code stringlengths 87 55.2k | code_codestyle int64 0 349 | style_context stringlengths 135 49.1k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase_ : str = logging.get_logger(__name__)
lowerCamelCase_ : str = {
"""google/pegasus-large""": """https://huggingface.co/google/pegasus-large/resolve/main/config.json""",
# See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}
class __A ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
__lowerCAmelCase = "pegasus"
__lowerCAmelCase = ["past_key_values"]
__lowerCAmelCase = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
def __init__( self , __A=5_0265 , __A=1024 , __A=12 , __A=4096 , __A=16 , __A=12 , __A=4096 , __A=16 , __A=0.0 , __A=0.0 , __A=True , __A=True , __A="gelu" , __A=1024 , __A=0.1 , __A=0.0 , __A=0.0 , __A=0.02 , __A=0 , __A=False , __A=0 , __A=1 , __A=1 , **__A , ) -> Union[str, Any]:
a =vocab_size
a =max_position_embeddings
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 =encoder_layerdrop
a =decoder_layerdrop
a =use_cache
a =encoder_layers
a =scale_embedding # scale factor will be sqrt(d_model) if True
super().__init__(
pad_token_id=__A , eos_token_id=__A , is_encoder_decoder=__A , decoder_start_token_id=__A , forced_eos_token_id=__A , **__A , )
@property
def SCREAMING_SNAKE_CASE ( self ) -> int:
return self.encoder_attention_heads
@property
def SCREAMING_SNAKE_CASE ( self ) -> int:
return self.d_model | 81 | """simple docstring"""
import math
def __a ( _SCREAMING_SNAKE_CASE ) ->bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(_SCREAMING_SNAKE_CASE ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def __a ( _SCREAMING_SNAKE_CASE = 0.1 ) ->int:
a__: str = 3
a__: Optional[Any] = 3
while primes / (2 * j - 1) >= ratio:
for i in range(j * j + j + 1 , (j + 2) * (j + 2) , j + 1 ):
primes += is_prime(_SCREAMING_SNAKE_CASE )
j += 2
return j
if __name__ == "__main__":
import doctest
doctest.testmod()
| 290 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
A__ = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ = ["""NllbTokenizer"""]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ = ["""NllbTokenizerFast"""]
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_nllb import NllbTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_nllb_fast import NllbTokenizerFast
else:
import sys
A__ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 82 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import _LazyModule
lowercase__ = {'processing_wav2vec2_with_lm': ['Wav2Vec2ProcessorWithLM']}
if TYPE_CHECKING:
from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM
else:
import sys
lowercase__ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 290 | 0 |
'''simple docstring'''
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
snake_case_ : str = '\\n\n'
snake_case_ : str = '\nPerplexity (PPL) is one of the most common metrics for evaluating language models.\nIt is defined as the exponentiated average negative log-likelihood of a sequence.\n\nFor more information, see https://huggingface.co/docs/transformers/perplexity\n'
snake_case_ : List[str] = '\nArgs:\n model_id (str): model used for calculating Perplexity\n NOTE: Perplexity can only be calculated for causal language models.\n This includes models such as gpt2, causal variations of bert,\n causal versions of t5, and more (the full list can be found\n in the AutoModelForCausalLM documentation here:\n https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCausalLM )\n\n input_texts (list of str): input text, each separate text snippet\n is one list entry.\n batch_size (int): the batch size to run texts through the model. Defaults to 16.\n add_start_token (bool): whether to add the start token to the texts,\n so the perplexity can include the probability of the first word. Defaults to True.\n device (str): device to run on, defaults to \'cuda\' when available\nReturns:\n perplexity: dictionary containing the perplexity scores for the texts\n in the input list, as well as the mean perplexity. If one of the input texts is\n longer than the max input length of the model, then it is truncated to the\n max length for the perplexity computation.\nExamples:\n Example 1:\n >>> perplexity = datasets.load_metric("perplexity")\n >>> input_texts = ["lorem ipsum", "Happy Birthday!", "Bienvenue"]\n >>> results = perplexity.compute(model_id=\'gpt2\',\n ... add_start_token=False,\n ... input_texts=input_texts) # doctest:+ELLIPSIS\n >>> print(list(results.keys()))\n [\'perplexities\', \'mean_perplexity\']\n >>> print(round(results["mean_perplexity"], 2))\n 78.22\n >>> print(round(results["perplexities"][0], 2))\n 11.11\n\n Example 2:\n >>> perplexity = datasets.load_metric("perplexity")\n >>> input_texts = datasets.load_dataset("wikitext",\n ... "wikitext-2-raw-v1",\n ... split="test")["text"][:50] # doctest:+ELLIPSIS\n [...]\n >>> input_texts = [s for s in input_texts if s!=\'\']\n >>> results = perplexity.compute(model_id=\'gpt2\',\n ... input_texts=input_texts) # doctest:+ELLIPSIS\n >>> print(list(results.keys()))\n [\'perplexities\', \'mean_perplexity\']\n >>> print(round(results["mean_perplexity"], 2))\n 60.35\n >>> print(round(results["perplexities"][0], 2))\n 81.12\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class lowercase__ ( datasets.Metric ):
def UpperCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION ,citation=_CITATION ,inputs_description=_KWARGS_DESCRIPTION ,features=datasets.Features(
{
'input_texts': datasets.Value('string' ),
} ) ,reference_urls=['https://huggingface.co/docs/transformers/perplexity'] ,)
def UpperCamelCase_ ( self : int ,lowerCamelCase__ : Tuple ,lowerCamelCase__ : int ,lowerCamelCase__ : int = 16 ,lowerCamelCase__ : bool = True ,lowerCamelCase__ : int=None ):
'''simple docstring'''
if device is not None:
assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu."
if device == "gpu":
_UpperCamelCase : int = 'cuda'
else:
_UpperCamelCase : Optional[Any] = 'cuda' if torch.cuda.is_available() else 'cpu'
_UpperCamelCase : Dict = AutoModelForCausalLM.from_pretrained(lowerCamelCase__ )
_UpperCamelCase : Dict = model.to(lowerCamelCase__ )
_UpperCamelCase : int = AutoTokenizer.from_pretrained(lowerCamelCase__ )
# if batch_size > 1 (which generally leads to padding being required), and
# if there is not an already assigned pad_token, assign an existing
# special token to also be the padding token
if tokenizer.pad_token is None and batch_size > 1:
_UpperCamelCase : List[Any] = list(tokenizer.special_tokens_map_extended.values() )
# check that the model already has at least one special token defined
assert (
len(lowerCamelCase__ ) > 0
), "If batch_size > 1, model must have at least one special token to use for padding. Please use a different model or set batch_size=1."
# assign one of the special tokens to also be the pad token
tokenizer.add_special_tokens({'pad_token': existing_special_tokens[0]} )
if add_start_token:
# leave room for <BOS> token to be added:
assert (
tokenizer.bos_token is not None
), "Input model must already have a BOS token if using add_start_token=True. Please use a different model, or set add_start_token=False"
_UpperCamelCase : List[Any] = model.config.max_length - 1
else:
_UpperCamelCase : Tuple = model.config.max_length
_UpperCamelCase : Tuple = tokenizer(
lowerCamelCase__ ,add_special_tokens=lowerCamelCase__ ,padding=lowerCamelCase__ ,truncation=lowerCamelCase__ ,max_length=lowerCamelCase__ ,return_tensors='pt' ,return_attention_mask=lowerCamelCase__ ,).to(lowerCamelCase__ )
_UpperCamelCase : str = encodings['input_ids']
_UpperCamelCase : int = encodings['attention_mask']
# check that each input is long enough:
if add_start_token:
assert torch.all(torch.ge(attn_masks.sum(1 ) ,1 ) ), "Each input text must be at least one token long."
else:
assert torch.all(
torch.ge(attn_masks.sum(1 ) ,2 ) ), "When add_start_token=False, each input text must be at least two tokens long. Run with add_start_token=True if inputting strings of only one token, and remove all empty input strings."
_UpperCamelCase : List[Any] = []
_UpperCamelCase : Dict = CrossEntropyLoss(reduction='none' )
for start_index in logging.tqdm(range(0 ,len(lowerCamelCase__ ) ,lowerCamelCase__ ) ):
_UpperCamelCase : str = min(start_index + batch_size ,len(lowerCamelCase__ ) )
_UpperCamelCase : Tuple = encoded_texts[start_index:end_index]
_UpperCamelCase : List[str] = attn_masks[start_index:end_index]
if add_start_token:
_UpperCamelCase : Dict = torch.tensor([[tokenizer.bos_token_id]] * encoded_batch.size(dim=0 ) ).to(lowerCamelCase__ )
_UpperCamelCase : List[Any] = torch.cat([bos_tokens_tensor, encoded_batch] ,dim=1 )
_UpperCamelCase : Tuple = torch.cat(
[torch.ones(bos_tokens_tensor.size() ,dtype=torch.intaa ).to(lowerCamelCase__ ), attn_mask] ,dim=1 )
_UpperCamelCase : Tuple = encoded_batch
with torch.no_grad():
_UpperCamelCase : Any = model(lowerCamelCase__ ,attention_mask=lowerCamelCase__ ).logits
_UpperCamelCase : Dict = out_logits[..., :-1, :].contiguous()
_UpperCamelCase : List[str] = labels[..., 1:].contiguous()
_UpperCamelCase : Optional[int] = attn_mask[..., 1:].contiguous()
_UpperCamelCase : Union[str, Any] = torch.expa(
(loss_fct(shift_logits.transpose(1 ,2 ) ,lowerCamelCase__ ) * shift_attention_mask_batch).sum(1 )
/ shift_attention_mask_batch.sum(1 ) )
ppls += perplexity_batch.tolist()
return {"perplexities": ppls, "mean_perplexity": np.mean(lowerCamelCase__ )}
| 83 | """simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, 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 __snake_case ( __lowerCAmelCase , unittest.TestCase ):
a__ = KandinskyInpaintPipeline
a__ = ["""prompt""", """image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
a__ = [
"""prompt""",
"""negative_prompt""",
"""image_embeds""",
"""negative_image_embeds""",
"""image""",
"""mask_image""",
]
a__ = [
"""generator""",
"""height""",
"""width""",
"""latents""",
"""guidance_scale""",
"""negative_prompt""",
"""num_inference_steps""",
"""return_dict""",
"""guidance_scale""",
"""num_images_per_prompt""",
"""output_type""",
"""return_dict""",
]
a__ = False
@property
def lowerCamelCase_ ( self) -> Optional[int]:
'''simple docstring'''
return 32
@property
def lowerCamelCase_ ( self) -> Tuple:
'''simple docstring'''
return 32
@property
def lowerCamelCase_ ( self) -> Dict:
'''simple docstring'''
return self.time_input_dim
@property
def lowerCamelCase_ ( self) -> Dict:
'''simple docstring'''
return self.time_input_dim * 4
@property
def lowerCamelCase_ ( self) -> List[Any]:
'''simple docstring'''
return 1_00
@property
def lowerCamelCase_ ( self) -> List[Any]:
'''simple docstring'''
a__: Optional[int] = XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base')
return tokenizer
@property
def lowerCamelCase_ ( self) -> Any:
'''simple docstring'''
torch.manual_seed(0)
a__: Dict = MCLIPConfig(
numDims=self.cross_attention_dim , transformerDimensions=self.text_embedder_hidden_size , hidden_size=self.text_embedder_hidden_size , intermediate_size=37 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=10_05 , )
a__: Optional[Any] = MultilingualCLIP(lowercase)
a__: int = text_encoder.eval()
return text_encoder
@property
def lowerCamelCase_ ( self) -> List[str]:
'''simple docstring'''
torch.manual_seed(0)
a__: Any = {
'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__: str = UNetaDConditionModel(**lowercase)
return model
@property
def lowerCamelCase_ ( self) -> Union[str, Any]:
'''simple docstring'''
return {
"block_out_channels": [32, 64],
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def lowerCamelCase_ ( self) -> List[Any]:
'''simple docstring'''
torch.manual_seed(0)
a__: Any = VQModel(**self.dummy_movq_kwargs)
return model
def lowerCamelCase_ ( self) -> Any:
'''simple docstring'''
a__: Dict = self.dummy_text_encoder
a__: int = self.dummy_tokenizer
a__: str = self.dummy_unet
a__: Any = self.dummy_movq
a__: Tuple = DDIMScheduler(
num_train_timesteps=10_00 , beta_schedule='linear' , beta_start=0.00085 , beta_end=0.012 , clip_sample=lowercase , set_alpha_to_one=lowercase , steps_offset=1 , prediction_type='epsilon' , thresholding=lowercase , )
a__: Tuple = {
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'unet': unet,
'scheduler': scheduler,
'movq': movq,
}
return components
def lowerCamelCase_ ( self , lowercase , lowercase=0) -> Any:
'''simple docstring'''
a__: List[Any] = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(lowercase)).to(lowercase)
a__: int = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(seed + 1)).to(lowercase)
# create init_image
a__: Optional[int] = floats_tensor((1, 3, 64, 64) , rng=random.Random(lowercase)).to(lowercase)
a__: int = image.cpu().permute(0 , 2 , 3 , 1)[0]
a__: Optional[int] = Image.fromarray(np.uinta(lowercase)).convert('RGB').resize((2_56, 2_56))
# create mask
a__: Tuple = np.ones((64, 64) , dtype=np.floataa)
a__: Optional[Any] = 0
if str(lowercase).startswith('mps'):
a__: str = torch.manual_seed(lowercase)
else:
a__: Dict = torch.Generator(device=lowercase).manual_seed(lowercase)
a__: Optional[int] = {
'prompt': 'horse',
'image': init_image,
'mask_image': mask,
'image_embeds': image_embeds,
'negative_image_embeds': negative_image_embeds,
'generator': generator,
'height': 64,
'width': 64,
'num_inference_steps': 2,
'guidance_scale': 4.0,
'output_type': 'np',
}
return inputs
def lowerCamelCase_ ( self) -> str:
'''simple docstring'''
a__: Optional[Any] = 'cpu'
a__: List[Any] = self.get_dummy_components()
a__: Optional[Any] = self.pipeline_class(**lowercase)
a__: str = pipe.to(lowercase)
pipe.set_progress_bar_config(disable=lowercase)
a__: Optional[int] = pipe(**self.get_dummy_inputs(lowercase))
a__: List[str] = output.images
a__: int = pipe(
**self.get_dummy_inputs(lowercase) , return_dict=lowercase , )[0]
a__: Optional[Any] = image[0, -3:, -3:, -1]
a__: List[Any] = image_from_tuple[0, -3:, -3:, -1]
print(f'image.shape {image.shape}')
assert image.shape == (1, 64, 64, 3)
a__: str = np.array(
[0.8326919, 0.73790467, 0.20918581, 0.9309612, 0.5511791, 0.43713328, 0.5513321, 0.49922934, 0.59497786])
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 lowerCamelCase_ ( self) -> str:
'''simple docstring'''
super().test_inference_batch_single_identical(expected_max_diff=3e-3)
@slow
@require_torch_gpu
class __snake_case ( unittest.TestCase ):
def lowerCamelCase_ ( self) -> Optional[Any]:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase_ ( self) -> Dict:
'''simple docstring'''
a__: List[Any] = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy')
a__: int = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png')
a__: Union[str, Any] = np.ones((7_68, 7_68) , dtype=np.floataa)
a__: int = 0
a__: Optional[int] = 'a hat'
a__: int = KandinskyPriorPipeline.from_pretrained(
'kandinsky-community/kandinsky-2-1-prior' , torch_dtype=torch.floataa)
pipe_prior.to(lowercase)
a__: Any = KandinskyInpaintPipeline.from_pretrained(
'kandinsky-community/kandinsky-2-1-inpaint' , torch_dtype=torch.floataa)
a__: Optional[Any] = pipeline.to(lowercase)
pipeline.set_progress_bar_config(disable=lowercase)
a__: Dict = torch.Generator(device='cpu').manual_seed(0)
a__ , a__: Optional[Any] = pipe_prior(
lowercase , generator=lowercase , num_inference_steps=5 , negative_prompt='' , ).to_tuple()
a__: List[str] = pipeline(
lowercase , image=lowercase , mask_image=lowercase , image_embeds=lowercase , negative_image_embeds=lowercase , generator=lowercase , num_inference_steps=1_00 , height=7_68 , width=7_68 , output_type='np' , )
a__: str = output.images[0]
assert image.shape == (7_68, 7_68, 3)
assert_mean_pixel_difference(lowercase , lowercase)
| 290 | 0 |
"""simple docstring"""
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
UNetaDConditionModel,
VideoToVideoSDPipeline,
)
from diffusers.utils import floats_tensor, is_xformers_available, skip_mps
from diffusers.utils.testing_utils import enable_full_determinism, slow, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
@skip_mps
class _SCREAMING_SNAKE_CASE ( A__ , unittest.TestCase ):
UpperCAmelCase_ :Dict = VideoToVideoSDPipeline
UpperCAmelCase_ :List[Any] = TEXT_GUIDED_IMAGE_VARIATION_PARAMS.union({"video"} ) - {"image", "width", "height"}
UpperCAmelCase_ :int = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"video"} ) - {"image"}
UpperCAmelCase_ :int = PipelineTesterMixin.required_optional_params - {"latents"}
UpperCAmelCase_ :Union[str, Any] = False
# No `output_type`.
UpperCAmelCase_ :Optional[Any] = frozenset(
[
"num_inference_steps",
"generator",
"latents",
"return_dict",
"callback",
"callback_steps",
] )
def __lowerCAmelCase ( self ) -> Tuple:
torch.manual_seed(0 )
lowerCAmelCase_ :Any = UNetaDConditionModel(
block_out_channels=(32, 64, 64, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("""CrossAttnDownBlock3D""", """CrossAttnDownBlock3D""", """CrossAttnDownBlock3D""", """DownBlock3D""") , up_block_types=("""UpBlock3D""", """CrossAttnUpBlock3D""", """CrossAttnUpBlock3D""", """CrossAttnUpBlock3D""") , cross_attention_dim=32 , attention_head_dim=4 , )
lowerCAmelCase_ :int = DDIMScheduler(
beta_start=0.0_0_0_8_5 , beta_end=0.0_1_2 , beta_schedule="""scaled_linear""" , clip_sample=__A , set_alpha_to_one=__A , )
torch.manual_seed(0 )
lowerCAmelCase_ :int = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] , up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] , latent_channels=4 , sample_size=128 , )
torch.manual_seed(0 )
lowerCAmelCase_ :Optional[int] = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , hidden_act="""gelu""" , projection_dim=512 , )
lowerCAmelCase_ :Any = CLIPTextModel(__A )
lowerCAmelCase_ :Optional[int] = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
lowerCAmelCase_ :List[str] = {
"""unet""": unet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
}
return components
def __lowerCAmelCase ( self , __A , __A=0 ) -> Dict:
# 3 frames
lowerCAmelCase_ :str = floats_tensor((1, 3, 3, 32, 32) , rng=random.Random(__A ) ).to(__A )
if str(__A ).startswith("""mps""" ):
lowerCAmelCase_ :List[str] = torch.manual_seed(__A )
else:
lowerCAmelCase_ :Optional[Any] = torch.Generator(device=__A ).manual_seed(__A )
lowerCAmelCase_ :List[Any] = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""video""": video,
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 6.0,
"""output_type""": """pt""",
}
return inputs
def __lowerCAmelCase ( self ) -> Optional[Any]:
lowerCAmelCase_ :Optional[int] = """cpu""" # ensure determinism for the device-dependent torch.Generator
lowerCAmelCase_ :Optional[Any] = self.get_dummy_components()
lowerCAmelCase_ :Optional[int] = VideoToVideoSDPipeline(**__A )
lowerCAmelCase_ :List[Any] = sd_pipe.to(__A )
sd_pipe.set_progress_bar_config(disable=__A )
lowerCAmelCase_ :int = self.get_dummy_inputs(__A )
lowerCAmelCase_ :List[str] = """np"""
lowerCAmelCase_ :List[str] = sd_pipe(**__A ).frames
lowerCAmelCase_ :Any = frames[0][-3:, -3:, -1]
assert frames[0].shape == (32, 32, 3)
lowerCAmelCase_ :Union[str, Any] = np.array([106, 117, 113, 174, 137, 112, 148, 151, 131] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() , reason="""XFormers attention is only available with CUDA and `xformers` installed""" , )
def __lowerCAmelCase ( self ) -> Tuple:
self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=__A , expected_max_diff=5E-3 )
@unittest.skip(reason="""Batching needs to be properly figured out first for this pipeline.""" )
def __lowerCAmelCase ( self ) -> Union[str, Any]:
pass
@unittest.skip(reason="""Batching needs to be properly figured out first for this pipeline.""" )
def __lowerCAmelCase ( self ) -> List[str]:
pass
@unittest.skip(reason="""`num_images_per_prompt` argument is not supported for this pipeline.""" )
def __lowerCAmelCase ( self ) -> Any:
pass
def __lowerCAmelCase ( self ) -> str:
return super().test_progress_bar()
@slow
@skip_mps
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __lowerCAmelCase ( self ) -> Dict:
lowerCAmelCase_ :str = VideoToVideoSDPipeline.from_pretrained("""cerspense/zeroscope_v2_XL""" , torch_dtype=torch.floataa )
pipe.enable_model_cpu_offload()
# 10 frames
lowerCAmelCase_ :int = torch.Generator(device="""cpu""" ).manual_seed(0 )
lowerCAmelCase_ :Union[str, Any] = torch.randn((1, 10, 3, 1024, 576) , generator=__A )
lowerCAmelCase_ :Optional[int] = video.to("""cuda""" )
lowerCAmelCase_ :Tuple = """Spiderman is surfing"""
lowerCAmelCase_ :Union[str, Any] = pipe(__A , video=__A , generator=__A , num_inference_steps=3 , output_type="""pt""" ).frames
lowerCAmelCase_ :str = np.array([-1.0_4_5_8_9_8_4, -1.1_2_7_9_2_9_7, -0.9_6_6_3_0_8_6, -0.9_1_5_0_3_9_0_6, -0.7_5_0_9_7_6_5_6] )
assert np.abs(video_frames.cpu().numpy()[0, 0, 0, 0, -5:] - expected_array ).sum() < 1E-2
| 84 | """simple docstring"""
import argparse
import torch
from transformers import (
EncodecConfig,
EncodecFeatureExtractor,
EncodecModel,
logging,
)
# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/main/compression_state_dict.bin
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_48khz-7e698e3e.th
logging.set_verbosity_info()
lowercase__ = logging.get_logger('transformers.models.encodec')
lowercase__ = {
'quantizer.vq.layers.*._codebook.inited': 'quantizer.layers.*.codebook.inited',
'quantizer.vq.layers.*._codebook.cluster_size': 'quantizer.layers.*.codebook.cluster_size',
'quantizer.vq.layers.*._codebook.embed': 'quantizer.layers.*.codebook.embed',
'quantizer.vq.layers.*._codebook.embed_avg': 'quantizer.layers.*.codebook.embed_avg',
}
lowercase__ = {
'encoder.model.0.conv.conv': 'encoder.layers.0.conv',
'encoder.model.1.block.1.conv.conv': 'encoder.layers.1.block.1.conv',
'encoder.model.1.block.3.conv.conv': 'encoder.layers.1.block.3.conv',
'encoder.model.1.shortcut.conv.conv': 'encoder.layers.1.shortcut.conv',
'encoder.model.3.conv.conv': 'encoder.layers.3.conv',
'encoder.model.4.block.1.conv.conv': 'encoder.layers.4.block.1.conv',
'encoder.model.4.block.3.conv.conv': 'encoder.layers.4.block.3.conv',
'encoder.model.4.shortcut.conv.conv': 'encoder.layers.4.shortcut.conv',
'encoder.model.6.conv.conv': 'encoder.layers.6.conv',
'encoder.model.7.block.1.conv.conv': 'encoder.layers.7.block.1.conv',
'encoder.model.7.block.3.conv.conv': 'encoder.layers.7.block.3.conv',
'encoder.model.7.shortcut.conv.conv': 'encoder.layers.7.shortcut.conv',
'encoder.model.9.conv.conv': 'encoder.layers.9.conv',
'encoder.model.10.block.1.conv.conv': 'encoder.layers.10.block.1.conv',
'encoder.model.10.block.3.conv.conv': 'encoder.layers.10.block.3.conv',
'encoder.model.10.shortcut.conv.conv': 'encoder.layers.10.shortcut.conv',
'encoder.model.12.conv.conv': 'encoder.layers.12.conv',
'encoder.model.13.lstm': 'encoder.layers.13.lstm',
'encoder.model.15.conv.conv': 'encoder.layers.15.conv',
}
lowercase__ = {
'encoder.model.0.conv.norm': 'encoder.layers.0.norm',
'encoder.model.1.block.1.conv.norm': 'encoder.layers.1.block.1.norm',
'encoder.model.1.block.3.conv.norm': 'encoder.layers.1.block.3.norm',
'encoder.model.1.shortcut.conv.norm': 'encoder.layers.1.shortcut.norm',
'encoder.model.3.conv.norm': 'encoder.layers.3.norm',
'encoder.model.4.block.1.conv.norm': 'encoder.layers.4.block.1.norm',
'encoder.model.4.block.3.conv.norm': 'encoder.layers.4.block.3.norm',
'encoder.model.4.shortcut.conv.norm': 'encoder.layers.4.shortcut.norm',
'encoder.model.6.conv.norm': 'encoder.layers.6.norm',
'encoder.model.7.block.1.conv.norm': 'encoder.layers.7.block.1.norm',
'encoder.model.7.block.3.conv.norm': 'encoder.layers.7.block.3.norm',
'encoder.model.7.shortcut.conv.norm': 'encoder.layers.7.shortcut.norm',
'encoder.model.9.conv.norm': 'encoder.layers.9.norm',
'encoder.model.10.block.1.conv.norm': 'encoder.layers.10.block.1.norm',
'encoder.model.10.block.3.conv.norm': 'encoder.layers.10.block.3.norm',
'encoder.model.10.shortcut.conv.norm': 'encoder.layers.10.shortcut.norm',
'encoder.model.12.conv.norm': 'encoder.layers.12.norm',
'encoder.model.15.conv.norm': 'encoder.layers.15.norm',
}
lowercase__ = {
'decoder.model.0.conv.conv': 'decoder.layers.0.conv',
'decoder.model.1.lstm': 'decoder.layers.1.lstm',
'decoder.model.3.convtr.convtr': 'decoder.layers.3.conv',
'decoder.model.4.block.1.conv.conv': 'decoder.layers.4.block.1.conv',
'decoder.model.4.block.3.conv.conv': 'decoder.layers.4.block.3.conv',
'decoder.model.4.shortcut.conv.conv': 'decoder.layers.4.shortcut.conv',
'decoder.model.6.convtr.convtr': 'decoder.layers.6.conv',
'decoder.model.7.block.1.conv.conv': 'decoder.layers.7.block.1.conv',
'decoder.model.7.block.3.conv.conv': 'decoder.layers.7.block.3.conv',
'decoder.model.7.shortcut.conv.conv': 'decoder.layers.7.shortcut.conv',
'decoder.model.9.convtr.convtr': 'decoder.layers.9.conv',
'decoder.model.10.block.1.conv.conv': 'decoder.layers.10.block.1.conv',
'decoder.model.10.block.3.conv.conv': 'decoder.layers.10.block.3.conv',
'decoder.model.10.shortcut.conv.conv': 'decoder.layers.10.shortcut.conv',
'decoder.model.12.convtr.convtr': 'decoder.layers.12.conv',
'decoder.model.13.block.1.conv.conv': 'decoder.layers.13.block.1.conv',
'decoder.model.13.block.3.conv.conv': 'decoder.layers.13.block.3.conv',
'decoder.model.13.shortcut.conv.conv': 'decoder.layers.13.shortcut.conv',
'decoder.model.15.conv.conv': 'decoder.layers.15.conv',
}
lowercase__ = {
'decoder.model.0.conv.norm': 'decoder.layers.0.norm',
'decoder.model.3.convtr.norm': 'decoder.layers.3.norm',
'decoder.model.4.block.1.conv.norm': 'decoder.layers.4.block.1.norm',
'decoder.model.4.block.3.conv.norm': 'decoder.layers.4.block.3.norm',
'decoder.model.4.shortcut.conv.norm': 'decoder.layers.4.shortcut.norm',
'decoder.model.6.convtr.norm': 'decoder.layers.6.norm',
'decoder.model.7.block.1.conv.norm': 'decoder.layers.7.block.1.norm',
'decoder.model.7.block.3.conv.norm': 'decoder.layers.7.block.3.norm',
'decoder.model.7.shortcut.conv.norm': 'decoder.layers.7.shortcut.norm',
'decoder.model.9.convtr.norm': 'decoder.layers.9.norm',
'decoder.model.10.block.1.conv.norm': 'decoder.layers.10.block.1.norm',
'decoder.model.10.block.3.conv.norm': 'decoder.layers.10.block.3.norm',
'decoder.model.10.shortcut.conv.norm': 'decoder.layers.10.shortcut.norm',
'decoder.model.12.convtr.norm': 'decoder.layers.12.norm',
'decoder.model.13.block.1.conv.norm': 'decoder.layers.13.block.1.norm',
'decoder.model.13.block.3.conv.norm': 'decoder.layers.13.block.3.norm',
'decoder.model.13.shortcut.conv.norm': 'decoder.layers.13.shortcut.norm',
'decoder.model.15.conv.norm': 'decoder.layers.15.norm',
}
lowercase__ = {
**MAPPING_QUANTIZER,
**MAPPING_ENCODER,
**MAPPING_DECODER,
}
lowercase__ = {
**MAPPING_QUANTIZER,
**MAPPING_ENCODER,
**MAPPING_ENCODER_48K,
**MAPPING_DECODER,
**MAPPING_DECODER_48K,
}
lowercase__ = []
lowercase__ = []
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->int:
for attribute in key.split('.' ):
a__: str = getattr(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
if weight_type is not None:
a__: List[str] = getattr(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ).shape
else:
a__: Optional[Any] = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
F'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
F' {value.shape} for {full_name}' )
if weight_type == "weight":
a__: str = value
elif weight_type == "weight_g":
a__: int = value
elif weight_type == "weight_v":
a__: Tuple = value
elif weight_type == "bias":
a__: Dict = value
elif weight_type == "running_mean":
a__: Any = value
elif weight_type == "running_var":
a__: Tuple = value
elif weight_type == "num_batches_tracked":
a__: List[str] = value
elif weight_type == "weight_ih_l0":
a__: List[Any] = value
elif weight_type == "weight_hh_l0":
a__: List[Any] = value
elif weight_type == "bias_ih_l0":
a__: List[Any] = value
elif weight_type == "bias_hh_l0":
a__: List[Any] = value
elif weight_type == "weight_ih_l1":
a__: int = value
elif weight_type == "weight_hh_l1":
a__: str = value
elif weight_type == "bias_ih_l1":
a__: Union[str, Any] = value
elif weight_type == "bias_hh_l1":
a__: Any = value
else:
a__: Union[str, Any] = value
logger.info(F'{key + ("." + weight_type if weight_type is not None else "")} was initialized from {full_name}.' )
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->Dict:
for key in ignore_keys:
if key.endswith('.*' ):
if name.startswith(key[:-1] ):
return True
elif ".*." in key:
a__ , a__: Optional[Any] = key.split('.*.' )
if prefix in name and suffix in name:
return True
elif key in name:
return True
return False
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->List[str]:
a__: List[Any] = []
if model_name == "encodec_24khz" or "encodec_32khz":
a__: Optional[int] = MAPPING_24K
elif model_name == "encodec_48khz":
a__: List[Any] = MAPPING_48K
else:
raise ValueError(F'Unsupported model: {model_name}' )
for name, value in orig_dict.items():
if should_ignore(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
logger.info(F'{name} was ignored' )
continue
a__: int = False
for key, mapped_key in MAPPING.items():
if "*" in key:
a__ , a__: str = key.split('.*.' )
if prefix in name and suffix in name:
a__: List[str] = suffix
if key in name:
# HACK otherwise .embed gets initialized with .embed_avg too
if key.endswith('embed' ) and name.endswith('embed_avg' ):
continue
a__: List[str] = True
if "*" in mapped_key:
a__: List[str] = name.split(_SCREAMING_SNAKE_CASE )[0].split('.' )[-2]
a__: str = mapped_key.replace('*' , _SCREAMING_SNAKE_CASE )
if "weight_g" in name:
a__: int = 'weight_g'
elif "weight_v" in name:
a__: Dict = 'weight_v'
elif "weight_ih_l0" in name:
a__: int = 'weight_ih_l0'
elif "weight_hh_l0" in name:
a__: Union[str, Any] = 'weight_hh_l0'
elif "bias_ih_l0" in name:
a__: Optional[Any] = 'bias_ih_l0'
elif "bias_hh_l0" in name:
a__: Optional[int] = 'bias_hh_l0'
elif "weight_ih_l1" in name:
a__: Dict = 'weight_ih_l1'
elif "weight_hh_l1" in name:
a__: Optional[Any] = 'weight_hh_l1'
elif "bias_ih_l1" in name:
a__: List[str] = 'bias_ih_l1'
elif "bias_hh_l1" in name:
a__: Optional[Any] = 'bias_hh_l1'
elif "bias" in name:
a__: List[str] = 'bias'
elif "weight" in name:
a__: Any = 'weight'
elif "running_mean" in name:
a__: Dict = 'running_mean'
elif "running_var" in name:
a__: Dict = 'running_var'
elif "num_batches_tracked" in name:
a__: Dict = 'num_batches_tracked'
else:
a__: List[str] = None
set_recursively(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
continue
if not is_used:
unused_weights.append(_SCREAMING_SNAKE_CASE )
logger.warning(F'Unused weights: {unused_weights}' )
@torch.no_grad()
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , ) ->int:
if config_path is not None:
a__: Dict = EncodecConfig.from_pretrained(_SCREAMING_SNAKE_CASE )
else:
a__: Tuple = EncodecConfig()
if model_name == "encodec_24khz":
pass # config is already correct
elif model_name == "encodec_32khz":
a__: Any = [8, 5, 4, 4]
a__: List[str] = [2.2]
a__: List[Any] = 64
a__: Dict = 32000
a__: Union[str, Any] = 2048
a__: Union[str, Any] = False
a__: Any = False
a__: Optional[Any] = False
elif model_name == "encodec_48khz":
a__: Optional[int] = [8, 5, 4, 2]
a__: Union[str, Any] = [3.0, 6.0, 12.0, 24.0]
a__: List[str] = 48000
a__: Tuple = 2
a__: Optional[Any] = False
a__: Optional[int] = 'time_group_norm'
a__: Union[str, Any] = True
a__: Dict = 1.0
a__: str = 0.01
else:
raise ValueError(F'Unknown model name: {model_name}' )
a__: Optional[int] = EncodecModel(_SCREAMING_SNAKE_CASE )
a__: List[str] = EncodecFeatureExtractor(
feature_size=config.audio_channels , sampling_rate=config.sampling_rate , chunk_length_s=config.chunk_length_s , overlap=config.overlap , )
feature_extractor.save_pretrained(_SCREAMING_SNAKE_CASE )
a__: int = torch.load(_SCREAMING_SNAKE_CASE )
if "best_state" in original_checkpoint:
# we might have a training state saved, in which case discard the yaml results and just retain the weights
a__: str = original_checkpoint['best_state']
recursively_load_weights(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
model.save_pretrained(_SCREAMING_SNAKE_CASE )
if repo_id:
print('Pushing to the hub...' )
feature_extractor.push_to_hub(_SCREAMING_SNAKE_CASE )
model.push_to_hub(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
lowercase__ = argparse.ArgumentParser()
parser.add_argument(
'--model',
default='encodec_24khz',
type=str,
help='The model to convert. Should be one of \'encodec_24khz\', \'encodec_32khz\', \'encodec_48khz\'.',
)
parser.add_argument('--checkpoint_path', required=True, default=None, type=str, help='Path to original checkpoint')
parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
parser.add_argument(
'--pytorch_dump_folder_path', required=True, default=None, type=str, help='Path to the output PyTorch model.'
)
parser.add_argument(
'--push_to_hub', default=None, type=str, help='Where to upload the converted model on the 🤗 hub.'
)
lowercase__ = parser.parse_args()
convert_checkpoint(
args.model,
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.config_path,
args.push_to_hub,
)
| 290 | 0 |
'''simple docstring'''
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
_SCREAMING_SNAKE_CASE : List[str] = re.compile(r"\b(a|an|the)\b", re.UNICODE)
_SCREAMING_SNAKE_CASE : List[str] = None
def UpperCamelCase_( ):
'''simple docstring'''
snake_case_ = argparse.ArgumentParser("Official evaluation script for SQuAD version 2.0." )
parser.add_argument("data_file" , metavar="data.json" , help="Input data JSON file." )
parser.add_argument("pred_file" , metavar="pred.json" , help="Model predictions." )
parser.add_argument(
"--out-file" , "-o" , metavar="eval.json" , help="Write accuracy metrics to file (default is stdout)." )
parser.add_argument(
"--na-prob-file" , "-n" , metavar="na_prob.json" , help="Model estimates of probability of no answer." )
parser.add_argument(
"--na-prob-thresh" , "-t" , type=snake_case , default=1.0 , help="Predict \"\" if no-answer probability exceeds this (default = 1.0)." , )
parser.add_argument(
"--out-image-dir" , "-p" , metavar="out_images" , default=snake_case , help="Save precision-recall curves to directory." )
parser.add_argument("--verbose" , "-v" , action="store_true" )
if len(sys.argv ) == 1:
parser.print_help()
sys.exit(1 )
return parser.parse_args()
def UpperCamelCase_( snake_case : int ):
'''simple docstring'''
snake_case_ = {}
for article in dataset:
for p in article["paragraphs"]:
for qa in p["qas"]:
snake_case_ = bool(qa["answers"]["text"] )
return qid_to_has_ans
def UpperCamelCase_( snake_case : Optional[int] ):
'''simple docstring'''
def remove_articles(snake_case : Tuple ):
return ARTICLES_REGEX.sub(" " , snake_case )
def white_space_fix(snake_case : Dict ):
return " ".join(text.split() )
def remove_punc(snake_case : Optional[Any] ):
snake_case_ = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(snake_case : Dict ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(snake_case ) ) ) )
def UpperCamelCase_( snake_case : str ):
'''simple docstring'''
if not s:
return []
return normalize_answer(snake_case ).split()
def UpperCamelCase_( snake_case : Optional[Any] , snake_case : int ):
'''simple docstring'''
return int(normalize_answer(snake_case ) == normalize_answer(snake_case ) )
def UpperCamelCase_( snake_case : Union[str, Any] , snake_case : int ):
'''simple docstring'''
snake_case_ = get_tokens(snake_case )
snake_case_ = get_tokens(snake_case )
snake_case_ = collections.Counter(snake_case ) & collections.Counter(snake_case )
snake_case_ = sum(common.values() )
if len(snake_case ) == 0 or len(snake_case ) == 0:
# If either is no-answer, then F1 is 1 if they agree, 0 otherwise
return int(gold_toks == pred_toks )
if num_same == 0:
return 0
snake_case_ = 1.0 * num_same / len(snake_case )
snake_case_ = 1.0 * num_same / len(snake_case )
snake_case_ = (2 * precision * recall) / (precision + recall)
return fa
def UpperCamelCase_( snake_case : str , snake_case : str ):
'''simple docstring'''
snake_case_ = {}
snake_case_ = {}
for article in dataset:
for p in article["paragraphs"]:
for qa in p["qas"]:
snake_case_ = qa["id"]
snake_case_ = [t for t in qa["answers"]["text"] if normalize_answer(snake_case )]
if not gold_answers:
# For unanswerable questions, only correct answer is empty string
snake_case_ = [""]
if qid not in preds:
print(f'Missing prediction for {qid}' )
continue
snake_case_ = preds[qid]
# Take max over all gold answers
snake_case_ = max(compute_exact(snake_case , snake_case ) for a in gold_answers )
snake_case_ = max(compute_fa(snake_case , snake_case ) for a in gold_answers )
return exact_scores, fa_scores
def UpperCamelCase_( snake_case : List[str] , snake_case : Union[str, Any] , snake_case : Union[str, Any] , snake_case : int ):
'''simple docstring'''
snake_case_ = {}
for qid, s in scores.items():
snake_case_ = na_probs[qid] > na_prob_thresh
if pred_na:
snake_case_ = float(not qid_to_has_ans[qid] )
else:
snake_case_ = s
return new_scores
def UpperCamelCase_( snake_case : Tuple , snake_case : str , snake_case : Union[str, Any]=None ):
'''simple docstring'''
if not qid_list:
snake_case_ = len(snake_case )
return collections.OrderedDict(
[
("exact", 100.0 * sum(exact_scores.values() ) / total),
("f1", 100.0 * sum(fa_scores.values() ) / total),
("total", total),
] )
else:
snake_case_ = len(snake_case )
return collections.OrderedDict(
[
("exact", 100.0 * sum(exact_scores[k] for k in qid_list ) / total),
("f1", 100.0 * sum(fa_scores[k] for k in qid_list ) / total),
("total", total),
] )
def UpperCamelCase_( snake_case : Optional[int] , snake_case : Optional[Any] , snake_case : Optional[Any] ):
'''simple docstring'''
for k in new_eval:
snake_case_ = new_eval[k]
def UpperCamelCase_( snake_case : Optional[int] , snake_case : List[Any] , snake_case : Optional[int] , snake_case : str ):
'''simple docstring'''
plt.step(snake_case , snake_case , color="b" , alpha=0.2 , where="post" )
plt.fill_between(snake_case , snake_case , step="post" , alpha=0.2 , color="b" )
plt.xlabel("Recall" )
plt.ylabel("Precision" )
plt.xlim([0.0, 1.05] )
plt.ylim([0.0, 1.05] )
plt.title(snake_case )
plt.savefig(snake_case )
plt.clf()
def UpperCamelCase_( snake_case : Optional[Any] , snake_case : int , snake_case : Union[str, Any] , snake_case : int , snake_case : Optional[Any]=None , snake_case : Optional[Any]=None ):
'''simple docstring'''
snake_case_ = sorted(snake_case , key=lambda snake_case : na_probs[k] )
snake_case_ = 0.0
snake_case_ = 1.0
snake_case_ = 0.0
snake_case_ = [1.0]
snake_case_ = [0.0]
snake_case_ = 0.0
for i, qid in enumerate(snake_case ):
if qid_to_has_ans[qid]:
true_pos += scores[qid]
snake_case_ = true_pos / float(i + 1 )
snake_case_ = true_pos / float(snake_case )
if i == len(snake_case ) - 1 or na_probs[qid] != na_probs[qid_list[i + 1]]:
# i.e., if we can put a threshold after this point
avg_prec += cur_p * (cur_r - recalls[-1])
precisions.append(snake_case )
recalls.append(snake_case )
if out_image:
plot_pr_curve(snake_case , snake_case , snake_case , snake_case )
return {"ap": 100.0 * avg_prec}
def UpperCamelCase_( snake_case : Optional[int] , snake_case : Dict , snake_case : Optional[int] , snake_case : Dict , snake_case : List[str] , snake_case : Dict ):
'''simple docstring'''
if out_image_dir and not os.path.exists(snake_case ):
os.makedirs(snake_case )
snake_case_ = sum(1 for v in qid_to_has_ans.values() if v )
if num_true_pos == 0:
return
snake_case_ = make_precision_recall_eval(
snake_case , snake_case , snake_case , snake_case , out_image=os.path.join(snake_case , "pr_exact.png" ) , title="Precision-Recall curve for Exact Match score" , )
snake_case_ = make_precision_recall_eval(
snake_case , snake_case , snake_case , snake_case , out_image=os.path.join(snake_case , "pr_f1.png" ) , title="Precision-Recall curve for F1 score" , )
snake_case_ = {k: float(snake_case ) for k, v in qid_to_has_ans.items()}
snake_case_ = make_precision_recall_eval(
snake_case , snake_case , snake_case , snake_case , out_image=os.path.join(snake_case , "pr_oracle.png" ) , title="Oracle Precision-Recall curve (binary task of HasAns vs. NoAns)" , )
merge_eval(snake_case , snake_case , "pr_exact" )
merge_eval(snake_case , snake_case , "pr_f1" )
merge_eval(snake_case , snake_case , "pr_oracle" )
def UpperCamelCase_( snake_case : int , snake_case : Optional[int] , snake_case : str , snake_case : Any ):
'''simple docstring'''
if not qid_list:
return
snake_case_ = [na_probs[k] for k in qid_list]
snake_case_ = np.ones_like(snake_case ) / float(len(snake_case ) )
plt.hist(snake_case , weights=snake_case , bins=2_0 , range=(0.0, 1.0) )
plt.xlabel("Model probability of no-answer" )
plt.ylabel("Proportion of dataset" )
plt.title(f'Histogram of no-answer probability: {name}' )
plt.savefig(os.path.join(snake_case , f'na_prob_hist_{name}.png' ) )
plt.clf()
def UpperCamelCase_( snake_case : Dict , snake_case : int , snake_case : Optional[int] , snake_case : Optional[int] ):
'''simple docstring'''
snake_case_ = sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k] )
snake_case_ = num_no_ans
snake_case_ = cur_score
snake_case_ = 0.0
snake_case_ = sorted(snake_case , key=lambda snake_case : na_probs[k] )
for i, qid in enumerate(snake_case ):
if qid not in scores:
continue
if qid_to_has_ans[qid]:
snake_case_ = scores[qid]
else:
if preds[qid]:
snake_case_ = -1
else:
snake_case_ = 0
cur_score += diff
if cur_score > best_score:
snake_case_ = cur_score
snake_case_ = na_probs[qid]
return 100.0 * best_score / len(snake_case ), best_thresh
def UpperCamelCase_( snake_case : Dict , snake_case : List[Any] , snake_case : int , snake_case : str , snake_case : Dict , snake_case : Dict ):
'''simple docstring'''
snake_case_ , snake_case_ = find_best_thresh(snake_case , snake_case , snake_case , snake_case )
snake_case_ , snake_case_ = find_best_thresh(snake_case , snake_case , snake_case , snake_case )
snake_case_ = best_exact
snake_case_ = exact_thresh
snake_case_ = best_fa
snake_case_ = fa_thresh
def UpperCamelCase_( ):
'''simple docstring'''
with open(OPTS.data_file ) as f:
snake_case_ = json.load(snake_case )
snake_case_ = dataset_json["data"]
with open(OPTS.pred_file ) as f:
snake_case_ = json.load(snake_case )
if OPTS.na_prob_file:
with open(OPTS.na_prob_file ) as f:
snake_case_ = json.load(snake_case )
else:
snake_case_ = {k: 0.0 for k in preds}
snake_case_ = make_qid_to_has_ans(snake_case ) # maps qid to True/False
snake_case_ = [k for k, v in qid_to_has_ans.items() if v]
snake_case_ = [k for k, v in qid_to_has_ans.items() if not v]
snake_case_ , snake_case_ = get_raw_scores(snake_case , snake_case )
snake_case_ = apply_no_ans_threshold(snake_case , snake_case , snake_case , OPTS.na_prob_thresh )
snake_case_ = apply_no_ans_threshold(snake_case , snake_case , snake_case , OPTS.na_prob_thresh )
snake_case_ = make_eval_dict(snake_case , snake_case )
if has_ans_qids:
snake_case_ = make_eval_dict(snake_case , snake_case , qid_list=snake_case )
merge_eval(snake_case , snake_case , "HasAns" )
if no_ans_qids:
snake_case_ = make_eval_dict(snake_case , snake_case , qid_list=snake_case )
merge_eval(snake_case , snake_case , "NoAns" )
if OPTS.na_prob_file:
find_all_best_thresh(snake_case , snake_case , snake_case , snake_case , snake_case , snake_case )
if OPTS.na_prob_file and OPTS.out_image_dir:
run_precision_recall_analysis(snake_case , snake_case , snake_case , snake_case , snake_case , OPTS.out_image_dir )
histogram_na_prob(snake_case , snake_case , OPTS.out_image_dir , "hasAns" )
histogram_na_prob(snake_case , snake_case , OPTS.out_image_dir , "noAns" )
if OPTS.out_file:
with open(OPTS.out_file , "w" ) as f:
json.dump(snake_case , snake_case )
else:
print(json.dumps(snake_case , indent=2 ) )
if __name__ == "__main__":
_SCREAMING_SNAKE_CASE : Any = parse_args()
if OPTS.out_image_dir:
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
main()
| 85 | """simple docstring"""
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->Union[str, Any]:
if height >= 1:
move_tower(height - 1 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
move_disk(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
move_tower(height - 1 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->Union[str, Any]:
print('moving disk from' , _SCREAMING_SNAKE_CASE , 'to' , _SCREAMING_SNAKE_CASE )
def __a ( ) ->List[str]:
a__: Dict = int(input('Height of hanoi: ' ).strip() )
move_tower(_SCREAMING_SNAKE_CASE , 'A' , 'B' , 'C' )
if __name__ == "__main__":
main()
| 290 | 0 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamembertModel
@require_tf
@require_sentencepiece
@require_tokenizers
class A__ ( unittest.TestCase):
@slow
def __lowerCamelCase ( self ):
__lowerCAmelCase : List[str] = TFCamembertModel.from_pretrained('jplu/tf-camembert-base' )
__lowerCAmelCase : Optional[Any] = tf.convert_to_tensor(
[[5, 1_21, 11, 6_60, 16, 7_30, 2_55_43, 1_10, 83, 6]] , dtype=tf.intaa , ) # J'aime le camembert !"
