooler_output''', {0: '''batch'''})] )
@property
def __lowercase ( self ) -> float:
return 1e-4
| 14 | 0 |
'''simple docstring'''
# flake8: noqa
# Lint as: python3
from typing import Dict, List, Optional, Type
from .. import config
from ..utils import logging
from .formatting import (
ArrowFormatter,
CustomFormatter,
Formatter,
PandasFormatter,
PythonFormatter,
TensorFormatter,
format_table,
query_table,
)
from .np_formatter import NumpyFormatter
UpperCAmelCase_ : int = logging.get_logger(__name__)
UpperCAmelCase_ : Dict = {}
UpperCAmelCase_ : List[Any] = {}
UpperCAmelCase_ : int = {}
def _UpperCamelCase (_lowerCamelCase : type , _lowerCamelCase : Optional[str] , _lowerCamelCase : Optional[List[str]] = None , )-> Any:
'''simple docstring'''
__snake_case = aliases if aliases is not None else []
if format_type in _FORMAT_TYPES:
logger.warning(
f'''Overwriting format type \'{format_type}\' ({_FORMAT_TYPES[format_type].__name__} -> {formatter_cls.__name__})''' )
__snake_case = formatter_cls
for alias in set(aliases + [format_type] ):
if alias in _FORMAT_TYPES_ALIASES:
logger.warning(
f'''Overwriting format type alias \'{alias}\' ({_FORMAT_TYPES_ALIASES[alias]} -> {format_type})''' )
__snake_case = format_type
def _UpperCamelCase (_lowerCamelCase : Exception , _lowerCamelCase : Optional[str] , _lowerCamelCase : Optional[List[str]] = None )-> str:
'''simple docstring'''
__snake_case = aliases if aliases is not None else []
for alias in set(aliases + [format_type] ):
__snake_case = unavailable_error
# Here we define all the available formatting functions that can be used by `Dataset.set_format`
_register_formatter(PythonFormatter, None, aliases=['''python'''])
_register_formatter(ArrowFormatter, '''arrow''', aliases=['''pa''', '''pyarrow'''])
_register_formatter(NumpyFormatter, '''numpy''', aliases=['''np'''])
_register_formatter(PandasFormatter, '''pandas''', aliases=['''pd'''])
_register_formatter(CustomFormatter, '''custom''')
if config.TORCH_AVAILABLE:
from .torch_formatter import TorchFormatter
_register_formatter(TorchFormatter, '''torch''', aliases=['''pt''', '''pytorch'''])
else:
UpperCAmelCase_ : str = ValueError('''PyTorch needs to be installed to be able to return PyTorch tensors.''')
_register_unavailable_formatter(_torch_error, '''torch''', aliases=['''pt''', '''pytorch'''])
if config.TF_AVAILABLE:
from .tf_formatter import TFFormatter
_register_formatter(TFFormatter, '''tensorflow''', aliases=['''tf'''])
else:
UpperCAmelCase_ : Union[str, Any] = ValueError('''Tensorflow needs to be installed to be able to return Tensorflow tensors.''')
_register_unavailable_formatter(_tf_error, '''tensorflow''', aliases=['''tf'''])
if config.JAX_AVAILABLE:
from .jax_formatter import JaxFormatter
_register_formatter(JaxFormatter, '''jax''', aliases=[])
else:
UpperCAmelCase_ : Union[str, Any] = ValueError('''JAX needs to be installed to be able to return JAX arrays.''')
_register_unavailable_formatter(_jax_error, '''jax''', aliases=[])
def _UpperCamelCase (_lowerCamelCase : Optional[str] )-> Optional[str]:
'''simple docstring'''
if format_type in _FORMAT_TYPES_ALIASES:
return _FORMAT_TYPES_ALIASES[format_type]
else:
return format_type
def _UpperCamelCase (_lowerCamelCase : Optional[str] , **_lowerCamelCase : Optional[Any] )-> Formatter:
'''simple docstring'''
__snake_case = get_format_type_from_alias(__a )
if format_type in _FORMAT_TYPES:
return _FORMAT_TYPES[format_type](**__a )
if format_type in _FORMAT_TYPES_ALIASES_UNAVAILABLE:
raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]
else:
raise ValueError(
f'''Return type should be None or selected in {list(type for type in _FORMAT_TYPES.keys() if type != None )}, but got \'{format_type}\'''' )
| 24 |
a__ = '''Input must be a string of 8 numbers plus letter'''
a__ = '''TRWAGMYFPDXBNJZSQVHLCKE'''
def __UpperCAmelCase ( __a : str ) -> bool:
"""simple docstring"""
if not isinstance(__a ,__a ):
_a : List[str] = F"""Expected string as input, found {type(__a ).__name__}"""
raise TypeError(__a )
_a : List[Any] = spanish_id.replace('''-''' ,'''''' ).upper()
if len(__a ) != 9:
raise ValueError(__a )
try:
_a : Any = int(spanish_id_clean[0:8] )
_a : str = spanish_id_clean[8]
except ValueError as ex:
raise ValueError(__a ) from ex
if letter.isdigit():
raise ValueError(__a )
return letter == LOOKUP_LETTERS[number % 23]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 14 | 0 |
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import ViTForImageClassification, ViTForMaskedImageModeling, ViTModel
from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class _lowerCAmelCase :
def __init__( self , _UpperCamelCase , _UpperCamelCase=13 , _UpperCamelCase=30 , _UpperCamelCase=2 , _UpperCamelCase=3 , _UpperCamelCase=True , _UpperCamelCase=True , _UpperCamelCase=32 , _UpperCamelCase=5 , _UpperCamelCase=4 , _UpperCamelCase=37 , _UpperCamelCase="gelu" , _UpperCamelCase=0.1 , _UpperCamelCase=0.1 , _UpperCamelCase=10 , _UpperCamelCase=0.02 , _UpperCamelCase=None , _UpperCamelCase=2 , ) -> List[Any]:
lowerCAmelCase_ = parent
lowerCAmelCase_ = batch_size
lowerCAmelCase_ = image_size
lowerCAmelCase_ = patch_size
lowerCAmelCase_ = num_channels
lowerCAmelCase_ = is_training
lowerCAmelCase_ = use_labels
lowerCAmelCase_ = hidden_size
lowerCAmelCase_ = num_hidden_layers
lowerCAmelCase_ = num_attention_heads
lowerCAmelCase_ = intermediate_size
lowerCAmelCase_ = hidden_act
lowerCAmelCase_ = hidden_dropout_prob
lowerCAmelCase_ = attention_probs_dropout_prob
lowerCAmelCase_ = type_sequence_label_size
lowerCAmelCase_ = initializer_range
lowerCAmelCase_ = scope
lowerCAmelCase_ = encoder_stride
# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
lowerCAmelCase_ = (image_size // patch_size) ** 2
lowerCAmelCase_ = num_patches + 1
def __a ( self ) -> Any:
lowerCAmelCase_ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowerCAmelCase_ = None
if self.use_labels:
lowerCAmelCase_ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowerCAmelCase_ = self.get_config()
return config, pixel_values, labels
def __a ( self ) -> List[Any]:
return ViTConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=_a , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , )
def __a ( self , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> Any:
lowerCAmelCase_ = ViTModel(config=_a )
model.to(_a )
model.eval()
lowerCAmelCase_ = model(_a )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __a ( self , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> Dict:
lowerCAmelCase_ = ViTForMaskedImageModeling(config=_a )
model.to(_a )
model.eval()
lowerCAmelCase_ = model(_a )
self.parent.assertEqual(
result.reconstruction.shape , (self.batch_size, self.num_channels, self.image_size, self.image_size) )
# test greyscale images
lowerCAmelCase_ = 1
lowerCAmelCase_ = ViTForMaskedImageModeling(_a )
model.to(_a )
model.eval()
lowerCAmelCase_ = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
lowerCAmelCase_ = model(_a )
self.parent.assertEqual(result.reconstruction.shape , (self.batch_size, 1, self.image_size, self.image_size) )
def __a ( self , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> List[str]:
lowerCAmelCase_ = self.type_sequence_label_size
lowerCAmelCase_ = ViTForImageClassification(_a )
model.to(_a )
model.eval()
lowerCAmelCase_ = model(_a , labels=_a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
lowerCAmelCase_ = 1
lowerCAmelCase_ = ViTForImageClassification(_a )
model.to(_a )
model.eval()
lowerCAmelCase_ = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
lowerCAmelCase_ = model(_a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def __a ( self ) -> int:
lowerCAmelCase_ = self.prepare_config_and_inputs()
(
lowerCAmelCase_
) = config_and_inputs
lowerCAmelCase_ = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class _lowerCAmelCase ( __lowercase , __lowercase , unittest.TestCase ):
_lowercase =(
(
ViTModel,
ViTForImageClassification,
ViTForMaskedImageModeling,
)
if is_torch_available()
else ()
)
_lowercase =(
{"feature-extraction": ViTModel, "image-classification": ViTForImageClassification}
if is_torch_available()
else {}
)
_lowercase =True
_lowercase =False
_lowercase =False
_lowercase =False
def __a ( self ) -> str:
lowerCAmelCase_ = ViTModelTester(self )
lowerCAmelCase_ = ConfigTester(self , config_class=_a , has_text_modality=_a , hidden_size=37 )
def __a ( self ) -> Union[str, Any]:
self.config_tester.run_common_tests()
@unittest.skip(reason="ViT does not use inputs_embeds" )
def __a ( self ) -> Optional[Any]:
pass
def __a ( self ) -> Tuple:
lowerCAmelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase_ = model_class(_a )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
lowerCAmelCase_ = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(_a , nn.Linear ) )
def __a ( self ) -> Any:
lowerCAmelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase_ = model_class(_a )
lowerCAmelCase_ = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowerCAmelCase_ = [*signature.parameters.keys()]
lowerCAmelCase_ = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , _a )
def __a ( self ) -> Optional[Any]:
lowerCAmelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_a )
def __a ( self ) -> int:
lowerCAmelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*_a )
def __a ( self ) -> Optional[int]:
lowerCAmelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_a )
@slow
def __a ( self ) -> Union[str, Any]:
for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCAmelCase_ = ViTModel.from_pretrained(_a )
self.assertIsNotNone(_a )
def lowerCamelCase__ ( ):
"""simple docstring"""
lowerCAmelCase_ = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
@require_vision
class _lowerCAmelCase ( unittest.TestCase ):
@cached_property
def __a ( self ) -> Union[str, Any]:
return ViTImageProcessor.from_pretrained("google/vit-base-patch16-224" ) if is_vision_available() else None
@slow
def __a ( self ) -> Tuple:
lowerCAmelCase_ = ViTForImageClassification.from_pretrained("google/vit-base-patch16-224" ).to(_a )
lowerCAmelCase_ = self.default_image_processor
lowerCAmelCase_ = prepare_img()
lowerCAmelCase_ = image_processor(images=_a , return_tensors="pt" ).to(_a )
# forward pass
with torch.no_grad():
lowerCAmelCase_ = model(**_a )
# verify the logits
lowerCAmelCase_ = torch.Size((1, 1_000) )
self.assertEqual(outputs.logits.shape , _a )
lowerCAmelCase_ = torch.tensor([-0.2744, 0.8215, -0.0836] ).to(_a )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , _a , atol=1e-4 ) )
@slow
def __a ( self ) -> int:
# ViT models have an `interpolate_pos_encoding` argument in their forward method,
# allowing to interpolate the pre-trained position embeddings in order to use
# the model on higher resolutions. The DINO model by Facebook AI leverages this
# to visualize self-attention on higher resolution images.
lowerCAmelCase_ = ViTModel.from_pretrained("facebook/dino-vits8" ).to(_a )
lowerCAmelCase_ = ViTImageProcessor.from_pretrained("facebook/dino-vits8" , size=480 )
lowerCAmelCase_ = prepare_img()
lowerCAmelCase_ = image_processor(images=_a , return_tensors="pt" )
lowerCAmelCase_ = inputs.pixel_values.to(_a )
# forward pass
with torch.no_grad():
lowerCAmelCase_ = model(_a , interpolate_pos_encoding=_a )
# verify the logits
lowerCAmelCase_ = torch.Size((1, 3_601, 384) )
self.assertEqual(outputs.last_hidden_state.shape , _a )
lowerCAmelCase_ = torch.tensor(
[[4.2340, 4.3906, -6.6692], [4.5463, 1.8928, -6.7257], [4.4429, 0.8496, -5.8585]] ).to(_a )
self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3] , _a , atol=1e-4 ) )
@slow
@require_accelerate
@require_torch_gpu
def __a ( self ) -> Union[str, Any]:
lowerCAmelCase_ = ViTModel.from_pretrained("facebook/dino-vits8" , torch_dtype=torch.floataa , device_map="auto" )
lowerCAmelCase_ = self.default_image_processor
lowerCAmelCase_ = prepare_img()
lowerCAmelCase_ = image_processor(images=_a , return_tensors="pt" )
lowerCAmelCase_ = inputs.pixel_values.to(_a )
# forward pass to make sure inference works in fp16
with torch.no_grad():
lowerCAmelCase_ = model(_a )
| 290 |
from random import randint
from tempfile import TemporaryFile
import numpy as np
def __UpperCAmelCase ( __a : Optional[Any] ,__a : int ,__a : Any ) -> int:
"""simple docstring"""
_a : int = 0
if start < end:
_a : Tuple = randint(__a ,__a )
_a : Tuple = a[end]
_a : List[str] = a[pivot]
_a : Any = temp
_a , _a : Optional[int] = _in_place_partition(__a ,__a ,__a )
count += _in_place_quick_sort(__a ,__a ,p - 1 )
count += _in_place_quick_sort(__a ,p + 1 ,__a )
return count
def __UpperCAmelCase ( __a : List[Any] ,__a : Tuple ,__a : Dict ) -> Dict:
"""simple docstring"""
_a : Dict = 0
_a : Tuple = randint(__a ,__a )
_a : List[Any] = a[end]
_a : str = a[pivot]
_a : str = temp
_a : Dict = start - 1
for index in range(__a ,__a ):
count += 1
if a[index] < a[end]: # check if current val is less than pivot value
_a : int = new_pivot_index + 1
_a : Any = a[new_pivot_index]
_a : Optional[int] = a[index]
_a : str = temp
_a : Union[str, Any] = a[new_pivot_index + 1]
_a : Tuple = a[end]
_a : Any = temp
return new_pivot_index + 1, count
a__ = TemporaryFile()
a__ = 100 # 1000 elements are to be sorted
a__ , a__ = 0, 1 # mean and standard deviation
a__ = np.random.normal(mu, sigma, p)
np.save(outfile, X)
print('''The array is''')
print(X)
outfile.seek(0) # using the same array
a__ = np.load(outfile)
a__ = len(M) - 1
a__ = _in_place_quick_sort(M, 0, r)
print(
'''No of Comparisons for 100 elements selected from a standard normal distribution'''
'''is :'''
)
print(z)
| 14 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCamelCase = {"""configuration_vit_msn""": ["""VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViTMSNConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase = [
"""VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""ViTMSNModel""",
"""ViTMSNForImageClassification""",
"""ViTMSNPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vit_msn import (
VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST,
ViTMSNForImageClassification,
ViTMSNModel,
ViTMSNPreTrainedModel,
)
else:
import sys
_lowerCamelCase = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 71 |
import json
import os
import unittest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class UpperCAmelCase_ ( __lowercase , unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase__ : List[Any] = MgpstrTokenizer
UpperCAmelCase__ : int = False
UpperCAmelCase__ : Union[str, Any] = {}
UpperCAmelCase__ : List[Any] = False
def __lowercase ( self ) -> Any:
super().setUp()
# fmt: off
_a : Tuple = ['''[GO]''', '''[s]''', '''0''', '''1''', '''2''', '''3''', '''4''', '''5''', '''6''', '''7''', '''8''', '''9''', '''a''', '''b''', '''c''', '''d''', '''e''', '''f''', '''g''', '''h''', '''i''', '''j''', '''k''', '''l''', '''m''', '''n''', '''o''', '''p''', '''q''', '''r''', '''s''', '''t''', '''u''', '''v''', '''w''', '''x''', '''y''', '''z''']
# fmt: on
_a : Optional[int] = dict(zip(_a , range(len(_a ) ) ) )
_a : Any = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write(json.dumps(_a ) + '''\n''' )
def __lowercase ( self , **_a ) -> Dict:
return MgpstrTokenizer.from_pretrained(self.tmpdirname , **_a )
def __lowercase ( self , _a ) -> Tuple:
_a : List[str] = '''tester'''
_a : Optional[Any] = '''tester'''
return input_text, output_text
@unittest.skip('''MGP-STR always lower cases letters.''' )
def __lowercase ( self ) -> Any:
pass
def __lowercase ( self ) -> Any:
_a : Union[str, Any] = self.get_tokenizers(do_lower_case=_a )
for tokenizer in tokenizers:
with self.subTest(F"""{tokenizer.__class__.__name__}""" ):
_a : int = '''[SPECIAL_TOKEN]'''
tokenizer.add_special_tokens({'''cls_token''': special_token} )
_a : Tuple = tokenizer.encode([special_token] , add_special_tokens=_a )
self.assertEqual(len(_a ) , 1 )
_a : Tuple = tokenizer.decode(_a , skip_special_tokens=_a )
self.assertTrue(special_token not in decoded )
def __lowercase ( self ) -> Tuple:
_a : List[Any] = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(F"""{tokenizer.__class__.__name__}""" ):
_a , _a : int = self.get_input_output_texts(_a )
_a : List[str] = tokenizer.tokenize(_a )
_a : Optional[int] = tokenizer.convert_tokens_to_ids(_a )
_a : Tuple = tokenizer.encode(_a , add_special_tokens=_a )
self.assertListEqual(_a , _a )
_a : Optional[int] = tokenizer.convert_ids_to_tokens(_a )
self.assertNotEqual(len(_a ) , 0 )
_a : int = tokenizer.decode(_a )
self.assertIsInstance(_a , _a )
self.assertEqual(text_a.replace(''' ''' , '''''' ) , _a )
@unittest.skip('''MGP-STR tokenizer only handles one sequence.''' )
def __lowercase ( self ) -> List[str]:
pass
@unittest.skip('''inputs cannot be pretokenized in MgpstrTokenizer''' )
def __lowercase ( self ) -> Optional[Any]:
pass
| 14 | 0 |
"""simple docstring"""
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
sys.path.append(str(Path(__file__).parent.parent / "utils"))
from test_module.custom_feature_extraction import CustomFeatureExtractor # noqa E402
__A : List[str] = get_tests_dir("fixtures")
class _a ( unittest.TestCase):
"""simple docstring"""
def lowercase__ ( self : List[str] )->Tuple:
# A mock response for an HTTP head request to emulate server down
_UpperCAmelCase = mock.Mock()
_UpperCAmelCase = 5_0_0
_UpperCAmelCase = {}
_UpperCAmelCase = HTTPError
_UpperCAmelCase = {}
# Download this model to make sure it's in the cache.
_UpperCAmelCase = WavaVecaFeatureExtractor.from_pretrained('''hf-internal-testing/tiny-random-wav2vec2''' )
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch('''requests.Session.request''' , return_value=_a ) as mock_head:
_UpperCAmelCase = WavaVecaFeatureExtractor.from_pretrained('''hf-internal-testing/tiny-random-wav2vec2''' )
# This check we did call the fake head request
mock_head.assert_called()
def lowercase__ ( self : Any )->Tuple:
# This test is for deprecated behavior and can be removed in v5
_UpperCAmelCase = WavaVecaFeatureExtractor.from_pretrained(
'''https://huggingface.co/hf-internal-testing/tiny-random-wav2vec2/resolve/main/preprocessor_config.json''' )
@is_staging_test
class _a ( unittest.TestCase):
"""simple docstring"""
@classmethod
def lowercase__ ( cls : Optional[int] )->Optional[Any]:
_UpperCAmelCase = TOKEN
HfFolder.save_token(_a )
@classmethod
def lowercase__ ( cls : Tuple )->Tuple:
try:
delete_repo(token=cls._token , repo_id='''test-feature-extractor''' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='''valid_org/test-feature-extractor-org''' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='''test-dynamic-feature-extractor''' )
except HTTPError:
pass
def lowercase__ ( self : Optional[Any] )->str:
_UpperCAmelCase = WavaVecaFeatureExtractor.from_pretrained(_a )
feature_extractor.push_to_hub('''test-feature-extractor''' , use_auth_token=self._token )
_UpperCAmelCase = WavaVecaFeatureExtractor.from_pretrained(F'{USER}/test-feature-extractor' )
for k, v in feature_extractor.__dict__.items():
self.assertEqual(_a , getattr(_a , _a ) )
# Reset repo
delete_repo(token=self._token , repo_id='''test-feature-extractor''' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
feature_extractor.save_pretrained(
_a , repo_id='''test-feature-extractor''' , push_to_hub=_a , use_auth_token=self._token )
_UpperCAmelCase = WavaVecaFeatureExtractor.from_pretrained(F'{USER}/test-feature-extractor' )
for k, v in feature_extractor.__dict__.items():
self.assertEqual(_a , getattr(_a , _a ) )
def lowercase__ ( self : int )->Dict:
_UpperCAmelCase = WavaVecaFeatureExtractor.from_pretrained(_a )
feature_extractor.push_to_hub('''valid_org/test-feature-extractor''' , use_auth_token=self._token )
_UpperCAmelCase = WavaVecaFeatureExtractor.from_pretrained('''valid_org/test-feature-extractor''' )
for k, v in feature_extractor.__dict__.items():
self.assertEqual(_a , getattr(_a , _a ) )
# Reset repo
delete_repo(token=self._token , repo_id='''valid_org/test-feature-extractor''' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
feature_extractor.save_pretrained(
_a , repo_id='''valid_org/test-feature-extractor-org''' , push_to_hub=_a , use_auth_token=self._token )
_UpperCAmelCase = WavaVecaFeatureExtractor.from_pretrained('''valid_org/test-feature-extractor-org''' )
for k, v in feature_extractor.__dict__.items():
self.assertEqual(_a , getattr(_a , _a ) )
def lowercase__ ( self : int )->Any:
CustomFeatureExtractor.register_for_auto_class()
_UpperCAmelCase = CustomFeatureExtractor.from_pretrained(_a )
feature_extractor.push_to_hub('''test-dynamic-feature-extractor''' , use_auth_token=self._token )
# This has added the proper auto_map field to the config
self.assertDictEqual(
feature_extractor.auto_map , {'''AutoFeatureExtractor''': '''custom_feature_extraction.CustomFeatureExtractor'''} , )
_UpperCAmelCase = AutoFeatureExtractor.from_pretrained(
F'{USER}/test-dynamic-feature-extractor' , trust_remote_code=_a )
# Can't make an isinstance check because the new_feature_extractor is from the CustomFeatureExtractor class of a dynamic module
self.assertEqual(new_feature_extractor.__class__.__name__ , '''CustomFeatureExtractor''' )
| 602 |
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 UpperCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
def __lowercase ( self ) -> List[Any]:
_a : int = 0
def __lowercase ( self ) -> List[str]:
_a : Dict = AutoImageProcessor.from_pretrained('''openai/clip-vit-base-patch32''' )
self.assertIsInstance(_a , _a )
def __lowercase ( self ) -> Union[str, Any]:
with tempfile.TemporaryDirectory() as tmpdirname:
_a : Tuple = Path(_a ) / '''preprocessor_config.json'''
_a : Optional[Any] = Path(_a ) / '''config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(_a , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(_a , '''w''' ) )
_a : List[str] = AutoImageProcessor.from_pretrained(_a )
self.assertIsInstance(_a , _a )
def __lowercase ( self ) -> Optional[Any]:
# Ensure we can load the image processor from the feature extractor config
with tempfile.TemporaryDirectory() as tmpdirname:
_a : Optional[int] = Path(_a ) / '''preprocessor_config.json'''
_a : Any = Path(_a ) / '''config.json'''
json.dump(
{'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''} , open(_a , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(_a , '''w''' ) )
_a : Optional[Any] = AutoImageProcessor.from_pretrained(_a )
self.assertIsInstance(_a , _a )
def __lowercase ( self ) -> Optional[Any]:
with tempfile.TemporaryDirectory() as tmpdirname:
_a : Dict = CLIPConfig()
# Create a dummy config file with image_proceesor_type
_a : Tuple = Path(_a ) / '''preprocessor_config.json'''
_a : List[str] = Path(_a ) / '''config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(_a , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(_a , '''w''' ) )
# remove image_processor_type to make sure config.json alone is enough to load image processor locally
_a : Tuple = AutoImageProcessor.from_pretrained(_a ).to_dict()
config_dict.pop('''image_processor_type''' )
_a : Tuple = CLIPImageProcessor(**_a )
# save in new folder
model_config.save_pretrained(_a )
config.save_pretrained(_a )
_a : List[str] = AutoImageProcessor.from_pretrained(_a )
# make sure private variable is not incorrectly saved
_a : Optional[int] = json.loads(config.to_json_string() )
self.assertTrue('''_processor_class''' not in dict_as_saved )
self.assertIsInstance(_a , _a )
def __lowercase ( self ) -> Dict:
with tempfile.TemporaryDirectory() as tmpdirname:
_a : Optional[int] = Path(_a ) / '''preprocessor_config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(_a , '''w''' ) , )
_a : List[str] = AutoImageProcessor.from_pretrained(_a )
self.assertIsInstance(_a , _a )
def __lowercase ( self ) -> Any:
with self.assertRaisesRegex(
_a , '''clip-base is not a local folder and is not a valid model identifier''' ):
_a : Dict = AutoImageProcessor.from_pretrained('''clip-base''' )
def __lowercase ( self ) -> List[Any]:
with self.assertRaisesRegex(
_a , R'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)''' ):
_a : List[str] = AutoImageProcessor.from_pretrained(_a , revision='''aaaaaa''' )
def __lowercase ( self ) -> Dict:
with self.assertRaisesRegex(
_a , '''hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.''' , ):
_a : Optional[int] = AutoImageProcessor.from_pretrained('''hf-internal-testing/config-no-model''' )
def __lowercase ( self ) -> Union[str, Any]:
# If remote code is not set, we will time out when asking whether to load the model.
with self.assertRaises(_a ):
_a : str = AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor''' )
# If remote code is disabled, we can't load this config.
with self.assertRaises(_a ):
_a : Optional[Any] = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=_a )
_a : Union[str, Any] = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=_a )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
# Test image processor can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(_a )
_a : Optional[Any] = AutoImageProcessor.from_pretrained(_a , trust_remote_code=_a )
self.assertEqual(reloaded_image_processor.__class__.__name__ , '''NewImageProcessor''' )
def __lowercase ( self ) -> Dict:
try:
AutoConfig.register('''custom''' , _a )
AutoImageProcessor.register(_a , _a )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(_a ):
AutoImageProcessor.register(_a , _a )
with tempfile.TemporaryDirectory() as tmpdirname:
_a : int = Path(_a ) / '''preprocessor_config.json'''
_a : int = Path(_a ) / '''config.json'''
json.dump(
{'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''} , open(_a , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(_a , '''w''' ) )
_a : int = CustomImageProcessor.from_pretrained(_a )
# 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(_a )
_a : Optional[Any] = AutoImageProcessor.from_pretrained(_a )
self.assertIsInstance(_a , _a )
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 __lowercase ( self ) -> Union[str, Any]:
class UpperCAmelCase_ ( __lowercase ):
"""simple docstring"""
UpperCAmelCase__ : Tuple = True
try:
AutoConfig.register('''custom''' , _a )
AutoImageProcessor.register(_a , _a )
# If remote code is not set, the default is to use local
_a : str = 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 : int = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=_a )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
self.assertTrue(image_processor.is_local )
# If remote is enabled, we load from the Hub
_a : Dict = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=_a )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
self.assertTrue(not hasattr(_a , '''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]
| 14 | 0 |
"""simple docstring"""
import argparse
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
__SCREAMING_SNAKE_CASE : Tuple = '''src/diffusers'''
# Matches is_xxx_available()
__SCREAMING_SNAKE_CASE : Optional[Any] = re.compile(R'''is\_([a-z_]*)_available\(\)''')
# Matches from xxx import bla
__SCREAMING_SNAKE_CASE : Any = re.compile(R'''\s+from\s+\S*\s+import\s+([^\(\s].*)\n''')
__SCREAMING_SNAKE_CASE : Any = '''
{0} = None
'''
__SCREAMING_SNAKE_CASE : Any = '''
class {0}(metaclass=DummyObject):
_backends = {1}
def __init__(self, *args, **kwargs):
requires_backends(self, {1})
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, {1})
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, {1})
'''
__SCREAMING_SNAKE_CASE : str = '''
def {0}(*args, **kwargs):
requires_backends({0}, {1})
'''
def lowerCAmelCase_( lowercase_ : Dict ) -> str:
_lowerCamelCase = _re_backend.findall(__a )
if len(__a ) == 0:
return None
return "_and_".join(__a )
def lowerCAmelCase_( ) -> Union[str, Any]:
with open(os.path.join(__a , '''__init__.py''' ) , '''r''' , encoding='''utf-8''' , newline='''\n''' ) as f:
_lowerCamelCase = f.readlines()
# Get to the point we do the actual imports for type checking
_lowerCamelCase = 0
_lowerCamelCase = {}
# Go through the end of the file
while line_index < len(__a ):
# If the line contains is_backend_available, we grab all objects associated with the `else` block
_lowerCamelCase = find_backend(lines[line_index] )
if backend is not None:
while not lines[line_index].startswith('''else:''' ):
line_index += 1
line_index += 1
_lowerCamelCase = []
# Until we unindent, add backend objects to the list
while line_index < len(__a ) and len(lines[line_index] ) > 1:
_lowerCamelCase = lines[line_index]
_lowerCamelCase = _re_single_line_import.search(__a )
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(''', ''' ) )
elif line.startswith(''' ''' * 8 ):
objects.append(line[8:-2] )
line_index += 1
if len(__a ) > 0:
_lowerCamelCase = objects
else:
line_index += 1
return backend_specific_objects
def lowerCAmelCase_( lowercase_ : str , lowercase_ : str ) -> str:
if name.isupper():
return DUMMY_CONSTANT.format(__a )
elif name.islower():
return DUMMY_FUNCTION.format(__a , __a )
else:
return DUMMY_CLASS.format(__a , __a )
def lowerCAmelCase_( lowercase_ : List[Any]=None ) -> Tuple:
if backend_specific_objects is None:
_lowerCamelCase = read_init()
# For special correspondence backend to module name as used in the function requires_modulename
_lowerCamelCase = {}
for backend, objects in backend_specific_objects.items():
_lowerCamelCase = '''[''' + ''', '''.join(F"""\"{b}\"""" for b in backend.split('''_and_''' ) ) + ''']'''
_lowerCamelCase = '''# This file is autogenerated by the command `make fix-copies`, do not edit.\n'''
dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
dummy_file += "\n".join([create_dummy_object(__a , __a ) for o in objects] )
_lowerCamelCase = dummy_file
return dummy_files
def lowerCAmelCase_( lowercase_ : Union[str, Any]=False ) -> Optional[Any]:
_lowerCamelCase = create_dummy_files()
# For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
_lowerCamelCase = {'''torch''': '''pt'''}
# Locate actual dummy modules and read their content.
_lowerCamelCase = os.path.join(__a , '''utils''' )
_lowerCamelCase = {
backend: os.path.join(__a , F"""dummy_{short_names.get(__a , __a )}_objects.py""" )
for backend in dummy_files.keys()
}
_lowerCamelCase = {}
for backend, file_path in dummy_file_paths.items():
if os.path.isfile(__a ):
with open(__a , '''r''' , encoding='''utf-8''' , newline='''\n''' ) as f:
_lowerCamelCase = f.read()
else:
_lowerCamelCase = ''''''
for backend in dummy_files.keys():
if dummy_files[backend] != actual_dummies[backend]:
if overwrite:
print(
F"""Updating diffusers.utils.dummy_{short_names.get(__a , __a )}_objects.py as the main """
'''__init__ has new objects.''' )
with open(dummy_file_paths[backend] , '''w''' , encoding='''utf-8''' , newline='''\n''' ) as f:
f.write(dummy_files[backend] )
else:
raise ValueError(
'''The main __init__ has objects that are not present in '''
F"""diffusers.utils.dummy_{short_names.get(__a , __a )}_objects.py. Run `make fix-copies` """
'''to fix this.''' )
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE : int = argparse.ArgumentParser()
parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
__SCREAMING_SNAKE_CASE : Any = parser.parse_args()
check_dummies(args.fix_and_overwrite)
| 661 |
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class UpperCAmelCase_ :
"""simple docstring"""
UpperCAmelCase__ : float
UpperCAmelCase__ : TreeNode | None = None
UpperCAmelCase__ : TreeNode | None = None
def __UpperCAmelCase ( __a : TreeNode | None ) -> bool:
"""simple docstring"""
def is_valid_tree(__a : TreeNode | None ) -> bool:
if node is None:
return True
if not isinstance(__a ,__a ):
return False
try:
float(node.data )
except (TypeError, ValueError):
return False
return is_valid_tree(node.left ) and is_valid_tree(node.right )
if not is_valid_tree(__a ):
raise ValueError(
'''Each node should be type of TreeNode and data should be float.''' )
def is_binary_search_tree_recursive_check(
__a : TreeNode | None ,__a : float ,__a : float ) -> bool:
if node is None:
return True
return (
left_bound < node.data < right_bound
and is_binary_search_tree_recursive_check(node.left ,__a ,node.data )
and is_binary_search_tree_recursive_check(
node.right ,node.data ,__a )
)
return is_binary_search_tree_recursive_check(__a ,-float('''inf''' ) ,float('''inf''' ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 14 | 0 |
import numpy as np
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModelWithProjection, PreTrainedModel
from ...utils import logging
_lowercase = logging.get_logger(__name__)
class __A ( __lowercase ):
UpperCamelCase :List[Any] = CLIPConfig
UpperCamelCase :List[Any] = ["CLIPEncoderLayer"]
def __init__(self , __magic_name__ ):
super().__init__(_a )
lowerCamelCase__ : List[str] = CLIPVisionModelWithProjection(config.vision_config )
lowerCamelCase__ : Any = nn.Linear(config.vision_config.projection_dim , 1 )
lowerCamelCase__ : Any = nn.Linear(config.vision_config.projection_dim , 1 )
@torch.no_grad()
def _snake_case (self , __magic_name__ , __magic_name__ , __magic_name__=0.5 , __magic_name__=0.5 ):
lowerCamelCase__ : Dict = self.vision_model(_a )[0]
lowerCamelCase__ : Union[str, Any] = self.p_head(_a )
lowerCamelCase__ : str = nsfw_detected.flatten()
lowerCamelCase__ : Optional[int] = nsfw_detected > p_threshold
lowerCamelCase__ : Dict = nsfw_detected.tolist()
if any(_a ):
logger.warning(
"""Potential NSFW content was detected in one or more images. A black image will be returned instead."""
""" Try again with a different prompt and/or seed.""" )
for idx, nsfw_detected_ in enumerate(_a ):
if nsfw_detected_:
lowerCamelCase__ : Union[str, Any] = np.zeros(images[idx].shape )
lowerCamelCase__ : str = self.w_head(_a )
lowerCamelCase__ : Dict = watermark_detected.flatten()
lowerCamelCase__ : Optional[Any] = watermark_detected > w_threshold
lowerCamelCase__ : str = watermark_detected.tolist()
if any(_a ):
logger.warning(
"""Potential watermarked content was detected in one or more images. A black image will be returned instead."""