__lowerCAmelCase : Optional[int] = model(_SCREAMING_SNAKE_CASE )['last_hidden_state']
__lowerCAmelCase : Optional[Any] = tf.TensorShape((1, 10, 7_68) )
self.assertEqual(output.shape , _SCREAMING_SNAKE_CASE )
# compare the actual values for a slice.
__lowerCAmelCase : Optional[Any] = tf.convert_to_tensor(
[[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]] , dtype=tf.floataa , )
# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
# camembert.eval()
# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()
self.assertTrue(np.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1E-4 ) ) | 86 | """simple docstring"""
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = False ) ->str:
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
a__: Optional[int] = F'Expected string as input, found {type(_SCREAMING_SNAKE_CASE )}'
raise ValueError(_SCREAMING_SNAKE_CASE )
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
a__: List[str] = F'Expected boolean as use_pascal parameter, found {type(_SCREAMING_SNAKE_CASE )}'
raise ValueError(_SCREAMING_SNAKE_CASE )
a__: int = input_str.split('_' )
a__: List[str] = 0 if use_pascal else 1
a__: List[str] = words[start_index:]
a__: List[str] = [word[0].upper() + word[1:] for word in words_to_capitalize]
a__: List[str] = '' if use_pascal else words[0]
return "".join([initial_word, *capitalized_words] )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 290 | 0 |
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class snake_case_ ( __A ):
__A : List[Any] = (UnCLIPScheduler,)
def __UpperCamelCase ( self : Union[str, Any] , **lowercase_ : List[str] ) -> List[str]:
lowercase__ : int = {
"num_train_timesteps": 10_00,
"variance_type": "fixed_small_log",
"clip_sample": True,
"clip_sample_range": 1.0,
"prediction_type": "epsilon",
}
config.update(**lowercase_ )
return config
def __UpperCamelCase ( self : Tuple ) -> Tuple:
for timesteps in [1, 5, 1_00, 10_00]:
self.check_over_configs(num_train_timesteps=lowercase_ )
def __UpperCamelCase ( self : Optional[int] ) -> Union[str, Any]:
for variance in ["fixed_small_log", "learned_range"]:
self.check_over_configs(variance_type=lowercase_ )
def __UpperCamelCase ( self : int ) -> List[Any]:
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=lowercase_ )
def __UpperCamelCase ( self : str ) -> int:
for clip_sample_range in [1, 5, 10, 20]:
self.check_over_configs(clip_sample_range=lowercase_ )
def __UpperCamelCase ( self : int ) -> Optional[int]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(prediction_type=lowercase_ )
def __UpperCamelCase ( self : str ) -> Union[str, Any]:
for time_step in [0, 5_00, 9_99]:
for prev_timestep in [None, 5, 1_00, 2_50, 5_00, 7_50]:
if prev_timestep is not None and prev_timestep >= time_step:
continue
self.check_over_forward(time_step=lowercase_ , prev_timestep=lowercase_ )
def __UpperCamelCase ( self : Union[str, Any] ) -> int:
lowercase__ : Dict = self.scheduler_classes[0]
lowercase__ : Optional[Any] = self.get_scheduler_config(variance_type="fixed_small_log" )
lowercase__ : Optional[int] = scheduler_class(**lowercase_ )
assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 1.00_00E-10 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(4_87 ) - 0.0_54_96_25 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(9_99 ) - 0.9_99_49_87 ) ) < 1E-5
def __UpperCamelCase ( self : Union[str, Any] ) -> Optional[Any]:
lowercase__ : Dict = self.scheduler_classes[0]
lowercase__ : Optional[Any] = self.get_scheduler_config(variance_type="learned_range" )
lowercase__ : Any = scheduler_class(**lowercase_ )
lowercase__ : Tuple = 0.5
assert scheduler._get_variance(1 , predicted_variance=lowercase_ ) - -10.1_71_27_90 < 1E-5
assert scheduler._get_variance(4_87 , predicted_variance=lowercase_ ) - -5.7_99_80_52 < 1E-5
assert scheduler._get_variance(9_99 , predicted_variance=lowercase_ ) - -0.0_01_00_11 < 1E-5
def __UpperCamelCase ( self : str ) -> List[Any]:
lowercase__ : Union[str, Any] = self.scheduler_classes[0]
lowercase__ : List[Any] = self.get_scheduler_config()
lowercase__ : Optional[Any] = scheduler_class(**lowercase_ )
lowercase__ : Union[str, Any] = scheduler.timesteps
lowercase__ : List[Any] = self.dummy_model()
lowercase__ : str = self.dummy_sample_deter
lowercase__ : int = torch.manual_seed(0 )
for i, t in enumerate(lowercase_ ):
# 1. predict noise residual
lowercase__ : Optional[int] = model(lowercase_ , lowercase_ )
# 2. predict previous mean of sample x_t-1
lowercase__ : int = scheduler.step(lowercase_ , lowercase_ , lowercase_ , generator=lowercase_ ).prev_sample
lowercase__ : List[str] = pred_prev_sample
lowercase__ : List[str] = torch.sum(torch.abs(lowercase_ ) )
lowercase__ : List[Any] = torch.mean(torch.abs(lowercase_ ) )
assert abs(result_sum.item() - 2_52.2_68_24_95 ) < 1E-2
assert abs(result_mean.item() - 0.3_28_47_43 ) < 1E-3
def __UpperCamelCase ( self : Optional[Any] ) -> List[str]:
lowercase__ : Optional[Any] = self.scheduler_classes[0]
lowercase__ : Any = self.get_scheduler_config()
lowercase__ : List[Any] = scheduler_class(**lowercase_ )
scheduler.set_timesteps(25 )
lowercase__ : Union[str, Any] = scheduler.timesteps
lowercase__ : Tuple = self.dummy_model()
lowercase__ : int = self.dummy_sample_deter
lowercase__ : Optional[Any] = torch.manual_seed(0 )
for i, t in enumerate(lowercase_ ):
# 1. predict noise residual
lowercase__ : Tuple = model(lowercase_ , lowercase_ )
if i + 1 == timesteps.shape[0]:
lowercase__ : int = None
else:
lowercase__ : Union[str, Any] = timesteps[i + 1]
# 2. predict previous mean of sample x_t-1
lowercase__ : str = scheduler.step(
lowercase_ , lowercase_ , lowercase_ , prev_timestep=lowercase_ , generator=lowercase_ ).prev_sample
lowercase__ : str = pred_prev_sample
lowercase__ : Dict = torch.sum(torch.abs(lowercase_ ) )
lowercase__ : List[Any] = torch.mean(torch.abs(lowercase_ ) )
assert abs(result_sum.item() - 2_58.2_04_49_83 ) < 1E-2
assert abs(result_mean.item() - 0.3_36_20_38 ) < 1E-3
def __UpperCamelCase ( self : Any ) -> Tuple:
pass
def __UpperCamelCase ( self : Optional[int] ) -> Optional[Any]:
pass
| 87 | """simple docstring"""
class __snake_case :
def __init__( self , lowercase , lowercase=None , lowercase=None) -> List[str]:
'''simple docstring'''
a__: Dict = data
a__: List[Any] = previous
a__: Any = next_node
def __str__( self) -> str:
'''simple docstring'''
return f'{self.data}'
def lowerCamelCase_ ( self) -> int:
'''simple docstring'''
return self.data
def lowerCamelCase_ ( self) -> Union[str, Any]:
'''simple docstring'''
return self.next
def lowerCamelCase_ ( self) -> Optional[Any]:
'''simple docstring'''
return self.previous
class __snake_case :
def __init__( self , lowercase) -> Dict:
'''simple docstring'''
a__: List[Any] = head
def __iter__( self) -> List[Any]:
'''simple docstring'''
return self
def lowerCamelCase_ ( self) -> Optional[Any]:
'''simple docstring'''
if not self.current:
raise StopIteration
else:
a__: Dict = self.current.get_data()
a__: Optional[Any] = self.current.get_next()
return value
class __snake_case :
def __init__( self) -> Dict:
'''simple docstring'''
a__: List[Any] = None # First node in list
a__: Optional[int] = None # Last node in list
def __str__( self) -> Optional[Any]:
'''simple docstring'''
a__: Dict = self.head
a__: Optional[Any] = []
while current is not None:
nodes.append(current.get_data())
a__: str = current.get_next()
return " ".join(str(lowercase) for node in nodes)
def __contains__( self , lowercase) -> Optional[int]:
'''simple docstring'''
a__: Optional[int] = self.head
while current:
if current.get_data() == value:
return True
a__: Dict = current.get_next()
return False
def __iter__( self) -> int:
'''simple docstring'''
return LinkedListIterator(self.head)
def lowerCamelCase_ ( self) -> List[Any]:
'''simple docstring'''
if self.head:
return self.head.get_data()
return None
def lowerCamelCase_ ( self) -> Optional[Any]:
'''simple docstring'''
if self.tail:
return self.tail.get_data()
return None
def lowerCamelCase_ ( self , lowercase) -> None:
'''simple docstring'''
if self.head is None:
a__: Optional[Any] = node
a__: Optional[Any] = node
else:
self.insert_before_node(self.head , lowercase)
def lowerCamelCase_ ( self , lowercase) -> None:
'''simple docstring'''
if self.head is None:
self.set_head(lowercase)
else:
self.insert_after_node(self.tail , lowercase)
def lowerCamelCase_ ( self , lowercase) -> None:
'''simple docstring'''
a__: Tuple = Node(lowercase)
if self.head is None:
self.set_head(lowercase)
else:
self.set_tail(lowercase)
def lowerCamelCase_ ( self , lowercase , lowercase) -> None:
'''simple docstring'''
a__: Union[str, Any] = node
a__: Optional[Any] = node.previous
if node.get_previous() is None:
a__: Tuple = node_to_insert
else:
a__: int = node_to_insert
a__: Optional[int] = node_to_insert
def lowerCamelCase_ ( self , lowercase , lowercase) -> None:
'''simple docstring'''
a__: Optional[int] = node
a__: Tuple = node.next
if node.get_next() is None:
a__: Optional[int] = node_to_insert
else:
a__: Any = node_to_insert
a__: str = node_to_insert
def lowerCamelCase_ ( self , lowercase , lowercase) -> None:
'''simple docstring'''
a__: Any = 1
a__: Tuple = Node(lowercase)
a__: Tuple = self.head
while node:
if current_position == position:
self.insert_before_node(lowercase , lowercase)
return
current_position += 1
a__: List[Any] = node.next
self.insert_after_node(self.tail , lowercase)
def lowerCamelCase_ ( self , lowercase) -> Node:
'''simple docstring'''
a__: Tuple = self.head
while node:
if node.get_data() == item:
return node
a__: List[str] = node.get_next()
raise Exception('Node not found')
def lowerCamelCase_ ( self , lowercase) -> Any:
'''simple docstring'''
if (node := self.get_node(lowercase)) is not None:
if node == self.head:
a__: Any = self.head.get_next()
if node == self.tail:
a__: List[Any] = self.tail.get_previous()
self.remove_node_pointers(lowercase)
@staticmethod
def lowerCamelCase_ ( lowercase) -> None:
'''simple docstring'''
if node.get_next():
a__: Any = node.previous
if node.get_previous():
a__: List[str] = node.next
a__: int = None
a__: Union[str, Any] = None
def lowerCamelCase_ ( self) -> str:
'''simple docstring'''
return self.head is None
def __a ( ) ->None:
pass
if __name__ == "__main__":
import doctest
doctest.testmod()
| 290 | 0 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_torch
class UpperCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
@slow
def _lowercase ( self : Optional[Any] ) -> List[str]:
"""simple docstring"""
__magic_name__ = XLMRobertaModel.from_pretrained("""xlm-roberta-base""" )
__magic_name__ = torch.tensor([[0, 581, 1_0269, 83, 9_9942, 136, 6_0742, 23, 70, 8_0583, 1_8276, 2]] )
# The dog is cute and lives in the garden house
__magic_name__ = torch.Size((1, 12, 768) ) # batch_size, sequence_length, embedding_vector_dim
__magic_name__ = torch.tensor(
[[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]] )
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
# xlmr.eval()
# expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
with torch.no_grad():
__magic_name__ = model(UpperCamelCase__ )["""last_hidden_state"""].detach()
self.assertEqual(output.shape , UpperCamelCase__ )
# compare the actual values for a slice of last dim
self.assertTrue(torch.allclose(output[:, :, -1] , UpperCamelCase__ , atol=1E-3 ) )
@slow
def _lowercase ( self : Optional[Any] ) -> Any:
"""simple docstring"""
__magic_name__ = XLMRobertaModel.from_pretrained("""xlm-roberta-large""" )
__magic_name__ = torch.tensor([[0, 581, 1_0269, 83, 9_9942, 136, 6_0742, 23, 70, 8_0583, 1_8276, 2]] )
# The dog is cute and lives in the garden house
__magic_name__ = torch.Size((1, 12, 1024) ) # batch_size, sequence_length, embedding_vector_dim
__magic_name__ = torch.tensor(
[[-0.0699, -0.0318, 0.0705, -0.1241, 0.0999, -0.0520, 0.1004, -0.1838, -0.4704, 0.1437, 0.0821, 0.0126]] )
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
# xlmr.eval()
# expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
with torch.no_grad():
__magic_name__ = model(UpperCamelCase__ )["""last_hidden_state"""].detach()
self.assertEqual(output.shape , UpperCamelCase__ )
# compare the actual values for a slice of last dim
self.assertTrue(torch.allclose(output[:, :, -1] , UpperCamelCase__ , atol=1E-3 ) )
| 88 | """simple docstring"""
import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable
import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization import from_bytes, to_bytes
from flax.training import train_state
from flax.training.common_utils import shard
from tqdm.auto import tqdm
from transformers import BigBirdConfig, FlaxBigBirdForQuestionAnswering
from transformers.models.big_bird.modeling_flax_big_bird import FlaxBigBirdForQuestionAnsweringModule
class __snake_case ( __lowerCAmelCase ):
a__ = 42
a__ = jnp.floataa
a__ = True
def lowerCamelCase_ ( self) -> int:
'''simple docstring'''
super().setup()
a__: int = nn.Dense(5 , dtype=self.dtype)
def __call__( self , *lowercase , **lowercase) -> Dict:
'''simple docstring'''
a__: Dict = super().__call__(*lowercase , **lowercase)
a__: str = self.cls(outputs[2])
return outputs[:2] + (cls_out,)
class __snake_case ( __lowerCAmelCase ):
a__ = FlaxBigBirdForNaturalQuestionsModule
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->Optional[Any]:
def cross_entropy(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None ):
a__: Any = logits.shape[-1]
a__: List[Any] = (labels[..., None] == jnp.arange(_SCREAMING_SNAKE_CASE )[None]).astype('f4' )
a__: List[str] = jax.nn.log_softmax(_SCREAMING_SNAKE_CASE , axis=-1 )
a__: Dict = -jnp.sum(labels * logits , axis=-1 )
if reduction is not None:
a__: str = reduction(_SCREAMING_SNAKE_CASE )
return loss
a__: Tuple = partial(_SCREAMING_SNAKE_CASE , reduction=jnp.mean )
a__: List[str] = cross_entropy(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
a__: Union[str, Any] = cross_entropy(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
a__: Any = cross_entropy(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
return (start_loss + end_loss + pooled_loss) / 3
@dataclass
class __snake_case :
a__ = "google/bigbird-roberta-base"
a__ = 3000
a__ = 1_0500
a__ = 128
a__ = 3
a__ = 1
a__ = 5
# tx_args
a__ = 3e-5
a__ = 0.0
a__ = 2_0000
a__ = 0.0095
a__ = "bigbird-roberta-natural-questions"
a__ = "training-expt"
a__ = "data/nq-training.jsonl"
a__ = "data/nq-validation.jsonl"
def lowerCamelCase_ ( self) -> Optional[Any]:
'''simple docstring'''
os.makedirs(self.base_dir , exist_ok=lowercase)
a__: str = os.path.join(self.base_dir , self.save_dir)
a__: List[str] = self.batch_size_per_device * jax.device_count()
@dataclass
class __snake_case :
a__ = 42
a__ = 4096 # no dynamic padding on TPUs
def __call__( self , lowercase) -> List[Any]:
'''simple docstring'''
a__: int = self.collate_fn(lowercase)
a__: Optional[int] = jax.tree_util.tree_map(lowercase , lowercase)
return batch
def lowerCamelCase_ ( self , lowercase) -> Dict:
'''simple docstring'''
a__ , a__: Dict = self.fetch_inputs(features['input_ids'])
a__: List[Any] = {
'input_ids': jnp.array(lowercase , dtype=jnp.intaa),
'attention_mask': jnp.array(lowercase , dtype=jnp.intaa),
'start_labels': jnp.array(features['start_token'] , dtype=jnp.intaa),
'end_labels': jnp.array(features['end_token'] , dtype=jnp.intaa),
'pooled_labels': jnp.array(features['category'] , dtype=jnp.intaa),
}
return batch
def lowerCamelCase_ ( self , lowercase) -> List[str]:
'''simple docstring'''
a__: List[Any] = [self._fetch_inputs(lowercase) for ids in input_ids]
return zip(*lowercase)
def lowerCamelCase_ ( self , lowercase) -> Dict:
'''simple docstring'''
a__: Union[str, Any] = [1 for _ in range(len(lowercase))]
while len(lowercase) < self.max_length:
input_ids.append(self.pad_id)
attention_mask.append(0)
return input_ids, attention_mask
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None ) ->List[Any]:
if seed is not None:
a__: int = dataset.shuffle(seed=_SCREAMING_SNAKE_CASE )
for i in range(len(_SCREAMING_SNAKE_CASE ) // batch_size ):
a__: Union[str, Any] = dataset[i * batch_size : (i + 1) * batch_size]
yield dict(_SCREAMING_SNAKE_CASE )
@partial(jax.pmap , axis_name='batch' )
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ) ->Any:
def loss_fn(_SCREAMING_SNAKE_CASE ):
a__: str = model_inputs.pop('start_labels' )
a__: Dict = model_inputs.pop('end_labels' )
a__: Optional[int] = model_inputs.pop('pooled_labels' )
a__: Optional[Any] = state.apply_fn(**_SCREAMING_SNAKE_CASE , params=_SCREAMING_SNAKE_CASE , dropout_rng=_SCREAMING_SNAKE_CASE , train=_SCREAMING_SNAKE_CASE )
a__ , a__ , a__: Optional[int] = outputs
return state.loss_fn(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , )
a__ , a__: Union[str, Any] = jax.random.split(_SCREAMING_SNAKE_CASE )
a__: List[Any] = jax.value_and_grad(_SCREAMING_SNAKE_CASE )
a__ , a__: str = grad_fn(state.params )
a__: Optional[int] = jax.lax.pmean({'loss': loss} , axis_name='batch' )
a__: int = jax.lax.pmean(_SCREAMING_SNAKE_CASE , 'batch' )
a__: Union[str, Any] = state.apply_gradients(grads=_SCREAMING_SNAKE_CASE )
return state, metrics, new_drp_rng
@partial(jax.pmap , axis_name='batch' )
def __a ( _SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ) ->Optional[Any]:
a__: Optional[int] = model_inputs.pop('start_labels' )
a__: int = model_inputs.pop('end_labels' )
a__: Dict = model_inputs.pop('pooled_labels' )
a__: Union[str, Any] = state.apply_fn(**_SCREAMING_SNAKE_CASE , params=state.params , train=_SCREAMING_SNAKE_CASE )
a__ , a__ , a__: int = outputs
a__: Optional[int] = state.loss_fn(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
a__: Tuple = jax.lax.pmean({'loss': loss} , axis_name='batch' )
return metrics
class __snake_case ( train_state.TrainState ):
a__ = struct.field(pytree_node=__lowerCAmelCase )
@dataclass
class __snake_case :
a__ = 42
a__ = 42
a__ = 42
a__ = 42
a__ = 42
a__ = 42
a__ = None
def lowerCamelCase_ ( self , lowercase , lowercase , lowercase , lowercase=None) -> Optional[int]:
'''simple docstring'''
a__: Dict = model.params
a__: Any = TrainState.create(
apply_fn=model.__call__ , params=lowercase , tx=lowercase , loss_fn=lowercase , )
if ckpt_dir is not None:
a__ , a__ , a__ , a__ , a__: Any = restore_checkpoint(lowercase , lowercase)
a__: Any = {
'lr': args.lr,
'init_lr': args.init_lr,
'warmup_steps': args.warmup_steps,
'num_train_steps': num_train_steps,
'weight_decay': args.weight_decay,
}
a__ , a__: str = build_tx(**lowercase)
a__: Optional[Any] = train_state.TrainState(
step=lowercase , apply_fn=model.__call__ , params=lowercase , tx=lowercase , opt_state=lowercase , )
a__: int = args
a__: Union[str, Any] = data_collator
a__: Any = lr
a__: Dict = params
a__: Tuple = jax_utils.replicate(lowercase)
return state
def lowerCamelCase_ ( self , lowercase , lowercase , lowercase) -> int:
'''simple docstring'''
a__: int = self.args
a__: str = len(lowercase) // args.batch_size
a__: Tuple = jax.random.PRNGKey(0)
a__: List[Any] = jax.random.split(lowercase , jax.device_count())
for epoch in range(args.max_epochs):
a__: str = jnp.array(0 , dtype=jnp.floataa)
a__: Tuple = get_batched_dataset(lowercase , args.batch_size , seed=lowercase)
a__: Optional[int] = 0
for batch in tqdm(lowercase , total=lowercase , desc=f'Running EPOCH-{epoch}'):
a__: List[str] = self.data_collator(lowercase)
a__ , a__ , a__: int = self.train_step_fn(lowercase , lowercase , **lowercase)
running_loss += jax_utils.unreplicate(metrics['loss'])
i += 1
if i % args.logging_steps == 0:
a__: List[Any] = jax_utils.unreplicate(state.step)
a__: Tuple = running_loss.item() / i
a__: Optional[Any] = self.scheduler_fn(state_step - 1)
a__: List[Any] = self.evaluate(lowercase , lowercase)
a__: List[str] = {
'step': state_step.item(),
'eval_loss': eval_loss.item(),
'tr_loss': tr_loss,
'lr': lr.item(),
}
tqdm.write(str(lowercase))
self.logger.log(lowercase , commit=lowercase)
if i % args.save_steps == 0:
self.save_checkpoint(args.save_dir + f'-e{epoch}-s{i}' , state=lowercase)
def lowerCamelCase_ ( self , lowercase , lowercase) -> List[Any]:
'''simple docstring'''
a__: Tuple = get_batched_dataset(lowercase , self.args.batch_size)
a__: Dict = len(lowercase) // self.args.batch_size
a__: Tuple = jnp.array(0 , dtype=jnp.floataa)
a__: List[Any] = 0
for batch in tqdm(lowercase , total=lowercase , desc='Evaluating ... '):
a__: str = self.data_collator(lowercase)
a__: List[str] = self.val_step_fn(lowercase , **lowercase)
running_loss += jax_utils.unreplicate(metrics['loss'])
i += 1
return running_loss / i
def lowerCamelCase_ ( self , lowercase , lowercase) -> Any:
'''simple docstring'''
a__: List[Any] = jax_utils.unreplicate(lowercase)
print(f'SAVING CHECKPOINT IN {save_dir}' , end=' ... ')
self.model_save_fn(lowercase , params=state.params)
with open(os.path.join(lowercase , 'opt_state.msgpack') , 'wb') as f:
f.write(to_bytes(state.opt_state))
joblib.dump(self.args , os.path.join(lowercase , 'args.joblib'))
joblib.dump(self.data_collator , os.path.join(lowercase , 'data_collator.joblib'))
with open(os.path.join(lowercase , 'training_state.json') , 'w') as f:
json.dump({'step': state.step.item()} , lowercase)
print('DONE')
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->Optional[Any]:
print(F'RESTORING CHECKPOINT FROM {save_dir}' , end=' ... ' )
with open(os.path.join(_SCREAMING_SNAKE_CASE , 'flax_model.msgpack' ) , 'rb' ) as f:
a__: int = from_bytes(state.params , f.read() )
with open(os.path.join(_SCREAMING_SNAKE_CASE , 'opt_state.msgpack' ) , 'rb' ) as f:
a__: Optional[Any] = from_bytes(state.opt_state , f.read() )
a__: Optional[Any] = joblib.load(os.path.join(_SCREAMING_SNAKE_CASE , 'args.joblib' ) )
a__: int = joblib.load(os.path.join(_SCREAMING_SNAKE_CASE , 'data_collator.joblib' ) )
with open(os.path.join(_SCREAMING_SNAKE_CASE , 'training_state.json' ) , 'r' ) as f:
a__: Any = json.load(_SCREAMING_SNAKE_CASE )
a__: Optional[Any] = training_state['step']
print('DONE' )
return params, opt_state, step, args, data_collator
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->Optional[int]:
a__: str = num_train_steps - warmup_steps
a__: str = optax.linear_schedule(init_value=_SCREAMING_SNAKE_CASE , end_value=_SCREAMING_SNAKE_CASE , transition_steps=_SCREAMING_SNAKE_CASE )
a__: List[Any] = optax.linear_schedule(init_value=_SCREAMING_SNAKE_CASE , end_value=1e-7 , transition_steps=_SCREAMING_SNAKE_CASE )
a__: int = optax.join_schedules(schedules=[warmup_fn, decay_fn] , boundaries=[warmup_steps] )
return lr
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->Tuple:
def weight_decay_mask(_SCREAMING_SNAKE_CASE ):
a__: List[Any] = traverse_util.flatten_dict(_SCREAMING_SNAKE_CASE )
a__: List[str] = {k: (v[-1] != 'bias' and v[-2:] != ('LayerNorm', 'scale')) for k, v in params.items()}
return traverse_util.unflatten_dict(_SCREAMING_SNAKE_CASE )
a__: List[str] = scheduler_fn(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
a__: Any = optax.adamw(learning_rate=_SCREAMING_SNAKE_CASE , weight_decay=_SCREAMING_SNAKE_CASE , mask=_SCREAMING_SNAKE_CASE )
return tx, lr
| 290 | 0 |
'''simple docstring'''
__lowerCAmelCase = '''
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install git+https://github.com/huggingface/transformers.git
'''
__lowerCAmelCase = [{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
__lowerCAmelCase = {
'''{processor_class}''': '''FakeProcessorClass''',
'''{model_class}''': '''FakeModelClass''',
'''{object_class}''': '''FakeObjectClass''',
}
| 89 | """simple docstring"""
from typing import List, Optional, Tuple, Union
import PIL
import torch
from torchvision import transforms
from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor
lowercase__ = transforms.Compose(
[
transforms.Resize((256, 256)),
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5]),
]
)
def __a ( _SCREAMING_SNAKE_CASE ) ->Any:
if isinstance(_SCREAMING_SNAKE_CASE , torch.Tensor ):
return image
elif isinstance(_SCREAMING_SNAKE_CASE , PIL.Image.Image ):
a__: Optional[int] = [image]
a__: str = [trans(img.convert('RGB' ) ) for img in image]
a__: Any = torch.stack(_SCREAMING_SNAKE_CASE )
return image
class __snake_case ( __lowerCAmelCase ):
def __init__( self , lowercase , lowercase) -> Optional[int]:
'''simple docstring'''
super().__init__()
# make sure scheduler can always be converted to DDIM
a__: Dict = DDIMScheduler.from_config(scheduler.config)
self.register_modules(unet=lowercase , scheduler=lowercase)
def lowerCamelCase_ ( self , lowercase) -> int:
'''simple docstring'''
if strength < 0 or strength > 1:
raise ValueError(f'The value of strength should in [0.0, 1.0] but is {strength}')
def lowerCamelCase_ ( self , lowercase , lowercase , lowercase) -> Dict:
'''simple docstring'''
a__: int = min(int(num_inference_steps * strength) , lowercase)
a__: Any = max(num_inference_steps - init_timestep , 0)
a__: Union[str, Any] = self.scheduler.timesteps[t_start:]
return timesteps, num_inference_steps - t_start
def lowerCamelCase_ ( self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase=None) -> List[Any]:
'''simple docstring'''
if not isinstance(lowercase , (torch.Tensor, PIL.Image.Image, list)):
raise ValueError(
f'`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(lowercase)}')
a__: Tuple = image.to(device=lowercase , dtype=lowercase)
if isinstance(lowercase , lowercase) and len(lowercase) != batch_size:
raise ValueError(
f'You have passed a list of generators of length {len(lowercase)}, but requested an effective batch'
f' size of {batch_size}. Make sure the batch size matches the length of the generators.')
a__: List[str] = init_latents.shape
a__: List[Any] = randn_tensor(lowercase , generator=lowercase , device=lowercase , dtype=lowercase)
# get latents
print('add noise to latents at timestep' , lowercase)
a__: int = self.scheduler.add_noise(lowercase , lowercase , lowercase)
a__: Dict = init_latents
return latents
@torch.no_grad()
def __call__( self , lowercase = None , lowercase = 0.8 , lowercase = 1 , lowercase = None , lowercase = 0.0 , lowercase = 50 , lowercase = None , lowercase = "pil" , lowercase = True , ) -> Union[ImagePipelineOutput, Tuple]:
'''simple docstring'''
self.check_inputs(lowercase)
# 2. Preprocess image
a__: Tuple = preprocess(lowercase)
# 3. set timesteps
self.scheduler.set_timesteps(lowercase , device=self.device)
a__ , a__: Union[str, Any] = self.get_timesteps(lowercase , lowercase , self.device)
a__: Optional[int] = timesteps[:1].repeat(lowercase)
# 4. Prepare latent variables
a__: Union[str, Any] = self.prepare_latents(lowercase , lowercase , lowercase , self.unet.dtype , self.device , lowercase)
a__: Optional[Any] = latents
# 5. Denoising loop
for t in self.progress_bar(lowercase):
# 1. predict noise model_output
a__: Dict = self.unet(lowercase , lowercase).sample
# 2. predict previous mean of image x_t-1 and add variance depending on eta
# eta corresponds to η in paper and should be between [0, 1]
# do x_t -> x_t-1
a__: Optional[Any] = self.scheduler.step(
lowercase , lowercase , lowercase , eta=lowercase , use_clipped_model_output=lowercase , generator=lowercase , ).prev_sample
a__: Union[str, Any] = (image / 2 + 0.5).clamp(0 , 1)
a__: Optional[int] = image.cpu().permute(0 , 2 , 3 , 1).numpy()
if output_type == "pil":
a__: Dict = self.numpy_to_pil(lowercase)
if not return_dict:
return (image, latent_timestep.item())
return ImagePipelineOutput(images=lowercase)
| 290 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
"naver-clova-ix/donut-base": "https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json",
# See all Donut models at https://huggingface.co/models?filter=donut-swin
}
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
snake_case_ = '''donut-swin'''
snake_case_ = {
'''num_attention_heads''': '''num_heads''',
'''num_hidden_layers''': '''num_layers''',
}
def __init__( self , lowerCamelCase__=224 , lowerCamelCase__=4 , lowerCamelCase__=3 , lowerCamelCase__=96 , lowerCamelCase__=[2, 2, 6, 2] , lowerCamelCase__=[3, 6, 12, 24] , lowerCamelCase__=7 , lowerCamelCase__=4.0 , lowerCamelCase__=True , lowerCamelCase__=0.0 , lowerCamelCase__=0.0 , lowerCamelCase__=0.1 , lowerCamelCase__="gelu" , lowerCamelCase__=False , lowerCamelCase__=0.02 , lowerCamelCase__=1e-5 , **lowerCamelCase__ , ) -> str:
'''simple docstring'''
super().__init__(**lowerCamelCase__ )
__lowerCamelCase = image_size
__lowerCamelCase = patch_size
__lowerCamelCase = num_channels
__lowerCamelCase = embed_dim
__lowerCamelCase = depths
__lowerCamelCase = len(lowerCamelCase__ )
__lowerCamelCase = num_heads
__lowerCamelCase = window_size
__lowerCamelCase = mlp_ratio
__lowerCamelCase = qkv_bias
__lowerCamelCase = hidden_dropout_prob
__lowerCamelCase = attention_probs_dropout_prob
__lowerCamelCase = drop_path_rate
__lowerCamelCase = hidden_act
__lowerCamelCase = use_absolute_embeddings
__lowerCamelCase = layer_norm_eps
__lowerCamelCase = initializer_range
# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
__lowerCamelCase = int(embed_dim * 2 ** (len(lowerCamelCase__ ) - 1) )
| 90 | """simple docstring"""
import shutil
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_tf_cross_test,
require_tf,
require_torch,
require_torchvision,
require_vision,
)
from transformers.utils import is_tf_available, is_torch_available, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, SamImageProcessor, SamProcessor
if is_torch_available():
import torch
if is_tf_available():
import tensorflow as tf
@require_vision
@require_torchvision
class __snake_case ( unittest.TestCase ):
def lowerCamelCase_ ( self) -> Any:
'''simple docstring'''
a__: Optional[Any] = tempfile.mkdtemp()
a__: Optional[int] = SamImageProcessor()
a__: Tuple = SamProcessor(lowercase)
processor.save_pretrained(self.tmpdirname)
def lowerCamelCase_ ( self , **lowercase) -> List[Any]:
'''simple docstring'''
return AutoProcessor.from_pretrained(self.tmpdirname , **lowercase).image_processor
def lowerCamelCase_ ( self) -> Optional[int]:
'''simple docstring'''
shutil.rmtree(self.tmpdirname)
def lowerCamelCase_ ( self) -> int:
'''simple docstring'''
a__: Any = [np.random.randint(2_55 , size=(3, 30, 4_00) , dtype=np.uinta)]
a__: Optional[Any] = [Image.fromarray(np.moveaxis(lowercase , 0 , -1)) for x in image_inputs]
return image_inputs
def lowerCamelCase_ ( self) -> str:
'''simple docstring'''
a__: List[str] = SamProcessor(image_processor=self.get_image_processor())
processor.save_pretrained(self.tmpdirname)
a__: Optional[int] = self.get_image_processor(do_normalize=lowercase , padding_value=1.0)
a__: List[Any] = SamProcessor.from_pretrained(self.tmpdirname , do_normalize=lowercase , padding_value=1.0)
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string())
self.assertIsInstance(processor.image_processor , lowercase)
def lowerCamelCase_ ( self) -> Tuple:
'''simple docstring'''
a__: Union[str, Any] = self.get_image_processor()
a__: List[Any] = SamProcessor(image_processor=lowercase)
a__: Optional[int] = self.prepare_image_inputs()
a__: Optional[Any] = image_processor(lowercase , return_tensors='np')
a__: Tuple = processor(images=lowercase , return_tensors='np')
input_feat_extract.pop('original_sizes') # pop original_sizes as it is popped in the processor
input_feat_extract.pop('reshaped_input_sizes') # pop original_sizes as it is popped in the processor
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1e-2)
@require_torch
def lowerCamelCase_ ( self) -> Any:
'''simple docstring'''
a__: int = self.get_image_processor()
a__: List[str] = SamProcessor(image_processor=lowercase)
a__: Optional[Any] = [torch.ones((1, 3, 5, 5))]
a__: Union[str, Any] = [[17_64, 26_46]]
a__: Optional[Any] = [[6_83, 10_24]]
a__: int = processor.post_process_masks(lowercase , lowercase , lowercase)
self.assertEqual(masks[0].shape , (1, 3, 17_64, 26_46))
a__: Optional[int] = processor.post_process_masks(
lowercase , torch.tensor(lowercase) , torch.tensor(lowercase))
self.assertEqual(masks[0].shape , (1, 3, 17_64, 26_46))
# should also work with np
a__: Dict = [np.ones((1, 3, 5, 5))]
a__: Tuple = processor.post_process_masks(lowercase , np.array(lowercase) , np.array(lowercase))
self.assertEqual(masks[0].shape , (1, 3, 17_64, 26_46))
a__: Tuple = [[1, 0], [0, 1]]
with self.assertRaises(lowercase):
a__: List[Any] = processor.post_process_masks(lowercase , np.array(lowercase) , np.array(lowercase))
@require_vision
@require_tf
class __snake_case ( unittest.TestCase ):
def lowerCamelCase_ ( self) -> List[Any]:
'''simple docstring'''
a__: Optional[Any] = tempfile.mkdtemp()
a__: List[Any] = SamImageProcessor()
a__: Optional[int] = SamProcessor(lowercase)
processor.save_pretrained(self.tmpdirname)
def lowerCamelCase_ ( self , **lowercase) -> int:
'''simple docstring'''
return AutoProcessor.from_pretrained(self.tmpdirname , **lowercase).image_processor
def lowerCamelCase_ ( self) -> Tuple:
'''simple docstring'''
shutil.rmtree(self.tmpdirname)
def lowerCamelCase_ ( self) -> Tuple:
'''simple docstring'''
a__: Optional[Any] = [np.random.randint(2_55 , size=(3, 30, 4_00) , dtype=np.uinta)]
a__: Optional[int] = [Image.fromarray(np.moveaxis(lowercase , 0 , -1)) for x in image_inputs]
return image_inputs
def lowerCamelCase_ ( self) -> Any:
'''simple docstring'''
a__: List[str] = SamProcessor(image_processor=self.get_image_processor())
processor.save_pretrained(self.tmpdirname)
a__: Dict = self.get_image_processor(do_normalize=lowercase , padding_value=1.0)
a__: Union[str, Any] = SamProcessor.from_pretrained(self.tmpdirname , do_normalize=lowercase , padding_value=1.0)
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string())
self.assertIsInstance(processor.image_processor , lowercase)
def lowerCamelCase_ ( self) -> int:
'''simple docstring'''
a__: Optional[Any] = self.get_image_processor()
a__: str = SamProcessor(image_processor=lowercase)
a__: int = self.prepare_image_inputs()
a__: int = image_processor(lowercase , return_tensors='np')
a__: Dict = processor(images=lowercase , return_tensors='np')
input_feat_extract.pop('original_sizes') # pop original_sizes as it is popped in the processor
input_feat_extract.pop('reshaped_input_sizes') # pop reshaped_input_sizes as it is popped in the processor
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1e-2)
@require_tf
def lowerCamelCase_ ( self) -> Any:
'''simple docstring'''
a__: Tuple = self.get_image_processor()
a__: Any = SamProcessor(image_processor=lowercase)
a__: str = [tf.ones((1, 3, 5, 5))]
a__: List[Any] = [[17_64, 26_46]]
a__: List[Any] = [[6_83, 10_24]]
a__: List[Any] = processor.post_process_masks(lowercase , lowercase , lowercase , return_tensors='tf')
self.assertEqual(masks[0].shape , (1, 3, 17_64, 26_46))
a__: Tuple = processor.post_process_masks(
lowercase , tf.convert_to_tensor(lowercase) , tf.convert_to_tensor(lowercase) , return_tensors='tf' , )
self.assertEqual(masks[0].shape , (1, 3, 17_64, 26_46))
# should also work with np
a__: Optional[Any] = [np.ones((1, 3, 5, 5))]
a__: int = processor.post_process_masks(
lowercase , np.array(lowercase) , np.array(lowercase) , return_tensors='tf')
self.assertEqual(masks[0].shape , (1, 3, 17_64, 26_46))
a__: List[str] = [[1, 0], [0, 1]]
with self.assertRaises(tf.errors.InvalidArgumentError):
a__: Any = processor.post_process_masks(
lowercase , np.array(lowercase) , np.array(lowercase) , return_tensors='tf')
@require_vision
@require_torchvision
class __snake_case ( unittest.TestCase ):
def lowerCamelCase_ ( self) -> Dict:
'''simple docstring'''
a__: str = tempfile.mkdtemp()
a__: int = SamImageProcessor()
a__: Union[str, Any] = SamProcessor(lowercase)
processor.save_pretrained(self.tmpdirname)
def lowerCamelCase_ ( self , **lowercase) -> Optional[int]:
'''simple docstring'''
return AutoProcessor.from_pretrained(self.tmpdirname , **lowercase).image_processor
def lowerCamelCase_ ( self) -> Tuple:
'''simple docstring'''
shutil.rmtree(self.tmpdirname)
def lowerCamelCase_ ( self) -> Tuple:
'''simple docstring'''
a__: Any = [np.random.randint(2_55 , size=(3, 30, 4_00) , dtype=np.uinta)]
a__: Any = [Image.fromarray(np.moveaxis(lowercase , 0 , -1)) for x in image_inputs]
return image_inputs
@is_pt_tf_cross_test
def lowerCamelCase_ ( self) -> int:
'''simple docstring'''
a__: Optional[int] = self.get_image_processor()
a__: int = SamProcessor(image_processor=lowercase)
a__: int = np.random.randint(0 , 2 , size=(1, 3, 5, 5)).astype(np.floataa)
a__: Dict = [tf.convert_to_tensor(lowercase)]
a__: Union[str, Any] = [torch.tensor(lowercase)]
a__: List[Any] = [[17_64, 26_46]]
a__: Optional[Any] = [[6_83, 10_24]]
a__: Tuple = processor.post_process_masks(
lowercase , lowercase , lowercase , return_tensors='tf')
a__: str = processor.post_process_masks(
lowercase , lowercase , lowercase , return_tensors='pt')
self.assertTrue(np.all(tf_masks[0].numpy() == pt_masks[0].numpy()))
@is_pt_tf_cross_test
def lowerCamelCase_ ( self) -> str:
'''simple docstring'''
a__: Tuple = self.get_image_processor()
a__: Dict = SamProcessor(image_processor=lowercase)
a__: Any = self.prepare_image_inputs()
a__: List[Any] = image_processor(lowercase , return_tensors='pt')['pixel_values'].numpy()
a__: Tuple = processor(images=lowercase , return_tensors='pt')['pixel_values'].numpy()
a__: Any = image_processor(lowercase , return_tensors='tf')['pixel_values'].numpy()
a__: Any = processor(images=lowercase , return_tensors='tf')['pixel_values'].numpy()
self.assertTrue(np.allclose(lowercase , lowercase))
self.assertTrue(np.allclose(lowercase , lowercase))
self.assertTrue(np.allclose(lowercase , lowercase))
| 290 | 0 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Generator
def _A () -> Generator[int, None, None]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : dict[int, int] = {}
SCREAMING_SNAKE_CASE_ : List[Any] = 2
while True:
SCREAMING_SNAKE_CASE_ : int = factor_map.pop(__a , __a )
if factor:
SCREAMING_SNAKE_CASE_ : Union[str, Any] = factor + prime
while x in factor_map:
x += factor
SCREAMING_SNAKE_CASE_ : List[str] = factor
else:
SCREAMING_SNAKE_CASE_ : List[str] = prime
yield prime
prime += 1
def _A (__a = 1e10 ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Tuple = sieve()
SCREAMING_SNAKE_CASE_ : Optional[int] = 1
while True:
SCREAMING_SNAKE_CASE_ : Union[str, Any] = next(__a )
if (2 * prime * n) > limit:
return n
# Ignore the next prime as the reminder will be 2.
next(__a )
n += 2
if __name__ == "__main__":
print(solution())
| 91 | """simple docstring"""
from math import pow, sqrt
def __a ( *_SCREAMING_SNAKE_CASE ) ->bool:
a__: Union[str, Any] = len(_SCREAMING_SNAKE_CASE ) > 0 and all(value > 0.0 for value in values )
return result
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->float | ValueError:
return (
round(sqrt(molar_mass_a / molar_mass_a ) , 6 )
if validate(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
else ValueError('Input Error: Molar mass values must greater than 0.' )
)
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->float | ValueError:
return (
round(effusion_rate * sqrt(molar_mass_a / molar_mass_a ) , 6 )
if validate(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
else ValueError(
'Input Error: Molar mass and effusion rate values must greater than 0.' )
)
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->float | ValueError:
return (
round(effusion_rate / sqrt(molar_mass_a / molar_mass_a ) , 6 )
if validate(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
else ValueError(
'Input Error: Molar mass and effusion rate values must greater than 0.' )
)
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->float | ValueError:
return (
round(molar_mass / pow(effusion_rate_a / effusion_rate_a , 2 ) , 6 )
if validate(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
else ValueError(
'Input Error: Molar mass and effusion rate values must greater than 0.' )
)
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->float | ValueError:
return (
round(pow(effusion_rate_a / effusion_rate_a , 2 ) / molar_mass , 6 )
if validate(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
else ValueError(
'Input Error: Molar mass and effusion rate values must greater than 0.' )
)
| 290 | 0 |
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image
from transformers import (
AutoTokenizer,
BlipaConfig,
BlipaForConditionalGeneration,
BlipaProcessor,
BlipaVisionConfig,
BlipImageProcessor,
OPTConfig,
TaConfig,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD
def _a ( ):
__lowerCAmelCase = "https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png"
__lowerCAmelCase = Image.open(requests.get(SCREAMING_SNAKE_CASE_ , stream=SCREAMING_SNAKE_CASE_ ).raw ).convert("RGB" )
return image
def _a ( SCREAMING_SNAKE_CASE_ : List[Any] ):
__lowerCAmelCase = []
# fmt: off
# vision encoder
rename_keys.append(("visual_encoder.cls_token", "vision_model.embeddings.class_embedding") )
rename_keys.append(("visual_encoder.pos_embed", "vision_model.embeddings.position_embedding") )
rename_keys.append(("visual_encoder.patch_embed.proj.weight", "vision_model.embeddings.patch_embedding.weight") )
rename_keys.append(("visual_encoder.patch_embed.proj.bias", "vision_model.embeddings.patch_embedding.bias") )
rename_keys.append(("ln_vision.weight", "vision_model.post_layernorm.weight") )
rename_keys.append(("ln_vision.bias", "vision_model.post_layernorm.bias") )
for i in range(config.vision_config.num_hidden_layers ):
rename_keys.append((F"""visual_encoder.blocks.{i}.norm1.weight""", F"""vision_model.encoder.layers.{i}.layer_norm1.weight""") )
rename_keys.append((F"""visual_encoder.blocks.{i}.norm1.bias""", F"""vision_model.encoder.layers.{i}.layer_norm1.bias""") )
rename_keys.append((F"""visual_encoder.blocks.{i}.norm2.weight""", F"""vision_model.encoder.layers.{i}.layer_norm2.weight""") )
rename_keys.append((F"""visual_encoder.blocks.{i}.norm2.bias""", F"""vision_model.encoder.layers.{i}.layer_norm2.bias""") )
rename_keys.append((F"""visual_encoder.blocks.{i}.attn.qkv.weight""", F"""vision_model.encoder.layers.{i}.self_attn.qkv.weight""") )
rename_keys.append((F"""visual_encoder.blocks.{i}.attn.proj.weight""", F"""vision_model.encoder.layers.{i}.self_attn.projection.weight""",) )
rename_keys.append((F"""visual_encoder.blocks.{i}.attn.proj.bias""", F"""vision_model.encoder.layers.{i}.self_attn.projection.bias""") )
rename_keys.append((F"""visual_encoder.blocks.{i}.mlp.fc1.weight""", F"""vision_model.encoder.layers.{i}.mlp.fc1.weight""") )
rename_keys.append((F"""visual_encoder.blocks.{i}.mlp.fc1.bias""", F"""vision_model.encoder.layers.{i}.mlp.fc1.bias""") )
rename_keys.append((F"""visual_encoder.blocks.{i}.mlp.fc2.weight""", F"""vision_model.encoder.layers.{i}.mlp.fc2.weight""") )
rename_keys.append((F"""visual_encoder.blocks.{i}.mlp.fc2.bias""", F"""vision_model.encoder.layers.{i}.mlp.fc2.bias""") )
# QFormer
rename_keys.append(("Qformer.bert.embeddings.LayerNorm.weight", "qformer.layernorm.weight") )
rename_keys.append(("Qformer.bert.embeddings.LayerNorm.bias", "qformer.layernorm.bias") )
# fmt: on
return rename_keys
def _a ( SCREAMING_SNAKE_CASE_ : Dict , SCREAMING_SNAKE_CASE_ : Tuple , SCREAMING_SNAKE_CASE_ : Dict ):
__lowerCAmelCase = dct.pop(SCREAMING_SNAKE_CASE_ )
__lowerCAmelCase = val
def _a ( SCREAMING_SNAKE_CASE_ : Union[str, Any] , SCREAMING_SNAKE_CASE_ : Union[str, Any] ):
for i in range(config.vision_config.num_hidden_layers ):
# read in original q and v biases
__lowerCAmelCase = state_dict.pop(F"""visual_encoder.blocks.{i}.attn.q_bias""" )
__lowerCAmelCase = state_dict.pop(F"""visual_encoder.blocks.{i}.attn.v_bias""" )
# next, set bias in the state dict
__lowerCAmelCase = torch.cat((q_bias, torch.zeros_like(SCREAMING_SNAKE_CASE_ , requires_grad=SCREAMING_SNAKE_CASE_ ), v_bias) )
__lowerCAmelCase = qkv_bias
def _a ( SCREAMING_SNAKE_CASE_ : Any , SCREAMING_SNAKE_CASE_ : Any ):
__lowerCAmelCase = 3_64 if "coco" in model_name else 2_24
__lowerCAmelCase = BlipaVisionConfig(image_size=SCREAMING_SNAKE_CASE_ ).to_dict()
# make sure the models have proper bos_token_id and eos_token_id set (important for generation)
# seems like flan-T5 models don't have bos_token_id properly set?
if "opt-2.7b" in model_name:
__lowerCAmelCase = OPTConfig.from_pretrained("facebook/opt-2.7b" , eos_token_id=SCREAMING_SNAKE_CASE_ ).to_dict()
elif "opt-6.7b" in model_name:
__lowerCAmelCase = OPTConfig.from_pretrained("facebook/opt-6.7b" , eos_token_id=SCREAMING_SNAKE_CASE_ ).to_dict()
elif "t5-xl" in model_name:
__lowerCAmelCase = TaConfig.from_pretrained("google/flan-t5-xl" , dense_act_fn="gelu" , bos_token_id=1 ).to_dict()
elif "t5-xxl" in model_name:
__lowerCAmelCase = TaConfig.from_pretrained("google/flan-t5-xxl" , dense_act_fn="gelu" , bos_token_id=1 ).to_dict()
__lowerCAmelCase = BlipaConfig(vision_config=SCREAMING_SNAKE_CASE_ , text_config=SCREAMING_SNAKE_CASE_ )
return config, image_size
@torch.no_grad()
def _a ( SCREAMING_SNAKE_CASE_ : Any , SCREAMING_SNAKE_CASE_ : Dict=None , SCREAMING_SNAKE_CASE_ : Union[str, Any]=False ):
__lowerCAmelCase = (
AutoTokenizer.from_pretrained("facebook/opt-2.7b" )
if "opt" in model_name
else AutoTokenizer.from_pretrained("google/flan-t5-xl" )
)
__lowerCAmelCase = tokenizer("\n" , add_special_tokens=SCREAMING_SNAKE_CASE_ ).input_ids[0]
__lowerCAmelCase , __lowerCAmelCase = get_blipa_config(SCREAMING_SNAKE_CASE_ , eos_token_id=SCREAMING_SNAKE_CASE_ )
__lowerCAmelCase = BlipaForConditionalGeneration(SCREAMING_SNAKE_CASE_ ).eval()
__lowerCAmelCase = {
"blip2-opt-2.7b": ("blip2_opt", "pretrain_opt2.7b"),
"blip2-opt-6.7b": ("blip2_opt", "pretrain_opt6.7b"),
"blip2-opt-2.7b-coco": ("blip2_opt", "caption_coco_opt2.7b"),
"blip2-opt-6.7b-coco": ("blip2_opt", "caption_coco_opt6.7b"),
"blip2-flan-t5-xl": ("blip2_t5", "pretrain_flant5xl"),
"blip2-flan-t5-xl-coco": ("blip2_t5", "caption_coco_flant5xl"),
"blip2-flan-t5-xxl": ("blip2_t5", "pretrain_flant5xxl"),
}
__lowerCAmelCase , __lowerCAmelCase = model_name_to_original[model_name]
# load original model
print("Loading original model..." )
__lowerCAmelCase = "cuda" if torch.cuda.is_available() else "cpu"
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = load_model_and_preprocess(
name=SCREAMING_SNAKE_CASE_ , model_type=SCREAMING_SNAKE_CASE_ , is_eval=SCREAMING_SNAKE_CASE_ , device=SCREAMING_SNAKE_CASE_ )
original_model.eval()
print("Done!" )
# update state dict keys
__lowerCAmelCase = original_model.state_dict()
__lowerCAmelCase = create_rename_keys(SCREAMING_SNAKE_CASE_ )
for src, dest in rename_keys:
rename_key(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# some keys can be renamed efficiently
for key, val in state_dict.copy().items():
__lowerCAmelCase = state_dict.pop(SCREAMING_SNAKE_CASE_ )
if key.startswith("Qformer.bert" ):
__lowerCAmelCase = key.replace("Qformer.bert" , "qformer" )
if "attention.self" in key:
__lowerCAmelCase = key.replace("self" , "attention" )
if "opt_proj" in key:
__lowerCAmelCase = key.replace("opt_proj" , "language_projection" )
if "t5_proj" in key:
__lowerCAmelCase = key.replace("t5_proj" , "language_projection" )
if key.startswith("opt" ):
__lowerCAmelCase = key.replace("opt" , "language" )
if key.startswith("t5" ):
__lowerCAmelCase = key.replace("t5" , "language" )
__lowerCAmelCase = val
# read in qv biases
read_in_q_v_bias(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
__lowerCAmelCase , __lowerCAmelCase = hf_model.load_state_dict(SCREAMING_SNAKE_CASE_ , strict=SCREAMING_SNAKE_CASE_ )
assert len(SCREAMING_SNAKE_CASE_ ) == 0
assert unexpected_keys == ["qformer.embeddings.position_ids"]
__lowerCAmelCase = load_demo_image()
__lowerCAmelCase = vis_processors["eval"](SCREAMING_SNAKE_CASE_ ).unsqueeze(0 ).to(SCREAMING_SNAKE_CASE_ )
__lowerCAmelCase = tokenizer(["\n"] , return_tensors="pt" ).input_ids.to(SCREAMING_SNAKE_CASE_ )
# create processor
__lowerCAmelCase = BlipImageProcessor(
size={"height": image_size, "width": image_size} , image_mean=SCREAMING_SNAKE_CASE_ , image_std=SCREAMING_SNAKE_CASE_ )
__lowerCAmelCase = BlipaProcessor(image_processor=SCREAMING_SNAKE_CASE_ , tokenizer=SCREAMING_SNAKE_CASE_ )
__lowerCAmelCase = processor(images=SCREAMING_SNAKE_CASE_ , return_tensors="pt" ).pixel_values.to(SCREAMING_SNAKE_CASE_ )
# make sure processor creates exact same pixel values
assert torch.allclose(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
original_model.to(SCREAMING_SNAKE_CASE_ )
hf_model.to(SCREAMING_SNAKE_CASE_ )
with torch.no_grad():
if "opt" in model_name:
__lowerCAmelCase = original_model({"image": original_pixel_values, "text_input": [""]} ).logits
__lowerCAmelCase = hf_model(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ).logits
else:
__lowerCAmelCase = original_model(
{"image": original_pixel_values, "text_input": ["\n"], "text_output": ["\n"]} ).logits
__lowerCAmelCase = input_ids.masked_fill(input_ids == tokenizer.pad_token_id , -1_00 )
__lowerCAmelCase = hf_model(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ ).logits
assert original_logits.shape == logits.shape
print("First values of original logits:" , original_logits[0, :3, :3] )
print("First values of HF logits:" , logits[0, :3, :3] )
# assert values
if model_name == "blip2-flan-t5-xl":
__lowerCAmelCase = torch.tensor(
[[-41.58_50, -4.44_40, -8.99_22], [-47.43_22, -5.91_43, -1.73_40]] , device=SCREAMING_SNAKE_CASE_ )
assert torch.allclose(logits[0, :3, :3] , SCREAMING_SNAKE_CASE_ , atol=1E-4 )
elif model_name == "blip2-flan-t5-xl-coco":
__lowerCAmelCase = torch.tensor(
[[-57.01_09, -9.89_67, -12.62_80], [-68.65_78, -12.71_91, -10.50_65]] , device=SCREAMING_SNAKE_CASE_ )
else:
# cast to same type
__lowerCAmelCase = logits.dtype
assert torch.allclose(original_logits.to(SCREAMING_SNAKE_CASE_ ) , SCREAMING_SNAKE_CASE_ , atol=1E-2 )
print("Looks ok!" )
print("Generating a caption..." )
__lowerCAmelCase = ""
__lowerCAmelCase = tokenizer(SCREAMING_SNAKE_CASE_ , return_tensors="pt" ).input_ids.to(SCREAMING_SNAKE_CASE_ )
__lowerCAmelCase = original_model.generate({"image": original_pixel_values} )
__lowerCAmelCase = hf_model.generate(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , do_sample=SCREAMING_SNAKE_CASE_ , num_beams=5 , max_length=30 , min_length=1 , top_p=0.9 , repetition_penalty=1.0 , length_penalty=1.0 , temperature=1 , )
print("Original generation:" , SCREAMING_SNAKE_CASE_ )
__lowerCAmelCase = input_ids.shape[1]
__lowerCAmelCase = processor.batch_decode(outputs[:, prompt_length:] , skip_special_tokens=SCREAMING_SNAKE_CASE_ )
__lowerCAmelCase = [text.strip() for text in output_text]
print("HF generation:" , SCREAMING_SNAKE_CASE_ )
if pytorch_dump_folder_path is not None:
processor.save_pretrained(SCREAMING_SNAKE_CASE_ )
hf_model.save_pretrained(SCREAMING_SNAKE_CASE_ )
if push_to_hub:
processor.push_to_hub(F"""nielsr/{model_name}""" )
hf_model.push_to_hub(F"""nielsr/{model_name}""" )
if __name__ == "__main__":
UpperCamelCase__ = argparse.ArgumentParser()
UpperCamelCase__ = [
"""blip2-opt-2.7b""",
"""blip2-opt-6.7b""",
"""blip2-opt-2.7b-coco""",
"""blip2-opt-6.7b-coco""",
"""blip2-flan-t5-xl""",
"""blip2-flan-t5-xl-coco""",
"""blip2-flan-t5-xxl""",
]
parser.add_argument(
"""--model_name""",
default="""blip2-opt-2.7b""",
choices=choices,
type=str,
help="""Path to hf config.json of model to convert""",
)
parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
parser.add_argument(
"""--push_to_hub""",
action="""store_true""",
help="""Whether to push the model and processor to the hub after converting""",
)
UpperCamelCase__ = parser.parse_args()
convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 92 | """simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
'andreasmadsen/efficient_mlm_m0.40': (
'https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json'
),
}
class __snake_case ( __lowerCAmelCase ):
a__ = """roberta-prelayernorm"""
def __init__( self , lowercase=5_02_65 , lowercase=7_68 , lowercase=12 , lowercase=12 , lowercase=30_72 , lowercase="gelu" , lowercase=0.1 , lowercase=0.1 , lowercase=5_12 , lowercase=2 , lowercase=0.02 , lowercase=1e-12 , lowercase=1 , lowercase=0 , lowercase=2 , lowercase="absolute" , lowercase=True , lowercase=None , **lowercase , ) -> Any:
'''simple docstring'''
super().__init__(pad_token_id=lowercase , bos_token_id=lowercase , eos_token_id=lowercase , **lowercase)
a__: Union[str, Any] = vocab_size
a__: str = hidden_size
a__: Tuple = num_hidden_layers
a__: List[str] = num_attention_heads
a__: Dict = hidden_act
a__: int = intermediate_size
a__: Tuple = hidden_dropout_prob
a__: str = attention_probs_dropout_prob
a__: Tuple = max_position_embeddings
a__: Tuple = type_vocab_size
a__: Optional[Any] = initializer_range
a__: Tuple = layer_norm_eps
a__: Optional[int] = position_embedding_type
a__: Any = use_cache
a__: Dict = classifier_dropout
class __snake_case ( __lowerCAmelCase ):
@property
def lowerCamelCase_ ( self) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
if self.task == "multiple-choice":
a__: str = {0: 'batch', 1: 'choice', 2: 'sequence'}
else:
a__: Union[str, Any] = {0: 'batch', 1: 'sequence'}
return OrderedDict(
[
('input_ids', dynamic_axis),
('attention_mask', dynamic_axis),
])
| 290 | 0 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase : Optional[Any] = logging.get_logger(__name__)
_lowercase : List[str] = {
"google/pix2struct-textcaps-base": (
"https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json"
),
}
class lowerCAmelCase__ ( lowerCamelCase_ ):
lowerCAmelCase_ = '''pix2struct_text_model'''
lowerCAmelCase_ = ['''past_key_values''']
lowerCAmelCase_ = {
'''hidden_size''': '''hidden_size''',
'''num_attention_heads''': '''num_heads''',
'''num_hidden_layers''': '''num_layers''',
}
def __init__( self , __SCREAMING_SNAKE_CASE=5_02_44 , __SCREAMING_SNAKE_CASE=7_68 , __SCREAMING_SNAKE_CASE=64 , __SCREAMING_SNAKE_CASE=20_48 , __SCREAMING_SNAKE_CASE=12 , __SCREAMING_SNAKE_CASE=12 , __SCREAMING_SNAKE_CASE=32 , __SCREAMING_SNAKE_CASE=1_28 , __SCREAMING_SNAKE_CASE=0.1 , __SCREAMING_SNAKE_CASE=1E-6 , __SCREAMING_SNAKE_CASE=1.0 , __SCREAMING_SNAKE_CASE="gelu_new" , __SCREAMING_SNAKE_CASE=0 , __SCREAMING_SNAKE_CASE=False , __SCREAMING_SNAKE_CASE=0 , __SCREAMING_SNAKE_CASE=1 , __SCREAMING_SNAKE_CASE=False , __SCREAMING_SNAKE_CASE=True , **__SCREAMING_SNAKE_CASE , ):
"""simple docstring"""
lowercase_ : Any = vocab_size
lowercase_ : Tuple = hidden_size
lowercase_ : Optional[Any] = d_kv
lowercase_ : List[str] = d_ff
lowercase_ : List[str] = num_layers
lowercase_ : Optional[Any] = num_heads
lowercase_ : Union[str, Any] = relative_attention_num_buckets
lowercase_ : Optional[int] = relative_attention_max_distance
lowercase_ : Union[str, Any] = dropout_rate
lowercase_ : Dict = layer_norm_epsilon
lowercase_ : Dict = initializer_factor
lowercase_ : List[Any] = use_cache
lowercase_ : Optional[int] = eos_token_id
lowercase_ : Optional[int] = decoder_start_token_id
# for backwards compatibility
lowercase_ : Any = dense_act_fn
super().__init__(
pad_token_id=__SCREAMING_SNAKE_CASE , eos_token_id=__SCREAMING_SNAKE_CASE , decoder_start_token_id=__SCREAMING_SNAKE_CASE , tie_word_embeddings=__SCREAMING_SNAKE_CASE , is_decoder=__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE , )
@classmethod
def _snake_case ( cls , __SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE ):
"""simple docstring"""
cls._set_token_in_kwargs(__SCREAMING_SNAKE_CASE )
lowercase_ , lowercase_ : Optional[int] = cls.get_config_dict(__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
# get the text config dict if we are loading from Pix2StructConfig
if config_dict.get('''model_type''' ) == "pix2struct":
lowercase_ : List[Any] = config_dict['''text_config''']
if "model_type" in config_dict and hasattr(cls , '''model_type''' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
class lowerCAmelCase__ ( lowerCamelCase_ ):
lowerCAmelCase_ = '''pix2struct_vision_model'''
def __init__( self , __SCREAMING_SNAKE_CASE=7_68 , __SCREAMING_SNAKE_CASE=7_68 , __SCREAMING_SNAKE_CASE=20_48 , __SCREAMING_SNAKE_CASE=64 , __SCREAMING_SNAKE_CASE=12 , __SCREAMING_SNAKE_CASE=12 , __SCREAMING_SNAKE_CASE="gelu_new" , __SCREAMING_SNAKE_CASE=1E-6 , __SCREAMING_SNAKE_CASE=0.0 , __SCREAMING_SNAKE_CASE=0.0 , __SCREAMING_SNAKE_CASE=1E-1_0 , __SCREAMING_SNAKE_CASE=1.0 , __SCREAMING_SNAKE_CASE=40_96 , __SCREAMING_SNAKE_CASE=32 , __SCREAMING_SNAKE_CASE=1_28 , **__SCREAMING_SNAKE_CASE , ):
"""simple docstring"""
super().__init__(**__SCREAMING_SNAKE_CASE )
lowercase_ : Union[str, Any] = hidden_size
lowercase_ : Any = patch_embed_hidden_size
lowercase_ : List[Any] = d_ff
lowercase_ : Dict = dropout_rate
lowercase_ : Any = num_hidden_layers
lowercase_ : Any = num_attention_heads
lowercase_ : int = initializer_range
lowercase_ : Dict = initializer_factor
lowercase_ : Dict = attention_dropout
lowercase_ : Optional[Any] = layer_norm_eps
lowercase_ : str = dense_act_fn
lowercase_ : Dict = seq_len
lowercase_ : List[Any] = relative_attention_num_buckets
lowercase_ : int = relative_attention_max_distance
lowercase_ : Optional[int] = d_kv
@classmethod
def _snake_case ( cls , __SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE ):
"""simple docstring"""
cls._set_token_in_kwargs(__SCREAMING_SNAKE_CASE )
lowercase_ , lowercase_ : str = cls.get_config_dict(__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
# get the vision config dict if we are loading from Pix2StructConfig
if config_dict.get('''model_type''' ) == "pix2struct":
lowercase_ : Optional[int] = config_dict['''vision_config''']
if "model_type" in config_dict and hasattr(cls , '''model_type''' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
class lowerCAmelCase__ ( lowerCamelCase_ ):
lowerCAmelCase_ = '''pix2struct'''
lowerCAmelCase_ = True
def __init__( self , __SCREAMING_SNAKE_CASE=None , __SCREAMING_SNAKE_CASE=None , __SCREAMING_SNAKE_CASE=1.0 , __SCREAMING_SNAKE_CASE=0.02 , __SCREAMING_SNAKE_CASE=False , __SCREAMING_SNAKE_CASE=False , __SCREAMING_SNAKE_CASE=True , **__SCREAMING_SNAKE_CASE , ):
"""simple docstring"""
super().__init__(tie_word_embeddings=__SCREAMING_SNAKE_CASE , is_encoder_decoder=__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
if text_config is None:
lowercase_ : Optional[Any] = {}
logger.info('''text_config is None. Initializing the Pix2StructTextConfig with default values.''' )
if vision_config is None:
lowercase_ : Dict = {}
logger.info('''vision_config is None. Initializing the Pix2StructVisionConfig with default values.''' )
lowercase_ : str = PixaStructTextConfig(**__SCREAMING_SNAKE_CASE )
lowercase_ : Tuple = PixaStructVisionConfig(**__SCREAMING_SNAKE_CASE )
lowercase_ : Optional[Any] = self.text_config.decoder_start_token_id
lowercase_ : Union[str, Any] = self.text_config.pad_token_id
lowercase_ : Union[str, Any] = self.text_config.eos_token_id
lowercase_ : int = initializer_factor
lowercase_ : Any = initializer_range
lowercase_ : str = self.initializer_range
lowercase_ : str = self.initializer_range
lowercase_ : int = is_vqa
@classmethod
def _snake_case ( cls , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE ):
"""simple docstring"""
return cls(text_config=text_config.to_dict() , vision_config=vision_config.to_dict() , **__SCREAMING_SNAKE_CASE )
def _snake_case ( self ):
"""simple docstring"""
lowercase_ : Tuple = copy.deepcopy(self.__dict__ )
lowercase_ : Any = self.text_config.to_dict()
lowercase_ : Optional[Any] = self.vision_config.to_dict()
lowercase_ : Optional[int] = self.__class__.model_type
return output
| 93 | """simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
'MIT/ast-finetuned-audioset-10-10-0.4593': (
'https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve/main/config.json'
),
}
class __snake_case ( __lowerCAmelCase ):
a__ = """audio-spectrogram-transformer"""
def __init__( self , lowercase=7_68 , lowercase=12 , lowercase=12 , lowercase=30_72 , lowercase="gelu" , lowercase=0.0 , lowercase=0.0 , lowercase=0.02 , lowercase=1e-12 , lowercase=16 , lowercase=True , lowercase=10 , lowercase=10 , lowercase=10_24 , lowercase=1_28 , **lowercase , ) -> str:
'''simple docstring'''
super().__init__(**lowercase)
a__: Any = hidden_size
a__: int = num_hidden_layers
a__: Union[str, Any] = num_attention_heads
a__: Any = intermediate_size
a__: Union[str, Any] = hidden_act
a__: int = hidden_dropout_prob
a__: str = attention_probs_dropout_prob
a__: str = initializer_range
a__: Tuple = layer_norm_eps
a__: Any = patch_size
a__: int = qkv_bias
a__: Optional[Any] = frequency_stride
a__: int = time_stride
a__: List[str] = max_length
a__: Tuple = num_mel_bins
| 290 | 0 |
from __future__ import annotations
from collections.abc import Callable
snake_case : List[Any] = list[list[float | int]]
def __lowerCamelCase ( UpperCAmelCase_ : Matrix , UpperCAmelCase_ : Matrix ):
"""simple docstring"""
a :int = len(UpperCAmelCase_ )
a :Matrix = [[0 for _ in range(size + 1 )] for _ in range(UpperCAmelCase_ )]
a :int
a :int
a :int
a :int
a :int
a :float
for row in range(UpperCAmelCase_ ):
for col in range(UpperCAmelCase_ ):
a :Union[str, Any] = matrix[row][col]
a :Optional[int] = vector[row][0]
a :Optional[Any] = 0
a :List[Any] = 0
while row < size and col < size:
# pivoting
a :List[str] = max((abs(augmented[rowa][col] ), rowa) for rowa in range(UpperCAmelCase_ , UpperCAmelCase_ ) )[
1
]
if augmented[pivot_row][col] == 0:
col += 1
continue
else:
a , a :Dict = augmented[pivot_row], augmented[row]
for rowa in range(row + 1 , UpperCAmelCase_ ):
a :Tuple = augmented[rowa][col] / augmented[row][col]
a :List[str] = 0
for cola in range(col + 1 , size + 1 ):
augmented[rowa][cola] -= augmented[row][cola] * ratio
row += 1
col += 1
# back substitution
for col in range(1 , UpperCAmelCase_ ):
for row in range(UpperCAmelCase_ ):
a :Optional[Any] = augmented[row][col] / augmented[col][col]
for cola in range(UpperCAmelCase_ , size + 1 ):
augmented[row][cola] -= augmented[col][cola] * ratio
# round to get rid of numbers like 2.000000000000004
return [
[round(augmented[row][size] / augmented[row][row] , 10 )] for row in range(UpperCAmelCase_ )
]
def __lowerCamelCase ( UpperCAmelCase_ : list[int] ):
"""simple docstring"""
a :int = len(UpperCAmelCase_ )
a :Matrix = [[0 for _ in range(UpperCAmelCase_ )] for _ in range(UpperCAmelCase_ )]
a :Matrix = [[0] for _ in range(UpperCAmelCase_ )]
a :Matrix
a :int
a :int
a :int
for x_val, y_val in enumerate(UpperCAmelCase_ ):
for col in range(UpperCAmelCase_ ):
a :List[str] = (x_val + 1) ** (size - col - 1)
a :Any = y_val
a :Optional[int] = solve(UpperCAmelCase_ , UpperCAmelCase_ )
def interpolated_func(UpperCAmelCase_ : int ) -> int:
return sum(
round(coeffs[x_val][0] ) * (var ** (size - x_val - 1))
for x_val in range(UpperCAmelCase_ ) )
return interpolated_func
def __lowerCamelCase ( UpperCAmelCase_ : int ):
"""simple docstring"""
return (
1
- variable
+ variable**2
- variable**3
+ variable**4
- variable**5
+ variable**6
- variable**7
+ variable**8
- variable**9
+ variable**10
)
def __lowerCamelCase ( UpperCAmelCase_ : Callable[[int], int] = question_function , UpperCAmelCase_ : int = 10 ):
"""simple docstring"""
a :list[int] = [func(UpperCAmelCase_ ) for x_val in range(1 , order + 1 )]
a :list[Callable[[int], int]] = [
interpolate(data_points[:max_coeff] ) for max_coeff in range(1 , order + 1 )
]
a :int = 0
a :Callable[[int], int]
a :int
for poly in polynomials:
a :Union[str, Any] = 1
while func(UpperCAmelCase_ ) == poly(UpperCAmelCase_ ):
x_val += 1
ret += poly(UpperCAmelCase_ )
return ret
if __name__ == "__main__":
print(F"""{solution() = }""")
| 94 | """simple docstring"""
import unittest
from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
lowercase__ = get_tests_dir('fixtures/test_sentencepiece.model')
lowercase__ = get_tests_dir('fixtures/test_sentencepiece_bpe.model')
lowercase__ = 'pt' if is_torch_available() else 'tf'
@require_sentencepiece
@require_tokenizers
class __snake_case ( __lowerCAmelCase , unittest.TestCase ):
a__ = CamembertTokenizer
a__ = CamembertTokenizerFast
a__ = True
a__ = True
def lowerCamelCase_ ( self) -> Union[str, Any]:
'''simple docstring'''
super().setUp()
# We have a SentencePiece fixture for testing
a__: Tuple = CamembertTokenizer(lowercase)
tokenizer.save_pretrained(self.tmpdirname)
def lowerCamelCase_ ( self) -> List[str]:
'''simple docstring'''
a__: Optional[Any] = '<pad>'
a__: List[Any] = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowercase) , lowercase)
self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowercase) , lowercase)
def lowerCamelCase_ ( self) -> Any:
'''simple docstring'''
a__: str = list(self.get_tokenizer().get_vocab().keys())
self.assertEqual(vocab_keys[0] , '<s>NOTUSED')
self.assertEqual(vocab_keys[1] , '<pad>')
self.assertEqual(vocab_keys[-1] , '<mask>')
self.assertEqual(len(lowercase) , 10_04)
def lowerCamelCase_ ( self) -> Any:
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size , 10_05)
def lowerCamelCase_ ( self) -> Optional[Any]:
'''simple docstring'''
a__: Optional[Any] = CamembertTokenizer(lowercase)
tokenizer.save_pretrained(self.tmpdirname)
a__: List[Any] = CamembertTokenizerFast.from_pretrained(self.tmpdirname)
a__: Dict = 'I was born in 92000, and this is falsé.'
a__: Optional[int] = tokenizer.encode(lowercase)
a__: Any = rust_tokenizer.encode(lowercase)
self.assertListEqual(lowercase , lowercase)
a__: Optional[Any] = tokenizer.encode(lowercase , add_special_tokens=lowercase)
a__: str = rust_tokenizer.encode(lowercase , add_special_tokens=lowercase)
self.assertListEqual(lowercase , lowercase)
# <unk> tokens are not the same for `rust` than for `slow`.
# Because spm gives back raw token instead of `unk` in EncodeAsPieces
# tokens = tokenizer.tokenize(sequence)
a__: Tuple = tokenizer.convert_ids_to_tokens(lowercase)
a__: Tuple = rust_tokenizer.tokenize(lowercase)
self.assertListEqual(lowercase , lowercase)
def lowerCamelCase_ ( self) -> Dict:
'''simple docstring'''
if not self.test_rust_tokenizer:
return
a__: Dict = self.get_tokenizer()
a__: str = self.get_rust_tokenizer()
a__: int = 'I was born in 92000, and this is falsé.'
a__: Optional[Any] = tokenizer.tokenize(lowercase)
a__: List[Any] = rust_tokenizer.tokenize(lowercase)
self.assertListEqual(lowercase , lowercase)
a__: str = tokenizer.encode(lowercase , add_special_tokens=lowercase)
a__: str = rust_tokenizer.encode(lowercase , add_special_tokens=lowercase)
self.assertListEqual(lowercase , lowercase)
a__: Tuple = self.get_rust_tokenizer()
a__: Union[str, Any] = tokenizer.encode(lowercase)
a__: List[Any] = rust_tokenizer.encode(lowercase)
self.assertListEqual(lowercase , lowercase)
@slow
def lowerCamelCase_ ( self) -> Optional[Any]:
'''simple docstring'''
a__: Union[str, Any] = {'input_ids': [[5, 54, 71_96, 2_97, 30, 23, 7_76, 18, 11, 32_15, 37_05, 82_52, 22, 31_64, 11_81, 21_16, 29, 16, 8_13, 25, 7_91, 33_14, 20, 34_46, 38, 2_75_75, 1_20, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 4_68, 17, 11, 90_88, 20, 15_17, 8, 2_28_04, 1_88_18, 10, 38, 6_29, 6_07, 6_07, 1_42, 19, 71_96, 8_67, 56, 1_03_26, 24, 22_67, 20, 4_16, 50_72, 1_56_12, 2_33, 7_34, 7, 23_99, 27, 16, 30_15, 16_49, 7, 24, 20, 43_38, 23_99, 27, 13, 34_00, 14, 13, 61_89, 8, 9_30, 9, 6]], '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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501
# fmt: on
# camembert is a french model. So we also use french texts.
a__: int = [
'Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '
'utilisé principalement dans le domaine du traitement automatique des langues (TAL).',
'À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '
'pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '
'telles que la traduction et la synthèse de texte.',
]
self.tokenizer_integration_test_util(
expected_encoding=lowercase , model_name='camembert-base' , revision='3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf' , sequences=lowercase , )
| 290 | 0 |
from __future__ import annotations
import math
UpperCAmelCase : Any = """2020.9.26"""
UpperCAmelCase : Optional[Any] = """xcodz-dot, cclaus, dhruvmanila"""
def _A ( SCREAMING_SNAKE_CASE : float , SCREAMING_SNAKE_CASE : float , SCREAMING_SNAKE_CASE : float , SCREAMING_SNAKE_CASE : float , SCREAMING_SNAKE_CASE : float ):
"""simple docstring"""
if not all(isinstance(SCREAMING_SNAKE_CASE , (float, int) ) for val in locals().values() ):
a__ : Optional[Any] =f'''Input values must either be float or int: {list(locals().values() )}'''
raise TypeError(SCREAMING_SNAKE_CASE )
a__ : List[str] =((x * distance) / (z + distance)) * scale
a__ : Dict =((y * distance) / (z + distance)) * scale
return projected_x, projected_y
def _A ( SCREAMING_SNAKE_CASE : float , SCREAMING_SNAKE_CASE : float , SCREAMING_SNAKE_CASE : float , SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : float ):
"""simple docstring"""
if not isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
raise TypeError("Axis must be a str" )
a__ : Optional[int] =locals()
del input_variables["axis"]
if not all(isinstance(SCREAMING_SNAKE_CASE , (float, int) ) for val in input_variables.values() ):
a__ : List[Any] =(
"Input values except axis must either be float or int: "
f'''{list(input_variables.values() )}'''
)
raise TypeError(SCREAMING_SNAKE_CASE )
a__ : List[Any] =(angle % 360) / 450 * 180 / math.pi
if axis == "z":
a__ : Tuple =x * math.cos(SCREAMING_SNAKE_CASE ) - y * math.sin(SCREAMING_SNAKE_CASE )
a__ : int =y * math.cos(SCREAMING_SNAKE_CASE ) + x * math.sin(SCREAMING_SNAKE_CASE )
a__ : Optional[Any] =z
elif axis == "x":
a__ : str =y * math.cos(SCREAMING_SNAKE_CASE ) - z * math.sin(SCREAMING_SNAKE_CASE )
a__ : Union[str, Any] =z * math.cos(SCREAMING_SNAKE_CASE ) + y * math.sin(SCREAMING_SNAKE_CASE )
a__ : str =x
elif axis == "y":
a__ : List[str] =x * math.cos(SCREAMING_SNAKE_CASE ) - z * math.sin(SCREAMING_SNAKE_CASE )
a__ : Any =z * math.cos(SCREAMING_SNAKE_CASE ) + x * math.sin(SCREAMING_SNAKE_CASE )
a__ : Optional[Any] =y
else:
raise ValueError("not a valid axis, choose one of 'x', 'y', 'z'" )
return new_x, new_y, new_z
if __name__ == "__main__":
import doctest
doctest.testmod()
print(F"""{convert_to_ad(1.0, 2.0, 3.0, 1_0.0, 1_0.0) = }""")
print(F"""{rotate(1.0, 2.0, 3.0, 'y', 9_0.0) = }""")
| 95 | """simple docstring"""
def __a ( _SCREAMING_SNAKE_CASE = 1000000 ) ->int:
a__: int = limit + 1
a__: Optional[int] = [0] * limit
for first_term in range(1 , _SCREAMING_SNAKE_CASE ):
for n in range(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
a__: List[Any] = first_term + n / first_term
if common_difference % 4: # d must be divisble by 4
continue
else:
common_difference /= 4
if (
first_term > common_difference
and first_term < 4 * common_difference
): # since x,y,z are positive integers
frequency[n] += 1 # so z>0 and a>d ,also 4d<a
a__: Any = sum(1 for x in frequency[1:limit] if x == 10 )
return count
if __name__ == "__main__":
print(f"{solution() = }")
| 290 | 0 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ..models.auto import AutoModelForVisionaSeq
from ..utils import requires_backends
from .base import PipelineTool
if TYPE_CHECKING:
from PIL import Image
class lowerCAmelCase__ ( lowercase ):
'''simple docstring'''
lowerCamelCase__ = """Salesforce/blip-image-captioning-base"""
lowerCamelCase__ = (
"""This is a tool that generates a description of an image. It takes an input named `image` which should be the """
"""image to caption, and returns a text that contains the description in English."""
)
lowerCamelCase__ = """image_captioner"""
lowerCamelCase__ = AutoModelForVisionaSeq
lowerCamelCase__ = ["""image"""]
lowerCamelCase__ = ["""text"""]
def __init__( self , *lowercase , **lowercase ):
requires_backends(self , ['vision'] )
super().__init__(*lowercase , **lowercase )
def A_ ( self , lowercase ):
return self.pre_processor(images=lowercase , return_tensors='pt' )
def A_ ( self , lowercase ):
return self.model.generate(**lowercase )
def A_ ( self , lowercase ):
return self.pre_processor.batch_decode(lowercase , skip_special_tokens=lowercase )[0].strip() | 96 | """simple docstring"""
import os
from typing import Dict, List, Tuple, TypeVar, Union
lowercase__ = TypeVar('T')
lowercase__ = Union[List[T], Tuple[T, ...]]
lowercase__ = Union[T, List[T], Dict[str, T]]
lowercase__ = Union[str, bytes, os.PathLike]
| 290 | 0 |
'''simple docstring'''
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
from .timesteps import (
fastaa_timesteps,
smartaa_timesteps,
smartaa_timesteps,
smartaaa_timesteps,
smartaaa_timesteps,
superaa_timesteps,
superaa_timesteps,
superaaa_timesteps,
)
@dataclass
class lowercase ( A__ ):
"""simple docstring"""
_a = 42
_a = 42
_a = 42
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .pipeline_if import IFPipeline
from .pipeline_if_imgaimg import IFImgaImgPipeline
from .pipeline_if_imgaimg_superresolution import IFImgaImgSuperResolutionPipeline
from .pipeline_if_inpainting import IFInpaintingPipeline
from .pipeline_if_inpainting_superresolution import IFInpaintingSuperResolutionPipeline
from .pipeline_if_superresolution import IFSuperResolutionPipeline
from .safety_checker import IFSafetyChecker
from .watermark import IFWatermarker | 97 | """simple docstring"""
from math import pi, sqrt, tan
def __a ( _SCREAMING_SNAKE_CASE ) ->float:
if side_length < 0:
raise ValueError('surface_area_cube() only accepts non-negative values' )
return 6 * side_length**2
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->float:
if length < 0 or breadth < 0 or height < 0:
raise ValueError('surface_area_cuboid() only accepts non-negative values' )
return 2 * ((length * breadth) + (breadth * height) + (length * height))
def __a ( _SCREAMING_SNAKE_CASE ) ->float:
if radius < 0:
raise ValueError('surface_area_sphere() only accepts non-negative values' )
return 4 * pi * radius**2
def __a ( _SCREAMING_SNAKE_CASE ) ->float:
if radius < 0:
raise ValueError('surface_area_hemisphere() only accepts non-negative values' )
return 3 * pi * radius**2
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->float:
if radius < 0 or height < 0:
raise ValueError('surface_area_cone() only accepts non-negative values' )
return pi * radius * (radius + (height**2 + radius**2) ** 0.5)
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->float:
if radius_a < 0 or radius_a < 0 or height < 0:
raise ValueError(
'surface_area_conical_frustum() only accepts non-negative values' )
a__: List[Any] = (height**2 + (radius_a - radius_a) ** 2) ** 0.5
return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->float:
if radius < 0 or height < 0:
raise ValueError('surface_area_cylinder() only accepts non-negative values' )
return 2 * pi * radius * (height + radius)
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->float:
if torus_radius < 0 or tube_radius < 0:
raise ValueError('surface_area_torus() only accepts non-negative values' )
if torus_radius < tube_radius:
raise ValueError(
'surface_area_torus() does not support spindle or self intersecting tori' )
return 4 * pow(_SCREAMING_SNAKE_CASE , 2 ) * torus_radius * tube_radius
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->float:
if length < 0 or width < 0:
raise ValueError('area_rectangle() only accepts non-negative values' )
return length * width
def __a ( _SCREAMING_SNAKE_CASE ) ->float:
if side_length < 0:
raise ValueError('area_square() only accepts non-negative values' )
return side_length**2
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->float:
if base < 0 or height < 0:
raise ValueError('area_triangle() only accepts non-negative values' )
return (base * height) / 2
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->float:
if sidea < 0 or sidea < 0 or sidea < 0:
raise ValueError('area_triangle_three_sides() only accepts non-negative values' )
elif sidea + sidea < sidea or sidea + sidea < sidea or sidea + sidea < sidea:
raise ValueError('Given three sides do not form a triangle' )
a__: int = (sidea + sidea + sidea) / 2
a__: Tuple = sqrt(
semi_perimeter
* (semi_perimeter - sidea)
* (semi_perimeter - sidea)
* (semi_perimeter - sidea) )
return area
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->float:
if base < 0 or height < 0:
raise ValueError('area_parallelogram() only accepts non-negative values' )
return base * height
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->float:
if basea < 0 or basea < 0 or height < 0:
raise ValueError('area_trapezium() only accepts non-negative values' )
return 1 / 2 * (basea + basea) * height
def __a ( _SCREAMING_SNAKE_CASE ) ->float:
if radius < 0:
raise ValueError('area_circle() only accepts non-negative values' )
return pi * radius**2
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->float:
if radius_x < 0 or radius_y < 0:
raise ValueError('area_ellipse() only accepts non-negative values' )
return pi * radius_x * radius_y
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->float:
if diagonal_a < 0 or diagonal_a < 0:
raise ValueError('area_rhombus() only accepts non-negative values' )
return 1 / 2 * diagonal_a * diagonal_a
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->float:
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) or sides < 3:
raise ValueError(
'area_reg_polygon() only accepts integers greater than or \
equal to three as number of sides' )
elif length < 0:
raise ValueError(
'area_reg_polygon() only accepts non-negative values as \
length of a side' )
return (sides * length**2) / (4 * tan(pi / sides ))
return (sides * length**2) / (4 * tan(pi / sides ))
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True) # verbose so we can see methods missing tests
print('[DEMO] Areas of various geometric shapes: \n')
print(f"Rectangle: {area_rectangle(10, 20) = }")
print(f"Square: {area_square(10) = }")
print(f"Triangle: {area_triangle(10, 10) = }")
print(f"Triangle: {area_triangle_three_sides(5, 12, 13) = }")
print(f"Parallelogram: {area_parallelogram(10, 20) = }")
print(f"Rhombus: {area_rhombus(10, 20) = }")
print(f"Trapezium: {area_trapezium(10, 20, 30) = }")
print(f"Circle: {area_circle(20) = }")
print(f"Ellipse: {area_ellipse(10, 20) = }")
print('\nSurface Areas of various geometric shapes: \n')
print(f"Cube: {surface_area_cube(20) = }")
print(f"Cuboid: {surface_area_cuboid(10, 20, 30) = }")
print(f"Sphere: {surface_area_sphere(20) = }")
print(f"Hemisphere: {surface_area_hemisphere(20) = }")
print(f"Cone: {surface_area_cone(10, 20) = }")
print(f"Conical Frustum: {surface_area_conical_frustum(10, 20, 30) = }")
print(f"Cylinder: {surface_area_cylinder(10, 20) = }")
print(f"Torus: {surface_area_torus(20, 10) = }")
print(f"Equilateral Triangle: {area_reg_polygon(3, 10) = }")
print(f"Square: {area_reg_polygon(4, 10) = }")
print(f"Reqular Pentagon: {area_reg_polygon(5, 10) = }")
| 290 | 0 |
"""simple docstring"""
import argparse
import logging
import sys
from unittest.mock import patch
import run_glue_deebert
from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow
logging.basicConfig(level=logging.DEBUG)
lowerCAmelCase__ : Optional[Any] = logging.getLogger()
def a_ ( ):
UpperCAmelCase__ = argparse.ArgumentParser()
parser.add_argument('-f' )
UpperCAmelCase__ = parser.parse_args()
return args.f
class snake_case ( __UpperCAmelCase ):
"""simple docstring"""
def __lowerCAmelCase ( self : int ):
UpperCAmelCase__ = logging.StreamHandler(sys.stdout )
logger.addHandler(lowerCamelCase__ )
def __lowerCAmelCase ( self : Optional[Any] ,lowerCamelCase__ : Union[str, Any] ):
UpperCAmelCase__ = get_gpu_count()
if n_gpu > 1:
pass
# XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
# script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
# distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
# cmd = [sys.executable] + distributed_args + args
# execute_subprocess_async(cmd, env=self.get_env())
# XXX: test the results - need to save them first into .json file
else:
args.insert(0 ,'run_glue_deebert.py' )
with patch.object(lowerCamelCase__ ,'argv' ,lowerCamelCase__ ):
UpperCAmelCase__ = run_glue_deebert.main()
for value in result.values():
self.assertGreaterEqual(lowerCamelCase__ ,0.6_6_6 )
@slow
@require_torch_non_multi_gpu
def __lowerCAmelCase ( self : List[str] ):
UpperCAmelCase__ = '\n --model_type roberta\n --model_name_or_path roberta-base\n --task_name MRPC\n --do_train\n --do_eval\n --do_lower_case\n --data_dir ./tests/fixtures/tests_samples/MRPC/\n --max_seq_length 128\n --per_gpu_eval_batch_size=1\n --per_gpu_train_batch_size=8\n --learning_rate 2e-4\n --num_train_epochs 3\n --overwrite_output_dir\n --seed 42\n --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n --plot_data_dir ./examples/deebert/results/\n --save_steps 0\n --overwrite_cache\n --eval_after_first_stage\n '.split()
self.run_and_check(lowerCamelCase__ )
UpperCAmelCase__ = '\n --model_type roberta\n --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n --task_name MRPC\n --do_eval\n --do_lower_case\n --data_dir ./tests/fixtures/tests_samples/MRPC/\n --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n --plot_data_dir ./examples/deebert/results/\n --max_seq_length 128\n --eval_each_highway\n --eval_highway\n --overwrite_cache\n --per_gpu_eval_batch_size=1\n '.split()
self.run_and_check(lowerCamelCase__ )
UpperCAmelCase__ = '\n --model_type roberta\n --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n --task_name MRPC\n --do_eval\n --do_lower_case\n --data_dir ./tests/fixtures/tests_samples/MRPC/\n --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n --plot_data_dir ./examples/deebert/results/\n --max_seq_length 128\n --early_exit_entropy 0.1\n --eval_highway\n --overwrite_cache\n --per_gpu_eval_batch_size=1\n '.split()
self.run_and_check(lowerCamelCase__ )
| 98 | """simple docstring"""
import itertools
import random
import unittest
import numpy as np
from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
lowercase__ = random.Random()
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=1.0 , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None ) ->Optional[int]:
if rng is None:
a__: Any = global_rng
a__: int = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
class __snake_case ( unittest.TestCase ):
def __init__( self , lowercase , lowercase=7 , lowercase=4_00 , lowercase=20_00 , lowercase=1 , lowercase=0.0 , lowercase=1_60_00 , lowercase=True , lowercase=True , ) -> Union[str, Any]:
'''simple docstring'''
a__: Tuple = parent
a__: Optional[int] = batch_size
a__: Optional[Any] = min_seq_length
a__: Optional[int] = max_seq_length
a__: Tuple = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
a__: Dict = feature_size
a__: Any = padding_value
a__: Optional[Any] = sampling_rate
a__: Optional[Any] = return_attention_mask
a__: str = do_normalize
def lowerCamelCase_ ( self) -> Dict:
'''simple docstring'''
return {
"feature_size": self.feature_size,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"return_attention_mask": self.return_attention_mask,
"do_normalize": self.do_normalize,
}
def lowerCamelCase_ ( self , lowercase=False , lowercase=False) -> Tuple:
'''simple docstring'''
def _flatten(lowercase):
return list(itertools.chain(*lowercase))
if equal_length:
a__: Dict = floats_list((self.batch_size, self.max_seq_length))
else:
# make sure that inputs increase in size
a__: List[Any] = [
_flatten(floats_list((x, self.feature_size)))
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff)
]
if numpify:
a__: str = [np.asarray(lowercase) for x in speech_inputs]
return speech_inputs
class __snake_case ( __lowerCAmelCase , unittest.TestCase ):
a__ = WavaVecaFeatureExtractor
def lowerCamelCase_ ( self) -> Tuple:
'''simple docstring'''
a__: Optional[int] = WavaVecaFeatureExtractionTester(self)
def lowerCamelCase_ ( self , lowercase) -> List[Any]:
'''simple docstring'''
self.assertTrue(np.all(np.mean(lowercase , axis=0) < 1e-3))
self.assertTrue(np.all(np.abs(np.var(lowercase , axis=0) - 1) < 1e-3))
def lowerCamelCase_ ( self) -> List[str]:
'''simple docstring'''
a__: List[Any] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
# create three inputs of length 800, 1000, and 1200
a__: Optional[Any] = [floats_list((1, x))[0] for x in range(8_00 , 14_00 , 2_00)]
a__: List[str] = [np.asarray(lowercase) for speech_input in speech_inputs]
# Test not batched input
a__: Optional[Any] = feat_extract(speech_inputs[0] , return_tensors='np').input_values
a__: Dict = feat_extract(np_speech_inputs[0] , return_tensors='np').input_values
self.assertTrue(np.allclose(lowercase , lowercase , atol=1e-3))
# Test batched
a__: Dict = feat_extract(lowercase , return_tensors='np').input_values
a__: int = feat_extract(lowercase , return_tensors='np').input_values
for enc_seq_a, enc_seq_a in zip(lowercase , lowercase):
self.assertTrue(np.allclose(lowercase , lowercase , atol=1e-3))
# Test 2-D numpy arrays are batched.
a__: int = [floats_list((1, x))[0] for x in (8_00, 8_00, 8_00)]
a__: Union[str, Any] = np.asarray(lowercase)
a__: int = feat_extract(lowercase , return_tensors='np').input_values
a__: Any = feat_extract(lowercase , return_tensors='np').input_values
for enc_seq_a, enc_seq_a in zip(lowercase , lowercase):
self.assertTrue(np.allclose(lowercase , lowercase , atol=1e-3))
def lowerCamelCase_ ( self) -> List[Any]:
'''simple docstring'''
a__: Tuple = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
a__: List[Any] = [floats_list((1, x))[0] for x in range(8_00 , 14_00 , 2_00)]
a__: Optional[int] = ['longest', 'max_length', 'do_not_pad']
a__: List[Any] = [None, 16_00, None]
for max_length, padding in zip(lowercase , lowercase):
a__: Dict = feat_extract(lowercase , padding=lowercase , max_length=lowercase , return_tensors='np')
a__: Union[str, Any] = processed.input_values
self._check_zero_mean_unit_variance(input_values[0][:8_00])
self.assertTrue(input_values[0][8_00:].sum() < 1e-6)
self._check_zero_mean_unit_variance(input_values[1][:10_00])
self.assertTrue(input_values[0][10_00:].sum() < 1e-6)
self._check_zero_mean_unit_variance(input_values[2][:12_00])
def lowerCamelCase_ ( self) -> Optional[int]:
'''simple docstring'''
a__: str = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
a__: Optional[int] = range(8_00 , 14_00 , 2_00)
a__: List[str] = [floats_list((1, x))[0] for x in lengths]
a__: Tuple = ['longest', 'max_length', 'do_not_pad']
a__: Dict = [None, 16_00, None]
for max_length, padding in zip(lowercase , lowercase):
a__: int = feat_extract(lowercase , max_length=lowercase , padding=lowercase)
a__: Any = processed.input_values
self._check_zero_mean_unit_variance(input_values[0][:8_00])
self._check_zero_mean_unit_variance(input_values[1][:10_00])
self._check_zero_mean_unit_variance(input_values[2][:12_00])
def lowerCamelCase_ ( self) -> List[str]:
'''simple docstring'''
a__: Any = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
a__: Any = [floats_list((1, x))[0] for x in range(8_00 , 14_00 , 2_00)]
a__: Dict = feat_extract(
lowercase , truncation=lowercase , max_length=10_00 , padding='max_length' , return_tensors='np')
a__: int = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :8_00])
self._check_zero_mean_unit_variance(input_values[1])
self._check_zero_mean_unit_variance(input_values[2])
def lowerCamelCase_ ( self) -> Dict:
'''simple docstring'''
a__: Tuple = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
a__: int = [floats_list((1, x))[0] for x in range(8_00 , 14_00 , 2_00)]
a__: str = feat_extract(
lowercase , truncation=lowercase , max_length=10_00 , padding='longest' , return_tensors='np')
a__: Any = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :8_00])
self._check_zero_mean_unit_variance(input_values[1, :10_00])
self._check_zero_mean_unit_variance(input_values[2])
# make sure that if max_length < longest -> then pad to max_length
self.assertTrue(input_values.shape == (3, 10_00))
a__: Dict = [floats_list((1, x))[0] for x in range(8_00 , 14_00 , 2_00)]
a__: Tuple = feat_extract(
lowercase , truncation=lowercase , max_length=20_00 , padding='longest' , return_tensors='np')
a__: str = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :8_00])
self._check_zero_mean_unit_variance(input_values[1, :10_00])
self._check_zero_mean_unit_variance(input_values[2])
# make sure that if max_length > longest -> then pad to longest
self.assertTrue(input_values.shape == (3, 12_00))
@require_torch
def lowerCamelCase_ ( self) -> Tuple:
'''simple docstring'''
import torch
a__: Optional[int] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
a__: Tuple = np.random.rand(1_00).astype(np.floataa)
a__: Tuple = np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
a__: Any = feature_extractor.pad([{'input_values': inputs}] , return_tensors='np')
self.assertTrue(np_processed.input_values.dtype == np.floataa)
a__: Optional[Any] = feature_extractor.pad([{'input_values': inputs}] , return_tensors='pt')
self.assertTrue(pt_processed.input_values.dtype == torch.floataa)
@slow
@require_torch
def lowerCamelCase_ ( self) -> Optional[Any]:
'''simple docstring'''
for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
a__: str = WavaVecaConfig.from_pretrained(lowercase)
a__: str = WavaVecaFeatureExtractor.from_pretrained(lowercase)
# only "layer" feature extraction norm should make use of
# attention_mask
self.assertEqual(feat_extract.return_attention_mask , config.feat_extract_norm == 'layer')
| 290 | 0 |
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def A_ ( A__ ) -> str:
a__ : Any = filter(lambda A__ : p.requires_grad , model.parameters() )
a__ : Union[str, Any] = sum([np.prod(p.size() ) for p in model_parameters] )
return params
lowercase : Union[str, Any] = logging.getLogger(__name__)
def A_ ( A__ , A__ ) -> int:
if metric == "rouge2":
a__ : Any = '{val_avg_rouge2:.4f}-{step_count}'
elif metric == "bleu":
a__ : Tuple = '{val_avg_bleu:.4f}-{step_count}'
elif metric == "em":
a__ : Union[str, Any] = '{val_avg_em:.4f}-{step_count}'
elif metric == "loss":
a__ : int = '{val_avg_loss:.4f}-{step_count}'
else:
raise NotImplementedError(
F'seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this'
' function.' )
a__ : Tuple = ModelCheckpoint(
dirpath=A__ , filename=A__ , monitor=F'val_{metric}' , mode='max' , save_top_k=1 , every_n_epochs=1 , )
return checkpoint_callback
def A_ ( A__ , A__ ) -> str:
return EarlyStopping(
monitor=F'val_{metric}' , mode='min' if 'loss' in metric else 'max' , patience=A__ , verbose=A__ , )
class A__ ( pl.Callback ):
"""simple docstring"""
def __lowercase ( self , lowercase , lowercase) -> Optional[int]:
'''simple docstring'''
a__ : Any = {F'lr_group_{i}': param['lr'] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups)}
pl_module.logger.log_metrics(lowercase)
@rank_zero_only
def __lowercase ( self , lowercase , lowercase , lowercase , lowercase=True) -> None:
'''simple docstring'''
logger.info(F'***** {type_path} results at step {trainer.global_step:05d} *****')
a__ : Optional[Any] = trainer.callback_metrics
trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ['log', 'progress_bar', 'preds']})
# Log results
a__ : Any = Path(pl_module.hparams.output_dir)
if type_path == "test":
a__ : Dict = od / 'test_results.txt'
a__ : str = od / 'test_generations.txt'
else:
# this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
# If people want this it will be easy enough to add back.
a__ : Any = od / F'{type_path}_results/{trainer.global_step:05d}.txt'
a__ : Union[str, Any] = od / F'{type_path}_generations/{trainer.global_step:05d}.txt'
results_file.parent.mkdir(exist_ok=lowercase)
generations_file.parent.mkdir(exist_ok=lowercase)
with open(lowercase , 'a+') as writer:
for key in sorted(lowercase):
if key in ["log", "progress_bar", "preds"]:
continue
a__ : Dict = metrics[key]
if isinstance(lowercase , torch.Tensor):
a__ : Optional[Any] = val.item()
a__ : Dict = F'{key}: {val:.6f}\n'
writer.write(lowercase)
if not save_generations:
return
if "preds" in metrics:
a__ : Tuple = '\n'.join(metrics['preds'])
generations_file.open('w+').write(lowercase)
@rank_zero_only
def __lowercase ( self , lowercase , lowercase) -> int:
'''simple docstring'''
try:
a__ : int = pl_module.model.model.num_parameters()
except AttributeError:
a__ : str = pl_module.model.num_parameters()
a__ : Union[str, Any] = count_trainable_parameters(lowercase)
# mp stands for million parameters
trainer.logger.log_metrics({'n_params': npars, 'mp': npars / 1e6, 'grad_mp': n_trainable_pars / 1e6})
@rank_zero_only
def __lowercase ( self , lowercase , lowercase) -> Any:
'''simple docstring'''
save_json(pl_module.metrics , pl_module.metrics_save_path)
return self._write_logs(lowercase , lowercase , 'test')
@rank_zero_only
def __lowercase ( self , lowercase , lowercase) -> Dict:
'''simple docstring'''
save_json(pl_module.metrics , pl_module.metrics_save_path)
# Uncommenting this will save val generations
# return self._write_logs(trainer, pl_module, "valid")
| 99 | """simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
'edbeeching/decision-transformer-gym-hopper-medium': (
'https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json'
),
# See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}
class __snake_case ( __lowerCAmelCase ):
a__ = """decision_transformer"""
a__ = ["""past_key_values"""]
a__ = {
"""max_position_embeddings""": """n_positions""",
"""num_attention_heads""": """n_head""",
"""num_hidden_layers""": """n_layer""",
}
def __init__( self , lowercase=17 , lowercase=4 , lowercase=1_28 , lowercase=40_96 , lowercase=True , lowercase=1 , lowercase=10_24 , lowercase=3 , lowercase=1 , lowercase=None , lowercase="relu" , lowercase=0.1 , lowercase=0.1 , lowercase=0.1 , lowercase=1e-5 , lowercase=0.02 , lowercase=True , lowercase=True , lowercase=5_02_56 , lowercase=5_02_56 , lowercase=False , lowercase=False , **lowercase , ) -> Tuple:
'''simple docstring'''
a__: List[str] = state_dim
a__: int = act_dim
a__: List[Any] = hidden_size
a__: List[str] = max_ep_len
a__: List[Any] = action_tanh
a__: Optional[Any] = vocab_size
a__: Tuple = n_positions
a__: Dict = n_layer
a__: Optional[int] = n_head
a__: Optional[int] = n_inner
a__: Any = activation_function
a__: Union[str, Any] = resid_pdrop
a__: Any = embd_pdrop
a__: Any = attn_pdrop
a__: List[Any] = layer_norm_epsilon
a__: Optional[Any] = initializer_range
a__: Any = scale_attn_weights
a__: Dict = use_cache
a__: Optional[int] = scale_attn_by_inverse_layer_idx
a__: List[str] = reorder_and_upcast_attn
a__: Any = bos_token_id
a__: int = eos_token_id
super().__init__(bos_token_id=lowercase , eos_token_id=lowercase , **lowercase)
| 290 | 0 |
"""simple docstring"""
import math
def _lowerCAmelCase ( UpperCamelCase_ , UpperCamelCase_ ):
if 0 not in (x, y):
# We use the relation x^y = y*log10(x), where 10 is the base.
return y * math.logaa(UpperCamelCase_ )
else:
if x == 0: # 0 raised to any number is 0
return 0
elif y == 0:
return 1 # any number raised to 0 is 1
raise AssertionError("""This should never happen""" )
if __name__ == "__main__": # Main function
# Read two numbers from input and typecast them to int using map function.
# Here x is the base and y is the power.
__magic_name__ = "Enter the base and the power separated by a comma: "
__magic_name__, __magic_name__ = map(int, input(prompt).split(","))
__magic_name__, __magic_name__ = map(int, input(prompt).split(","))
# We find the log of each number, using the function res(), which takes two
# arguments.
__magic_name__ = res(xa, ya)
__magic_name__ = res(xa, ya)
# We check for the largest number
if resa > resa:
print("Largest number is", xa, "^", ya)
elif resa > resa:
print("Largest number is", xa, "^", ya)
else:
print("Both are equal")
| 100 | """simple docstring"""
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->int:
while a != 0:
a__ , a__: List[str] = b % a, a
return b
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->int:
if gcd(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) != 1:
a__: Dict = F'mod inverse of {a!r} and {m!r} does not exist'
raise ValueError(_SCREAMING_SNAKE_CASE )
a__ , a__ , a__: Union[str, Any] = 1, 0, a
a__ , a__ , a__: Any = 0, 1, m
while va != 0:
a__: int = ua // va
a__ , a__ , a__ , a__ , a__ , a__: Any = (ua - q * va), (ua - q * va), (ua - q * va), va, va, va
return ua % m
| 290 | 0 |
def UpperCamelCase ( lowerCAmelCase__ , lowerCAmelCase__ ):
'''simple docstring'''
if mass < 0:
raise ValueError('''The mass of a body cannot be negative''' )
return 0.5 * mass * abs(lowerCAmelCase__ ) * abs(lowerCAmelCase__ )
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
| 101 | """simple docstring"""
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
lowercase__ = logging.getLogger(__name__)
class __snake_case :
def __init__( self) -> Optional[int]:
'''simple docstring'''
a__: Optional[Any] = False
def lowerCamelCase_ ( self , lowercase , lowercase , lowercase , lowercase) -> str:
'''simple docstring'''
if not self.initialized:
a__: Optional[int] = RagRetriever(
lowercase , question_encoder_tokenizer=lowercase , generator_tokenizer=lowercase , index=lowercase , init_retrieval=lowercase , )
a__: Optional[int] = True
def lowerCamelCase_ ( self) -> int:
'''simple docstring'''
self.retriever.index.init_index()
def lowerCamelCase_ ( self , lowercase , lowercase) -> Union[str, Any]:
'''simple docstring'''
a__ , a__: str = self.retriever._main_retrieve(lowercase , lowercase)
return doc_ids, retrieved_doc_embeds
class __snake_case ( __lowerCAmelCase ):
def __init__( self , lowercase , lowercase , lowercase , lowercase , lowercase=None) -> int:
'''simple docstring'''
if index is not None and index.is_initialized() and len(lowercase) > 0:
raise ValueError(
'When using Ray for distributed fine-tuning, '
'you\'ll need to provide the paths instead, '
'as the dataset and the index are loaded '
'separately. More info in examples/rag/use_own_knowledge_dataset.py ')
super().__init__(
lowercase , question_encoder_tokenizer=lowercase , generator_tokenizer=lowercase , index=lowercase , init_retrieval=lowercase , )
a__: Any = retrieval_workers
if len(self.retrieval_workers) > 0:
ray.get(
[
worker.create_rag_retriever.remote(lowercase , lowercase , lowercase , lowercase)
for worker in self.retrieval_workers
])
def lowerCamelCase_ ( self) -> Any:
'''simple docstring'''
logger.info('initializing retrieval')
if len(self.retrieval_workers) > 0:
ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers])
else:
# Non-distributed training. Load index into this same process.
self.index.init_index()
def lowerCamelCase_ ( self , lowercase , lowercase) -> Union[str, Any]:
'''simple docstring'''
if len(self.retrieval_workers) > 0:
# Select a random retrieval actor.
a__: int = self.retrieval_workers[random.randint(0 , len(self.retrieval_workers) - 1)]
a__ , a__: List[Any] = ray.get(random_worker.retrieve.remote(lowercase , lowercase))
else:
a__ , a__: Dict = self._main_retrieve(lowercase , lowercase)
return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(lowercase)
@classmethod
def lowerCamelCase_ ( cls , lowercase , lowercase=None , **lowercase) -> Tuple:
'''simple docstring'''
return super(lowercase , cls).get_tokenizers(lowercase , lowercase , **lowercase)
@classmethod
def lowerCamelCase_ ( cls , lowercase , lowercase , lowercase=None , **lowercase) -> Union[str, Any]:
'''simple docstring'''
a__: Optional[int] = kwargs.pop('config' , lowercase) or RagConfig.from_pretrained(lowercase , **lowercase)
a__: Union[str, Any] = RagTokenizer.from_pretrained(lowercase , config=lowercase)
a__: int = rag_tokenizer.question_encoder
a__: Any = rag_tokenizer.generator
if indexed_dataset is not None:
a__: List[Any] = 'custom'
a__: Optional[Any] = CustomHFIndex(config.retrieval_vector_size , lowercase)
else:
a__: Dict = cls._build_index(lowercase)
return cls(
lowercase , question_encoder_tokenizer=lowercase , generator_tokenizer=lowercase , retrieval_workers=lowercase , index=lowercase , )
| 290 | 0 |
"""simple docstring"""
import argparse
import torch
from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert
logging.set_verbosity_info()
def lowercase ( _snake_case : Union[str, Any] , _snake_case : Dict , _snake_case : Union[str, Any] ) ->Optional[Any]:
"""simple docstring"""
__snake_case : Optional[Any] = AlbertConfig.from_json_file(_snake_case )
print(f"""Building PyTorch model from configuration: {config}""" )
__snake_case : Tuple = AlbertForPreTraining(_snake_case )
# Load weights from tf checkpoint
load_tf_weights_in_albert(_snake_case , _snake_case , _snake_case )
# Save pytorch-model
print(f"""Save PyTorch model to {pytorch_dump_path}""" )
torch.save(model.state_dict() , _snake_case )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE : Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
)
parser.add_argument(
"""--albert_config_file""",
default=None,
type=str,
required=True,
help=(
"""The config json file corresponding to the pre-trained ALBERT model. \n"""
"""This specifies the model architecture."""
),
)
parser.add_argument(
"""--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
)
SCREAMING_SNAKE_CASE : Union[str, Any] = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)
| 102 | """simple docstring"""
import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter
import requests
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None ) ->Optional[int]:
a__: int = None
if token is not None:
a__: Tuple = {'Accept': 'application/vnd.github+json', 'Authorization': F'Bearer {token}'}
a__: Optional[Any] = F'https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100'
a__: str = requests.get(_SCREAMING_SNAKE_CASE , headers=_SCREAMING_SNAKE_CASE ).json()
a__: str = {}
try:
job_links.update({job['name']: job['html_url'] for job in result['jobs']} )
a__: int = math.ceil((result['total_count'] - 100) / 100 )
for i in range(_SCREAMING_SNAKE_CASE ):
a__: Dict = requests.get(url + F'&page={i + 2}' , headers=_SCREAMING_SNAKE_CASE ).json()
job_links.update({job['name']: job['html_url'] for job in result['jobs']} )
return job_links
except Exception:
print(F'Unknown error, could not fetch links:\n{traceback.format_exc()}' )
return {}
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None ) ->Dict:
a__: Dict = None
if token is not None:
a__: List[str] = {'Accept': 'application/vnd.github+json', 'Authorization': F'Bearer {token}'}
a__: Dict = F'https://api.github.com/repos/huggingface/transformers/actions/runs/{worflow_run_id}/artifacts?per_page=100'
a__: Union[str, Any] = requests.get(_SCREAMING_SNAKE_CASE , headers=_SCREAMING_SNAKE_CASE ).json()
a__: List[Any] = {}
try:
artifacts.update({artifact['name']: artifact['archive_download_url'] for artifact in result['artifacts']} )
a__: Dict = math.ceil((result['total_count'] - 100) / 100 )
for i in range(_SCREAMING_SNAKE_CASE ):
a__: Optional[int] = requests.get(url + F'&page={i + 2}' , headers=_SCREAMING_SNAKE_CASE ).json()
artifacts.update({artifact['name']: artifact['archive_download_url'] for artifact in result['artifacts']} )
return artifacts
except Exception:
print(F'Unknown error, could not fetch links:\n{traceback.format_exc()}' )
return {}
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->str:
a__: List[Any] = None
if token is not None:
a__: Optional[int] = {'Accept': 'application/vnd.github+json', 'Authorization': F'Bearer {token}'}
a__: Union[str, Any] = requests.get(_SCREAMING_SNAKE_CASE , headers=_SCREAMING_SNAKE_CASE , allow_redirects=_SCREAMING_SNAKE_CASE )
a__: Optional[Any] = result.headers['Location']
a__: Optional[int] = requests.get(_SCREAMING_SNAKE_CASE , allow_redirects=_SCREAMING_SNAKE_CASE )
a__: int = os.path.join(_SCREAMING_SNAKE_CASE , F'{artifact_name}.zip' )
with open(_SCREAMING_SNAKE_CASE , 'wb' ) as fp:
fp.write(response.content )
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None ) ->List[Any]:
a__: List[Any] = []
a__: Optional[Any] = []
a__: List[Any] = None
with zipfile.ZipFile(_SCREAMING_SNAKE_CASE ) as z:
for filename in z.namelist():
if not os.path.isdir(_SCREAMING_SNAKE_CASE ):
# read the file
if filename in ["failures_line.txt", "summary_short.txt", "job_name.txt"]:
with z.open(_SCREAMING_SNAKE_CASE ) as f:
for line in f:
a__: Optional[int] = line.decode('UTF-8' ).strip()
if filename == "failures_line.txt":
try:
# `error_line` is the place where `error` occurs
a__: Union[str, Any] = line[: line.index(': ' )]
a__: Union[str, Any] = line[line.index(': ' ) + len(': ' ) :]
errors.append([error_line, error] )
except Exception:
# skip un-related lines
pass
elif filename == "summary_short.txt" and line.startswith('FAILED ' ):
# `test` is the test method that failed
a__: Optional[int] = line[len('FAILED ' ) :]
failed_tests.append(_SCREAMING_SNAKE_CASE )
elif filename == "job_name.txt":
a__: Union[str, Any] = line
if len(_SCREAMING_SNAKE_CASE ) != len(_SCREAMING_SNAKE_CASE ):
raise ValueError(
F'`errors` and `failed_tests` should have the same number of elements. Got {len(_SCREAMING_SNAKE_CASE )} for `errors` '
F'and {len(_SCREAMING_SNAKE_CASE )} for `failed_tests` instead. The test reports in {artifact_zip_path} have some'
' problem.' )
a__: Tuple = None
if job_name and job_links:
a__: Dict = job_links.get(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# A list with elements of the form (line of error, error, failed test)
a__: int = [x + [y] + [job_link] for x, y in zip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )]
return result
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None ) ->str:
a__: int = []
a__: Optional[int] = [os.path.join(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) for p in os.listdir(_SCREAMING_SNAKE_CASE ) if p.endswith('.zip' )]
for p in paths:
errors.extend(get_errors_from_single_artifact(_SCREAMING_SNAKE_CASE , job_links=_SCREAMING_SNAKE_CASE ) )
return errors
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None ) ->Any:
a__: str = Counter()
counter.update([x[1] for x in logs] )
a__: int = counter.most_common()
a__: Any = {}
for error, count in counts:
if error_filter is None or error not in error_filter:
a__: List[str] = {'count': count, 'failed_tests': [(x[2], x[0]) for x in logs if x[1] == error]}
a__: Optional[Any] = dict(sorted(r.items() , key=lambda _SCREAMING_SNAKE_CASE : item[1]["count"] , reverse=_SCREAMING_SNAKE_CASE ) )
return r
def __a ( _SCREAMING_SNAKE_CASE ) ->Union[str, Any]:
a__: List[str] = test.split('::' )[0]
if test.startswith('tests/models/' ):
a__: Dict = test.split('/' )[2]
else:
a__: Any = None
return test
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None ) ->List[str]:
a__: int = [(x[0], x[1], get_model(x[2] )) for x in logs]
a__: List[Any] = [x for x in logs if x[2] is not None]
a__: Optional[Any] = {x[2] for x in logs}
a__: Dict = {}
for test in tests:
a__: Union[str, Any] = Counter()
# count by errors in `test`
counter.update([x[1] for x in logs if x[2] == test] )
a__: Union[str, Any] = counter.most_common()
a__: List[str] = {error: count for error, count in counts if (error_filter is None or error not in error_filter)}
a__: List[Any] = sum(error_counts.values() )
if n_errors > 0:
a__: Any = {'count': n_errors, 'errors': error_counts}
a__: Optional[int] = dict(sorted(r.items() , key=lambda _SCREAMING_SNAKE_CASE : item[1]["count"] , reverse=_SCREAMING_SNAKE_CASE ) )
return r
def __a ( _SCREAMING_SNAKE_CASE ) ->Union[str, Any]:
a__: Any = '| no. | error | status |'
a__: Any = '|-:|:-|:-|'
a__: str = [header, sep]
for error in reduced_by_error:
a__: int = reduced_by_error[error]['count']
a__: Tuple = F'| {count} | {error[:100]} | |'
lines.append(_SCREAMING_SNAKE_CASE )
return "\n".join(_SCREAMING_SNAKE_CASE )
def __a ( _SCREAMING_SNAKE_CASE ) ->str:
a__: List[str] = '| model | no. of errors | major error | count |'
a__: str = '|-:|-:|-:|-:|'
a__: int = [header, sep]
for model in reduced_by_model:
a__: Tuple = reduced_by_model[model]['count']
a__ , a__: Dict = list(reduced_by_model[model]['errors'].items() )[0]
a__: Dict = F'| {model} | {count} | {error[:60]} | {_count} |'
lines.append(_SCREAMING_SNAKE_CASE )
return "\n".join(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
lowercase__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument('--workflow_run_id', type=str, required=True, help='A GitHub Actions workflow run id.')
parser.add_argument(
'--output_dir',
type=str,
required=True,
help='Where to store the downloaded artifacts and other result files.',
)
parser.add_argument('--token', default=None, type=str, help='A token that has actions:read permission.')
lowercase__ = parser.parse_args()
os.makedirs(args.output_dir, exist_ok=True)
lowercase__ = get_job_links(args.workflow_run_id, token=args.token)
lowercase__ = {}
# To deal with `workflow_call` event, where a job name is the combination of the job names in the caller and callee.
# For example, `PyTorch 1.11 / Model tests (models/albert, single-gpu)`.
if _job_links:
for k, v in _job_links.items():
# This is how GitHub actions combine job names.
if " / " in k:
lowercase__ = k.find(' / ')
lowercase__ = k[index + len(' / ') :]
lowercase__ = v
with open(os.path.join(args.output_dir, 'job_links.json'), 'w', encoding='UTF-8') as fp:
json.dump(job_links, fp, ensure_ascii=False, indent=4)
lowercase__ = get_artifacts_links(args.workflow_run_id, token=args.token)
with open(os.path.join(args.output_dir, 'artifacts.json'), 'w', encoding='UTF-8') as fp:
json.dump(artifacts, fp, ensure_ascii=False, indent=4)
for idx, (name, url) in enumerate(artifacts.items()):
download_artifact(name, url, args.output_dir, args.token)
# Be gentle to GitHub
time.sleep(1)
lowercase__ = get_all_errors(args.output_dir, job_links=job_links)
# `e[1]` is the error
lowercase__ = Counter()
counter.update([e[1] for e in errors])
# print the top 30 most common test errors
lowercase__ = counter.most_common(30)
for item in most_common:
print(item)
with open(os.path.join(args.output_dir, 'errors.json'), 'w', encoding='UTF-8') as fp:
json.dump(errors, fp, ensure_ascii=False, indent=4)
lowercase__ = reduce_by_error(errors)
lowercase__ = reduce_by_model(errors)
lowercase__ = make_github_table(reduced_by_error)
lowercase__ = make_github_table_per_model(reduced_by_model)
with open(os.path.join(args.output_dir, 'reduced_by_error.txt'), 'w', encoding='UTF-8') as fp:
fp.write(sa)
with open(os.path.join(args.output_dir, 'reduced_by_model.txt'), 'w', encoding='UTF-8') as fp:
fp.write(sa)
| 290 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
A__ : int = logging.get_logger(__name__)
A__ : int = {
'''google/bit-50''': '''https://huggingface.co/google/bit-50/resolve/main/config.json''',
}
class __snake_case ( UpperCamelCase_ ,UpperCamelCase_ ):
_a = '''bit'''
_a = ['''preactivation''', '''bottleneck''']
_a = ['''SAME''', '''VALID''']
def __init__( self : Any , A_ : Dict=3 , A_ : Optional[Any]=6_4 , A_ : Any=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8] , A_ : Optional[Any]=[3, 4, 6, 3] , A_ : Tuple="preactivation" , A_ : Tuple="relu" , A_ : List[str]=None , A_ : Dict=3_2 , A_ : Union[str, Any]=0.0 , A_ : Union[str, Any]=False , A_ : Any=3_2 , A_ : List[str]=1 , A_ : Any=None , A_ : Tuple=None , **A_ : Tuple , ):
super().__init__(**A_)
if layer_type not in self.layer_types:
raise ValueError(F"""layer_type={layer_type} is not one of {",".join(self.layer_types)}""")
if global_padding is not None:
if global_padding.upper() in self.supported_padding:
lowerCAmelCase_ : str = global_padding.upper()
else:
raise ValueError(F"""Padding strategy {global_padding} not supported""")
lowerCAmelCase_ : str = num_channels
lowerCAmelCase_ : int = embedding_size
lowerCAmelCase_ : Optional[Any] = hidden_sizes
lowerCAmelCase_ : Any = depths
lowerCAmelCase_ : Optional[int] = layer_type
lowerCAmelCase_ : List[str] = hidden_act
lowerCAmelCase_ : Dict = global_padding
lowerCAmelCase_ : int = num_groups
lowerCAmelCase_ : Tuple = drop_path_rate
lowerCAmelCase_ : Dict = embedding_dynamic_padding
lowerCAmelCase_ : Optional[int] = output_stride
lowerCAmelCase_ : Tuple = width_factor
lowerCAmelCase_ : str = ['''stem'''] + [F"""stage{idx}""" for idx in range(1 , len(A_) + 1)]
lowerCAmelCase_ , lowerCAmelCase_ : Any = get_aligned_output_features_output_indices(
out_features=A_ , out_indices=A_ , stage_names=self.stage_names)
| 103 | """simple docstring"""
import math
def __a ( _SCREAMING_SNAKE_CASE ) ->bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(_SCREAMING_SNAKE_CASE ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def __a ( _SCREAMING_SNAKE_CASE = 0.1 ) ->int:
a__: str = 3
a__: Optional[Any] = 3
while primes / (2 * j - 1) >= ratio:
for i in range(j * j + j + 1 , (j + 2) * (j + 2) , j + 1 ):
primes += is_prime(_SCREAMING_SNAKE_CASE )
j += 2
return j
if __name__ == "__main__":
import doctest
doctest.testmod()
| 290 | 0 |
'''simple docstring'''
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
def _A ( A__ ):
"""simple docstring"""
__lowercase , __lowercase = image.size
__lowercase , __lowercase = (x - x % 32 for x in (w, h)) # resize to integer multiple of 32
__lowercase = image.resize((w, h) , resample=PIL_INTERPOLATION['''lanczos'''] )
__lowercase = np.array(A__ ).astype(np.floataa ) / 2_5_5.0
__lowercase = image[None].transpose(0 , 3 , 1 , 2 )
__lowercase = torch.from_numpy(A__ )
return 2.0 * image - 1.0
class lowercase_ (lowerCamelCase__ ):
"""simple docstring"""
def __init__( self : Union[str, Any] ,lowercase__ : VQModel ,lowercase__ : UNetaDModel ,lowercase__ : Union[
DDIMScheduler,
PNDMScheduler,
LMSDiscreteScheduler,
EulerDiscreteScheduler,
EulerAncestralDiscreteScheduler,
DPMSolverMultistepScheduler,
] ,):
super().__init__()
self.register_modules(vqvae=lowercase__ ,unet=lowercase__ ,scheduler=lowercase__ )
@torch.no_grad()
def __call__( self : Dict ,lowercase__ : Union[torch.Tensor, PIL.Image.Image] = None ,lowercase__ : Optional[int] = 1 ,lowercase__ : Optional[int] = 1_0_0 ,lowercase__ : Optional[float] = 0.0 ,lowercase__ : Optional[Union[torch.Generator, List[torch.Generator]]] = None ,lowercase__ : Optional[str] = "pil" ,lowercase__ : bool = True ,):
if isinstance(lowercase__ ,PIL.Image.Image ):
__lowercase = 1
elif isinstance(lowercase__ ,torch.Tensor ):
__lowercase = image.shape[0]
else:
raise ValueError(F"`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(lowercase__ )}" )
if isinstance(lowercase__ ,PIL.Image.Image ):
__lowercase = preprocess(lowercase__ )
__lowercase , __lowercase = image.shape[-2:]
# in_channels should be 6: 3 for latents, 3 for low resolution image
__lowercase = (batch_size, self.unet.config.in_channels // 2, height, width)
__lowercase = next(self.unet.parameters() ).dtype
__lowercase = randn_tensor(lowercase__ ,generator=lowercase__ ,device=self.device ,dtype=lowercase__ )
__lowercase = image.to(device=self.device ,dtype=lowercase__ )
# set timesteps and move to the correct device
self.scheduler.set_timesteps(lowercase__ ,device=self.device )
__lowercase = self.scheduler.timesteps
# scale the initial noise by the standard deviation required by the scheduler
__lowercase = latents * self.scheduler.init_noise_sigma
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
__lowercase = '''eta''' in set(inspect.signature(self.scheduler.step ).parameters.keys() )
__lowercase = {}
if accepts_eta:
__lowercase = eta
for t in self.progress_bar(lowercase__ ):
# concat latents and low resolution image in the channel dimension.
__lowercase = torch.cat([latents, image] ,dim=1 )
__lowercase = self.scheduler.scale_model_input(lowercase__ ,lowercase__ )
# predict the noise residual
__lowercase = self.unet(lowercase__ ,lowercase__ ).sample
# compute the previous noisy sample x_t -> x_t-1
__lowercase = self.scheduler.step(lowercase__ ,lowercase__ ,lowercase__ ,**lowercase__ ).prev_sample
# decode the image latents with the VQVAE
__lowercase = self.vqvae.decode(lowercase__ ).sample
__lowercase = torch.clamp(lowercase__ ,-1.0 ,1.0 )
__lowercase = image / 2 + 0.5
__lowercase = image.cpu().permute(0 ,2 ,3 ,1 ).numpy()
if output_type == "pil":
__lowercase = self.numpy_to_pil(lowercase__ )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=lowercase__ )
| 104 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import _LazyModule
lowercase__ = {'processing_wav2vec2_with_lm': ['Wav2Vec2ProcessorWithLM']}
if TYPE_CHECKING:
from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM
else:
import sys
lowercase__ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 290 | 0 |
"""simple docstring"""
import subprocess
import sys
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class __UpperCamelCase ( a__ ):
@require_torch
def __a ( self ) -> str:
# this test is a bit tricky since TRANSFORMERS_OFFLINE can only be changed before
# `transformers` is loaded, and it's too late for inside pytest - so we are changing it
# while running an external program
# python one-liner segments
# this must be loaded before socket.socket is monkey-patched
a : Union[str, Any] = "\nfrom transformers import BertConfig, BertModel, BertTokenizer, pipeline\n "
a : Any = "\nmname = \"hf-internal-testing/tiny-random-bert\"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nBertTokenizer.from_pretrained(mname)\npipe = pipeline(task=\"fill-mask\", model=mname)\nprint(\"success\")\n "
a : Optional[int] = "\nimport socket\ndef offline_socket(*args, **kwargs): raise RuntimeError(\"Offline mode is enabled, we shouldn't access internet\")\nsocket.socket = offline_socket\n "
# Force fetching the files so that we can use the cache
a : Optional[int] = "hf-internal-testing/tiny-random-bert"
BertConfig.from_pretrained(lowerCAmelCase__ )
BertModel.from_pretrained(lowerCAmelCase__ )
BertTokenizer.from_pretrained(lowerCAmelCase__ )
pipeline(task="fill-mask" , model=lowerCAmelCase__ )
# baseline - just load from_pretrained with normal network
a : Optional[Any] = [sys.executable, "-c", "\n".join([load, run, mock] )]
# should succeed
a : Dict = self.get_env()
# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
a : Any = "1"
a : List[str] = subprocess.run(lowerCAmelCase__ , env=lowerCAmelCase__ , check=lowerCAmelCase__ , capture_output=lowerCAmelCase__ )
self.assertEqual(result.returncode , 0 , result.stderr )
self.assertIn("success" , result.stdout.decode() )
@require_torch
def __a ( self ) -> Optional[Any]:
# python one-liner segments
# this must be loaded before socket.socket is monkey-patched
a : str = "\nfrom transformers import BertConfig, BertModel, BertTokenizer, pipeline\n "
a : List[str] = "\nmname = \"hf-internal-testing/tiny-random-bert\"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nBertTokenizer.from_pretrained(mname)\npipe = pipeline(task=\"fill-mask\", model=mname)\nprint(\"success\")\n "
a : Optional[int] = "\nimport socket\ndef offline_socket(*args, **kwargs): raise socket.error(\"Faking flaky internet\")\nsocket.socket = offline_socket\n "
# Force fetching the files so that we can use the cache
a : List[Any] = "hf-internal-testing/tiny-random-bert"
BertConfig.from_pretrained(lowerCAmelCase__ )
BertModel.from_pretrained(lowerCAmelCase__ )
BertTokenizer.from_pretrained(lowerCAmelCase__ )
pipeline(task="fill-mask" , model=lowerCAmelCase__ )
# baseline - just load from_pretrained with normal network
a : int = [sys.executable, "-c", "\n".join([load, run, mock] )]
# should succeed
a : str = self.get_env()
a : int = subprocess.run(lowerCAmelCase__ , env=lowerCAmelCase__ , check=lowerCAmelCase__ , capture_output=lowerCAmelCase__ )
self.assertEqual(result.returncode , 0 , result.stderr )
self.assertIn("success" , result.stdout.decode() )
@require_torch
def __a ( self ) -> Tuple:
# this test is a bit tricky since TRANSFORMERS_OFFLINE can only be changed before
# `transformers` is loaded, and it's too late for inside pytest - so we are changing it
# while running an external program
# python one-liner segments
# this must be loaded before socket.socket is monkey-patched
a : Optional[int] = "\nfrom transformers import BertConfig, BertModel, BertTokenizer\n "
a : Any = "\nmname = \"hf-internal-testing/tiny-random-bert-sharded\"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nprint(\"success\")\n "
a : List[str] = "\nimport socket\ndef offline_socket(*args, **kwargs): raise ValueError(\"Offline mode is enabled\")\nsocket.socket = offline_socket\n "
# baseline - just load from_pretrained with normal network
a : int = [sys.executable, "-c", "\n".join([load, run] )]
# should succeed
a : Dict = self.get_env()
a : Optional[Any] = subprocess.run(lowerCAmelCase__ , env=lowerCAmelCase__ , check=lowerCAmelCase__ , capture_output=lowerCAmelCase__ )
self.assertEqual(result.returncode , 0 , result.stderr )
self.assertIn("success" , result.stdout.decode() )
# next emulate no network
a : Tuple = [sys.executable, "-c", "\n".join([load, mock, run] )]