""" Try again with a different prompt and/or seed.""" )
for idx, watermark_detected_ in enumerate(_a ):
if watermark_detected_:
lowerCamelCase__ : Dict = np.zeros(images[idx].shape )
return images, nsfw_detected, watermark_detected
| 157 |
from __future__ import annotations
import matplotlib.pyplot as plt # type: ignore
import numpy
# initial triangle of Koch snowflake
a__ = numpy.array([0, 0])
a__ = numpy.array([0.5, 0.8660254])
a__ = numpy.array([1, 0])
a__ = [VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]
def __UpperCAmelCase ( __a : list[numpy.ndarray] ,__a : int ) -> list[numpy.ndarray]:
"""simple docstring"""
_a : Tuple = initial_vectors
for _ in range(__a ):
_a : int = iteration_step(__a )
return vectors
def __UpperCAmelCase ( __a : list[numpy.ndarray] ) -> list[numpy.ndarray]:
"""simple docstring"""
_a : Tuple = []
for i, start_vector in enumerate(vectors[:-1] ):
_a : str = vectors[i + 1]
new_vectors.append(__a )
_a : Optional[int] = end_vector - start_vector
new_vectors.append(start_vector + difference_vector / 3 )
new_vectors.append(
start_vector + difference_vector / 3 + rotate(difference_vector / 3 ,60 ) )
new_vectors.append(start_vector + difference_vector * 2 / 3 )
new_vectors.append(vectors[-1] )
return new_vectors
def __UpperCAmelCase ( __a : numpy.ndarray ,__a : float ) -> numpy.ndarray:
"""simple docstring"""
_a : Tuple = numpy.radians(__a )
_a , _a : List[Any] = numpy.cos(__a ), numpy.sin(__a )
_a : Dict = numpy.array(((c, -s), (s, c)) )
return numpy.dot(__a ,__a )
def __UpperCAmelCase ( __a : list[numpy.ndarray] ) -> None:
"""simple docstring"""
_a : str = plt.gca()
axes.set_aspect('''equal''' )
# matplotlib.pyplot.plot takes a list of all x-coordinates and a list of all
# y-coordinates as inputs, which are constructed from the vector-list using
# zip()
_a , _a : Optional[int] = zip(*__a )
plt.plot(__a ,__a )
plt.show()
if __name__ == "__main__":
import doctest
doctest.testmod()
a__ = iterate(INITIAL_VECTORS, 5)
plot(processed_vectors)
| 14 | 0 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel
if is_vision_available():
from transformers import MaskFormerImageProcessor
if is_vision_available():
from PIL import Image
class __lowerCAmelCase :
def __init__( self , __UpperCAmelCase , __UpperCAmelCase=2 , __UpperCAmelCase=True , __UpperCAmelCase=False , __UpperCAmelCase=10 , __UpperCAmelCase=3 , __UpperCAmelCase=32 * 4 , __UpperCAmelCase=32 * 6 , __UpperCAmelCase=4 , __UpperCAmelCase=32 , ):
'''simple docstring'''
__lowerCamelCase = parent
__lowerCamelCase = batch_size
__lowerCamelCase = is_training
__lowerCamelCase = use_auxiliary_loss
__lowerCamelCase = num_queries
__lowerCamelCase = num_channels
__lowerCamelCase = min_size
__lowerCamelCase = max_size
__lowerCamelCase = num_labels
__lowerCamelCase = mask_feature_size
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
_a )
__lowerCamelCase = torch.ones([self.batch_size, self.min_size, self.max_size] , device=_a )
__lowerCamelCase = (
torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] , device=_a ) > 0.5
).float()
__lowerCamelCase = (torch.rand((self.batch_size, self.num_labels) , device=_a ) > 0.5).long()
__lowerCamelCase = self.get_config()
return config, pixel_values, pixel_mask, mask_labels, class_labels
def lowerCamelCase ( self ):
'''simple docstring'''
return MaskFormerConfig.from_backbone_and_decoder_configs(
backbone_config=SwinConfig(
depths=[1, 1, 1, 1] , ) , decoder_config=DetrConfig(
decoder_ffn_dim=128 , num_queries=self.num_queries , decoder_attention_heads=2 , d_model=self.mask_feature_size , ) , mask_feature_size=self.mask_feature_size , fpn_feature_size=self.mask_feature_size , num_channels=self.num_channels , num_labels=self.num_labels , )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.prepare_config_and_inputs()
__lowerCamelCase = {'''pixel_values''': pixel_values, '''pixel_mask''': pixel_mask}
return config, inputs_dict
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = output.encoder_hidden_states
__lowerCamelCase = output.pixel_decoder_hidden_states
__lowerCamelCase = output.transformer_decoder_hidden_states
self.parent.assertTrue(len(_a ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(_a ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(_a ) , config.decoder_config.decoder_layers )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase=False ):
'''simple docstring'''
with torch.no_grad():
__lowerCamelCase = MaskFormerModel(config=_a )
model.to(_a )
model.eval()
__lowerCamelCase = model(pixel_values=_a , pixel_mask=_a )
__lowerCamelCase = model(_a , output_hidden_states=_a )
# the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
# encoder and pixel decoder
self.parent.assertEqual(
output.transformer_decoder_last_hidden_state.shape , (self.batch_size, self.num_queries, self.mask_feature_size) , )
# let's ensure the other two hidden state exists
self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(output.encoder_last_hidden_state is not None )
if output_hidden_states:
self.check_output_hidden_state(_a , _a )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = MaskFormerForInstanceSegmentation(config=_a )
model.to(_a )
model.eval()
def comm_check_on_output(__UpperCAmelCase ):
# let's still check that all the required stuff is there
self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.encoder_last_hidden_state is not None )
# okay, now we need to check the logits shape
# due to the encoder compression, masks have a //4 spatial size
self.parent.assertEqual(
result.masks_queries_logits.shape , (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) , )
# + 1 for null class
self.parent.assertEqual(
result.class_queries_logits.shape , (self.batch_size, self.num_queries, self.num_labels + 1) )
with torch.no_grad():
__lowerCamelCase = model(pixel_values=_a , pixel_mask=_a )
__lowerCamelCase = model(_a )
comm_check_on_output(_a )
__lowerCamelCase = model(
pixel_values=_a , pixel_mask=_a , mask_labels=_a , class_labels=_a )
comm_check_on_output(_a )
self.parent.assertTrue(result.loss is not None )
self.parent.assertEqual(result.loss.shape , torch.Size([1] ) )
@require_torch
class __lowerCAmelCase ( __lowercase , __lowercase , unittest.TestCase ):
lowerCAmelCase__ = (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
lowerCAmelCase__ = (
{"feature-extraction": MaskFormerModel, "image-segmentation": MaskFormerForInstanceSegmentation}
if is_torch_available()
else {}
)
lowerCAmelCase__ = False
lowerCAmelCase__ = False
lowerCAmelCase__ = False
lowerCAmelCase__ = False
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = MaskFormerModelTester(self )
__lowerCamelCase = ConfigTester(self , config_class=_a , has_text_modality=_a )
def lowerCamelCase ( self ):
'''simple docstring'''
self.config_tester.run_common_tests()
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(_a , **_a , output_hidden_states=_a )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*_a )
@unittest.skip(reason='''MaskFormer does not use inputs_embeds''' )
def lowerCamelCase ( self ):
'''simple docstring'''
pass
@unittest.skip(reason='''MaskFormer does not have a get_input_embeddings method''' )
def lowerCamelCase ( self ):
'''simple docstring'''
pass
@unittest.skip(reason='''MaskFormer is not a generative model''' )
def lowerCamelCase ( self ):
'''simple docstring'''
pass
@unittest.skip(reason='''MaskFormer does not use token embeddings''' )
def lowerCamelCase ( self ):
'''simple docstring'''
pass
@require_torch_multi_gpu
@unittest.skip(
reason='''MaskFormer has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`''' )
def lowerCamelCase ( self ):
'''simple docstring'''
pass
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def lowerCamelCase ( self ):
'''simple docstring'''
pass
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowerCamelCase = model_class(_a )
__lowerCamelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__lowerCamelCase = [*signature.parameters.keys()]
__lowerCamelCase = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , _a )
@slow
def lowerCamelCase ( self ):
'''simple docstring'''
for model_name in ["facebook/maskformer-swin-small-coco"]:
__lowerCamelCase = MaskFormerModel.from_pretrained(_a )
self.assertIsNotNone(_a )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = (self.model_tester.min_size,) * 2
__lowerCamelCase = {
'''pixel_values''': torch.randn((2, 3, *size) , device=_a ),
'''mask_labels''': torch.randn((2, 10, *size) , device=_a ),
'''class_labels''': torch.zeros(2 , 10 , device=_a ).long(),
}
__lowerCamelCase = MaskFormerForInstanceSegmentation(MaskFormerConfig() ).to(_a )
__lowerCamelCase = model(**_a )
self.assertTrue(outputs.loss is not None )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(_a , **_a , output_hidden_states=_a )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowerCamelCase = model_class(_a ).to(_a )
__lowerCamelCase = model(**_a , output_attentions=_a )
self.assertTrue(outputs.attentions is not None )
def lowerCamelCase ( self ):
'''simple docstring'''
if not self.model_tester.is_training:
return
# only MaskFormerForInstanceSegmentation has the loss
__lowerCamelCase = self.all_model_classes[1]
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
__lowerCamelCase = model_class(_a )
model.to(_a )
model.train()
__lowerCamelCase = model(_a , mask_labels=_a , class_labels=_a ).loss
loss.backward()
def lowerCamelCase ( self ):
'''simple docstring'''
# only MaskFormerForInstanceSegmentation has the loss
__lowerCamelCase = self.all_model_classes[1]
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
__lowerCamelCase = True
__lowerCamelCase = True
__lowerCamelCase = model_class(_a )
model.to(_a )
model.train()
__lowerCamelCase = model(_a , mask_labels=_a , class_labels=_a )
__lowerCamelCase = outputs.encoder_hidden_states[0]
encoder_hidden_states.retain_grad()
__lowerCamelCase = outputs.pixel_decoder_hidden_states[0]
pixel_decoder_hidden_states.retain_grad()
# we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
__lowerCamelCase = outputs.transformer_decoder_hidden_states[0]
transformer_decoder_hidden_states.retain_grad()
__lowerCamelCase = outputs.attentions[0]
attentions.retain_grad()
outputs.loss.backward(retain_graph=_a )
self.assertIsNotNone(encoder_hidden_states.grad )
self.assertIsNotNone(pixel_decoder_hidden_states.grad )
self.assertIsNotNone(transformer_decoder_hidden_states.grad )
self.assertIsNotNone(attentions.grad )
a_ = 1E-4
def a__ ( ):
__lowerCamelCase = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_vision
@slow
class __lowerCAmelCase ( unittest.TestCase ):
@cached_property
def lowerCamelCase ( self ):
'''simple docstring'''
return (
MaskFormerImageProcessor.from_pretrained('''facebook/maskformer-swin-small-coco''' )
if is_vision_available()
else None
)
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = MaskFormerModel.from_pretrained('''facebook/maskformer-swin-small-coco''' ).to(_a )
__lowerCamelCase = self.default_image_processor
__lowerCamelCase = prepare_img()
__lowerCamelCase = image_processor(_a , return_tensors='''pt''' ).to(_a )
__lowerCamelCase = inputs['''pixel_values'''].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(_a , (1, 3, 800, 1088) )
with torch.no_grad():
__lowerCamelCase = model(**_a )
__lowerCamelCase = torch.tensor(
[[-0.0_482, 0.9_228, 0.4_951], [-0.2_547, 0.8_017, 0.8_527], [-0.0_069, 0.3_385, -0.0_089]] ).to(_a )
self.assertTrue(
torch.allclose(
outputs.encoder_last_hidden_state[0, 0, :3, :3] , _a , atol=_a ) )
__lowerCamelCase = torch.tensor(
[[-0.8_422, -0.8_434, -0.9_718], [-1.0_144, -0.5_565, -0.4_195], [-1.0_038, -0.4_484, -0.1_961]] ).to(_a )
self.assertTrue(
torch.allclose(
outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] , _a , atol=_a ) )
__lowerCamelCase = torch.tensor(
[[0.2_852, -0.0_159, 0.9_735], [0.6_254, 0.1_858, 0.8_529], [-0.0_680, -0.4_116, 1.8_413]] ).to(_a )
self.assertTrue(
torch.allclose(
outputs.transformer_decoder_last_hidden_state[0, :3, :3] , _a , atol=_a ) )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = (
MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco''' )
.to(_a )
.eval()
)
__lowerCamelCase = self.default_image_processor
__lowerCamelCase = prepare_img()
__lowerCamelCase = image_processor(_a , return_tensors='''pt''' ).to(_a )
__lowerCamelCase = inputs['''pixel_values'''].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(_a , (1, 3, 800, 1088) )
with torch.no_grad():
__lowerCamelCase = model(**_a )
# masks_queries_logits
__lowerCamelCase = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
__lowerCamelCase = [
[-1.3_737_124, -1.7_724_937, -1.9_364_233],
[-1.5_977_281, -1.9_867_939, -2.1_523_695],
[-1.5_795_398, -1.9_269_832, -2.093_942],
]
__lowerCamelCase = torch.tensor(_a ).to(_a )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , _a , atol=_a ) )
# class_queries_logits
__lowerCamelCase = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
__lowerCamelCase = torch.tensor(
[
[1.6_5_1_2E0_0, -5.2_5_7_2E0_0, -3.3_5_1_9E0_0],
[3.6_1_6_9E-0_2, -5.9_0_2_5E0_0, -2.9_3_1_3E0_0],
[1.0_7_6_6E-0_4, -7.7_6_3_0E0_0, -5.1_2_6_3E0_0],
] ).to(_a )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , _a , atol=_a ) )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = (
MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-resnet101-coco-stuff''' )
.to(_a )
.eval()
)
__lowerCamelCase = self.default_image_processor
__lowerCamelCase = prepare_img()
__lowerCamelCase = image_processor(_a , return_tensors='''pt''' ).to(_a )
__lowerCamelCase = inputs['''pixel_values'''].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(_a , (1, 3, 800, 1088) )
with torch.no_grad():
__lowerCamelCase = model(**_a )
# masks_queries_logits
__lowerCamelCase = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
__lowerCamelCase = [[-0.9_046, -2.6_366, -4.6_062], [-3.4_179, -5.7_890, -8.8_057], [-4.9_179, -7.6_560, -10.7_711]]
__lowerCamelCase = torch.tensor(_a ).to(_a )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , _a , atol=_a ) )
# class_queries_logits
__lowerCamelCase = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
__lowerCamelCase = torch.tensor(
[[4.7_188, -3.2_585, -2.8_857], [6.6_871, -2.9_181, -1.2_487], [7.2_449, -2.2_764, -2.1_874]] ).to(_a )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , _a , atol=_a ) )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = (
MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco''' )
.to(_a )
.eval()
)
__lowerCamelCase = self.default_image_processor
__lowerCamelCase = image_processor(
[np.zeros((3, 800, 1333) ), np.zeros((3, 800, 1333) )] , segmentation_maps=[np.zeros((384, 384) ).astype(np.floataa ), np.zeros((384, 384) ).astype(np.floataa )] , return_tensors='''pt''' , )
__lowerCamelCase = inputs['''pixel_values'''].to(_a )
__lowerCamelCase = [el.to(_a ) for el in inputs['''mask_labels''']]
__lowerCamelCase = [el.to(_a ) for el in inputs['''class_labels''']]
with torch.no_grad():
__lowerCamelCase = model(**_a )
self.assertTrue(outputs.loss is not None )
| 175 |
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def __UpperCAmelCase ( __a : Tuple ,__a : Dict ,__a : List[str] ,__a : Optional[Any] ,__a : Tuple ) -> Dict:
"""simple docstring"""
with open(__a ) as metadata_file:
_a : Optional[Any] = json.load(__a )
_a : List[Any] = LukeConfig(use_entity_aware_attention=__a ,**metadata['''model_config'''] )
# Load in the weights from the checkpoint_path
_a : Optional[Any] = torch.load(__a ,map_location='''cpu''' )['''module''']
# Load the entity vocab file
_a : Any = load_original_entity_vocab(__a )
# add an entry for [MASK2]
_a : Union[str, Any] = max(entity_vocab.values() ) + 1
config.entity_vocab_size += 1
_a : Dict = XLMRobertaTokenizer.from_pretrained(metadata['''model_config''']['''bert_model_name'''] )
# Add special tokens to the token vocabulary for downstream tasks
_a : Optional[int] = AddedToken('''<ent>''' ,lstrip=__a ,rstrip=__a )
_a : Tuple = AddedToken('''<ent2>''' ,lstrip=__a ,rstrip=__a )
tokenizer.add_special_tokens({'''additional_special_tokens''': [entity_token_a, entity_token_a]} )
config.vocab_size += 2
print(F"""Saving tokenizer to {pytorch_dump_folder_path}""" )
tokenizer.save_pretrained(__a )
with open(os.path.join(__a ,'''tokenizer_config.json''' ) ,'''r''' ) as f:
_a : List[str] = json.load(__a )
_a : Tuple = '''MLukeTokenizer'''
with open(os.path.join(__a ,'''tokenizer_config.json''' ) ,'''w''' ) as f:
json.dump(__a ,__a )
with open(os.path.join(__a ,MLukeTokenizer.vocab_files_names['''entity_vocab_file'''] ) ,'''w''' ) as f:
json.dump(__a ,__a )
_a : Optional[int] = MLukeTokenizer.from_pretrained(__a )
# Initialize the embeddings of the special tokens
_a : str = tokenizer.convert_tokens_to_ids(['''@'''] )[0]
_a : Tuple = tokenizer.convert_tokens_to_ids(['''#'''] )[0]
_a : Any = state_dict['''embeddings.word_embeddings.weight''']
_a : Optional[int] = word_emb[ent_init_index].unsqueeze(0 )
_a : Any = word_emb[enta_init_index].unsqueeze(0 )
_a : Union[str, Any] = torch.cat([word_emb, ent_emb, enta_emb] )
# add special tokens for 'entity_predictions.bias'
for bias_name in ["lm_head.decoder.bias", "lm_head.bias"]:
_a : Tuple = state_dict[bias_name]
_a : Optional[Any] = decoder_bias[ent_init_index].unsqueeze(0 )
_a : Optional[int] = decoder_bias[enta_init_index].unsqueeze(0 )
_a : Dict = torch.cat([decoder_bias, ent_decoder_bias, enta_decoder_bias] )
# Initialize the query layers of the entity-aware self-attention mechanism
for layer_index in range(config.num_hidden_layers ):
for matrix_name in ["query.weight", "query.bias"]:
_a : Tuple = F"""encoder.layer.{layer_index}.attention.self."""
_a : List[Any] = state_dict[prefix + matrix_name]
_a : Dict = state_dict[prefix + matrix_name]
_a : List[Any] = state_dict[prefix + matrix_name]
# Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
_a : Union[str, Any] = state_dict['''entity_embeddings.entity_embeddings.weight''']
_a : Optional[int] = entity_emb[entity_vocab['''[MASK]''']].unsqueeze(0 )
_a : Any = torch.cat([entity_emb, entity_mask_emb] )
# add [MASK2] for 'entity_predictions.bias'
_a : int = state_dict['''entity_predictions.bias''']
_a : int = entity_prediction_bias[entity_vocab['''[MASK]''']].unsqueeze(0 )
_a : Optional[Any] = torch.cat([entity_prediction_bias, entity_mask_bias] )
_a : Optional[int] = LukeForMaskedLM(config=__a ).eval()
state_dict.pop('''entity_predictions.decoder.weight''' )
state_dict.pop('''lm_head.decoder.weight''' )
state_dict.pop('''lm_head.decoder.bias''' )
_a : int = OrderedDict()
for key, value in state_dict.items():
if not (key.startswith('''lm_head''' ) or key.startswith('''entity_predictions''' )):
_a : Optional[int] = state_dict[key]
else:
_a : Tuple = state_dict[key]
_a , _a : int = model.load_state_dict(__a ,strict=__a )
if set(__a ) != {"luke.embeddings.position_ids"}:
raise ValueError(F"""Unexpected unexpected_keys: {unexpected_keys}""" )
if set(__a ) != {
"lm_head.decoder.weight",
"lm_head.decoder.bias",
"entity_predictions.decoder.weight",
}:
raise ValueError(F"""Unexpected missing_keys: {missing_keys}""" )
model.tie_weights()
assert (model.luke.embeddings.word_embeddings.weight == model.lm_head.decoder.weight).all()
assert (model.luke.entity_embeddings.entity_embeddings.weight == model.entity_predictions.decoder.weight).all()
# Check outputs
_a : Optional[int] = MLukeTokenizer.from_pretrained(__a ,task='''entity_classification''' )
_a : int = '''ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan).'''
_a : List[Any] = (0, 9)
_a : Tuple = tokenizer(__a ,entity_spans=[span] ,return_tensors='''pt''' )
_a : int = model(**__a )
# Verify word hidden states
if model_size == "large":
raise NotImplementedError
else: # base
_a : List[str] = torch.Size((1, 33, 768) )
_a : Union[str, Any] = torch.tensor([[0.08_92, 0.05_96, -0.28_19], [0.01_34, 0.11_99, 0.05_73], [-0.01_69, 0.09_27, 0.06_44]] )
if not (outputs.last_hidden_state.shape == expected_shape):
raise ValueError(
F"""Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}""" )
if not torch.allclose(outputs.last_hidden_state[0, :3, :3] ,__a ,atol=1E-4 ):
raise ValueError
# Verify entity hidden states
if model_size == "large":
raise NotImplementedError
else: # base
_a : str = torch.Size((1, 1, 768) )
_a : List[Any] = torch.tensor([[-0.14_82, 0.06_09, 0.03_22]] )
if not (outputs.entity_last_hidden_state.shape == expected_shape):
raise ValueError(
F"""Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is"""
F""" {expected_shape}""" )
if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3] ,__a ,atol=1E-4 ):
raise ValueError
# Verify masked word/entity prediction
_a : Optional[int] = MLukeTokenizer.from_pretrained(__a )
_a : Dict = '''Tokyo is the capital of <mask>.'''
_a : List[str] = (24, 30)
_a : Optional[int] = tokenizer(__a ,entity_spans=[span] ,return_tensors='''pt''' )
_a : Optional[Any] = model(**__a )
_a : Any = encoding['''input_ids'''][0].tolist()
_a : Optional[Any] = input_ids.index(tokenizer.convert_tokens_to_ids('''<mask>''' ) )
_a : Any = outputs.logits[0][mask_position_id].argmax(dim=-1 )
assert "Japan" == tokenizer.decode(__a )
_a : Any = outputs.entity_logits[0][0].argmax().item()
_a : Optional[Any] = [
entity for entity, entity_id in tokenizer.entity_vocab.items() if entity_id == predicted_entity_id
]
assert [e for e in multilingual_predicted_entities if e.startswith('''en:''' )][0] == "en:Japan"
# Finally, save our PyTorch model and tokenizer
print('''Saving PyTorch model to {}'''.format(__a ) )
model.save_pretrained(__a )
def __UpperCAmelCase ( __a : List[Any] ) -> int:
"""simple docstring"""
_a : Union[str, Any] = ['''[MASK]''', '''[PAD]''', '''[UNK]''']
_a : int = [json.loads(__a ) for line in open(__a )]
_a : List[Any] = {}
for entry in data:
_a : int = entry['''id''']
for entity_name, language in entry["entities"]:
if entity_name in SPECIAL_TOKENS:
_a : List[Any] = entity_id
break
_a : Dict = F"""{language}:{entity_name}"""
_a : int = entity_id
return new_mapping
if __name__ == "__main__":
a__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument('''--checkpoint_path''', type=str, help='''Path to a pytorch_model.bin file.''')
parser.add_argument(
'''--metadata_path''', default=None, type=str, help='''Path to a metadata.json file, defining the configuration.'''
)
parser.add_argument(
'''--entity_vocab_path''',
default=None,
type=str,
help='''Path to an entity_vocab.tsv file, containing the entity vocabulary.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to where to dump the output PyTorch model.'''
)
parser.add_argument(
'''--model_size''', default='''base''', type=str, choices=['''base''', '''large'''], help='''Size of the model to be converted.'''
)
a__ = parser.parse_args()
convert_luke_checkpoint(
args.checkpoint_path,
args.metadata_path,
args.entity_vocab_path,
args.pytorch_dump_folder_path,
args.model_size,
)
| 14 | 0 |
'''simple docstring'''
import darl # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline
UpperCAmelCase__ = {
'''n_samples''': 6_4,
'''horizon''': 3_2,
'''num_inference_steps''': 2_0,
'''n_guide_steps''': 2, # can set to 0 for faster sampling, does not use value network
'''scale_grad_by_std''': True,
'''scale''': 0.1,
'''eta''': 0.0,
'''t_grad_cutoff''': 2,
'''device''': '''cpu''',
}
if __name__ == "__main__":
UpperCAmelCase__ = '''hopper-medium-v2'''
UpperCAmelCase__ = gym.make(env_name)
UpperCAmelCase__ = ValueGuidedRLPipeline.from_pretrained(
'''bglick13/hopper-medium-v2-value-function-hor32''',
env=env,
)
env.seed(0)
UpperCAmelCase__ = env.reset()
UpperCAmelCase__ = 0
UpperCAmelCase__ = 0
UpperCAmelCase__ = 1_0_0_0
UpperCAmelCase__ = [obs.copy()]
try:
for t in tqdm.tqdm(range(T)):
# call the policy
UpperCAmelCase__ = pipeline(obs, planning_horizon=3_2)
# execute action in environment
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ = env.step(denorm_actions)
UpperCAmelCase__ = env.get_normalized_score(total_reward)
# update return
total_reward += reward
total_score += score
print(
F"""Step: {t}, Reward: {reward}, Total Reward: {total_reward}, Score: {score}, Total Score:"""
F""" {total_score}"""
)
# save observations for rendering
rollout.append(next_observation.copy())
UpperCAmelCase__ = next_observation
except KeyboardInterrupt:
pass
print(F"""Total reward: {total_reward}""")
| 186 |
from scipy.stats import spearmanr
import datasets
a__ = '''
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlations imply that as data in dataset x increases, so
does data in dataset y. Negative correlations imply that as x increases,
y decreases. Correlations of -1 or +1 imply an exact monotonic relationship.
Unlike the Pearson correlation, the Spearman correlation does not
assume that both datasets are normally distributed.
The p-value roughly indicates the probability of an uncorrelated system
producing datasets that have a Spearman correlation at least as extreme
as the one computed from these datasets. The p-values are not entirely
reliable but are probably reasonable for datasets larger than 500 or so.
'''
a__ = '''
Args:
predictions (`List[float]`): Predicted labels, as returned by a model.
references (`List[float]`): Ground truth labels.
return_pvalue (`bool`): If `True`, returns the p-value. If `False`, returns
only the spearmanr score. Defaults to `False`.
Returns:
spearmanr (`float`): Spearman correlation coefficient.
p-value (`float`): p-value. **Note**: is only returned if `return_pvalue=True` is input.
Examples:
Example 1:
>>> spearmanr_metric = datasets.load_metric("spearmanr")
>>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], predictions=[10, 9, 2.5, 6, 4])
>>> print(results)
{\'spearmanr\': -0.7}
Example 2:
>>> spearmanr_metric = datasets.load_metric("spearmanr")
>>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5],
... predictions=[10, 9, 2.5, 6, 4],
... return_pvalue=True)
>>> print(results[\'spearmanr\'])
-0.7
>>> print(round(results[\'spearmanr_pvalue\'], 2))
0.19
'''
a__ = R'''\
@book{kokoska2000crc,
title={CRC standard probability and statistics tables and formulae},
author={Kokoska, Stephen and Zwillinger, Daniel},
year={2000},
publisher={Crc Press}
}
@article{2020SciPy-NMeth,
author = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
Haberland, Matt and Reddy, Tyler and Cournapeau, David and
Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
Kern, Robert and Larson, Eric and Carey, C J and
Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and
{VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
Harris, Charles R. and Archibald, Anne M. and
Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and
{van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
title = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
Computing in Python}},
journal = {Nature Methods},
year = {2020},
volume = {17},
pages = {261--272},
adsurl = {https://rdcu.be/b08Wh},
doi = {10.1038/s41592-019-0686-2},
}
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class UpperCAmelCase_ ( datasets.Metric ):
"""simple docstring"""
def __lowercase ( self ) -> int:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Value('''float''' ),
'''references''': datasets.Value('''float''' ),
} ) , reference_urls=['''https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.spearmanr.html'''] , )
def __lowercase ( self , _a , _a , _a=False ) -> str:
_a : int = spearmanr(_a , _a )
if return_pvalue:
return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
else:
return {"spearmanr": results[0]}
| 14 | 0 |
def UpperCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase ) -> list:
__lowercase : Tuple = len(__a )
__lowercase : str = []
for i in range(len(__a ) - pat_len + 1 ):
__lowercase : Any = True
for j in range(__a ):
if s[i + j] != pattern[j]:
__lowercase : Optional[int] = False
break
if match_found:
position.append(__a )
return position
if __name__ == "__main__":
assert naive_pattern_search("ABCDEFG", "DE") == [3]
print(naive_pattern_search("ABAAABCDBBABCDDEBCABC", "ABC"))
| 509 |
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def __UpperCAmelCase ( __a : bytes ,__a : int ) -> np.array:
"""simple docstring"""
_a : int = F"""{sampling_rate}"""
_a : str = '''1'''
_a : Optional[int] = '''f32le'''
_a : Optional[Any] = [
'''ffmpeg''',
'''-i''',
'''pipe:0''',
'''-ac''',
ac,
'''-ar''',
ar,
'''-f''',
format_for_conversion,
'''-hide_banner''',
'''-loglevel''',
'''quiet''',
'''pipe:1''',
]
try:
with subprocess.Popen(__a ,stdin=subprocess.PIPE ,stdout=subprocess.PIPE ) as ffmpeg_process:
_a : Any = ffmpeg_process.communicate(__a )
except FileNotFoundError as error:
raise ValueError('''ffmpeg was not found but is required to load audio files from filename''' ) from error
_a : Optional[Any] = output_stream[0]
_a : Optional[int] = np.frombuffer(__a ,np.floataa )
if audio.shape[0] == 0:
raise ValueError('''Malformed soundfile''' )
return audio
def __UpperCAmelCase ( __a : int ,__a : float ,__a : str = "f32le" ,) -> str:
"""simple docstring"""
_a : Dict = F"""{sampling_rate}"""
_a : Optional[Any] = '''1'''
if format_for_conversion == "s16le":
_a : Dict = 2
elif format_for_conversion == "f32le":
_a : Optional[Any] = 4
else:
raise ValueError(F"""Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`""" )
_a : Dict = platform.system()
if system == "Linux":
_a : Dict = '''alsa'''
_a : Union[str, Any] = '''default'''
elif system == "Darwin":
_a : Union[str, Any] = '''avfoundation'''
_a : List[str] = ''':0'''
elif system == "Windows":
_a : Optional[int] = '''dshow'''
_a : str = '''default'''
_a : Tuple = [
'''ffmpeg''',
'''-f''',
format_,
'''-i''',
input_,
'''-ac''',
ac,
'''-ar''',
ar,
'''-f''',
format_for_conversion,
'''-fflags''',
'''nobuffer''',
'''-hide_banner''',
'''-loglevel''',
'''quiet''',
'''pipe:1''',
]
_a : Any = int(round(sampling_rate * chunk_length_s ) ) * size_of_sample
_a : str = _ffmpeg_stream(__a ,__a )
for item in iterator:
yield item
def __UpperCAmelCase ( __a : int ,__a : float ,__a : Optional[int] = None ,__a : Optional[Union[Tuple[float, float], float]] = None ,__a : str = "f32le" ,) -> Optional[int]:
"""simple docstring"""
if stream_chunk_s is not None:
_a : Tuple = stream_chunk_s
else:
_a : Tuple = chunk_length_s
_a : Tuple = ffmpeg_microphone(__a ,__a ,format_for_conversion=__a )
if format_for_conversion == "s16le":
_a : Any = np.intaa
_a : Optional[int] = 2
elif format_for_conversion == "f32le":
_a : Dict = np.floataa
_a : List[Any] = 4
else:
raise ValueError(F"""Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`""" )
if stride_length_s is None:
_a : List[Any] = chunk_length_s / 6
_a : Optional[int] = int(round(sampling_rate * chunk_length_s ) ) * size_of_sample
if isinstance(__a ,(int, float) ):
_a : Optional[Any] = [stride_length_s, stride_length_s]
_a : Optional[Any] = int(round(sampling_rate * stride_length_s[0] ) ) * size_of_sample
_a : str = int(round(sampling_rate * stride_length_s[1] ) ) * size_of_sample
_a : Optional[Any] = datetime.datetime.now()
_a : Tuple = datetime.timedelta(seconds=__a )
for item in chunk_bytes_iter(__a ,__a ,stride=(stride_left, stride_right) ,stream=__a ):
# Put everything back in numpy scale
_a : Dict = np.frombuffer(item['''raw'''] ,dtype=__a )
_a : Dict = (
item['''stride'''][0] // size_of_sample,
item['''stride'''][1] // size_of_sample,
)
_a : str = sampling_rate
audio_time += delta
if datetime.datetime.now() > audio_time + 10 * delta:
# We're late !! SKIP
continue
yield item
def __UpperCAmelCase ( __a : Optional[int] ,__a : int ,__a : Tuple[int, int] ,__a : bool = False ) -> Optional[int]:
"""simple docstring"""
_a : Any = b''''''
_a , _a : List[str] = stride
if stride_left + stride_right >= chunk_len:
raise ValueError(
F"""Stride needs to be strictly smaller than chunk_len: ({stride_left}, {stride_right}) vs {chunk_len}""" )
_a : List[str] = 0
for raw in iterator:
acc += raw
if stream and len(__a ) < chunk_len:
_a : Dict = (_stride_left, 0)
yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
else:
while len(__a ) >= chunk_len:
# We are flushing the accumulator
_a : List[str] = (_stride_left, stride_right)
_a : List[Any] = {'''raw''': acc[:chunk_len], '''stride''': stride}
if stream:
_a : List[Any] = False
yield item
_a : Optional[Any] = stride_left
_a : Optional[Any] = acc[chunk_len - stride_left - stride_right :]
# Last chunk
if len(__a ) > stride_left:
_a : Optional[Any] = {'''raw''': acc, '''stride''': (_stride_left, 0)}
if stream:
_a : Dict = False
yield item
def __UpperCAmelCase ( __a : int ,__a : int ) -> Tuple:
"""simple docstring"""
_a : Dict = 2**24 # 16Mo
try:
with subprocess.Popen(__a ,stdout=subprocess.PIPE ,bufsize=__a ) as ffmpeg_process:
while True:
_a : int = ffmpeg_process.stdout.read(__a )
if raw == b"":
break
yield raw
except FileNotFoundError as error:
raise ValueError('''ffmpeg was not found but is required to stream audio files from filename''' ) from error
| 14 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE :Union[str, Any] = {
'configuration_mobilebert': [
'MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'MobileBertConfig',
'MobileBertOnnxConfig',
],
'tokenization_mobilebert': ['MobileBertTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE :Optional[Any] = ['MobileBertTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE :List[Any] = [
'MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'MobileBertForMaskedLM',
'MobileBertForMultipleChoice',
'MobileBertForNextSentencePrediction',
'MobileBertForPreTraining',
'MobileBertForQuestionAnswering',
'MobileBertForSequenceClassification',
'MobileBertForTokenClassification',
'MobileBertLayer',
'MobileBertModel',
'MobileBertPreTrainedModel',
'load_tf_weights_in_mobilebert',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE :Tuple = [
'TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFMobileBertForMaskedLM',
'TFMobileBertForMultipleChoice',
'TFMobileBertForNextSentencePrediction',
'TFMobileBertForPreTraining',
'TFMobileBertForQuestionAnswering',
'TFMobileBertForSequenceClassification',
'TFMobileBertForTokenClassification',
'TFMobileBertMainLayer',
'TFMobileBertModel',
'TFMobileBertPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_mobilebert import (
MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
MobileBertConfig,
MobileBertOnnxConfig,
)
from .tokenization_mobilebert import MobileBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mobilebert_fast import MobileBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mobilebert import (
MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
MobileBertForMaskedLM,
MobileBertForMultipleChoice,
MobileBertForNextSentencePrediction,
MobileBertForPreTraining,
MobileBertForQuestionAnswering,
MobileBertForSequenceClassification,
MobileBertForTokenClassification,
MobileBertLayer,
MobileBertModel,
MobileBertPreTrainedModel,
load_tf_weights_in_mobilebert,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_mobilebert import (
TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFMobileBertForMaskedLM,
TFMobileBertForMultipleChoice,
TFMobileBertForNextSentencePrediction,
TFMobileBertForPreTraining,
TFMobileBertForQuestionAnswering,
TFMobileBertForSequenceClassification,
TFMobileBertForTokenClassification,
TFMobileBertMainLayer,
TFMobileBertModel,
TFMobileBertPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE :Optional[int] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 55 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, 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 UpperCAmelCase_ ( __lowercase , unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase__ : Union[str, Any] = KandinskyInpaintPipeline
UpperCAmelCase__ : Optional[int] = ["prompt", "image_embeds", "negative_image_embeds", "image", "mask_image"]
UpperCAmelCase__ : Optional[Any] = [
"prompt",
"negative_prompt",
"image_embeds",
"negative_image_embeds",
"image",
"mask_image",
]
UpperCAmelCase__ : Optional[int] = [
"generator",
"height",
"width",
"latents",
"guidance_scale",
"negative_prompt",
"num_inference_steps",
"return_dict",
"guidance_scale",
"num_images_per_prompt",
"output_type",
"return_dict",
]
UpperCAmelCase__ : Any = False
@property
def __lowercase ( self ) -> Optional[int]:
return 3_2
@property
def __lowercase ( self ) -> int:
return 3_2
@property
def __lowercase ( self ) -> List[str]:
return self.time_input_dim
@property
def __lowercase ( self ) -> List[str]:
return self.time_input_dim * 4
@property
def __lowercase ( self ) -> Optional[Any]:
return 1_0_0
@property
def __lowercase ( self ) -> Optional[Any]:
_a : Any = XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base''' )
return tokenizer
@property
def __lowercase ( self ) -> str:
torch.manual_seed(0 )
_a : List[Any] = MCLIPConfig(
numDims=self.cross_attention_dim , transformerDimensions=self.text_embedder_hidden_size , hidden_size=self.text_embedder_hidden_size , intermediate_size=3_7 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=1_0_0_5 , )
_a : Optional[int] = MultilingualCLIP(_a )
_a : Tuple = text_encoder.eval()
return text_encoder
@property
def __lowercase ( self ) -> str:
torch.manual_seed(0 )
_a : List[str] = {
'''in_channels''': 9,
# 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 : Dict = UNetaDConditionModel(**_a )
return model
@property
def __lowercase ( self ) -> Optional[int]:
return {
"block_out_channels": [3_2, 6_4],
"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": 1_2,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def __lowercase ( self ) -> Tuple:
torch.manual_seed(0 )
_a : List[Any] = VQModel(**self.dummy_movq_kwargs )
return model
def __lowercase ( self ) -> Any:
_a : List[Any] = self.dummy_text_encoder
_a : Optional[Any] = self.dummy_tokenizer
_a : Optional[Any] = self.dummy_unet
_a : Union[str, Any] = self.dummy_movq
_a : Tuple = DDIMScheduler(
num_train_timesteps=1_0_0_0 , beta_schedule='''linear''' , beta_start=0.0_0085 , beta_end=0.012 , clip_sample=_a , set_alpha_to_one=_a , steps_offset=1 , prediction_type='''epsilon''' , thresholding=_a , )
_a : str = {
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''unet''': unet,
'''scheduler''': scheduler,
'''movq''': movq,
}
return components
def __lowercase ( self , _a , _a=0 ) -> int:
_a : Union[str, Any] = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(_a ) ).to(_a )
_a : List[str] = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(seed + 1 ) ).to(_a )
# create init_image
_a : Tuple = floats_tensor((1, 3, 6_4, 6_4) , rng=random.Random(_a ) ).to(_a )
_a : Dict = image.cpu().permute(0 , 2 , 3 , 1 )[0]
_a : Optional[int] = Image.fromarray(np.uinta(_a ) ).convert('''RGB''' ).resize((2_5_6, 2_5_6) )
# create mask
_a : Union[str, Any] = np.ones((6_4, 6_4) , dtype=np.floataa )
_a : List[str] = 0
if str(_a ).startswith('''mps''' ):
_a : Tuple = torch.manual_seed(_a )
else:
_a : Any = torch.Generator(device=_a ).manual_seed(_a )
_a : Any = {
'''prompt''': '''horse''',
'''image''': init_image,
'''mask_image''': mask,
'''image_embeds''': image_embeds,
'''negative_image_embeds''': negative_image_embeds,
'''generator''': generator,
'''height''': 6_4,
'''width''': 6_4,
'''num_inference_steps''': 2,
'''guidance_scale''': 4.0,
'''output_type''': '''np''',
}
return inputs
def __lowercase ( self ) -> Optional[Any]:
_a : Optional[Any] = '''cpu'''
_a : List[Any] = self.get_dummy_components()
_a : Tuple = self.pipeline_class(**_a )
_a : int = pipe.to(_a )
pipe.set_progress_bar_config(disable=_a )
_a : Any = pipe(**self.get_dummy_inputs(_a ) )
_a : str = output.images
_a : Tuple = pipe(
**self.get_dummy_inputs(_a ) , return_dict=_a , )[0]
_a : Union[str, Any] = image[0, -3:, -3:, -1]
_a : Tuple = image_from_tuple[0, -3:, -3:, -1]
print(F"""image.shape {image.shape}""" )
assert image.shape == (1, 6_4, 6_4, 3)
_a : str = np.array(
[0.832_6919, 0.7379_0467, 0.2091_8581, 0.930_9612, 0.551_1791, 0.4371_3328, 0.551_3321, 0.4992_2934, 0.5949_7786] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
), F""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
), F""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""
def __lowercase ( self ) -> Dict:
super().test_inference_batch_single_identical(expected_max_diff=3e-3 )
@slow
@require_torch_gpu
class UpperCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
def __lowercase ( self ) -> str:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __lowercase ( self ) -> Union[str, Any]:
_a : Tuple = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy''' )
_a : str = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png''' )
_a : Tuple = np.ones((7_6_8, 7_6_8) , dtype=np.floataa )
_a : Any = 0
_a : Optional[Any] = '''a hat'''
_a : Optional[Any] = KandinskyPriorPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-1-prior''' , torch_dtype=torch.floataa )
pipe_prior.to(_a )
_a : Tuple = KandinskyInpaintPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-1-inpaint''' , torch_dtype=torch.floataa )
_a : Union[str, Any] = pipeline.to(_a )
pipeline.set_progress_bar_config(disable=_a )
_a : Union[str, Any] = torch.Generator(device='''cpu''' ).manual_seed(0 )
_a , _a : Dict = pipe_prior(
_a , generator=_a , num_inference_steps=5 , negative_prompt='''''' , ).to_tuple()
_a : Optional[int] = pipeline(
_a , image=_a , mask_image=_a , image_embeds=_a , negative_image_embeds=_a , generator=_a , num_inference_steps=1_0_0 , height=7_6_8 , width=7_6_8 , output_type='''np''' , )
_a : Optional[int] = output.images[0]
assert image.shape == (7_6_8, 7_6_8, 3)
assert_mean_pixel_difference(_a , _a )
| 14 | 0 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
__A : Optional[Any] = logging.get_logger(__name__)
class lowerCamelCase( __lowercase ):
'''simple docstring'''
__magic_name__ = ["pixel_values"]
def __init__( self , snake_case_ = True , snake_case_ = None , snake_case_ = None , snake_case_ = PILImageResampling.BILINEAR , snake_case_ = True , snake_case_ = 1 / 255 , snake_case_ = True , snake_case_ = None , snake_case_ = None , **snake_case_ , ):
super().__init__(**_a )
_A = size if size is not None else {'''shortest_edge''': 384}
_A = get_size_dict(_a , default_to_square=_a )
_A = do_resize
_A = size
# Default value set here for backwards compatibility where the value in config is None
_A = crop_pct if crop_pct is not None else 224 / 256
_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 lowerCAmelCase__ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ = PILImageResampling.BICUBIC , snake_case_ = None , **snake_case_ , ):
_A = get_size_dict(_a , default_to_square=_a )
if "shortest_edge" not in size:
raise ValueError(F"Size dictionary must contain 'shortest_edge' key. Got {size.keys()}" )
_A = size['''shortest_edge''']
if shortest_edge < 384:
# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
_A = int(shortest_edge / crop_pct )
_A = get_resize_output_image_size(_a , size=_a , default_to_square=_a )
_A = resize(image=_a , size=_a , resample=_a , data_format=_a , **_a )
# then crop to (shortest_edge, shortest_edge)
return center_crop(image=_a , size=(shortest_edge, shortest_edge) , data_format=_a , **_a )
else:
# warping (no cropping) when evaluated at 384 or larger
return resize(
_a , size=(shortest_edge, shortest_edge) , resample=_a , data_format=_a , **_a )
def lowerCAmelCase__ ( self , snake_case_ , snake_case_ , snake_case_ = None , **snake_case_ , ):
return rescale(_a , scale=_a , data_format=_a , **_a )
def lowerCAmelCase__ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ = None , **snake_case_ , ):
return normalize(_a , mean=_a , std=_a , data_format=_a , **_a )
def lowerCAmelCase__ ( self , snake_case_ , snake_case_ = None , snake_case_ = None , snake_case_ = None , snake_case_ = None , snake_case_ = None , snake_case_ = None , snake_case_ = None , snake_case_ = None , snake_case_ = None , snake_case_ = None , snake_case_ = ChannelDimension.FIRST , **snake_case_ , ):
_A = do_resize if do_resize is not None else self.do_resize
_A = crop_pct if crop_pct is not None else self.crop_pct
_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 = size if size is not None else self.size
_A = get_size_dict(_a , default_to_square=_a )
_A = make_list_of_images(_a )
if not valid_images(_a ):
raise ValueError(
'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
'torch.Tensor, tf.Tensor or jax.ndarray.' )
if do_resize and size is None or resample is None:
raise ValueError('Size and resample must be specified if do_resize is True.' )
if do_resize and size["shortest_edge"] < 384 and crop_pct is None:
raise ValueError('crop_pct must be specified if size < 384.' )
if do_rescale and rescale_factor is None:
raise ValueError('Rescale factor must be specified if do_rescale is True.' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('Image mean and std must be specified if do_normalize is True.' )
# All transformations expect numpy arrays.
_A = [to_numpy_array(_a ) for image in images]
if do_resize:
_A = [self.resize(image=_a , size=_a , crop_pct=_a , resample=_a ) for image in images]
if do_rescale:
_A = [self.rescale(image=_a , scale=_a ) for image in images]
if do_normalize:
_A = [self.normalize(image=_a , mean=_a , std=_a ) for image in images]
_A = [to_channel_dimension_format(_a , _a ) for image in images]
_A = {'''pixel_values''': images}
return BatchFeature(data=_a , tensor_type=_a )
| 27 |
import argparse
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt
if __name__ == "__main__":
a__ = argparse.ArgumentParser()
parser.add_argument(
'''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
)
parser.add_argument(
'''--original_config_file''',
type=str,
required=True,
help='''The YAML config file corresponding to the original architecture.''',
)
parser.add_argument(
'''--num_in_channels''',
default=None,
type=int,
help='''The number of input channels. If `None` number of input channels will be automatically inferred.''',
)
parser.add_argument(
'''--image_size''',
default=512,
type=int,
help=(
'''The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2'''
''' Base. Use 768 for Stable Diffusion v2.'''
),
)
parser.add_argument(
'''--extract_ema''',
action='''store_true''',
help=(
'''Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights'''
''' or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield'''
''' higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.'''
),
)
parser.add_argument(
'''--upcast_attention''',
action='''store_true''',
help=(
'''Whether the attention computation should always be upcasted. This is necessary when running stable'''
''' diffusion 2.1.'''