# Doesn't fail anymore since the model is in the cache due to other tests, so commenting this.
# env["TRANSFORMERS_OFFLINE"] = "0"
# result = subprocess.run(cmd, env=env, check=False, capture_output=True)
# self.assertEqual(result.returncode, 1, result.stderr)
# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
a : List[Any] = "1"
a : Optional[int] = subprocess.run(lowerCAmelCase__ , env=lowerCAmelCase__ , check=lowerCAmelCase__ , capture_output=lowerCAmelCase__ )
self.assertEqual(result.returncode , 0 , result.stderr )
self.assertIn("success" , result.stdout.decode() )
@require_torch
def __a ( self ) -> Optional[Any]:
a : Union[str, Any] = "\nfrom transformers import pipeline\n "
a : Any = "\nmname = \"hf-internal-testing/tiny-random-bert\"\npipe = pipeline(model=mname)\n "
a : List[Any] = "\nimport socket\ndef offline_socket(*args, **kwargs): raise socket.error(\"Offline mode is enabled\")\nsocket.socket = offline_socket\n "
a : List[Any] = self.get_env()
a : Optional[int] = "1"
a : Tuple = [sys.executable, "-c", "\n".join([load, mock, run] )]
a : List[Any] = subprocess.run(lowerCAmelCase__ , env=lowerCAmelCase__ , check=lowerCAmelCase__ , capture_output=lowerCAmelCase__ )
self.assertEqual(result.returncode , 1 , result.stderr )
self.assertIn(
"You cannot infer task automatically within `pipeline` when using offline mode" , result.stderr.decode().replace("\n" , "" ) , )
@require_torch
def __a ( self ) -> Tuple:
a : Optional[int] = "\nfrom transformers import AutoModel\n "
a : int = "\nmname = \"hf-internal-testing/test_dynamic_model\"\nAutoModel.from_pretrained(mname, trust_remote_code=True)\nprint(\"success\")\n "
# baseline - just load from_pretrained with normal network
a : Optional[Any] = [sys.executable, "-c", "\n".join([load, run] )]
# should succeed
a : List[str] = self.get_env()
a : Optional[int] = subprocess.run(lowerCAmelCase__ , env=lowerCAmelCase__ , check=lowerCAmelCase__ , capture_output=lowerCAmelCase__ )
self.assertEqual(result.returncode , 0 , result.stderr )
self.assertIn("success" , result.stdout.decode() )
# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
a : Union[str, Any] = "1"
a : Dict = subprocess.run(lowerCAmelCase__ , env=lowerCAmelCase__ , check=lowerCAmelCase__ , capture_output=lowerCAmelCase__ )
self.assertEqual(result.returncode , 0 , result.stderr )
self.assertIn("success" , result.stdout.decode() )
| 105 | """simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, 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 __snake_case ( __lowerCAmelCase , unittest.TestCase ):
a__ = KandinskyInpaintPipeline
a__ = ["""prompt""", """image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
a__ = [
"""prompt""",
"""negative_prompt""",
"""image_embeds""",
"""negative_image_embeds""",
"""image""",
"""mask_image""",
]
a__ = [
"""generator""",
"""height""",
"""width""",
"""latents""",
"""guidance_scale""",
"""negative_prompt""",
"""num_inference_steps""",
"""return_dict""",
"""guidance_scale""",
"""num_images_per_prompt""",
"""output_type""",
"""return_dict""",
]
a__ = False
@property
def lowerCamelCase_ ( self) -> Optional[int]:
'''simple docstring'''
return 32
@property
def lowerCamelCase_ ( self) -> Tuple:
'''simple docstring'''
return 32
@property
def lowerCamelCase_ ( self) -> Dict:
'''simple docstring'''
return self.time_input_dim
@property
def lowerCamelCase_ ( self) -> Dict:
'''simple docstring'''
return self.time_input_dim * 4
@property
def lowerCamelCase_ ( self) -> List[Any]:
'''simple docstring'''
return 1_00
@property
def lowerCamelCase_ ( self) -> List[Any]:
'''simple docstring'''
a__: Optional[int] = XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base')
return tokenizer
@property
def lowerCamelCase_ ( self) -> Any:
'''simple docstring'''
torch.manual_seed(0)
a__: Dict = MCLIPConfig(
numDims=self.cross_attention_dim , transformerDimensions=self.text_embedder_hidden_size , hidden_size=self.text_embedder_hidden_size , intermediate_size=37 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=10_05 , )
a__: Optional[Any] = MultilingualCLIP(lowercase)
a__: int = text_encoder.eval()
return text_encoder
@property
def lowerCamelCase_ ( self) -> List[str]:
'''simple docstring'''
torch.manual_seed(0)
a__: Any = {
'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__: str = UNetaDConditionModel(**lowercase)
return model
@property
def lowerCamelCase_ ( self) -> Union[str, Any]:
'''simple docstring'''
return {
"block_out_channels": [32, 64],
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def lowerCamelCase_ ( self) -> List[Any]:
'''simple docstring'''
torch.manual_seed(0)
a__: Any = VQModel(**self.dummy_movq_kwargs)
return model
def lowerCamelCase_ ( self) -> Any:
'''simple docstring'''
a__: Dict = self.dummy_text_encoder
a__: int = self.dummy_tokenizer
a__: str = self.dummy_unet
a__: Any = self.dummy_movq
a__: Tuple = DDIMScheduler(
num_train_timesteps=10_00 , beta_schedule='linear' , beta_start=0.00085 , beta_end=0.012 , clip_sample=lowercase , set_alpha_to_one=lowercase , steps_offset=1 , prediction_type='epsilon' , thresholding=lowercase , )
a__: Tuple = {
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'unet': unet,
'scheduler': scheduler,
'movq': movq,
}
return components
def lowerCamelCase_ ( self , lowercase , lowercase=0) -> Any:
'''simple docstring'''
a__: List[Any] = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(lowercase)).to(lowercase)
a__: int = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(seed + 1)).to(lowercase)
# create init_image
a__: Optional[int] = floats_tensor((1, 3, 64, 64) , rng=random.Random(lowercase)).to(lowercase)
a__: int = image.cpu().permute(0 , 2 , 3 , 1)[0]
a__: Optional[int] = Image.fromarray(np.uinta(lowercase)).convert('RGB').resize((2_56, 2_56))
# create mask
a__: Tuple = np.ones((64, 64) , dtype=np.floataa)
a__: Optional[Any] = 0
if str(lowercase).startswith('mps'):
a__: str = torch.manual_seed(lowercase)
else:
a__: Dict = torch.Generator(device=lowercase).manual_seed(lowercase)
a__: Optional[int] = {
'prompt': 'horse',
'image': init_image,
'mask_image': mask,
'image_embeds': image_embeds,
'negative_image_embeds': negative_image_embeds,
'generator': generator,
'height': 64,
'width': 64,
'num_inference_steps': 2,
'guidance_scale': 4.0,
'output_type': 'np',
}
return inputs
def lowerCamelCase_ ( self) -> str:
'''simple docstring'''
a__: Optional[Any] = 'cpu'
a__: List[Any] = self.get_dummy_components()
a__: Optional[Any] = self.pipeline_class(**lowercase)
a__: str = pipe.to(lowercase)
pipe.set_progress_bar_config(disable=lowercase)
a__: Optional[int] = pipe(**self.get_dummy_inputs(lowercase))
a__: List[str] = output.images
a__: int = pipe(
**self.get_dummy_inputs(lowercase) , return_dict=lowercase , )[0]
a__: Optional[Any] = image[0, -3:, -3:, -1]
a__: List[Any] = image_from_tuple[0, -3:, -3:, -1]
print(f'image.shape {image.shape}')
assert image.shape == (1, 64, 64, 3)
a__: str = np.array(
[0.8326919, 0.73790467, 0.20918581, 0.9309612, 0.5511791, 0.43713328, 0.5513321, 0.49922934, 0.59497786])
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 lowerCamelCase_ ( self) -> str:
'''simple docstring'''
super().test_inference_batch_single_identical(expected_max_diff=3e-3)
@slow
@require_torch_gpu
class __snake_case ( unittest.TestCase ):
def lowerCamelCase_ ( self) -> Optional[Any]:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase_ ( self) -> Dict:
'''simple docstring'''
a__: List[Any] = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy')
a__: int = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png')
a__: Union[str, Any] = np.ones((7_68, 7_68) , dtype=np.floataa)
a__: int = 0
a__: Optional[int] = 'a hat'
a__: int = KandinskyPriorPipeline.from_pretrained(
'kandinsky-community/kandinsky-2-1-prior' , torch_dtype=torch.floataa)
pipe_prior.to(lowercase)
a__: Any = KandinskyInpaintPipeline.from_pretrained(
'kandinsky-community/kandinsky-2-1-inpaint' , torch_dtype=torch.floataa)
a__: Optional[Any] = pipeline.to(lowercase)
pipeline.set_progress_bar_config(disable=lowercase)
a__: Dict = torch.Generator(device='cpu').manual_seed(0)
a__ , a__: Optional[Any] = pipe_prior(
lowercase , generator=lowercase , num_inference_steps=5 , negative_prompt='' , ).to_tuple()
a__: List[str] = pipeline(
lowercase , image=lowercase , mask_image=lowercase , image_embeds=lowercase , negative_image_embeds=lowercase , generator=lowercase , num_inference_steps=1_00 , height=7_68 , width=7_68 , output_type='np' , )
a__: str = output.images[0]
assert image.shape == (7_68, 7_68, 3)
assert_mean_pixel_difference(lowercase , lowercase)
| 290 | 0 |
"""simple docstring"""
import os
import textwrap
import pyarrow as pa
import pytest
from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv
from ..utils import require_pil
@pytest.fixture
def __SCREAMING_SNAKE_CASE ( A_ ):
lowerCAmelCase__ : Union[str, Any] = tmp_path / '''file.csv'''
lowerCAmelCase__ : List[str] = textwrap.dedent(
'''\
header1,header2
1,2
10,20
''' )
with open(A_ , '''w''' ) as f:
f.write(A_ )
return str(A_ )
@pytest.fixture
def __SCREAMING_SNAKE_CASE ( A_ ):
lowerCAmelCase__ : Optional[Any] = tmp_path / '''malformed_file.csv'''
lowerCAmelCase__ : Tuple = textwrap.dedent(
'''\
header1,header2
1,2
10,20,
''' )
with open(A_ , '''w''' ) as f:
f.write(A_ )
return str(A_ )
@pytest.fixture
def __SCREAMING_SNAKE_CASE ( A_ , A_ ):
lowerCAmelCase__ : Optional[int] = tmp_path / '''csv_with_image.csv'''
lowerCAmelCase__ : Dict = textwrap.dedent(
f'\\n image\n {image_file}\n ' )
with open(A_ , '''w''' ) as f:
f.write(A_ )
return str(A_ )
@pytest.fixture
def __SCREAMING_SNAKE_CASE ( A_ ):
lowerCAmelCase__ : List[str] = tmp_path / '''csv_with_label.csv'''
lowerCAmelCase__ : Tuple = textwrap.dedent(
'''\
label
good
bad
good
''' )
with open(A_ , '''w''' ) as f:
f.write(A_ )
return str(A_ )
@pytest.fixture
def __SCREAMING_SNAKE_CASE ( A_ ):
lowerCAmelCase__ : Tuple = tmp_path / '''csv_with_int_list.csv'''
lowerCAmelCase__ : Dict = textwrap.dedent(
'''\
int_list
1 2 3
4 5 6
7 8 9
''' )
with open(A_ , '''w''' ) as f:
f.write(A_ )
return str(A_ )
def __SCREAMING_SNAKE_CASE ( A_ , A_ , A_ ):
lowerCAmelCase__ : Any = Csv()
lowerCAmelCase__ : Union[str, Any] = csv._generate_tables([[csv_file, malformed_csv_file]] )
with pytest.raises(A_ , match='''Error tokenizing data''' ):
for _ in generator:
pass
assert any(
record.levelname == '''ERROR'''
and '''Failed to read file''' in record.message
and os.path.basename(A_ ) in record.message
for record in caplog.records )
@require_pil
def __SCREAMING_SNAKE_CASE ( A_ ):
with open(A_ , encoding='''utf-8''' ) as f:
lowerCAmelCase__ : int = f.read().splitlines()[1]
lowerCAmelCase__ : Tuple = Csv(encoding='''utf-8''' , features=Features({'''image''': Image()} ) )
lowerCAmelCase__ : Any = csv._generate_tables([[csv_file_with_image]] )
lowerCAmelCase__ : str = pa.concat_tables([table for _, table in generator] )
assert pa_table.schema.field('''image''' ).type == Image()()
lowerCAmelCase__ : List[Any] = pa_table.to_pydict()['''image''']
assert generated_content == [{"path": image_file, "bytes": None}]
def __SCREAMING_SNAKE_CASE ( A_ ):
with open(A_ , encoding='''utf-8''' ) as f:
lowerCAmelCase__ : List[Any] = f.read().splitlines()[1:]
lowerCAmelCase__ : str = Csv(encoding='''utf-8''' , features=Features({'''label''': ClassLabel(names=['''good''', '''bad'''] )} ) )
lowerCAmelCase__ : str = csv._generate_tables([[csv_file_with_label]] )
lowerCAmelCase__ : List[Any] = pa.concat_tables([table for _, table in generator] )
assert pa_table.schema.field('''label''' ).type == ClassLabel(names=['''good''', '''bad'''] )()
lowerCAmelCase__ : Optional[Any] = pa_table.to_pydict()['''label''']
assert generated_content == [ClassLabel(names=['''good''', '''bad'''] ).straint(A_ ) for label in labels]
def __SCREAMING_SNAKE_CASE ( A_ ):
lowerCAmelCase__ : Any = Csv(encoding='''utf-8''' , sep=''',''' , converters={'''int_list''': lambda A_ : [int(A_ ) for i in x.split()]} )
lowerCAmelCase__ : int = csv._generate_tables([[csv_file_with_int_list]] )
lowerCAmelCase__ : List[Any] = pa.concat_tables([table for _, table in generator] )
assert pa.types.is_list(pa_table.schema.field('''int_list''' ).type )
lowerCAmelCase__ : List[Any] = pa_table.to_pydict()['''int_list''']
assert generated_content == [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
| 106 | """simple docstring"""
import argparse
import torch
from transformers import (
EncodecConfig,
EncodecFeatureExtractor,
EncodecModel,
logging,
)
# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/main/compression_state_dict.bin
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_48khz-7e698e3e.th
logging.set_verbosity_info()
lowercase__ = logging.get_logger('transformers.models.encodec')
lowercase__ = {
'quantizer.vq.layers.*._codebook.inited': 'quantizer.layers.*.codebook.inited',
'quantizer.vq.layers.*._codebook.cluster_size': 'quantizer.layers.*.codebook.cluster_size',
'quantizer.vq.layers.*._codebook.embed': 'quantizer.layers.*.codebook.embed',
'quantizer.vq.layers.*._codebook.embed_avg': 'quantizer.layers.*.codebook.embed_avg',
}
lowercase__ = {
'encoder.model.0.conv.conv': 'encoder.layers.0.conv',
'encoder.model.1.block.1.conv.conv': 'encoder.layers.1.block.1.conv',
'encoder.model.1.block.3.conv.conv': 'encoder.layers.1.block.3.conv',
'encoder.model.1.shortcut.conv.conv': 'encoder.layers.1.shortcut.conv',
'encoder.model.3.conv.conv': 'encoder.layers.3.conv',
'encoder.model.4.block.1.conv.conv': 'encoder.layers.4.block.1.conv',
'encoder.model.4.block.3.conv.conv': 'encoder.layers.4.block.3.conv',
'encoder.model.4.shortcut.conv.conv': 'encoder.layers.4.shortcut.conv',
'encoder.model.6.conv.conv': 'encoder.layers.6.conv',
'encoder.model.7.block.1.conv.conv': 'encoder.layers.7.block.1.conv',
'encoder.model.7.block.3.conv.conv': 'encoder.layers.7.block.3.conv',
'encoder.model.7.shortcut.conv.conv': 'encoder.layers.7.shortcut.conv',
'encoder.model.9.conv.conv': 'encoder.layers.9.conv',
'encoder.model.10.block.1.conv.conv': 'encoder.layers.10.block.1.conv',
'encoder.model.10.block.3.conv.conv': 'encoder.layers.10.block.3.conv',
'encoder.model.10.shortcut.conv.conv': 'encoder.layers.10.shortcut.conv',
'encoder.model.12.conv.conv': 'encoder.layers.12.conv',
'encoder.model.13.lstm': 'encoder.layers.13.lstm',
'encoder.model.15.conv.conv': 'encoder.layers.15.conv',
}
lowercase__ = {
'encoder.model.0.conv.norm': 'encoder.layers.0.norm',
'encoder.model.1.block.1.conv.norm': 'encoder.layers.1.block.1.norm',
'encoder.model.1.block.3.conv.norm': 'encoder.layers.1.block.3.norm',
'encoder.model.1.shortcut.conv.norm': 'encoder.layers.1.shortcut.norm',
'encoder.model.3.conv.norm': 'encoder.layers.3.norm',
'encoder.model.4.block.1.conv.norm': 'encoder.layers.4.block.1.norm',
'encoder.model.4.block.3.conv.norm': 'encoder.layers.4.block.3.norm',
'encoder.model.4.shortcut.conv.norm': 'encoder.layers.4.shortcut.norm',
'encoder.model.6.conv.norm': 'encoder.layers.6.norm',
'encoder.model.7.block.1.conv.norm': 'encoder.layers.7.block.1.norm',
'encoder.model.7.block.3.conv.norm': 'encoder.layers.7.block.3.norm',
'encoder.model.7.shortcut.conv.norm': 'encoder.layers.7.shortcut.norm',
'encoder.model.9.conv.norm': 'encoder.layers.9.norm',
'encoder.model.10.block.1.conv.norm': 'encoder.layers.10.block.1.norm',
'encoder.model.10.block.3.conv.norm': 'encoder.layers.10.block.3.norm',
'encoder.model.10.shortcut.conv.norm': 'encoder.layers.10.shortcut.norm',
'encoder.model.12.conv.norm': 'encoder.layers.12.norm',
'encoder.model.15.conv.norm': 'encoder.layers.15.norm',
}
lowercase__ = {
'decoder.model.0.conv.conv': 'decoder.layers.0.conv',
'decoder.model.1.lstm': 'decoder.layers.1.lstm',
'decoder.model.3.convtr.convtr': 'decoder.layers.3.conv',
'decoder.model.4.block.1.conv.conv': 'decoder.layers.4.block.1.conv',
'decoder.model.4.block.3.conv.conv': 'decoder.layers.4.block.3.conv',
'decoder.model.4.shortcut.conv.conv': 'decoder.layers.4.shortcut.conv',
'decoder.model.6.convtr.convtr': 'decoder.layers.6.conv',
'decoder.model.7.block.1.conv.conv': 'decoder.layers.7.block.1.conv',
'decoder.model.7.block.3.conv.conv': 'decoder.layers.7.block.3.conv',
'decoder.model.7.shortcut.conv.conv': 'decoder.layers.7.shortcut.conv',
'decoder.model.9.convtr.convtr': 'decoder.layers.9.conv',
'decoder.model.10.block.1.conv.conv': 'decoder.layers.10.block.1.conv',
'decoder.model.10.block.3.conv.conv': 'decoder.layers.10.block.3.conv',
'decoder.model.10.shortcut.conv.conv': 'decoder.layers.10.shortcut.conv',
'decoder.model.12.convtr.convtr': 'decoder.layers.12.conv',
'decoder.model.13.block.1.conv.conv': 'decoder.layers.13.block.1.conv',
'decoder.model.13.block.3.conv.conv': 'decoder.layers.13.block.3.conv',
'decoder.model.13.shortcut.conv.conv': 'decoder.layers.13.shortcut.conv',
'decoder.model.15.conv.conv': 'decoder.layers.15.conv',
}
lowercase__ = {
'decoder.model.0.conv.norm': 'decoder.layers.0.norm',
'decoder.model.3.convtr.norm': 'decoder.layers.3.norm',
'decoder.model.4.block.1.conv.norm': 'decoder.layers.4.block.1.norm',
'decoder.model.4.block.3.conv.norm': 'decoder.layers.4.block.3.norm',
'decoder.model.4.shortcut.conv.norm': 'decoder.layers.4.shortcut.norm',
'decoder.model.6.convtr.norm': 'decoder.layers.6.norm',
'decoder.model.7.block.1.conv.norm': 'decoder.layers.7.block.1.norm',
'decoder.model.7.block.3.conv.norm': 'decoder.layers.7.block.3.norm',
'decoder.model.7.shortcut.conv.norm': 'decoder.layers.7.shortcut.norm',
'decoder.model.9.convtr.norm': 'decoder.layers.9.norm',
'decoder.model.10.block.1.conv.norm': 'decoder.layers.10.block.1.norm',
'decoder.model.10.block.3.conv.norm': 'decoder.layers.10.block.3.norm',
'decoder.model.10.shortcut.conv.norm': 'decoder.layers.10.shortcut.norm',
'decoder.model.12.convtr.norm': 'decoder.layers.12.norm',
'decoder.model.13.block.1.conv.norm': 'decoder.layers.13.block.1.norm',
'decoder.model.13.block.3.conv.norm': 'decoder.layers.13.block.3.norm',
'decoder.model.13.shortcut.conv.norm': 'decoder.layers.13.shortcut.norm',
'decoder.model.15.conv.norm': 'decoder.layers.15.norm',
}
lowercase__ = {
**MAPPING_QUANTIZER,
**MAPPING_ENCODER,
**MAPPING_DECODER,
}
lowercase__ = {
**MAPPING_QUANTIZER,
**MAPPING_ENCODER,
**MAPPING_ENCODER_48K,
**MAPPING_DECODER,
**MAPPING_DECODER_48K,
}
lowercase__ = []
lowercase__ = []
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->int:
for attribute in key.split('.' ):
a__: str = getattr(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
if weight_type is not None:
a__: List[str] = getattr(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ).shape
else:
a__: Optional[Any] = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
F'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
F' {value.shape} for {full_name}' )
if weight_type == "weight":
a__: str = value
elif weight_type == "weight_g":
a__: int = value
elif weight_type == "weight_v":
a__: Tuple = value
elif weight_type == "bias":
a__: Dict = value
elif weight_type == "running_mean":
a__: Any = value
elif weight_type == "running_var":
a__: Tuple = value
elif weight_type == "num_batches_tracked":
a__: List[str] = value
elif weight_type == "weight_ih_l0":
a__: List[Any] = value
elif weight_type == "weight_hh_l0":
a__: List[Any] = value
elif weight_type == "bias_ih_l0":
a__: List[Any] = value
elif weight_type == "bias_hh_l0":
a__: List[Any] = value
elif weight_type == "weight_ih_l1":
a__: int = value
elif weight_type == "weight_hh_l1":
a__: str = value
elif weight_type == "bias_ih_l1":
a__: Union[str, Any] = value
elif weight_type == "bias_hh_l1":
a__: Any = value
else:
a__: Union[str, Any] = value
logger.info(F'{key + ("." + weight_type if weight_type is not None else "")} was initialized from {full_name}.' )
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->Dict:
for key in ignore_keys:
if key.endswith('.*' ):
if name.startswith(key[:-1] ):
return True
elif ".*." in key:
a__ , a__: Optional[Any] = key.split('.*.' )
if prefix in name and suffix in name:
return True
elif key in name:
return True
return False
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->List[str]:
a__: List[Any] = []
if model_name == "encodec_24khz" or "encodec_32khz":
a__: Optional[int] = MAPPING_24K
elif model_name == "encodec_48khz":
a__: List[Any] = MAPPING_48K
else:
raise ValueError(F'Unsupported model: {model_name}' )
for name, value in orig_dict.items():
if should_ignore(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
logger.info(F'{name} was ignored' )
continue
a__: int = False
for key, mapped_key in MAPPING.items():
if "*" in key:
a__ , a__: str = key.split('.*.' )
if prefix in name and suffix in name:
a__: List[str] = suffix
if key in name:
# HACK otherwise .embed gets initialized with .embed_avg too
if key.endswith('embed' ) and name.endswith('embed_avg' ):
continue
a__: List[str] = True
if "*" in mapped_key:
a__: List[str] = name.split(_SCREAMING_SNAKE_CASE )[0].split('.' )[-2]
a__: str = mapped_key.replace('*' , _SCREAMING_SNAKE_CASE )
if "weight_g" in name:
a__: int = 'weight_g'
elif "weight_v" in name:
a__: Dict = 'weight_v'
elif "weight_ih_l0" in name:
a__: int = 'weight_ih_l0'
elif "weight_hh_l0" in name:
a__: Union[str, Any] = 'weight_hh_l0'
elif "bias_ih_l0" in name:
a__: Optional[Any] = 'bias_ih_l0'
elif "bias_hh_l0" in name:
a__: Optional[int] = 'bias_hh_l0'
elif "weight_ih_l1" in name:
a__: Dict = 'weight_ih_l1'
elif "weight_hh_l1" in name:
a__: Optional[Any] = 'weight_hh_l1'
elif "bias_ih_l1" in name:
a__: List[str] = 'bias_ih_l1'
elif "bias_hh_l1" in name:
a__: Optional[Any] = 'bias_hh_l1'
elif "bias" in name:
a__: List[str] = 'bias'
elif "weight" in name:
a__: Any = 'weight'
elif "running_mean" in name:
a__: Dict = 'running_mean'
elif "running_var" in name:
a__: Dict = 'running_var'
elif "num_batches_tracked" in name:
a__: Dict = 'num_batches_tracked'
else:
a__: List[str] = None
set_recursively(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
continue
if not is_used:
unused_weights.append(_SCREAMING_SNAKE_CASE )
logger.warning(F'Unused weights: {unused_weights}' )
@torch.no_grad()
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , ) ->int:
if config_path is not None:
a__: Dict = EncodecConfig.from_pretrained(_SCREAMING_SNAKE_CASE )
else:
a__: Tuple = EncodecConfig()
if model_name == "encodec_24khz":
pass # config is already correct
elif model_name == "encodec_32khz":
a__: Any = [8, 5, 4, 4]
a__: List[str] = [2.2]
a__: List[Any] = 64
a__: Dict = 32000
a__: Union[str, Any] = 2048
a__: Union[str, Any] = False
a__: Any = False
a__: Optional[Any] = False
elif model_name == "encodec_48khz":
a__: Optional[int] = [8, 5, 4, 2]
a__: Union[str, Any] = [3.0, 6.0, 12.0, 24.0]
a__: List[str] = 48000
a__: Tuple = 2
a__: Optional[Any] = False
a__: Optional[int] = 'time_group_norm'
a__: Union[str, Any] = True
a__: Dict = 1.0
a__: str = 0.01
else:
raise ValueError(F'Unknown model name: {model_name}' )
a__: Optional[int] = EncodecModel(_SCREAMING_SNAKE_CASE )
a__: List[str] = EncodecFeatureExtractor(
feature_size=config.audio_channels , sampling_rate=config.sampling_rate , chunk_length_s=config.chunk_length_s , overlap=config.overlap , )
feature_extractor.save_pretrained(_SCREAMING_SNAKE_CASE )
a__: int = torch.load(_SCREAMING_SNAKE_CASE )
if "best_state" in original_checkpoint:
# we might have a training state saved, in which case discard the yaml results and just retain the weights
a__: str = original_checkpoint['best_state']
recursively_load_weights(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
model.save_pretrained(_SCREAMING_SNAKE_CASE )
if repo_id:
print('Pushing to the hub...' )
feature_extractor.push_to_hub(_SCREAMING_SNAKE_CASE )
model.push_to_hub(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
lowercase__ = argparse.ArgumentParser()
parser.add_argument(
'--model',
default='encodec_24khz',
type=str,
help='The model to convert. Should be one of \'encodec_24khz\', \'encodec_32khz\', \'encodec_48khz\'.',
)
parser.add_argument('--checkpoint_path', required=True, default=None, type=str, help='Path to original checkpoint')
parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
parser.add_argument(
'--pytorch_dump_folder_path', required=True, default=None, type=str, help='Path to the output PyTorch model.'
)
parser.add_argument(
'--push_to_hub', default=None, type=str, help='Where to upload the converted model on the 🤗 hub.'
)
lowercase__ = parser.parse_args()
convert_checkpoint(
args.model,
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.config_path,
args.push_to_hub,
)
| 290 | 0 |
def __magic_name__ ( A : list ):
'''simple docstring'''
def merge(A : list, A : list ) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[0] else right).pop(0 )
yield from left
yield from right
return list(_merge() )
if len(A ) <= 1:
return collection
a = len(A ) // 2
return merge(merge_sort(collection[:mid] ), merge_sort(collection[mid:] ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
__lowerCAmelCase : List[Any] = input('Enter numbers separated by a comma:\n').strip()
__lowerCAmelCase : List[Any] = [int(item) for item in user_input.split(',')]
print(*merge_sort(unsorted), sep=',')
| 107 | """simple docstring"""
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->Union[str, Any]:
if height >= 1:
move_tower(height - 1 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
move_disk(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
move_tower(height - 1 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->Union[str, Any]:
print('moving disk from' , _SCREAMING_SNAKE_CASE , 'to' , _SCREAMING_SNAKE_CASE )
def __a ( ) ->List[str]:
a__: Dict = int(input('Height of hanoi: ' ).strip() )
move_tower(_SCREAMING_SNAKE_CASE , 'A' , 'B' , 'C' )
if __name__ == "__main__":
main()
| 290 | 0 |
"""simple docstring"""
def a__ ( SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
return int((input_a, input_a).count(0 ) != 0 )
def a__ ( ):
'''simple docstring'''
assert nand_gate(0 , 0 ) == 1
assert nand_gate(0 , 1 ) == 1
assert nand_gate(1 , 0 ) == 1
assert nand_gate(1 , 1 ) == 0
if __name__ == "__main__":
print(nand_gate(0, 0))
print(nand_gate(0, 1))
print(nand_gate(1, 0))
print(nand_gate(1, 1))
| 108 | """simple docstring"""
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = False ) ->str:
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
a__: Optional[int] = F'Expected string as input, found {type(_SCREAMING_SNAKE_CASE )}'
raise ValueError(_SCREAMING_SNAKE_CASE )
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
a__: List[str] = F'Expected boolean as use_pascal parameter, found {type(_SCREAMING_SNAKE_CASE )}'
raise ValueError(_SCREAMING_SNAKE_CASE )
a__: int = input_str.split('_' )
a__: List[str] = 0 if use_pascal else 1
a__: List[str] = words[start_index:]
a__: List[str] = [word[0].upper() + word[1:] for word in words_to_capitalize]
a__: List[str] = '' if use_pascal else words[0]
return "".join([initial_word, *capitalized_words] )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 290 | 0 |
"""simple docstring"""
from __future__ import annotations
import requests
def _snake_case ( UpperCamelCase : str ):
UpperCAmelCase : Tuple = F"https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty"
return requests.get(UpperCamelCase ).json()
def _snake_case ( UpperCamelCase : int = 10 ):
UpperCAmelCase : Any = """https://hacker-news.firebaseio.com/v0/topstories.json?print=pretty"""
UpperCAmelCase : str = requests.get(UpperCamelCase ).json()[:max_stories]
return [get_hackernews_story(UpperCamelCase ) for story_id in story_ids]
def _snake_case ( UpperCamelCase : int = 10 ):
UpperCAmelCase : Optional[int] = hackernews_top_stories(UpperCamelCase )
return "\n".join("""* [{title}]({url})""".format(**UpperCamelCase ) for story in stories )
if __name__ == "__main__":
print(hackernews_top_stories_as_markdown())
| 109 | """simple docstring"""
class __snake_case :
def __init__( self , lowercase , lowercase=None , lowercase=None) -> List[str]:
'''simple docstring'''
a__: Dict = data
a__: List[Any] = previous
a__: Any = next_node
def __str__( self) -> str:
'''simple docstring'''
return f'{self.data}'
def lowerCamelCase_ ( self) -> int:
'''simple docstring'''
return self.data
def lowerCamelCase_ ( self) -> Union[str, Any]:
'''simple docstring'''
return self.next
def lowerCamelCase_ ( self) -> Optional[Any]:
'''simple docstring'''
return self.previous
class __snake_case :
def __init__( self , lowercase) -> Dict:
'''simple docstring'''
a__: List[Any] = head
def __iter__( self) -> List[Any]:
'''simple docstring'''
return self
def lowerCamelCase_ ( self) -> Optional[Any]:
'''simple docstring'''
if not self.current:
raise StopIteration
else:
a__: Dict = self.current.get_data()
a__: Optional[Any] = self.current.get_next()
return value
class __snake_case :
def __init__( self) -> Dict:
'''simple docstring'''
a__: List[Any] = None # First node in list
a__: Optional[int] = None # Last node in list
def __str__( self) -> Optional[Any]:
'''simple docstring'''
a__: Dict = self.head
a__: Optional[Any] = []
while current is not None:
nodes.append(current.get_data())
a__: str = current.get_next()
return " ".join(str(lowercase) for node in nodes)
def __contains__( self , lowercase) -> Optional[int]:
'''simple docstring'''
a__: Optional[int] = self.head
while current:
if current.get_data() == value:
return True
a__: Dict = current.get_next()
return False
def __iter__( self) -> int:
'''simple docstring'''
return LinkedListIterator(self.head)
def lowerCamelCase_ ( self) -> List[Any]:
'''simple docstring'''
if self.head:
return self.head.get_data()
return None
def lowerCamelCase_ ( self) -> Optional[Any]:
'''simple docstring'''
if self.tail:
return self.tail.get_data()
return None
def lowerCamelCase_ ( self , lowercase) -> None:
'''simple docstring'''
if self.head is None:
a__: Optional[Any] = node
a__: Optional[Any] = node
else:
self.insert_before_node(self.head , lowercase)
def lowerCamelCase_ ( self , lowercase) -> None:
'''simple docstring'''
if self.head is None:
self.set_head(lowercase)
else:
self.insert_after_node(self.tail , lowercase)
def lowerCamelCase_ ( self , lowercase) -> None:
'''simple docstring'''
a__: Tuple = Node(lowercase)
if self.head is None:
self.set_head(lowercase)
else:
self.set_tail(lowercase)
def lowerCamelCase_ ( self , lowercase , lowercase) -> None:
'''simple docstring'''
a__: Union[str, Any] = node
a__: Optional[Any] = node.previous
if node.get_previous() is None:
a__: Tuple = node_to_insert
else:
a__: int = node_to_insert
a__: Optional[int] = node_to_insert
def lowerCamelCase_ ( self , lowercase , lowercase) -> None:
'''simple docstring'''
a__: Optional[int] = node
a__: Tuple = node.next
if node.get_next() is None:
a__: Optional[int] = node_to_insert
else:
a__: Any = node_to_insert
a__: str = node_to_insert
def lowerCamelCase_ ( self , lowercase , lowercase) -> None:
'''simple docstring'''
a__: Any = 1
a__: Tuple = Node(lowercase)
a__: Tuple = self.head
while node:
if current_position == position:
self.insert_before_node(lowercase , lowercase)
return
current_position += 1
a__: List[Any] = node.next
self.insert_after_node(self.tail , lowercase)
def lowerCamelCase_ ( self , lowercase) -> Node:
'''simple docstring'''
a__: Tuple = self.head
while node:
if node.get_data() == item:
return node
a__: List[str] = node.get_next()
raise Exception('Node not found')
def lowerCamelCase_ ( self , lowercase) -> Any:
'''simple docstring'''
if (node := self.get_node(lowercase)) is not None:
if node == self.head:
a__: Any = self.head.get_next()
if node == self.tail:
a__: List[Any] = self.tail.get_previous()
self.remove_node_pointers(lowercase)
@staticmethod
def lowerCamelCase_ ( lowercase) -> None:
'''simple docstring'''
if node.get_next():
a__: Any = node.previous
if node.get_previous():
a__: List[str] = node.next
a__: int = None
a__: Union[str, Any] = None
def lowerCamelCase_ ( self) -> str:
'''simple docstring'''
return self.head is None
def __a ( ) ->None:
pass
if __name__ == "__main__":
import doctest
doctest.testmod()
| 290 | 0 |
"""simple docstring"""
_A : Optional[int] = {str(digit): digit**5 for digit in range(10)}
def __magic_name__ ( __snake_case : int ) -> int:
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(_SCREAMING_SNAKE_CASE ) )
def __magic_name__ ( ) -> int:
return sum(
number
for number in range(1000 , 100_0000 )
if number == digits_fifth_powers_sum(_SCREAMING_SNAKE_CASE ) )
if __name__ == "__main__":
print(solution())
| 202 | """simple docstring"""
import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable
import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization import from_bytes, to_bytes
from flax.training import train_state
from flax.training.common_utils import shard
from tqdm.auto import tqdm
from transformers import BigBirdConfig, FlaxBigBirdForQuestionAnswering
from transformers.models.big_bird.modeling_flax_big_bird import FlaxBigBirdForQuestionAnsweringModule
class __snake_case ( __lowerCAmelCase ):
a__ = 42
a__ = jnp.floataa
a__ = True
def lowerCamelCase_ ( self) -> int:
'''simple docstring'''
super().setup()
a__: int = nn.Dense(5 , dtype=self.dtype)
def __call__( self , *lowercase , **lowercase) -> Dict:
'''simple docstring'''
a__: Dict = super().__call__(*lowercase , **lowercase)
a__: str = self.cls(outputs[2])
return outputs[:2] + (cls_out,)
class __snake_case ( __lowerCAmelCase ):
a__ = FlaxBigBirdForNaturalQuestionsModule
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->Optional[Any]:
def cross_entropy(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None ):
a__: Any = logits.shape[-1]
a__: List[Any] = (labels[..., None] == jnp.arange(_SCREAMING_SNAKE_CASE )[None]).astype('f4' )
a__: List[str] = jax.nn.log_softmax(_SCREAMING_SNAKE_CASE , axis=-1 )
a__: Dict = -jnp.sum(labels * logits , axis=-1 )
if reduction is not None:
a__: str = reduction(_SCREAMING_SNAKE_CASE )
return loss
a__: Tuple = partial(_SCREAMING_SNAKE_CASE , reduction=jnp.mean )
a__: List[str] = cross_entropy(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
a__: Union[str, Any] = cross_entropy(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
a__: Any = cross_entropy(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
return (start_loss + end_loss + pooled_loss) / 3
@dataclass
class __snake_case :
a__ = "google/bigbird-roberta-base"
a__ = 3000
a__ = 1_0500
a__ = 128
a__ = 3
a__ = 1
a__ = 5
# tx_args
a__ = 3e-5
a__ = 0.0
a__ = 2_0000
a__ = 0.0095
a__ = "bigbird-roberta-natural-questions"
a__ = "training-expt"
a__ = "data/nq-training.jsonl"
a__ = "data/nq-validation.jsonl"
def lowerCamelCase_ ( self) -> Optional[Any]:
'''simple docstring'''
os.makedirs(self.base_dir , exist_ok=lowercase)
a__: str = os.path.join(self.base_dir , self.save_dir)
a__: List[str] = self.batch_size_per_device * jax.device_count()
@dataclass
class __snake_case :
a__ = 42
a__ = 4096 # no dynamic padding on TPUs
def __call__( self , lowercase) -> List[Any]:
'''simple docstring'''
a__: int = self.collate_fn(lowercase)
a__: Optional[int] = jax.tree_util.tree_map(lowercase , lowercase)
return batch
def lowerCamelCase_ ( self , lowercase) -> Dict:
'''simple docstring'''
a__ , a__: Dict = self.fetch_inputs(features['input_ids'])
a__: List[Any] = {
'input_ids': jnp.array(lowercase , dtype=jnp.intaa),
'attention_mask': jnp.array(lowercase , dtype=jnp.intaa),
'start_labels': jnp.array(features['start_token'] , dtype=jnp.intaa),
'end_labels': jnp.array(features['end_token'] , dtype=jnp.intaa),
'pooled_labels': jnp.array(features['category'] , dtype=jnp.intaa),
}
return batch
def lowerCamelCase_ ( self , lowercase) -> List[str]:
'''simple docstring'''
a__: List[Any] = [self._fetch_inputs(lowercase) for ids in input_ids]
return zip(*lowercase)
def lowerCamelCase_ ( self , lowercase) -> Dict:
'''simple docstring'''
a__: Union[str, Any] = [1 for _ in range(len(lowercase))]
while len(lowercase) < self.max_length:
input_ids.append(self.pad_id)
attention_mask.append(0)
return input_ids, attention_mask
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None ) ->List[Any]:
if seed is not None:
a__: int = dataset.shuffle(seed=_SCREAMING_SNAKE_CASE )
for i in range(len(_SCREAMING_SNAKE_CASE ) // batch_size ):
a__: Union[str, Any] = dataset[i * batch_size : (i + 1) * batch_size]
yield dict(_SCREAMING_SNAKE_CASE )
@partial(jax.pmap , axis_name='batch' )
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ) ->Any:
def loss_fn(_SCREAMING_SNAKE_CASE ):
a__: str = model_inputs.pop('start_labels' )
a__: Dict = model_inputs.pop('end_labels' )
a__: Optional[int] = model_inputs.pop('pooled_labels' )
a__: Optional[Any] = state.apply_fn(**_SCREAMING_SNAKE_CASE , params=_SCREAMING_SNAKE_CASE , dropout_rng=_SCREAMING_SNAKE_CASE , train=_SCREAMING_SNAKE_CASE )
a__ , a__ , a__: Optional[int] = outputs
return state.loss_fn(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , )
a__ , a__: Union[str, Any] = jax.random.split(_SCREAMING_SNAKE_CASE )
a__: List[Any] = jax.value_and_grad(_SCREAMING_SNAKE_CASE )
a__ , a__: str = grad_fn(state.params )
a__: Optional[int] = jax.lax.pmean({'loss': loss} , axis_name='batch' )
a__: int = jax.lax.pmean(_SCREAMING_SNAKE_CASE , 'batch' )
a__: Union[str, Any] = state.apply_gradients(grads=_SCREAMING_SNAKE_CASE )
return state, metrics, new_drp_rng
@partial(jax.pmap , axis_name='batch' )
def __a ( _SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ) ->Optional[Any]:
a__: Optional[int] = model_inputs.pop('start_labels' )
a__: int = model_inputs.pop('end_labels' )
a__: Dict = model_inputs.pop('pooled_labels' )
a__: Union[str, Any] = state.apply_fn(**_SCREAMING_SNAKE_CASE , params=state.params , train=_SCREAMING_SNAKE_CASE )
a__ , a__ , a__: int = outputs
a__: Optional[int] = state.loss_fn(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
a__: Tuple = jax.lax.pmean({'loss': loss} , axis_name='batch' )
return metrics
class __snake_case ( train_state.TrainState ):
a__ = struct.field(pytree_node=__lowerCAmelCase )
@dataclass
class __snake_case :
a__ = 42
a__ = 42
a__ = 42
a__ = 42
a__ = 42
a__ = 42
a__ = None
def lowerCamelCase_ ( self , lowercase , lowercase , lowercase , lowercase=None) -> Optional[int]:
'''simple docstring'''
a__: Dict = model.params
a__: Any = TrainState.create(
apply_fn=model.__call__ , params=lowercase , tx=lowercase , loss_fn=lowercase , )
if ckpt_dir is not None:
a__ , a__ , a__ , a__ , a__: Any = restore_checkpoint(lowercase , lowercase)
a__: Any = {
'lr': args.lr,
'init_lr': args.init_lr,
'warmup_steps': args.warmup_steps,
'num_train_steps': num_train_steps,
'weight_decay': args.weight_decay,
}
a__ , a__: str = build_tx(**lowercase)
a__: Optional[Any] = train_state.TrainState(
step=lowercase , apply_fn=model.__call__ , params=lowercase , tx=lowercase , opt_state=lowercase , )
a__: int = args
a__: Union[str, Any] = data_collator
a__: Any = lr
a__: Dict = params
a__: Tuple = jax_utils.replicate(lowercase)
return state
def lowerCamelCase_ ( self , lowercase , lowercase , lowercase) -> int:
'''simple docstring'''
a__: int = self.args
a__: str = len(lowercase) // args.batch_size
a__: Tuple = jax.random.PRNGKey(0)
a__: List[Any] = jax.random.split(lowercase , jax.device_count())
for epoch in range(args.max_epochs):
a__: str = jnp.array(0 , dtype=jnp.floataa)
a__: Tuple = get_batched_dataset(lowercase , args.batch_size , seed=lowercase)
a__: Optional[int] = 0
for batch in tqdm(lowercase , total=lowercase , desc=f'Running EPOCH-{epoch}'):
a__: List[str] = self.data_collator(lowercase)
a__ , a__ , a__: int = self.train_step_fn(lowercase , lowercase , **lowercase)
running_loss += jax_utils.unreplicate(metrics['loss'])
i += 1
if i % args.logging_steps == 0:
a__: List[Any] = jax_utils.unreplicate(state.step)
a__: Tuple = running_loss.item() / i
a__: Optional[Any] = self.scheduler_fn(state_step - 1)
a__: List[Any] = self.evaluate(lowercase , lowercase)
a__: List[str] = {
'step': state_step.item(),
'eval_loss': eval_loss.item(),
'tr_loss': tr_loss,
'lr': lr.item(),
}
tqdm.write(str(lowercase))
self.logger.log(lowercase , commit=lowercase)
if i % args.save_steps == 0:
self.save_checkpoint(args.save_dir + f'-e{epoch}-s{i}' , state=lowercase)
def lowerCamelCase_ ( self , lowercase , lowercase) -> List[Any]:
'''simple docstring'''
a__: Tuple = get_batched_dataset(lowercase , self.args.batch_size)
a__: Dict = len(lowercase) // self.args.batch_size
a__: Tuple = jnp.array(0 , dtype=jnp.floataa)
a__: List[Any] = 0
for batch in tqdm(lowercase , total=lowercase , desc='Evaluating ... '):
a__: str = self.data_collator(lowercase)
a__: List[str] = self.val_step_fn(lowercase , **lowercase)
running_loss += jax_utils.unreplicate(metrics['loss'])
i += 1
return running_loss / i
def lowerCamelCase_ ( self , lowercase , lowercase) -> Any:
'''simple docstring'''
a__: List[Any] = jax_utils.unreplicate(lowercase)
print(f'SAVING CHECKPOINT IN {save_dir}' , end=' ... ')
self.model_save_fn(lowercase , params=state.params)
with open(os.path.join(lowercase , 'opt_state.msgpack') , 'wb') as f:
f.write(to_bytes(state.opt_state))
joblib.dump(self.args , os.path.join(lowercase , 'args.joblib'))
joblib.dump(self.data_collator , os.path.join(lowercase , 'data_collator.joblib'))
with open(os.path.join(lowercase , 'training_state.json') , 'w') as f:
json.dump({'step': state.step.item()} , lowercase)
print('DONE')
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->Optional[Any]:
print(F'RESTORING CHECKPOINT FROM {save_dir}' , end=' ... ' )
with open(os.path.join(_SCREAMING_SNAKE_CASE , 'flax_model.msgpack' ) , 'rb' ) as f:
a__: int = from_bytes(state.params , f.read() )
with open(os.path.join(_SCREAMING_SNAKE_CASE , 'opt_state.msgpack' ) , 'rb' ) as f:
a__: Optional[Any] = from_bytes(state.opt_state , f.read() )
a__: Optional[Any] = joblib.load(os.path.join(_SCREAMING_SNAKE_CASE , 'args.joblib' ) )
a__: int = joblib.load(os.path.join(_SCREAMING_SNAKE_CASE , 'data_collator.joblib' ) )
with open(os.path.join(_SCREAMING_SNAKE_CASE , 'training_state.json' ) , 'r' ) as f:
a__: Any = json.load(_SCREAMING_SNAKE_CASE )
a__: Optional[Any] = training_state['step']
print('DONE' )
return params, opt_state, step, args, data_collator
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->Optional[int]:
a__: str = num_train_steps - warmup_steps
a__: str = optax.linear_schedule(init_value=_SCREAMING_SNAKE_CASE , end_value=_SCREAMING_SNAKE_CASE , transition_steps=_SCREAMING_SNAKE_CASE )
a__: List[Any] = optax.linear_schedule(init_value=_SCREAMING_SNAKE_CASE , end_value=1e-7 , transition_steps=_SCREAMING_SNAKE_CASE )
a__: int = optax.join_schedules(schedules=[warmup_fn, decay_fn] , boundaries=[warmup_steps] )
return lr
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->Tuple:
def weight_decay_mask(_SCREAMING_SNAKE_CASE ):
a__: List[Any] = traverse_util.flatten_dict(_SCREAMING_SNAKE_CASE )
a__: List[str] = {k: (v[-1] != 'bias' and v[-2:] != ('LayerNorm', 'scale')) for k, v in params.items()}
return traverse_util.unflatten_dict(_SCREAMING_SNAKE_CASE )
a__: List[str] = scheduler_fn(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
a__: Any = optax.adamw(learning_rate=_SCREAMING_SNAKE_CASE , weight_decay=_SCREAMING_SNAKE_CASE , mask=_SCREAMING_SNAKE_CASE )
return tx, lr
| 290 | 0 |
'''simple docstring'''
def UpperCAmelCase_ ( __lowercase : Tuple ) -> list:
'''simple docstring'''
if len(_SCREAMING_SNAKE_CASE ) <= 1:
return lst
_UpperCAmelCase = 1
while i < len(_SCREAMING_SNAKE_CASE ):
if lst[i - 1] <= lst[i]:
i += 1
else:
_UpperCAmelCase = lst[i], lst[i - 1]
i -= 1
if i == 0:
_UpperCAmelCase = 1
return lst
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE :Union[str, Any] = input('''Enter numbers separated by a comma:\n''').strip()
__SCREAMING_SNAKE_CASE :Optional[int] = [int(item) for item in user_input.split(''',''')]
print(gnome_sort(unsorted))
| 22 | """simple docstring"""
from typing import List, Optional, Tuple, Union
import PIL
import torch
from torchvision import transforms
from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor
lowercase__ = transforms.Compose(
[
transforms.Resize((256, 256)),
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5]),
]
)
def __a ( _SCREAMING_SNAKE_CASE ) ->Any:
if isinstance(_SCREAMING_SNAKE_CASE , torch.Tensor ):
return image
elif isinstance(_SCREAMING_SNAKE_CASE , PIL.Image.Image ):
a__: Optional[int] = [image]
a__: str = [trans(img.convert('RGB' ) ) for img in image]
a__: Any = torch.stack(_SCREAMING_SNAKE_CASE )
return image
class __snake_case ( __lowerCAmelCase ):
def __init__( self , lowercase , lowercase) -> Optional[int]:
'''simple docstring'''
super().__init__()
# make sure scheduler can always be converted to DDIM
a__: Dict = DDIMScheduler.from_config(scheduler.config)
self.register_modules(unet=lowercase , scheduler=lowercase)
def lowerCamelCase_ ( self , lowercase) -> int:
'''simple docstring'''
if strength < 0 or strength > 1:
raise ValueError(f'The value of strength should in [0.0, 1.0] but is {strength}')
def lowerCamelCase_ ( self , lowercase , lowercase , lowercase) -> Dict:
'''simple docstring'''
a__: int = min(int(num_inference_steps * strength) , lowercase)
a__: Any = max(num_inference_steps - init_timestep , 0)
a__: Union[str, Any] = self.scheduler.timesteps[t_start:]
return timesteps, num_inference_steps - t_start
def lowerCamelCase_ ( self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase=None) -> List[Any]:
'''simple docstring'''
if not isinstance(lowercase , (torch.Tensor, PIL.Image.Image, list)):
raise ValueError(
f'`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(lowercase)}')
a__: Tuple = image.to(device=lowercase , dtype=lowercase)
if isinstance(lowercase , lowercase) and len(lowercase) != batch_size:
raise ValueError(
f'You have passed a list of generators of length {len(lowercase)}, but requested an effective batch'
f' size of {batch_size}. Make sure the batch size matches the length of the generators.')