),
)
parser.add_argument(
'''--from_safetensors''',
action='''store_true''',
help='''If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.''',
)
parser.add_argument(
'''--to_safetensors''',
action='''store_true''',
help='''Whether to store pipeline in safetensors format or not.''',
)
parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')
parser.add_argument('''--device''', type=str, help='''Device to use (e.g. cpu, cuda:0, cuda:1, etc.)''')
def __UpperCAmelCase ( __a : Any ) -> List[Any]:
"""simple docstring"""
if string == "True":
return True
elif string == "False":
return False
else:
raise ValueError(F"""could not parse string as bool {string}""" )
parser.add_argument(
'''--use_linear_projection''', help='''Override for use linear projection''', required=False, type=parse_bool
)
parser.add_argument('''--cross_attention_dim''', help='''Override for cross attention_dim''', required=False, type=int)
a__ = parser.parse_args()
a__ = download_controlnet_from_original_ckpt(
checkpoint_path=args.checkpoint_path,
original_config_file=args.original_config_file,
image_size=args.image_size,
extract_ema=args.extract_ema,
num_in_channels=args.num_in_channels,
upcast_attention=args.upcast_attention,
from_safetensors=args.from_safetensors,
device=args.device,
use_linear_projection=args.use_linear_projection,
cross_attention_dim=args.cross_attention_dim,
)
controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
| 14 | 0 |
'''simple docstring'''
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from tqdm import tqdm
import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, StoppingCriteria, StoppingCriteriaList
UpperCAmelCase_ : Any = ['''\nclass''', '''\ndef''', '''\n#''', '''\n@''', '''\nprint''', '''\nif''']
class lowerCAmelCase ( __lowercase):
def __init__( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE=None , __SCREAMING_SNAKE_CASE=1 ) -> Optional[int]:
'''simple docstring'''
__snake_case = tokenizer
__snake_case = dataset
__snake_case = len(_a ) if n_tasks is None else n_tasks
__snake_case = n_copies
def __iter__( self ) -> Optional[int]:
'''simple docstring'''
__snake_case = []
for task in range(self.n_tasks ):
# without strip, the model generate commented codes ...
prompts.append(self.tokenizer.eos_token + self.dataset[task]['''prompt'''].strip() )
__snake_case = self.tokenizer(_a , padding=_a , return_tensors='''pt''' )
for task in range(self.n_tasks ):
for _ in range(self.n_copies ):
yield {
"ids": outputs.input_ids[task],
"task_id": task,
"input_len": outputs.attention_mask[task].sum(),
}
class lowerCAmelCase ( __lowercase):
def __init__( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) -> List[Any]:
'''simple docstring'''
__snake_case = start_length
__snake_case = eof_strings
__snake_case = tokenizer
def __call__( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE ) -> Union[str, Any]:
'''simple docstring'''
__snake_case = self.tokenizer.batch_decode(input_ids[:, self.start_length :] )
__snake_case = []
for decoded_generation in decoded_generations:
done.append(any(stop_string in decoded_generation for stop_string in self.eof_strings ) )
return all(_a )
def _UpperCamelCase (_lowerCamelCase : Tuple )-> Union[str, Any]:
'''simple docstring'''
__snake_case = re.split('''(%s)''' % '''|'''.join(__a ) , __a )
# last string should be ""
return "".join(string_list[:-2] )
def _UpperCamelCase (_lowerCamelCase : Dict , _lowerCamelCase : List[Any] , _lowerCamelCase : int , _lowerCamelCase : List[Any] , _lowerCamelCase : List[Any] , _lowerCamelCase : Union[str, Any]=20 , **_lowerCamelCase : Union[str, Any] )-> Union[str, Any]:
'''simple docstring'''
__snake_case = defaultdict(__a ) # dict of list of generated tokens
for step, batch in tqdm(enumerate(__a ) ):
with torch.no_grad():
__snake_case = batch['''ids'''].shape[-1]
__snake_case = accelerator.unwrap_model(__a ).generate(
input_ids=batch['''ids'''][:, : batch['''input_len''']] , num_return_sequences=__a , **__a )
# each task is generated batch_size times
__snake_case = batch['''task_id'''].repeat(__a )
__snake_case = accelerator.pad_across_processes(
__a , dim=1 , pad_index=tokenizer.pad_token_id )
__snake_case = accelerator.gather((generated_tokens, generated_tasks) )
__snake_case = generated_tokens.cpu().numpy()
__snake_case = generated_tasks.cpu().numpy()
for task, generated_tokens in zip(__a , __a ):
gen_token_dict[task].append(__a )
__snake_case = [[] for _ in range(__a )]
for task, generated_tokens in gen_token_dict.items():
for s in generated_tokens:
__snake_case = tokenizer.decode(__a , skip_special_tokens=__a , clean_up_tokenization_spaces=__a )
code_gens[task].append(remove_last_block(__a ) )
return code_gens
def _UpperCamelCase ()-> str:
'''simple docstring'''
__snake_case = HfArgumentParser(__a )
__snake_case = parser.parse_args()
transformers.logging.set_verbosity_error()
# enables code execution in code_eval metric
__snake_case = args.HF_ALLOW_CODE_EVAL
# make sure tokenizer plays nice with multiprocessing
__snake_case = '''false'''
if args.num_workers is None:
__snake_case = multiprocessing.cpu_count()
# Use dataset load to feed to accelerate
__snake_case = Accelerator()
set_seed(args.seed , device_specific=__a )
# Load model and tokenizer
__snake_case = AutoTokenizer.from_pretrained(args.model_ckpt )
__snake_case = tokenizer.eos_token
__snake_case = AutoModelForCausalLM.from_pretrained(args.model_ckpt )
# Generation settings
__snake_case = {
'''do_sample''': args.do_sample,
'''temperature''': args.temperature,
'''max_new_tokens''': args.max_new_tokens,
'''top_p''': args.top_p,
'''top_k''': args.top_k,
'''stopping_criteria''': StoppingCriteriaList([EndOfFunctionCriteria(0 , __a , __a )] ),
}
# Load evaluation dataset and metric
__snake_case = load_dataset('''openai_humaneval''' )
__snake_case = load_metric('''code_eval''' )
__snake_case = args.num_tasks if args.num_tasks is not None else len(human_eval['''test'''] )
__snake_case = args.n_samples // args.batch_size
__snake_case = TokenizedDataset(__a , human_eval['''test'''] , n_copies=__a , n_tasks=__a )
# do not confuse args.batch_size, which is actually the num_return_sequences
__snake_case = DataLoader(__a , batch_size=1 )
# Run a quick test to see if code evaluation is enabled
try:
__snake_case = code_eval_metric.compute(references=[''''''] , predictions=[['''''']] )
except ValueError as exception:
print(
'''Code evaluation not enabled. Read the warning below carefully and then use `--HF_ALLOW_CODE_EVAL="1"`'''
''' flag to enable code evaluation.''' )
raise exception
__snake_case = accelerator.prepare(__a , __a )
__snake_case = complete_code(
__a , __a , __a , __a , n_tasks=__a , batch_size=args.batch_size , **__a , )
if accelerator.is_main_process:
__snake_case = []
for task in tqdm(range(__a ) ):
__snake_case = human_eval['''test'''][task]['''test''']
__snake_case = f'''check({human_eval["test"][task]["entry_point"]})'''
references.append('''\n''' + test_func + '''\n''' + entry_point )
# Evaluate completions with "code_eval" metric
__snake_case = code_eval_metric.compute(
references=__a , predictions=__a , num_workers=args.num_workers )
print(f'''Results: {pass_at_k}''' )
# Save results to json file
with open(args.output_file , '''w''' ) as fp:
json.dump(__a , __a )
# For some reason the folliwng seems to be necessary sometimes for code_eval to work nice with multiprocessing
# https://stackoverflow.com/questions/60804599/python-multiprocessing-keeps-spawning-the-whole-script
if __name__ == "__main__":
main()
| 24 |
class UpperCAmelCase_ :
"""simple docstring"""
def __init__( self , _a , _a , _a ) -> List[str]:
_a : List[Any] = name
_a : List[str] = value
_a : List[str] = weight
def __repr__( self ) -> Optional[int]:
return F"""{self.__class__.__name__}({self.name}, {self.value}, {self.weight})"""
def __lowercase ( self ) -> List[Any]:
return self.value
def __lowercase ( self ) -> int:
return self.name
def __lowercase ( self ) -> Optional[int]:
return self.weight
def __lowercase ( self ) -> Optional[Any]:
return self.value / self.weight
def __UpperCAmelCase ( __a : Optional[int] ,__a : Tuple ,__a : List[str] ) -> List[str]:
"""simple docstring"""
_a : Optional[int] = []
for i in range(len(__a ) ):
menu.append(Things(name[i] ,value[i] ,weight[i] ) )
return menu
def __UpperCAmelCase ( __a : int ,__a : Union[str, Any] ,__a : int ) -> Union[str, Any]:
"""simple docstring"""
_a : Union[str, Any] = sorted(__a ,key=__a ,reverse=__a )
_a : Any = []
_a , _a : Optional[int] = 0.0, 0.0
for i in range(len(__a ) ):
if (total_cost + items_copy[i].get_weight()) <= max_cost:
result.append(items_copy[i] )
total_cost += items_copy[i].get_weight()
total_value += items_copy[i].get_value()
return (result, total_value)
def __UpperCAmelCase ( ) -> int:
"""simple docstring"""
if __name__ == "__main__":
import doctest
doctest.testmod()
| 14 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A = logging.get_logger(__name__)
_A = {
"uclanlp/visualbert-vqa": "https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json",
"uclanlp/visualbert-vqa-pre": "https://huggingface.co/uclanlp/visualbert-vqa-pre/resolve/main/config.json",
"uclanlp/visualbert-vqa-coco-pre": (
"https://huggingface.co/uclanlp/visualbert-vqa-coco-pre/resolve/main/config.json"
),
"uclanlp/visualbert-vcr": "https://huggingface.co/uclanlp/visualbert-vcr/resolve/main/config.json",
"uclanlp/visualbert-vcr-pre": "https://huggingface.co/uclanlp/visualbert-vcr-pre/resolve/main/config.json",
"uclanlp/visualbert-vcr-coco-pre": (
"https://huggingface.co/uclanlp/visualbert-vcr-coco-pre/resolve/main/config.json"
),
"uclanlp/visualbert-nlvr2": "https://huggingface.co/uclanlp/visualbert-nlvr2/resolve/main/config.json",
"uclanlp/visualbert-nlvr2-pre": "https://huggingface.co/uclanlp/visualbert-nlvr2-pre/resolve/main/config.json",
"uclanlp/visualbert-nlvr2-coco-pre": (
"https://huggingface.co/uclanlp/visualbert-nlvr2-coco-pre/resolve/main/config.json"
)
# See all VisualBERT models at https://huggingface.co/models?filter=visual_bert
}
class _lowerCAmelCase ( __lowercase ):
_lowercase ="visual_bert"
def __init__( self , _UpperCamelCase=30_522 , _UpperCamelCase=768 , _UpperCamelCase=512 , _UpperCamelCase=12 , _UpperCamelCase=12 , _UpperCamelCase=3_072 , _UpperCamelCase="gelu" , _UpperCamelCase=0.1 , _UpperCamelCase=0.1 , _UpperCamelCase=512 , _UpperCamelCase=2 , _UpperCamelCase=0.02 , _UpperCamelCase=1e-1_2 , _UpperCamelCase=False , _UpperCamelCase=True , _UpperCamelCase=1 , _UpperCamelCase=0 , _UpperCamelCase=2 , **_UpperCamelCase , ) -> str:
super().__init__(pad_token_id=_a , bos_token_id=_a , eos_token_id=_a , **_a )
lowerCAmelCase_ = vocab_size
lowerCAmelCase_ = max_position_embeddings
lowerCAmelCase_ = hidden_size
lowerCAmelCase_ = visual_embedding_dim
lowerCAmelCase_ = num_hidden_layers
lowerCAmelCase_ = num_attention_heads
lowerCAmelCase_ = intermediate_size
lowerCAmelCase_ = hidden_act
lowerCAmelCase_ = hidden_dropout_prob
lowerCAmelCase_ = attention_probs_dropout_prob
lowerCAmelCase_ = initializer_range
lowerCAmelCase_ = type_vocab_size
lowerCAmelCase_ = layer_norm_eps
lowerCAmelCase_ = bypass_transformer
lowerCAmelCase_ = special_visual_initialize
| 290 |
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import SwiftFormerForImageClassification, SwiftFormerModel
from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class UpperCAmelCase_ :
"""simple docstring"""
def __init__( self , _a , _a=1_3 , _a=3 , _a=True , _a=True , _a=0.1 , _a=0.1 , _a=2_2_4 , _a=1_0_0_0 , _a=[3, 3, 6, 4] , _a=[4_8, 5_6, 1_1_2, 2_2_0] , ) -> Tuple:
_a : Dict = parent
_a : Optional[int] = batch_size
_a : Optional[Any] = num_channels
_a : Union[str, Any] = is_training
_a : Tuple = use_labels
_a : Dict = hidden_dropout_prob
_a : List[Any] = attention_probs_dropout_prob
_a : Dict = num_labels
_a : List[str] = image_size
_a : Dict = layer_depths
_a : str = embed_dims
def __lowercase ( self ) -> Optional[Any]:
_a : Dict = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_a : int = None
if self.use_labels:
_a : Optional[Any] = ids_tensor([self.batch_size] , self.num_labels )
_a : Dict = self.get_config()
return config, pixel_values, labels
def __lowercase ( self ) -> int:
return SwiftFormerConfig(
depths=self.layer_depths , embed_dims=self.embed_dims , mlp_ratio=4 , downsamples=[True, True, True, True] , hidden_act='''gelu''' , num_labels=self.num_labels , down_patch_size=3 , down_stride=2 , down_pad=1 , drop_rate=0.0 , drop_path_rate=0.0 , use_layer_scale=_a , layer_scale_init_value=1e-5 , )
def __lowercase ( self , _a , _a , _a ) -> str:
_a : List[Any] = SwiftFormerModel(config=_a )
model.to(_a )
model.eval()
_a : Optional[int] = model(_a )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.embed_dims[-1], 7, 7) )
def __lowercase ( self , _a , _a , _a ) -> Optional[Any]:
_a : List[str] = self.num_labels
_a : Optional[int] = SwiftFormerForImageClassification(_a )
model.to(_a )
model.eval()
_a : List[str] = model(_a , labels=_a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
_a : Union[str, Any] = SwiftFormerForImageClassification(_a )
model.to(_a )
model.eval()
_a : Dict = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_a : Optional[Any] = model(_a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __lowercase ( self ) -> Tuple:
((_a) , (_a) , (_a)) : Optional[int] = self.prepare_config_and_inputs()
_a : List[Any] = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class UpperCAmelCase_ ( __lowercase , __lowercase , unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase__ : List[str] = (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
UpperCAmelCase__ : Optional[int] = (
{"feature-extraction": SwiftFormerModel, "image-classification": SwiftFormerForImageClassification}
if is_torch_available()
else {}
)
UpperCAmelCase__ : Optional[Any] = False
UpperCAmelCase__ : str = False
UpperCAmelCase__ : Tuple = False
UpperCAmelCase__ : Tuple = False
UpperCAmelCase__ : str = False
def __lowercase ( self ) -> Optional[int]:
_a : Union[str, Any] = SwiftFormerModelTester(self )
_a : int = ConfigTester(
self , config_class=_a , has_text_modality=_a , hidden_size=3_7 , num_attention_heads=1_2 , num_hidden_layers=1_2 , )
def __lowercase ( self ) -> int:
self.config_tester.run_common_tests()
@unittest.skip(reason='''SwiftFormer does not use inputs_embeds''' )
def __lowercase ( self ) -> Union[str, Any]:
pass
def __lowercase ( self ) -> Dict:
_a , _a : int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_a : Any = model_class(_a )
_a : int = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(_a , nn.Linear ) )
def __lowercase ( self ) -> str:
_a , _a : str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_a : Optional[int] = model_class(_a )
_a : Tuple = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_a : Tuple = [*signature.parameters.keys()]
_a : List[str] = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , _a )
def __lowercase ( self ) -> int:
_a : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_a )
def __lowercase ( self ) -> Optional[int]:
_a : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_a )
@slow
def __lowercase ( self ) -> Optional[Any]:
for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_a : Any = SwiftFormerModel.from_pretrained(_a )
self.assertIsNotNone(_a )
@unittest.skip(reason='''SwiftFormer does not output attentions''' )
def __lowercase ( self ) -> List[Any]:
pass
def __lowercase ( self ) -> int:
def check_hidden_states_output(_a , _a , _a ):
_a : Optional[int] = model_class(_a )
model.to(_a )
model.eval()
with torch.no_grad():
_a : Union[str, Any] = model(**self._prepare_for_class(_a , _a ) )
_a : Optional[Any] = outputs.hidden_states
_a : Union[str, Any] = 8
self.assertEqual(len(_a ) , _a ) # TODO
# SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
# with the width and height being successively divided by 2, after every 2 blocks
for i in range(len(_a ) ):
self.assertEqual(
hidden_states[i].shape , torch.Size(
[
self.model_tester.batch_size,
self.model_tester.embed_dims[i // 2],
(self.model_tester.image_size // 4) // 2 ** (i // 2),
(self.model_tester.image_size // 4) // 2 ** (i // 2),
] ) , )
_a , _a : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_a : str = True
check_hidden_states_output(_a , _a , _a )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_a : List[str] = True
check_hidden_states_output(_a , _a , _a )
def __lowercase ( self ) -> str:
def _config_zero_init(_a ):
_a : List[Any] = copy.deepcopy(_a )
for key in configs_no_init.__dict__.keys():
if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
setattr(_a , _a , 1e-1_0 )
if isinstance(getattr(_a , _a , _a ) , _a ):
_a : int = _config_zero_init(getattr(_a , _a ) )
setattr(_a , _a , _a )
return configs_no_init
_a , _a : Any = self.model_tester.prepare_config_and_inputs_for_common()
_a : Dict = _config_zero_init(_a )
for model_class in self.all_model_classes:
_a : Dict = model_class(config=_a )
for name, param in model.named_parameters():
if param.requires_grad:
self.assertIn(
((param.data.mean() * 1e9) / 1e9).round().item() , [0.0, 1.0] , msg=F"""Parameter {name} of model {model_class} seems not properly initialized""" , )
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def __lowercase ( self ) -> Optional[Any]:
pass
def __UpperCAmelCase ( ) -> Optional[Any]:
"""simple docstring"""
_a : Optional[int] = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class UpperCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def __lowercase ( self ) -> str:
return ViTImageProcessor.from_pretrained('''MBZUAI/swiftformer-xs''' ) if is_vision_available() else None
@slow
def __lowercase ( self ) -> Dict:
_a : Any = SwiftFormerForImageClassification.from_pretrained('''MBZUAI/swiftformer-xs''' ).to(_a )
_a : Any = self.default_image_processor
_a : Any = prepare_img()
_a : Any = image_processor(images=_a , return_tensors='''pt''' ).to(_a )
# forward pass
with torch.no_grad():
_a : Optional[Any] = model(**_a )
# verify the logits
_a : List[str] = torch.Size((1, 1_0_0_0) )
self.assertEqual(outputs.logits.shape , _a )
_a : int = torch.tensor([[-2.1_7_0_3e0_0, 2.1_1_0_7e0_0, -2.0_8_1_1e0_0]] ).to(_a )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , _a , atol=1e-4 ) )
| 14 | 0 |
'''simple docstring'''
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def a__ ( _SCREAMING_SNAKE_CASE : Tuple , _SCREAMING_SNAKE_CASE : Tuple=7 ) -> Any:
"""simple docstring"""
UpperCAmelCase_ : Union[str, Any] = None
if token is not None:
UpperCAmelCase_ : List[str] = {'''Accept''': '''application/vnd.github+json''', '''Authorization''': F'''Bearer {token}'''}
# The id of a workflow (not of a workflow run)
UpperCAmelCase_ : Any = '''636036'''
UpperCAmelCase_ : Any = F'''https://api.github.com/repos/huggingface/transformers/actions/workflows/{workflow_id}/runs'''
# On `main` branch + event being `schedule` + not returning PRs + only `num_runs` results
url += F'''?branch=main&event=schedule&exclude_pull_requests=true&per_page={num_runs}'''
UpperCAmelCase_ : List[Any] = requests.get(__a , headers=__a ).json()
return result["workflow_runs"]
def a__ ( _SCREAMING_SNAKE_CASE : Union[str, Any] ) -> Tuple:
"""simple docstring"""
UpperCAmelCase_ : str = get_daily_ci_runs(__a )
UpperCAmelCase_ : List[Any] = None
for workflow_run in workflow_runs:
if workflow_run["status"] == "completed":
UpperCAmelCase_ : int = workflow_run['''id''']
break
return workflow_run_id
def a__ ( _SCREAMING_SNAKE_CASE : Dict , _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
UpperCAmelCase_ : Dict = get_last_daily_ci_runs(__a )
if workflow_run_id is not None:
UpperCAmelCase_ : Optional[int] = get_artifacts_links(worflow_run_id=__a , token=__a )
for artifact_name in artifact_names:
if artifact_name in artifacts_links:
UpperCAmelCase_ : List[str] = artifacts_links[artifact_name]
download_artifact(
artifact_name=__a , artifact_url=__a , output_dir=__a , token=__a )
def a__ ( _SCREAMING_SNAKE_CASE : List[Any] , _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : Tuple ) -> Union[str, Any]:
"""simple docstring"""
get_last_daily_ci_artifacts(__a , __a , __a )
UpperCAmelCase_ : List[Any] = {}
for artifact_name in artifact_names:
UpperCAmelCase_ : List[str] = os.path.join(__a , F'''{artifact_name}.zip''' )
if os.path.isfile(__a ):
UpperCAmelCase_ : Any = {}
with zipfile.ZipFile(__a ) as z:
for filename in z.namelist():
if not os.path.isdir(__a ):
# read the file
with z.open(__a ) as f:
UpperCAmelCase_ : Optional[int] = f.read().decode("UTF-8" )
return results
| 71 |
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging
logging.set_verbosity_info()
a__ = logging.get_logger(__name__)
def __UpperCAmelCase ( __a : str ) -> List[Any]:
"""simple docstring"""
_a : Tuple = SwinConfig.from_pretrained(
'''microsoft/swin-tiny-patch4-window7-224''' ,out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4'''] )
_a : Dict = MaskFormerConfig(backbone_config=__a )
_a : Optional[Any] = '''huggingface/label-files'''
if "ade20k-full" in model_name:
# this should be ok
_a : Optional[Any] = 847
_a : List[Any] = '''maskformer-ade20k-full-id2label.json'''
elif "ade" in model_name:
# this should be ok
_a : Union[str, Any] = 150
_a : Any = '''ade20k-id2label.json'''
elif "coco-stuff" in model_name:
# this should be ok
_a : int = 171
_a : List[str] = '''maskformer-coco-stuff-id2label.json'''
elif "coco" in model_name:
# TODO
_a : Dict = 133
_a : Optional[Any] = '''coco-panoptic-id2label.json'''
elif "cityscapes" in model_name:
# this should be ok
_a : List[Any] = 19
_a : Optional[Any] = '''cityscapes-id2label.json'''
elif "vistas" in model_name:
# this should be ok
_a : List[Any] = 65
_a : Dict = '''mapillary-vistas-id2label.json'''
_a : Optional[int] = json.load(open(hf_hub_download(__a ,__a ,repo_type='''dataset''' ) ,'''r''' ) )
_a : Tuple = {int(__a ): v for k, v in idalabel.items()}
return config
def __UpperCAmelCase ( __a : Optional[Any] ) -> Tuple:
"""simple docstring"""
_a : Optional[Any] = []
# stem
# fmt: off
rename_keys.append(('''backbone.patch_embed.proj.weight''', '''model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight''') )
rename_keys.append(('''backbone.patch_embed.proj.bias''', '''model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias''') )
rename_keys.append(('''backbone.patch_embed.norm.weight''', '''model.pixel_level_module.encoder.model.embeddings.norm.weight''') )
rename_keys.append(('''backbone.patch_embed.norm.bias''', '''model.pixel_level_module.encoder.model.embeddings.norm.bias''') )
# stages
for i in range(len(config.backbone_config.depths ) ):
for j in range(config.backbone_config.depths[i] ):
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm1.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm1.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.relative_position_index""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.proj.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.proj.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm2.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm2.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc1.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc1.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc2.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc2.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias""") )
if i < 3:
rename_keys.append((F"""backbone.layers.{i}.downsample.reduction.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight""") )
rename_keys.append((F"""backbone.layers.{i}.downsample.norm.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight""") )
rename_keys.append((F"""backbone.layers.{i}.downsample.norm.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias""") )
rename_keys.append((F"""backbone.norm{i}.weight""", F"""model.pixel_level_module.encoder.hidden_states_norms.{i}.weight""") )
rename_keys.append((F"""backbone.norm{i}.bias""", F"""model.pixel_level_module.encoder.hidden_states_norms.{i}.bias""") )
# FPN
rename_keys.append(('''sem_seg_head.layer_4.weight''', '''model.pixel_level_module.decoder.fpn.stem.0.weight''') )
rename_keys.append(('''sem_seg_head.layer_4.norm.weight''', '''model.pixel_level_module.decoder.fpn.stem.1.weight''') )
rename_keys.append(('''sem_seg_head.layer_4.norm.bias''', '''model.pixel_level_module.decoder.fpn.stem.1.bias''') )
for source_index, target_index in zip(range(3 ,0 ,-1 ) ,range(0 ,3 ) ):
rename_keys.append((F"""sem_seg_head.adapter_{source_index}.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight""") )
rename_keys.append((F"""sem_seg_head.adapter_{source_index}.norm.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight""") )
rename_keys.append((F"""sem_seg_head.adapter_{source_index}.norm.bias""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias""") )
rename_keys.append((F"""sem_seg_head.layer_{source_index}.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight""") )
rename_keys.append((F"""sem_seg_head.layer_{source_index}.norm.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight""") )
rename_keys.append((F"""sem_seg_head.layer_{source_index}.norm.bias""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias""") )
rename_keys.append(('''sem_seg_head.mask_features.weight''', '''model.pixel_level_module.decoder.mask_projection.weight''') )
rename_keys.append(('''sem_seg_head.mask_features.bias''', '''model.pixel_level_module.decoder.mask_projection.bias''') )
# Transformer decoder
for idx in range(config.decoder_config.decoder_layers ):
# self-attention out projection
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight""", F"""model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias""", F"""model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias""") )
# cross-attention out projection
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias""") )
# MLP 1
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight""", F"""model.transformer_module.decoder.layers.{idx}.fc1.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias""", F"""model.transformer_module.decoder.layers.{idx}.fc1.bias""") )
# MLP 2
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight""", F"""model.transformer_module.decoder.layers.{idx}.fc2.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias""", F"""model.transformer_module.decoder.layers.{idx}.fc2.bias""") )
# layernorm 1 (self-attention layernorm)
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight""", F"""model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias""", F"""model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias""") )
# layernorm 2 (cross-attention layernorm)
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias""") )
# layernorm 3 (final layernorm)
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight""", F"""model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias""", F"""model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias""") )
rename_keys.append(('''sem_seg_head.predictor.transformer.decoder.norm.weight''', '''model.transformer_module.decoder.layernorm.weight''') )
rename_keys.append(('''sem_seg_head.predictor.transformer.decoder.norm.bias''', '''model.transformer_module.decoder.layernorm.bias''') )
# heads on top
rename_keys.append(('''sem_seg_head.predictor.query_embed.weight''', '''model.transformer_module.queries_embedder.weight''') )
rename_keys.append(('''sem_seg_head.predictor.input_proj.weight''', '''model.transformer_module.input_projection.weight''') )
rename_keys.append(('''sem_seg_head.predictor.input_proj.bias''', '''model.transformer_module.input_projection.bias''') )
rename_keys.append(('''sem_seg_head.predictor.class_embed.weight''', '''class_predictor.weight''') )
rename_keys.append(('''sem_seg_head.predictor.class_embed.bias''', '''class_predictor.bias''') )
for i in range(3 ):
rename_keys.append((F"""sem_seg_head.predictor.mask_embed.layers.{i}.weight""", F"""mask_embedder.{i}.0.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.mask_embed.layers.{i}.bias""", F"""mask_embedder.{i}.0.bias""") )
# fmt: on
return rename_keys
def __UpperCAmelCase ( __a : List[str] ,__a : List[Any] ,__a : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
_a : str = dct.pop(__a )
_a : str = val
def __UpperCAmelCase ( __a : List[Any] ,__a : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
_a : Union[str, Any] = [int(backbone_config.embed_dim * 2**i ) for i in range(len(backbone_config.depths ) )]
for i in range(len(backbone_config.depths ) ):
_a : Optional[Any] = num_features[i]
for j in range(backbone_config.depths[i] ):
# fmt: off
# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
_a : List[Any] = state_dict.pop(F"""backbone.layers.{i}.blocks.{j}.attn.qkv.weight""" )
_a : Optional[int] = state_dict.pop(F"""backbone.layers.{i}.blocks.{j}.attn.qkv.bias""" )
# next, add query, keys and values (in that order) to the state dict
_a : Optional[int] = in_proj_weight[:dim, :]
_a : List[Any] = in_proj_bias[: dim]
_a : Optional[int] = in_proj_weight[
dim : dim * 2, :
]
_a : Tuple = in_proj_bias[
dim : dim * 2
]
_a : int = in_proj_weight[
-dim :, :
]
_a : Optional[int] = in_proj_bias[-dim :]
# fmt: on
def __UpperCAmelCase ( __a : List[str] ,__a : List[Any] ) -> List[Any]:
"""simple docstring"""
_a : Optional[int] = config.decoder_config.hidden_size
for idx in range(config.decoder_config.decoder_layers ):
# read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
_a : Union[str, Any] = state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight""" )
_a : List[Any] = state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias""" )
# next, add query, keys and values (in that order) to the state dict
_a : Union[str, Any] = in_proj_weight[: hidden_size, :]
_a : List[Any] = in_proj_bias[:config.hidden_size]
_a : Dict = in_proj_weight[hidden_size : hidden_size * 2, :]
_a : Any = in_proj_bias[hidden_size : hidden_size * 2]
_a : Tuple = in_proj_weight[-hidden_size :, :]
_a : List[Any] = in_proj_bias[-hidden_size :]
# read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
_a : List[Any] = state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight""" )
_a : List[str] = state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias""" )
# next, add query, keys and values (in that order) to the state dict
_a : Optional[Any] = in_proj_weight[: hidden_size, :]
_a : Any = in_proj_bias[:config.hidden_size]
_a : List[str] = in_proj_weight[hidden_size : hidden_size * 2, :]
_a : Optional[Any] = in_proj_bias[hidden_size : hidden_size * 2]
_a : List[str] = in_proj_weight[-hidden_size :, :]
_a : int = in_proj_bias[-hidden_size :]
# fmt: on
def __UpperCAmelCase ( ) -> torch.Tensor:
"""simple docstring"""
_a : str = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
_a : Dict = Image.open(requests.get(__a ,stream=__a ).raw )
return im
@torch.no_grad()
def __UpperCAmelCase ( __a : str ,__a : str ,__a : str ,__a : bool = False ) -> Union[str, Any]:
"""simple docstring"""
_a : Optional[Any] = get_maskformer_config(__a )
# load original state_dict
with open(__a ,'''rb''' ) as f:
_a : str = pickle.load(__a )
_a : Union[str, Any] = data['''model''']
# for name, param in state_dict.items():
# print(name, param.shape)
# rename keys
_a : Any = create_rename_keys(__a )
for src, dest in rename_keys:
rename_key(__a ,__a ,__a )
read_in_swin_q_k_v(__a ,config.backbone_config )
read_in_decoder_q_k_v(__a ,__a )
# update to torch tensors
for key, value in state_dict.items():
_a : Optional[int] = torch.from_numpy(__a )
# load 🤗 model
_a : Dict = MaskFormerForInstanceSegmentation(__a )
model.eval()
for name, param in model.named_parameters():
print(__a ,param.shape )
_a , _a : Tuple = model.load_state_dict(__a ,strict=__a )
assert missing_keys == [
"model.pixel_level_module.encoder.model.layernorm.weight",
"model.pixel_level_module.encoder.model.layernorm.bias",
]
assert len(__a ) == 0, F"""Unexpected keys: {unexpected_keys}"""
# verify results
_a : Union[str, Any] = prepare_img()
if "vistas" in model_name:
_a : int = 65
elif "cityscapes" in model_name:
_a : Tuple = 65_535
else:
_a : str = 255
_a : Dict = True if '''ade''' in model_name else False
_a : Optional[Any] = MaskFormerImageProcessor(ignore_index=__a ,reduce_labels=__a )
_a : Optional[Any] = image_processor(__a ,return_tensors='''pt''' )
_a : int = model(**__a )
print('''Logits:''' ,outputs.class_queries_logits[0, :3, :3] )
if model_name == "maskformer-swin-tiny-ade":
_a : Union[str, Any] = torch.tensor(
[[3.63_53, -4.47_70, -2.60_65], [0.50_81, -4.23_94, -3.53_43], [2.19_09, -5.03_53, -1.93_23]] )
assert torch.allclose(outputs.class_queries_logits[0, :3, :3] ,__a ,atol=1E-4 )
print('''Looks ok!''' )
if pytorch_dump_folder_path is not None:
print(F"""Saving model and image processor to {pytorch_dump_folder_path}""" )
Path(__a ).mkdir(exist_ok=__a )
model.save_pretrained(__a )
image_processor.save_pretrained(__a )
if push_to_hub:
print('''Pushing model and image processor to the hub...''' )
model.push_to_hub(F"""nielsr/{model_name}""" )
image_processor.push_to_hub(F"""nielsr/{model_name}""" )
if __name__ == "__main__":
a__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--model_name''',
default='''maskformer-swin-tiny-ade''',
type=str,
help=('''Name of the MaskFormer model you\'d like to convert''',),
)
parser.add_argument(
'''--checkpoint_path''',
default='''/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl''',
type=str,
help='''Path to the original state dict (.pth file).''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
)
parser.add_argument(
'''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
)
a__ = parser.parse_args()
convert_maskformer_checkpoint(
args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
)
| 14 | 0 |
"""simple docstring"""
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = 42
UpperCamelCase__ = None
UpperCamelCase__ = None
def lowercase ( _SCREAMING_SNAKE_CASE : TreeNode | None ):
'''simple docstring'''
def is_valid_tree(_SCREAMING_SNAKE_CASE : TreeNode | None ) -> bool:
if node is None:
return True
if not isinstance(__a , __a ):
return False
try:
float(node.data )
except (TypeError, ValueError):
return False
return is_valid_tree(node.left ) and is_valid_tree(node.right )
if not is_valid_tree(__a ):
raise ValueError(
'''Each node should be type of TreeNode and data should be float.''' )
def is_binary_search_tree_recursive_check(
_SCREAMING_SNAKE_CASE : TreeNode | None , _SCREAMING_SNAKE_CASE : float , _SCREAMING_SNAKE_CASE : float ) -> bool:
if node is None:
return True
return (
left_bound < node.data < right_bound
and is_binary_search_tree_recursive_check(node.left , __a , node.data )
and is_binary_search_tree_recursive_check(
node.right , node.data , __a )
)
return is_binary_search_tree_recursive_check(__a , -float('''inf''' ) , float('''inf''' ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 602 |
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
a__ = get_tests_dir('''fixtures/test_sentencepiece.model''')
@require_sentencepiece
class UpperCAmelCase_ ( __lowercase , unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase__ : List[str] = XLMProphetNetTokenizer
UpperCAmelCase__ : Optional[int] = False
UpperCAmelCase__ : List[Any] = True
def __lowercase ( self ) -> int:
super().setUp()
# We have a SentencePiece fixture for testing
_a : List[Any] = XLMProphetNetTokenizer(_a , keep_accents=_a )
tokenizer.save_pretrained(self.tmpdirname )
def __lowercase ( self ) -> Any:
_a : Tuple = '''[PAD]'''
_a : int = 0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(_a ) , _a )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(_a ) , _a )
def __lowercase ( self ) -> str:
_a : Any = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''[PAD]''' )
self.assertEqual(vocab_keys[1] , '''[CLS]''' )
self.assertEqual(vocab_keys[-1] , '''j''' )
self.assertEqual(len(_a ) , 1_0_1_2 )
def __lowercase ( self ) -> Union[str, Any]:
self.assertEqual(self.get_tokenizer().vocab_size , 1_0_1_2 )
def __lowercase ( self ) -> str:
_a : Tuple = XLMProphetNetTokenizer(_a , keep_accents=_a )
_a : Union[str, Any] = tokenizer.tokenize('''This is a test''' )
self.assertListEqual(_a , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(_a ) , [value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]] , )
_a : Optional[int] = tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
_a , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''9''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''é''',
'''.''',
] , )
_a : List[Any] = tokenizer.convert_tokens_to_ids(_a )
self.assertListEqual(
_a , [
value + tokenizer.fairseq_offset
for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, -9, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, -9, 4]
] , )
_a : List[str] = tokenizer.convert_ids_to_tokens(_a )
self.assertListEqual(
_a , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''[UNK]''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''[UNK]''',
'''.''',
] , )
@cached_property
def __lowercase ( self ) -> List[str]:
return XLMProphetNetTokenizer.from_pretrained('''microsoft/xprophetnet-large-wiki100-cased''' )
@slow
def __lowercase ( self ) -> Tuple:
_a : str = '''Hello World!'''
_a : Tuple = [3_5_3_8_9, 6_6_7_2, 4_9, 2]
self.assertListEqual(_a , self.big_tokenizer.encode(_a ) )
@slow
def __lowercase ( self ) -> str:
# fmt: off
_a : str = {'''input_ids''': [[1_1_0_7_3, 8_2_7_8_3, 1_8, 2_6, 8_2_7_8_3, 5_4_9, 5_1_5_4_0, 2_4_8, 1_7_2_0_9, 1_3_0_1, 2_1_7, 2_0, 2_1_5_1_8_6, 1_3_2_5, 1_4_7, 1_7_2_0_9, 1_3_0_1, 2_1_7, 2_0, 5_6_3_7_0, 5_3, 1_2_2_0_2_0, 2_0, 1_6_4_7_7, 2_7, 8_7_3_5_5, 4_5_4_8, 2_0, 4_7_2_8, 7_8_3_9_2, 1_7, 1_5_9_9_6_9, 1_8, 2_6, 2_4_4_9_1, 6_2_9, 1_5, 5_3_8, 2_2_7_0_4, 5_4_3_9, 1_5, 2_7_8_8, 2_4_4_9_1, 9_8_8_5, 1_5, 4_3_5_3_4, 6_0_5, 1_5, 8_1_4, 1_8_4_0_3, 3_3_2_0_0, 2_9, 1_5, 4_3_5_3_4, 2_4_4_5_8, 1_2_4_1_0, 1_1_1, 2_4_9_6_6, 8_3_6_6_9, 9_6_3_7, 1_4_4_0_6_8, 2_6, 8_5_0, 2_2_3_4_6, 2_7, 1_4_7, 2_4_9_6_6, 8_3_6_6_9, 8_3_4_9_0, 2_6, 3_9_1_1_3, 7_3_5, 2_7, 6_8_9, 6_5_6, 2_8_0_0, 1_3_3_9, 4_6_0_0, 5_3, 1_2_2_0_2_0, 1_1_5_7_8_5, 3_4, 8_1_6, 1_3_3_9, 4_6_8_8_7, 1_8, 1_4_7, 5_3_9_0_5, 1_9_5_1, 4_2_2_3_8, 4_1_1_7_0, 1_7_7_3_2, 8_3_4, 4_3_6, 1_5, 2_7_5_2_3, 9_8_7_3_3, 2_1_7, 1_4_7, 5_5_4_2, 4_9_8_1, 9_3_0, 1_7_3_4_7, 1_6, 2], [2_0_0_9_1, 6_2_9, 9_4, 8_2_7_8_6, 5_8, 4_9_0, 2_0, 1_5_2_8, 8_4, 5_3_9_0_5, 3_4_4, 8_0_5_9_2, 1_1_0_1_2_8, 1_8_8_2_2, 5_2_6_7, 1_3_0_6, 6_2, 1_5_2_5_3_7, 3_0_8, 7_9_9_7, 4_0_1, 1_2_4_4_2_7, 5_4_9, 3_5_4_4_2, 2_2_5, 1_0_9, 1_5_0_5_5, 2_5_7_4_8, 1_4_7, 7_1_1_9, 4_3_7_1_2, 3_4, 7_6_7, 1_3_5_3_6_6, 1_8, 1_6, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_9_2, 6_3_7_8_4, 1_1_9_4_6_6, 1_7, 1_4_7_8_0_8, 8_8_2_1_4, 1_8, 6_5_6, 8_1, 3_2, 3_2_9_6, 1_0_2_8_0, 1_6, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=_a , model_name='''microsoft/xprophetnet-large-wiki100-cased''' , revision='''1acad1643ddd54a44df6a1b797ada8373685d90e''' , )
| 14 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__SCREAMING_SNAKE_CASE : Optional[Any] = {
'''configuration_perceiver''': ['''PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PerceiverConfig''', '''PerceiverOnnxConfig'''],
'''tokenization_perceiver''': ['''PerceiverTokenizer'''],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE : List[str] = ['''PerceiverFeatureExtractor''']
__SCREAMING_SNAKE_CASE : Tuple = ['''PerceiverImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE : Optional[int] = [
'''PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''PerceiverForImageClassificationConvProcessing''',
'''PerceiverForImageClassificationFourier''',
'''PerceiverForImageClassificationLearned''',
'''PerceiverForMaskedLM''',
'''PerceiverForMultimodalAutoencoding''',
'''PerceiverForOpticalFlow''',
'''PerceiverForSequenceClassification''',
'''PerceiverLayer''',
'''PerceiverModel''',
'''PerceiverPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_perceiver import PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP, PerceiverConfig, PerceiverOnnxConfig
from .tokenization_perceiver import PerceiverTokenizer
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_perceiver import PerceiverFeatureExtractor
from .image_processing_perceiver import PerceiverImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_perceiver import (
PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST,
PerceiverForImageClassificationConvProcessing,
PerceiverForImageClassificationFourier,
PerceiverForImageClassificationLearned,
PerceiverForMaskedLM,
PerceiverForMultimodalAutoencoding,
PerceiverForOpticalFlow,
PerceiverForSequenceClassification,
PerceiverLayer,
PerceiverModel,
PerceiverPreTrainedModel,
)
else:
import sys
__SCREAMING_SNAKE_CASE : Optional[int] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 661 |
import os
import unittest
from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class UpperCAmelCase_ ( __lowercase , unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase__ : Any = LxmertTokenizer
UpperCAmelCase__ : Optional[Any] = LxmertTokenizerFast
UpperCAmelCase__ : Any = True
UpperCAmelCase__ : Dict = True
def __lowercase ( self ) -> Union[str, Any]:
super().setUp()
_a : int = [
'''[UNK]''',
'''[CLS]''',
'''[SEP]''',
'''want''',
'''##want''',
'''##ed''',
'''wa''',
'''un''',
'''runn''',
'''##ing''',
''',''',
'''low''',
'''lowest''',
]
_a : Any = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as vocab_writer:
vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens] ) )
def __lowercase ( self , _a ) -> List[str]:
_a : Tuple = '''UNwant\u00E9d,running'''
_a : str = '''unwanted, running'''
return input_text, output_text
def __lowercase ( self ) -> List[Any]:
_a : str = self.tokenizer_class(self.vocab_file )
_a : str = tokenizer.tokenize('''UNwant\u00E9d,running''' )
self.assertListEqual(_a , ['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing'''] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(_a ) , [7, 4, 5, 1_0, 8, 9] )
def __lowercase ( self ) -> List[Any]:
if not self.test_rust_tokenizer:
return
_a : Optional[Any] = self.get_tokenizer()
_a : str = self.get_rust_tokenizer()
_a : Optional[Any] = '''I was born in 92000, and this is falsé.'''