a__: List[str] = init_latents.shape
a__: List[Any] = randn_tensor(lowercase , generator=lowercase , device=lowercase , dtype=lowercase)
# get latents
print('add noise to latents at timestep' , lowercase)
a__: int = self.scheduler.add_noise(lowercase , lowercase , lowercase)
a__: Dict = init_latents
return latents
@torch.no_grad()
def __call__( self , lowercase = None , lowercase = 0.8 , lowercase = 1 , lowercase = None , lowercase = 0.0 , lowercase = 50 , lowercase = None , lowercase = "pil" , lowercase = True , ) -> Union[ImagePipelineOutput, Tuple]:
'''simple docstring'''
self.check_inputs(lowercase)
# 2. Preprocess image
a__: Tuple = preprocess(lowercase)
# 3. set timesteps
self.scheduler.set_timesteps(lowercase , device=self.device)
a__ , a__: Union[str, Any] = self.get_timesteps(lowercase , lowercase , self.device)
a__: Optional[int] = timesteps[:1].repeat(lowercase)
# 4. Prepare latent variables
a__: Union[str, Any] = self.prepare_latents(lowercase , lowercase , lowercase , self.unet.dtype , self.device , lowercase)
a__: Optional[Any] = latents
# 5. Denoising loop
for t in self.progress_bar(lowercase):
# 1. predict noise model_output
a__: Dict = self.unet(lowercase , lowercase).sample
# 2. predict previous mean of image x_t-1 and add variance depending on eta
# eta corresponds to η in paper and should be between [0, 1]
# do x_t -> x_t-1
a__: Optional[Any] = self.scheduler.step(
lowercase , lowercase , lowercase , eta=lowercase , use_clipped_model_output=lowercase , generator=lowercase , ).prev_sample
a__: Union[str, Any] = (image / 2 + 0.5).clamp(0 , 1)
a__: Optional[int] = image.cpu().permute(0 , 2 , 3 , 1).numpy()
if output_type == "pil":
a__: Dict = self.numpy_to_pil(lowercase)
if not return_dict:
return (image, latent_timestep.item())
return ImagePipelineOutput(images=lowercase)
| 290 | 0 |
'''simple docstring'''
from __future__ import annotations
import os
from collections.abc import Mapping
_lowerCamelCase : Optional[Any] = tuple[int, int]
class __UpperCAmelCase :
'''simple docstring'''
def __init__(self : Tuple , _lowerCAmelCase : Tuple , _lowerCAmelCase : Optional[int] ):
A = vertices
A = {
(min(_lowerCAmelCase ), max(_lowerCAmelCase )): weight for edge, weight in edges.items()
}
def A (self : Any , _lowerCAmelCase : Dict , _lowerCAmelCase : Union[str, Any] ):
self.vertices.add(edge[0] )
self.vertices.add(edge[1] )
A = weight
def A (self : Optional[int] ):
A = Graph({min(self.vertices )} , {} )
A = 42
A = 42
A = 42
A = 42
while len(subgraph.vertices ) < len(self.vertices ):
A = max(self.edges.values() ) + 1
for edge, weight in self.edges.items():
if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
if weight < min_weight:
A = edge
A = weight
subgraph.add_edge(_lowerCAmelCase , _lowerCAmelCase )
return subgraph
def __a ( UpperCAmelCase = "p107_network.txt" ) ->int:
"""simple docstring"""
A = os.path.abspath(os.path.dirname(_SCREAMING_SNAKE_CASE ) )
A = os.path.join(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
A = {}
A = 42
A = 42
A = 42
with open(_SCREAMING_SNAKE_CASE ) as f:
A = f.read().strip().split("""\n""" )
A = [line.split(""",""" ) for line in data]
for edgea in range(1 , len(_SCREAMING_SNAKE_CASE ) ):
for edgea in range(_SCREAMING_SNAKE_CASE ):
if adjaceny_matrix[edgea][edgea] != "-":
A = int(adjaceny_matrix[edgea][edgea] )
A = Graph(set(range(len(_SCREAMING_SNAKE_CASE ) ) ) , _SCREAMING_SNAKE_CASE )
A = graph.prims_algorithm()
A = sum(graph.edges.values() )
A = sum(subgraph.edges.values() )
return initial_total - optimal_total
if __name__ == "__main__":
print(f"{solution() = }")
| 258 | """simple docstring"""
import shutil
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_tf_cross_test,
require_tf,
require_torch,
require_torchvision,
require_vision,
)
from transformers.utils import is_tf_available, is_torch_available, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, SamImageProcessor, SamProcessor
if is_torch_available():
import torch
if is_tf_available():
import tensorflow as tf
@require_vision
@require_torchvision
class __snake_case ( unittest.TestCase ):
def lowerCamelCase_ ( self) -> Any:
'''simple docstring'''
a__: Optional[Any] = tempfile.mkdtemp()
a__: Optional[int] = SamImageProcessor()
a__: Tuple = SamProcessor(lowercase)
processor.save_pretrained(self.tmpdirname)
def lowerCamelCase_ ( self , **lowercase) -> List[Any]:
'''simple docstring'''
return AutoProcessor.from_pretrained(self.tmpdirname , **lowercase).image_processor
def lowerCamelCase_ ( self) -> Optional[int]:
'''simple docstring'''
shutil.rmtree(self.tmpdirname)
def lowerCamelCase_ ( self) -> int:
'''simple docstring'''
a__: Any = [np.random.randint(2_55 , size=(3, 30, 4_00) , dtype=np.uinta)]
a__: Optional[Any] = [Image.fromarray(np.moveaxis(lowercase , 0 , -1)) for x in image_inputs]
return image_inputs
def lowerCamelCase_ ( self) -> str:
'''simple docstring'''
a__: List[str] = SamProcessor(image_processor=self.get_image_processor())
processor.save_pretrained(self.tmpdirname)
a__: Optional[int] = self.get_image_processor(do_normalize=lowercase , padding_value=1.0)
a__: List[Any] = SamProcessor.from_pretrained(self.tmpdirname , do_normalize=lowercase , padding_value=1.0)
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string())
self.assertIsInstance(processor.image_processor , lowercase)
def lowerCamelCase_ ( self) -> Tuple:
'''simple docstring'''
a__: Union[str, Any] = self.get_image_processor()
a__: List[Any] = SamProcessor(image_processor=lowercase)
a__: Optional[int] = self.prepare_image_inputs()
a__: Optional[Any] = image_processor(lowercase , return_tensors='np')
a__: Tuple = processor(images=lowercase , return_tensors='np')
input_feat_extract.pop('original_sizes') # pop original_sizes as it is popped in the processor
input_feat_extract.pop('reshaped_input_sizes') # pop original_sizes as it is popped in the processor
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1e-2)
@require_torch
def lowerCamelCase_ ( self) -> Any:
'''simple docstring'''
a__: int = self.get_image_processor()
a__: List[str] = SamProcessor(image_processor=lowercase)
a__: Optional[Any] = [torch.ones((1, 3, 5, 5))]
a__: Union[str, Any] = [[17_64, 26_46]]
a__: Optional[Any] = [[6_83, 10_24]]
a__: int = processor.post_process_masks(lowercase , lowercase , lowercase)
self.assertEqual(masks[0].shape , (1, 3, 17_64, 26_46))
a__: Optional[int] = processor.post_process_masks(
lowercase , torch.tensor(lowercase) , torch.tensor(lowercase))
self.assertEqual(masks[0].shape , (1, 3, 17_64, 26_46))
# should also work with np
a__: Dict = [np.ones((1, 3, 5, 5))]
a__: Tuple = processor.post_process_masks(lowercase , np.array(lowercase) , np.array(lowercase))
self.assertEqual(masks[0].shape , (1, 3, 17_64, 26_46))
a__: Tuple = [[1, 0], [0, 1]]
with self.assertRaises(lowercase):
a__: List[Any] = processor.post_process_masks(lowercase , np.array(lowercase) , np.array(lowercase))
@require_vision
@require_tf
class __snake_case ( unittest.TestCase ):
def lowerCamelCase_ ( self) -> List[Any]:
'''simple docstring'''
a__: Optional[Any] = tempfile.mkdtemp()
a__: List[Any] = SamImageProcessor()
a__: Optional[int] = SamProcessor(lowercase)
processor.save_pretrained(self.tmpdirname)
def lowerCamelCase_ ( self , **lowercase) -> int:
'''simple docstring'''
return AutoProcessor.from_pretrained(self.tmpdirname , **lowercase).image_processor
def lowerCamelCase_ ( self) -> Tuple:
'''simple docstring'''
shutil.rmtree(self.tmpdirname)
def lowerCamelCase_ ( self) -> Tuple:
'''simple docstring'''
a__: Optional[Any] = [np.random.randint(2_55 , size=(3, 30, 4_00) , dtype=np.uinta)]
a__: Optional[int] = [Image.fromarray(np.moveaxis(lowercase , 0 , -1)) for x in image_inputs]
return image_inputs
def lowerCamelCase_ ( self) -> Any:
'''simple docstring'''
a__: List[str] = SamProcessor(image_processor=self.get_image_processor())
processor.save_pretrained(self.tmpdirname)
a__: Dict = self.get_image_processor(do_normalize=lowercase , padding_value=1.0)
a__: Union[str, Any] = SamProcessor.from_pretrained(self.tmpdirname , do_normalize=lowercase , padding_value=1.0)
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string())
self.assertIsInstance(processor.image_processor , lowercase)
def lowerCamelCase_ ( self) -> int:
'''simple docstring'''
a__: Optional[Any] = self.get_image_processor()
a__: str = SamProcessor(image_processor=lowercase)
a__: int = self.prepare_image_inputs()
a__: int = image_processor(lowercase , return_tensors='np')
a__: Dict = processor(images=lowercase , return_tensors='np')
input_feat_extract.pop('original_sizes') # pop original_sizes as it is popped in the processor
input_feat_extract.pop('reshaped_input_sizes') # pop reshaped_input_sizes as it is popped in the processor
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1e-2)
@require_tf
def lowerCamelCase_ ( self) -> Any:
'''simple docstring'''
a__: Tuple = self.get_image_processor()
a__: Any = SamProcessor(image_processor=lowercase)
a__: str = [tf.ones((1, 3, 5, 5))]
a__: List[Any] = [[17_64, 26_46]]
a__: List[Any] = [[6_83, 10_24]]
a__: List[Any] = processor.post_process_masks(lowercase , lowercase , lowercase , return_tensors='tf')
self.assertEqual(masks[0].shape , (1, 3, 17_64, 26_46))
a__: Tuple = processor.post_process_masks(
lowercase , tf.convert_to_tensor(lowercase) , tf.convert_to_tensor(lowercase) , return_tensors='tf' , )
self.assertEqual(masks[0].shape , (1, 3, 17_64, 26_46))
# should also work with np
a__: Optional[Any] = [np.ones((1, 3, 5, 5))]
a__: int = processor.post_process_masks(
lowercase , np.array(lowercase) , np.array(lowercase) , return_tensors='tf')
self.assertEqual(masks[0].shape , (1, 3, 17_64, 26_46))
a__: List[str] = [[1, 0], [0, 1]]
with self.assertRaises(tf.errors.InvalidArgumentError):
a__: Any = processor.post_process_masks(
lowercase , np.array(lowercase) , np.array(lowercase) , return_tensors='tf')
@require_vision
@require_torchvision
class __snake_case ( unittest.TestCase ):
def lowerCamelCase_ ( self) -> Dict:
'''simple docstring'''
a__: str = tempfile.mkdtemp()
a__: int = SamImageProcessor()
a__: Union[str, Any] = SamProcessor(lowercase)
processor.save_pretrained(self.tmpdirname)
def lowerCamelCase_ ( self , **lowercase) -> Optional[int]:
'''simple docstring'''
return AutoProcessor.from_pretrained(self.tmpdirname , **lowercase).image_processor
def lowerCamelCase_ ( self) -> Tuple:
'''simple docstring'''
shutil.rmtree(self.tmpdirname)
def lowerCamelCase_ ( self) -> Tuple:
'''simple docstring'''
a__: Any = [np.random.randint(2_55 , size=(3, 30, 4_00) , dtype=np.uinta)]
a__: Any = [Image.fromarray(np.moveaxis(lowercase , 0 , -1)) for x in image_inputs]
return image_inputs
@is_pt_tf_cross_test
def lowerCamelCase_ ( self) -> int:
'''simple docstring'''
a__: Optional[int] = self.get_image_processor()
a__: int = SamProcessor(image_processor=lowercase)
a__: int = np.random.randint(0 , 2 , size=(1, 3, 5, 5)).astype(np.floataa)
a__: Dict = [tf.convert_to_tensor(lowercase)]
a__: Union[str, Any] = [torch.tensor(lowercase)]
a__: List[Any] = [[17_64, 26_46]]
a__: Optional[Any] = [[6_83, 10_24]]
a__: Tuple = processor.post_process_masks(
lowercase , lowercase , lowercase , return_tensors='tf')
a__: str = processor.post_process_masks(
lowercase , lowercase , lowercase , return_tensors='pt')
self.assertTrue(np.all(tf_masks[0].numpy() == pt_masks[0].numpy()))
@is_pt_tf_cross_test
def lowerCamelCase_ ( self) -> str:
'''simple docstring'''
a__: Tuple = self.get_image_processor()
a__: Dict = SamProcessor(image_processor=lowercase)
a__: Any = self.prepare_image_inputs()
a__: List[Any] = image_processor(lowercase , return_tensors='pt')['pixel_values'].numpy()
a__: Tuple = processor(images=lowercase , return_tensors='pt')['pixel_values'].numpy()
a__: Any = image_processor(lowercase , return_tensors='tf')['pixel_values'].numpy()
a__: Any = processor(images=lowercase , return_tensors='tf')['pixel_values'].numpy()
self.assertTrue(np.allclose(lowercase , lowercase))
self.assertTrue(np.allclose(lowercase , lowercase))
self.assertTrue(np.allclose(lowercase , lowercase))
| 290 | 0 |
'''simple docstring'''
import warnings
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__SCREAMING_SNAKE_CASE : int = logging.get_logger(__name__)
class lowerCamelCase_ (__lowerCAmelCase ):
'''simple docstring'''
__UpperCamelCase: Tuple = ["input_ids", "attention_mask"]
def __init__( self : Optional[int] , A : Optional[Any]="</s>" , A : List[Any]="<unk>" , A : Optional[int]="<pad>" , A : Union[str, Any]=125 , A : List[str]=None , **A : str , ):
if extra_ids > 0 and additional_special_tokens is None:
_UpperCAmelCase : Optional[Any] = [F"""<extra_id_{i}>""" for i in range(A )]
elif extra_ids > 0 and additional_special_tokens is not None:
# Check that we have the right number of extra_id special tokens
_UpperCAmelCase : Optional[int] = len(set(filter(lambda A : bool("extra_id" in str(A ) ) , A ) ) )
if extra_tokens != extra_ids:
raise ValueError(
F"""Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"""
" provided to ByT5Tokenizer. In this case the additional_special_tokens must include the"
" extra_ids tokens" )
_UpperCAmelCase : Dict = AddedToken(A , lstrip=A , rstrip=A ) if isinstance(A , A ) else pad_token
_UpperCAmelCase : Optional[int] = AddedToken(A , lstrip=A , rstrip=A ) if isinstance(A , A ) else eos_token
_UpperCAmelCase : Dict = AddedToken(A , lstrip=A , rstrip=A ) if isinstance(A , A ) else unk_token
super().__init__(
eos_token=A , unk_token=A , pad_token=A , extra_ids=A , additional_special_tokens=A , **A , )
_UpperCAmelCase : Union[str, Any] = extra_ids
_UpperCAmelCase : str = 2**8 # utf is 8 bits
# define special tokens dict
_UpperCAmelCase : Dict[int, str] = {
self.pad_token: 0,
self.eos_token: 1,
self.unk_token: 2,
}
_UpperCAmelCase : Dict = len(self.special_tokens_encoder )
_UpperCAmelCase : List[Any] = len(A )
for i, token in enumerate(A ):
_UpperCAmelCase : Union[str, Any] = self.vocab_size + i - n
_UpperCAmelCase : Dict[str, int] = {v: k for k, v in self.special_tokens_encoder.items()}
@property
def _A ( self : Any ):
return self._utf_vocab_size + self._num_special_tokens + self._extra_ids
def _A ( self : int , A : Tuple , A : Optional[Any] = None , A : int = 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 )
# normal case: some special tokens
if token_ids_a is None:
return ([0] * len(A )) + [1]
return ([0] * len(A )) + [1] + ([0] * len(A )) + [1]
def _A ( self : Union[str, Any] , A : int ):
if len(A ) > 0 and token_ids[-1] == self.eos_token_id:
warnings.warn(
F"""This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated"""
" eos tokens being added." )
return token_ids
else:
return token_ids + [self.eos_token_id]
def _A ( self : Tuple , A : Any , A : int = None ):
_UpperCAmelCase : Any = [self.eos_token_id]
if token_ids_a is None:
return len(token_ids_a + eos ) * [0]
return len(token_ids_a + eos + token_ids_a + eos ) * [0]
def _A ( self : Optional[int] , A : List[Any] , A : Dict = None ):
_UpperCAmelCase : Dict = self._add_eos_if_not_present(A )
if token_ids_a is None:
return token_ids_a
else:
_UpperCAmelCase : Optional[Any] = self._add_eos_if_not_present(A )
return token_ids_a + token_ids_a
def _A ( self : Optional[Any] , A : str ):
_UpperCAmelCase : int = [chr(A ) for i in text.encode("utf-8" )]
return tokens
def _A ( self : Dict , A : Optional[Any] ):
if token in self.special_tokens_encoder:
_UpperCAmelCase : List[str] = self.special_tokens_encoder[token]
elif token in self.added_tokens_encoder:
_UpperCAmelCase : Union[str, Any] = self.added_tokens_encoder[token]
elif len(A ) != 1:
_UpperCAmelCase : List[Any] = self.unk_token_id
else:
_UpperCAmelCase : int = ord(A ) + self._num_special_tokens
return token_id
def _A ( self : Optional[int] , A : List[Any] ):
if index in self.special_tokens_decoder:
_UpperCAmelCase : Tuple = self.special_tokens_decoder[index]
else:
_UpperCAmelCase : Tuple = chr(index - self._num_special_tokens )
return token
def _A ( self : List[str] , A : Optional[int] ):
_UpperCAmelCase : Optional[Any] = B''
for token in tokens:
if token in self.special_tokens_decoder:
_UpperCAmelCase : Optional[Any] = self.special_tokens_decoder[token].encode("utf-8" )
elif token in self.added_tokens_decoder:
_UpperCAmelCase : List[Any] = self.special_tokens_decoder[token].encode("utf-8" )
elif token in self.special_tokens_encoder:
_UpperCAmelCase : Any = token.encode("utf-8" )
elif token in self.added_tokens_encoder:
_UpperCAmelCase : Union[str, Any] = token.encode("utf-8" )
else:
_UpperCAmelCase : Union[str, Any] = bytes([ord(A )] )
bstring += tok_string
_UpperCAmelCase : str = bstring.decode("utf-8" , errors="ignore" )
return string
def _A ( self : str , A : Optional[Any] , A : Union[str, Any] = None ):
return ()
| 31 | """simple docstring"""
from math import pow, sqrt
def __a ( *_SCREAMING_SNAKE_CASE ) ->bool:
a__: Union[str, Any] = len(_SCREAMING_SNAKE_CASE ) > 0 and all(value > 0.0 for value in values )
return result
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->float | ValueError:
return (
round(sqrt(molar_mass_a / molar_mass_a ) , 6 )
if validate(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
else ValueError('Input Error: Molar mass values must greater than 0.' )
)
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->float | ValueError:
return (
round(effusion_rate * sqrt(molar_mass_a / molar_mass_a ) , 6 )
if validate(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
else ValueError(
'Input Error: Molar mass and effusion rate values must greater than 0.' )
)
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->float | ValueError:
return (
round(effusion_rate / sqrt(molar_mass_a / molar_mass_a ) , 6 )
if validate(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
else ValueError(
'Input Error: Molar mass and effusion rate values must greater than 0.' )
)
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->float | ValueError:
return (
round(molar_mass / pow(effusion_rate_a / effusion_rate_a , 2 ) , 6 )
if validate(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
else ValueError(
'Input Error: Molar mass and effusion rate values must greater than 0.' )
)
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->float | ValueError:
return (
round(pow(effusion_rate_a / effusion_rate_a , 2 ) / molar_mass , 6 )
if validate(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
else ValueError(
'Input Error: Molar mass and effusion rate values must greater than 0.' )
)
| 290 | 0 |
import math_equivalence # From: git+https://github.com/hendrycks/math.git
import datasets
lowerCAmelCase__ :List[Any] = '''\\n@article{hendrycksmath2021,\n title={Measuring Mathematical Problem Solving With the MATH Dataset},\n author={Dan Hendrycks\n and Collin Burns\n and Saurav Kadavath\n and Akul Arora\n and Steven Basart\n and Eric Tang\n and Dawn Song\n and Jacob Steinhardt},\n journal={arXiv preprint arXiv:2103.03874},\n year={2021}\n}\n'''
lowerCAmelCase__ :Tuple = '''\\nThis metric is used to assess performance on the Mathematics Aptitude Test of Heuristics (MATH) dataset.\nIt first canonicalizes the inputs (e.g., converting "1/2" to "\\frac{1}{2}") and then computes accuracy.\n'''
lowerCAmelCase__ :str = R'''\nCalculates accuracy after canonicalizing inputs.\n\nArgs:\n predictions: list of predictions to score. Each prediction\n is a string that contains natural language and LaTex.\n references: list of reference for each prediction. Each\n reference is a string that contains natural language\n and LaTex.\nReturns:\n accuracy: accuracy after canonicalizing inputs\n (e.g., converting "1/2" to "\\frac{1}{2}")\n\nExamples:\n >>> metric = datasets.load_metric("competition_math")\n >>> results = metric.compute(references=["\\frac{1}{2}"], predictions=["1/2"])\n >>> print(results)\n {\'accuracy\': 1.0}\n'''
@datasets.utils.file_utils.add_end_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __a ( datasets.Metric ):
def UpperCAmelCase__ ( self ) -> Optional[Any]:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'predictions': datasets.Value('string' ),
'references': datasets.Value('string' ),
} ) , homepage='https://github.com/hendrycks/math' , codebase_urls=['https://github.com/hendrycks/math'] , )
def UpperCAmelCase__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase = 0.0
for i, j in zip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
n_correct += 1.0 if math_equivalence.is_equiv(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) else 0.0
_UpperCAmelCase = n_correct / len(_SCREAMING_SNAKE_CASE )
return {
"accuracy": accuracy,
}
| 329 | """simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
'andreasmadsen/efficient_mlm_m0.40': (
'https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json'
),
}
class __snake_case ( __lowerCAmelCase ):
a__ = """roberta-prelayernorm"""
def __init__( self , lowercase=5_02_65 , lowercase=7_68 , lowercase=12 , lowercase=12 , lowercase=30_72 , lowercase="gelu" , lowercase=0.1 , lowercase=0.1 , lowercase=5_12 , lowercase=2 , lowercase=0.02 , lowercase=1e-12 , lowercase=1 , lowercase=0 , lowercase=2 , lowercase="absolute" , lowercase=True , lowercase=None , **lowercase , ) -> Any:
'''simple docstring'''
super().__init__(pad_token_id=lowercase , bos_token_id=lowercase , eos_token_id=lowercase , **lowercase)
a__: Union[str, Any] = vocab_size
a__: str = hidden_size
a__: Tuple = num_hidden_layers
a__: List[str] = num_attention_heads
a__: Dict = hidden_act
a__: int = intermediate_size
a__: Tuple = hidden_dropout_prob
a__: str = attention_probs_dropout_prob
a__: Tuple = max_position_embeddings
a__: Tuple = type_vocab_size
a__: Optional[Any] = initializer_range
a__: Tuple = layer_norm_eps
a__: Optional[int] = position_embedding_type
a__: Any = use_cache
a__: Dict = classifier_dropout
class __snake_case ( __lowerCAmelCase ):
@property
def lowerCamelCase_ ( self) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
if self.task == "multiple-choice":
a__: str = {0: 'batch', 1: 'choice', 2: 'sequence'}
else:
a__: Union[str, Any] = {0: 'batch', 1: 'sequence'}
return OrderedDict(
[
('input_ids', dynamic_axis),
('attention_mask', dynamic_axis),
])
| 290 | 0 |
'''simple docstring'''
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class A_ ( __lowerCAmelCase , unittest.TestCase ):
'''simple docstring'''
UpperCAmelCase_ : str = CodeGenTokenizer
UpperCAmelCase_ : List[Any] = CodeGenTokenizerFast
UpperCAmelCase_ : Dict = True
UpperCAmelCase_ : Dict = {"""add_prefix_space""": True}
UpperCAmelCase_ : Optional[Any] = False
def UpperCAmelCase_ ( self : Union[str, Any] ) -> Any:
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
UpperCAmelCase : int = [
'l',
'o',
'w',
'e',
'r',
's',
't',
'i',
'd',
'n',
'\u0120',
'\u0120l',
'\u0120n',
'\u0120lo',
'\u0120low',
'er',
'\u0120lowest',
'\u0120newer',
'\u0120wider',
'<unk>',
'<|endoftext|>',
]
UpperCAmelCase : List[str] = dict(zip(lowercase_ , range(len(lowercase_ ) ) ) )
UpperCAmelCase : Optional[int] = ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
UpperCAmelCase : Tuple = {'unk_token': '<unk>'}
UpperCAmelCase : Optional[Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
UpperCAmelCase : Dict = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['merges_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as fp:
fp.write(json.dumps(lowercase_ ) + '\n' )
with open(self.merges_file , 'w' , encoding='utf-8' ) as fp:
fp.write('\n'.join(lowercase_ ) )
def UpperCAmelCase_ ( self : str , **lowercase_ : Optional[int] ) -> Optional[int]:
kwargs.update(self.special_tokens_map )
return CodeGenTokenizer.from_pretrained(self.tmpdirname , **lowercase_ )
def UpperCAmelCase_ ( self : Union[str, Any] , **lowercase_ : Dict ) -> Tuple:
kwargs.update(self.special_tokens_map )
return CodeGenTokenizerFast.from_pretrained(self.tmpdirname , **lowercase_ )
def UpperCAmelCase_ ( self : int , lowercase_ : Optional[int] ) -> Optional[int]:
UpperCAmelCase : List[str] = 'lower newer'
UpperCAmelCase : str = 'lower newer'
return input_text, output_text
def UpperCAmelCase_ ( self : Optional[Any] ) -> Union[str, Any]:
UpperCAmelCase : List[Any] = CodeGenTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
UpperCAmelCase : Union[str, Any] = 'lower newer'
UpperCAmelCase : Any = ['\u0120low', 'er', '\u0120', 'n', 'e', 'w', 'er']
UpperCAmelCase : Tuple = tokenizer.tokenize(lowercase_ , add_prefix_space=lowercase_ )
self.assertListEqual(lowercase_ , lowercase_ )
UpperCAmelCase : int = tokens + [tokenizer.unk_token]
UpperCAmelCase : Any = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase_ ) , lowercase_ )
def UpperCAmelCase_ ( self : Any ) -> Tuple:
if not self.test_rust_tokenizer:
return
UpperCAmelCase : Union[str, Any] = self.get_tokenizer()
UpperCAmelCase : int = self.get_rust_tokenizer(add_prefix_space=lowercase_ )
UpperCAmelCase : Any = 'lower newer'
# Testing tokenization
UpperCAmelCase : Any = tokenizer.tokenize(lowercase_ , add_prefix_space=lowercase_ )
UpperCAmelCase : Tuple = rust_tokenizer.tokenize(lowercase_ )
self.assertListEqual(lowercase_ , lowercase_ )
# Testing conversion to ids without special tokens
UpperCAmelCase : Optional[Any] = tokenizer.encode(lowercase_ , add_special_tokens=lowercase_ , add_prefix_space=lowercase_ )
UpperCAmelCase : Dict = rust_tokenizer.encode(lowercase_ , add_special_tokens=lowercase_ )
self.assertListEqual(lowercase_ , lowercase_ )
# Testing conversion to ids with special tokens
UpperCAmelCase : Dict = self.get_rust_tokenizer(add_prefix_space=lowercase_ )
UpperCAmelCase : Tuple = tokenizer.encode(lowercase_ , add_prefix_space=lowercase_ )
UpperCAmelCase : Tuple = rust_tokenizer.encode(lowercase_ )
self.assertListEqual(lowercase_ , lowercase_ )
# Testing the unknown token
UpperCAmelCase : Any = tokens + [rust_tokenizer.unk_token]
UpperCAmelCase : Any = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(lowercase_ ) , lowercase_ )
def UpperCAmelCase_ ( self : int , *lowercase_ : List[Any] , **lowercase_ : Union[str, Any] ) -> List[Any]:
pass
def UpperCAmelCase_ ( self : Optional[Any] , lowercase_ : List[Any]=15 ) -> int:
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
UpperCAmelCase : Optional[Any] = self.rust_tokenizer_class.from_pretrained(lowercase_ , **lowercase_ )
# Simple input
UpperCAmelCase : Any = 'This is a simple input'
UpperCAmelCase : str = ['This is a simple input 1', 'This is a simple input 2']
UpperCAmelCase : Any = ('This is a simple input', 'This is a pair')
UpperCAmelCase : int = [
('This is a simple input 1', 'This is a simple input 2'),
('This is a simple pair 1', 'This is a simple pair 2'),
]
# Simple input tests
self.assertRaises(lowercase_ , tokenizer_r.encode , lowercase_ , max_length=lowercase_ , padding='max_length' )
# Simple input
self.assertRaises(lowercase_ , tokenizer_r.encode_plus , lowercase_ , max_length=lowercase_ , padding='max_length' )
# Simple input
self.assertRaises(
lowercase_ , tokenizer_r.batch_encode_plus , lowercase_ , max_length=lowercase_ , padding='max_length' , )
# Pair input
self.assertRaises(lowercase_ , tokenizer_r.encode , lowercase_ , max_length=lowercase_ , padding='max_length' )
# Pair input
self.assertRaises(lowercase_ , tokenizer_r.encode_plus , lowercase_ , max_length=lowercase_ , padding='max_length' )
# Pair input
self.assertRaises(
lowercase_ , tokenizer_r.batch_encode_plus , lowercase_ , max_length=lowercase_ , padding='max_length' , )
def UpperCAmelCase_ ( self : Any ) -> str:
UpperCAmelCase : str = CodeGenTokenizer.from_pretrained(self.tmpdirname , pad_token='<pad>' )
# Simple input
UpperCAmelCase : str = 'This is a simple input'
UpperCAmelCase : List[Any] = ['This is a simple input looooooooong', 'This is a simple input']
UpperCAmelCase : int = ('This is a simple input', 'This is a pair')
UpperCAmelCase : str = [
('This is a simple input loooooong', 'This is a simple input'),
('This is a simple pair loooooong', 'This is a simple pair'),
]
UpperCAmelCase : List[Any] = tokenizer.pad_token_id
UpperCAmelCase : Optional[Any] = tokenizer(lowercase_ , padding='max_length' , max_length=30 , return_tensors='np' )
UpperCAmelCase : Union[str, Any] = tokenizer(lowercase_ , padding=lowercase_ , truncate=lowercase_ , return_tensors='np' )
UpperCAmelCase : Union[str, Any] = tokenizer(*lowercase_ , padding='max_length' , max_length=60 , return_tensors='np' )
UpperCAmelCase : Optional[Any] = tokenizer(lowercase_ , padding=lowercase_ , truncate=lowercase_ , return_tensors='np' )
# s
# test single string max_length padding
self.assertEqual(out_s['input_ids'].shape[-1] , 30 )
self.assertTrue(pad_token_id in out_s['input_ids'] )
self.assertTrue(0 in out_s['attention_mask'] )
# s2
# test automatic padding
self.assertEqual(out_sa['input_ids'].shape[-1] , 33 )
# long slice doesn't have padding
self.assertFalse(pad_token_id in out_sa['input_ids'][0] )
self.assertFalse(0 in out_sa['attention_mask'][0] )
# short slice does have padding
self.assertTrue(pad_token_id in out_sa['input_ids'][1] )
self.assertTrue(0 in out_sa['attention_mask'][1] )
# p
# test single pair max_length padding
self.assertEqual(out_p['input_ids'].shape[-1] , 60 )
self.assertTrue(pad_token_id in out_p['input_ids'] )
self.assertTrue(0 in out_p['attention_mask'] )
# p2
# test automatic padding pair
self.assertEqual(out_pa['input_ids'].shape[-1] , 52 )
# long slice pair doesn't have padding
self.assertFalse(pad_token_id in out_pa['input_ids'][0] )
self.assertFalse(0 in out_pa['attention_mask'][0] )
# short slice pair does have padding
self.assertTrue(pad_token_id in out_pa['input_ids'][1] )
self.assertTrue(0 in out_pa['attention_mask'][1] )
def UpperCAmelCase_ ( self : List[str] ) -> Tuple:
UpperCAmelCase : Any = '$$$'
UpperCAmelCase : Union[str, Any] = CodeGenTokenizer.from_pretrained(self.tmpdirname , bos_token=lowercase_ , add_bos_token=lowercase_ )
UpperCAmelCase : List[str] = 'This is a simple input'
UpperCAmelCase : List[Any] = ['This is a simple input 1', 'This is a simple input 2']
UpperCAmelCase : Tuple = tokenizer.bos_token_id
UpperCAmelCase : Optional[Any] = tokenizer(lowercase_ )
UpperCAmelCase : Dict = tokenizer(lowercase_ )
self.assertEqual(out_s.input_ids[0] , lowercase_ )
self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids ) )
UpperCAmelCase : str = tokenizer.decode(out_s.input_ids )
UpperCAmelCase : List[str] = tokenizer.batch_decode(out_sa.input_ids )
self.assertEqual(decode_s.split()[0] , lowercase_ )
self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa ) )
@slow
def UpperCAmelCase_ ( self : Union[str, Any] ) -> Union[str, Any]:
UpperCAmelCase : Optional[int] = CodeGenTokenizer.from_pretrained('Salesforce/codegen-350M-mono' )
UpperCAmelCase : int = '\nif len_a > len_b:\n result = a\nelse:\n result = b\n\n\n\n#'
UpperCAmelCase : Tuple = '\nif len_a > len_b: result = a\nelse: result = b'
UpperCAmelCase : Dict = tokenizer.encode(lowercase_ )
UpperCAmelCase : Union[str, Any] = ['^#', re.escape('<|endoftext|>' ), '^\'\'\'', '^"""', '\n\n\n']
UpperCAmelCase : List[str] = tokenizer.decode(lowercase_ , truncate_before_pattern=lowercase_ )
self.assertEqual(lowercase_ , lowercase_ )
def UpperCAmelCase_ ( self : str ) -> int:
pass
| 151 | """simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
'MIT/ast-finetuned-audioset-10-10-0.4593': (
'https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve/main/config.json'
),
}
class __snake_case ( __lowerCAmelCase ):
a__ = """audio-spectrogram-transformer"""
def __init__( self , lowercase=7_68 , lowercase=12 , lowercase=12 , lowercase=30_72 , lowercase="gelu" , lowercase=0.0 , lowercase=0.0 , lowercase=0.02 , lowercase=1e-12 , lowercase=16 , lowercase=True , lowercase=10 , lowercase=10 , lowercase=10_24 , lowercase=1_28 , **lowercase , ) -> str:
'''simple docstring'''
super().__init__(**lowercase)
a__: Any = hidden_size
a__: int = num_hidden_layers
a__: Union[str, Any] = num_attention_heads
a__: Any = intermediate_size
a__: Union[str, Any] = hidden_act
a__: int = hidden_dropout_prob
a__: str = attention_probs_dropout_prob
a__: str = initializer_range
a__: Tuple = layer_norm_eps
a__: Any = patch_size
a__: int = qkv_bias
a__: Optional[Any] = frequency_stride
a__: int = time_stride
a__: List[str] = max_length
a__: Tuple = num_mel_bins
| 290 | 0 |
from __future__ import annotations
from random import choice
def _a ( SCREAMING_SNAKE_CASE : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
return choice(_SCREAMING_SNAKE_CASE )
def _a ( SCREAMING_SNAKE_CASE : Optional[Any] , SCREAMING_SNAKE_CASE : Tuple ) -> int:
"""simple docstring"""
__lowerCAmelCase: List[str] = random_pivot(_SCREAMING_SNAKE_CASE )
# partition based on pivot
# linear time
__lowerCAmelCase: Optional[int] = [e for e in lst if e < pivot]
__lowerCAmelCase: int = [e for e in lst if e > pivot]
# if we get lucky, pivot might be the element we want.
# we can easily see this:
# small (elements smaller than k)
# + pivot (kth element)
# + big (elements larger than k)
if len(_SCREAMING_SNAKE_CASE ) == k - 1:
return pivot
# pivot is in elements bigger than k
elif len(_SCREAMING_SNAKE_CASE ) < k - 1:
return kth_number(_SCREAMING_SNAKE_CASE , k - len(_SCREAMING_SNAKE_CASE ) - 1 )
# pivot is in elements smaller than k
else:
return kth_number(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 322 | """simple docstring"""
import unittest
from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
lowercase__ = get_tests_dir('fixtures/test_sentencepiece.model')
lowercase__ = get_tests_dir('fixtures/test_sentencepiece_bpe.model')
lowercase__ = 'pt' if is_torch_available() else 'tf'
@require_sentencepiece
@require_tokenizers
class __snake_case ( __lowerCAmelCase , unittest.TestCase ):
a__ = CamembertTokenizer
a__ = CamembertTokenizerFast
a__ = True
a__ = True
def lowerCamelCase_ ( self) -> Union[str, Any]:
'''simple docstring'''
super().setUp()
# We have a SentencePiece fixture for testing
a__: Tuple = CamembertTokenizer(lowercase)
tokenizer.save_pretrained(self.tmpdirname)
def lowerCamelCase_ ( self) -> List[str]:
'''simple docstring'''
a__: Optional[Any] = '<pad>'
a__: List[Any] = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowercase) , lowercase)
self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowercase) , lowercase)
def lowerCamelCase_ ( self) -> Any:
'''simple docstring'''
a__: str = list(self.get_tokenizer().get_vocab().keys())
self.assertEqual(vocab_keys[0] , '<s>NOTUSED')
self.assertEqual(vocab_keys[1] , '<pad>')
self.assertEqual(vocab_keys[-1] , '<mask>')
self.assertEqual(len(lowercase) , 10_04)
def lowerCamelCase_ ( self) -> Any:
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size , 10_05)
def lowerCamelCase_ ( self) -> Optional[Any]:
'''simple docstring'''
a__: Optional[Any] = CamembertTokenizer(lowercase)
tokenizer.save_pretrained(self.tmpdirname)
a__: List[Any] = CamembertTokenizerFast.from_pretrained(self.tmpdirname)
a__: Dict = 'I was born in 92000, and this is falsé.'
a__: Optional[int] = tokenizer.encode(lowercase)
a__: Any = rust_tokenizer.encode(lowercase)
self.assertListEqual(lowercase , lowercase)
a__: Optional[Any] = tokenizer.encode(lowercase , add_special_tokens=lowercase)
a__: str = rust_tokenizer.encode(lowercase , add_special_tokens=lowercase)
self.assertListEqual(lowercase , lowercase)
# <unk> tokens are not the same for `rust` than for `slow`.
# Because spm gives back raw token instead of `unk` in EncodeAsPieces
# tokens = tokenizer.tokenize(sequence)
a__: Tuple = tokenizer.convert_ids_to_tokens(lowercase)
a__: Tuple = rust_tokenizer.tokenize(lowercase)
self.assertListEqual(lowercase , lowercase)
def lowerCamelCase_ ( self) -> Dict:
'''simple docstring'''
if not self.test_rust_tokenizer:
return
a__: Dict = self.get_tokenizer()
a__: str = self.get_rust_tokenizer()
a__: int = 'I was born in 92000, and this is falsé.'
a__: Optional[Any] = tokenizer.tokenize(lowercase)
a__: List[Any] = rust_tokenizer.tokenize(lowercase)
self.assertListEqual(lowercase , lowercase)
a__: str = tokenizer.encode(lowercase , add_special_tokens=lowercase)
a__: str = rust_tokenizer.encode(lowercase , add_special_tokens=lowercase)
self.assertListEqual(lowercase , lowercase)
a__: Tuple = self.get_rust_tokenizer()
a__: Union[str, Any] = tokenizer.encode(lowercase)
a__: List[Any] = rust_tokenizer.encode(lowercase)
self.assertListEqual(lowercase , lowercase)
@slow
def lowerCamelCase_ ( self) -> Optional[Any]:
'''simple docstring'''
a__: Union[str, Any] = {'input_ids': [[5, 54, 71_96, 2_97, 30, 23, 7_76, 18, 11, 32_15, 37_05, 82_52, 22, 31_64, 11_81, 21_16, 29, 16, 8_13, 25, 7_91, 33_14, 20, 34_46, 38, 2_75_75, 1_20, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 4_68, 17, 11, 90_88, 20, 15_17, 8, 2_28_04, 1_88_18, 10, 38, 6_29, 6_07, 6_07, 1_42, 19, 71_96, 8_67, 56, 1_03_26, 24, 22_67, 20, 4_16, 50_72, 1_56_12, 2_33, 7_34, 7, 23_99, 27, 16, 30_15, 16_49, 7, 24, 20, 43_38, 23_99, 27, 13, 34_00, 14, 13, 61_89, 8, 9_30, 9, 6]], '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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501
# fmt: on
# camembert is a french model. So we also use french texts.
a__: int = [
'Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '
'utilisé principalement dans le domaine du traitement automatique des langues (TAL).',
'À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '
'pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '
'telles que la traduction et la synthèse de texte.',
]
self.tokenizer_integration_test_util(
expected_encoding=lowercase , model_name='camembert-base' , revision='3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf' , sequences=lowercase , )
| 290 | 0 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
UniSpeechConfig,
UniSpeechForCTC,
UniSpeechForPreTraining,
WavaVecaFeatureExtractor,
WavaVecaPhonemeCTCTokenizer,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
lowercase__ : List[str] = logging.get_logger(__name__)
lowercase__ : str = {
'''post_extract_proj''': '''feature_projection.projection''',
'''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
'''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
'''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
'''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
'''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
'''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
'''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
'''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
'''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
'''encoder.layer_norm''': '''encoder.layer_norm''',
'''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
'''quantizer.weight_proj''': '''quantizer.weight_proj''',
'''quantizer.vars''': '''quantizer.codevectors''',
'''project_q''': '''project_q''',
'''final_proj''': '''project_hid''',
'''w2v_encoder.proj''': '''ctc_proj''',
'''mask_emb''': '''masked_spec_embed''',
}
lowercase__ : Tuple = [
'''ctc_proj''',
'''quantizer.weight_proj''',
'''quantizer.codevectors''',
'''project_q''',
'''project_hid''',
]
def __lowercase ( _a , _a , _a , _a , _a , _a ):
for attribute in key.split('''.''' ):
if is_finetuned:
if attribute in ["quantizer", "project_q", "project_hid"]:
# those layers are only relevant for pretraining and should be dropped
return
if attribute == "ctc_proj":
# we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
snake_case_ : str = 'lm_head'
snake_case_ : List[str] = getattr(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
if weight_type is not None:
snake_case_ : Tuple = getattr(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ).shape
else:
snake_case_ : Optional[Any] = hf_pointer.shape
assert hf_shape == value.shape, (
f"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"
f" {value.shape} for {full_name}"
)
if weight_type == "weight":
snake_case_ : Any = value
elif weight_type == "weight_g":
snake_case_ : Optional[int] = value
elif weight_type == "weight_v":
snake_case_ : str = value
elif weight_type == "bias":
snake_case_ : Optional[Any] = value
else:
snake_case_ : List[Any] = value
logger.info(f"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}." )
def __lowercase ( _a , _a , _a ):
snake_case_ : str = []
snake_case_ : Dict = fairseq_model.state_dict()
snake_case_ : Any = hf_model.unispeech.feature_extractor
for name, value in fairseq_dict.items():
snake_case_ : Tuple = False
if "conv_layers" in name:
load_conv_layer(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , hf_model.config.feat_extract_norm == '''group''' , )
snake_case_ : List[str] = True
else:
for key, mapped_key in MAPPING.items():
snake_case_ : Tuple = 'unispeech.' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split('''w2v_model.''' )[-1] == name.split('''.''' )[0]:
snake_case_ : Optional[Any] = True
if "*" in mapped_key:
snake_case_ : List[Any] = name.split(_SCREAMING_SNAKE_CASE )[0].split('''.''' )[-2]
snake_case_ : Dict = mapped_key.replace('''*''' , _SCREAMING_SNAKE_CASE )
if "weight_g" in name:
snake_case_ : Tuple = 'weight_g'
elif "weight_v" in name:
snake_case_ : str = 'weight_v'
elif "bias" in name:
snake_case_ : Optional[int] = 'bias'
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
snake_case_ : List[Any] = 'weight'
else:
snake_case_ : List[Any] = None
set_recursively(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
continue
if not is_used:
unused_weights.append(_SCREAMING_SNAKE_CASE )
logger.warning(f"Unused weights: {unused_weights}" )
def __lowercase ( _a , _a , _a , _a , _a ):
snake_case_ : str = full_name.split('''conv_layers.''' )[-1]
snake_case_ : List[Any] = name.split('''.''' )
snake_case_ : List[Any] = int(items[0] )
snake_case_ : Tuple = int(items[1] )
if type_id == 0:
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
f"{full_name} has size {value.shape}, but"
f" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."
)
snake_case_ : int = value
logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}." )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
f"{full_name} has size {value.shape}, but"
f" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."
)
snake_case_ : Dict = value
logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}." )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
f"{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"
" found."
)
snake_case_ : Optional[int] = value
logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}." )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
f"{full_name} has size {value.shape}, but"
f" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."