_a : Optional[Any] = tokenizer.tokenize(_a )
_a : List[Any] = rust_tokenizer.tokenize(_a )
self.assertListEqual(_a , _a )
_a : List[Any] = tokenizer.encode(_a , add_special_tokens=_a )
_a : Any = rust_tokenizer.encode(_a , add_special_tokens=_a )
self.assertListEqual(_a , _a )
_a : Dict = self.get_rust_tokenizer()
_a : Optional[int] = tokenizer.encode(_a )
_a : Dict = rust_tokenizer.encode(_a )
self.assertListEqual(_a , _a )
| 14 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_lowercase = {
'''configuration_falcon''': ['''FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FalconConfig'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = [
'''FALCON_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''FalconForCausalLM''',
'''FalconModel''',
'''FalconPreTrainedModel''',
'''FalconForSequenceClassification''',
'''FalconForTokenClassification''',
'''FalconForQuestionAnswering''',
]
if TYPE_CHECKING:
from .configuration_falcon import FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP, FalconConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_falcon import (
FALCON_PRETRAINED_MODEL_ARCHIVE_LIST,
FalconForCausalLM,
FalconForQuestionAnswering,
FalconForSequenceClassification,
FalconForTokenClassification,
FalconModel,
FalconPreTrainedModel,
)
else:
import sys
_lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 157 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch
if is_vision_available():
from transformers import TvltImageProcessor
if is_speech_available():
from transformers import TvltFeatureExtractor
from transformers import TvltProcessor
@require_torch
class UpperCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
def __lowercase ( self ) -> int:
_a : Dict = '''ZinengTang/tvlt-base'''
_a : List[str] = tempfile.mkdtemp()
def __lowercase ( self , **_a ) -> int:
return TvltImageProcessor.from_pretrained(self.checkpoint , **_a )
def __lowercase ( self , **_a ) -> List[Any]:
return TvltFeatureExtractor.from_pretrained(self.checkpoint , **_a )
def __lowercase ( self ) -> Optional[int]:
shutil.rmtree(self.tmpdirname )
def __lowercase ( self ) -> Dict:
_a : Union[str, Any] = self.get_image_processor()
_a : Dict = self.get_feature_extractor()
_a : Optional[int] = TvltProcessor(image_processor=_a , feature_extractor=_a )
processor.save_pretrained(self.tmpdirname )
_a : Any = TvltProcessor.from_pretrained(self.tmpdirname )
self.assertIsInstance(processor.feature_extractor , _a )
self.assertIsInstance(processor.image_processor , _a )
def __lowercase ( self ) -> Any:
_a : Optional[Any] = self.get_image_processor()
_a : Dict = self.get_feature_extractor()
_a : Dict = TvltProcessor(image_processor=_a , feature_extractor=_a )
_a : Union[str, Any] = np.ones([1_2_0_0_0] )
_a : Dict = feature_extractor(_a , return_tensors='''np''' )
_a : Tuple = processor(audio=_a , return_tensors='''np''' )
for key in audio_dict.keys():
self.assertAlmostEqual(audio_dict[key].sum() , input_processor[key].sum() , delta=1e-2 )
def __lowercase ( self ) -> int:
_a : Optional[Any] = self.get_image_processor()
_a : Union[str, Any] = self.get_feature_extractor()
_a : Optional[Any] = TvltProcessor(image_processor=_a , feature_extractor=_a )
_a : List[Any] = np.ones([3, 2_2_4, 2_2_4] )
_a : int = image_processor(_a , return_tensors='''np''' )
_a : Optional[int] = processor(images=_a , return_tensors='''np''' )
for key in image_dict.keys():
self.assertAlmostEqual(image_dict[key].sum() , input_processor[key].sum() , delta=1e-2 )
def __lowercase ( self ) -> Union[str, Any]:
_a : int = self.get_image_processor()
_a : Union[str, Any] = self.get_feature_extractor()
_a : Any = TvltProcessor(image_processor=_a , feature_extractor=_a )
_a : List[str] = np.ones([1_2_0_0_0] )
_a : Optional[int] = np.ones([3, 2_2_4, 2_2_4] )
_a : int = processor(audio=_a , images=_a )
self.assertListEqual(list(inputs.keys() ) , ['''audio_values''', '''audio_mask''', '''pixel_values''', '''pixel_mask'''] )
# test if it raises when no input is passed
with pytest.raises(_a ):
processor()
def __lowercase ( self ) -> Union[str, Any]:
_a : str = self.get_image_processor()
_a : Union[str, Any] = self.get_feature_extractor()
_a : Dict = TvltProcessor(image_processor=_a , feature_extractor=_a )
self.assertListEqual(
processor.model_input_names , image_processor.model_input_names + feature_extractor.model_input_names , msg='''`processor` and `image_processor`+`feature_extractor` model input names do not match''' , )
| 14 | 0 |
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def a__ ( _UpperCamelCase : Tuple ,_UpperCamelCase : Dict ,_UpperCamelCase : List[str] ,_UpperCamelCase : Optional[Any] ,_UpperCamelCase : Tuple ):
with open(__a ) as metadata_file:
__lowerCamelCase = json.load(__a )
__lowerCamelCase = LukeConfig(use_entity_aware_attention=__a ,**metadata['''model_config'''] )
# Load in the weights from the checkpoint_path
__lowerCamelCase = torch.load(__a ,map_location='''cpu''' )['''module''']
# Load the entity vocab file
__lowerCamelCase = load_original_entity_vocab(__a )
# add an entry for [MASK2]
__lowerCamelCase = max(entity_vocab.values() ) + 1
config.entity_vocab_size += 1
__lowerCamelCase = XLMRobertaTokenizer.from_pretrained(metadata['''model_config''']['''bert_model_name'''] )
# Add special tokens to the token vocabulary for downstream tasks
__lowerCamelCase = AddedToken('''<ent>''' ,lstrip=__a ,rstrip=__a )
__lowerCamelCase = AddedToken('''<ent2>''' ,lstrip=__a ,rstrip=__a )
tokenizer.add_special_tokens({'''additional_special_tokens''': [entity_token_a, entity_token_a]} )
config.vocab_size += 2
print(F"""Saving tokenizer to {pytorch_dump_folder_path}""" )
tokenizer.save_pretrained(__a )
with open(os.path.join(__a ,'''tokenizer_config.json''' ) ,'''r''' ) as f:
__lowerCamelCase = json.load(__a )
__lowerCamelCase = '''MLukeTokenizer'''
with open(os.path.join(__a ,'''tokenizer_config.json''' ) ,'''w''' ) as f:
json.dump(__a ,__a )
with open(os.path.join(__a ,MLukeTokenizer.vocab_files_names['''entity_vocab_file'''] ) ,'''w''' ) as f:
json.dump(__a ,__a )
__lowerCamelCase = MLukeTokenizer.from_pretrained(__a )
# Initialize the embeddings of the special tokens
__lowerCamelCase = tokenizer.convert_tokens_to_ids(['''@'''] )[0]
__lowerCamelCase = tokenizer.convert_tokens_to_ids(['''#'''] )[0]
__lowerCamelCase = state_dict['''embeddings.word_embeddings.weight''']
__lowerCamelCase = word_emb[ent_init_index].unsqueeze(0 )
__lowerCamelCase = word_emb[enta_init_index].unsqueeze(0 )
__lowerCamelCase = torch.cat([word_emb, ent_emb, enta_emb] )
# add special tokens for 'entity_predictions.bias'
for bias_name in ["lm_head.decoder.bias", "lm_head.bias"]:
__lowerCamelCase = state_dict[bias_name]
__lowerCamelCase = decoder_bias[ent_init_index].unsqueeze(0 )
__lowerCamelCase = decoder_bias[enta_init_index].unsqueeze(0 )
__lowerCamelCase = torch.cat([decoder_bias, ent_decoder_bias, enta_decoder_bias] )
# Initialize the query layers of the entity-aware self-attention mechanism
for layer_index in range(config.num_hidden_layers ):
for matrix_name in ["query.weight", "query.bias"]:
__lowerCamelCase = F"""encoder.layer.{layer_index}.attention.self."""
__lowerCamelCase = state_dict[prefix + matrix_name]
__lowerCamelCase = state_dict[prefix + matrix_name]
__lowerCamelCase = state_dict[prefix + matrix_name]
# Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
__lowerCamelCase = state_dict['''entity_embeddings.entity_embeddings.weight''']
__lowerCamelCase = entity_emb[entity_vocab['''[MASK]''']].unsqueeze(0 )
__lowerCamelCase = torch.cat([entity_emb, entity_mask_emb] )
# add [MASK2] for 'entity_predictions.bias'
__lowerCamelCase = state_dict['''entity_predictions.bias''']
__lowerCamelCase = entity_prediction_bias[entity_vocab['''[MASK]''']].unsqueeze(0 )
__lowerCamelCase = torch.cat([entity_prediction_bias, entity_mask_bias] )
__lowerCamelCase = LukeForMaskedLM(config=__a ).eval()
state_dict.pop('''entity_predictions.decoder.weight''' )
state_dict.pop('''lm_head.decoder.weight''' )
state_dict.pop('''lm_head.decoder.bias''' )
__lowerCamelCase = OrderedDict()
for key, value in state_dict.items():
if not (key.startswith('''lm_head''' ) or key.startswith('''entity_predictions''' )):
__lowerCamelCase = state_dict[key]
else:
__lowerCamelCase = state_dict[key]
__lowerCamelCase = model.load_state_dict(__a ,strict=__a )
if set(__a ) != {"luke.embeddings.position_ids"}:
raise ValueError(F"""Unexpected unexpected_keys: {unexpected_keys}""" )
if set(__a ) != {
"lm_head.decoder.weight",
"lm_head.decoder.bias",
"entity_predictions.decoder.weight",
}:
raise ValueError(F"""Unexpected missing_keys: {missing_keys}""" )
model.tie_weights()
assert (model.luke.embeddings.word_embeddings.weight == model.lm_head.decoder.weight).all()
assert (model.luke.entity_embeddings.entity_embeddings.weight == model.entity_predictions.decoder.weight).all()
# Check outputs
__lowerCamelCase = MLukeTokenizer.from_pretrained(__a ,task='''entity_classification''' )
__lowerCamelCase = '''ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan).'''
__lowerCamelCase = (0, 9)
__lowerCamelCase = tokenizer(__a ,entity_spans=[span] ,return_tensors='''pt''' )
__lowerCamelCase = model(**__a )
# Verify word hidden states
if model_size == "large":
raise NotImplementedError
else: # base
__lowerCamelCase = torch.Size((1, 33, 7_68) )
__lowerCamelCase = torch.tensor([[0.0_892, 0.0_596, -0.2_819], [0.0_134, 0.1_199, 0.0_573], [-0.0_169, 0.0_927, 0.0_644]] )
if not (outputs.last_hidden_state.shape == expected_shape):
raise ValueError(
F"""Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}""" )
if not torch.allclose(outputs.last_hidden_state[0, :3, :3] ,__a ,atol=1e-4 ):
raise ValueError
# Verify entity hidden states
if model_size == "large":
raise NotImplementedError
else: # base
__lowerCamelCase = torch.Size((1, 1, 7_68) )
__lowerCamelCase = torch.tensor([[-0.1_482, 0.0_609, 0.0_322]] )
if not (outputs.entity_last_hidden_state.shape == expected_shape):
raise ValueError(
F"""Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is"""
F""" {expected_shape}""" )
if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3] ,__a ,atol=1e-4 ):
raise ValueError
# Verify masked word/entity prediction
__lowerCamelCase = MLukeTokenizer.from_pretrained(__a )
__lowerCamelCase = '''Tokyo is the capital of <mask>.'''
__lowerCamelCase = (24, 30)
__lowerCamelCase = tokenizer(__a ,entity_spans=[span] ,return_tensors='''pt''' )
__lowerCamelCase = model(**__a )
__lowerCamelCase = encoding['''input_ids'''][0].tolist()
__lowerCamelCase = input_ids.index(tokenizer.convert_tokens_to_ids('''<mask>''' ) )
__lowerCamelCase = outputs.logits[0][mask_position_id].argmax(dim=-1 )
assert "Japan" == tokenizer.decode(__a )
__lowerCamelCase = outputs.entity_logits[0][0].argmax().item()
__lowerCamelCase = [
entity for entity, entity_id in tokenizer.entity_vocab.items() if entity_id == predicted_entity_id
]
assert [e for e in multilingual_predicted_entities if e.startswith('''en:''' )][0] == "en:Japan"
# Finally, save our PyTorch model and tokenizer
print('''Saving PyTorch model to {}'''.format(__a ) )
model.save_pretrained(__a )
def a__ ( _UpperCamelCase : List[Any] ):
__lowerCamelCase = ['''[MASK]''', '''[PAD]''', '''[UNK]''']
__lowerCamelCase = [json.loads(__a ) for line in open(__a )]
__lowerCamelCase = {}
for entry in data:
__lowerCamelCase = entry['''id''']
for entity_name, language in entry["entities"]:
if entity_name in SPECIAL_TOKENS:
__lowerCamelCase = entity_id
break
__lowerCamelCase = F"""{language}:{entity_name}"""
__lowerCamelCase = entity_id
return new_mapping
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument("""--checkpoint_path""", type=str, help="""Path to a pytorch_model.bin file.""")
parser.add_argument(
"""--metadata_path""", default=None, type=str, help="""Path to a metadata.json file, defining the configuration."""
)
parser.add_argument(
"""--entity_vocab_path""",
default=None,
type=str,
help="""Path to an entity_vocab.tsv file, containing the entity vocabulary.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to where to dump the output PyTorch model."""
)
parser.add_argument(
"""--model_size""", default="""base""", type=str, choices=["""base""", """large"""], help="""Size of the model to be converted."""
)
a_ = parser.parse_args()
convert_luke_checkpoint(
args.checkpoint_path,
args.metadata_path,
args.entity_vocab_path,
args.pytorch_dump_folder_path,
args.model_size,
)
| 175 |
def __UpperCAmelCase ( __a : str ) -> list:
"""simple docstring"""
if n_term == "":
return []
_a : list = []
for temp in range(int(__a ) ):
series.append(F"""1/{temp + 1}""" if series else '''1''' )
return series
if __name__ == "__main__":
a__ = input('''Enter the last number (nth term) of the Harmonic Series''')
print('''Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n''')
print(harmonic_series(nth_term))
| 14 | 0 |
'''simple docstring'''
def UpperCAmelCase__( _SCREAMING_SNAKE_CASE : int = 1000 ):
"""simple docstring"""
__A= 2**power
__A= 0
while n:
__A= r + n % 10, n // 10
return r
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 186 |
import argparse
import collections
import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def __UpperCAmelCase ( __a : List[Any] ,__a : Optional[Any] ,__a : Optional[int] ) -> Dict:
"""simple docstring"""
return params[F"""{prefix}/{prefix}/relpos_bias/rel_embedding"""][:, i, :]
def __UpperCAmelCase ( __a : List[Any] ,__a : Optional[int] ,__a : int ,__a : List[str]="attention" ) -> List[str]:
"""simple docstring"""
_a : str = np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/key/kernel"""][:, i, :, :] )
_a : Tuple = k_tmp.reshape(k_tmp.shape[0] ,k_tmp.shape[1] * k_tmp.shape[2] )
_a : Any = np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/out/kernel"""][:, i, :, :] )
_a : Dict = o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1] ,o_tmp.shape[2] )
_a : Union[str, Any] = np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/query/kernel"""][:, i, :, :] )
_a : Any = q_tmp.reshape(q_tmp.shape[0] ,q_tmp.shape[1] * q_tmp.shape[2] )
_a : Tuple = np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/value/kernel"""][:, i, :, :] )
_a : int = v_tmp.reshape(v_tmp.shape[0] ,v_tmp.shape[1] * v_tmp.shape[2] )
return k, o, q, v
def __UpperCAmelCase ( __a : Union[str, Any] ,__a : Union[str, Any] ,__a : List[Any] ,__a : Any=False ) -> Any:
"""simple docstring"""
if split_mlp_wi:
_a : Union[str, Any] = params[F"""{prefix}/{prefix}/mlp/wi_0/kernel"""][:, i, :]
_a : Union[str, Any] = params[F"""{prefix}/{prefix}/mlp/wi_1/kernel"""][:, i, :]
_a : List[str] = (wi_a, wi_a)
else:
_a : List[str] = params[F"""{prefix}/{prefix}/mlp/wi/kernel"""][:, i, :]
_a : Optional[int] = params[F"""{prefix}/{prefix}/mlp/wo/kernel"""][:, i, :]
return wi, wo
def __UpperCAmelCase ( __a : List[Any] ,__a : Optional[Any] ,__a : Union[str, Any] ,__a : str ) -> List[str]:
"""simple docstring"""
return params[F"""{prefix}/{prefix}/{layer_name}/scale"""][:, i]
def __UpperCAmelCase ( __a : dict ,*, __a : int ,__a : bool ,__a : bool = False ) -> Any:
"""simple docstring"""
_a : Dict = traverse_util.flatten_dict(variables['''target'''] )
_a : Any = {'''/'''.join(__a ): v for k, v in old.items()}
# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
_a : Optional[int] = '''encoder/encoder/mlp/wi_0/kernel''' in old
print('''Split MLP:''' ,__a )
_a : Tuple = collections.OrderedDict()
# Shared embeddings.
_a : Any = old['''token_embedder/embedding''']
# Encoder.
for i in range(__a ):
# Block i, layer 0 (Self Attention).
_a : Optional[Any] = tax_layer_norm_lookup(__a ,__a ,'''encoder''' ,'''pre_attention_layer_norm''' )
_a , _a , _a , _a : List[str] = tax_attention_lookup(__a ,__a ,'''encoder''' ,'''attention''' )
_a : List[str] = layer_norm
_a : Optional[Any] = k.T
_a : str = o.T
_a : List[Any] = q.T
_a : Tuple = v.T
# Block i, layer 1 (MLP).
_a : str = tax_layer_norm_lookup(__a ,__a ,'''encoder''' ,'''pre_mlp_layer_norm''' )
_a , _a : Any = tax_mlp_lookup(__a ,__a ,'''encoder''' ,__a )
_a : str = layer_norm
if split_mlp_wi:
_a : List[Any] = wi[0].T
_a : Any = wi[1].T
else:
_a : Any = wi.T
_a : Optional[Any] = wo.T
if scalable_attention:
# convert the rel_embedding of each layer
_a : Dict = tax_relpos_bias_lookup(
__a ,__a ,'''encoder''' ).T
_a : List[str] = old['''encoder/encoder_norm/scale''']
if not scalable_attention:
_a : List[Any] = tax_relpos_bias_lookup(
__a ,0 ,'''encoder''' ).T
_a : Optional[Any] = tax_relpos_bias_lookup(
__a ,0 ,'''decoder''' ).T
if not is_encoder_only:
# Decoder.
for i in range(__a ):
# Block i, layer 0 (Self Attention).
_a : Union[str, Any] = tax_layer_norm_lookup(__a ,__a ,'''decoder''' ,'''pre_self_attention_layer_norm''' )
_a , _a , _a , _a : Optional[Any] = tax_attention_lookup(__a ,__a ,'''decoder''' ,'''self_attention''' )
_a : Optional[Any] = layer_norm
_a : Dict = k.T
_a : str = o.T
_a : str = q.T
_a : List[str] = v.T
# Block i, layer 1 (Cross Attention).
_a : Any = tax_layer_norm_lookup(__a ,__a ,'''decoder''' ,'''pre_cross_attention_layer_norm''' )
_a , _a , _a , _a : str = tax_attention_lookup(__a ,__a ,'''decoder''' ,'''encoder_decoder_attention''' )
_a : Optional[Any] = layer_norm
_a : Optional[int] = k.T
_a : Dict = o.T
_a : str = q.T
_a : int = v.T
# Block i, layer 2 (MLP).
_a : Optional[int] = tax_layer_norm_lookup(__a ,__a ,'''decoder''' ,'''pre_mlp_layer_norm''' )
_a , _a : Tuple = tax_mlp_lookup(__a ,__a ,'''decoder''' ,__a )
_a : Optional[Any] = layer_norm
if split_mlp_wi:
_a : List[str] = wi[0].T
_a : List[Any] = wi[1].T
else:
_a : Dict = wi.T
_a : str = wo.T
if scalable_attention:
# convert the rel_embedding of each layer
_a : Tuple = tax_relpos_bias_lookup(__a ,__a ,'''decoder''' ).T
_a : Tuple = old['''decoder/decoder_norm/scale''']
# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
if "decoder/logits_dense/kernel" in old:
_a : Any = old['''decoder/logits_dense/kernel'''].T
return new
def __UpperCAmelCase ( __a : Dict ,__a : bool ) -> Tuple:
"""simple docstring"""
_a : Tuple = collections.OrderedDict([(k, torch.from_numpy(v.copy() )) for (k, v) in converted_params.items()] )
# Add what is missing.
if "encoder.embed_tokens.weight" not in state_dict:
_a : Any = state_dict['''shared.weight''']
if not is_encoder_only:
if "decoder.embed_tokens.weight" not in state_dict:
_a : Optional[int] = state_dict['''shared.weight''']
if "lm_head.weight" not in state_dict: # For old 1.0 models.
print('''Using shared word embeddings as lm_head.''' )
_a : str = state_dict['''shared.weight''']
return state_dict
def __UpperCAmelCase ( __a : List[str] ,__a : Union[str, Any] ,__a : Dict ,__a : Union[str, Any] ,__a : List[Any] ) -> int:
"""simple docstring"""
_a : List[str] = checkpoints.load_tax_checkpoint(__a )
_a : str = convert_tax_to_pytorch(
__a ,num_layers=config.num_layers ,is_encoder_only=__a ,scalable_attention=__a )
_a : str = make_state_dict(__a ,__a )
model.load_state_dict(__a ,strict=__a )
def __UpperCAmelCase ( __a : List[Any] ,__a : Any ,__a : Union[str, Any] ,__a : bool = False ,__a : bool = False ,) -> Optional[Any]:
"""simple docstring"""
_a : List[str] = MTaConfig.from_json_file(__a )
print(F"""Building PyTorch model from configuration: {config}""" )
# Non-v1.1 checkpoints could also use T5Model, but this works for all.
# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
if is_encoder_only:
_a : Any = UMTaEncoderModel(__a )
else:
_a : Tuple = UMTaForConditionalGeneration(__a )
# Load weights from tf checkpoint
load_tax_weights_in_ta(__a ,__a ,__a ,__a ,__a )
# Save pytorch-model
print(F"""Save PyTorch model to {pytorch_dump_path}""" )
model.save_pretrained(__a )
# Verify that we can load the checkpoint.
model.from_pretrained(__a )
print('''Done''' )
if __name__ == "__main__":
a__ = argparse.ArgumentParser(description='''Converts a native T5X checkpoint into a PyTorch checkpoint.''')
# Required parameters
parser.add_argument(
'''--t5x_checkpoint_path''', default=None, type=str, required=True, help='''Path to the T5X checkpoint.'''
)
parser.add_argument(
'''--config_file''',
default=None,
type=str,
required=True,
help='''The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.''',
)
parser.add_argument(
'''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
parser.add_argument(
'''--is_encoder_only''', action='''store_true''', help='''Check if the model is encoder-decoder model''', default=False
)
parser.add_argument(
'''--scalable_attention''',
action='''store_true''',
help='''Whether the model uses scaled attention (umt5 model)''',
default=False,
)
a__ = parser.parse_args()
convert_tax_checkpoint_to_pytorch(
args.tax_checkpoint_path,
args.config_file,
args.pytorch_dump_path,
args.is_encoder_only,
args.scalable_attention,
)
| 14 | 0 |
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCAmelCase : Optional[int] = logging.get_logger(__name__)
__lowerCAmelCase : str = {
"xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/config.json",
"xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/config.json",
}
class __lowerCAmelCase ( __lowercase ):
"""simple docstring"""
A__ : Tuple = "xlnet"
A__ : Dict = ["mems"]
A__ : str = {
"n_token": "vocab_size", # Backward compatibility
"hidden_size": "d_model",
"num_attention_heads": "n_head",
"num_hidden_layers": "n_layer",
}
def __init__( self : Optional[Any] , _snake_case : Union[str, Any]=3_2000 , _snake_case : Optional[Any]=1024 , _snake_case : int=24 , _snake_case : List[Any]=16 , _snake_case : Union[str, Any]=4096 , _snake_case : Optional[int]="gelu" , _snake_case : int=True , _snake_case : Union[str, Any]="bi" , _snake_case : Any=0.02 , _snake_case : Dict=1E-1_2 , _snake_case : Optional[Any]=0.1 , _snake_case : Union[str, Any]=512 , _snake_case : List[str]=None , _snake_case : int=True , _snake_case : List[str]=False , _snake_case : List[Any]=False , _snake_case : List[Any]=-1 , _snake_case : Tuple=False , _snake_case : Any="last" , _snake_case : List[str]=True , _snake_case : Union[str, Any]="tanh" , _snake_case : Dict=0.1 , _snake_case : Dict=5 , _snake_case : Optional[int]=5 , _snake_case : List[Any]=5 , _snake_case : List[Any]=1 , _snake_case : Union[str, Any]=2 , **_snake_case : str , ):
__lowercase : Any = vocab_size
__lowercase : Any = d_model
__lowercase : Any = n_layer
__lowercase : Optional[Any] = n_head
if d_model % n_head != 0:
raise ValueError(F'\'d_model % n_head\' ({d_model % n_head}) should be equal to 0' )
if "d_head" in kwargs:
if kwargs["d_head"] != d_model // n_head:
raise ValueError(
F'`d_head` ({kwargs["d_head"]}) should be equal to `d_model // n_head` ({d_model // n_head})' )
__lowercase : Tuple = d_model // n_head
__lowercase : int = ff_activation
__lowercase : str = d_inner
__lowercase : int = untie_r
__lowercase : List[str] = attn_type
__lowercase : Union[str, Any] = initializer_range
__lowercase : Union[str, Any] = layer_norm_eps
__lowercase : Tuple = dropout
__lowercase : Optional[int] = mem_len
__lowercase : Union[str, Any] = reuse_len
__lowercase : Dict = bi_data
__lowercase : List[str] = clamp_len
__lowercase : Tuple = same_length
__lowercase : List[Any] = summary_type
__lowercase : Tuple = summary_use_proj
__lowercase : Dict = summary_activation
__lowercase : Dict = summary_last_dropout
__lowercase : int = start_n_top
__lowercase : int = end_n_top
__lowercase : Any = bos_token_id
__lowercase : Optional[Any] = pad_token_id
__lowercase : int = eos_token_id
if "use_cache" in kwargs:
warnings.warn(
'''The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems_eval`'''
''' instead.''' , _a , )
__lowercase : Optional[Any] = kwargs['''use_cache''']
__lowercase : List[str] = use_mems_eval
__lowercase : List[Any] = use_mems_train
super().__init__(pad_token_id=_a , bos_token_id=_a , eos_token_id=_a , **_a )
@property
def snake_case_ ( self : int ):
logger.info(F'The model {self.model_type} is one of the few models that has no sequence length limit.' )
return -1
@max_position_embeddings.setter
def snake_case_ ( self : Tuple , _snake_case : List[str] ):
# Message copied from Transformer-XL documentation
raise NotImplementedError(
F'The model {self.model_type} is one of the few models that has no sequence length limit.' )
| 509 |
import random
import sys
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap
a__ = '''Usage of script: script_name <size_of_canvas:int>'''
a__ = [0] * 100 + [1] * 10
random.shuffle(choice)
def __UpperCAmelCase ( __a : int ) -> list[list[bool]]:
"""simple docstring"""
_a : int = [[False for i in range(__a )] for j in range(__a )]
return canvas
def __UpperCAmelCase ( __a : list[list[bool]] ) -> None:
"""simple docstring"""
for i, row in enumerate(__a ):
for j, _ in enumerate(__a ):
_a : Optional[int] = bool(random.getrandbits(1 ) )
def __UpperCAmelCase ( __a : list[list[bool]] ) -> list[list[bool]]:
"""simple docstring"""
_a : Any = np.array(__a )
_a : Optional[int] = np.array(create_canvas(current_canvas.shape[0] ) )
for r, row in enumerate(__a ):
for c, pt in enumerate(__a ):
_a : Tuple = __judge_point(
__a ,current_canvas[r - 1 : r + 2, c - 1 : c + 2] )
_a : List[str] = next_gen_canvas
del next_gen_canvas # cleaning memory as we move on.
_a : list[list[bool]] = current_canvas.tolist()
return return_canvas
def __UpperCAmelCase ( __a : bool ,__a : list[list[bool]] ) -> bool:
"""simple docstring"""
_a : Optional[Any] = 0
_a : str = 0
# finding dead or alive neighbours count.
for i in neighbours:
for status in i:
if status:
alive += 1
else:
dead += 1
# handling duplicate entry for focus pt.
if pt:
alive -= 1
else:
dead -= 1
# running the rules of game here.
_a : Optional[int] = pt
if pt:
if alive < 2:
_a : Dict = False
elif alive == 2 or alive == 3:
_a : Optional[Any] = True
elif alive > 3:
_a : str = False
else:
if alive == 3:
_a : int = True
return state
if __name__ == "__main__":
if len(sys.argv) != 2:
raise Exception(usage_doc)
a__ = int(sys.argv[1])
# main working structure of this module.
a__ = create_canvas(canvas_size)
seed(c)
a__ , a__ = plt.subplots()
fig.show()
a__ = ListedColormap(['''w''', '''k'''])
try:
while True:
a__ = run(c)
ax.matshow(c, cmap=cmap)
fig.canvas.draw()
ax.cla()
except KeyboardInterrupt:
# do nothing.
pass
| 14 | 0 |
def UpperCAmelCase ( a_ , a_ , a_ ) -> Optional[Any]:
"""simple docstring"""
if n == 0:
return 1
elif n % 2 == 1:
return (binary_exponentiation(__a , n - 1 , __a ) * a) % mod
else:
__A = binary_exponentiation(__a , n / 2 , __a )
return (b * b) % mod
# a prime number
SCREAMING_SNAKE_CASE :Any = 701
SCREAMING_SNAKE_CASE :int = 10_0000_0000
SCREAMING_SNAKE_CASE :Tuple = 10
# using binary exponentiation function, O(log(p)):
print((a / b) % p == (a * binary_exponentiation(b, p - 2, p)) % p)
print((a / b) % p == (a * b ** (p - 2)) % p)
| 55 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__ = logging.get_logger(__name__)
a__ = {
'''funnel-transformer/small''': '''https://huggingface.co/funnel-transformer/small/resolve/main/config.json''',
'''funnel-transformer/small-base''': '''https://huggingface.co/funnel-transformer/small-base/resolve/main/config.json''',
'''funnel-transformer/medium''': '''https://huggingface.co/funnel-transformer/medium/resolve/main/config.json''',
'''funnel-transformer/medium-base''': '''https://huggingface.co/funnel-transformer/medium-base/resolve/main/config.json''',
'''funnel-transformer/intermediate''': (
'''https://huggingface.co/funnel-transformer/intermediate/resolve/main/config.json'''
),
'''funnel-transformer/intermediate-base''': (
'''https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/config.json'''
),
'''funnel-transformer/large''': '''https://huggingface.co/funnel-transformer/large/resolve/main/config.json''',
'''funnel-transformer/large-base''': '''https://huggingface.co/funnel-transformer/large-base/resolve/main/config.json''',
'''funnel-transformer/xlarge''': '''https://huggingface.co/funnel-transformer/xlarge/resolve/main/config.json''',
'''funnel-transformer/xlarge-base''': '''https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/config.json''',
}
class UpperCAmelCase_ ( __lowercase ):
"""simple docstring"""
UpperCAmelCase__ : Optional[int] = "funnel"
UpperCAmelCase__ : Tuple = {
"hidden_size": "d_model",
"num_attention_heads": "n_head",
}
def __init__( self , _a=3_0_5_2_2 , _a=[4, 4, 4] , _a=None , _a=2 , _a=7_6_8 , _a=1_2 , _a=6_4 , _a=3_0_7_2 , _a="gelu_new" , _a=0.1 , _a=0.1 , _a=0.0 , _a=0.1 , _a=None , _a=1e-9 , _a="mean" , _a="relative_shift" , _a=True , _a=True , _a=True , **_a , ) -> List[Any]:
_a : Optional[int] = vocab_size
_a : Dict = block_sizes
_a : Optional[int] = [1] * len(_a ) if block_repeats is None else block_repeats
assert len(_a ) == len(
self.block_repeats ), "`block_sizes` and `block_repeats` should have the same length."
_a : int = num_decoder_layers
_a : List[str] = d_model
_a : Optional[Any] = n_head
_a : Tuple = d_head
_a : Dict = d_inner
_a : List[str] = hidden_act
_a : int = hidden_dropout
_a : Union[str, Any] = attention_dropout
_a : Tuple = activation_dropout
_a : Optional[Any] = initializer_range
_a : Dict = initializer_std
_a : Union[str, Any] = layer_norm_eps
assert pooling_type in [
"mean",
"max",
], F"""Got {pooling_type} for `pooling_type` but only 'mean' and 'max' are supported."""
_a : Any = pooling_type
assert attention_type in [
"relative_shift",
"factorized",
], F"""Got {attention_type} for `attention_type` but only 'relative_shift' and 'factorized' are supported."""
_a : Optional[Any] = attention_type
_a : int = separate_cls
_a : Tuple = truncate_seq
_a : List[Any] = pool_q_only
super().__init__(**_a )
@property
def __lowercase ( self ) -> Tuple:
return sum(self.block_sizes )
@num_hidden_layers.setter
def __lowercase ( self , _a ) -> List[str]:
raise NotImplementedError(
'''This model does not support the setting of `num_hidden_layers`. Please set `block_sizes`.''' )
@property
def __lowercase ( self ) -> Optional[int]:
return len(self.block_sizes )
@num_blocks.setter
def __lowercase ( self , _a ) -> Dict:
raise NotImplementedError('''This model does not support the setting of `num_blocks`. Please set `block_sizes`.''' )
| 14 | 0 |
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class lowerCamelCase( unittest.TestCase ):
'''simple docstring'''
def lowerCAmelCase__ ( self ):
_A = 10
def lowerCAmelCase__ ( self ):
_A = [1, 2, 3, 4]
_A = [1, 2, 3, 4, 0, 0, 0, 0, 0, 0]
self.assertEqual(truncate_or_pad(_a , self.block_size , 0 ) , _a )
def lowerCAmelCase__ ( self ):
_A = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
_A = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
self.assertEqual(truncate_or_pad(_a , self.block_size , 0 ) , _a )
def lowerCAmelCase__ ( self ):
_A = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]
_A = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
self.assertEqual(truncate_or_pad(_a , self.block_size , 0 ) , _a )
def lowerCAmelCase__ ( self ):
_A = '''It was the year of Our Lord one thousand seven hundred and
seventy-five.\n\nSpiritual revelations were conceded to England at that
favoured period, as at this.'''
_A = process_story(_a )
self.assertEqual(_a , [] )
def lowerCAmelCase__ ( self ):
_A = ''''''
_A = process_story(_a )
self.assertEqual(_a , [] )
self.assertEqual(_a , [] )
def lowerCAmelCase__ ( self ):
_A = (
'''It was the year of Our Lord one thousand seven hundred and '''
'''seventy-five\n\nSpiritual revelations were conceded to England '''
'''at that favoured period, as at this.\n@highlight\n\nIt was the best of times'''
)
_A = process_story(_a )
_A = [
'''It was the year of Our Lord one thousand seven hundred and seventy-five.''',
'''Spiritual revelations were conceded to England at that favoured period, as at this.''',
]
self.assertEqual(_a , _a )
_A = ['''It was the best of times.''']