)
snake_case_ : Union[str, Any] = value
logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}." )
else:
unused_weights.append(_SCREAMING_SNAKE_CASE )
@torch.no_grad()
def __lowercase ( _a , _a , _a=None , _a=None , _a=True ):
if config_path is not None:
snake_case_ : Optional[int] = UniSpeechConfig.from_pretrained(_SCREAMING_SNAKE_CASE )
else:
snake_case_ : List[str] = UniSpeechConfig()
if is_finetuned:
if dict_path:
snake_case_ : Tuple = Dictionary.load_from_json(_SCREAMING_SNAKE_CASE )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
snake_case_ : List[str] = target_dict.pad_index
snake_case_ : Dict = target_dict.bos_index
snake_case_ : List[str] = target_dict.eos_index
snake_case_ : Any = len(target_dict.symbols )
snake_case_ : Optional[int] = os.path.join(_SCREAMING_SNAKE_CASE , '''vocab.json''' )
if not os.path.isdir(_SCREAMING_SNAKE_CASE ):
logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(_SCREAMING_SNAKE_CASE ) )
return
os.makedirs(_SCREAMING_SNAKE_CASE , exist_ok=_SCREAMING_SNAKE_CASE )
snake_case_ : Optional[int] = target_dict.indices
# fairseq has the <pad> and <s> switched
snake_case_ : List[Any] = 42
snake_case_ : Optional[Any] = 43
with open(_SCREAMING_SNAKE_CASE , '''w''' , encoding='''utf-8''' ) as vocab_handle:
json.dump(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
snake_case_ : Dict = WavaVecaPhonemeCTCTokenizer(
_SCREAMING_SNAKE_CASE , unk_token=target_dict.unk_word , pad_token=target_dict.pad_word , bos_token=target_dict.bos_word , eos_token=target_dict.eos_word , word_delimiter_token='''|''' , do_lower_case=_SCREAMING_SNAKE_CASE , )
snake_case_ : Union[str, Any] = True if config.feat_extract_norm == 'layer' else False
snake_case_ : str = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=16_000 , padding_value=0 , do_normalize=_SCREAMING_SNAKE_CASE , return_attention_mask=_SCREAMING_SNAKE_CASE , )
snake_case_ : List[Any] = WavaVecaProcessor(feature_extractor=_SCREAMING_SNAKE_CASE , tokenizer=_SCREAMING_SNAKE_CASE )
processor.save_pretrained(_SCREAMING_SNAKE_CASE )
snake_case_ : Dict = UniSpeechForCTC(_SCREAMING_SNAKE_CASE )
else:
snake_case_ : List[Any] = UniSpeechForPreTraining(_SCREAMING_SNAKE_CASE )
if is_finetuned:
snake_case_ : List[str] = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={'''data''': '''/'''.join(dict_path.split('''/''' )[:-1] ), '''w2v_path''': checkpoint_path} )
else:
snake_case_ : List[str] = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] )
snake_case_ : str = model[0].eval()
recursively_load_weights(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
hf_unispeech.save_pretrained(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
lowercase__ : Any = argparse.ArgumentParser()
parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
parser.add_argument(
'''--not_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
)
lowercase__ : Tuple = parser.parse_args()
convert_unispeech_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
)
| 264 | """simple docstring"""
def __a ( _SCREAMING_SNAKE_CASE = 1000000 ) ->int:
a__: int = limit + 1
a__: Optional[int] = [0] * limit
for first_term in range(1 , _SCREAMING_SNAKE_CASE ):
for n in range(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
a__: List[Any] = first_term + n / first_term
if common_difference % 4: # d must be divisble by 4
continue
else:
common_difference /= 4
if (
first_term > common_difference
and first_term < 4 * common_difference
): # since x,y,z are positive integers
frequency[n] += 1 # so z>0 and a>d ,also 4d<a
a__: Any = sum(1 for x in frequency[1:limit] if x == 10 )
return count
if __name__ == "__main__":
print(f"{solution() = }")
| 290 | 0 |
'''simple docstring'''
from __future__ import annotations
import copy
import tempfile
import unittest
from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
DUMMY_UNKNOWN_IDENTIFIER,
SMALL_MODEL_IDENTIFIER,
RequestCounter,
require_tensorflow_probability,
require_tf,
slow,
)
from ..bert.test_modeling_bert import BertModelTester
if is_tf_available():
from transformers import (
TFAutoModel,
TFAutoModelForCausalLM,
TFAutoModelForMaskedLM,
TFAutoModelForPreTraining,
TFAutoModelForQuestionAnswering,
TFAutoModelForSeqaSeqLM,
TFAutoModelForSequenceClassification,
TFAutoModelForTableQuestionAnswering,
TFAutoModelForTokenClassification,
TFAutoModelWithLMHead,
TFBertForMaskedLM,
TFBertForPreTraining,
TFBertForQuestionAnswering,
TFBertForSequenceClassification,
TFBertModel,
TFFunnelBaseModel,
TFFunnelModel,
TFGPTaLMHeadModel,
TFRobertaForMaskedLM,
TFTaForConditionalGeneration,
TFTapasForQuestionAnswering,
)
from transformers.models.auto.modeling_tf_auto import (
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_MASKED_LM_MAPPING,
TF_MODEL_FOR_PRETRAINING_MAPPING,
TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
TF_MODEL_MAPPING,
)
from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.tapas.modeling_tf_tapas import TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST
class snake_case ( __lowerCAmelCase ):
"""simple docstring"""
_lowerCamelCase = "new-model"
if is_tf_available():
class snake_case ( __lowerCAmelCase ):
"""simple docstring"""
_lowerCamelCase = NewModelConfig
@require_tf
class snake_case ( unittest.TestCase ):
"""simple docstring"""
@slow
def snake_case ( self ):
"""simple docstring"""
lowerCamelCase_ = 'bert-base-cased'
lowerCamelCase_ = AutoConfig.from_pretrained(UpperCamelCase )
self.assertIsNotNone(UpperCamelCase )
self.assertIsInstance(UpperCamelCase , UpperCamelCase )
lowerCamelCase_ = TFAutoModel.from_pretrained(UpperCamelCase )
self.assertIsNotNone(UpperCamelCase )
self.assertIsInstance(UpperCamelCase , UpperCamelCase )
@slow
def snake_case ( self ):
"""simple docstring"""
lowerCamelCase_ = 'bert-base-cased'
lowerCamelCase_ = AutoConfig.from_pretrained(UpperCamelCase )
self.assertIsNotNone(UpperCamelCase )
self.assertIsInstance(UpperCamelCase , UpperCamelCase )
lowerCamelCase_ = TFAutoModelForPreTraining.from_pretrained(UpperCamelCase )
self.assertIsNotNone(UpperCamelCase )
self.assertIsInstance(UpperCamelCase , UpperCamelCase )
@slow
def snake_case ( self ):
"""simple docstring"""
for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCamelCase_ = AutoConfig.from_pretrained(UpperCamelCase )
self.assertIsNotNone(UpperCamelCase )
self.assertIsInstance(UpperCamelCase , UpperCamelCase )
lowerCamelCase_ = TFAutoModelForCausalLM.from_pretrained(UpperCamelCase )
lowerCamelCase_ = TFAutoModelForCausalLM.from_pretrained(UpperCamelCase , output_loading_info=UpperCamelCase )
self.assertIsNotNone(UpperCamelCase )
self.assertIsInstance(UpperCamelCase , UpperCamelCase )
@slow
def snake_case ( self ):
"""simple docstring"""
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCamelCase_ = AutoConfig.from_pretrained(UpperCamelCase )
self.assertIsNotNone(UpperCamelCase )
self.assertIsInstance(UpperCamelCase , UpperCamelCase )
lowerCamelCase_ = TFAutoModelWithLMHead.from_pretrained(UpperCamelCase )
self.assertIsNotNone(UpperCamelCase )
self.assertIsInstance(UpperCamelCase , UpperCamelCase )
@slow
def snake_case ( self ):
"""simple docstring"""
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCamelCase_ = AutoConfig.from_pretrained(UpperCamelCase )
self.assertIsNotNone(UpperCamelCase )
self.assertIsInstance(UpperCamelCase , UpperCamelCase )
lowerCamelCase_ = TFAutoModelForMaskedLM.from_pretrained(UpperCamelCase )
lowerCamelCase_ = TFAutoModelForMaskedLM.from_pretrained(UpperCamelCase , output_loading_info=UpperCamelCase )
self.assertIsNotNone(UpperCamelCase )
self.assertIsInstance(UpperCamelCase , UpperCamelCase )
@slow
def snake_case ( self ):
"""simple docstring"""
for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCamelCase_ = AutoConfig.from_pretrained(UpperCamelCase )
self.assertIsNotNone(UpperCamelCase )
self.assertIsInstance(UpperCamelCase , UpperCamelCase )
lowerCamelCase_ = TFAutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase )
lowerCamelCase_ = TFAutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase , output_loading_info=UpperCamelCase )
self.assertIsNotNone(UpperCamelCase )
self.assertIsInstance(UpperCamelCase , UpperCamelCase )
@slow
def snake_case ( self ):
"""simple docstring"""
for model_name in ["bert-base-uncased"]:
lowerCamelCase_ = AutoConfig.from_pretrained(UpperCamelCase )
self.assertIsNotNone(UpperCamelCase )
self.assertIsInstance(UpperCamelCase , UpperCamelCase )
lowerCamelCase_ = TFAutoModelForSequenceClassification.from_pretrained(UpperCamelCase )
self.assertIsNotNone(UpperCamelCase )
self.assertIsInstance(UpperCamelCase , UpperCamelCase )
@slow
def snake_case ( self ):
"""simple docstring"""
for model_name in ["bert-base-uncased"]:
lowerCamelCase_ = AutoConfig.from_pretrained(UpperCamelCase )
self.assertIsNotNone(UpperCamelCase )
self.assertIsInstance(UpperCamelCase , UpperCamelCase )
lowerCamelCase_ = TFAutoModelForQuestionAnswering.from_pretrained(UpperCamelCase )
self.assertIsNotNone(UpperCamelCase )
self.assertIsInstance(UpperCamelCase , UpperCamelCase )
@slow
@require_tensorflow_probability
def snake_case ( self ):
"""simple docstring"""
for model_name in TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST[5:6]:
lowerCamelCase_ = AutoConfig.from_pretrained(UpperCamelCase )
self.assertIsNotNone(UpperCamelCase )
self.assertIsInstance(UpperCamelCase , UpperCamelCase )
lowerCamelCase_ = TFAutoModelForTableQuestionAnswering.from_pretrained(UpperCamelCase )
lowerCamelCase_ = TFAutoModelForTableQuestionAnswering.from_pretrained(
UpperCamelCase , output_loading_info=UpperCamelCase )
self.assertIsNotNone(UpperCamelCase )
self.assertIsInstance(UpperCamelCase , UpperCamelCase )
def snake_case ( self ):
"""simple docstring"""
lowerCamelCase_ = TFAutoModelWithLMHead.from_pretrained(UpperCamelCase )
self.assertIsInstance(UpperCamelCase , UpperCamelCase )
self.assertEqual(model.num_parameters() , 1_4410 )
self.assertEqual(model.num_parameters(only_trainable=UpperCamelCase ) , 1_4410 )
def snake_case ( self ):
"""simple docstring"""
lowerCamelCase_ = TFAutoModelWithLMHead.from_pretrained(UpperCamelCase )
self.assertIsInstance(UpperCamelCase , UpperCamelCase )
self.assertEqual(model.num_parameters() , 1_4410 )
self.assertEqual(model.num_parameters(only_trainable=UpperCamelCase ) , 1_4410 )
def snake_case ( self ):
"""simple docstring"""
lowerCamelCase_ = TFAutoModel.from_pretrained("sgugger/funnel-random-tiny" )
self.assertIsInstance(UpperCamelCase , UpperCamelCase )
lowerCamelCase_ = copy.deepcopy(model.config )
lowerCamelCase_ = ['FunnelBaseModel']
lowerCamelCase_ = TFAutoModel.from_config(UpperCamelCase )
self.assertIsInstance(UpperCamelCase , UpperCamelCase )
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(UpperCamelCase )
lowerCamelCase_ = TFAutoModel.from_pretrained(UpperCamelCase )
self.assertIsInstance(UpperCamelCase , UpperCamelCase )
def snake_case ( self ):
"""simple docstring"""
try:
AutoConfig.register("new-model" , UpperCamelCase )
lowerCamelCase_ = [
TFAutoModel,
TFAutoModelForCausalLM,
TFAutoModelForMaskedLM,
TFAutoModelForPreTraining,
TFAutoModelForQuestionAnswering,
TFAutoModelForSequenceClassification,
TFAutoModelForTokenClassification,
]
for auto_class in auto_classes:
with self.subTest(auto_class.__name__ ):
# Wrong config class will raise an error
with self.assertRaises(UpperCamelCase ):
auto_class.register(UpperCamelCase , UpperCamelCase )
auto_class.register(UpperCamelCase , UpperCamelCase )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(UpperCamelCase ):
auto_class.register(UpperCamelCase , UpperCamelCase )
# Now that the config is registered, it can be used as any other config with the auto-API
lowerCamelCase_ = BertModelTester(self ).get_config()
lowerCamelCase_ = NewModelConfig(**tiny_config.to_dict() )
lowerCamelCase_ = auto_class.from_config(UpperCamelCase )
self.assertIsInstance(UpperCamelCase , UpperCamelCase )
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(UpperCamelCase )
lowerCamelCase_ = auto_class.from_pretrained(UpperCamelCase )
self.assertIsInstance(UpperCamelCase , UpperCamelCase )
finally:
if "new-model" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["new-model"]
for mapping in (
TF_MODEL_MAPPING,
TF_MODEL_FOR_PRETRAINING_MAPPING,
TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_MASKED_LM_MAPPING,
):
if NewModelConfig in mapping._extra_content:
del mapping._extra_content[NewModelConfig]
def snake_case ( self ):
"""simple docstring"""
with self.assertRaisesRegex(
UpperCamelCase , "bert-base is not a local folder and is not a valid model identifier" ):
lowerCamelCase_ = TFAutoModel.from_pretrained("bert-base" )
def snake_case ( self ):
"""simple docstring"""
with self.assertRaisesRegex(
UpperCamelCase , r"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)" ):
lowerCamelCase_ = TFAutoModel.from_pretrained(UpperCamelCase , revision="aaaaaa" )
def snake_case ( self ):
"""simple docstring"""
with self.assertRaisesRegex(
UpperCamelCase , "hf-internal-testing/config-no-model does not appear to have a file named pytorch_model.bin" , ):
lowerCamelCase_ = TFAutoModel.from_pretrained("hf-internal-testing/config-no-model" )
def snake_case ( self ):
"""simple docstring"""
with self.assertRaisesRegex(UpperCamelCase , "Use `from_pt=True` to load this model" ):
lowerCamelCase_ = TFAutoModel.from_pretrained("hf-internal-testing/tiny-bert-pt-only" )
def snake_case ( self ):
"""simple docstring"""
lowerCamelCase_ = TFAutoModel.from_pretrained("hf-internal-testing/tiny-random-bert" )
with RequestCounter() as counter:
lowerCamelCase_ = TFAutoModel.from_pretrained("hf-internal-testing/tiny-random-bert" )
self.assertEqual(counter.get_request_count , 0 )
self.assertEqual(counter.head_request_count , 1 )
self.assertEqual(counter.other_request_count , 0 )
# With a sharded checkpoint
lowerCamelCase_ = TFAutoModel.from_pretrained("ArthurZ/tiny-random-bert-sharded" )
with RequestCounter() as counter:
lowerCamelCase_ = TFAutoModel.from_pretrained("ArthurZ/tiny-random-bert-sharded" )
self.assertEqual(counter.get_request_count , 0 )
self.assertEqual(counter.head_request_count , 1 )
self.assertEqual(counter.other_request_count , 0 )
| 55 | """simple docstring"""
import os
from typing import Dict, List, Tuple, TypeVar, Union
lowercase__ = TypeVar('T')
lowercase__ = Union[List[T], Tuple[T, ...]]
lowercase__ = Union[T, List[T], Dict[str, T]]
lowercase__ = Union[str, bytes, os.PathLike]
| 290 | 0 |
'''simple docstring'''
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
a__ : Any = logging.get_logger(__name__)
a__ : Optional[int] = {
'post_extract_proj': 'feature_projection.projection',
'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',
'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',
'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
'self_attn_layer_norm': 'encoder.layers.*.layer_norm',
'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',
'fc2': 'encoder.layers.*.feed_forward.output_dense',
'final_layer_norm': 'encoder.layers.*.final_layer_norm',
'encoder.layer_norm': 'encoder.layer_norm',
'encoder.layer_norm_for_extract': 'layer_norm_for_extract',
'w2v_model.layer_norm': 'feature_projection.layer_norm',
'quantizer.weight_proj': 'quantizer.weight_proj',
'quantizer.vars': 'quantizer.codevectors',
'project_q': 'project_q',
'final_proj': 'project_hid',
'w2v_encoder.proj': 'lm_head',
'label_embs_concat': 'label_embeddings_concat',
'mask_emb': 'masked_spec_embed',
'spk_proj': 'speaker_proj',
}
a__ : int = [
'lm_head',
'quantizer.weight_proj',
'quantizer.codevectors',
'project_q',
'project_hid',
'label_embeddings_concat',
'speaker_proj',
'layer_norm_for_extract',
]
def _lowercase ( __A ,__A ,__A ,__A ,__A ):
'''simple docstring'''
for attribute in key.split(""".""" ):
__UpperCamelCase = getattr(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE )
if weight_type is not None:
__UpperCamelCase = getattr(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ).shape
else:
__UpperCamelCase = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
f"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"
f" {value.shape} for {full_name}" )
if weight_type == "weight":
__UpperCamelCase = value
elif weight_type == "weight_g":
__UpperCamelCase = value
elif weight_type == "weight_v":
__UpperCamelCase = value
elif weight_type == "bias":
__UpperCamelCase = value
else:
__UpperCamelCase = value
logger.info(f"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}." )
def _lowercase ( __A ,__A ):
'''simple docstring'''
__UpperCamelCase = []
__UpperCamelCase = fairseq_model.state_dict()
__UpperCamelCase = hf_model.unispeech_sat.feature_extractor
for name, value in fairseq_dict.items():
__UpperCamelCase = False
if "conv_layers" in name:
load_conv_layer(
_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,hf_model.config.feat_extract_norm == """group""" ,)
__UpperCamelCase = True
else:
for key, mapped_key in MAPPING.items():
__UpperCamelCase = 'unispeech_sat.' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split("""w2v_model.""" )[-1] == name.split(""".""" )[0]:
if "layer_norm_for_extract" in name and (".".join(name.split(""".""" )[:-1] ) != key):
# special case since naming is very similar
continue
__UpperCamelCase = True
if "*" in mapped_key:
__UpperCamelCase = name.split(_SCREAMING_SNAKE_CASE )[0].split(""".""" )[-2]
__UpperCamelCase = mapped_key.replace("""*""" ,_SCREAMING_SNAKE_CASE )
if "weight_g" in name:
__UpperCamelCase = 'weight_g'
elif "weight_v" in name:
__UpperCamelCase = 'weight_v'
elif "bias" in name:
__UpperCamelCase = 'bias'
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
__UpperCamelCase = 'weight'
else:
__UpperCamelCase = None
set_recursively(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE )
continue
if not is_used:
unused_weights.append(_SCREAMING_SNAKE_CASE )
logger.warning(f"Unused weights: {unused_weights}" )
def _lowercase ( __A ,__A ,__A ,__A ,__A ):
'''simple docstring'''
__UpperCamelCase = full_name.split("""conv_layers.""" )[-1]
__UpperCamelCase = name.split(""".""" )
__UpperCamelCase = int(items[0] )
__UpperCamelCase = int(items[1] )
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
f"{full_name} has size {value.shape}, but"
f" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found." )
__UpperCamelCase = value
logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}." )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
f"{full_name} has size {value.shape}, but"
f" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found." )
__UpperCamelCase = value
logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}." )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
f"{full_name} has size {value.shape}, but"
f" {feature_extractor[layer_id].layer_norm.bias.data.shape} was found." )
__UpperCamelCase = value
logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}." )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
f"{full_name} has size {value.shape}, but"
f" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found." )
__UpperCamelCase = value
logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}." )
else:
unused_weights.append(_SCREAMING_SNAKE_CASE )
@torch.no_grad()
def _lowercase ( __A ,__A ,__A=None ,__A=None ,__A=True ):
'''simple docstring'''
if config_path is not None:
__UpperCamelCase = UniSpeechSatConfig.from_pretrained(_SCREAMING_SNAKE_CASE )
else:
__UpperCamelCase = UniSpeechSatConfig()
__UpperCamelCase = ''
if is_finetuned:
__UpperCamelCase = UniSpeechSatForCTC(_SCREAMING_SNAKE_CASE )
else:
__UpperCamelCase = UniSpeechSatForPreTraining(_SCREAMING_SNAKE_CASE )
__UpperCamelCase = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] ,arg_overrides={"""data""": """/""".join(dict_path.split("""/""" )[:-1] )} )
__UpperCamelCase = model[0].eval()
recursively_load_weights(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE )
hf_wavavec.save_pretrained(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
a__ : str = argparse.ArgumentParser()
parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
parser.add_argument(
'--not_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
)
a__ : List[Any] = parser.parse_args()
convert_unispeech_sat_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
)
| 349 | """simple docstring"""
from math import pi, sqrt, tan
def __a ( _SCREAMING_SNAKE_CASE ) ->float:
if side_length < 0:
raise ValueError('surface_area_cube() only accepts non-negative values' )
return 6 * side_length**2
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->float:
if length < 0 or breadth < 0 or height < 0:
raise ValueError('surface_area_cuboid() only accepts non-negative values' )
return 2 * ((length * breadth) + (breadth * height) + (length * height))
def __a ( _SCREAMING_SNAKE_CASE ) ->float:
if radius < 0:
raise ValueError('surface_area_sphere() only accepts non-negative values' )
return 4 * pi * radius**2
def __a ( _SCREAMING_SNAKE_CASE ) ->float:
if radius < 0:
raise ValueError('surface_area_hemisphere() only accepts non-negative values' )
return 3 * pi * radius**2
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->float:
if radius < 0 or height < 0:
raise ValueError('surface_area_cone() only accepts non-negative values' )
return pi * radius * (radius + (height**2 + radius**2) ** 0.5)
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->float:
if radius_a < 0 or radius_a < 0 or height < 0:
raise ValueError(
'surface_area_conical_frustum() only accepts non-negative values' )
a__: List[Any] = (height**2 + (radius_a - radius_a) ** 2) ** 0.5
return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->float:
if radius < 0 or height < 0:
raise ValueError('surface_area_cylinder() only accepts non-negative values' )
return 2 * pi * radius * (height + radius)
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->float:
if torus_radius < 0 or tube_radius < 0:
raise ValueError('surface_area_torus() only accepts non-negative values' )
if torus_radius < tube_radius:
raise ValueError(
'surface_area_torus() does not support spindle or self intersecting tori' )
return 4 * pow(_SCREAMING_SNAKE_CASE , 2 ) * torus_radius * tube_radius
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->float:
if length < 0 or width < 0:
raise ValueError('area_rectangle() only accepts non-negative values' )
return length * width
def __a ( _SCREAMING_SNAKE_CASE ) ->float:
if side_length < 0:
raise ValueError('area_square() only accepts non-negative values' )
return side_length**2
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->float:
if base < 0 or height < 0:
raise ValueError('area_triangle() only accepts non-negative values' )
return (base * height) / 2
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->float:
if sidea < 0 or sidea < 0 or sidea < 0:
raise ValueError('area_triangle_three_sides() only accepts non-negative values' )
elif sidea + sidea < sidea or sidea + sidea < sidea or sidea + sidea < sidea:
raise ValueError('Given three sides do not form a triangle' )
a__: int = (sidea + sidea + sidea) / 2
a__: Tuple = sqrt(
semi_perimeter
* (semi_perimeter - sidea)
* (semi_perimeter - sidea)
* (semi_perimeter - sidea) )
return area
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->float:
if base < 0 or height < 0:
raise ValueError('area_parallelogram() only accepts non-negative values' )
return base * height
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->float:
if basea < 0 or basea < 0 or height < 0:
raise ValueError('area_trapezium() only accepts non-negative values' )
return 1 / 2 * (basea + basea) * height
def __a ( _SCREAMING_SNAKE_CASE ) ->float:
if radius < 0:
raise ValueError('area_circle() only accepts non-negative values' )
return pi * radius**2
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->float:
if radius_x < 0 or radius_y < 0:
raise ValueError('area_ellipse() only accepts non-negative values' )
return pi * radius_x * radius_y
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->float:
if diagonal_a < 0 or diagonal_a < 0:
raise ValueError('area_rhombus() only accepts non-negative values' )
return 1 / 2 * diagonal_a * diagonal_a
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->float:
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) or sides < 3:
raise ValueError(
'area_reg_polygon() only accepts integers greater than or \
equal to three as number of sides' )
elif length < 0:
raise ValueError(
'area_reg_polygon() only accepts non-negative values as \
length of a side' )
return (sides * length**2) / (4 * tan(pi / sides ))
return (sides * length**2) / (4 * tan(pi / sides ))
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True) # verbose so we can see methods missing tests
print('[DEMO] Areas of various geometric shapes: \n')
print(f"Rectangle: {area_rectangle(10, 20) = }")
print(f"Square: {area_square(10) = }")
print(f"Triangle: {area_triangle(10, 10) = }")
print(f"Triangle: {area_triangle_three_sides(5, 12, 13) = }")
print(f"Parallelogram: {area_parallelogram(10, 20) = }")
print(f"Rhombus: {area_rhombus(10, 20) = }")
print(f"Trapezium: {area_trapezium(10, 20, 30) = }")
print(f"Circle: {area_circle(20) = }")
print(f"Ellipse: {area_ellipse(10, 20) = }")
print('\nSurface Areas of various geometric shapes: \n')
print(f"Cube: {surface_area_cube(20) = }")
print(f"Cuboid: {surface_area_cuboid(10, 20, 30) = }")
print(f"Sphere: {surface_area_sphere(20) = }")
print(f"Hemisphere: {surface_area_hemisphere(20) = }")
print(f"Cone: {surface_area_cone(10, 20) = }")
print(f"Conical Frustum: {surface_area_conical_frustum(10, 20, 30) = }")
print(f"Cylinder: {surface_area_cylinder(10, 20) = }")
print(f"Torus: {surface_area_torus(20, 10) = }")
print(f"Equilateral Triangle: {area_reg_polygon(3, 10) = }")
print(f"Square: {area_reg_polygon(4, 10) = }")
print(f"Reqular Pentagon: {area_reg_polygon(5, 10) = }")
| 290 | 0 |
'''simple docstring'''
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def _A ( lowercase__ , lowercase__ ):
lowercase__ = f'''{sampling_rate}'''
lowercase__ = '1'
lowercase__ = 'f32le'
lowercase__ = [
'ffmpeg',
'-i',
'pipe:0',
'-ac',
ac,
'-ar',
ar,
'-f',
format_for_conversion,
'-hide_banner',
'-loglevel',
'quiet',
'pipe:1',
]
try:
with subprocess.Popen(_SCREAMING_SNAKE_CASE , stdin=subprocess.PIPE , stdout=subprocess.PIPE ) as ffmpeg_process:
lowercase__ = ffmpeg_process.communicate(_SCREAMING_SNAKE_CASE )
except FileNotFoundError as error:
raise ValueError("""ffmpeg was not found but is required to load audio files from filename""" ) from error
lowercase__ = output_stream[0]
lowercase__ = np.frombuffer(_SCREAMING_SNAKE_CASE , np.floataa )
if audio.shape[0] == 0:
raise ValueError("""Malformed soundfile""" )
return audio
def _A ( lowercase__ , lowercase__ , lowercase__ = "f32le" , ):
lowercase__ = f'''{sampling_rate}'''
lowercase__ = '1'
if format_for_conversion == "s16le":
lowercase__ = 2
elif format_for_conversion == "f32le":
lowercase__ = 4
else:
raise ValueError(f'''Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`''' )
lowercase__ = platform.system()
if system == "Linux":
lowercase__ = 'alsa'
lowercase__ = 'default'
elif system == "Darwin":
lowercase__ = 'avfoundation'
lowercase__ = ':0'
elif system == "Windows":
lowercase__ = 'dshow'
lowercase__ = 'default'
lowercase__ = [
'ffmpeg',
'-f',
format_,
'-i',
input_,
'-ac',
ac,
'-ar',
ar,
'-f',
format_for_conversion,
'-fflags',
'nobuffer',
'-hide_banner',
'-loglevel',
'quiet',
'pipe:1',
]
lowercase__ = int(round(sampling_rate * chunk_length_s ) ) * size_of_sample
lowercase__ = _ffmpeg_stream(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
for item in iterator:
yield item
def _A ( lowercase__ , lowercase__ , lowercase__ = None , lowercase__ = None , lowercase__ = "f32le" , ):
if stream_chunk_s is not None:
lowercase__ = stream_chunk_s
else:
lowercase__ = chunk_length_s
lowercase__ = ffmpeg_microphone(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , format_for_conversion=_SCREAMING_SNAKE_CASE )
if format_for_conversion == "s16le":
lowercase__ = np.intaa
lowercase__ = 2
elif format_for_conversion == "f32le":
lowercase__ = np.floataa
lowercase__ = 4
else:
raise ValueError(f'''Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`''' )
if stride_length_s is None:
lowercase__ = chunk_length_s / 6
lowercase__ = int(round(sampling_rate * chunk_length_s ) ) * size_of_sample
if isinstance(_SCREAMING_SNAKE_CASE , (int, float) ):
lowercase__ = [stride_length_s, stride_length_s]
lowercase__ = int(round(sampling_rate * stride_length_s[0] ) ) * size_of_sample
lowercase__ = int(round(sampling_rate * stride_length_s[1] ) ) * size_of_sample
lowercase__ = datetime.datetime.now()
lowercase__ = datetime.timedelta(seconds=_SCREAMING_SNAKE_CASE )
for item in chunk_bytes_iter(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , stride=(stride_left, stride_right) , stream=_SCREAMING_SNAKE_CASE ):
# Put everything back in numpy scale
lowercase__ = np.frombuffer(item["""raw"""] , dtype=_SCREAMING_SNAKE_CASE )
lowercase__ = (
item['stride'][0] // size_of_sample,
item['stride'][1] // size_of_sample,
)
lowercase__ = sampling_rate
audio_time += delta
if datetime.datetime.now() > audio_time + 10 * delta:
# We're late !! SKIP
continue
yield item
def _A ( lowercase__ , lowercase__ , lowercase__ , lowercase__ = False ):
lowercase__ = b''
lowercase__ = 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}''' )
lowercase__ = 0
for raw in iterator:
acc += raw
if stream and len(_SCREAMING_SNAKE_CASE ) < chunk_len:
lowercase__ = (_stride_left, 0)
yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
else:
while len(_SCREAMING_SNAKE_CASE ) >= chunk_len:
# We are flushing the accumulator
lowercase__ = (_stride_left, stride_right)
lowercase__ = {'raw': acc[:chunk_len], 'stride': stride}
if stream:
lowercase__ = False
yield item
lowercase__ = stride_left
lowercase__ = acc[chunk_len - stride_left - stride_right :]
# Last chunk
if len(_SCREAMING_SNAKE_CASE ) > stride_left:
lowercase__ = {'raw': acc, 'stride': (_stride_left, 0)}
if stream:
lowercase__ = False
yield item
def _A ( lowercase__ , lowercase__ ):
lowercase__ = 2**24 # 16Mo
try:
with subprocess.Popen(_SCREAMING_SNAKE_CASE , stdout=subprocess.PIPE , bufsize=_SCREAMING_SNAKE_CASE ) as ffmpeg_process:
while True:
lowercase__ = ffmpeg_process.stdout.read(_SCREAMING_SNAKE_CASE )
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
| 164 | """simple docstring"""
import itertools
import random
import unittest
import numpy as np
from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
lowercase__ = random.Random()
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=1.0 , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None ) ->Optional[int]:
if rng is None:
a__: Any = global_rng
a__: int = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
class __snake_case ( unittest.TestCase ):
def __init__( self , lowercase , lowercase=7 , lowercase=4_00 , lowercase=20_00 , lowercase=1 , lowercase=0.0 , lowercase=1_60_00 , lowercase=True , lowercase=True , ) -> Union[str, Any]:
'''simple docstring'''
a__: Tuple = parent
a__: Optional[int] = batch_size
a__: Optional[Any] = min_seq_length
a__: Optional[int] = max_seq_length
a__: Tuple = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
a__: Dict = feature_size
a__: Any = padding_value
a__: Optional[Any] = sampling_rate
a__: Optional[Any] = return_attention_mask
a__: str = do_normalize
def lowerCamelCase_ ( self) -> Dict:
'''simple docstring'''
return {
"feature_size": self.feature_size,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"return_attention_mask": self.return_attention_mask,
"do_normalize": self.do_normalize,
}
def lowerCamelCase_ ( self , lowercase=False , lowercase=False) -> Tuple:
'''simple docstring'''
def _flatten(lowercase):
return list(itertools.chain(*lowercase))
if equal_length:
a__: Dict = floats_list((self.batch_size, self.max_seq_length))
else:
# make sure that inputs increase in size
a__: List[Any] = [
_flatten(floats_list((x, self.feature_size)))
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff)
]
if numpify:
a__: str = [np.asarray(lowercase) for x in speech_inputs]
return speech_inputs
class __snake_case ( __lowerCAmelCase , unittest.TestCase ):
a__ = WavaVecaFeatureExtractor
def lowerCamelCase_ ( self) -> Tuple:
'''simple docstring'''
a__: Optional[int] = WavaVecaFeatureExtractionTester(self)
def lowerCamelCase_ ( self , lowercase) -> List[Any]:
'''simple docstring'''
self.assertTrue(np.all(np.mean(lowercase , axis=0) < 1e-3))
self.assertTrue(np.all(np.abs(np.var(lowercase , axis=0) - 1) < 1e-3))
def lowerCamelCase_ ( self) -> List[str]:
'''simple docstring'''
a__: List[Any] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
# create three inputs of length 800, 1000, and 1200
a__: Optional[Any] = [floats_list((1, x))[0] for x in range(8_00 , 14_00 , 2_00)]
a__: List[str] = [np.asarray(lowercase) for speech_input in speech_inputs]
# Test not batched input
a__: Optional[Any] = feat_extract(speech_inputs[0] , return_tensors='np').input_values
a__: Dict = feat_extract(np_speech_inputs[0] , return_tensors='np').input_values
self.assertTrue(np.allclose(lowercase , lowercase , atol=1e-3))
# Test batched
a__: Dict = feat_extract(lowercase , return_tensors='np').input_values
a__: int = feat_extract(lowercase , return_tensors='np').input_values
for enc_seq_a, enc_seq_a in zip(lowercase , lowercase):
self.assertTrue(np.allclose(lowercase , lowercase , atol=1e-3))
# Test 2-D numpy arrays are batched.
a__: int = [floats_list((1, x))[0] for x in (8_00, 8_00, 8_00)]
a__: Union[str, Any] = np.asarray(lowercase)
a__: int = feat_extract(lowercase , return_tensors='np').input_values
a__: Any = feat_extract(lowercase , return_tensors='np').input_values
for enc_seq_a, enc_seq_a in zip(lowercase , lowercase):
self.assertTrue(np.allclose(lowercase , lowercase , atol=1e-3))
def lowerCamelCase_ ( self) -> List[Any]:
'''simple docstring'''
a__: Tuple = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
a__: List[Any] = [floats_list((1, x))[0] for x in range(8_00 , 14_00 , 2_00)]
a__: Optional[int] = ['longest', 'max_length', 'do_not_pad']
a__: List[Any] = [None, 16_00, None]
for max_length, padding in zip(lowercase , lowercase):
a__: Dict = feat_extract(lowercase , padding=lowercase , max_length=lowercase , return_tensors='np')
a__: Union[str, Any] = processed.input_values
self._check_zero_mean_unit_variance(input_values[0][:8_00])
self.assertTrue(input_values[0][8_00:].sum() < 1e-6)
self._check_zero_mean_unit_variance(input_values[1][:10_00])
self.assertTrue(input_values[0][10_00:].sum() < 1e-6)
self._check_zero_mean_unit_variance(input_values[2][:12_00])
def lowerCamelCase_ ( self) -> Optional[int]:
'''simple docstring'''
a__: str = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
a__: Optional[int] = range(8_00 , 14_00 , 2_00)
a__: List[str] = [floats_list((1, x))[0] for x in lengths]
a__: Tuple = ['longest', 'max_length', 'do_not_pad']
a__: Dict = [None, 16_00, None]
for max_length, padding in zip(lowercase , lowercase):
a__: int = feat_extract(lowercase , max_length=lowercase , padding=lowercase)
a__: Any = processed.input_values
self._check_zero_mean_unit_variance(input_values[0][:8_00])
self._check_zero_mean_unit_variance(input_values[1][:10_00])
self._check_zero_mean_unit_variance(input_values[2][:12_00])
def lowerCamelCase_ ( self) -> List[str]:
'''simple docstring'''
a__: Any = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
a__: Any = [floats_list((1, x))[0] for x in range(8_00 , 14_00 , 2_00)]
a__: Dict = feat_extract(
lowercase , truncation=lowercase , max_length=10_00 , padding='max_length' , return_tensors='np')
a__: int = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :8_00])
self._check_zero_mean_unit_variance(input_values[1])
self._check_zero_mean_unit_variance(input_values[2])
def lowerCamelCase_ ( self) -> Dict:
'''simple docstring'''
a__: Tuple = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
a__: int = [floats_list((1, x))[0] for x in range(8_00 , 14_00 , 2_00)]
a__: str = feat_extract(
lowercase , truncation=lowercase , max_length=10_00 , padding='longest' , return_tensors='np')
a__: Any = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :8_00])
self._check_zero_mean_unit_variance(input_values[1, :10_00])
self._check_zero_mean_unit_variance(input_values[2])
# make sure that if max_length < longest -> then pad to max_length
self.assertTrue(input_values.shape == (3, 10_00))
a__: Dict = [floats_list((1, x))[0] for x in range(8_00 , 14_00 , 2_00)]
a__: Tuple = feat_extract(
lowercase , truncation=lowercase , max_length=20_00 , padding='longest' , return_tensors='np')
a__: str = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :8_00])
self._check_zero_mean_unit_variance(input_values[1, :10_00])
self._check_zero_mean_unit_variance(input_values[2])
# make sure that if max_length > longest -> then pad to longest
self.assertTrue(input_values.shape == (3, 12_00))
@require_torch
def lowerCamelCase_ ( self) -> Tuple:
'''simple docstring'''
import torch
a__: Optional[int] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
a__: Tuple = np.random.rand(1_00).astype(np.floataa)
a__: Tuple = np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
a__: Any = feature_extractor.pad([{'input_values': inputs}] , return_tensors='np')
self.assertTrue(np_processed.input_values.dtype == np.floataa)
a__: Optional[Any] = feature_extractor.pad([{'input_values': inputs}] , return_tensors='pt')
self.assertTrue(pt_processed.input_values.dtype == torch.floataa)
@slow
@require_torch
def lowerCamelCase_ ( self) -> Optional[Any]:
'''simple docstring'''
for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
a__: str = WavaVecaConfig.from_pretrained(lowercase)
a__: str = WavaVecaFeatureExtractor.from_pretrained(lowercase)
# only "layer" feature extraction norm should make use of
# attention_mask
self.assertEqual(feat_extract.return_attention_mask , config.feat_extract_norm == 'layer')
| 290 | 0 |
"""simple docstring"""
_A : Any = [
"""DownloadConfig""",
"""DownloadManager""",
"""DownloadMode""",
"""StreamingDownloadManager""",
]
from .download_config import DownloadConfig
from .download_manager import DownloadManager, DownloadMode
from .streaming_download_manager import StreamingDownloadManager
| 202 | """simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
'edbeeching/decision-transformer-gym-hopper-medium': (
'https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json'
),
# See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}
class __snake_case ( __lowerCAmelCase ):
a__ = """decision_transformer"""
a__ = ["""past_key_values"""]
a__ = {
"""max_position_embeddings""": """n_positions""",
"""num_attention_heads""": """n_head""",
"""num_hidden_layers""": """n_layer""",
}
def __init__( self , lowercase=17 , lowercase=4 , lowercase=1_28 , lowercase=40_96 , lowercase=True , lowercase=1 , lowercase=10_24 , lowercase=3 , lowercase=1 , lowercase=None , lowercase="relu" , lowercase=0.1 , lowercase=0.1 , lowercase=0.1 , lowercase=1e-5 , lowercase=0.02 , lowercase=True , lowercase=True , lowercase=5_02_56 , lowercase=5_02_56 , lowercase=False , lowercase=False , **lowercase , ) -> Tuple:
'''simple docstring'''
a__: List[str] = state_dim
a__: int = act_dim
a__: List[Any] = hidden_size
a__: List[str] = max_ep_len
a__: List[Any] = action_tanh
a__: Optional[Any] = vocab_size
a__: Tuple = n_positions
a__: Dict = n_layer
a__: Optional[int] = n_head
a__: Optional[int] = n_inner
a__: Any = activation_function
a__: Union[str, Any] = resid_pdrop
a__: Any = embd_pdrop
a__: Any = attn_pdrop
a__: List[Any] = layer_norm_epsilon
a__: Optional[Any] = initializer_range
a__: Any = scale_attn_weights
a__: Dict = use_cache
a__: Optional[int] = scale_attn_by_inverse_layer_idx
a__: List[str] = reorder_and_upcast_attn
a__: Any = bos_token_id
a__: int = eos_token_id
super().__init__(bos_token_id=lowercase , eos_token_id=lowercase , **lowercase)
| 290 | 0 |
'''simple docstring'''
import itertools
import random
import unittest
import numpy as np
from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
__SCREAMING_SNAKE_CASE :List[str] = random.Random()
def UpperCAmelCase_ ( __lowercase : int , __lowercase : List[str]=1.0 , __lowercase : Optional[Any]=None , __lowercase : str=None ) -> Optional[int]:
'''simple docstring'''
if rng is None:
_UpperCAmelCase = global_rng
_UpperCAmelCase = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
class A_ ( unittest.TestCase ):
def __init__( self : List[Any] , snake_case_ : Optional[Any] , snake_case_ : int=7 , snake_case_ : Optional[int]=4_0_0 , snake_case_ : Optional[int]=2_0_0_0 , snake_case_ : int=1 , snake_case_ : Union[str, Any]=0.0 , snake_case_ : Any=1_6_0_0_0 , snake_case_ : Optional[int]=True , snake_case_ : List[Any]=True , ):
_UpperCAmelCase = parent
_UpperCAmelCase = batch_size
_UpperCAmelCase = min_seq_length
_UpperCAmelCase = max_seq_length
_UpperCAmelCase = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
_UpperCAmelCase = feature_size
_UpperCAmelCase = padding_value
_UpperCAmelCase = sampling_rate
_UpperCAmelCase = return_attention_mask
_UpperCAmelCase = do_normalize
def lowercase ( self : Optional[Any] ):
return {
"feature_size": self.feature_size,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"return_attention_mask": self.return_attention_mask,
"do_normalize": self.do_normalize,
}
def lowercase ( self : Dict , snake_case_ : Any=False , snake_case_ : List[Any]=False ):
def _flatten(snake_case_ : Any ):
return list(itertools.chain(*snake_case_ ) )
if equal_length:
_UpperCAmelCase = floats_list((self.batch_size, self.max_seq_length) )
else:
# make sure that inputs increase in size
_UpperCAmelCase = [
_flatten(floats_list((x, self.feature_size) ) )
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff )
]
if numpify:
_UpperCAmelCase = [np.asarray(snake_case_ ) for x in speech_inputs]
return speech_inputs
class A_ ( __lowerCAmelCase , unittest.TestCase ):
_lowerCamelCase : Any = WavaVecaFeatureExtractor
def lowercase ( self : Dict ):
_UpperCAmelCase = WavaVecaFeatureExtractionTester(self )
def lowercase ( self : Optional[Any] , snake_case_ : Optional[int] ):
self.assertTrue(np.all(np.mean(snake_case_ , axis=0 ) < 1e-3 ) )
self.assertTrue(np.all(np.abs(np.var(snake_case_ , axis=0 ) - 1 ) < 1e-3 ) )
def lowercase ( self : str ):
_UpperCAmelCase = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
_UpperCAmelCase = [floats_list((1, x) )[0] for x in range(8_0_0 , 1_4_0_0 , 2_0_0 )]
_UpperCAmelCase = [np.asarray(snake_case_ ) for speech_input in speech_inputs]
# Test not batched input
_UpperCAmelCase = feat_extract(speech_inputs[0] , return_tensors="np" ).input_values
_UpperCAmelCase = feat_extract(np_speech_inputs[0] , return_tensors="np" ).input_values
self.assertTrue(np.allclose(snake_case_ , snake_case_ , atol=1e-3 ) )
# Test batched
_UpperCAmelCase = feat_extract(snake_case_ , return_tensors="np" ).input_values
_UpperCAmelCase = feat_extract(snake_case_ , return_tensors="np" ).input_values
for enc_seq_a, enc_seq_a in zip(snake_case_ , snake_case_ ):
self.assertTrue(np.allclose(snake_case_ , snake_case_ , atol=1e-3 ) )
# Test 2-D numpy arrays are batched.
_UpperCAmelCase = [floats_list((1, x) )[0] for x in (8_0_0, 8_0_0, 8_0_0)]
_UpperCAmelCase = np.asarray(snake_case_ )
_UpperCAmelCase = feat_extract(snake_case_ , return_tensors="np" ).input_values
_UpperCAmelCase = feat_extract(snake_case_ , return_tensors="np" ).input_values
for enc_seq_a, enc_seq_a in zip(snake_case_ , snake_case_ ):
self.assertTrue(np.allclose(snake_case_ , snake_case_ , atol=1e-3 ) )
def lowercase ( self : List[Any] ):
_UpperCAmelCase = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
_UpperCAmelCase = [floats_list((1, x) )[0] for x in range(8_0_0 , 1_4_0_0 , 2_0_0 )]
_UpperCAmelCase = ['longest', 'max_length', 'do_not_pad']
_UpperCAmelCase = [None, 1_6_0_0, None]
for max_length, padding in zip(snake_case_ , snake_case_ ):
_UpperCAmelCase = feat_extract(snake_case_ , padding=snake_case_ , max_length=snake_case_ , return_tensors="np" )
_UpperCAmelCase = processed.input_values
self._check_zero_mean_unit_variance(input_values[0][:8_0_0] )
self.assertTrue(input_values[0][8_0_0:].sum() < 1e-6 )
self._check_zero_mean_unit_variance(input_values[1][:1_0_0_0] )
self.assertTrue(input_values[0][1_0_0_0:].sum() < 1e-6 )
self._check_zero_mean_unit_variance(input_values[2][:1_2_0_0] )
def lowercase ( self : Dict ):
_UpperCAmelCase = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
_UpperCAmelCase = range(8_0_0 , 1_4_0_0 , 2_0_0 )
_UpperCAmelCase = [floats_list((1, x) )[0] for x in lengths]
_UpperCAmelCase = ['longest', 'max_length', 'do_not_pad']
_UpperCAmelCase = [None, 1_6_0_0, None]
for max_length, padding in zip(snake_case_ , snake_case_ ):
_UpperCAmelCase = feat_extract(snake_case_ , max_length=snake_case_ , padding=snake_case_ )
_UpperCAmelCase = processed.input_values
self._check_zero_mean_unit_variance(input_values[0][:8_0_0] )
self._check_zero_mean_unit_variance(input_values[1][:1_0_0_0] )
self._check_zero_mean_unit_variance(input_values[2][:1_2_0_0] )
def lowercase ( self : Union[str, Any] ):
_UpperCAmelCase = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
_UpperCAmelCase = [floats_list((1, x) )[0] for x in range(8_0_0 , 1_4_0_0 , 2_0_0 )]
_UpperCAmelCase = feat_extract(
snake_case_ , truncation=snake_case_ , max_length=1_0_0_0 , padding="max_length" , return_tensors="np" )
_UpperCAmelCase = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :8_0_0] )
self._check_zero_mean_unit_variance(input_values[1] )
self._check_zero_mean_unit_variance(input_values[2] )
def lowercase ( self : Union[str, Any] ):
_UpperCAmelCase = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
_UpperCAmelCase = [floats_list((1, x) )[0] for x in range(8_0_0 , 1_4_0_0 , 2_0_0 )]
_UpperCAmelCase = feat_extract(
snake_case_ , truncation=snake_case_ , max_length=1_0_0_0 , padding="longest" , return_tensors="np" )
_UpperCAmelCase = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :8_0_0] )
self._check_zero_mean_unit_variance(input_values[1, :1_0_0_0] )
self._check_zero_mean_unit_variance(input_values[2] )
# make sure that if max_length < longest -> then pad to max_length
self.assertTrue(input_values.shape == (3, 1_0_0_0) )
_UpperCAmelCase = [floats_list((1, x) )[0] for x in range(8_0_0 , 1_4_0_0 , 2_0_0 )]
_UpperCAmelCase = feat_extract(
snake_case_ , truncation=snake_case_ , max_length=2_0_0_0 , padding="longest" , return_tensors="np" )
_UpperCAmelCase = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :8_0_0] )
self._check_zero_mean_unit_variance(input_values[1, :1_0_0_0] )
self._check_zero_mean_unit_variance(input_values[2] )
# make sure that if max_length > longest -> then pad to longest
self.assertTrue(input_values.shape == (3, 1_2_0_0) )
@require_torch
def lowercase ( self : Any ):
import torch
_UpperCAmelCase = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
_UpperCAmelCase = np.random.rand(1_0_0 ).astype(np.floataa )
_UpperCAmelCase = np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
_UpperCAmelCase = feature_extractor.pad([{"input_values": inputs}] , return_tensors="np" )
self.assertTrue(np_processed.input_values.dtype == np.floataa )
_UpperCAmelCase = feature_extractor.pad([{"input_values": inputs}] , return_tensors="pt" )
self.assertTrue(pt_processed.input_values.dtype == torch.floataa )
@slow
@require_torch
def lowercase ( self : Optional[Any] ):
for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
_UpperCAmelCase = WavaVecaConfig.from_pretrained(snake_case_ )
_UpperCAmelCase = WavaVecaFeatureExtractor.from_pretrained(snake_case_ )
# only "layer" feature extraction norm should make use of
# attention_mask
self.assertEqual(feat_extract.return_attention_mask , config.feat_extract_norm == "layer" )
| 22 | """simple docstring"""
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->int:
while a != 0:
a__ , a__: List[str] = b % a, a
return b
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->int:
if gcd(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) != 1:
a__: Dict = F'mod inverse of {a!r} and {m!r} does not exist'
raise ValueError(_SCREAMING_SNAKE_CASE )
a__ , a__ , a__: Union[str, Any] = 1, 0, a
a__ , a__ , a__: Any = 0, 1, m
while va != 0:
a__: int = ua // va
a__ , a__ , a__ , a__ , a__ , a__: Any = (ua - q * va), (ua - q * va), (ua - q * va), va, va, va
return ua % m
| 290 | 0 |
'''simple docstring'''
import math
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin
class __UpperCAmelCase ( __lowerCAmelCase , __lowerCAmelCase ):
'''simple docstring'''
@register_to_config
def __init__(self : Dict , _lowerCAmelCase : int = 128 , _lowerCAmelCase : Tuple = 256 , _lowerCAmelCase : Union[str, Any] = 2_000.0 , _lowerCAmelCase : List[Any] = 768 , _lowerCAmelCase : Union[str, Any] = 12 , _lowerCAmelCase : Dict = 12 , _lowerCAmelCase : List[Any] = 64 , _lowerCAmelCase : Optional[Any] = 2048 , _lowerCAmelCase : List[Any] = 0.1 , ):
super().__init__()
A = nn.Sequential(
nn.Linear(_lowerCAmelCase , d_model * 4 , bias=_lowerCAmelCase ) , nn.SiLU() , nn.Linear(d_model * 4 , d_model * 4 , bias=_lowerCAmelCase ) , nn.SiLU() , )
A = nn.Embedding(_lowerCAmelCase , _lowerCAmelCase )
A = False
A = nn.Linear(_lowerCAmelCase , _lowerCAmelCase , bias=_lowerCAmelCase )
A = nn.Dropout(p=_lowerCAmelCase )
A = nn.ModuleList()
for lyr_num in range(_lowerCAmelCase ):
# FiLM conditional T5 decoder
A = DecoderLayer(d_model=_lowerCAmelCase , d_kv=_lowerCAmelCase , num_heads=_lowerCAmelCase , d_ff=_lowerCAmelCase , dropout_rate=_lowerCAmelCase )
self.decoders.append(_lowerCAmelCase )
A = TaLayerNorm(_lowerCAmelCase )
A = nn.Dropout(p=_lowerCAmelCase )
A = nn.Linear(_lowerCAmelCase , _lowerCAmelCase , bias=_lowerCAmelCase )
def A (self : List[Any] , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Optional[Any] ):
A = torch.mul(query_input.unsqueeze(-1 ) , key_input.unsqueeze(-2 ) )
return mask.unsqueeze(-3 )
def A (self : Union[str, Any] , _lowerCAmelCase : Dict , _lowerCAmelCase : Dict , _lowerCAmelCase : Optional[Any] ):
A = decoder_input_tokens.shape
assert decoder_noise_time.shape == (batch,)