self.assertEqual(_a , _a )
def lowerCAmelCase__ ( self ):
_A = torch.tensor([1, 2, 3, 4] )
_A = torch.tensor([1, 1, 1, 1] )
np.testing.assert_array_equal(build_mask(_a , 0 ).numpy() , expected.numpy() )
def lowerCAmelCase__ ( self ):
_A = torch.tensor([1, 2, 3, 4, 23, 23, 23] )
_A = torch.tensor([1, 1, 1, 1, 0, 0, 0] )
np.testing.assert_array_equal(build_mask(_a , 23 ).numpy() , expected.numpy() )
def lowerCAmelCase__ ( self ):
_A = torch.tensor([8, 2, 3, 4, 1, 1, 1] )
_A = torch.tensor([1, 1, 1, 1, 0, 0, 0] )
np.testing.assert_array_equal(build_mask(_a , 1 ).numpy() , expected.numpy() )
def lowerCAmelCase__ ( self ):
_A = 101
_A = torch.tensor([[1, 2, 3, 4, 5, 6], [1, 2, 3, 101, 5, 6], [1, 101, 3, 4, 101, 6]] )
_A = torch.tensor([[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 1]] )
_A = compute_token_type_ids(_a , _a )
np.testing.assert_array_equal(_a , _a )
| 27 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a__ = logging.get_logger(__name__)
a__ = {
'''google/mobilenet_v1_1.0_224''': '''https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json''',
'''google/mobilenet_v1_0.75_192''': '''https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json''',
# See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}
class UpperCAmelCase_ ( __lowercase ):
"""simple docstring"""
UpperCAmelCase__ : int = "mobilenet_v1"
def __init__( self , _a=3 , _a=2_2_4 , _a=1.0 , _a=8 , _a="relu6" , _a=True , _a=0.999 , _a=0.02 , _a=0.001 , **_a , ) -> List[Any]:
super().__init__(**_a )
if depth_multiplier <= 0:
raise ValueError('''depth_multiplier must be greater than zero.''' )
_a : Tuple = num_channels
_a : str = image_size
_a : Tuple = depth_multiplier
_a : Any = min_depth
_a : int = hidden_act
_a : Optional[Any] = tf_padding
_a : str = classifier_dropout_prob
_a : Optional[int] = initializer_range
_a : Any = layer_norm_eps
class UpperCAmelCase_ ( __lowercase ):
"""simple docstring"""
UpperCAmelCase__ : str = version.parse("1.11" )
@property
def __lowercase ( self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict([('''pixel_values''', {0: '''batch'''})] )
@property
def __lowercase ( self ) -> Mapping[str, Mapping[int, str]]:
if self.task == "image-classification":
return OrderedDict([('''logits''', {0: '''batch'''})] )
else:
return OrderedDict([('''last_hidden_state''', {0: '''batch'''}), ('''pooler_output''', {0: '''batch'''})] )
@property
def __lowercase ( self ) -> float:
return 1e-4
| 14 | 0 |
'''simple docstring'''
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def _UpperCamelCase (_lowerCamelCase : bytes , _lowerCamelCase : int )-> np.array:
'''simple docstring'''
__snake_case = f'''{sampling_rate}'''
__snake_case = '''1'''
__snake_case = '''f32le'''
__snake_case = [
'''ffmpeg''',
'''-i''',
'''pipe:0''',
'''-ac''',
ac,
'''-ar''',
ar,
'''-f''',
format_for_conversion,
'''-hide_banner''',
'''-loglevel''',
'''quiet''',
'''pipe:1''',
]
try:
with subprocess.Popen(__a , stdin=subprocess.PIPE , stdout=subprocess.PIPE ) as ffmpeg_process:
__snake_case = ffmpeg_process.communicate(__a )
except FileNotFoundError as error:
raise ValueError('''ffmpeg was not found but is required to load audio files from filename''' ) from error
__snake_case = output_stream[0]
__snake_case = np.frombuffer(__a , np.floataa )
if audio.shape[0] == 0:
raise ValueError('''Malformed soundfile''' )
return audio
def _UpperCamelCase (_lowerCamelCase : int , _lowerCamelCase : float , _lowerCamelCase : str = "f32le" , )-> str:
'''simple docstring'''
__snake_case = f'''{sampling_rate}'''
__snake_case = '''1'''
if format_for_conversion == "s16le":
__snake_case = 2
elif format_for_conversion == "f32le":
__snake_case = 4
else:
raise ValueError(f'''Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`''' )
__snake_case = platform.system()
if system == "Linux":
__snake_case = '''alsa'''
__snake_case = '''default'''
elif system == "Darwin":
__snake_case = '''avfoundation'''
__snake_case = ''':0'''
elif system == "Windows":
__snake_case = '''dshow'''
__snake_case = '''default'''
__snake_case = [
'''ffmpeg''',
'''-f''',
format_,
'''-i''',
input_,
'''-ac''',
ac,
'''-ar''',
ar,
'''-f''',
format_for_conversion,
'''-fflags''',
'''nobuffer''',
'''-hide_banner''',
'''-loglevel''',
'''quiet''',
'''pipe:1''',
]
__snake_case = int(round(sampling_rate * chunk_length_s ) ) * size_of_sample
__snake_case = _ffmpeg_stream(__a , __a )
for item in iterator:
yield item
def _UpperCamelCase (_lowerCamelCase : int , _lowerCamelCase : float , _lowerCamelCase : Optional[int] = None , _lowerCamelCase : Optional[Union[Tuple[float, float], float]] = None , _lowerCamelCase : str = "f32le" , )-> Optional[int]:
'''simple docstring'''
if stream_chunk_s is not None:
__snake_case = stream_chunk_s
else:
__snake_case = chunk_length_s
__snake_case = ffmpeg_microphone(__a , __a , format_for_conversion=__a )
if format_for_conversion == "s16le":
__snake_case = np.intaa
__snake_case = 2
elif format_for_conversion == "f32le":
__snake_case = np.floataa
__snake_case = 4
else:
raise ValueError(f'''Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`''' )
if stride_length_s is None:
__snake_case = chunk_length_s / 6
__snake_case = int(round(sampling_rate * chunk_length_s ) ) * size_of_sample
if isinstance(__a , (int, float) ):
__snake_case = [stride_length_s, stride_length_s]
__snake_case = int(round(sampling_rate * stride_length_s[0] ) ) * size_of_sample
__snake_case = int(round(sampling_rate * stride_length_s[1] ) ) * size_of_sample
__snake_case = datetime.datetime.now()
__snake_case = datetime.timedelta(seconds=__a )
for item in chunk_bytes_iter(__a , __a , stride=(stride_left, stride_right) , stream=__a ):
# Put everything back in numpy scale
__snake_case = np.frombuffer(item['''raw'''] , dtype=__a )
__snake_case = (
item['''stride'''][0] // size_of_sample,
item['''stride'''][1] // size_of_sample,
)
__snake_case = sampling_rate
audio_time += delta
if datetime.datetime.now() > audio_time + 10 * delta:
# We're late !! SKIP
continue
yield item
def _UpperCamelCase (_lowerCamelCase : Optional[int] , _lowerCamelCase : int , _lowerCamelCase : Tuple[int, int] , _lowerCamelCase : bool = False )-> Optional[int]:
'''simple docstring'''
__snake_case = b''''''
__snake_case = stride
if stride_left + stride_right >= chunk_len:
raise ValueError(
f'''Stride needs to be strictly smaller than chunk_len: ({stride_left}, {stride_right}) vs {chunk_len}''' )
__snake_case = 0
for raw in iterator:
acc += raw
if stream and len(__a ) < chunk_len:
__snake_case = (_stride_left, 0)
yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
else:
while len(__a ) >= chunk_len:
# We are flushing the accumulator
__snake_case = (_stride_left, stride_right)
__snake_case = {'''raw''': acc[:chunk_len], '''stride''': stride}
if stream:
__snake_case = False
yield item
__snake_case = stride_left
__snake_case = acc[chunk_len - stride_left - stride_right :]
# Last chunk
if len(__a ) > stride_left:
__snake_case = {'''raw''': acc, '''stride''': (_stride_left, 0)}
if stream:
__snake_case = False
yield item
def _UpperCamelCase (_lowerCamelCase : int , _lowerCamelCase : int )-> Tuple:
'''simple docstring'''
__snake_case = 2**24 # 16Mo
try:
with subprocess.Popen(__a , stdout=subprocess.PIPE , bufsize=__a ) as ffmpeg_process:
while True:
__snake_case = ffmpeg_process.stdout.read(__a )
if raw == b"":
break
yield raw
except FileNotFoundError as error:
raise ValueError('''ffmpeg was not found but is required to stream audio files from filename''' ) from error
| 24 |
a__ = '''Input must be a string of 8 numbers plus letter'''
a__ = '''TRWAGMYFPDXBNJZSQVHLCKE'''
def __UpperCAmelCase ( __a : str ) -> bool:
"""simple docstring"""
if not isinstance(__a ,__a ):
_a : List[str] = F"""Expected string as input, found {type(__a ).__name__}"""
raise TypeError(__a )
_a : List[Any] = spanish_id.replace('''-''' ,'''''' ).upper()
if len(__a ) != 9:
raise ValueError(__a )
try:
_a : Any = int(spanish_id_clean[0:8] )
_a : str = spanish_id_clean[8]
except ValueError as ex:
raise ValueError(__a ) from ex
if letter.isdigit():
raise ValueError(__a )
return letter == LOOKUP_LETTERS[number % 23]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 14 | 0 |
import requests
from bsa import BeautifulSoup
def lowerCamelCase__ ( __lowerCAmelCase : str = "https://www.worldometers.info/coronavirus" ):
"""simple docstring"""
lowerCAmelCase_ = BeautifulSoup(requests.get(__a ).text , "html.parser" )
lowerCAmelCase_ = soup.findAll("h1" )
lowerCAmelCase_ = soup.findAll("div" , {"class": "maincounter-number"} )
keys += soup.findAll("span" , {"class": "panel-title"} )
values += soup.findAll("div" , {"class": "number-table-main"} )
return {key.text.strip(): value.text.strip() for key, value in zip(__a , __a )}
if __name__ == "__main__":
print("\033[1m" + "COVID-19 Status of the World" + "\033[0m\n")
for key, value in world_covidaa_stats().items():
print(f"""{key}\n{value}\n""")
| 290 |
from random import randint
from tempfile import TemporaryFile
import numpy as np
def __UpperCAmelCase ( __a : Optional[Any] ,__a : int ,__a : Any ) -> int:
"""simple docstring"""
_a : int = 0
if start < end:
_a : Tuple = randint(__a ,__a )
_a : Tuple = a[end]
_a : List[str] = a[pivot]
_a : Any = temp
_a , _a : Optional[int] = _in_place_partition(__a ,__a ,__a )
count += _in_place_quick_sort(__a ,__a ,p - 1 )
count += _in_place_quick_sort(__a ,p + 1 ,__a )
return count
def __UpperCAmelCase ( __a : List[Any] ,__a : Tuple ,__a : Dict ) -> Dict:
"""simple docstring"""
_a : Dict = 0
_a : Tuple = randint(__a ,__a )
_a : List[Any] = a[end]
_a : str = a[pivot]
_a : str = temp
_a : Dict = start - 1
for index in range(__a ,__a ):
count += 1
if a[index] < a[end]: # check if current val is less than pivot value
_a : int = new_pivot_index + 1
_a : Any = a[new_pivot_index]
_a : Optional[int] = a[index]
_a : str = temp
_a : Union[str, Any] = a[new_pivot_index + 1]
_a : Tuple = a[end]
_a : Any = temp
return new_pivot_index + 1, count
a__ = TemporaryFile()
a__ = 100 # 1000 elements are to be sorted
a__ , a__ = 0, 1 # mean and standard deviation
a__ = np.random.normal(mu, sigma, p)
np.save(outfile, X)
print('''The array is''')
print(X)
outfile.seek(0) # using the same array
a__ = np.load(outfile)
a__ = len(M) - 1
a__ = _in_place_quick_sort(M, 0, r)
print(
'''No of Comparisons for 100 elements selected from a standard normal distribution'''
'''is :'''
)
print(z)
| 14 | 0 |
'''simple docstring'''
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import UperNetForSemanticSegmentation
from transformers.models.upernet.modeling_upernet import UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class _snake_case :
def __init__( self ,_snake_case ,_snake_case=13 ,_snake_case=32 ,_snake_case=3 ,_snake_case=4 ,_snake_case=[10, 20, 30, 40] ,_snake_case=[2, 2, 3, 2] ,_snake_case=True ,_snake_case=True ,_snake_case=37 ,_snake_case="gelu" ,_snake_case=10 ,_snake_case=0.02 ,_snake_case=["stage2", "stage3", "stage4"] ,_snake_case=3 ,_snake_case=None ,):
UpperCAmelCase_ : Dict = parent
UpperCAmelCase_ : List[str] = batch_size
UpperCAmelCase_ : Any = image_size
UpperCAmelCase_ : Optional[int] = num_channels
UpperCAmelCase_ : List[Any] = num_stages
UpperCAmelCase_ : List[str] = hidden_sizes
UpperCAmelCase_ : Tuple = depths
UpperCAmelCase_ : Any = is_training
UpperCAmelCase_ : Optional[int] = use_labels
UpperCAmelCase_ : Optional[Any] = intermediate_size
UpperCAmelCase_ : Optional[int] = hidden_act
UpperCAmelCase_ : Dict = type_sequence_label_size
UpperCAmelCase_ : str = initializer_range
UpperCAmelCase_ : Optional[int] = out_features
UpperCAmelCase_ : List[str] = num_labels
UpperCAmelCase_ : Optional[Any] = scope
UpperCAmelCase_ : Optional[int] = num_stages
def UpperCamelCase__ ( self ):
UpperCAmelCase_ : Any = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCAmelCase_ : Tuple = None
if self.use_labels:
UpperCAmelCase_ : Optional[int] = ids_tensor([self.batch_size] ,self.type_sequence_label_size )
UpperCAmelCase_ : Any = self.get_config()
return config, pixel_values, labels
def UpperCamelCase__ ( self ):
return ConvNextConfig(
num_channels=self.num_channels ,num_stages=self.num_stages ,hidden_sizes=self.hidden_sizes ,depths=self.depths ,is_training=self.is_training ,intermediate_size=self.intermediate_size ,hidden_act=self.hidden_act ,out_features=self.out_features ,)
def UpperCamelCase__ ( self ):
return UperNetConfig(
backbone_config=self.get_backbone_config() ,hidden_size=5_12 ,pool_scales=[1, 2, 3, 6] ,use_auxiliary_head=_a ,auxiliary_loss_weight=0.4 ,auxiliary_in_channels=40 ,auxiliary_channels=2_56 ,auxiliary_num_convs=1 ,auxiliary_concat_input=_a ,loss_ignore_index=2_55 ,num_labels=self.num_labels ,)
def UpperCamelCase__ ( self ,_snake_case ,_snake_case ,_snake_case ):
UpperCAmelCase_ : Dict = UperNetForSemanticSegmentation(config=_a )
model.to(_a )
model.eval()
UpperCAmelCase_ : List[str] = model(_a )
self.parent.assertEqual(
result.logits.shape ,(self.batch_size, self.num_labels, self.image_size, self.image_size) )
def UpperCamelCase__ ( self ):
UpperCAmelCase_ : Tuple = self.prepare_config_and_inputs()
(
UpperCAmelCase_
) : Dict = config_and_inputs
UpperCAmelCase_ : Union[str, Any] = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class _snake_case (__lowercase , __lowercase , unittest.TestCase):
__A : Tuple =(UperNetForSemanticSegmentation,) if is_torch_available() else ()
__A : Union[str, Any] ={"image-segmentation": UperNetForSemanticSegmentation} if is_torch_available() else {}
__A : Tuple =False
__A : int =False
__A : List[Any] =False
__A : List[Any] =False
__A : Tuple =False
__A : Tuple =False
def UpperCamelCase__ ( self ):
UpperCAmelCase_ : Dict = UperNetModelTester(self )
UpperCAmelCase_ : Tuple = ConfigTester(self ,config_class=_a ,has_text_modality=_a ,hidden_size=37 )
def UpperCamelCase__ ( self ):
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def UpperCamelCase__ ( self ):
return
def UpperCamelCase__ ( self ):
UpperCAmelCase_ : int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase_ : Tuple = model_class(_a )
UpperCAmelCase_ : int = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCAmelCase_ : Any = [*signature.parameters.keys()]
UpperCAmelCase_ : Any = ['''pixel_values''']
self.assertListEqual(arg_names[:1] ,_a )
def UpperCamelCase__ ( self ):
UpperCAmelCase_ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*_a )
@unittest.skip(reason="UperNet does not use inputs_embeds" )
def UpperCamelCase__ ( self ):
pass
@unittest.skip(reason="UperNet does not support input and output embeddings" )
def UpperCamelCase__ ( self ):
pass
@unittest.skip(reason="UperNet does not have a base model" )
def UpperCamelCase__ ( self ):
pass
@unittest.skip(reason="UperNet does not have a base model" )
def UpperCamelCase__ ( self ):
pass
@require_torch_multi_gpu
@unittest.skip(reason="UperNet has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`" )
def UpperCamelCase__ ( self ):
pass
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
def UpperCamelCase__ ( self ):
pass
def UpperCamelCase__ ( self ):
def check_hidden_states_output(_snake_case ,_snake_case ,_snake_case ):
UpperCAmelCase_ : Union[str, Any] = model_class(_a )
model.to(_a )
model.eval()
with torch.no_grad():
UpperCAmelCase_ : Any = model(**self._prepare_for_class(_a ,_a ) )
UpperCAmelCase_ : Optional[Any] = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
UpperCAmelCase_ : Optional[int] = self.model_tester.num_stages
self.assertEqual(len(_a ) ,expected_num_stages + 1 )
# ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) ,[self.model_tester.image_size // 4, self.model_tester.image_size // 4] ,)
UpperCAmelCase_ : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase_ : int = True
check_hidden_states_output(_a ,_a ,_a )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
UpperCAmelCase_ : int = True
check_hidden_states_output(_a ,_a ,_a )
def UpperCamelCase__ ( self ):
UpperCAmelCase_ : int = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase_ : List[str] = _config_zero_init(_a )
UpperCAmelCase_ : Tuple = _config_zero_init(configs_no_init.backbone_config )
for model_class in self.all_model_classes:
UpperCAmelCase_ : Dict = model_class(config=_a )
for name, param in model.named_parameters():
if param.requires_grad:
self.assertIn(
((param.data.mean() * 1E9).round() / 1E9).item() ,[0.0, 1.0] ,msg=f'''Parameter {name} of model {model_class} seems not properly initialized''' ,)
@unittest.skip(reason="UperNet does not have tied weights" )
def UpperCamelCase__ ( self ):
pass
@slow
def UpperCamelCase__ ( self ):
for model_name in UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCAmelCase_ : Tuple = UperNetForSemanticSegmentation.from_pretrained(_a )
self.assertIsNotNone(_a )
def a__ ( ) -> Dict:
"""simple docstring"""
UpperCAmelCase_ : str = hf_hub_download(
repo_id="hf-internal-testing/fixtures_ade20k" , repo_type="dataset" , filename="ADE_val_00000001.jpg" )
UpperCAmelCase_ : Optional[int] = Image.open(__a ).convert("RGB" )
return image
@require_torch
@require_vision
@slow
class _snake_case (unittest.TestCase):
def UpperCamelCase__ ( self ):
UpperCAmelCase_ : List[str] = AutoImageProcessor.from_pretrained("openmmlab/upernet-swin-tiny" )
UpperCAmelCase_ : List[str] = UperNetForSemanticSegmentation.from_pretrained("openmmlab/upernet-swin-tiny" ).to(_a )
UpperCAmelCase_ : List[str] = prepare_img()
UpperCAmelCase_ : Union[str, Any] = processor(images=_a ,return_tensors="pt" ).to(_a )
with torch.no_grad():
UpperCAmelCase_ : Union[str, Any] = model(**_a )
UpperCAmelCase_ : str = torch.Size((1, model.config.num_labels, 5_12, 5_12) )
self.assertEqual(outputs.logits.shape ,_a )
UpperCAmelCase_ : Tuple = torch.tensor(
[[-7.5958, -7.5958, -7.4302], [-7.5958, -7.5958, -7.4302], [-7.4797, -7.4797, -7.3068]] ).to(_a )
self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3] ,_a ,atol=1E-4 ) )
def UpperCamelCase__ ( self ):
UpperCAmelCase_ : Tuple = AutoImageProcessor.from_pretrained("openmmlab/upernet-convnext-tiny" )
UpperCAmelCase_ : Union[str, Any] = UperNetForSemanticSegmentation.from_pretrained("openmmlab/upernet-convnext-tiny" ).to(_a )
UpperCAmelCase_ : Dict = prepare_img()
UpperCAmelCase_ : Union[str, Any] = processor(images=_a ,return_tensors="pt" ).to(_a )
with torch.no_grad():
UpperCAmelCase_ : Dict = model(**_a )
UpperCAmelCase_ : int = torch.Size((1, model.config.num_labels, 5_12, 5_12) )
self.assertEqual(outputs.logits.shape ,_a )
UpperCAmelCase_ : Any = torch.tensor(
[[-8.8110, -8.8110, -8.6521], [-8.8110, -8.8110, -8.6521], [-8.7746, -8.7746, -8.6130]] ).to(_a )
self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3] ,_a ,atol=1E-4 ) )
| 71 |
import json
import os
import unittest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class UpperCAmelCase_ ( __lowercase , unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase__ : List[Any] = MgpstrTokenizer
UpperCAmelCase__ : int = False
UpperCAmelCase__ : Union[str, Any] = {}
UpperCAmelCase__ : List[Any] = False
def __lowercase ( self ) -> Any:
super().setUp()
# fmt: off
_a : Tuple = ['''[GO]''', '''[s]''', '''0''', '''1''', '''2''', '''3''', '''4''', '''5''', '''6''', '''7''', '''8''', '''9''', '''a''', '''b''', '''c''', '''d''', '''e''', '''f''', '''g''', '''h''', '''i''', '''j''', '''k''', '''l''', '''m''', '''n''', '''o''', '''p''', '''q''', '''r''', '''s''', '''t''', '''u''', '''v''', '''w''', '''x''', '''y''', '''z''']
# fmt: on
_a : Optional[int] = dict(zip(_a , range(len(_a ) ) ) )
_a : Any = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write(json.dumps(_a ) + '''\n''' )
def __lowercase ( self , **_a ) -> Dict:
return MgpstrTokenizer.from_pretrained(self.tmpdirname , **_a )
def __lowercase ( self , _a ) -> Tuple:
_a : List[str] = '''tester'''
_a : Optional[Any] = '''tester'''
return input_text, output_text
@unittest.skip('''MGP-STR always lower cases letters.''' )
def __lowercase ( self ) -> Any:
pass
def __lowercase ( self ) -> Any:
_a : Union[str, Any] = self.get_tokenizers(do_lower_case=_a )
for tokenizer in tokenizers:
with self.subTest(F"""{tokenizer.__class__.__name__}""" ):
_a : int = '''[SPECIAL_TOKEN]'''
tokenizer.add_special_tokens({'''cls_token''': special_token} )
_a : Tuple = tokenizer.encode([special_token] , add_special_tokens=_a )
self.assertEqual(len(_a ) , 1 )
_a : Tuple = tokenizer.decode(_a , skip_special_tokens=_a )
self.assertTrue(special_token not in decoded )
def __lowercase ( self ) -> Tuple:
_a : List[Any] = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(F"""{tokenizer.__class__.__name__}""" ):
_a , _a : int = self.get_input_output_texts(_a )
_a : List[str] = tokenizer.tokenize(_a )
_a : Optional[int] = tokenizer.convert_tokens_to_ids(_a )
_a : Tuple = tokenizer.encode(_a , add_special_tokens=_a )
self.assertListEqual(_a , _a )
_a : Optional[int] = tokenizer.convert_ids_to_tokens(_a )
self.assertNotEqual(len(_a ) , 0 )
_a : int = tokenizer.decode(_a )
self.assertIsInstance(_a , _a )
self.assertEqual(text_a.replace(''' ''' , '''''' ) , _a )
@unittest.skip('''MGP-STR tokenizer only handles one sequence.''' )
def __lowercase ( self ) -> List[str]:
pass
@unittest.skip('''inputs cannot be pretokenized in MgpstrTokenizer''' )
def __lowercase ( self ) -> Optional[Any]:
pass
| 14 | 0 |
"""simple docstring"""
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
__A : Union[str, Any] = logging.get_logger(__name__)
class _a :
"""simple docstring"""
def __init__( self : str , __UpperCamelCase : Tuple = None , __UpperCamelCase : int = None , __UpperCamelCase : Union[str, Any]=None , __UpperCamelCase : int=None )->Union[str, Any]:
if not conversation_id:
_UpperCAmelCase = uuid.uuida()
if past_user_inputs is None:
_UpperCAmelCase = []
if generated_responses is None:
_UpperCAmelCase = []
_UpperCAmelCase = conversation_id
_UpperCAmelCase = past_user_inputs
_UpperCAmelCase = generated_responses
_UpperCAmelCase = text
def __eq__( self : List[Any] , __UpperCamelCase : Optional[int] )->Any:
if not isinstance(_a , _a ):
return False
if self.uuid == other.uuid:
return True
return (
self.new_user_input == other.new_user_input
and self.past_user_inputs == other.past_user_inputs
and self.generated_responses == other.generated_responses
)
def lowercase__ ( self : Dict , __UpperCamelCase : Optional[Any] , __UpperCamelCase : List[str] = False )->List[str]:
if self.new_user_input:
if overwrite:
logger.warning(
F'User input added while unprocessed input was existing: \"{self.new_user_input}\" was overwritten '
F'with: \"{text}\".' )
_UpperCAmelCase = text
else:
logger.warning(
F'User input added while unprocessed input was existing: \"{self.new_user_input}\" new input '
F'ignored: \"{text}\". Set `overwrite` to True to overwrite unprocessed user input' )
else:
_UpperCAmelCase = text
def lowercase__ ( self : str )->Optional[int]:
if self.new_user_input:
self.past_user_inputs.append(self.new_user_input )
_UpperCAmelCase = None
def lowercase__ ( self : int , __UpperCamelCase : Optional[int] )->Optional[Any]:
self.generated_responses.append(_a )
def lowercase__ ( self : List[Any] )->str:
for user_input, generated_response in zip(self.past_user_inputs , self.generated_responses ):
yield True, user_input
yield False, generated_response
if self.new_user_input:
yield True, self.new_user_input
def __repr__( self : List[str] )->Optional[int]:
_UpperCAmelCase = F'Conversation id: {self.uuid} \n'
for is_user, text in self.iter_texts():
_UpperCAmelCase = '''user''' if is_user else '''bot'''
output += F'{name} >> {text} \n'
return output
@add_end_docstrings(
__lowercase , r"""\n min_length_for_response (`int`, *optional*, defaults to 32):\n The minimum length (in number of tokens) for a response.\n minimum_tokens (`int`, *optional*, defaults to 10):\n The minimum length of tokens to leave for a response.\n """ , )
class _a ( __lowercase):
"""simple docstring"""
def __init__( self : Tuple , *__UpperCamelCase : Union[str, Any] , **__UpperCamelCase : int )->List[str]:
super().__init__(*_a , **_a )
if self.tokenizer.pad_token_id is None:
_UpperCAmelCase = self.tokenizer.eos_token
def lowercase__ ( self : Any , __UpperCamelCase : int=None , __UpperCamelCase : List[str]=None , __UpperCamelCase : Optional[Any]=None , **__UpperCamelCase : Optional[int] )->Union[str, Any]:
_UpperCAmelCase = {}
_UpperCAmelCase = {}
_UpperCAmelCase = {}
if min_length_for_response is not None:
_UpperCAmelCase = min_length_for_response
if minimum_tokens is not None:
_UpperCAmelCase = minimum_tokens
if "max_length" in generate_kwargs:
_UpperCAmelCase = generate_kwargs['''max_length''']
# self.max_length = generate_kwargs.get("max_length", self.model.config.max_length)
if clean_up_tokenization_spaces is not None:
_UpperCAmelCase = clean_up_tokenization_spaces
if generate_kwargs:
forward_params.update(_a )
return preprocess_params, forward_params, postprocess_params
def __call__( self : Dict , __UpperCamelCase : Optional[int] , __UpperCamelCase : int=0 , **__UpperCamelCase : int )->int:
_UpperCAmelCase = super().__call__(_a , num_workers=_a , **_a )
if isinstance(_a , _a ) and len(_a ) == 1:
return outputs[0]
return outputs
def lowercase__ ( self : Optional[int] , __UpperCamelCase : Any , __UpperCamelCase : Union[str, Any]=3_2 )->Dict[str, Any]:
if not isinstance(_a , _a ):
raise ValueError('''ConversationalPipeline, expects Conversation as inputs''' )
if conversation.new_user_input is None:
raise ValueError(
F'Conversation with UUID {type(conversation.uuid )} does not contain new user input to process. '
'''Add user inputs with the conversation\'s `add_user_input` method''' )
if hasattr(self.tokenizer , '''_build_conversation_input_ids''' ):
_UpperCAmelCase = self.tokenizer._build_conversation_input_ids(_a )
else:
# If the tokenizer cannot handle conversations, we default to only the old version
_UpperCAmelCase = self._legacy_parse_and_tokenize(_a )
if self.framework == "pt":
_UpperCAmelCase = torch.LongTensor([input_ids] )
elif self.framework == "tf":
_UpperCAmelCase = tf.constant([input_ids] )
return {"input_ids": input_ids, "conversation": conversation}
def lowercase__ ( self : Tuple , __UpperCamelCase : Any , __UpperCamelCase : str=1_0 , **__UpperCamelCase : Optional[int] )->Union[str, Any]:
_UpperCAmelCase = generate_kwargs.get('''max_length''' , self.model.config.max_length )
_UpperCAmelCase = model_inputs['''input_ids'''].shape[1]
if max_length - minimum_tokens < n:
logger.warning(F'Conversation input is to long ({n}), trimming it to ({max_length} - {minimum_tokens})' )
_UpperCAmelCase = max_length - minimum_tokens
_UpperCAmelCase = model_inputs['''input_ids'''][:, -trim:]
if "attention_mask" in model_inputs:
_UpperCAmelCase = model_inputs['''attention_mask'''][:, -trim:]
_UpperCAmelCase = model_inputs.pop('''conversation''' )
_UpperCAmelCase = max_length
_UpperCAmelCase = self.model.generate(**_a , **_a )
if self.model.config.is_encoder_decoder:
_UpperCAmelCase = 1
else:
_UpperCAmelCase = n
return {"output_ids": output_ids[:, start_position:], "conversation": conversation}
def lowercase__ ( self : Dict , __UpperCamelCase : List[str] , __UpperCamelCase : List[Any]=True )->Union[str, Any]:
_UpperCAmelCase = model_outputs['''output_ids''']
_UpperCAmelCase = self.tokenizer.decode(
output_ids[0] , skip_special_tokens=_a , clean_up_tokenization_spaces=_a , )
_UpperCAmelCase = model_outputs['''conversation''']
conversation.mark_processed()
conversation.append_response(_a )
return conversation
def lowercase__ ( self : Optional[int] , __UpperCamelCase : Any )->Dict:
_UpperCAmelCase = self.tokenizer.eos_token_id
_UpperCAmelCase = []
for is_user, text in conversation.iter_texts():
if eos_token_id is not None:
input_ids.extend(self.tokenizer.encode(_a , add_special_tokens=_a ) + [eos_token_id] )
else:
input_ids.extend(self.tokenizer.encode(_a , add_special_tokens=_a ) )
if len(_a ) > self.tokenizer.model_max_length:
_UpperCAmelCase = input_ids[-self.tokenizer.model_max_length :]
return input_ids
| 602 |
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 UpperCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
def __lowercase ( self ) -> List[Any]:
_a : int = 0
def __lowercase ( self ) -> List[str]:
_a : Dict = AutoImageProcessor.from_pretrained('''openai/clip-vit-base-patch32''' )
self.assertIsInstance(_a , _a )
def __lowercase ( self ) -> Union[str, Any]:
with tempfile.TemporaryDirectory() as tmpdirname:
_a : Tuple = Path(_a ) / '''preprocessor_config.json'''
_a : Optional[Any] = Path(_a ) / '''config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(_a , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(_a , '''w''' ) )
_a : List[str] = AutoImageProcessor.from_pretrained(_a )
self.assertIsInstance(_a , _a )
def __lowercase ( self ) -> Optional[Any]:
# Ensure we can load the image processor from the feature extractor config
with tempfile.TemporaryDirectory() as tmpdirname:
_a : Optional[int] = Path(_a ) / '''preprocessor_config.json'''
_a : Any = Path(_a ) / '''config.json'''
json.dump(
{'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''} , open(_a , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(_a , '''w''' ) )
_a : Optional[Any] = AutoImageProcessor.from_pretrained(_a )
self.assertIsInstance(_a , _a )
def __lowercase ( self ) -> Optional[Any]:
with tempfile.TemporaryDirectory() as tmpdirname:
_a : Dict = CLIPConfig()
# Create a dummy config file with image_proceesor_type
_a : Tuple = Path(_a ) / '''preprocessor_config.json'''
_a : List[str] = Path(_a ) / '''config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(_a , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(_a , '''w''' ) )
# remove image_processor_type to make sure config.json alone is enough to load image processor locally
_a : Tuple = AutoImageProcessor.from_pretrained(_a ).to_dict()
config_dict.pop('''image_processor_type''' )
_a : Tuple = CLIPImageProcessor(**_a )
# save in new folder
model_config.save_pretrained(_a )
config.save_pretrained(_a )
_a : List[str] = AutoImageProcessor.from_pretrained(_a )
# make sure private variable is not incorrectly saved
_a : Optional[int] = json.loads(config.to_json_string() )
self.assertTrue('''_processor_class''' not in dict_as_saved )
self.assertIsInstance(_a , _a )
def __lowercase ( self ) -> Dict:
with tempfile.TemporaryDirectory() as tmpdirname:
_a : Optional[int] = Path(_a ) / '''preprocessor_config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(_a , '''w''' ) , )
_a : List[str] = AutoImageProcessor.from_pretrained(_a )
self.assertIsInstance(_a , _a )
def __lowercase ( self ) -> Any:
with self.assertRaisesRegex(
_a , '''clip-base is not a local folder and is not a valid model identifier''' ):
_a : Dict = AutoImageProcessor.from_pretrained('''clip-base''' )
def __lowercase ( self ) -> List[Any]:
with self.assertRaisesRegex(
_a , R'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)''' ):
_a : List[str] = AutoImageProcessor.from_pretrained(_a , revision='''aaaaaa''' )
def __lowercase ( self ) -> Dict:
with self.assertRaisesRegex(
_a , '''hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.''' , ):
_a : Optional[int] = AutoImageProcessor.from_pretrained('''hf-internal-testing/config-no-model''' )
def __lowercase ( self ) -> Union[str, Any]:
# If remote code is not set, we will time out when asking whether to load the model.
with self.assertRaises(_a ):
_a : str = AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor''' )
# If remote code is disabled, we can't load this config.
with self.assertRaises(_a ):
_a : Optional[Any] = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=_a )
_a : Union[str, Any] = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=_a )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
# Test image processor can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(_a )
_a : Optional[Any] = AutoImageProcessor.from_pretrained(_a , trust_remote_code=_a )
self.assertEqual(reloaded_image_processor.__class__.__name__ , '''NewImageProcessor''' )
def __lowercase ( self ) -> Dict:
try:
AutoConfig.register('''custom''' , _a )
AutoImageProcessor.register(_a , _a )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(_a ):
AutoImageProcessor.register(_a , _a )
with tempfile.TemporaryDirectory() as tmpdirname:
_a : int = Path(_a ) / '''preprocessor_config.json'''
_a : int = Path(_a ) / '''config.json'''
json.dump(
{'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''} , open(_a , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(_a , '''w''' ) )
_a : int = CustomImageProcessor.from_pretrained(_a )
# 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(_a )
_a : Optional[Any] = AutoImageProcessor.from_pretrained(_a )
self.assertIsInstance(_a , _a )
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 __lowercase ( self ) -> Union[str, Any]:
class UpperCAmelCase_ ( __lowercase ):
"""simple docstring"""
UpperCAmelCase__ : Tuple = True
try:
AutoConfig.register('''custom''' , _a )
AutoImageProcessor.register(_a , _a )
# If remote code is not set, the default is to use local
_a : str = 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 : int = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=_a )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
self.assertTrue(image_processor.is_local )
# If remote is enabled, we load from the Hub
_a : Dict = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=_a )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
self.assertTrue(not hasattr(_a , '''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]
| 14 | 0 |
"""simple docstring"""
import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request
from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging import version
from .. import __version__
from . import DIFFUSERS_DYNAMIC_MODULE_NAME, HF_MODULES_CACHE, logging
__SCREAMING_SNAKE_CASE : int = (
'''https://raw.githubusercontent.com/huggingface/diffusers/{revision}/examples/community/{pipeline}.py'''
)
__SCREAMING_SNAKE_CASE : List[Any] = logging.get_logger(__name__) # pylint: disable=invalid-name
def lowerCAmelCase_( ) -> Union[str, Any]:
_lowerCamelCase = '''https://pypi.org/pypi/diffusers/json'''
_lowerCamelCase = json.loads(request.urlopen(__a ).read() )['''releases'''].keys()
return sorted(__a , key=lambda lowercase_ : version.Version(__a ) )
def lowerCAmelCase_( ) -> List[str]:
if HF_MODULES_CACHE in sys.path:
return
sys.path.append(__a )
os.makedirs(__a , exist_ok=__a )
_lowerCamelCase = Path(__a ) / '''__init__.py'''
if not init_path.exists():
init_path.touch()
def lowerCAmelCase_( lowercase_ : Union[str, os.PathLike] ) -> Optional[Any]:
init_hf_modules()
_lowerCamelCase = Path(__a ) / name
# If the parent module does not exist yet, recursively create it.
if not dynamic_module_path.parent.exists():
create_dynamic_module(dynamic_module_path.parent )
os.makedirs(__a , exist_ok=__a )
_lowerCamelCase = dynamic_module_path / '''__init__.py'''
if not init_path.exists():
init_path.touch()
def lowerCAmelCase_( lowercase_ : Union[str, Any] ) -> Union[str, Any]:
with open(__a , '''r''' , encoding='''utf-8''' ) as f:
_lowerCamelCase = f.read()
# Imports of the form `import .xxx`
_lowerCamelCase = re.findall('''^\s*import\s+\.(\S+)\s*$''' , __a , flags=re.MULTILINE )
# Imports of the form `from .xxx import yyy`
relative_imports += re.findall('''^\s*from\s+\.(\S+)\s+import''' , __a , flags=re.MULTILINE )
# Unique-ify
return list(set(__a ) )
def lowerCAmelCase_( lowercase_ : Union[str, Any] ) -> Union[str, Any]:
_lowerCamelCase = False
_lowerCamelCase = [module_file]
_lowerCamelCase = []
# Let's recurse through all relative imports
while not no_change:
_lowerCamelCase = []
for f in files_to_check:
new_imports.extend(get_relative_imports(__a ) )
_lowerCamelCase = Path(__a ).parent
_lowerCamelCase = [str(module_path / m ) for m in new_imports]
_lowerCamelCase = [f for f in new_import_files if f not in all_relative_imports]
_lowerCamelCase = [F"""{f}.py""" for f in new_import_files]
_lowerCamelCase = len(__a ) == 0
all_relative_imports.extend(__a )
return all_relative_imports
def lowerCAmelCase_( lowercase_ : str ) -> Any:
with open(__a , '''r''' , encoding='''utf-8''' ) as f:
_lowerCamelCase = f.read()
# Imports of the form `import xxx`
_lowerCamelCase = re.findall('''^\s*import\s+(\S+)\s*$''' , __a , flags=re.MULTILINE )
# Imports of the form `from xxx import yyy`
imports += re.findall('''^\s*from\s+(\S+)\s+import''' , __a , flags=re.MULTILINE )
# Only keep the top-level module
_lowerCamelCase = [imp.split('''.''' )[0] for imp in imports if not imp.startswith('''.''' )]
# Unique-ify and test we got them all
_lowerCamelCase = list(set(__a ) )
_lowerCamelCase = []
for imp in imports:
try:
importlib.import_module(__a )
except ImportError:
missing_packages.append(__a )
if len(__a ) > 0:
raise ImportError(
'''This modeling file requires the following packages that were not found in your environment: '''
F"""{", ".join(__a )}. Run `pip install {" ".join(__a )}`""" )
return get_relative_imports(__a )
def lowerCAmelCase_( lowercase_ : Optional[int] , lowercase_ : Optional[Any] ) -> Optional[int]:
_lowerCamelCase = module_path.replace(os.path.sep , '''.''' )
_lowerCamelCase = importlib.import_module(__a )
if class_name is None:
return find_pipeline_class(__a )
return getattr(__a , __a )
def lowerCAmelCase_( lowercase_ : Any ) -> List[Any]:
from ..pipelines import DiffusionPipeline
_lowerCamelCase = dict(inspect.getmembers(__a , inspect.isclass ) )
_lowerCamelCase = None
for cls_name, cls in cls_members.items():
if (
cls_name != DiffusionPipeline.__name__
and issubclass(cls , __a )
and cls.__module__.split('''.''' )[0] != "diffusers"
):
if pipeline_class is not None:
raise ValueError(
F"""Multiple classes that inherit from {DiffusionPipeline.__name__} have been found:"""
F""" {pipeline_class.__name__}, and {cls_name}. Please make sure to define only one in"""
F""" {loaded_module}.""" )
_lowerCamelCase = cls
return pipeline_class
def lowerCAmelCase_( lowercase_ : Union[str, os.PathLike] , lowercase_ : str , lowercase_ : Optional[Union[str, os.PathLike]] = None , lowercase_ : bool = False , lowercase_ : bool = False , lowercase_ : Optional[Dict[str, str]] = None , lowercase_ : Optional[Union[bool, str]] = None , lowercase_ : Optional[str] = None , lowercase_ : bool = False , ) -> Optional[Any]:
_lowerCamelCase = str(__a )
_lowerCamelCase = os.path.join(__a , __a )
if os.path.isfile(__a ):
_lowerCamelCase = module_file_or_url
_lowerCamelCase = '''local'''
elif pretrained_model_name_or_path.count('''/''' ) == 0:
_lowerCamelCase = get_diffusers_versions()
# cut ".dev0"
_lowerCamelCase = '''v''' + '''.'''.join(__version__.split('''.''' )[:3] )
# retrieve github version that matches
if revision is None:
_lowerCamelCase = latest_version if latest_version[1:] in available_versions else '''main'''
logger.info(F"""Defaulting to latest_version: {revision}.""" )
elif revision in available_versions:
_lowerCamelCase = F"""v{revision}"""
elif revision == "main":
_lowerCamelCase = revision
else:
raise ValueError(
F"""`custom_revision`: {revision} does not exist. Please make sure to choose one of"""
F""" {", ".join(available_versions + ["main"] )}.""" )
# community pipeline on GitHub
_lowerCamelCase = COMMUNITY_PIPELINES_URL.format(revision=__a , pipeline=__a )
try:
_lowerCamelCase = cached_download(
__a , cache_dir=__a , force_download=__a , proxies=__a , resume_download=__a , local_files_only=__a , use_auth_token=__a , )
_lowerCamelCase = '''git'''
_lowerCamelCase = pretrained_model_name_or_path + '''.py'''
except EnvironmentError:
logger.error(F"""Could not locate the {module_file} inside {pretrained_model_name_or_path}.""" )
raise
else:
try:
# Load from URL or cache if already cached
_lowerCamelCase = hf_hub_download(
__a , __a , cache_dir=__a , force_download=__a , proxies=__a , resume_download=__a , local_files_only=__a , use_auth_token=__a , )
_lowerCamelCase = os.path.join('''local''' , '''--'''.join(pretrained_model_name_or_path.split('''/''' ) ) )
except EnvironmentError:
logger.error(F"""Could not locate the {module_file} inside {pretrained_model_name_or_path}.""" )
raise
# Check we have all the requirements in our environment
_lowerCamelCase = check_imports(__a )
# Now we move the module inside our cached dynamic modules.
_lowerCamelCase = DIFFUSERS_DYNAMIC_MODULE_NAME + os.path.sep + submodule
create_dynamic_module(__a )
_lowerCamelCase = Path(__a ) / full_submodule
if submodule == "local" or submodule == "git":
# We always copy local files (we could hash the file to see if there was a change, and give them the name of
# that hash, to only copy when there is a modification but it seems overkill for now).
# The only reason we do the copy is to avoid putting too many folders in sys.path.
shutil.copy(__a , submodule_path / module_file )
for module_needed in modules_needed:
_lowerCamelCase = F"""{module_needed}.py"""
shutil.copy(os.path.join(__a , __a ) , submodule_path / module_needed )
else:
# Get the commit hash
# TODO: we will get this info in the etag soon, so retrieve it from there and not here.
if isinstance(__a , __a ):
_lowerCamelCase = use_auth_token
elif use_auth_token is True:
_lowerCamelCase = HfFolder.get_token()
else:
_lowerCamelCase = None
_lowerCamelCase = model_info(__a , revision=__a , token=__a ).sha
# The module file will end up being placed in a subfolder with the git hash of the repo. This way we get the
# benefit of versioning.
_lowerCamelCase = submodule_path / commit_hash
_lowerCamelCase = full_submodule + os.path.sep + commit_hash
create_dynamic_module(__a )
if not (submodule_path / module_file).exists():
shutil.copy(__a , submodule_path / module_file )
# Make sure we also have every file with relative
for module_needed in modules_needed:
if not (submodule_path / module_needed).exists():
get_cached_module_file(
__a , F"""{module_needed}.py""" , cache_dir=__a , force_download=__a , resume_download=__a , proxies=__a , use_auth_token=__a , revision=__a , local_files_only=__a , )
return os.path.join(__a , __a )
def lowerCAmelCase_( lowercase_ : Union[str, os.PathLike] , lowercase_ : str , lowercase_ : Optional[str] = None , lowercase_ : Optional[Union[str, os.PathLike]] = None , lowercase_ : bool = False , lowercase_ : bool = False , lowercase_ : Optional[Dict[str, str]] = None , lowercase_ : Optional[Union[bool, str]] = None , lowercase_ : Optional[str] = None , lowercase_ : bool = False , **lowercase_ : Tuple , ) -> int:
_lowerCamelCase = get_cached_module_file(
__a , __a , cache_dir=__a , force_download=__a , resume_download=__a , proxies=__a , use_auth_token=__a , revision=__a , local_files_only=__a , )
return get_class_in_module(__a , final_module.replace('''.py''' , '''''' ) )
| 661 |
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class UpperCAmelCase_ :
"""simple docstring"""
UpperCAmelCase__ : float
UpperCAmelCase__ : TreeNode | None = None
UpperCAmelCase__ : TreeNode | None = None
def __UpperCAmelCase ( __a : TreeNode | None ) -> bool:
"""simple docstring"""
def is_valid_tree(__a : TreeNode | None ) -> bool:
if node is None:
return True
if not isinstance(__a ,__a ):
return False
try:
float(node.data )
except (TypeError, ValueError):
return False
return is_valid_tree(node.left ) and is_valid_tree(node.right )
if not is_valid_tree(__a ):
raise ValueError(
'''Each node should be type of TreeNode and data should be float.''' )
def is_binary_search_tree_recursive_check(
__a : TreeNode | None ,__a : float ,__a : float ) -> bool:
if node is None:
return True
return (
left_bound < node.data < right_bound
and is_binary_search_tree_recursive_check(node.left ,__a ,node.data )
and is_binary_search_tree_recursive_check(
node.right ,node.data ,__a )
)
return is_binary_search_tree_recursive_check(__a ,-float('''inf''' ) ,float('''inf''' ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 14 | 0 |
from argparse import ArgumentParser, Namespace
from ..utils import logging
from . import BaseTransformersCLICommand
def _A (UpperCamelCase : Namespace ) ->Dict:
'''simple docstring'''
return ConvertCommand(
args.model_type , args.tf_checkpoint , args.pytorch_dump_output , args.config , args.finetuning_task_name )
_lowercase = '''
transformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires
TensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.
'''
class __A ( __lowercase ):
@staticmethod
def _snake_case (__magic_name__ ):
lowerCamelCase__ : Union[str, Any] = parser.add_parser(
"""convert""" , help="""CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints.""" , )
train_parser.add_argument("""--model_type""" , type=_a , required=_a , help="""Model\'s type.""" )
train_parser.add_argument(
"""--tf_checkpoint""" , type=_a , required=_a , help="""TensorFlow checkpoint path or folder.""" )
train_parser.add_argument(
"""--pytorch_dump_output""" , type=_a , required=_a , help="""Path to the PyTorch saved model output.""" )
train_parser.add_argument("""--config""" , type=_a , default="""""" , help="""Configuration file path or folder.""" )
train_parser.add_argument(
"""--finetuning_task_name""" , type=_a , default=_a , help="""Optional fine-tuning task name if the TF model was a finetuned model.""" , )
train_parser.set_defaults(func=_a )
def __init__(self , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , *__magic_name__ , ):
lowerCamelCase__ : str = logging.get_logger("""transformers-cli/converting""" )
self._logger.info(f"Loading model {model_type}" )
lowerCamelCase__ : int = model_type
lowerCamelCase__ : Optional[Any] = tf_checkpoint
lowerCamelCase__ : Union[str, Any] = pytorch_dump_output
lowerCamelCase__ : Tuple = config
lowerCamelCase__ : str = finetuning_task_name
def _snake_case (self ):
if self._model_type == "albert":
try:
from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
convert_tf_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(_a )
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "bert":
try:
from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
convert_tf_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(_a )
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "funnel":
try:
from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
convert_tf_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(_a )
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "t5":
try:
from ..models.ta.convert_ta_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
except ImportError:
raise ImportError(_a )
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "gpt":
from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
convert_openai_checkpoint_to_pytorch,
)
convert_openai_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "transfo_xl":
try:
from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
convert_transfo_xl_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(_a )
if "ckpt" in self._tf_checkpoint.lower():
lowerCamelCase__ : Union[str, Any] = self._tf_checkpoint
lowerCamelCase__ : str = ''''''
else:
lowerCamelCase__ : int = self._tf_checkpoint
lowerCamelCase__ : str = ''''''
convert_transfo_xl_checkpoint_to_pytorch(
_a , self._config , self._pytorch_dump_output , _a )
elif self._model_type == "gpt2":
try:
from ..models.gpta.convert_gpta_original_tf_checkpoint_to_pytorch import (
convert_gpta_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(_a )
convert_gpta_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "xlnet":
try:
from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
convert_xlnet_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(_a )
convert_xlnet_checkpoint_to_pytorch(
self._tf_checkpoint , self._config , self._pytorch_dump_output , self._finetuning_task_name )
elif self._model_type == "xlm":
from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
convert_xlm_checkpoint_to_pytorch,
)
convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint , self._pytorch_dump_output )
elif self._model_type == "lxmert":
from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
convert_lxmert_checkpoint_to_pytorch,
)
convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint , self._pytorch_dump_output )
elif self._model_type == "rembert":
from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
convert_rembert_tf_checkpoint_to_pytorch,
)
convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
else:
raise ValueError(
"""--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]""" )
| 157 |
from __future__ import annotations
import matplotlib.pyplot as plt # type: ignore
import numpy
# initial triangle of Koch snowflake
a__ = numpy.array([0, 0])
a__ = numpy.array([0.5, 0.8660254])
a__ = numpy.array([1, 0])
a__ = [VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]
def __UpperCAmelCase ( __a : list[numpy.ndarray] ,__a : int ) -> list[numpy.ndarray]:
"""simple docstring"""
_a : Tuple = initial_vectors
for _ in range(__a ):
_a : int = iteration_step(__a )
return vectors
def __UpperCAmelCase ( __a : list[numpy.ndarray] ) -> list[numpy.ndarray]:
"""simple docstring"""
_a : Tuple = []
for i, start_vector in enumerate(vectors[:-1] ):
_a : str = vectors[i + 1]
new_vectors.append(__a )
_a : Optional[int] = end_vector - start_vector
new_vectors.append(start_vector + difference_vector / 3 )
new_vectors.append(
start_vector + difference_vector / 3 + rotate(difference_vector / 3 ,60 ) )
new_vectors.append(start_vector + difference_vector * 2 / 3 )
new_vectors.append(vectors[-1] )
return new_vectors
def __UpperCAmelCase ( __a : numpy.ndarray ,__a : float ) -> numpy.ndarray:
"""simple docstring"""
_a : Tuple = numpy.radians(__a )
_a , _a : List[Any] = numpy.cos(__a ), numpy.sin(__a )
_a : Dict = numpy.array(((c, -s), (s, c)) )
return numpy.dot(__a ,__a )
def __UpperCAmelCase ( __a : list[numpy.ndarray] ) -> None:
"""simple docstring"""
_a : str = plt.gca()
axes.set_aspect('''equal''' )
# matplotlib.pyplot.plot takes a list of all x-coordinates and a list of all
# y-coordinates as inputs, which are constructed from the vector-list using
# zip()
_a , _a : Optional[int] = zip(*__a )
plt.plot(__a ,__a )
plt.show()
if __name__ == "__main__":
import doctest
doctest.testmod()
a__ = iterate(INITIAL_VECTORS, 5)
plot(processed_vectors)
| 14 | 0 |
import argparse
import json
import torch
from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def a__ ( _UpperCamelCase : int ,_UpperCamelCase : Dict=1 ):
if n_shave_prefix_segments >= 0:
return ".".join(path.split('''.''' )[n_shave_prefix_segments:] )
else:
return ".".join(path.split('''.''' )[:n_shave_prefix_segments] )
def a__ ( _UpperCamelCase : List[Any] ,_UpperCamelCase : List[str]=0 ):
__lowerCamelCase = []
for old_item in old_list:
__lowerCamelCase = old_item.replace('''in_layers.0''' ,'''norm1''' )
__lowerCamelCase = new_item.replace('''in_layers.2''' ,'''conv1''' )
__lowerCamelCase = new_item.replace('''out_layers.0''' ,'''norm2''' )
__lowerCamelCase = new_item.replace('''out_layers.3''' ,'''conv2''' )
__lowerCamelCase = new_item.replace('''emb_layers.1''' ,'''time_emb_proj''' )
__lowerCamelCase = new_item.replace('''skip_connection''' ,'''conv_shortcut''' )
__lowerCamelCase = shave_segments(__a ,n_shave_prefix_segments=__a )
mapping.append({'''old''': old_item, '''new''': new_item} )
return mapping
def a__ ( _UpperCamelCase : str ,_UpperCamelCase : Tuple=0 ):
__lowerCamelCase = []
for old_item in old_list:
__lowerCamelCase = old_item
__lowerCamelCase = new_item.replace('''norm.weight''' ,'''group_norm.weight''' )
__lowerCamelCase = new_item.replace('''norm.bias''' ,'''group_norm.bias''' )
__lowerCamelCase = new_item.replace('''proj_out.weight''' ,'''proj_attn.weight''' )
__lowerCamelCase = new_item.replace('''proj_out.bias''' ,'''proj_attn.bias''' )
__lowerCamelCase = shave_segments(__a ,n_shave_prefix_segments=__a )
mapping.append({'''old''': old_item, '''new''': new_item} )
return mapping
def a__ ( _UpperCamelCase : Tuple ,_UpperCamelCase : Dict ,_UpperCamelCase : Dict ,_UpperCamelCase : List[str]=None ,_UpperCamelCase : Any=None ,_UpperCamelCase : List[str]=None ):
assert isinstance(__a ,__a ), "Paths should be a list of dicts containing 'old' and 'new' keys."
# Splits the attention layers into three variables.
if attention_paths_to_split is not None:
for path, path_map in attention_paths_to_split.items():
__lowerCamelCase = old_checkpoint[path]
__lowerCamelCase = old_tensor.shape[0] // 3
__lowerCamelCase = (-1, channels) if len(old_tensor.shape ) == 3 else (-1)
__lowerCamelCase = old_tensor.shape[0] // config['''num_head_channels'''] // 3
__lowerCamelCase = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:] )
__lowerCamelCase = old_tensor.split(channels // num_heads ,dim=1 )
__lowerCamelCase = query.reshape(__a )
__lowerCamelCase = key.reshape(__a )
__lowerCamelCase = value.reshape(__a )
for path in paths:
__lowerCamelCase = path['''new''']
# These have already been assigned
if attention_paths_to_split is not None and new_path in attention_paths_to_split:
continue
# Global renaming happens here
__lowerCamelCase = new_path.replace('''middle_block.0''' ,'''mid_block.resnets.0''' )
__lowerCamelCase = new_path.replace('''middle_block.1''' ,'''mid_block.attentions.0''' )
__lowerCamelCase = new_path.replace('''middle_block.2''' ,'''mid_block.resnets.1''' )
if additional_replacements is not None:
for replacement in additional_replacements:
__lowerCamelCase = new_path.replace(replacement['''old'''] ,replacement['''new'''] )
# proj_attn.weight has to be converted from conv 1D to linear
if "proj_attn.weight" in new_path:
__lowerCamelCase = old_checkpoint[path['''old''']][:, :, 0]
else:
__lowerCamelCase = old_checkpoint[path['''old''']]
def a__ ( _UpperCamelCase : List[Any] ,_UpperCamelCase : Dict ):
__lowerCamelCase = {}
__lowerCamelCase = checkpoint['''time_embed.0.weight''']
__lowerCamelCase = checkpoint['''time_embed.0.bias''']
__lowerCamelCase = checkpoint['''time_embed.2.weight''']
__lowerCamelCase = checkpoint['''time_embed.2.bias''']
__lowerCamelCase = checkpoint['''input_blocks.0.0.weight''']
__lowerCamelCase = checkpoint['''input_blocks.0.0.bias''']
__lowerCamelCase = checkpoint['''out.0.weight''']
__lowerCamelCase = checkpoint['''out.0.bias''']
__lowerCamelCase = checkpoint['''out.2.weight''']
__lowerCamelCase = checkpoint['''out.2.bias''']
# Retrieves the keys for the input blocks only
__lowerCamelCase = len({'''.'''.join(layer.split('''.''' )[:2] ) for layer in checkpoint if '''input_blocks''' in layer} )
__lowerCamelCase = {
layer_id: [key for key in checkpoint if F"""input_blocks.{layer_id}""" in key]
for layer_id in range(__a )
}
# Retrieves the keys for the middle blocks only
__lowerCamelCase = len({'''.'''.join(layer.split('''.''' )[:2] ) for layer in checkpoint if '''middle_block''' in layer} )
__lowerCamelCase = {
layer_id: [key for key in checkpoint if F"""middle_block.{layer_id}""" in key]
for layer_id in range(__a )
}
# Retrieves the keys for the output blocks only
__lowerCamelCase = len({'''.'''.join(layer.split('''.''' )[:2] ) for layer in checkpoint if '''output_blocks''' in layer} )
__lowerCamelCase = {
layer_id: [key for key in checkpoint if F"""output_blocks.{layer_id}""" in key]
for layer_id in range(__a )
}
for i in range(1 ,__a ):
__lowerCamelCase = (i - 1) // (config['''num_res_blocks'''] + 1)
__lowerCamelCase = (i - 1) % (config['''num_res_blocks'''] + 1)
__lowerCamelCase = [key for key in input_blocks[i] if F"""input_blocks.{i}.0""" in key]
__lowerCamelCase = [key for key in input_blocks[i] if F"""input_blocks.{i}.1""" in key]
if F"""input_blocks.{i}.0.op.weight""" in checkpoint:
__lowerCamelCase = checkpoint[
F"""input_blocks.{i}.0.op.weight"""
]
__lowerCamelCase = checkpoint[
F"""input_blocks.{i}.0.op.bias"""
]
continue
__lowerCamelCase = renew_resnet_paths(__a )
__lowerCamelCase = {'''old''': F"""input_blocks.{i}.0""", '''new''': F"""down_blocks.{block_id}.resnets.{layer_in_block_id}"""}
__lowerCamelCase = {'''old''': '''resnets.2.op''', '''new''': '''downsamplers.0.op'''}
assign_to_checkpoint(
__a ,__a ,__a ,additional_replacements=[meta_path, resnet_op] ,config=__a )
if len(__a ):
__lowerCamelCase = renew_attention_paths(__a )
__lowerCamelCase = {
'''old''': F"""input_blocks.{i}.1""",
'''new''': F"""down_blocks.{block_id}.attentions.{layer_in_block_id}""",
}
__lowerCamelCase = {
F"""input_blocks.{i}.1.qkv.bias""": {
'''key''': F"""down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias""",
'''query''': F"""down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias""",
'''value''': F"""down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias""",
},
F"""input_blocks.{i}.1.qkv.weight""": {
'''key''': F"""down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight""",
'''query''': F"""down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight""",
'''value''': F"""down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight""",
},
}
assign_to_checkpoint(
__a ,__a ,__a ,additional_replacements=[meta_path] ,attention_paths_to_split=__a ,config=__a ,)
__lowerCamelCase = middle_blocks[0]
__lowerCamelCase = middle_blocks[1]
__lowerCamelCase = middle_blocks[2]
__lowerCamelCase = renew_resnet_paths(__a )
assign_to_checkpoint(__a ,__a ,__a ,config=__a )
__lowerCamelCase = renew_resnet_paths(__a )
assign_to_checkpoint(__a ,__a ,__a ,config=__a )
__lowerCamelCase = renew_attention_paths(__a )
__lowerCamelCase = {
'''middle_block.1.qkv.bias''': {
'''key''': '''mid_block.attentions.0.key.bias''',
'''query''': '''mid_block.attentions.0.query.bias''',
'''value''': '''mid_block.attentions.0.value.bias''',
},
'''middle_block.1.qkv.weight''': {
'''key''': '''mid_block.attentions.0.key.weight''',
'''query''': '''mid_block.attentions.0.query.weight''',
'''value''': '''mid_block.attentions.0.value.weight''',
},
}
assign_to_checkpoint(
__a ,__a ,__a ,attention_paths_to_split=__a ,config=__a )
for i in range(__a ):
__lowerCamelCase = i // (config['''num_res_blocks'''] + 1)
__lowerCamelCase = i % (config['''num_res_blocks'''] + 1)
__lowerCamelCase = [shave_segments(__a ,2 ) for name in output_blocks[i]]
__lowerCamelCase = {}
for layer in output_block_layers:
__lowerCamelCase = layer.split('''.''' )[0], shave_segments(__a ,1 )
if layer_id in output_block_list:
output_block_list[layer_id].append(__a )
else:
__lowerCamelCase = [layer_name]
if len(__a ) > 1:
__lowerCamelCase = [key for key in output_blocks[i] if F"""output_blocks.{i}.0""" in key]
__lowerCamelCase = [key for key in output_blocks[i] if F"""output_blocks.{i}.1""" in key]
__lowerCamelCase = renew_resnet_paths(__a )
__lowerCamelCase = renew_resnet_paths(__a )
__lowerCamelCase = {'''old''': F"""output_blocks.{i}.0""", '''new''': F"""up_blocks.{block_id}.resnets.{layer_in_block_id}"""}
assign_to_checkpoint(__a ,__a ,__a ,additional_replacements=[meta_path] ,config=__a )
if ["conv.weight", "conv.bias"] in output_block_list.values():
__lowerCamelCase = list(output_block_list.values() ).index(['''conv.weight''', '''conv.bias'''] )
__lowerCamelCase = checkpoint[
F"""output_blocks.{i}.{index}.conv.weight"""
]
__lowerCamelCase = checkpoint[
F"""output_blocks.{i}.{index}.conv.bias"""
]
# Clear attentions as they have been attributed above.
if len(__a ) == 2:
__lowerCamelCase = []
if len(__a ):
__lowerCamelCase = renew_attention_paths(__a )
__lowerCamelCase = {
'''old''': F"""output_blocks.{i}.1""",
'''new''': F"""up_blocks.{block_id}.attentions.{layer_in_block_id}""",
}
__lowerCamelCase = {
F"""output_blocks.{i}.1.qkv.bias""": {
'''key''': F"""up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias""",
'''query''': F"""up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias""",
'''value''': F"""up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias""",
},
F"""output_blocks.{i}.1.qkv.weight""": {
'''key''': F"""up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight""",
'''query''': F"""up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight""",
'''value''': F"""up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight""",
},
}
assign_to_checkpoint(
__a ,__a ,__a ,additional_replacements=[meta_path] ,attention_paths_to_split=to_split if any('''qkv''' in key for key in attentions ) else None ,config=__a ,)
else:
__lowerCamelCase = renew_resnet_paths(__a ,n_shave_prefix_segments=1 )
for path in resnet_0_paths:
__lowerCamelCase = '''.'''.join(['''output_blocks''', str(__a ), path['''old''']] )
__lowerCamelCase = '''.'''.join(['''up_blocks''', str(__a ), '''resnets''', str(__a ), path['''new''']] )
__lowerCamelCase = checkpoint[old_path]
return new_checkpoint
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
parser.add_argument(
"""--checkpoint_path""", default=None, type=str, required=True, help="""Path to the checkpoint to convert."""
)
parser.add_argument(
"""--config_file""",
default=None,
type=str,
required=True,
help="""The config json file corresponding to the architecture.""",
)
parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")
a_ = parser.parse_args()
a_ = torch.load(args.checkpoint_path)
with open(args.config_file) as f:
a_ = json.loads(f.read())
a_ = convert_ldm_checkpoint(checkpoint, config)
if "ldm" in config:
del config["ldm"]
a_ = UNetaDModel(**config)
model.load_state_dict(converted_checkpoint)
try:
a_ = DDPMScheduler.from_config("""/""".join(args.checkpoint_path.split("""/""")[:-1]))
a_ = VQModel.from_pretrained("""/""".join(args.checkpoint_path.split("""/""")[:-1]))
a_ = LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
pipe.save_pretrained(args.dump_path)
except: # noqa: E722
model.save_pretrained(args.dump_path)
| 175 |
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def __UpperCAmelCase ( __a : Tuple ,__a : Dict ,__a : List[str] ,__a : Optional[Any] ,__a : Tuple ) -> Dict:
"""simple docstring"""
with open(__a ) as metadata_file:
_a : Optional[Any] = json.load(__a )
_a : List[Any] = LukeConfig(use_entity_aware_attention=__a ,**metadata['''model_config'''] )
# Load in the weights from the checkpoint_path
_a : Optional[Any] = torch.load(__a ,map_location='''cpu''' )['''module''']
# Load the entity vocab file
_a : Any = load_original_entity_vocab(__a )
# add an entry for [MASK2]
_a : Union[str, Any] = max(entity_vocab.values() ) + 1
config.entity_vocab_size += 1
_a : Dict = XLMRobertaTokenizer.from_pretrained(metadata['''model_config''']['''bert_model_name'''] )
# Add special tokens to the token vocabulary for downstream tasks
_a : Optional[int] = AddedToken('''<ent>''' ,lstrip=__a ,rstrip=__a )
_a : Tuple = AddedToken('''<ent2>''' ,lstrip=__a ,rstrip=__a )
tokenizer.add_special_tokens({'''additional_special_tokens''': [entity_token_a, entity_token_a]} )
config.vocab_size += 2
print(F"""Saving tokenizer to {pytorch_dump_folder_path}""" )
tokenizer.save_pretrained(__a )
with open(os.path.join(__a ,'''tokenizer_config.json''' ) ,'''r''' ) as f:
_a : List[str] = json.load(__a )
_a : Tuple = '''MLukeTokenizer'''
with open(os.path.join(__a ,'''tokenizer_config.json''' ) ,'''w''' ) as f:
json.dump(__a ,__a )
with open(os.path.join(__a ,MLukeTokenizer.vocab_files_names['''entity_vocab_file'''] ) ,'''w''' ) as f:
json.dump(__a ,__a )
_a : Optional[int] = MLukeTokenizer.from_pretrained(__a )
# Initialize the embeddings of the special tokens
_a : str = tokenizer.convert_tokens_to_ids(['''@'''] )[0]
_a : Tuple = tokenizer.convert_tokens_to_ids(['''#'''] )[0]
_a : Any = state_dict['''embeddings.word_embeddings.weight''']
_a : Optional[int] = word_emb[ent_init_index].unsqueeze(0 )
_a : Any = word_emb[enta_init_index].unsqueeze(0 )
_a : Union[str, Any] = torch.cat([word_emb, ent_emb, enta_emb] )
# add special tokens for 'entity_predictions.bias'
for bias_name in ["lm_head.decoder.bias", "lm_head.bias"]:
_a : Tuple = state_dict[bias_name]
_a : Optional[Any] = decoder_bias[ent_init_index].unsqueeze(0 )
_a : Optional[int] = decoder_bias[enta_init_index].unsqueeze(0 )
_a : Dict = torch.cat([decoder_bias, ent_decoder_bias, enta_decoder_bias] )
# Initialize the query layers of the entity-aware self-attention mechanism
for layer_index in range(config.num_hidden_layers ):
for matrix_name in ["query.weight", "query.bias"]:
_a : Tuple = F"""encoder.layer.{layer_index}.attention.self."""
_a : List[Any] = state_dict[prefix + matrix_name]
_a : Dict = state_dict[prefix + matrix_name]
_a : List[Any] = state_dict[prefix + matrix_name]
# Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
_a : Union[str, Any] = state_dict['''entity_embeddings.entity_embeddings.weight''']
_a : Optional[int] = entity_emb[entity_vocab['''[MASK]''']].unsqueeze(0 )
_a : Any = torch.cat([entity_emb, entity_mask_emb] )
# add [MASK2] for 'entity_predictions.bias'
_a : int = state_dict['''entity_predictions.bias''']
_a : int = entity_prediction_bias[entity_vocab['''[MASK]''']].unsqueeze(0 )
_a : Optional[Any] = torch.cat([entity_prediction_bias, entity_mask_bias] )
_a : Optional[int] = LukeForMaskedLM(config=__a ).eval()
state_dict.pop('''entity_predictions.decoder.weight''' )
state_dict.pop('''lm_head.decoder.weight''' )
state_dict.pop('''lm_head.decoder.bias''' )
_a : int = OrderedDict()
for key, value in state_dict.items():
if not (key.startswith('''lm_head''' ) or key.startswith('''entity_predictions''' )):
_a : Optional[int] = state_dict[key]
else:
_a : Tuple = state_dict[key]
_a , _a : int = model.load_state_dict(__a ,strict=__a )
if set(__a ) != {"luke.embeddings.position_ids"}:
raise ValueError(F"""Unexpected unexpected_keys: {unexpected_keys}""" )
if set(__a ) != {
"lm_head.decoder.weight",
"lm_head.decoder.bias",
"entity_predictions.decoder.weight",
}:
raise ValueError(F"""Unexpected missing_keys: {missing_keys}""" )
model.tie_weights()
assert (model.luke.embeddings.word_embeddings.weight == model.lm_head.decoder.weight).all()
assert (model.luke.entity_embeddings.entity_embeddings.weight == model.entity_predictions.decoder.weight).all()
# Check outputs
_a : Optional[int] = MLukeTokenizer.from_pretrained(__a ,task='''entity_classification''' )
_a : int = '''ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan).'''
_a : List[Any] = (0, 9)
_a : Tuple = tokenizer(__a ,entity_spans=[span] ,return_tensors='''pt''' )
_a : int = model(**__a )
# Verify word hidden states
if model_size == "large":
raise NotImplementedError
else: # base
_a : List[str] = torch.Size((1, 33, 768) )
_a : Union[str, Any] = torch.tensor([[0.08_92, 0.05_96, -0.28_19], [0.01_34, 0.11_99, 0.05_73], [-0.01_69, 0.09_27, 0.06_44]] )
if not (outputs.last_hidden_state.shape == expected_shape):
raise ValueError(
F"""Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}""" )
if not torch.allclose(outputs.last_hidden_state[0, :3, :3] ,__a ,atol=1E-4 ):
raise ValueError
# Verify entity hidden states
if model_size == "large":
raise NotImplementedError
else: # base
_a : str = torch.Size((1, 1, 768) )
_a : List[Any] = torch.tensor([[-0.14_82, 0.06_09, 0.03_22]] )
if not (outputs.entity_last_hidden_state.shape == expected_shape):
raise ValueError(
F"""Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is"""
F""" {expected_shape}""" )
if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3] ,__a ,atol=1E-4 ):
raise ValueError
# Verify masked word/entity prediction
_a : Optional[int] = MLukeTokenizer.from_pretrained(__a )
_a : Dict = '''Tokyo is the capital of <mask>.'''
_a : List[str] = (24, 30)
_a : Optional[int] = tokenizer(__a ,entity_spans=[span] ,return_tensors='''pt''' )
_a : Optional[Any] = model(**__a )
_a : Any = encoding['''input_ids'''][0].tolist()
_a : Optional[Any] = input_ids.index(tokenizer.convert_tokens_to_ids('''<mask>''' ) )
_a : Any = outputs.logits[0][mask_position_id].argmax(dim=-1 )
assert "Japan" == tokenizer.decode(__a )
_a : Any = outputs.entity_logits[0][0].argmax().item()
_a : Optional[Any] = [
entity for entity, entity_id in tokenizer.entity_vocab.items() if entity_id == predicted_entity_id
]
assert [e for e in multilingual_predicted_entities if e.startswith('''en:''' )][0] == "en:Japan"
# Finally, save our PyTorch model and tokenizer
print('''Saving PyTorch model to {}'''.format(__a ) )
model.save_pretrained(__a )
def __UpperCAmelCase ( __a : List[Any] ) -> int:
"""simple docstring"""
_a : Union[str, Any] = ['''[MASK]''', '''[PAD]''', '''[UNK]''']
_a : int = [json.loads(__a ) for line in open(__a )]
_a : List[Any] = {}
for entry in data:
_a : int = entry['''id''']
for entity_name, language in entry["entities"]:
if entity_name in SPECIAL_TOKENS:
_a : List[Any] = entity_id
break
_a : Dict = F"""{language}:{entity_name}"""
_a : int = entity_id
return new_mapping
if __name__ == "__main__":
a__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument('''--checkpoint_path''', type=str, help='''Path to a pytorch_model.bin file.''')
parser.add_argument(
'''--metadata_path''', default=None, type=str, help='''Path to a metadata.json file, defining the configuration.'''
)
parser.add_argument(
'''--entity_vocab_path''',
default=None,
type=str,
help='''Path to an entity_vocab.tsv file, containing the entity vocabulary.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to where to dump the output PyTorch model.'''
)
parser.add_argument(
'''--model_size''', default='''base''', type=str, choices=['''base''', '''large'''], help='''Size of the model to be converted.'''
)
a__ = parser.parse_args()
convert_luke_checkpoint(
args.checkpoint_path,
args.metadata_path,
args.entity_vocab_path,
args.pytorch_dump_folder_path,
args.model_size,
)
| 14 | 0 |
'''simple docstring'''
import copy
from collections import OrderedDict
from typing import Dict, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
'''facebook/detr-resnet-50''': '''https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json''',
# See all DETR models at https://huggingface.co/models?filter=detr
}
class a__ ( __lowercase ):
'''simple docstring'''
A : Optional[Any] = "detr"
A : int = ["past_key_values"]
A : int = {
"hidden_size": "d_model",
"num_attention_heads": "encoder_attention_heads",
}
def __init__( self : Optional[int] , lowerCAmelCase_ : List[str]=True , lowerCAmelCase_ : List[Any]=None , lowerCAmelCase_ : str=3 , lowerCAmelCase_ : Dict=100 , lowerCAmelCase_ : Optional[int]=6 , lowerCAmelCase_ : Optional[Any]=2_048 , lowerCAmelCase_ : Any=8 , lowerCAmelCase_ : List[str]=6 , lowerCAmelCase_ : Dict=2_048 , lowerCAmelCase_ : int=8 , lowerCAmelCase_ : str=0.0 , lowerCAmelCase_ : List[str]=0.0 , lowerCAmelCase_ : Optional[Any]=True , lowerCAmelCase_ : Optional[int]="relu" , lowerCAmelCase_ : Optional[int]=256 , lowerCAmelCase_ : List[Any]=0.1 , lowerCAmelCase_ : Any=0.0 , lowerCAmelCase_ : Optional[int]=0.0 , lowerCAmelCase_ : Any=0.02 , lowerCAmelCase_ : List[Any]=1.0 , lowerCAmelCase_ : List[Any]=False , lowerCAmelCase_ : Union[str, Any]="sine" , lowerCAmelCase_ : Optional[Any]="resnet50" , lowerCAmelCase_ : List[str]=True , lowerCAmelCase_ : int=False , lowerCAmelCase_ : Dict=1 , lowerCAmelCase_ : List[Any]=5 , lowerCAmelCase_ : Dict=2 , lowerCAmelCase_ : List[Any]=1 , lowerCAmelCase_ : List[Any]=1 , lowerCAmelCase_ : List[Any]=5 , lowerCAmelCase_ : List[str]=2 , lowerCAmelCase_ : Tuple=0.1 , **lowerCAmelCase_ : List[Any] , ) -> Any:
if backbone_config is not None and use_timm_backbone:
raise ValueError('You can\'t specify both `backbone_config` and `use_timm_backbone`.' )
if not use_timm_backbone:
if backbone_config is None:
logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.' )
__A= CONFIG_MAPPING['''resnet'''](out_features=['stage4'] )
elif isinstance(_a , _a ):
__A= backbone_config.get('model_type' )
__A= CONFIG_MAPPING[backbone_model_type]
__A= config_class.from_dict(_a )
# set timm attributes to None
__A= None, None, None
__A= use_timm_backbone
__A= backbone_config
__A= num_channels
__A= num_queries
__A= d_model
__A= encoder_ffn_dim
__A= encoder_layers
__A= encoder_attention_heads
__A= decoder_ffn_dim
__A= decoder_layers
__A= decoder_attention_heads
__A= dropout
__A= attention_dropout
__A= activation_dropout
__A= activation_function
__A= init_std
__A= init_xavier_std
__A= encoder_layerdrop
__A= decoder_layerdrop
__A= encoder_layers
__A= auxiliary_loss
__A= position_embedding_type
__A= backbone
__A= use_pretrained_backbone
__A= dilation
# Hungarian matcher
__A= class_cost
__A= bbox_cost
__A= giou_cost
# Loss coefficients
__A= mask_loss_coefficient
__A= dice_loss_coefficient
__A= bbox_loss_coefficient
__A= giou_loss_coefficient
__A= eos_coefficient
super().__init__(is_encoder_decoder=_a , **_a )
@property
def lowerCAmelCase ( self : Union[str, Any] ) -> int:
return self.encoder_attention_heads
@property
def lowerCAmelCase ( self : Optional[Any] ) -> int:
return self.d_model
@classmethod
def lowerCAmelCase ( cls : Union[str, Any] , lowerCAmelCase_ : List[Any] , **lowerCAmelCase_ : int ) -> Optional[int]:
return cls(backbone_config=_a , **_a )
def lowerCAmelCase ( self : Tuple ) -> Dict[str, any]:
__A= copy.deepcopy(self.__dict__ )
if output["backbone_config"] is not None:
__A= self.backbone_config.to_dict()
__A= self.__class__.model_type
return output
class a__ ( __lowercase ):
'''simple docstring'''
A : List[str] = version.parse('''1.11''' )
@property
def lowerCAmelCase ( self : Union[str, Any] ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
('pixel_mask', {0: 'batch'}),
] )
@property
def lowerCAmelCase ( self : str ) -> float:
return 1E-5
@property
def lowerCAmelCase ( self : Union[str, Any] ) -> int:
return 12
| 186 |
from scipy.stats import spearmanr
import datasets
a__ = '''
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlations imply that as data in dataset x increases, so
does data in dataset y. Negative correlations imply that as x increases,
y decreases. Correlations of -1 or +1 imply an exact monotonic relationship.
Unlike the Pearson correlation, the Spearman correlation does not
assume that both datasets are normally distributed.
The p-value roughly indicates the probability of an uncorrelated system
producing datasets that have a Spearman correlation at least as extreme
as the one computed from these datasets. The p-values are not entirely
reliable but are probably reasonable for datasets larger than 500 or so.
'''
a__ = '''
Args:
predictions (`List[float]`): Predicted labels, as returned by a model.
references (`List[float]`): Ground truth labels.
return_pvalue (`bool`): If `True`, returns the p-value. If `False`, returns
only the spearmanr score. Defaults to `False`.
Returns:
spearmanr (`float`): Spearman correlation coefficient.
p-value (`float`): p-value. **Note**: is only returned if `return_pvalue=True` is input.
Examples:
Example 1:
>>> spearmanr_metric = datasets.load_metric("spearmanr")
>>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], predictions=[10, 9, 2.5, 6, 4])
>>> print(results)
{\'spearmanr\': -0.7}
Example 2:
>>> spearmanr_metric = datasets.load_metric("spearmanr")
>>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5],
... predictions=[10, 9, 2.5, 6, 4],
... return_pvalue=True)
>>> print(results[\'spearmanr\'])
-0.7
>>> print(round(results[\'spearmanr_pvalue\'], 2))
0.19
'''
a__ = R'''\
@book{kokoska2000crc,
title={CRC standard probability and statistics tables and formulae},
author={Kokoska, Stephen and Zwillinger, Daniel},
year={2000},
publisher={Crc Press}
}
@article{2020SciPy-NMeth,
author = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
Haberland, Matt and Reddy, Tyler and Cournapeau, David and
Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
Kern, Robert and Larson, Eric and Carey, C J and
Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and
{VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
Harris, Charles R. and Archibald, Anne M. and
Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and
{van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
title = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
Computing in Python}},
journal = {Nature Methods},
year = {2020},
volume = {17},
pages = {261--272},
adsurl = {https://rdcu.be/b08Wh},
doi = {10.1038/s41592-019-0686-2},
}
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class UpperCAmelCase_ ( datasets.Metric ):
"""simple docstring"""
def __lowercase ( self ) -> int:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Value('''float''' ),
'''references''': datasets.Value('''float''' ),
} ) , reference_urls=['''https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.spearmanr.html'''] , )
def __lowercase ( self , _a , _a , _a=False ) -> str:
_a : int = spearmanr(_a , _a )
if return_pvalue:
return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
else:
return {"spearmanr": results[0]}
| 14 | 0 |
def UpperCAmelCase_ ( __lowerCAmelCase ) -> bool:
if not all(x.isalpha() for x in string ):
raise ValueError('''String must only contain alphabetic characters.''' )
__lowercase : Optional[Any] = sorted(string.lower() )
return len(__a ) == len(set(__a ) )
if __name__ == "__main__":
__lowerCAmelCase : Optional[int] = input("Enter a string ").strip()
__lowerCAmelCase : Optional[int] = is_isogram(input_str)
print(F'{input_str} is {"an" if isogram else "not an"} isogram.')
| 509 |
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def __UpperCAmelCase ( __a : bytes ,__a : int ) -> np.array:
"""simple docstring"""
_a : int = F"""{sampling_rate}"""
_a : str = '''1'''
_a : Optional[int] = '''f32le'''
_a : Optional[Any] = [
'''ffmpeg''',
'''-i''',
'''pipe:0''',
'''-ac''',
ac,
'''-ar''',
ar,
'''-f''',
format_for_conversion,
'''-hide_banner''',
'''-loglevel''',
'''quiet''',
'''pipe:1''',
]
try:
with subprocess.Popen(__a ,stdin=subprocess.PIPE ,stdout=subprocess.PIPE ) as ffmpeg_process:
_a : Any = ffmpeg_process.communicate(__a )
except FileNotFoundError as error:
raise ValueError('''ffmpeg was not found but is required to load audio files from filename''' ) from error
_a : Optional[Any] = output_stream[0]
_a : Optional[int] = np.frombuffer(__a ,np.floataa )
if audio.shape[0] == 0:
raise ValueError('''Malformed soundfile''' )
return audio
def __UpperCAmelCase ( __a : int ,__a : float ,__a : str = "f32le" ,) -> str:
"""simple docstring"""
_a : Dict = F"""{sampling_rate}"""
_a : Optional[Any] = '''1'''
if format_for_conversion == "s16le":
_a : Dict = 2
elif format_for_conversion == "f32le":
_a : Optional[Any] = 4
else:
raise ValueError(F"""Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`""" )
_a : Dict = platform.system()
if system == "Linux":
_a : Dict = '''alsa'''
_a : Union[str, Any] = '''default'''
elif system == "Darwin":
_a : Union[str, Any] = '''avfoundation'''
_a : List[str] = ''':0'''
elif system == "Windows":
_a : Optional[int] = '''dshow'''
_a : str = '''default'''
_a : Tuple = [
'''ffmpeg''',
'''-f''',
format_,
'''-i''',
input_,
'''-ac''',
ac,
'''-ar''',
ar,
'''-f''',
format_for_conversion,
'''-fflags''',
'''nobuffer''',
'''-hide_banner''',
'''-loglevel''',
'''quiet''',
'''pipe:1''',
]
_a : Any = int(round(sampling_rate * chunk_length_s ) ) * size_of_sample
_a : str = _ffmpeg_stream(__a ,__a )
for item in iterator:
yield item
def __UpperCAmelCase ( __a : int ,__a : float ,__a : Optional[int] = None ,__a : Optional[Union[Tuple[float, float], float]] = None ,__a : str = "f32le" ,) -> Optional[int]:
"""simple docstring"""
if stream_chunk_s is not None:
_a : Tuple = stream_chunk_s
else:
_a : Tuple = chunk_length_s
_a : Tuple = ffmpeg_microphone(__a ,__a ,format_for_conversion=__a )
if format_for_conversion == "s16le":
_a : Any = np.intaa
_a : Optional[int] = 2
elif format_for_conversion == "f32le":
_a : Dict = np.floataa
_a : List[Any] = 4
else:
raise ValueError(F"""Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`""" )
if stride_length_s is None:
_a : List[Any] = chunk_length_s / 6
_a : Optional[int] = int(round(sampling_rate * chunk_length_s ) ) * size_of_sample
if isinstance(__a ,(int, float) ):
_a : Optional[Any] = [stride_length_s, stride_length_s]
_a : Optional[Any] = int(round(sampling_rate * stride_length_s[0] ) ) * size_of_sample
_a : str = int(round(sampling_rate * stride_length_s[1] ) ) * size_of_sample
_a : Optional[Any] = datetime.datetime.now()
_a : Tuple = datetime.timedelta(seconds=__a )
for item in chunk_bytes_iter(__a ,__a ,stride=(stride_left, stride_right) ,stream=__a ):
# Put everything back in numpy scale
_a : Dict = np.frombuffer(item['''raw'''] ,dtype=__a )
_a : Dict = (
item['''stride'''][0] // size_of_sample,
item['''stride'''][1] // size_of_sample,
)
_a : str = sampling_rate
audio_time += delta
if datetime.datetime.now() > audio_time + 10 * delta:
# We're late !! SKIP
continue
yield item
def __UpperCAmelCase ( __a : Optional[int] ,__a : int ,__a : Tuple[int, int] ,__a : bool = False ) -> Optional[int]:
"""simple docstring"""
_a : Any = b''''''
_a , _a : List[str] = stride
if stride_left + stride_right >= chunk_len:
raise ValueError(
F"""Stride needs to be strictly smaller than chunk_len: ({stride_left}, {stride_right}) vs {chunk_len}""" )
_a : List[str] = 0
for raw in iterator:
acc += raw
if stream and len(__a ) < chunk_len:
_a : Dict = (_stride_left, 0)
yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
else:
while len(__a ) >= chunk_len:
# We are flushing the accumulator
_a : List[str] = (_stride_left, stride_right)
_a : List[Any] = {'''raw''': acc[:chunk_len], '''stride''': stride}
if stream:
_a : List[Any] = False
yield item
_a : Optional[Any] = stride_left
_a : Optional[Any] = acc[chunk_len - stride_left - stride_right :]
# Last chunk
if len(__a ) > stride_left:
_a : Optional[Any] = {'''raw''': acc, '''stride''': (_stride_left, 0)}
if stream:
_a : Dict = False
yield item
def __UpperCAmelCase ( __a : int ,__a : int ) -> Tuple:
"""simple docstring"""
_a : Dict = 2**24 # 16Mo
try:
with subprocess.Popen(__a ,stdout=subprocess.PIPE ,bufsize=__a ) as ffmpeg_process:
while True:
_a : int = ffmpeg_process.stdout.read(__a )
if raw == b"":
break
yield raw
except FileNotFoundError as error:
raise ValueError('''ffmpeg was not found but is required to stream audio files from filename''' ) from error
| 14 | 0 |
def UpperCAmelCase ( a_ ) -> bool:
"""simple docstring"""
__A = [int(__a ) for i in ip_va_address.split("." ) if i.isdigit()]
return len(__a ) == 4 and all(0 <= int(__a ) <= 2_5_4 for octet in octets )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE :Union[str, Any] = input().strip()
SCREAMING_SNAKE_CASE :Any = 'valid' if is_ip_va_address_valid(ip) else 'invalid'
print(f'''{ip} is a {valid_or_invalid} IP v4 address.''')
| 55 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, 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 UpperCAmelCase_ ( __lowercase , unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase__ : Union[str, Any] = KandinskyInpaintPipeline
UpperCAmelCase__ : Optional[int] = ["prompt", "image_embeds", "negative_image_embeds", "image", "mask_image"]
UpperCAmelCase__ : Optional[Any] = [
"prompt",
"negative_prompt",
"image_embeds",
"negative_image_embeds",
"image",
"mask_image",
]
UpperCAmelCase__ : Optional[int] = [
"generator",
"height",
"width",
"latents",
"guidance_scale",
"negative_prompt",
"num_inference_steps",
"return_dict",
"guidance_scale",
"num_images_per_prompt",
"output_type",
"return_dict",
]
UpperCAmelCase__ : Any = False
@property
def __lowercase ( self ) -> Optional[int]:
return 3_2
@property
def __lowercase ( self ) -> int:
return 3_2
@property
def __lowercase ( self ) -> List[str]:
return self.time_input_dim
@property
def __lowercase ( self ) -> List[str]:
return self.time_input_dim * 4
@property
def __lowercase ( self ) -> Optional[Any]:
return 1_0_0
@property
def __lowercase ( self ) -> Optional[Any]:
_a : Any = XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base''' )
return tokenizer
@property
def __lowercase ( self ) -> str:
torch.manual_seed(0 )
_a : List[Any] = MCLIPConfig(
numDims=self.cross_attention_dim , transformerDimensions=self.text_embedder_hidden_size , hidden_size=self.text_embedder_hidden_size , intermediate_size=3_7 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=1_0_0_5 , )
_a : Optional[int] = MultilingualCLIP(_a )
_a : Tuple = text_encoder.eval()
return text_encoder
@property
def __lowercase ( self ) -> str:
torch.manual_seed(0 )
_a : List[str] = {
'''in_channels''': 9,
# 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 : Dict = UNetaDConditionModel(**_a )
return model
@property
def __lowercase ( self ) -> Optional[int]:
return {
"block_out_channels": [3_2, 6_4],
"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": 1_2,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def __lowercase ( self ) -> Tuple:
torch.manual_seed(0 )
_a : List[Any] = VQModel(**self.dummy_movq_kwargs )
return model
def __lowercase ( self ) -> Any:
_a : List[Any] = self.dummy_text_encoder
_a : Optional[Any] = self.dummy_tokenizer
_a : Optional[Any] = self.dummy_unet
_a : Union[str, Any] = self.dummy_movq
_a : Tuple = DDIMScheduler(
num_train_timesteps=1_0_0_0 , beta_schedule='''linear''' , beta_start=0.0_0085 , beta_end=0.012 , clip_sample=_a , set_alpha_to_one=_a , steps_offset=1 , prediction_type='''epsilon''' , thresholding=_a , )
_a : str = {
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''unet''': unet,
'''scheduler''': scheduler,
'''movq''': movq,
}
return components
def __lowercase ( self , _a , _a=0 ) -> int:
_a : Union[str, Any] = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(_a ) ).to(_a )
_a : List[str] = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(seed + 1 ) ).to(_a )
# create init_image
_a : Tuple = floats_tensor((1, 3, 6_4, 6_4) , rng=random.Random(_a ) ).to(_a )
_a : Dict = image.cpu().permute(0 , 2 , 3 , 1 )[0]
_a : Optional[int] = Image.fromarray(np.uinta(_a ) ).convert('''RGB''' ).resize((2_5_6, 2_5_6) )
# create mask
_a : Union[str, Any] = np.ones((6_4, 6_4) , dtype=np.floataa )
_a : List[str] = 0
if str(_a ).startswith('''mps''' ):
_a : Tuple = torch.manual_seed(_a )
else:
_a : Any = torch.Generator(device=_a ).manual_seed(_a )
_a : Any = {
'''prompt''': '''horse''',
'''image''': init_image,
'''mask_image''': mask,
'''image_embeds''': image_embeds,
'''negative_image_embeds''': negative_image_embeds,
'''generator''': generator,
'''height''': 6_4,
'''width''': 6_4,
'''num_inference_steps''': 2,
'''guidance_scale''': 4.0,
'''output_type''': '''np''',
}
return inputs
def __lowercase ( self ) -> Optional[Any]:
_a : Optional[Any] = '''cpu'''
_a : List[Any] = self.get_dummy_components()
_a : Tuple = self.pipeline_class(**_a )
_a : int = pipe.to(_a )
pipe.set_progress_bar_config(disable=_a )
_a : Any = pipe(**self.get_dummy_inputs(_a ) )
_a : str = output.images
_a : Tuple = pipe(
**self.get_dummy_inputs(_a ) , return_dict=_a , )[0]
_a : Union[str, Any] = image[0, -3:, -3:, -1]
_a : Tuple = image_from_tuple[0, -3:, -3:, -1]
print(F"""image.shape {image.shape}""" )
assert image.shape == (1, 6_4, 6_4, 3)
_a : str = np.array(
[0.832_6919, 0.7379_0467, 0.2091_8581, 0.930_9612, 0.551_1791, 0.4371_3328, 0.551_3321, 0.4992_2934, 0.5949_7786] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
), F""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
), F""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""
def __lowercase ( self ) -> Dict:
super().test_inference_batch_single_identical(expected_max_diff=3e-3 )
@slow
@require_torch_gpu
class UpperCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
def __lowercase ( self ) -> str:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __lowercase ( self ) -> Union[str, Any]:
_a : Tuple = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy''' )
_a : str = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png''' )
_a : Tuple = np.ones((7_6_8, 7_6_8) , dtype=np.floataa )
_a : Any = 0
_a : Optional[Any] = '''a hat'''
_a : Optional[Any] = KandinskyPriorPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-1-prior''' , torch_dtype=torch.floataa )
pipe_prior.to(_a )
_a : Tuple = KandinskyInpaintPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-1-inpaint''' , torch_dtype=torch.floataa )
_a : Union[str, Any] = pipeline.to(_a )
pipeline.set_progress_bar_config(disable=_a )
_a : Union[str, Any] = torch.Generator(device='''cpu''' ).manual_seed(0 )
_a , _a : Dict = pipe_prior(
_a , generator=_a , num_inference_steps=5 , negative_prompt='''''' , ).to_tuple()
_a : Optional[int] = pipeline(
_a , image=_a , mask_image=_a , image_embeds=_a , negative_image_embeds=_a , generator=_a , num_inference_steps=1_0_0 , height=7_6_8 , width=7_6_8 , output_type='''np''' , )
_a : Optional[int] = output.images[0]
assert image.shape == (7_6_8, 7_6_8, 3)
assert_mean_pixel_difference(_a , _a )
| 14 | 0 |
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging
__A : str = logging.get_logger(__name__)
__A : Dict = {"vocab_file": "spiece.model"}
__A : str = {
"vocab_file": {
"TsinghuaAI/CPM-Generate": "https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model",
}
}
class lowerCamelCase( __lowercase ):
'''simple docstring'''
def __init__( self , snake_case_ , snake_case_=False , snake_case_=True , snake_case_=False , snake_case_="<s>" , snake_case_="</s>" , snake_case_="<unk>" , snake_case_="<sep>" , snake_case_="<pad>" , snake_case_="<cls>" , snake_case_="<mask>" , snake_case_=["<eop>", "<eod>"] , snake_case_ = None , **snake_case_ , ):
_A = AddedToken(_a , lstrip=_a , rstrip=_a ) if isinstance(_a , _a ) else mask_token
_A = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
do_lower_case=_a , remove_space=_a , keep_accents=_a , bos_token=_a , eos_token=_a , unk_token=_a , sep_token=_a , pad_token=_a , cls_token=_a , mask_token=_a , additional_special_tokens=_a , sp_model_kwargs=self.sp_model_kwargs , **_a , )
_A = 3
_A = do_lower_case
_A = remove_space
_A = keep_accents
_A = vocab_file
_A = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(_a )
try:
import jieba
except ModuleNotFoundError as error:
raise error.__class__(
'You need to install jieba to use CpmTokenizer or CpmTokenizerFast. '
'See https://pypi.org/project/jieba/ for installation.' )
_A = jieba
_A = str.maketrans(' \n' , '\u2582\u2583' )
@property
# Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
def lowerCAmelCase__ ( self ):
return len(self.sp_model )
def lowerCAmelCase__ ( self ):
_A = {self.convert_ids_to_tokens(_a ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self ):
_A = self.__dict__.copy()
_A = None
return state
def __setstate__( self , snake_case_ ):
_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 lowerCAmelCase__ ( self , snake_case_ ):
if self.remove_space:
_A = ''' '''.join(inputs.strip().split() )
else:
_A = inputs
_A = outputs.replace('``' , '"' ).replace('\'\'' , '"' )
if not self.keep_accents:
_A = unicodedata.normalize('NFKD' , _a )
_A = ''''''.join([c for c in outputs if not unicodedata.combining(_a )] )
if self.do_lower_case:
_A = outputs.lower()
return outputs
def lowerCAmelCase__ ( self , snake_case_ ):
_A = self.preprocess_text(_a )
_A = self.sp_model.encode(_a , out_type=_a )
_A = []
for piece in pieces:
if len(_a ) > 1 and piece[-1] == str(',' ) and piece[-2].isdigit():
_A = self.sp_model.EncodeAsPieces(piece[:-1].replace(_a , '' ) )
if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
if len(cur_pieces[0] ) == 1:
_A = cur_pieces[1:]
else:
_A = cur_pieces[0][1:]
cur_pieces.append(piece[-1] )
new_pieces.extend(_a )
else:
new_pieces.append(_a )
return new_pieces
def lowerCAmelCase__ ( self , snake_case_ ):
return self.sp_model.PieceToId(_a )
def lowerCAmelCase__ ( self , snake_case_ ):
return self.sp_model.IdToPiece(_a )
def lowerCAmelCase__ ( self , snake_case_ ):
_A = ''''''.join(_a ).replace(_a , ' ' ).strip()
return out_string
def lowerCAmelCase__ ( self , snake_case_ , snake_case_ = 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 lowerCAmelCase__ ( self , snake_case_ , snake_case_ = None , snake_case_ = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_a , token_ids_a=_a , already_has_special_tokens=_a )
if token_ids_a is not None:
return ([0] * len(_a )) + [1] + ([0] * len(_a )) + [1, 1]
return ([0] * len(_a )) + [1, 1]
def lowerCAmelCase__ ( self , snake_case_ , snake_case_ = 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 lowerCAmelCase__ ( self , snake_case_ , snake_case_ = None ):
if not os.path.isdir(_a ):
logger.error(F"Vocabulary path ({save_directory}) should be a directory" )
return
_A = os.path.join(
_a , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_a ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , _a )
elif not os.path.isfile(self.vocab_file ):
with open(_a , 'wb' ) as fi:
_A = self.sp_model.serialized_model_proto()
fi.write(_a )
return (out_vocab_file,)
def lowerCAmelCase__ ( self , *snake_case_ , **snake_case_ ):
_A = super()._decode(*_a , **_a )
_A = text.replace(' ' , '' ).replace('\u2582' , ' ' ).replace('\u2583' , '\n' )
return text
| 27 |
import argparse
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt
if __name__ == "__main__":
a__ = argparse.ArgumentParser()
parser.add_argument(
'''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
)
parser.add_argument(
'''--original_config_file''',
type=str,
required=True,
help='''The YAML config file corresponding to the original architecture.''',
)
parser.add_argument(
'''--num_in_channels''',
default=None,
type=int,
help='''The number of input channels. If `None` number of input channels will be automatically inferred.''',
)
parser.add_argument(
'''--image_size''',
default=512,
type=int,
help=(
'''The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2'''
''' Base. Use 768 for Stable Diffusion v2.'''
),
)
parser.add_argument(
'''--extract_ema''',
action='''store_true''',
help=(
'''Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights'''
''' or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield'''
''' higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.'''
),
)
parser.add_argument(
'''--upcast_attention''',
action='''store_true''',
help=(
'''Whether the attention computation should always be upcasted. This is necessary when running stable'''
''' diffusion 2.1.'''
),
)
parser.add_argument(
'''--from_safetensors''',
action='''store_true''',
help='''If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.''',
)
parser.add_argument(
'''--to_safetensors''',
action='''store_true''',
help='''Whether to store pipeline in safetensors format or not.''',
)
parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')
parser.add_argument('''--device''', type=str, help='''Device to use (e.g. cpu, cuda:0, cuda:1, etc.)''')
def __UpperCAmelCase ( __a : Any ) -> List[Any]:
"""simple docstring"""
if string == "True":
return True
elif string == "False":
return False
else:
raise ValueError(F"""could not parse string as bool {string}""" )
parser.add_argument(
'''--use_linear_projection''', help='''Override for use linear projection''', required=False, type=parse_bool
)
parser.add_argument('''--cross_attention_dim''', help='''Override for cross attention_dim''', required=False, type=int)
a__ = parser.parse_args()
a__ = download_controlnet_from_original_ckpt(
checkpoint_path=args.checkpoint_path,
original_config_file=args.original_config_file,
image_size=args.image_size,
extract_ema=args.extract_ema,
num_in_channels=args.num_in_channels,
upcast_attention=args.upcast_attention,
from_safetensors=args.from_safetensors,
device=args.device,
use_linear_projection=args.use_linear_projection,
cross_attention_dim=args.cross_attention_dim,
)
controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
| 14 | 0 |
'''simple docstring'''
import datasets
from .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py
UpperCAmelCase_ : str = '''\
@INPROCEEDINGS{Papineni02bleu:a,
author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},
title = {BLEU: a Method for Automatic Evaluation of Machine Translation},
booktitle = {},
year = {2002},
pages = {311--318}
}
@inproceedings{lin-och-2004-orange,
title = "{ORANGE}: a Method for Evaluating Automatic Evaluation Metrics for Machine Translation",
author = "Lin, Chin-Yew and
Och, Franz Josef",
booktitle = "{COLING} 2004: Proceedings of the 20th International Conference on Computational Linguistics",
month = "aug 23{--}aug 27",
year = "2004",
address = "Geneva, Switzerland",
publisher = "COLING",
url = "https://www.aclweb.org/anthology/C04-1072",
pages = "501--507",
}
'''
UpperCAmelCase_ : str = '''\
BLEU (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another.
Quality is considered to be the correspondence between a machine\'s output and that of a human: "the closer a machine translation is to a professional human translation,
the better it is" – this is the central idea behind BLEU. BLEU was one of the first metrics to claim a high correlation with human judgements of quality, and
remains one of the most popular automated and inexpensive metrics.
Scores are calculated for individual translated segments—generally sentences—by comparing them with a set of good quality reference translations.
Those scores are then averaged over the whole corpus to reach an estimate of the translation\'s overall quality. Intelligibility or grammatical correctness
are not taken into account[citation needed].
BLEU\'s output is always a number between 0 and 1. This value indicates how similar the candidate text is to the reference texts, with values closer to 1
representing more similar texts. Few human translations will attain a score of 1, since this would indicate that the candidate is identical to one of the
reference translations. For this reason, it is not necessary to attain a score of 1. Because there are more opportunities to match, adding additional
reference translations will increase the BLEU score.
'''
UpperCAmelCase_ : Union[str, Any] = '''
Computes BLEU score of translated segments against one or more references.
Args:
predictions: list of translations to score.
Each translation should be tokenized into a list of tokens.
references: list of lists of references for each translation.
Each reference should be tokenized into a list of tokens.
max_order: Maximum n-gram order to use when computing BLEU score.
smooth: Whether or not to apply Lin et al. 2004 smoothing.
Returns:
\'bleu\': bleu score,
\'precisions\': geometric mean of n-gram precisions,
\'brevity_penalty\': brevity penalty,
\'length_ratio\': ratio of lengths,
\'translation_length\': translation_length,
\'reference_length\': reference_length
Examples:
>>> predictions = [
... ["hello", "there", "general", "kenobi"], # tokenized prediction of the first sample
... ["foo", "bar", "foobar"] # tokenized prediction of the second sample
... ]
>>> references = [
... [["hello", "there", "general", "kenobi"], ["hello", "there", "!"]], # tokenized references for the first sample (2 references)
... [["foo", "bar", "foobar"]] # tokenized references for the second sample (1 reference)
... ]
>>> bleu = datasets.load_metric("bleu")
>>> results = bleu.compute(predictions=predictions, references=references)
>>> print(results["bleu"])
1.0
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION)
class lowerCAmelCase ( datasets.Metric):
def lowerCAmelCase ( self ) -> List[Any]:
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Sequence(datasets.Value('''string''' , id='''token''' ) , id='''sequence''' ),
'''references''': datasets.Sequence(
datasets.Sequence(datasets.Value('''string''' , id='''token''' ) , id='''sequence''' ) , id='''references''' ),
} ) , codebase_urls=['''https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py'''] , reference_urls=[
'''https://en.wikipedia.org/wiki/BLEU''',
'''https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213''',
] , )
def lowerCAmelCase ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE=4 , __SCREAMING_SNAKE_CASE=False ) -> Tuple:
'''simple docstring'''
__snake_case = compute_bleu(
reference_corpus=_a , translation_corpus=_a , max_order=_a , smooth=_a )
(__snake_case) = score
return {
"bleu": bleu,
"precisions": precisions,
"brevity_penalty": bp,
"length_ratio": ratio,
"translation_length": translation_length,
"reference_length": reference_length,
}
| 24 |
class UpperCAmelCase_ :
"""simple docstring"""
def __init__( self , _a , _a , _a ) -> List[str]:
_a : List[Any] = name
_a : List[str] = value
_a : List[str] = weight
def __repr__( self ) -> Optional[int]:
return F"""{self.__class__.__name__}({self.name}, {self.value}, {self.weight})"""
def __lowercase ( self ) -> List[Any]:
return self.value
def __lowercase ( self ) -> int:
return self.name
def __lowercase ( self ) -> Optional[int]:
return self.weight
def __lowercase ( self ) -> Optional[Any]:
return self.value / self.weight
def __UpperCAmelCase ( __a : Optional[int] ,__a : Tuple ,__a : List[str] ) -> List[str]:
"""simple docstring"""
_a : Optional[int] = []
for i in range(len(__a ) ):
menu.append(Things(name[i] ,value[i] ,weight[i] ) )
return menu
def __UpperCAmelCase ( __a : int ,__a : Union[str, Any] ,__a : int ) -> Union[str, Any]:
"""simple docstring"""
_a : Union[str, Any] = sorted(__a ,key=__a ,reverse=__a )
_a : Any = []
_a , _a : Optional[int] = 0.0, 0.0
for i in range(len(__a ) ):
if (total_cost + items_copy[i].get_weight()) <= max_cost:
result.append(items_copy[i] )
total_cost += items_copy[i].get_weight()
total_value += items_copy[i].get_value()
return (result, total_value)
def __UpperCAmelCase ( ) -> int:
"""simple docstring"""
if __name__ == "__main__":
import doctest
doctest.testmod()
| 14 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_A = {
"configuration_clipseg": [
"CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP",
"CLIPSegConfig",
"CLIPSegTextConfig",
"CLIPSegVisionConfig",
],
"processing_clipseg": ["CLIPSegProcessor"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_A = [
"CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST",
"CLIPSegModel",
"CLIPSegPreTrainedModel",
"CLIPSegTextModel",
"CLIPSegVisionModel",
"CLIPSegForImageSegmentation",
]
if TYPE_CHECKING:
from .configuration_clipseg import (
CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
CLIPSegConfig,
CLIPSegTextConfig,
CLIPSegVisionConfig,
)
from .processing_clipseg import CLIPSegProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_clipseg import (
CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
CLIPSegForImageSegmentation,
CLIPSegModel,
CLIPSegPreTrainedModel,
CLIPSegTextModel,
CLIPSegVisionModel,
)
else:
import sys
_A = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 290 |
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import SwiftFormerForImageClassification, SwiftFormerModel
from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class UpperCAmelCase_ :
"""simple docstring"""
def __init__( self , _a , _a=1_3 , _a=3 , _a=True , _a=True , _a=0.1 , _a=0.1 , _a=2_2_4 , _a=1_0_0_0 , _a=[3, 3, 6, 4] , _a=[4_8, 5_6, 1_1_2, 2_2_0] , ) -> Tuple:
_a : Dict = parent
_a : Optional[int] = batch_size
_a : Optional[Any] = num_channels
_a : Union[str, Any] = is_training
_a : Tuple = use_labels
_a : Dict = hidden_dropout_prob
_a : List[Any] = attention_probs_dropout_prob
_a : Dict = num_labels
_a : List[str] = image_size
_a : Dict = layer_depths
_a : str = embed_dims
def __lowercase ( self ) -> Optional[Any]:
_a : Dict = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_a : int = None
if self.use_labels:
_a : Optional[Any] = ids_tensor([self.batch_size] , self.num_labels )
_a : Dict = self.get_config()
return config, pixel_values, labels
def __lowercase ( self ) -> int:
return SwiftFormerConfig(
depths=self.layer_depths , embed_dims=self.embed_dims , mlp_ratio=4 , downsamples=[True, True, True, True] , hidden_act='''gelu''' , num_labels=self.num_labels , down_patch_size=3 , down_stride=2 , down_pad=1 , drop_rate=0.0 , drop_path_rate=0.0 , use_layer_scale=_a , layer_scale_init_value=1e-5 , )
def __lowercase ( self , _a , _a , _a ) -> str:
_a : List[Any] = SwiftFormerModel(config=_a )
model.to(_a )
model.eval()
_a : Optional[int] = model(_a )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.embed_dims[-1], 7, 7) )
def __lowercase ( self , _a , _a , _a ) -> Optional[Any]:
_a : List[str] = self.num_labels
_a : Optional[int] = SwiftFormerForImageClassification(_a )
model.to(_a )
model.eval()
_a : List[str] = model(_a , labels=_a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
_a : Union[str, Any] = SwiftFormerForImageClassification(_a )
model.to(_a )
model.eval()
_a : Dict = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_a : Optional[Any] = model(_a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __lowercase ( self ) -> Tuple:
((_a) , (_a) , (_a)) : Optional[int] = self.prepare_config_and_inputs()
_a : List[Any] = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class UpperCAmelCase_ ( __lowercase , __lowercase , unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase__ : List[str] = (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
UpperCAmelCase__ : Optional[int] = (
{"feature-extraction": SwiftFormerModel, "image-classification": SwiftFormerForImageClassification}
if is_torch_available()
else {}
)
UpperCAmelCase__ : Optional[Any] = False
UpperCAmelCase__ : str = False
UpperCAmelCase__ : Tuple = False
UpperCAmelCase__ : Tuple = False
UpperCAmelCase__ : str = False
def __lowercase ( self ) -> Optional[int]:
_a : Union[str, Any] = SwiftFormerModelTester(self )
_a : int = ConfigTester(
self , config_class=_a , has_text_modality=_a , hidden_size=3_7 , num_attention_heads=1_2 , num_hidden_layers=1_2 , )
def __lowercase ( self ) -> int:
self.config_tester.run_common_tests()
@unittest.skip(reason='''SwiftFormer does not use inputs_embeds''' )
def __lowercase ( self ) -> Union[str, Any]:
pass
def __lowercase ( self ) -> Dict:
_a , _a : int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_a : Any = model_class(_a )
_a : int = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(_a , nn.Linear ) )
def __lowercase ( self ) -> str:
_a , _a : str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_a : Optional[int] = model_class(_a )
_a : Tuple = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_a : Tuple = [*signature.parameters.keys()]
_a : List[str] = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , _a )
def __lowercase ( self ) -> int:
_a : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_a )
def __lowercase ( self ) -> Optional[int]:
_a : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_a )
@slow
def __lowercase ( self ) -> Optional[Any]:
for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_a : Any = SwiftFormerModel.from_pretrained(_a )
self.assertIsNotNone(_a )
@unittest.skip(reason='''SwiftFormer does not output attentions''' )
def __lowercase ( self ) -> List[Any]:
pass
def __lowercase ( self ) -> int:
def check_hidden_states_output(_a , _a , _a ):
_a : Optional[int] = model_class(_a )
model.to(_a )
model.eval()
with torch.no_grad():
_a : Union[str, Any] = model(**self._prepare_for_class(_a , _a ) )
_a : Optional[Any] = outputs.hidden_states
_a : Union[str, Any] = 8
self.assertEqual(len(_a ) , _a ) # TODO
# SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
# with the width and height being successively divided by 2, after every 2 blocks
for i in range(len(_a ) ):
self.assertEqual(
hidden_states[i].shape , torch.Size(
[
self.model_tester.batch_size,
self.model_tester.embed_dims[i // 2],
(self.model_tester.image_size // 4) // 2 ** (i // 2),
(self.model_tester.image_size // 4) // 2 ** (i // 2),
] ) , )
_a , _a : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_a : str = True
check_hidden_states_output(_a , _a , _a )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_a : List[str] = True
check_hidden_states_output(_a , _a , _a )
def __lowercase ( self ) -> str:
def _config_zero_init(_a ):
_a : List[Any] = copy.deepcopy(_a )
for key in configs_no_init.__dict__.keys():
if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
setattr(_a , _a , 1e-1_0 )
if isinstance(getattr(_a , _a , _a ) , _a ):
_a : int = _config_zero_init(getattr(_a , _a ) )
setattr(_a , _a , _a )
return configs_no_init
_a , _a : Any = self.model_tester.prepare_config_and_inputs_for_common()
_a : Dict = _config_zero_init(_a )
for model_class in self.all_model_classes:
_a : Dict = model_class(config=_a )
for name, param in model.named_parameters():
if param.requires_grad:
self.assertIn(
((param.data.mean() * 1e9) / 1e9).round().item() , [0.0, 1.0] , msg=F"""Parameter {name} of model {model_class} seems not properly initialized""" , )
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def __lowercase ( self ) -> Optional[Any]:
pass
def __UpperCAmelCase ( ) -> Optional[Any]:
"""simple docstring"""
_a : Optional[int] = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class UpperCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def __lowercase ( self ) -> str:
return ViTImageProcessor.from_pretrained('''MBZUAI/swiftformer-xs''' ) if is_vision_available() else None
@slow
def __lowercase ( self ) -> Dict:
_a : Any = SwiftFormerForImageClassification.from_pretrained('''MBZUAI/swiftformer-xs''' ).to(_a )
_a : Any = self.default_image_processor
_a : Any = prepare_img()
_a : Any = image_processor(images=_a , return_tensors='''pt''' ).to(_a )
# forward pass
with torch.no_grad():
_a : Optional[Any] = model(**_a )
# verify the logits
_a : List[str] = torch.Size((1, 1_0_0_0) )
self.assertEqual(outputs.logits.shape , _a )
_a : int = torch.tensor([[-2.1_7_0_3e0_0, 2.1_1_0_7e0_0, -2.0_8_1_1e0_0]] ).to(_a )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , _a , atol=1e-4 ) )
| 14 | 0 |
'''simple docstring'''
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=__lowercase)
class _snake_case (__lowercase):
__A : str =field(default="image-classification" , metadata={"include_in_asdict_even_if_is_default": True})
__A : ClassVar[Features] =Features({"image": Image()})
__A : ClassVar[Features] =Features({"labels": ClassLabel})
__A : str ="image"
__A : str ="labels"
def UpperCamelCase__ ( self ,_snake_case ):
if self.label_column not in features:
raise ValueError(f'''Column {self.label_column} is not present in features.''' )
if not isinstance(features[self.label_column] ,_a ):
raise ValueError(f'''Column {self.label_column} is not a ClassLabel.''' )
UpperCAmelCase_ : Dict = copy.deepcopy(self )
UpperCAmelCase_ : List[Any] = self.label_schema.copy()
UpperCAmelCase_ : Any = features[self.label_column]
UpperCAmelCase_ : int = label_schema
return task_template
@property
def UpperCamelCase__ ( self ):
return {
self.image_column: "image",
self.label_column: "labels",
}
| 71 |
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging
logging.set_verbosity_info()
a__ = logging.get_logger(__name__)
def __UpperCAmelCase ( __a : str ) -> List[Any]:
"""simple docstring"""
_a : Tuple = SwinConfig.from_pretrained(
'''microsoft/swin-tiny-patch4-window7-224''' ,out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4'''] )
_a : Dict = MaskFormerConfig(backbone_config=__a )
_a : Optional[Any] = '''huggingface/label-files'''
if "ade20k-full" in model_name:
# this should be ok
_a : Optional[Any] = 847
_a : List[Any] = '''maskformer-ade20k-full-id2label.json'''
elif "ade" in model_name:
# this should be ok
_a : Union[str, Any] = 150
_a : Any = '''ade20k-id2label.json'''
elif "coco-stuff" in model_name:
# this should be ok
_a : int = 171
_a : List[str] = '''maskformer-coco-stuff-id2label.json'''
elif "coco" in model_name:
# TODO
_a : Dict = 133
_a : Optional[Any] = '''coco-panoptic-id2label.json'''
elif "cityscapes" in model_name:
# this should be ok
_a : List[Any] = 19
_a : Optional[Any] = '''cityscapes-id2label.json'''
elif "vistas" in model_name:
# this should be ok
_a : List[Any] = 65
_a : Dict = '''mapillary-vistas-id2label.json'''
_a : Optional[int] = json.load(open(hf_hub_download(__a ,__a ,repo_type='''dataset''' ) ,'''r''' ) )
_a : Tuple = {int(__a ): v for k, v in idalabel.items()}
return config
def __UpperCAmelCase ( __a : Optional[Any] ) -> Tuple:
"""simple docstring"""
_a : Optional[Any] = []
# stem
# fmt: off
rename_keys.append(('''backbone.patch_embed.proj.weight''', '''model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight''') )
rename_keys.append(('''backbone.patch_embed.proj.bias''', '''model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias''') )
rename_keys.append(('''backbone.patch_embed.norm.weight''', '''model.pixel_level_module.encoder.model.embeddings.norm.weight''') )
rename_keys.append(('''backbone.patch_embed.norm.bias''', '''model.pixel_level_module.encoder.model.embeddings.norm.bias''') )
# stages
for i in range(len(config.backbone_config.depths ) ):
for j in range(config.backbone_config.depths[i] ):
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm1.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm1.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.relative_position_index""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.proj.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.proj.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm2.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm2.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc1.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc1.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc2.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc2.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias""") )
if i < 3:
rename_keys.append((F"""backbone.layers.{i}.downsample.reduction.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight""") )
rename_keys.append((F"""backbone.layers.{i}.downsample.norm.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight""") )
rename_keys.append((F"""backbone.layers.{i}.downsample.norm.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias""") )
rename_keys.append((F"""backbone.norm{i}.weight""", F"""model.pixel_level_module.encoder.hidden_states_norms.{i}.weight""") )
rename_keys.append((F"""backbone.norm{i}.bias""", F"""model.pixel_level_module.encoder.hidden_states_norms.{i}.bias""") )
# FPN
rename_keys.append(('''sem_seg_head.layer_4.weight''', '''model.pixel_level_module.decoder.fpn.stem.0.weight''') )
rename_keys.append(('''sem_seg_head.layer_4.norm.weight''', '''model.pixel_level_module.decoder.fpn.stem.1.weight''') )
rename_keys.append(('''sem_seg_head.layer_4.norm.bias''', '''model.pixel_level_module.decoder.fpn.stem.1.bias''') )
for source_index, target_index in zip(range(3 ,0 ,-1 ) ,range(0 ,3 ) ):
rename_keys.append((F"""sem_seg_head.adapter_{source_index}.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight""") )
rename_keys.append((F"""sem_seg_head.adapter_{source_index}.norm.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight""") )
rename_keys.append((F"""sem_seg_head.adapter_{source_index}.norm.bias""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias""") )
rename_keys.append((F"""sem_seg_head.layer_{source_index}.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight""") )
rename_keys.append((F"""sem_seg_head.layer_{source_index}.norm.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight""") )
rename_keys.append((F"""sem_seg_head.layer_{source_index}.norm.bias""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias""") )
rename_keys.append(('''sem_seg_head.mask_features.weight''', '''model.pixel_level_module.decoder.mask_projection.weight''') )
rename_keys.append(('''sem_seg_head.mask_features.bias''', '''model.pixel_level_module.decoder.mask_projection.bias''') )
# Transformer decoder
for idx in range(config.decoder_config.decoder_layers ):
# self-attention out projection
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight""", F"""model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias""", F"""model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias""") )
# cross-attention out projection
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias""") )
# MLP 1
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight""", F"""model.transformer_module.decoder.layers.{idx}.fc1.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias""", F"""model.transformer_module.decoder.layers.{idx}.fc1.bias""") )
# MLP 2
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight""", F"""model.transformer_module.decoder.layers.{idx}.fc2.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias""", F"""model.transformer_module.decoder.layers.{idx}.fc2.bias""") )
# layernorm 1 (self-attention layernorm)
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight""", F"""model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias""", F"""model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias""") )
# layernorm 2 (cross-attention layernorm)
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias""") )
# layernorm 3 (final layernorm)
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight""", F"""model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias""", F"""model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias""") )
rename_keys.append(('''sem_seg_head.predictor.transformer.decoder.norm.weight''', '''model.transformer_module.decoder.layernorm.weight''') )
rename_keys.append(('''sem_seg_head.predictor.transformer.decoder.norm.bias''', '''model.transformer_module.decoder.layernorm.bias''') )
# heads on top
rename_keys.append(('''sem_seg_head.predictor.query_embed.weight''', '''model.transformer_module.queries_embedder.weight''') )
rename_keys.append(('''sem_seg_head.predictor.input_proj.weight''', '''model.transformer_module.input_projection.weight''') )
rename_keys.append(('''sem_seg_head.predictor.input_proj.bias''', '''model.transformer_module.input_projection.bias''') )
rename_keys.append(('''sem_seg_head.predictor.class_embed.weight''', '''class_predictor.weight''') )
rename_keys.append(('''sem_seg_head.predictor.class_embed.bias''', '''class_predictor.bias''') )
for i in range(3 ):
rename_keys.append((F"""sem_seg_head.predictor.mask_embed.layers.{i}.weight""", F"""mask_embedder.{i}.0.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.mask_embed.layers.{i}.bias""", F"""mask_embedder.{i}.0.bias""") )
# fmt: on
return rename_keys
def __UpperCAmelCase ( __a : List[str] ,__a : List[Any] ,__a : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
_a : str = dct.pop(__a )
_a : str = val
def __UpperCAmelCase ( __a : List[Any] ,__a : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
_a : Union[str, Any] = [int(backbone_config.embed_dim * 2**i ) for i in range(len(backbone_config.depths ) )]
for i in range(len(backbone_config.depths ) ):
_a : Optional[Any] = num_features[i]
for j in range(backbone_config.depths[i] ):
# fmt: off
# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
_a : List[Any] = state_dict.pop(F"""backbone.layers.{i}.blocks.{j}.attn.qkv.weight""" )
_a : Optional[int] = state_dict.pop(F"""backbone.layers.{i}.blocks.{j}.attn.qkv.bias""" )
# next, add query, keys and values (in that order) to the state dict
_a : Optional[int] = in_proj_weight[:dim, :]
_a : List[Any] = in_proj_bias[: dim]
_a : Optional[int] = in_proj_weight[
dim : dim * 2, :
]
_a : Tuple = in_proj_bias[
dim : dim * 2
]
_a : int = in_proj_weight[
-dim :, :
]
_a : Optional[int] = in_proj_bias[-dim :]
# fmt: on
def __UpperCAmelCase ( __a : List[str] ,__a : List[Any] ) -> List[Any]:
"""simple docstring"""
_a : Optional[int] = config.decoder_config.hidden_size
for idx in range(config.decoder_config.decoder_layers ):
# read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
_a : Union[str, Any] = state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight""" )
_a : List[Any] = state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias""" )
# next, add query, keys and values (in that order) to the state dict
_a : Union[str, Any] = in_proj_weight[: hidden_size, :]
_a : List[Any] = in_proj_bias[:config.hidden_size]
_a : Dict = in_proj_weight[hidden_size : hidden_size * 2, :]
_a : Any = in_proj_bias[hidden_size : hidden_size * 2]
_a : Tuple = in_proj_weight[-hidden_size :, :]
_a : List[Any] = in_proj_bias[-hidden_size :]
# read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
_a : List[Any] = state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight""" )
_a : List[str] = state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias""" )
# next, add query, keys and values (in that order) to the state dict
_a : Optional[Any] = in_proj_weight[: hidden_size, :]
_a : Any = in_proj_bias[:config.hidden_size]
_a : List[str] = in_proj_weight[hidden_size : hidden_size * 2, :]
_a : Optional[Any] = in_proj_bias[hidden_size : hidden_size * 2]
_a : List[str] = in_proj_weight[-hidden_size :, :]
_a : int = in_proj_bias[-hidden_size :]
# fmt: on
def __UpperCAmelCase ( ) -> torch.Tensor:
"""simple docstring"""
_a : str = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
_a : Dict = Image.open(requests.get(__a ,stream=__a ).raw )
return im
@torch.no_grad()
def __UpperCAmelCase ( __a : str ,__a : str ,__a : str ,__a : bool = False ) -> Union[str, Any]:
"""simple docstring"""
_a : Optional[Any] = get_maskformer_config(__a )
# load original state_dict
with open(__a ,'''rb''' ) as f:
_a : str = pickle.load(__a )
_a : Union[str, Any] = data['''model''']
# for name, param in state_dict.items():
# print(name, param.shape)
# rename keys
_a : Any = create_rename_keys(__a )
for src, dest in rename_keys:
rename_key(__a ,__a ,__a )
read_in_swin_q_k_v(__a ,config.backbone_config )
read_in_decoder_q_k_v(__a ,__a )
# update to torch tensors
for key, value in state_dict.items():
_a : Optional[int] = torch.from_numpy(__a )
# load 🤗 model
_a : Dict = MaskFormerForInstanceSegmentation(__a )
model.eval()
for name, param in model.named_parameters():
print(__a ,param.shape )
_a , _a : Tuple = model.load_state_dict(__a ,strict=__a )
assert missing_keys == [
"model.pixel_level_module.encoder.model.layernorm.weight",
"model.pixel_level_module.encoder.model.layernorm.bias",
]
assert len(__a ) == 0, F"""Unexpected keys: {unexpected_keys}"""
# verify results
_a : Union[str, Any] = prepare_img()
if "vistas" in model_name:
_a : int = 65
elif "cityscapes" in model_name:
_a : Tuple = 65_535
else:
_a : str = 255
_a : Dict = True if '''ade''' in model_name else False
_a : Optional[Any] = MaskFormerImageProcessor(ignore_index=__a ,reduce_labels=__a )
_a : Optional[Any] = image_processor(__a ,return_tensors='''pt''' )
_a : int = model(**__a )
print('''Logits:''' ,outputs.class_queries_logits[0, :3, :3] )
if model_name == "maskformer-swin-tiny-ade":
_a : Union[str, Any] = torch.tensor(
[[3.63_53, -4.47_70, -2.60_65], [0.50_81, -4.23_94, -3.53_43], [2.19_09, -5.03_53, -1.93_23]] )
assert torch.allclose(outputs.class_queries_logits[0, :3, :3] ,__a ,atol=1E-4 )
print('''Looks ok!''' )
if pytorch_dump_folder_path is not None:
print(F"""Saving model and image processor to {pytorch_dump_folder_path}""" )
Path(__a ).mkdir(exist_ok=__a )
model.save_pretrained(__a )
image_processor.save_pretrained(__a )
if push_to_hub:
print('''Pushing model and image processor to the hub...''' )
model.push_to_hub(F"""nielsr/{model_name}""" )
image_processor.push_to_hub(F"""nielsr/{model_name}""" )
if __name__ == "__main__":
a__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--model_name''',
default='''maskformer-swin-tiny-ade''',
type=str,
help=('''Name of the MaskFormer model you\'d like to convert''',),
)
parser.add_argument(
'''--checkpoint_path''',
default='''/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl''',
type=str,
help='''Path to the original state dict (.pth file).''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
)
parser.add_argument(
'''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
)
a__ = parser.parse_args()
convert_maskformer_checkpoint(
args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
)
| 14 | 0 |
"""simple docstring"""
from argparse import ArgumentParser
from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import PTtoTFCommand
from .run import RunCommand
from .serving import ServeCommand
from .user import UserCommands
def lowercase ( ):
'''simple docstring'''
_UpperCAmelCase = ArgumentParser('''Transformers CLI tool''' , usage='''transformers-cli <command> [<args>]''' )
_UpperCAmelCase = parser.add_subparsers(help='''transformers-cli command helpers''' )
# Register commands
ConvertCommand.register_subcommand(__a )
DownloadCommand.register_subcommand(__a )
EnvironmentCommand.register_subcommand(__a )
RunCommand.register_subcommand(__a )
ServeCommand.register_subcommand(__a )
UserCommands.register_subcommand(__a )
AddNewModelCommand.register_subcommand(__a )
AddNewModelLikeCommand.register_subcommand(__a )
LfsCommands.register_subcommand(__a )
PTtoTFCommand.register_subcommand(__a )
# Let's go
_UpperCAmelCase = parser.parse_args()
if not hasattr(__a , '''func''' ):
parser.print_help()
exit(1 )
# Run
_UpperCAmelCase = args.func(__a )
service.run()
if __name__ == "__main__":
main()
| 602 |
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
a__ = get_tests_dir('''fixtures/test_sentencepiece.model''')
@require_sentencepiece
class UpperCAmelCase_ ( __lowercase , unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase__ : List[str] = XLMProphetNetTokenizer
UpperCAmelCase__ : Optional[int] = False
UpperCAmelCase__ : List[Any] = True
def __lowercase ( self ) -> int:
super().setUp()
# We have a SentencePiece fixture for testing
_a : List[Any] = XLMProphetNetTokenizer(_a , keep_accents=_a )
tokenizer.save_pretrained(self.tmpdirname )
def __lowercase ( self ) -> Any:
_a : Tuple = '''[PAD]'''
_a : int = 0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(_a ) , _a )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(_a ) , _a )
def __lowercase ( self ) -> str:
_a : Any = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''[PAD]''' )
self.assertEqual(vocab_keys[1] , '''[CLS]''' )
self.assertEqual(vocab_keys[-1] , '''j''' )
self.assertEqual(len(_a ) , 1_0_1_2 )
def __lowercase ( self ) -> Union[str, Any]:
self.assertEqual(self.get_tokenizer().vocab_size , 1_0_1_2 )
def __lowercase ( self ) -> str:
_a : Tuple = XLMProphetNetTokenizer(_a , keep_accents=_a )
_a : Union[str, Any] = tokenizer.tokenize('''This is a test''' )
self.assertListEqual(_a , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(_a ) , [value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]] , )
_a : Optional[int] = tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
_a , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''9''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''é''',
'''.''',
] , )
_a : List[Any] = tokenizer.convert_tokens_to_ids(_a )
self.assertListEqual(
_a , [
value + tokenizer.fairseq_offset
for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, -9, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, -9, 4]
] , )
_a : List[str] = tokenizer.convert_ids_to_tokens(_a )
self.assertListEqual(
_a , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''[UNK]''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''[UNK]''',
'''.''',
] , )
@cached_property
def __lowercase ( self ) -> List[str]:
return XLMProphetNetTokenizer.from_pretrained('''microsoft/xprophetnet-large-wiki100-cased''' )
@slow
def __lowercase ( self ) -> Tuple:
_a : str = '''Hello World!'''
_a : Tuple = [3_5_3_8_9, 6_6_7_2, 4_9, 2]
self.assertListEqual(_a , self.big_tokenizer.encode(_a ) )
@slow
def __lowercase ( self ) -> str:
# fmt: off
_a : str = {'''input_ids''': [[1_1_0_7_3, 8_2_7_8_3, 1_8, 2_6, 8_2_7_8_3, 5_4_9, 5_1_5_4_0, 2_4_8, 1_7_2_0_9, 1_3_0_1, 2_1_7, 2_0, 2_1_5_1_8_6, 1_3_2_5, 1_4_7, 1_7_2_0_9, 1_3_0_1, 2_1_7, 2_0, 5_6_3_7_0, 5_3, 1_2_2_0_2_0, 2_0, 1_6_4_7_7, 2_7, 8_7_3_5_5, 4_5_4_8, 2_0, 4_7_2_8, 7_8_3_9_2, 1_7, 1_5_9_9_6_9, 1_8, 2_6, 2_4_4_9_1, 6_2_9, 1_5, 5_3_8, 2_2_7_0_4, 5_4_3_9, 1_5, 2_7_8_8, 2_4_4_9_1, 9_8_8_5, 1_5, 4_3_5_3_4, 6_0_5, 1_5, 8_1_4, 1_8_4_0_3, 3_3_2_0_0, 2_9, 1_5, 4_3_5_3_4, 2_4_4_5_8, 1_2_4_1_0, 1_1_1, 2_4_9_6_6, 8_3_6_6_9, 9_6_3_7, 1_4_4_0_6_8, 2_6, 8_5_0, 2_2_3_4_6, 2_7, 1_4_7, 2_4_9_6_6, 8_3_6_6_9, 8_3_4_9_0, 2_6, 3_9_1_1_3, 7_3_5, 2_7, 6_8_9, 6_5_6, 2_8_0_0, 1_3_3_9, 4_6_0_0, 5_3, 1_2_2_0_2_0, 1_1_5_7_8_5, 3_4, 8_1_6, 1_3_3_9, 4_6_8_8_7, 1_8, 1_4_7, 5_3_9_0_5, 1_9_5_1, 4_2_2_3_8, 4_1_1_7_0, 1_7_7_3_2, 8_3_4, 4_3_6, 1_5, 2_7_5_2_3, 9_8_7_3_3, 2_1_7, 1_4_7, 5_5_4_2, 4_9_8_1, 9_3_0, 1_7_3_4_7, 1_6, 2], [2_0_0_9_1, 6_2_9, 9_4, 8_2_7_8_6, 5_8, 4_9_0, 2_0, 1_5_2_8, 8_4, 5_3_9_0_5, 3_4_4, 8_0_5_9_2, 1_1_0_1_2_8, 1_8_8_2_2, 5_2_6_7, 1_3_0_6, 6_2, 1_5_2_5_3_7, 3_0_8, 7_9_9_7, 4_0_1, 1_2_4_4_2_7, 5_4_9, 3_5_4_4_2, 2_2_5, 1_0_9, 1_5_0_5_5, 2_5_7_4_8, 1_4_7, 7_1_1_9, 4_3_7_1_2, 3_4, 7_6_7, 1_3_5_3_6_6, 1_8, 1_6, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_9_2, 6_3_7_8_4, 1_1_9_4_6_6, 1_7, 1_4_7_8_0_8, 8_8_2_1_4, 1_8, 6_5_6, 8_1, 3_2, 3_2_9_6, 1_0_2_8_0, 1_6, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=_a , model_name='''microsoft/xprophetnet-large-wiki100-cased''' , revision='''1acad1643ddd54a44df6a1b797ada8373685d90e''' , )
| 14 | 0 |
"""simple docstring"""
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers.utils import logging
logging.set_verbosity_info()
# should not include what is already done by the `from_pt` argument
__SCREAMING_SNAKE_CASE : Optional[Any] = {
'''/attention/''': '''/0/SelfAttention/''',
'''/self_attention/''': '''/0/SelfAttention/''',
'''/encoder_decoder_attention/''': '''/1/EncDecAttention/''',
'''value''': '''v''',
'''query''': '''q''',
'''key''': '''k''',
'''out''': '''o''',
'''pre_self_attention_layer_norm''': '''0/layer_norm''',
'''pre_cross_attention_layer_norm''': '''1/layer_norm''',
'''pre_attention_layer_norm''': '''0/layer_norm''', # previously 1, but seems wrong
'''token_embedder''': '''shared''',
'''encoder_norm''': '''final_layer_norm''',
'''decoder_norm''': '''final_layer_norm''',
'''relpos_bias/rel_embedding''': '''block/0/layer/0/SelfAttention/relative_attention_bias/weight''',
'''router/router_weights/w/''': '''router/classifier/''',
'''roer/roer_weights/w/''': '''router/classifier/''',
'''logits_dense''': '''lm_head''',
}
def lowerCAmelCase_( lowercase_ : List[str] ) -> str:
_lowerCamelCase = list(s_dict.keys() )
for key in keys:
_lowerCamelCase = R'''.*/layers_(\d+)'''
_lowerCamelCase = key
if re.match(__a , __a ):
_lowerCamelCase = re.sub(r'''layers_(\d+)''' , r'''block/\1/layer''' , __a )
_lowerCamelCase = R'''(encoder|decoder)\/'''
if re.match(__a , __a ):
_lowerCamelCase = re.match(__a , __a ).groups()
if groups[0] == "encoder":
_lowerCamelCase = re.sub(r'''/mlp/''' , r'''/1/mlp/''' , __a )
_lowerCamelCase = re.sub(r'''/pre_mlp_layer_norm/''' , r'''/1/layer_norm/''' , __a )
elif groups[0] == "decoder":
_lowerCamelCase = re.sub(r'''/mlp/''' , r'''/2/mlp/''' , __a )
_lowerCamelCase = re.sub(r'''/pre_mlp_layer_norm/''' , r'''/2/layer_norm/''' , __a )
# 2. Convert other classic mappings
for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
if old_key in new_key:
_lowerCamelCase = new_key.replace(__a , __a )
print(F"""{key} -> {new_key}""" )
_lowerCamelCase = s_dict.pop(__a )
if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
_lowerCamelCase = s_dict[
'''encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight'''
].T
if "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
_lowerCamelCase = s_dict[
'''decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight'''
].T
# 3. Take extra care of the EXPERTS layer
for key in list(s_dict.keys() ):
if "expert" in key:
_lowerCamelCase = s_dict[key].shape[0]
_lowerCamelCase = s_dict[key]
for idx in range(__a ):
_lowerCamelCase = expert_weihts[idx]
print(F"""{key} -> {key.replace("expert/" , "nested fstring" )}""" )
s_dict.pop(__a )
return s_dict
__SCREAMING_SNAKE_CASE : Dict = {
'''NUM_ENCODER_LAYERS''': '''num_layers''',
'''NUM_DECODER_LAYERS''': '''num_decoder_layers''',
'''NUM_HEADS''': '''num_heads''',
'''HEAD_DIM''': '''d_kv''',
'''EMBED_DIM''': '''d_model''',
'''MLP_DIM''': '''d_ff''',
'''NUM_SELECTED_EXPERTS''': '''num_selected_experts''',
'''NUM_ENCODER_SPARSE_LAYERS''': '''num_sparse_encoder_layers''',
'''NUM_DECODER_SPARSE_LAYERS''': '''num_sparse_decoder_layers''',
'''dense.MlpBlock.activations''': '''feed_forward_proj''',
}
def lowerCAmelCase_( lowercase_ : int , lowercase_ : Any ) -> Union[str, Any]:
import regex as re
with open(__a , '''r''' ) as f:
_lowerCamelCase = f.read()
_lowerCamelCase = re.findall(r'''(.*) = ([0-9.]*)''' , __a )
_lowerCamelCase = {}
for param, value in regex_match:
if param in GIN_TO_CONFIG_MAPPING and value != "":
_lowerCamelCase = float(__a ) if '''.''' in value else int(__a )
_lowerCamelCase = re.findall(r'''(.*activations) = \(\'(.*)\',\)''' , __a )[0]
_lowerCamelCase = str(activation[1] )
_lowerCamelCase = num_experts
_lowerCamelCase = SwitchTransformersConfig(**__a )
return config
def lowerCAmelCase_( lowercase_ : Dict , lowercase_ : Optional[int] , lowercase_ : Dict=None , lowercase_ : int="./" , lowercase_ : Union[str, Any]=8 ) -> Dict:
print(F"""Loading flax weights from : {flax_checkpoint_path}""" )
_lowerCamelCase = checkpoints.load_tax_checkpoint(__a )
if gin_file is not None:
_lowerCamelCase = convert_gin_to_config(__a , __a )
else:
_lowerCamelCase = SwitchTransformersConfig.from_pretrained(__a )
_lowerCamelCase = SwitchTransformersForConditionalGeneration(__a )
_lowerCamelCase = flax_params['''target''']
_lowerCamelCase = flatten_dict(__a , sep='''/''' )
_lowerCamelCase = rename_keys(__a )
_lowerCamelCase = unflatten_dict(__a , sep='''/''' )
# Load the flax params in the PT model
load_flax_weights_in_pytorch_model(__a , __a )
print(F"""Save PyTorch model to {pytorch_dump_path}""" )
pt_model.save_pretrained(__a )
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE : List[str] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--switch_t5x_checkpoint_path''',
default=None,
type=str,
required=True,
help=(
'''The config json file corresponding to the pre-trained SwitchTransformers model. \nThis specifies the'''
''' model architecture. If not provided, a `gin_file` has to be provided.'''
),
)
parser.add_argument(
'''--gin_file''',
default=None,
type=str,
required=False,
help='''Path to the gin config file. If not provided, a `config_file` has to be passed ''',
)
parser.add_argument(
'''--config_name''', default=None, type=str, required=False, help='''Config name of SwitchTransformers model.'''
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output pytorch model.'''
)
parser.add_argument('''--num_experts''', default=8, type=int, required=False, help='''Number of experts''')
__SCREAMING_SNAKE_CASE : Optional[Any] = parser.parse_args()
convert_flax_checkpoint_to_pytorch(
args.switch_tax_checkpoint_path,
args.config_name,
args.gin_file,
args.pytorch_dump_folder_path,
args.num_experts,
)
| 661 |
import os
import unittest
from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class UpperCAmelCase_ ( __lowercase , unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase__ : Any = LxmertTokenizer
UpperCAmelCase__ : Optional[Any] = LxmertTokenizerFast
UpperCAmelCase__ : Any = True
UpperCAmelCase__ : Dict = True
def __lowercase ( self ) -> Union[str, Any]:
super().setUp()
_a : int = [
'''[UNK]''',
'''[CLS]''',
'''[SEP]''',
'''want''',
'''##want''',
'''##ed''',
'''wa''',
'''un''',
'''runn''',
'''##ing''',
''',''',
'''low''',
'''lowest''',
]
_a : Any = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as vocab_writer:
vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens] ) )
def __lowercase ( self , _a ) -> List[str]:
_a : Tuple = '''UNwant\u00E9d,running'''
_a : str = '''unwanted, running'''
return input_text, output_text
def __lowercase ( self ) -> List[Any]:
_a : str = self.tokenizer_class(self.vocab_file )
_a : str = tokenizer.tokenize('''UNwant\u00E9d,running''' )
self.assertListEqual(_a , ['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing'''] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(_a ) , [7, 4, 5, 1_0, 8, 9] )
def __lowercase ( self ) -> List[Any]:
if not self.test_rust_tokenizer:
return
_a : Optional[Any] = self.get_tokenizer()
_a : str = self.get_rust_tokenizer()
_a : Optional[Any] = '''I was born in 92000, and this is falsé.'''
_a : Optional[Any] = tokenizer.tokenize(_a )
_a : List[Any] = rust_tokenizer.tokenize(_a )
self.assertListEqual(_a , _a )
_a : List[Any] = tokenizer.encode(_a , add_special_tokens=_a )
_a : Any = rust_tokenizer.encode(_a , add_special_tokens=_a )
self.assertListEqual(_a , _a )
_a : Dict = self.get_rust_tokenizer()
_a : Optional[int] = tokenizer.encode(_a )
_a : Dict = rust_tokenizer.encode(_a )
self.assertListEqual(_a , _a )
| 14 | 0 |
import inspect
import os
import unittest
from dataclasses import dataclass
import torch
from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler
@dataclass
class __A ( __lowercase ):
UpperCamelCase :int = 0
UpperCamelCase :bool = False
UpperCamelCase :float = 3.0
class __A ( unittest.TestCase ):
def _snake_case (self ):
# If no defaults are changed, `to_kwargs` returns an empty dict.
self.assertDictEqual(MockClass().to_kwargs() , {} )
self.assertDictEqual(MockClass(a=2 ).to_kwargs() , {"""a""": 2} )
self.assertDictEqual(MockClass(a=2 , b=_a ).to_kwargs() , {"""a""": 2, """b""": True} )
self.assertDictEqual(MockClass(a=2 , c=2.25 ).to_kwargs() , {"""a""": 2, """c""": 2.25} )
@require_cuda
def _snake_case (self ):
# If no defaults are changed, `to_kwargs` returns an empty dict.
lowerCamelCase__ : Tuple = GradScalerKwargs(init_scale=1024 , growth_factor=2 )
AcceleratorState._reset_state()
lowerCamelCase__ : List[Any] = Accelerator(mixed_precision="""fp16""" , kwargs_handlers=[scaler_handler] )
print(accelerator.use_fpaa )
lowerCamelCase__ : Union[str, Any] = accelerator.scaler
# Check the kwargs have been applied
self.assertEqual(scaler._init_scale , 10_24.0 )
self.assertEqual(scaler._growth_factor , 2.0 )
# Check the other values are at the default
self.assertEqual(scaler._backoff_factor , 0.5 )
self.assertEqual(scaler._growth_interval , 2000 )
self.assertEqual(scaler._enabled , _a )
@require_multi_gpu
def _snake_case (self ):
lowerCamelCase__ : int = ['''torchrun''', f"--nproc_per_node={torch.cuda.device_count()}", inspect.getfile(self.__class__ )]
execute_subprocess_async(_a , env=os.environ.copy() )
if __name__ == "__main__":
_lowercase = DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
_lowercase = Accelerator(kwargs_handlers=[ddp_scaler])
_lowercase = torch.nn.Linear(1_00, 2_00)
_lowercase = accelerator.prepare(model)
# Check the values changed in kwargs
_lowercase = ''''''
_lowercase = model.bucket_bytes_cap // (10_24 * 10_24)
if observed_bucket_cap_map != 15:
error_msg += F"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
if model.find_unused_parameters is not True:
error_msg += F"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"
# Check the values of the defaults
if model.dim != 0:
error_msg += F"Default value not respected, should have `0` but found {model.dim}.\n"
if model.broadcast_buffers is not True:
error_msg += F"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
if model.gradient_as_bucket_view is not False:
error_msg += F"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"
# Raise error at the end to make sure we don't stop at the first failure.
if len(error_msg) > 0:
raise ValueError(error_msg)
| 157 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch
if is_vision_available():
from transformers import TvltImageProcessor
if is_speech_available():
from transformers import TvltFeatureExtractor
from transformers import TvltProcessor
@require_torch
class UpperCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
def __lowercase ( self ) -> int:
_a : Dict = '''ZinengTang/tvlt-base'''
_a : List[str] = tempfile.mkdtemp()
def __lowercase ( self , **_a ) -> int:
return TvltImageProcessor.from_pretrained(self.checkpoint , **_a )
def __lowercase ( self , **_a ) -> List[Any]:
return TvltFeatureExtractor.from_pretrained(self.checkpoint , **_a )
def __lowercase ( self ) -> Optional[int]:
shutil.rmtree(self.tmpdirname )
def __lowercase ( self ) -> Dict:
_a : Union[str, Any] = self.get_image_processor()
_a : Dict = self.get_feature_extractor()
_a : Optional[int] = TvltProcessor(image_processor=_a , feature_extractor=_a )
processor.save_pretrained(self.tmpdirname )
_a : Any = TvltProcessor.from_pretrained(self.tmpdirname )
self.assertIsInstance(processor.feature_extractor , _a )
self.assertIsInstance(processor.image_processor , _a )
def __lowercase ( self ) -> Any:
_a : Optional[Any] = self.get_image_processor()
_a : Dict = self.get_feature_extractor()
_a : Dict = TvltProcessor(image_processor=_a , feature_extractor=_a )
_a : Union[str, Any] = np.ones([1_2_0_0_0] )
_a : Dict = feature_extractor(_a , return_tensors='''np''' )
_a : Tuple = processor(audio=_a , return_tensors='''np''' )
for key in audio_dict.keys():
self.assertAlmostEqual(audio_dict[key].sum() , input_processor[key].sum() , delta=1e-2 )
def __lowercase ( self ) -> int:
_a : Optional[Any] = self.get_image_processor()
_a : Union[str, Any] = self.get_feature_extractor()
_a : Optional[Any] = TvltProcessor(image_processor=_a , feature_extractor=_a )
_a : List[Any] = np.ones([3, 2_2_4, 2_2_4] )
_a : int = image_processor(_a , return_tensors='''np''' )
_a : Optional[int] = processor(images=_a , return_tensors='''np''' )
for key in image_dict.keys():
self.assertAlmostEqual(image_dict[key].sum() , input_processor[key].sum() , delta=1e-2 )
def __lowercase ( self ) -> Union[str, Any]:
_a : int = self.get_image_processor()
_a : Union[str, Any] = self.get_feature_extractor()
_a : Any = TvltProcessor(image_processor=_a , feature_extractor=_a )
_a : List[str] = np.ones([1_2_0_0_0] )
_a : Optional[int] = np.ones([3, 2_2_4, 2_2_4] )
_a : int = processor(audio=_a , images=_a )
self.assertListEqual(list(inputs.keys() ) , ['''audio_values''', '''audio_mask''', '''pixel_values''', '''pixel_mask'''] )
# test if it raises when no input is passed
with pytest.raises(_a ):
processor()
def __lowercase ( self ) -> Union[str, Any]:
_a : str = self.get_image_processor()
_a : Union[str, Any] = self.get_feature_extractor()
_a : Dict = TvltProcessor(image_processor=_a , feature_extractor=_a )
self.assertListEqual(
processor.model_input_names , image_processor.model_input_names + feature_extractor.model_input_names , msg='''`processor` and `image_processor`+`feature_extractor` model input names do not match''' , )
| 14 | 0 |
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 ( __lowercase , 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 lowerCamelCase ( self ):
'''simple docstring'''
torch.manual_seed(0 )
__lowerCamelCase = 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 , )
__lowerCamelCase = DDIMScheduler(
beta_start=0.00_085 , beta_end=0.012 , beta_schedule='''scaled_linear''' , clip_sample=_a , set_alpha_to_one=_a , )
torch.manual_seed(0 )
__lowerCamelCase = 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 )
__lowerCamelCase = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
__lowerCamelCase = CLIPTextModel(_a )
__lowerCamelCase = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
__lowerCamelCase = {
'''unet''': unet,
'''scheduler''': scheduler,
'''vqvae''': vae,
'''bert''': text_encoder,
'''tokenizer''': tokenizer,
}
return components
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase=0 ):
'''simple docstring'''
if str(_a ).startswith('''mps''' ):
__lowerCamelCase = torch.manual_seed(_a )
else:
__lowerCamelCase = torch.Generator(device=_a ).manual_seed(_a )
__lowerCamelCase = {
'''prompt''': '''A painting of a squirrel eating a burger''',
'''generator''': generator,
'''num_inference_steps''': 2,
'''guidance_scale''': 6.0,
'''output_type''': '''numpy''',
}
return inputs
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = '''cpu''' # ensure determinism for the device-dependent torch.Generator
__lowerCamelCase = self.get_dummy_components()
__lowerCamelCase = LDMTextToImagePipeline(**_a )
pipe.to(_a )
pipe.set_progress_bar_config(disable=_a )
__lowerCamelCase = self.get_dummy_inputs(_a )
__lowerCamelCase = pipe(**_a ).images
__lowerCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 16, 16, 3)
__lowerCamelCase = np.array([0.6_101, 0.6_156, 0.5_622, 0.4_895, 0.6_661, 0.3_804, 0.5_748, 0.6_136, 0.5_014] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
@slow
@require_torch_gpu
class __lowerCAmelCase ( unittest.TestCase ):
def lowerCamelCase ( self ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase=torch.floataa , __UpperCAmelCase=0 ):
'''simple docstring'''
__lowerCamelCase = torch.manual_seed(_a )
__lowerCamelCase = np.random.RandomState(_a ).standard_normal((1, 4, 32, 32) )
__lowerCamelCase = torch.from_numpy(_a ).to(device=_a , dtype=_a )
__lowerCamelCase = {
'''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 lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = LDMTextToImagePipeline.from_pretrained('''CompVis/ldm-text2im-large-256''' ).to(_a )
pipe.set_progress_bar_config(disable=_a )
__lowerCamelCase = self.get_inputs(_a )
__lowerCamelCase = pipe(**_a ).images
__lowerCamelCase = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 256, 256, 3)
__lowerCamelCase = np.array([0.51_825, 0.52_850, 0.52_543, 0.54_258, 0.52_304, 0.52_569, 0.54_363, 0.55_276, 0.56_878] )
__lowerCamelCase = np.abs(expected_slice - image_slice ).max()
assert max_diff < 1E-3
@nightly
@require_torch_gpu
class __lowerCAmelCase ( unittest.TestCase ):
def lowerCamelCase ( self ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase=torch.floataa , __UpperCAmelCase=0 ):
'''simple docstring'''
__lowerCamelCase = torch.manual_seed(_a )
__lowerCamelCase = np.random.RandomState(_a ).standard_normal((1, 4, 32, 32) )
__lowerCamelCase = torch.from_numpy(_a ).to(device=_a , dtype=_a )
__lowerCamelCase = {
'''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 lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = LDMTextToImagePipeline.from_pretrained('''CompVis/ldm-text2im-large-256''' ).to(_a )
pipe.set_progress_bar_config(disable=_a )
__lowerCamelCase = self.get_inputs(_a )
__lowerCamelCase = pipe(**_a ).images[0]
__lowerCamelCase = load_numpy(
'''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/ldm_text2img/ldm_large_256_ddim.npy''' )
__lowerCamelCase = np.abs(expected_image - image ).max()
assert max_diff < 1E-3
| 175 |
def __UpperCAmelCase ( __a : str ) -> list:
"""simple docstring"""
if n_term == "":
return []
_a : list = []
for temp in range(int(__a ) ):
series.append(F"""1/{temp + 1}""" if series else '''1''' )
return series
if __name__ == "__main__":
a__ = input('''Enter the last number (nth term) of the Harmonic Series''')
print('''Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n''')
print(harmonic_series(nth_term))
| 14 | 0 |
'''simple docstring'''
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SegformerConfig,
SegformerForImageClassification,
SegformerForSemanticSegmentation,
SegformerImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase__ = logging.get_logger(__name__)
def UpperCAmelCase__( _SCREAMING_SNAKE_CASE : Optional[Any],_SCREAMING_SNAKE_CASE : Dict=False ):
"""simple docstring"""
__A= OrderedDict()
for key, value in state_dict.items():
if encoder_only and not key.startswith('head' ):
__A= '''segformer.encoder.''' + key
if key.startswith('backbone' ):
__A= key.replace('backbone','segformer.encoder' )
if "patch_embed" in key:
# replace for example patch_embed1 by patch_embeddings.0
__A= key[key.find('patch_embed' ) + len('patch_embed' )]
__A= key.replace(f"""patch_embed{idx}""",f"""patch_embeddings.{int(__a )-1}""" )
if "norm" in key:
__A= key.replace('norm','layer_norm' )
if "segformer.encoder.layer_norm" in key:
# replace for example layer_norm1 by layer_norm.0
__A= key[key.find('segformer.encoder.layer_norm' ) + len('segformer.encoder.layer_norm' )]
__A= key.replace(f"""layer_norm{idx}""",f"""layer_norm.{int(__a )-1}""" )
if "layer_norm1" in key:
__A= key.replace('layer_norm1','layer_norm_1' )
if "layer_norm2" in key:
__A= key.replace('layer_norm2','layer_norm_2' )
if "block" in key:
# replace for example block1 by block.0
__A= key[key.find('block' ) + len('block' )]
__A= key.replace(f"""block{idx}""",f"""block.{int(__a )-1}""" )
if "attn.q" in key:
__A= key.replace('attn.q','attention.self.query' )
if "attn.proj" in key:
__A= key.replace('attn.proj','attention.output.dense' )
if "attn" in key:
__A= key.replace('attn','attention.self' )
if "fc1" in key:
__A= key.replace('fc1','dense1' )
if "fc2" in key:
__A= key.replace('fc2','dense2' )
if "linear_pred" in key:
__A= key.replace('linear_pred','classifier' )
if "linear_fuse" in key:
__A= key.replace('linear_fuse.conv','linear_fuse' )
__A= key.replace('linear_fuse.bn','batch_norm' )
if "linear_c" in key:
# replace for example linear_c4 by linear_c.3
__A= key[key.find('linear_c' ) + len('linear_c' )]
__A= key.replace(f"""linear_c{idx}""",f"""linear_c.{int(__a )-1}""" )
if key.startswith('head' ):
__A= key.replace('head','classifier' )
__A= value
return new_state_dict
def UpperCAmelCase__( _SCREAMING_SNAKE_CASE : str,_SCREAMING_SNAKE_CASE : Union[str, Any] ):
"""simple docstring"""
for i in range(config.num_encoder_blocks ):
for j in range(config.depths[i] ):
# read in weights + bias of keys and values (which is a single matrix in the original implementation)
__A= state_dict.pop(f"""segformer.encoder.block.{i}.{j}.attention.self.kv.weight""" )
__A= state_dict.pop(f"""segformer.encoder.block.{i}.{j}.attention.self.kv.bias""" )
# next, add keys and values (in that order) to the state dict
__A= kv_weight[
: config.hidden_sizes[i], :
]
__A= kv_bias[: config.hidden_sizes[i]]
__A= kv_weight[
config.hidden_sizes[i] :, :
]
__A= kv_bias[
config.hidden_sizes[i] :
]
def UpperCAmelCase__( ):
"""simple docstring"""
__A= '''http://images.cocodataset.org/val2017/000000039769.jpg'''
__A= Image.open(requests.get(__a,stream=__a ).raw )
return image
@torch.no_grad()
def UpperCAmelCase__( _SCREAMING_SNAKE_CASE : Dict,_SCREAMING_SNAKE_CASE : str,_SCREAMING_SNAKE_CASE : Optional[int] ):
"""simple docstring"""
__A= SegformerConfig()
__A= False
# set attributes based on model_name
__A= '''huggingface/label-files'''
if "segformer" in model_name:
__A= model_name[len('segformer.' ) : len('segformer.' ) + 2]
if "ade" in model_name:
__A= 150
__A= '''ade20k-id2label.json'''
__A= (1, 150, 128, 128)
elif "city" in model_name:
__A= 19
__A= '''cityscapes-id2label.json'''
__A= (1, 19, 128, 128)
else:
raise ValueError(f"""Model {model_name} not supported""" )
elif "mit" in model_name:
__A= True
__A= model_name[4:6]
__A= 1000
__A= '''imagenet-1k-id2label.json'''
__A= (1, 1000)
else:
raise ValueError(f"""Model {model_name} not supported""" )
# set config attributes
__A= json.load(open(hf_hub_download(__a,__a,repo_type='dataset' ),'r' ) )
__A= {int(__a ): v for k, v in idalabel.items()}
__A= idalabel
__A= {v: k for k, v in idalabel.items()}
if size == "b0":
pass
elif size == "b1":
__A= [64, 128, 320, 512]
__A= 256
elif size == "b2":
__A= [64, 128, 320, 512]
__A= 768
__A= [3, 4, 6, 3]
elif size == "b3":
__A= [64, 128, 320, 512]
__A= 768
__A= [3, 4, 18, 3]
elif size == "b4":
__A= [64, 128, 320, 512]
__A= 768
__A= [3, 8, 27, 3]
elif size == "b5":
__A= [64, 128, 320, 512]
__A= 768
__A= [3, 6, 40, 3]
else:
raise ValueError(f"""Size {size} not supported""" )
# load image processor (only resize + normalize)
__A= SegformerImageProcessor(
image_scale=(512, 512),keep_ratio=__a,align=__a,do_random_crop=__a )
# prepare image
__A= prepare_img()
__A= image_processor(images=__a,return_tensors='pt' ).pixel_values
logger.info(f"""Converting model {model_name}...""" )
# load original state dict
if encoder_only:
__A= torch.load(__a,map_location=torch.device('cpu' ) )
else:
__A= torch.load(__a,map_location=torch.device('cpu' ) )['''state_dict''']
# rename keys
__A= rename_keys(__a,encoder_only=__a )
if not encoder_only:
del state_dict["decode_head.conv_seg.weight"]
del state_dict["decode_head.conv_seg.bias"]
# key and value matrices need special treatment
read_in_k_v(__a,__a )
# create HuggingFace model and load state dict
if encoder_only:
__A= False
__A= SegformerForImageClassification(__a )
else:
__A= SegformerForSemanticSegmentation(__a )
model.load_state_dict(__a )
model.eval()
# forward pass
__A= model(__a )
__A= outputs.logits
# set expected_slice based on model name
# ADE20k checkpoints
if model_name == "segformer.b0.512x512.ade.160k":
__A= torch.tensor(
[
[[-4.63_10, -5.52_32, -6.23_56], [-5.19_21, -6.14_44, -6.59_96], [-5.44_24, -6.27_90, -6.75_74]],
[[-12.13_91, -13.31_22, -13.95_54], [-12.87_32, -13.93_52, -14.35_63], [-12.94_38, -13.82_26, -14.25_13]],
[[-12.51_34, -13.46_86, -14.49_15], [-12.86_69, -14.43_43, -14.77_58], [-13.25_23, -14.58_19, -15.06_94]],
] )
elif model_name == "segformer.b1.512x512.ade.160k":
__A= torch.tensor(
[
[[-7.58_20, -8.72_31, -8.32_15], [-8.06_00, -10.35_29, -10.03_04], [-7.52_08, -9.41_03, -9.62_39]],
[[-12.69_18, -13.89_94, -13.71_37], [-13.31_96, -15.75_23, -15.47_89], [-12.93_43, -14.87_57, -14.96_89]],
[[-11.19_11, -11.94_21, -11.32_43], [-11.33_42, -13.68_39, -13.35_81], [-10.39_09, -12.18_32, -12.48_58]],
] )
elif model_name == "segformer.b2.512x512.ade.160k":
__A= torch.tensor(
[
[[-11.81_73, -14.38_50, -16.31_28], [-14.56_48, -16.58_04, -18.65_68], [-14.72_23, -15.73_87, -18.42_18]],
[[-15.72_90, -17.91_71, -19.44_23], [-18.31_05, -19.94_48, -21.46_61], [-17.92_96, -18.64_97, -20.79_10]],
[[-15.07_83, -17.03_36, -18.27_89], [-16.87_71, -18.68_70, -20.16_12], [-16.24_54, -17.14_26, -19.50_55]],
] )
elif model_name == "segformer.b3.512x512.ade.160k":
__A= torch.tensor(
[
[[-9.08_78, -10.20_81, -10.18_91], [-9.31_44, -10.79_41, -10.98_43], [-9.22_94, -10.38_55, -10.57_04]],
[[-12.23_16, -13.90_68, -13.61_02], [-12.91_61, -14.37_02, -14.32_35], [-12.52_33, -13.71_74, -13.79_32]],
[[-14.62_75, -15.24_90, -14.97_27], [-14.34_00, -15.96_87, -16.28_27], [-14.14_84, -15.40_33, -15.89_37]],
] )
elif model_name == "segformer.b4.512x512.ade.160k":
__A= torch.tensor(
[
[[-12.31_44, -13.24_47, -14.08_02], [-13.36_14, -14.58_16, -15.61_17], [-13.33_40, -14.44_33, -16.22_19]],
[[-19.27_81, -20.41_28, -20.75_06], [-20.61_53, -21.65_66, -22.09_98], [-19.98_00, -21.04_30, -22.14_94]],
[[-18.87_39, -19.78_04, -21.18_34], [-20.12_33, -21.67_65, -23.29_44], [-20.03_15, -21.26_41, -23.69_44]],
] )
elif model_name == "segformer.b5.640x640.ade.160k":
__A= torch.tensor(
[
[[-9.55_24, -12.08_35, -11.73_48], [-10.52_29, -13.64_46, -14.56_62], [-9.58_42, -12.88_51, -13.94_14]],
[[-15.34_32, -17.53_23, -17.08_18], [-16.33_30, -18.92_55, -19.21_01], [-15.13_40, -17.78_48, -18.39_71]],
[[-12.60_72, -14.94_86, -14.66_31], [-13.76_29, -17.09_07, -17.77_45], [-12.78_99, -16.16_95, -17.16_71]],
] )
# Cityscapes checkpoints
elif model_name == "segformer.b0.1024x1024.city.160k":
__A= torch.tensor(
[
[[-11.92_95, -13.40_57, -14.81_06], [-13.34_31, -14.81_79, -15.37_81], [-14.28_36, -15.59_42, -16.15_88]],
[[-11.49_06, -12.80_67, -13.65_64], [-13.11_89, -14.05_00, -14.15_43], [-13.87_48, -14.51_36, -14.87_89]],
[[0.53_74, 0.10_67, -0.47_42], [0.11_41, -0.22_55, -0.70_99], [-0.30_00, -0.59_24, -1.31_05]],
] )
elif model_name == "segformer.b0.512x1024.city.160k":
__A= torch.tensor(
[
[[-7.82_17, -9.87_67, -10.17_17], [-9.44_38, -10.90_58, -11.40_47], [-9.79_39, -12.34_95, -12.10_79]],
[[-7.15_14, -9.53_36, -10.08_60], [-9.77_76, -11.68_22, -11.84_39], [-10.14_11, -12.76_55, -12.89_72]],
[[0.30_21, 0.08_05, -0.23_10], [-0.03_28, -0.16_05, -0.27_14], [-0.14_08, -0.54_77, -0.69_76]],
] )
elif model_name == "segformer.b0.640x1280.city.160k":
__A= torch.tensor(
[
[
[-1.1_3_7_2e0_1, -1.2_7_8_7e0_1, -1.3_4_7_7e0_1],
[-1.2_5_3_6e0_1, -1.4_1_9_4e0_1, -1.4_4_0_9e0_1],
[-1.3_2_1_7e0_1, -1.4_8_8_8e0_1, -1.5_3_2_7e0_1],
],
[
[-1.4_7_9_1e0_1, -1.7_1_2_2e0_1, -1.8_2_7_7e0_1],
[-1.7_1_6_3e0_1, -1.9_1_9_2e0_1, -1.9_5_3_3e0_1],
[-1.7_8_9_7e0_1, -1.9_9_9_1e0_1, -2.0_3_1_5e0_1],
],
[
[7.6_7_2_3e-0_1, 4.1_9_2_1e-0_1, -7.7_8_7_8e-0_2],
[4.7_7_7_2e-0_1, 9.5_5_5_7e-0_3, -2.8_0_8_2e-0_1],
[3.6_0_3_2e-0_1, -2.4_8_2_6e-0_1, -5.1_1_6_8e-0_1],
],
] )
elif model_name == "segformer.b0.768x768.city.160k":
__A= torch.tensor(
[
[[-9.49_59, -11.30_87, -11.74_79], [-11.00_25, -12.65_40, -12.33_19], [-11.40_64, -13.04_87, -12.99_05]],
[[-9.89_05, -11.30_84, -12.08_54], [-11.17_26, -12.76_98, -12.95_83], [-11.59_85, -13.32_78, -14.17_74]],
[[0.22_13, 0.01_92, -0.24_66], [-0.17_31, -0.42_13, -0.48_74], [-0.31_26, -0.65_41, -1.13_89]],
] )
elif model_name == "segformer.b1.1024x1024.city.160k":
__A= torch.tensor(
[
[[-13.57_48, -13.91_11, -12.65_00], [-14.35_00, -15.36_83, -14.23_28], [-14.75_32, -16.04_24, -15.60_87]],
[[-17.16_51, -15.87_25, -12.96_53], [-17.25_80, -17.37_18, -14.82_23], [-16.60_58, -16.87_83, -16.74_52]],
[[-3.64_56, -3.02_09, -1.42_03], [-3.07_97, -3.19_59, -2.00_00], [-1.87_57, -1.92_17, -1.69_97]],
] )
elif model_name == "segformer.b2.1024x1024.city.160k":
__A= torch.tensor(
[
[[-16.09_76, -16.48_56, -17.39_62], [-16.62_34, -19.03_42, -19.76_85], [-16.09_00, -18.06_61, -19.11_80]],
[[-18.47_50, -18.84_88, -19.50_74], [-19.40_30, -22.15_70, -22.59_77], [-19.11_91, -20.84_86, -22.37_83]],
[[-4.51_78, -5.50_37, -6.51_09], [-5.08_84, -7.21_74, -8.03_34], [-4.41_56, -5.81_17, -7.29_70]],
] )
elif model_name == "segformer.b3.1024x1024.city.160k":
__A= torch.tensor(
[
[[-14.20_81, -14.47_32, -14.19_77], [-14.58_67, -16.44_23, -16.63_56], [-13.44_41, -14.96_85, -16.86_96]],
[[-14.45_76, -14.70_73, -15.04_51], [-15.08_16, -17.62_37, -17.98_73], [-14.42_13, -16.01_99, -18.59_92]],
[[-4.73_49, -4.95_88, -5.09_66], [-4.32_10, -6.93_25, -7.25_91], [-3.43_12, -4.74_84, -7.19_17]],
] )
elif model_name == "segformer.b4.1024x1024.city.160k":
__A= torch.tensor(
[
[[-11.77_37, -11.95_26, -11.32_73], [-13.66_92, -14.45_74, -13.88_78], [-13.89_37, -14.69_24, -15.93_45]],
[[-14.67_06, -14.53_30, -14.13_06], [-16.15_02, -16.81_80, -16.42_69], [-16.83_38, -17.89_39, -20.17_46]],
[[1.04_91, 0.82_89, 1.03_10], [1.10_44, 0.52_19, 0.80_55], [1.08_99, 0.69_26, 0.55_90]],
] )
elif model_name == "segformer.b5.1024x1024.city.160k":
__A= torch.tensor(
[
[[-12.56_41, -13.47_77, -13.06_84], [-13.95_87, -15.89_83, -16.65_57], [-13.31_09, -15.73_50, -16.31_41]],
[[-14.70_74, -15.43_52, -14.59_44], [-16.63_53, -18.16_63, -18.61_20], [-15.17_02, -18.03_29, -18.15_47]],
[[-1.79_90, -2.09_51, -1.77_84], [-2.63_97, -3.82_45, -3.96_86], [-1.52_64, -2.81_26, -2.93_16]],
] )
else:
__A= logits.argmax(-1 ).item()
print('Predicted class:',model.config.idalabel[predicted_class_idx] )
# verify logits
if not encoder_only:
assert logits.shape == expected_shape
assert torch.allclose(logits[0, :3, :3, :3],__a,atol=1e-2 )
# finally, save model and image processor
logger.info(f"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}...""" )
Path(__a ).mkdir(exist_ok=__a )
model.save_pretrained(__a )
image_processor.save_pretrained(__a )
if __name__ == "__main__":
UpperCAmelCase__ = argparse.ArgumentParser()
parser.add_argument(
'''--model_name''',
default='''segformer.b0.512x512.ade.160k''',
type=str,
help='''Name of the model you\'d like to convert.''',
)
parser.add_argument(
'''--checkpoint_path''', default=None, type=str, help='''Path to the original PyTorch checkpoint (.pth file).'''
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
)
UpperCAmelCase__ = parser.parse_args()
convert_segformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)
| 186 |
import argparse
import collections
import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def __UpperCAmelCase ( __a : List[Any] ,__a : Optional[Any] ,__a : Optional[int] ) -> Dict:
"""simple docstring"""
return params[F"""{prefix}/{prefix}/relpos_bias/rel_embedding"""][:, i, :]
def __UpperCAmelCase ( __a : List[Any] ,__a : Optional[int] ,__a : int ,__a : List[str]="attention" ) -> List[str]:
"""simple docstring"""
_a : str = np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/key/kernel"""][:, i, :, :] )
_a : Tuple = k_tmp.reshape(k_tmp.shape[0] ,k_tmp.shape[1] * k_tmp.shape[2] )
_a : Any = np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/out/kernel"""][:, i, :, :] )
_a : Dict = o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1] ,o_tmp.shape[2] )
_a : Union[str, Any] = np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/query/kernel"""][:, i, :, :] )
_a : Any = q_tmp.reshape(q_tmp.shape[0] ,q_tmp.shape[1] * q_tmp.shape[2] )
_a : Tuple = np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/value/kernel"""][:, i, :, :] )
_a : int = v_tmp.reshape(v_tmp.shape[0] ,v_tmp.shape[1] * v_tmp.shape[2] )
return k, o, q, v
def __UpperCAmelCase ( __a : Union[str, Any] ,__a : Union[str, Any] ,__a : List[Any] ,__a : Any=False ) -> Any:
"""simple docstring"""
if split_mlp_wi:
_a : Union[str, Any] = params[F"""{prefix}/{prefix}/mlp/wi_0/kernel"""][:, i, :]
_a : Union[str, Any] = params[F"""{prefix}/{prefix}/mlp/wi_1/kernel"""][:, i, :]
_a : List[str] = (wi_a, wi_a)
else:
_a : List[str] = params[F"""{prefix}/{prefix}/mlp/wi/kernel"""][:, i, :]
_a : Optional[int] = params[F"""{prefix}/{prefix}/mlp/wo/kernel"""][:, i, :]
return wi, wo
def __UpperCAmelCase ( __a : List[Any] ,__a : Optional[Any] ,__a : Union[str, Any] ,__a : str ) -> List[str]:
"""simple docstring"""
return params[F"""{prefix}/{prefix}/{layer_name}/scale"""][:, i]
def __UpperCAmelCase ( __a : dict ,*, __a : int ,__a : bool ,__a : bool = False ) -> Any:
"""simple docstring"""
_a : Dict = traverse_util.flatten_dict(variables['''target'''] )
_a : Any = {'''/'''.join(__a ): v for k, v in old.items()}
# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
_a : Optional[int] = '''encoder/encoder/mlp/wi_0/kernel''' in old
print('''Split MLP:''' ,__a )
_a : Tuple = collections.OrderedDict()
# Shared embeddings.
_a : Any = old['''token_embedder/embedding''']
# Encoder.
for i in range(__a ):
# Block i, layer 0 (Self Attention).
_a : Optional[Any] = tax_layer_norm_lookup(__a ,__a ,'''encoder''' ,'''pre_attention_layer_norm''' )
_a , _a , _a , _a : List[str] = tax_attention_lookup(__a ,__a ,'''encoder''' ,'''attention''' )
_a : List[str] = layer_norm
_a : Optional[Any] = k.T
_a : str = o.T
_a : List[Any] = q.T
_a : Tuple = v.T
# Block i, layer 1 (MLP).
_a : str = tax_layer_norm_lookup(__a ,__a ,'''encoder''' ,'''pre_mlp_layer_norm''' )
_a , _a : Any = tax_mlp_lookup(__a ,__a ,'''encoder''' ,__a )
_a : str = layer_norm
if split_mlp_wi:
_a : List[Any] = wi[0].T
_a : Any = wi[1].T
else:
_a : Any = wi.T
_a : Optional[Any] = wo.T
if scalable_attention:
# convert the rel_embedding of each layer
_a : Dict = tax_relpos_bias_lookup(
__a ,__a ,'''encoder''' ).T
_a : List[str] = old['''encoder/encoder_norm/scale''']
if not scalable_attention:
_a : List[Any] = tax_relpos_bias_lookup(
__a ,0 ,'''encoder''' ).T
_a : Optional[Any] = tax_relpos_bias_lookup(
__a ,0 ,'''decoder''' ).T
if not is_encoder_only:
# Decoder.
for i in range(__a ):
# Block i, layer 0 (Self Attention).
_a : Union[str, Any] = tax_layer_norm_lookup(__a ,__a ,'''decoder''' ,'''pre_self_attention_layer_norm''' )
_a , _a , _a , _a : Optional[Any] = tax_attention_lookup(__a ,__a ,'''decoder''' ,'''self_attention''' )
_a : Optional[Any] = layer_norm
_a : Dict = k.T
_a : str = o.T
_a : str = q.T
_a : List[str] = v.T
# Block i, layer 1 (Cross Attention).
_a : Any = tax_layer_norm_lookup(__a ,__a ,'''decoder''' ,'''pre_cross_attention_layer_norm''' )
_a , _a , _a , _a : str = tax_attention_lookup(__a ,__a ,'''decoder''' ,'''encoder_decoder_attention''' )
_a : Optional[Any] = layer_norm
_a : Optional[int] = k.T
_a : Dict = o.T
_a : str = q.T
_a : int = v.T
# Block i, layer 2 (MLP).
_a : Optional[int] = tax_layer_norm_lookup(__a ,__a ,'''decoder''' ,'''pre_mlp_layer_norm''' )
_a , _a : Tuple = tax_mlp_lookup(__a ,__a ,'''decoder''' ,__a )
_a : Optional[Any] = layer_norm
if split_mlp_wi:
_a : List[str] = wi[0].T
_a : List[Any] = wi[1].T
else:
_a : Dict = wi.T
_a : str = wo.T
if scalable_attention:
# convert the rel_embedding of each layer
_a : Tuple = tax_relpos_bias_lookup(__a ,__a ,'''decoder''' ).T
_a : Tuple = old['''decoder/decoder_norm/scale''']
# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
if "decoder/logits_dense/kernel" in old:
_a : Any = old['''decoder/logits_dense/kernel'''].T
return new
def __UpperCAmelCase ( __a : Dict ,__a : bool ) -> Tuple:
"""simple docstring"""
_a : Tuple = collections.OrderedDict([(k, torch.from_numpy(v.copy() )) for (k, v) in converted_params.items()] )
# Add what is missing.
if "encoder.embed_tokens.weight" not in state_dict:
_a : Any = state_dict['''shared.weight''']
if not is_encoder_only:
if "decoder.embed_tokens.weight" not in state_dict:
_a : Optional[int] = state_dict['''shared.weight''']
if "lm_head.weight" not in state_dict: # For old 1.0 models.
print('''Using shared word embeddings as lm_head.''' )
_a : str = state_dict['''shared.weight''']
return state_dict
def __UpperCAmelCase ( __a : List[str] ,__a : Union[str, Any] ,__a : Dict ,__a : Union[str, Any] ,__a : List[Any] ) -> int:
"""simple docstring"""
_a : List[str] = checkpoints.load_tax_checkpoint(__a )
_a : str = convert_tax_to_pytorch(
__a ,num_layers=config.num_layers ,is_encoder_only=__a ,scalable_attention=__a )
_a : str = make_state_dict(__a ,__a )
model.load_state_dict(__a ,strict=__a )
def __UpperCAmelCase ( __a : List[Any] ,__a : Any ,__a : Union[str, Any] ,__a : bool = False ,__a : bool = False ,) -> Optional[Any]:
"""simple docstring"""
_a : List[str] = MTaConfig.from_json_file(__a )
print(F"""Building PyTorch model from configuration: {config}""" )
# Non-v1.1 checkpoints could also use T5Model, but this works for all.
# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
if is_encoder_only:
_a : Any = UMTaEncoderModel(__a )
else:
_a : Tuple = UMTaForConditionalGeneration(__a )
# Load weights from tf checkpoint
load_tax_weights_in_ta(__a ,__a ,__a ,__a ,__a )
# Save pytorch-model
print(F"""Save PyTorch model to {pytorch_dump_path}""" )
model.save_pretrained(__a )
# Verify that we can load the checkpoint.
model.from_pretrained(__a )
print('''Done''' )
if __name__ == "__main__":
a__ = argparse.ArgumentParser(description='''Converts a native T5X checkpoint into a PyTorch checkpoint.''')
# Required parameters
parser.add_argument(
'''--t5x_checkpoint_path''', default=None, type=str, required=True, help='''Path to the T5X checkpoint.'''
)
parser.add_argument(
'''--config_file''',
default=None,
type=str,
required=True,
help='''The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.''',
)
parser.add_argument(
'''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
parser.add_argument(
'''--is_encoder_only''', action='''store_true''', help='''Check if the model is encoder-decoder model''', default=False
)
parser.add_argument(
'''--scalable_attention''',
action='''store_true''',
help='''Whether the model uses scaled attention (umt5 model)''',
default=False,
)
a__ = parser.parse_args()
convert_tax_checkpoint_to_pytorch(
args.tax_checkpoint_path,
args.config_file,
args.pytorch_dump_path,
args.is_encoder_only,
args.scalable_attention,
)
| 14 | 0 |
def UpperCAmelCase_ ( __lowerCAmelCase ) -> str:
if not all(char in '''01''' for char in bin_string ):
raise ValueError('''Non-binary value was passed to the function''' )
if not bin_string:
raise ValueError('''Empty string was passed to the function''' )
__lowercase : Union[str, Any] = ''''''
while len(__a ) % 3 != 0:
__lowercase : Tuple = '''0''' + bin_string
__lowercase : int = [
bin_string[index : index + 3]
for index in range(len(__a ) )
if index % 3 == 0
]
for bin_group in bin_string_in_3_list:
__lowercase : str = 0
for index, val in enumerate(__a ):
oct_val += int(2 ** (2 - index) * int(__a ) )
oct_string += str(__a )
return oct_string
if __name__ == "__main__":
from doctest import testmod
testmod()
| 509 |
import random
import sys
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap
a__ = '''Usage of script: script_name <size_of_canvas:int>'''
a__ = [0] * 100 + [1] * 10
random.shuffle(choice)
def __UpperCAmelCase ( __a : int ) -> list[list[bool]]:
"""simple docstring"""
_a : int = [[False for i in range(__a )] for j in range(__a )]
return canvas
def __UpperCAmelCase ( __a : list[list[bool]] ) -> None:
"""simple docstring"""
for i, row in enumerate(__a ):
for j, _ in enumerate(__a ):
_a : Optional[int] = bool(random.getrandbits(1 ) )
def __UpperCAmelCase ( __a : list[list[bool]] ) -> list[list[bool]]:
"""simple docstring"""
_a : Any = np.array(__a )
_a : Optional[int] = np.array(create_canvas(current_canvas.shape[0] ) )
for r, row in enumerate(__a ):
for c, pt in enumerate(__a ):
_a : Tuple = __judge_point(
__a ,current_canvas[r - 1 : r + 2, c - 1 : c + 2] )
_a : List[str] = next_gen_canvas
del next_gen_canvas # cleaning memory as we move on.
_a : list[list[bool]] = current_canvas.tolist()
return return_canvas
def __UpperCAmelCase ( __a : bool ,__a : list[list[bool]] ) -> bool:
"""simple docstring"""
_a : Optional[Any] = 0
_a : str = 0
# finding dead or alive neighbours count.
for i in neighbours:
for status in i:
if status:
alive += 1
else:
dead += 1
# handling duplicate entry for focus pt.
if pt:
alive -= 1
else:
dead -= 1
# running the rules of game here.
_a : Optional[int] = pt
if pt:
if alive < 2:
_a : Dict = False
elif alive == 2 or alive == 3:
_a : Optional[Any] = True
elif alive > 3:
_a : str = False
else:
if alive == 3:
_a : int = True
return state
if __name__ == "__main__":
if len(sys.argv) != 2:
raise Exception(usage_doc)
a__ = int(sys.argv[1])
# main working structure of this module.
a__ = create_canvas(canvas_size)
seed(c)
a__ , a__ = plt.subplots()
fig.show()
a__ = ListedColormap(['''w''', '''k'''])
try:
while True:
a__ = run(c)
ax.matshow(c, cmap=cmap)
fig.canvas.draw()
ax.cla()
except KeyboardInterrupt:
# do nothing.
pass
| 14 | 0 |
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