# decoder_noise_time is in [0, 1), so rescale to expected timing range.
A = get_timestep_embedding(
decoder_noise_time * self.config.max_decoder_noise_time , embedding_dim=self.config.d_model , max_period=self.config.max_decoder_noise_time , ).to(dtype=self.dtype )
A = self.conditioning_emb(_lowerCAmelCase ).unsqueeze(1 )
assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4)
A = decoder_input_tokens.shape[1]
# If we want to use relative positions for audio context, we can just offset
# this sequence by the length of encodings_and_masks.
A = torch.broadcast_to(
torch.arange(_lowerCAmelCase , device=decoder_input_tokens.device ) , (batch, seq_length) , )
A = self.position_encoding(_lowerCAmelCase )
A = self.continuous_inputs_projection(_lowerCAmelCase )
inputs += position_encodings
A = self.dropout(_lowerCAmelCase )
# decoder: No padding present.
A = torch.ones(
decoder_input_tokens.shape[:2] , device=decoder_input_tokens.device , dtype=inputs.dtype )
# Translate encoding masks to encoder-decoder masks.
A = [(x, self.encoder_decoder_mask(_lowerCAmelCase , _lowerCAmelCase )) for x, y in encodings_and_masks]
# cross attend style: concat encodings
A = torch.cat([x[0] for x in encodings_and_encdec_masks] , dim=1 )
A = torch.cat([x[1] for x in encodings_and_encdec_masks] , dim=-1 )
for lyr in self.decoders:
A = lyr(
_lowerCAmelCase , conditioning_emb=_lowerCAmelCase , encoder_hidden_states=_lowerCAmelCase , encoder_attention_mask=_lowerCAmelCase , )[0]
A = self.decoder_norm(_lowerCAmelCase )
A = self.post_dropout(_lowerCAmelCase )
A = self.spec_out(_lowerCAmelCase )
return spec_out
class __UpperCAmelCase ( nn.Module ):
'''simple docstring'''
def __init__(self : str , _lowerCAmelCase : Any , _lowerCAmelCase : Dict , _lowerCAmelCase : List[Any] , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : str , _lowerCAmelCase : Union[str, Any]=1e-6 ):
super().__init__()
A = nn.ModuleList()
# cond self attention: layer 0
self.layer.append(
TaLayerSelfAttentionCond(d_model=_lowerCAmelCase , d_kv=_lowerCAmelCase , num_heads=_lowerCAmelCase , dropout_rate=_lowerCAmelCase ) )
# cross attention: layer 1
self.layer.append(
TaLayerCrossAttention(
d_model=_lowerCAmelCase , d_kv=_lowerCAmelCase , num_heads=_lowerCAmelCase , dropout_rate=_lowerCAmelCase , layer_norm_epsilon=_lowerCAmelCase , ) )
# Film Cond MLP + dropout: last layer
self.layer.append(
TaLayerFFCond(d_model=_lowerCAmelCase , d_ff=_lowerCAmelCase , dropout_rate=_lowerCAmelCase , layer_norm_epsilon=_lowerCAmelCase ) )
def A (self : Union[str, Any] , _lowerCAmelCase : List[str] , _lowerCAmelCase : Optional[int]=None , _lowerCAmelCase : int=None , _lowerCAmelCase : List[str]=None , _lowerCAmelCase : Optional[int]=None , _lowerCAmelCase : List[Any]=None , ):
A = self.layer[0](
_lowerCAmelCase , conditioning_emb=_lowerCAmelCase , attention_mask=_lowerCAmelCase , )
if encoder_hidden_states is not None:
A = torch.where(encoder_attention_mask > 0 , 0 , -1e10 ).to(
encoder_hidden_states.dtype )
A = self.layer[1](
_lowerCAmelCase , key_value_states=_lowerCAmelCase , attention_mask=_lowerCAmelCase , )
# Apply Film Conditional Feed Forward layer
A = self.layer[-1](_lowerCAmelCase , _lowerCAmelCase )
return (hidden_states,)
class __UpperCAmelCase ( nn.Module ):
'''simple docstring'''
def __init__(self : Tuple , _lowerCAmelCase : List[str] , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Union[str, Any] ):
super().__init__()
A = TaLayerNorm(_lowerCAmelCase )
A = TaFiLMLayer(in_features=d_model * 4 , out_features=_lowerCAmelCase )
A = Attention(query_dim=_lowerCAmelCase , heads=_lowerCAmelCase , dim_head=_lowerCAmelCase , out_bias=_lowerCAmelCase , scale_qk=_lowerCAmelCase )
A = nn.Dropout(_lowerCAmelCase )
def A (self : List[str] , _lowerCAmelCase : List[Any] , _lowerCAmelCase : Optional[int]=None , _lowerCAmelCase : Any=None , ):
A = self.layer_norm(_lowerCAmelCase )
if conditioning_emb is not None:
A = self.FiLMLayer(_lowerCAmelCase , _lowerCAmelCase )
# Self-attention block
A = self.attention(_lowerCAmelCase )
A = hidden_states + self.dropout(_lowerCAmelCase )
return hidden_states
class __UpperCAmelCase ( nn.Module ):
'''simple docstring'''
def __init__(self : Dict , _lowerCAmelCase : List[str] , _lowerCAmelCase : int , _lowerCAmelCase : Dict , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Union[str, Any] ):
super().__init__()
A = Attention(query_dim=_lowerCAmelCase , heads=_lowerCAmelCase , dim_head=_lowerCAmelCase , out_bias=_lowerCAmelCase , scale_qk=_lowerCAmelCase )
A = TaLayerNorm(_lowerCAmelCase , eps=_lowerCAmelCase )
A = nn.Dropout(_lowerCAmelCase )
def A (self : Any , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Optional[int]=None , _lowerCAmelCase : Any=None , ):
A = self.layer_norm(_lowerCAmelCase )
A = self.attention(
_lowerCAmelCase , encoder_hidden_states=_lowerCAmelCase , attention_mask=attention_mask.squeeze(1 ) , )
A = hidden_states + self.dropout(_lowerCAmelCase )
return layer_output
class __UpperCAmelCase ( nn.Module ):
'''simple docstring'''
def __init__(self : List[str] , _lowerCAmelCase : int , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : int , _lowerCAmelCase : Optional[int] ):
super().__init__()
A = TaDenseGatedActDense(d_model=_lowerCAmelCase , d_ff=_lowerCAmelCase , dropout_rate=_lowerCAmelCase )
A = TaFiLMLayer(in_features=d_model * 4 , out_features=_lowerCAmelCase )
A = TaLayerNorm(_lowerCAmelCase , eps=_lowerCAmelCase )
A = nn.Dropout(_lowerCAmelCase )
def A (self : Tuple , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Any=None ):
A = self.layer_norm(_lowerCAmelCase )
if conditioning_emb is not None:
A = self.film(_lowerCAmelCase , _lowerCAmelCase )
A = self.DenseReluDense(_lowerCAmelCase )
A = hidden_states + self.dropout(_lowerCAmelCase )
return hidden_states
class __UpperCAmelCase ( nn.Module ):
'''simple docstring'''
def __init__(self : List[str] , _lowerCAmelCase : Dict , _lowerCAmelCase : List[Any] , _lowerCAmelCase : List[str] ):
super().__init__()
A = nn.Linear(_lowerCAmelCase , _lowerCAmelCase , bias=_lowerCAmelCase )
A = nn.Linear(_lowerCAmelCase , _lowerCAmelCase , bias=_lowerCAmelCase )
A = nn.Linear(_lowerCAmelCase , _lowerCAmelCase , bias=_lowerCAmelCase )
A = nn.Dropout(_lowerCAmelCase )
A = NewGELUActivation()
def A (self : Optional[int] , _lowerCAmelCase : Tuple ):
A = self.act(self.wi_a(_lowerCAmelCase ) )
A = self.wi_a(_lowerCAmelCase )
A = hidden_gelu * hidden_linear
A = self.dropout(_lowerCAmelCase )
A = self.wo(_lowerCAmelCase )
return hidden_states
class __UpperCAmelCase ( nn.Module ):
'''simple docstring'''
def __init__(self : Optional[Any] , _lowerCAmelCase : Dict , _lowerCAmelCase : Optional[Any]=1e-6 ):
super().__init__()
A = nn.Parameter(torch.ones(_lowerCAmelCase ) )
A = eps
def A (self : List[str] , _lowerCAmelCase : Dict ):
A = hidden_states.to(torch.floataa ).pow(2 ).mean(-1 , keepdim=_lowerCAmelCase )
A = hidden_states * torch.rsqrt(variance + self.variance_epsilon )
# convert into half-precision if necessary
if self.weight.dtype in [torch.floataa, torch.bfloataa]:
A = hidden_states.to(self.weight.dtype )
return self.weight * hidden_states
class __UpperCAmelCase ( nn.Module ):
'''simple docstring'''
def A (self : Dict , _lowerCAmelCase : Tuple ):
return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi ) * (input + 0.044_715 * torch.pow(_lowerCAmelCase , 3.0 )) ))
class __UpperCAmelCase ( nn.Module ):
'''simple docstring'''
def __init__(self : Any , _lowerCAmelCase : str , _lowerCAmelCase : Dict ):
super().__init__()
A = nn.Linear(_lowerCAmelCase , out_features * 2 , bias=_lowerCAmelCase )
def A (self : str , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : List[Any] ):
A = self.scale_bias(_lowerCAmelCase )
A = torch.chunk(_lowerCAmelCase , 2 , -1 )
A = x * (1 + scale) + shift
return x
| 258 | """simple docstring"""
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
lowercase__ = logging.getLogger(__name__)
class __snake_case :
def __init__( self) -> Optional[int]:
'''simple docstring'''
a__: Optional[Any] = False
def lowerCamelCase_ ( self , lowercase , lowercase , lowercase , lowercase) -> str:
'''simple docstring'''
if not self.initialized:
a__: Optional[int] = RagRetriever(
lowercase , question_encoder_tokenizer=lowercase , generator_tokenizer=lowercase , index=lowercase , init_retrieval=lowercase , )
a__: Optional[int] = True
def lowerCamelCase_ ( self) -> int:
'''simple docstring'''
self.retriever.index.init_index()
def lowerCamelCase_ ( self , lowercase , lowercase) -> Union[str, Any]:
'''simple docstring'''
a__ , a__: str = self.retriever._main_retrieve(lowercase , lowercase)
return doc_ids, retrieved_doc_embeds
class __snake_case ( __lowerCAmelCase ):
def __init__( self , lowercase , lowercase , lowercase , lowercase , lowercase=None) -> int:
'''simple docstring'''
if index is not None and index.is_initialized() and len(lowercase) > 0:
raise ValueError(
'When using Ray for distributed fine-tuning, '
'you\'ll need to provide the paths instead, '
'as the dataset and the index are loaded '
'separately. More info in examples/rag/use_own_knowledge_dataset.py ')
super().__init__(
lowercase , question_encoder_tokenizer=lowercase , generator_tokenizer=lowercase , index=lowercase , init_retrieval=lowercase , )
a__: Any = retrieval_workers
if len(self.retrieval_workers) > 0:
ray.get(
[
worker.create_rag_retriever.remote(lowercase , lowercase , lowercase , lowercase)
for worker in self.retrieval_workers
])
def lowerCamelCase_ ( self) -> Any:
'''simple docstring'''
logger.info('initializing retrieval')
if len(self.retrieval_workers) > 0:
ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers])
else:
# Non-distributed training. Load index into this same process.
self.index.init_index()
def lowerCamelCase_ ( self , lowercase , lowercase) -> Union[str, Any]:
'''simple docstring'''
if len(self.retrieval_workers) > 0:
# Select a random retrieval actor.
a__: int = self.retrieval_workers[random.randint(0 , len(self.retrieval_workers) - 1)]
a__ , a__: List[Any] = ray.get(random_worker.retrieve.remote(lowercase , lowercase))
else:
a__ , a__: Dict = self._main_retrieve(lowercase , lowercase)
return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(lowercase)
@classmethod
def lowerCamelCase_ ( cls , lowercase , lowercase=None , **lowercase) -> Tuple:
'''simple docstring'''
return super(lowercase , cls).get_tokenizers(lowercase , lowercase , **lowercase)
@classmethod
def lowerCamelCase_ ( cls , lowercase , lowercase , lowercase=None , **lowercase) -> Union[str, Any]:
'''simple docstring'''
a__: Optional[int] = kwargs.pop('config' , lowercase) or RagConfig.from_pretrained(lowercase , **lowercase)
a__: Union[str, Any] = RagTokenizer.from_pretrained(lowercase , config=lowercase)
a__: int = rag_tokenizer.question_encoder
a__: Any = rag_tokenizer.generator
if indexed_dataset is not None:
a__: List[Any] = 'custom'
a__: Optional[Any] = CustomHFIndex(config.retrieval_vector_size , lowercase)
else:
a__: Dict = cls._build_index(lowercase)
return cls(
lowercase , question_encoder_tokenizer=lowercase , generator_tokenizer=lowercase , retrieval_workers=lowercase , index=lowercase , )
| 290 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE : List[Any] = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : Any = {
"""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_ (__lowerCAmelCase ):
'''simple docstring'''
__UpperCamelCase: Optional[int] = "funnel"
__UpperCamelCase: str = {
"hidden_size": "d_model",
"num_attention_heads": "n_head",
}
def __init__( self : Any , A : str=30522 , A : Any=[4, 4, 4] , A : str=None , A : str=2 , A : Tuple=768 , A : Union[str, Any]=12 , A : str=64 , A : Tuple=3072 , A : Dict="gelu_new" , A : Dict=0.1 , A : int=0.1 , A : Dict=0.0 , A : int=0.1 , A : int=None , A : List[str]=1E-9 , A : Any="mean" , A : Optional[Any]="relative_shift" , A : List[str]=True , A : List[str]=True , A : Dict=True , **A : int , ):
_UpperCAmelCase : Any = vocab_size
_UpperCAmelCase : List[str] = block_sizes
_UpperCAmelCase : str = [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."
_UpperCAmelCase : List[Any] = num_decoder_layers
_UpperCAmelCase : str = d_model
_UpperCAmelCase : Dict = n_head
_UpperCAmelCase : List[str] = d_head
_UpperCAmelCase : str = d_inner
_UpperCAmelCase : List[str] = hidden_act
_UpperCAmelCase : List[str] = hidden_dropout
_UpperCAmelCase : List[str] = attention_dropout
_UpperCAmelCase : Optional[Any] = activation_dropout
_UpperCAmelCase : List[str] = initializer_range
_UpperCAmelCase : Tuple = initializer_std
_UpperCAmelCase : Optional[Any] = layer_norm_eps
assert pooling_type in [
"mean",
"max",
], F"""Got {pooling_type} for `pooling_type` but only \'mean\' and \'max\' are supported."""
_UpperCAmelCase : Optional[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."""
_UpperCAmelCase : List[str] = attention_type
_UpperCAmelCase : Optional[int] = separate_cls
_UpperCAmelCase : Optional[Any] = truncate_seq
_UpperCAmelCase : List[Any] = pool_q_only
super().__init__(**A )
@property
def _A ( self : str ):
return sum(self.block_sizes )
@num_hidden_layers.setter
def _A ( self : Union[str, Any] , A : int ):
raise NotImplementedError(
"This model does not support the setting of `num_hidden_layers`. Please set `block_sizes`." )
@property
def _A ( self : Tuple ):
return len(self.block_sizes )
@num_blocks.setter
def _A ( self : Any , A : Optional[int] ):
raise NotImplementedError("This model does not support the setting of `num_blocks`. Please set `block_sizes`." )
| 31 | """simple docstring"""
import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter
import requests
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None ) ->Optional[int]:
a__: int = None
if token is not None:
a__: Tuple = {'Accept': 'application/vnd.github+json', 'Authorization': F'Bearer {token}'}
a__: Optional[Any] = F'https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100'
a__: str = requests.get(_SCREAMING_SNAKE_CASE , headers=_SCREAMING_SNAKE_CASE ).json()
a__: str = {}
try:
job_links.update({job['name']: job['html_url'] for job in result['jobs']} )
a__: int = math.ceil((result['total_count'] - 100) / 100 )
for i in range(_SCREAMING_SNAKE_CASE ):
a__: Dict = requests.get(url + F'&page={i + 2}' , headers=_SCREAMING_SNAKE_CASE ).json()
job_links.update({job['name']: job['html_url'] for job in result['jobs']} )
return job_links
except Exception:
print(F'Unknown error, could not fetch links:\n{traceback.format_exc()}' )
return {}
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None ) ->Dict:
a__: Dict = None
if token is not None:
a__: List[str] = {'Accept': 'application/vnd.github+json', 'Authorization': F'Bearer {token}'}
a__: Dict = F'https://api.github.com/repos/huggingface/transformers/actions/runs/{worflow_run_id}/artifacts?per_page=100'
a__: Union[str, Any] = requests.get(_SCREAMING_SNAKE_CASE , headers=_SCREAMING_SNAKE_CASE ).json()
a__: List[Any] = {}
try:
artifacts.update({artifact['name']: artifact['archive_download_url'] for artifact in result['artifacts']} )
a__: Dict = math.ceil((result['total_count'] - 100) / 100 )
for i in range(_SCREAMING_SNAKE_CASE ):
a__: Optional[int] = requests.get(url + F'&page={i + 2}' , headers=_SCREAMING_SNAKE_CASE ).json()
artifacts.update({artifact['name']: artifact['archive_download_url'] for artifact in result['artifacts']} )
return artifacts
except Exception:
print(F'Unknown error, could not fetch links:\n{traceback.format_exc()}' )
return {}
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->str:
a__: List[Any] = None
if token is not None:
a__: Optional[int] = {'Accept': 'application/vnd.github+json', 'Authorization': F'Bearer {token}'}
a__: Union[str, Any] = requests.get(_SCREAMING_SNAKE_CASE , headers=_SCREAMING_SNAKE_CASE , allow_redirects=_SCREAMING_SNAKE_CASE )
a__: Optional[Any] = result.headers['Location']
a__: Optional[int] = requests.get(_SCREAMING_SNAKE_CASE , allow_redirects=_SCREAMING_SNAKE_CASE )
a__: int = os.path.join(_SCREAMING_SNAKE_CASE , F'{artifact_name}.zip' )
with open(_SCREAMING_SNAKE_CASE , 'wb' ) as fp:
fp.write(response.content )
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None ) ->List[Any]:
a__: List[Any] = []
a__: Optional[Any] = []
a__: List[Any] = None
with zipfile.ZipFile(_SCREAMING_SNAKE_CASE ) as z:
for filename in z.namelist():
if not os.path.isdir(_SCREAMING_SNAKE_CASE ):
# read the file
if filename in ["failures_line.txt", "summary_short.txt", "job_name.txt"]:
with z.open(_SCREAMING_SNAKE_CASE ) as f:
for line in f:
a__: Optional[int] = line.decode('UTF-8' ).strip()
if filename == "failures_line.txt":
try:
# `error_line` is the place where `error` occurs
a__: Union[str, Any] = line[: line.index(': ' )]
a__: Union[str, Any] = line[line.index(': ' ) + len(': ' ) :]
errors.append([error_line, error] )
except Exception:
# skip un-related lines
pass
elif filename == "summary_short.txt" and line.startswith('FAILED ' ):
# `test` is the test method that failed
a__: Optional[int] = line[len('FAILED ' ) :]
failed_tests.append(_SCREAMING_SNAKE_CASE )
elif filename == "job_name.txt":
a__: Union[str, Any] = line
if len(_SCREAMING_SNAKE_CASE ) != len(_SCREAMING_SNAKE_CASE ):
raise ValueError(
F'`errors` and `failed_tests` should have the same number of elements. Got {len(_SCREAMING_SNAKE_CASE )} for `errors` '
F'and {len(_SCREAMING_SNAKE_CASE )} for `failed_tests` instead. The test reports in {artifact_zip_path} have some'
' problem.' )
a__: Tuple = None
if job_name and job_links:
a__: Dict = job_links.get(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# A list with elements of the form (line of error, error, failed test)
a__: int = [x + [y] + [job_link] for x, y in zip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )]
return result
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None ) ->str:
a__: int = []
a__: Optional[int] = [os.path.join(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) for p in os.listdir(_SCREAMING_SNAKE_CASE ) if p.endswith('.zip' )]
for p in paths:
errors.extend(get_errors_from_single_artifact(_SCREAMING_SNAKE_CASE , job_links=_SCREAMING_SNAKE_CASE ) )
return errors
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None ) ->Any:
a__: str = Counter()
counter.update([x[1] for x in logs] )
a__: int = counter.most_common()
a__: Any = {}
for error, count in counts:
if error_filter is None or error not in error_filter:
a__: List[str] = {'count': count, 'failed_tests': [(x[2], x[0]) for x in logs if x[1] == error]}
a__: Optional[Any] = dict(sorted(r.items() , key=lambda _SCREAMING_SNAKE_CASE : item[1]["count"] , reverse=_SCREAMING_SNAKE_CASE ) )
return r
def __a ( _SCREAMING_SNAKE_CASE ) ->Union[str, Any]:
a__: List[str] = test.split('::' )[0]
if test.startswith('tests/models/' ):
a__: Dict = test.split('/' )[2]
else:
a__: Any = None
return test
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None ) ->List[str]:
a__: int = [(x[0], x[1], get_model(x[2] )) for x in logs]
a__: List[Any] = [x for x in logs if x[2] is not None]
a__: Optional[Any] = {x[2] for x in logs}
a__: Dict = {}
for test in tests:
a__: Union[str, Any] = Counter()
# count by errors in `test`
counter.update([x[1] for x in logs if x[2] == test] )
a__: Union[str, Any] = counter.most_common()
a__: List[str] = {error: count for error, count in counts if (error_filter is None or error not in error_filter)}
a__: List[Any] = sum(error_counts.values() )
if n_errors > 0:
a__: Any = {'count': n_errors, 'errors': error_counts}
a__: Optional[int] = dict(sorted(r.items() , key=lambda _SCREAMING_SNAKE_CASE : item[1]["count"] , reverse=_SCREAMING_SNAKE_CASE ) )
return r
def __a ( _SCREAMING_SNAKE_CASE ) ->Union[str, Any]:
a__: Any = '| no. | error | status |'
a__: Any = '|-:|:-|:-|'
a__: str = [header, sep]
for error in reduced_by_error:
a__: int = reduced_by_error[error]['count']
a__: Tuple = F'| {count} | {error[:100]} | |'
lines.append(_SCREAMING_SNAKE_CASE )
return "\n".join(_SCREAMING_SNAKE_CASE )
def __a ( _SCREAMING_SNAKE_CASE ) ->str:
a__: List[str] = '| model | no. of errors | major error | count |'
a__: str = '|-:|-:|-:|-:|'
a__: int = [header, sep]
for model in reduced_by_model:
a__: Tuple = reduced_by_model[model]['count']
a__ , a__: Dict = list(reduced_by_model[model]['errors'].items() )[0]
a__: Dict = F'| {model} | {count} | {error[:60]} | {_count} |'
lines.append(_SCREAMING_SNAKE_CASE )
return "\n".join(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
lowercase__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument('--workflow_run_id', type=str, required=True, help='A GitHub Actions workflow run id.')
parser.add_argument(
'--output_dir',
type=str,
required=True,
help='Where to store the downloaded artifacts and other result files.',
)
parser.add_argument('--token', default=None, type=str, help='A token that has actions:read permission.')
lowercase__ = parser.parse_args()
os.makedirs(args.output_dir, exist_ok=True)
lowercase__ = get_job_links(args.workflow_run_id, token=args.token)
lowercase__ = {}
# To deal with `workflow_call` event, where a job name is the combination of the job names in the caller and callee.
# For example, `PyTorch 1.11 / Model tests (models/albert, single-gpu)`.
if _job_links:
for k, v in _job_links.items():
# This is how GitHub actions combine job names.
if " / " in k:
lowercase__ = k.find(' / ')
lowercase__ = k[index + len(' / ') :]
lowercase__ = v
with open(os.path.join(args.output_dir, 'job_links.json'), 'w', encoding='UTF-8') as fp:
json.dump(job_links, fp, ensure_ascii=False, indent=4)
lowercase__ = get_artifacts_links(args.workflow_run_id, token=args.token)
with open(os.path.join(args.output_dir, 'artifacts.json'), 'w', encoding='UTF-8') as fp:
json.dump(artifacts, fp, ensure_ascii=False, indent=4)
for idx, (name, url) in enumerate(artifacts.items()):
download_artifact(name, url, args.output_dir, args.token)
# Be gentle to GitHub
time.sleep(1)
lowercase__ = get_all_errors(args.output_dir, job_links=job_links)
# `e[1]` is the error
lowercase__ = Counter()
counter.update([e[1] for e in errors])
# print the top 30 most common test errors
lowercase__ = counter.most_common(30)
for item in most_common:
print(item)
with open(os.path.join(args.output_dir, 'errors.json'), 'w', encoding='UTF-8') as fp:
json.dump(errors, fp, ensure_ascii=False, indent=4)
lowercase__ = reduce_by_error(errors)
lowercase__ = reduce_by_model(errors)
lowercase__ = make_github_table(reduced_by_error)
lowercase__ = make_github_table_per_model(reduced_by_model)
with open(os.path.join(args.output_dir, 'reduced_by_error.txt'), 'w', encoding='UTF-8') as fp:
fp.write(sa)
with open(os.path.join(args.output_dir, 'reduced_by_model.txt'), 'w', encoding='UTF-8') as fp:
fp.write(sa)
| 290 | 0 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
lowerCAmelCase__ :Optional[Any] = logging.get_logger(__name__)
# TODO: upload to AWS
lowerCAmelCase__ :List[str] = {
'''yjernite/retribert-base-uncased''': (
'''https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/config.json'''
),
}
class __a ( __lowerCAmelCase ):
_a : str = 'retribert'
def __init__( self , _SCREAMING_SNAKE_CASE=30522 , _SCREAMING_SNAKE_CASE=768 , _SCREAMING_SNAKE_CASE=8 , _SCREAMING_SNAKE_CASE=12 , _SCREAMING_SNAKE_CASE=3072 , _SCREAMING_SNAKE_CASE="gelu" , _SCREAMING_SNAKE_CASE=0.1 , _SCREAMING_SNAKE_CASE=0.1 , _SCREAMING_SNAKE_CASE=512 , _SCREAMING_SNAKE_CASE=2 , _SCREAMING_SNAKE_CASE=0.02 , _SCREAMING_SNAKE_CASE=1e-1_2 , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=128 , _SCREAMING_SNAKE_CASE=0 , **_SCREAMING_SNAKE_CASE , ) -> Any:
"""simple docstring"""
super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = vocab_size
_UpperCAmelCase = hidden_size
_UpperCAmelCase = num_hidden_layers
_UpperCAmelCase = num_attention_heads
_UpperCAmelCase = hidden_act
_UpperCAmelCase = intermediate_size
_UpperCAmelCase = hidden_dropout_prob
_UpperCAmelCase = attention_probs_dropout_prob
_UpperCAmelCase = max_position_embeddings
_UpperCAmelCase = type_vocab_size
_UpperCAmelCase = initializer_range
_UpperCAmelCase = layer_norm_eps
_UpperCAmelCase = share_encoders
_UpperCAmelCase = projection_dim
| 329 | """simple docstring"""
import math
def __a ( _SCREAMING_SNAKE_CASE ) ->bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(_SCREAMING_SNAKE_CASE ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def __a ( _SCREAMING_SNAKE_CASE = 0.1 ) ->int:
a__: str = 3
a__: Optional[Any] = 3
while primes / (2 * j - 1) >= ratio:
for i in range(j * j + j + 1 , (j + 2) * (j + 2) , j + 1 ):
primes += is_prime(_SCREAMING_SNAKE_CASE )
j += 2
return j
if __name__ == "__main__":
import doctest
doctest.testmod()
| 290 | 0 |
'''simple docstring'''
lowercase__ = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
def UpperCamelCase( ):
UpperCAmelCase : Optional[Any] = input('Enter message: ' )
UpperCAmelCase : List[Any] = input('Enter key [alphanumeric]: ' )
UpperCAmelCase : Union[str, Any] = input('Encrypt/Decrypt [e/d]: ' )
if mode.lower().startswith('e' ):
UpperCAmelCase : Any = 'encrypt'
UpperCAmelCase : Optional[int] = encrypt_message(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
elif mode.lower().startswith('d' ):
UpperCAmelCase : int = 'decrypt'
UpperCAmelCase : Tuple = decrypt_message(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
print(F"""\n{mode.title()}ed message:""" )
print(_SCREAMING_SNAKE_CASE )
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ ):
return translate_message(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , 'encrypt' )
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ ):
return translate_message(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , 'decrypt' )
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ ):
UpperCAmelCase : int = []
UpperCAmelCase : Dict = 0
UpperCAmelCase : List[Any] = key.upper()
for symbol in message:
UpperCAmelCase : List[str] = LETTERS.find(symbol.upper() )
if num != -1:
if mode == "encrypt":
num += LETTERS.find(key[key_index] )
elif mode == "decrypt":
num -= LETTERS.find(key[key_index] )
num %= len(_SCREAMING_SNAKE_CASE )
if symbol.isupper():
translated.append(LETTERS[num] )
elif symbol.islower():
translated.append(LETTERS[num].lower() )
key_index += 1
if key_index == len(_SCREAMING_SNAKE_CASE ):
UpperCAmelCase : Optional[Any] = 0
else:
translated.append(_SCREAMING_SNAKE_CASE )
return "".join(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
main()
| 151 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import _LazyModule
lowercase__ = {'processing_wav2vec2_with_lm': ['Wav2Vec2ProcessorWithLM']}
if TYPE_CHECKING:
from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM
else:
import sys
lowercase__ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 290 | 0 |
import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs
_a = imread(R'''digital_image_processing/image_data/lena_small.jpg''')
_a = cvtColor(img, COLOR_BGR2GRAY)
def _a ( ) -> List[str]:
"""simple docstring"""
__lowerCAmelCase: Optional[int] = cn.convert_to_negative(_SCREAMING_SNAKE_CASE )
# assert negative_img array for at least one True
assert negative_img.any()
def _a ( ) -> List[str]:
"""simple docstring"""
with Image.open('digital_image_processing/image_data/lena_small.jpg' ) as img:
# Work around assertion for response
assert str(cc.change_contrast(_SCREAMING_SNAKE_CASE , 1_10 ) ).startswith(
'<PIL.Image.Image image mode=RGB size=100x100 at' )
def _a ( ) -> Optional[int]:
"""simple docstring"""
__lowerCAmelCase: Dict = canny.gen_gaussian_kernel(9 , sigma=1.4 )
# Assert ambiguous array
assert resp.all()
def _a ( ) -> int:
"""simple docstring"""
__lowerCAmelCase: Tuple = imread('digital_image_processing/image_data/lena_small.jpg' , 0 )
# assert ambiguous array for all == True
assert canny_img.all()
__lowerCAmelCase: Optional[Any] = canny.canny(_SCREAMING_SNAKE_CASE )
# assert canny array for at least one True
assert canny_array.any()
def _a ( ) -> List[Any]:
"""simple docstring"""
assert gg.gaussian_filter(_SCREAMING_SNAKE_CASE , 5 , sigma=0.9 ).all()
def _a ( ) -> Optional[int]:
"""simple docstring"""
__lowerCAmelCase: int = array([[0.2_5, 0.5, 0.2_5], [0.5, -3, 0.5], [0.2_5, 0.5, 0.2_5]] )
__lowerCAmelCase: Union[str, Any] = conv.img_convolve(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ).astype(_SCREAMING_SNAKE_CASE )
assert res.any()
def _a ( ) -> List[str]:
"""simple docstring"""
assert med.median_filter(_SCREAMING_SNAKE_CASE , 3 ).any()
def _a ( ) -> List[str]:
"""simple docstring"""
__lowerCAmelCase: int = sob.sobel_filter(_SCREAMING_SNAKE_CASE )
assert grad.any() and theta.any()
def _a ( ) -> Optional[int]:
"""simple docstring"""
__lowerCAmelCase: int = sp.make_sepia(_SCREAMING_SNAKE_CASE , 20 )
assert sepia.all()
def _a ( SCREAMING_SNAKE_CASE : Optional[Any] = "digital_image_processing/image_data/lena_small.jpg" ) -> List[Any]:
"""simple docstring"""
__lowerCAmelCase: Tuple = bs.Burkes(imread(_SCREAMING_SNAKE_CASE , 1 ) , 1_20 )
burkes.process()
assert burkes.output_img.any()
def _a ( SCREAMING_SNAKE_CASE : Optional[int] = "digital_image_processing/image_data/lena_small.jpg" , ) -> Optional[Any]:
"""simple docstring"""
__lowerCAmelCase: Any = rs.NearestNeighbour(imread(_SCREAMING_SNAKE_CASE , 1 ) , 4_00 , 2_00 )
nn.process()
assert nn.output.any()
def _a ( ) -> List[str]:
"""simple docstring"""
__lowerCAmelCase: Union[str, Any] = 'digital_image_processing/image_data/lena.jpg'
# Reading the image and converting it to grayscale.
__lowerCAmelCase: Any = imread(_SCREAMING_SNAKE_CASE , 0 )
# Test for get_neighbors_pixel function() return not None
__lowerCAmelCase: List[str] = 0
__lowerCAmelCase: Optional[int] = 0
__lowerCAmelCase: str = image[x_coordinate][y_coordinate]
__lowerCAmelCase: Any = lbp.get_neighbors_pixel(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
assert neighbors_pixels is not None
# Test for local_binary_pattern function()
# Create a numpy array as the same height and width of read image
__lowerCAmelCase: Dict = np.zeros((image.shape[0], image.shape[1]) )
# Iterating through the image and calculating the local binary pattern value
# for each pixel.
for i in range(0 , image.shape[0] ):
for j in range(0 , image.shape[1] ):
__lowerCAmelCase: List[str] = lbp.local_binary_value(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
assert lbp_image.any()
| 322 | """simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, 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 __snake_case ( __lowerCAmelCase , unittest.TestCase ):
a__ = KandinskyInpaintPipeline
a__ = ["""prompt""", """image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
a__ = [
"""prompt""",
"""negative_prompt""",
"""image_embeds""",
"""negative_image_embeds""",
"""image""",
"""mask_image""",
]
a__ = [
"""generator""",
"""height""",
"""width""",
"""latents""",
"""guidance_scale""",
"""negative_prompt""",
"""num_inference_steps""",
"""return_dict""",
"""guidance_scale""",
"""num_images_per_prompt""",
"""output_type""",
"""return_dict""",
]
a__ = False
@property
def lowerCamelCase_ ( self) -> Optional[int]:
'''simple docstring'''
return 32
@property
def lowerCamelCase_ ( self) -> Tuple:
'''simple docstring'''
return 32
@property
def lowerCamelCase_ ( self) -> Dict:
'''simple docstring'''
return self.time_input_dim
@property
def lowerCamelCase_ ( self) -> Dict:
'''simple docstring'''
return self.time_input_dim * 4
@property
def lowerCamelCase_ ( self) -> List[Any]:
'''simple docstring'''
return 1_00
@property
def lowerCamelCase_ ( self) -> List[Any]:
'''simple docstring'''
a__: Optional[int] = XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base')
return tokenizer
@property
def lowerCamelCase_ ( self) -> Any:
'''simple docstring'''
torch.manual_seed(0)
a__: Dict = MCLIPConfig(
numDims=self.cross_attention_dim , transformerDimensions=self.text_embedder_hidden_size , hidden_size=self.text_embedder_hidden_size , intermediate_size=37 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=10_05 , )
a__: Optional[Any] = MultilingualCLIP(lowercase)
a__: int = text_encoder.eval()
return text_encoder
@property
def lowerCamelCase_ ( self) -> List[str]:
'''simple docstring'''
torch.manual_seed(0)
a__: Any = {
'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__: str = UNetaDConditionModel(**lowercase)
return model
@property
def lowerCamelCase_ ( self) -> Union[str, Any]:
'''simple docstring'''
return {
"block_out_channels": [32, 64],
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def lowerCamelCase_ ( self) -> List[Any]:
'''simple docstring'''
torch.manual_seed(0)
a__: Any = VQModel(**self.dummy_movq_kwargs)
return model
def lowerCamelCase_ ( self) -> Any:
'''simple docstring'''
a__: Dict = self.dummy_text_encoder
a__: int = self.dummy_tokenizer
a__: str = self.dummy_unet
a__: Any = self.dummy_movq
a__: Tuple = DDIMScheduler(
num_train_timesteps=10_00 , beta_schedule='linear' , beta_start=0.00085 , beta_end=0.012 , clip_sample=lowercase , set_alpha_to_one=lowercase , steps_offset=1 , prediction_type='epsilon' , thresholding=lowercase , )
a__: Tuple = {
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'unet': unet,
'scheduler': scheduler,
'movq': movq,
}
return components
def lowerCamelCase_ ( self , lowercase , lowercase=0) -> Any:
'''simple docstring'''
a__: List[Any] = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(lowercase)).to(lowercase)
a__: int = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(seed + 1)).to(lowercase)
# create init_image
a__: Optional[int] = floats_tensor((1, 3, 64, 64) , rng=random.Random(lowercase)).to(lowercase)
a__: int = image.cpu().permute(0 , 2 , 3 , 1)[0]
a__: Optional[int] = Image.fromarray(np.uinta(lowercase)).convert('RGB').resize((2_56, 2_56))
# create mask
a__: Tuple = np.ones((64, 64) , dtype=np.floataa)
a__: Optional[Any] = 0
if str(lowercase).startswith('mps'):
a__: str = torch.manual_seed(lowercase)
else:
a__: Dict = torch.Generator(device=lowercase).manual_seed(lowercase)
a__: Optional[int] = {
'prompt': 'horse',
'image': init_image,
'mask_image': mask,
'image_embeds': image_embeds,
'negative_image_embeds': negative_image_embeds,
'generator': generator,
'height': 64,
'width': 64,
'num_inference_steps': 2,
'guidance_scale': 4.0,
'output_type': 'np',
}
return inputs
def lowerCamelCase_ ( self) -> str:
'''simple docstring'''
a__: Optional[Any] = 'cpu'
a__: List[Any] = self.get_dummy_components()
a__: Optional[Any] = self.pipeline_class(**lowercase)
a__: str = pipe.to(lowercase)
pipe.set_progress_bar_config(disable=lowercase)
a__: Optional[int] = pipe(**self.get_dummy_inputs(lowercase))
a__: List[str] = output.images
a__: int = pipe(
**self.get_dummy_inputs(lowercase) , return_dict=lowercase , )[0]
a__: Optional[Any] = image[0, -3:, -3:, -1]
a__: List[Any] = image_from_tuple[0, -3:, -3:, -1]
print(f'image.shape {image.shape}')
assert image.shape == (1, 64, 64, 3)
a__: str = np.array(
[0.8326919, 0.73790467, 0.20918581, 0.9309612, 0.5511791, 0.43713328, 0.5513321, 0.49922934, 0.59497786])
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 lowerCamelCase_ ( self) -> str:
'''simple docstring'''
super().test_inference_batch_single_identical(expected_max_diff=3e-3)
@slow
@require_torch_gpu
class __snake_case ( unittest.TestCase ):
def lowerCamelCase_ ( self) -> Optional[Any]:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase_ ( self) -> Dict:
'''simple docstring'''
a__: List[Any] = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy')
a__: int = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png')
a__: Union[str, Any] = np.ones((7_68, 7_68) , dtype=np.floataa)
a__: int = 0
a__: Optional[int] = 'a hat'
a__: int = KandinskyPriorPipeline.from_pretrained(
'kandinsky-community/kandinsky-2-1-prior' , torch_dtype=torch.floataa)
pipe_prior.to(lowercase)
a__: Any = KandinskyInpaintPipeline.from_pretrained(
'kandinsky-community/kandinsky-2-1-inpaint' , torch_dtype=torch.floataa)
a__: Optional[Any] = pipeline.to(lowercase)
pipeline.set_progress_bar_config(disable=lowercase)
a__: Dict = torch.Generator(device='cpu').manual_seed(0)
a__ , a__: Optional[Any] = pipe_prior(
lowercase , generator=lowercase , num_inference_steps=5 , negative_prompt='' , ).to_tuple()
a__: List[str] = pipeline(
lowercase , image=lowercase , mask_image=lowercase , image_embeds=lowercase , negative_image_embeds=lowercase , generator=lowercase , num_inference_steps=1_00 , height=7_68 , width=7_68 , output_type='np' , )
a__: str = output.images[0]
assert image.shape == (7_68, 7_68, 3)
assert_mean_pixel_difference(lowercase , lowercase)
| 290 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import _LazyModule
lowercase__ : str = {'''tokenization_bertweet''': ['''BertweetTokenizer''']}
if TYPE_CHECKING:
from .tokenization_bertweet import BertweetTokenizer
else:
import sys
lowercase__ : List[Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 264 | """simple docstring"""
import argparse
import torch
from transformers import (
EncodecConfig,
EncodecFeatureExtractor,
EncodecModel,
logging,
)
# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/main/compression_state_dict.bin
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_48khz-7e698e3e.th
logging.set_verbosity_info()
lowercase__ = logging.get_logger('transformers.models.encodec')
lowercase__ = {
'quantizer.vq.layers.*._codebook.inited': 'quantizer.layers.*.codebook.inited',
'quantizer.vq.layers.*._codebook.cluster_size': 'quantizer.layers.*.codebook.cluster_size',
'quantizer.vq.layers.*._codebook.embed': 'quantizer.layers.*.codebook.embed',
'quantizer.vq.layers.*._codebook.embed_avg': 'quantizer.layers.*.codebook.embed_avg',
}
lowercase__ = {
'encoder.model.0.conv.conv': 'encoder.layers.0.conv',
'encoder.model.1.block.1.conv.conv': 'encoder.layers.1.block.1.conv',
'encoder.model.1.block.3.conv.conv': 'encoder.layers.1.block.3.conv',
'encoder.model.1.shortcut.conv.conv': 'encoder.layers.1.shortcut.conv',
'encoder.model.3.conv.conv': 'encoder.layers.3.conv',
'encoder.model.4.block.1.conv.conv': 'encoder.layers.4.block.1.conv',
'encoder.model.4.block.3.conv.conv': 'encoder.layers.4.block.3.conv',
'encoder.model.4.shortcut.conv.conv': 'encoder.layers.4.shortcut.conv',
'encoder.model.6.conv.conv': 'encoder.layers.6.conv',
'encoder.model.7.block.1.conv.conv': 'encoder.layers.7.block.1.conv',
'encoder.model.7.block.3.conv.conv': 'encoder.layers.7.block.3.conv',
'encoder.model.7.shortcut.conv.conv': 'encoder.layers.7.shortcut.conv',
'encoder.model.9.conv.conv': 'encoder.layers.9.conv',
'encoder.model.10.block.1.conv.conv': 'encoder.layers.10.block.1.conv',
'encoder.model.10.block.3.conv.conv': 'encoder.layers.10.block.3.conv',
'encoder.model.10.shortcut.conv.conv': 'encoder.layers.10.shortcut.conv',
'encoder.model.12.conv.conv': 'encoder.layers.12.conv',
'encoder.model.13.lstm': 'encoder.layers.13.lstm',
'encoder.model.15.conv.conv': 'encoder.layers.15.conv',
}
lowercase__ = {
'encoder.model.0.conv.norm': 'encoder.layers.0.norm',
'encoder.model.1.block.1.conv.norm': 'encoder.layers.1.block.1.norm',
'encoder.model.1.block.3.conv.norm': 'encoder.layers.1.block.3.norm',
'encoder.model.1.shortcut.conv.norm': 'encoder.layers.1.shortcut.norm',
'encoder.model.3.conv.norm': 'encoder.layers.3.norm',
'encoder.model.4.block.1.conv.norm': 'encoder.layers.4.block.1.norm',
'encoder.model.4.block.3.conv.norm': 'encoder.layers.4.block.3.norm',
'encoder.model.4.shortcut.conv.norm': 'encoder.layers.4.shortcut.norm',
'encoder.model.6.conv.norm': 'encoder.layers.6.norm',
'encoder.model.7.block.1.conv.norm': 'encoder.layers.7.block.1.norm',
'encoder.model.7.block.3.conv.norm': 'encoder.layers.7.block.3.norm',
'encoder.model.7.shortcut.conv.norm': 'encoder.layers.7.shortcut.norm',
'encoder.model.9.conv.norm': 'encoder.layers.9.norm',
'encoder.model.10.block.1.conv.norm': 'encoder.layers.10.block.1.norm',
'encoder.model.10.block.3.conv.norm': 'encoder.layers.10.block.3.norm',
'encoder.model.10.shortcut.conv.norm': 'encoder.layers.10.shortcut.norm',
'encoder.model.12.conv.norm': 'encoder.layers.12.norm',
'encoder.model.15.conv.norm': 'encoder.layers.15.norm',
}
lowercase__ = {
'decoder.model.0.conv.conv': 'decoder.layers.0.conv',
'decoder.model.1.lstm': 'decoder.layers.1.lstm',
'decoder.model.3.convtr.convtr': 'decoder.layers.3.conv',
'decoder.model.4.block.1.conv.conv': 'decoder.layers.4.block.1.conv',
'decoder.model.4.block.3.conv.conv': 'decoder.layers.4.block.3.conv',
'decoder.model.4.shortcut.conv.conv': 'decoder.layers.4.shortcut.conv',
'decoder.model.6.convtr.convtr': 'decoder.layers.6.conv',
'decoder.model.7.block.1.conv.conv': 'decoder.layers.7.block.1.conv',
'decoder.model.7.block.3.conv.conv': 'decoder.layers.7.block.3.conv',
'decoder.model.7.shortcut.conv.conv': 'decoder.layers.7.shortcut.conv',
'decoder.model.9.convtr.convtr': 'decoder.layers.9.conv',
'decoder.model.10.block.1.conv.conv': 'decoder.layers.10.block.1.conv',
'decoder.model.10.block.3.conv.conv': 'decoder.layers.10.block.3.conv',
'decoder.model.10.shortcut.conv.conv': 'decoder.layers.10.shortcut.conv',
'decoder.model.12.convtr.convtr': 'decoder.layers.12.conv',
'decoder.model.13.block.1.conv.conv': 'decoder.layers.13.block.1.conv',
'decoder.model.13.block.3.conv.conv': 'decoder.layers.13.block.3.conv',
'decoder.model.13.shortcut.conv.conv': 'decoder.layers.13.shortcut.conv',
'decoder.model.15.conv.conv': 'decoder.layers.15.conv',
}
lowercase__ = {
'decoder.model.0.conv.norm': 'decoder.layers.0.norm',
'decoder.model.3.convtr.norm': 'decoder.layers.3.norm',
'decoder.model.4.block.1.conv.norm': 'decoder.layers.4.block.1.norm',
'decoder.model.4.block.3.conv.norm': 'decoder.layers.4.block.3.norm',
'decoder.model.4.shortcut.conv.norm': 'decoder.layers.4.shortcut.norm',
'decoder.model.6.convtr.norm': 'decoder.layers.6.norm',
'decoder.model.7.block.1.conv.norm': 'decoder.layers.7.block.1.norm',
'decoder.model.7.block.3.conv.norm': 'decoder.layers.7.block.3.norm',
'decoder.model.7.shortcut.conv.norm': 'decoder.layers.7.shortcut.norm',
'decoder.model.9.convtr.norm': 'decoder.layers.9.norm',
'decoder.model.10.block.1.conv.norm': 'decoder.layers.10.block.1.norm',
'decoder.model.10.block.3.conv.norm': 'decoder.layers.10.block.3.norm',
'decoder.model.10.shortcut.conv.norm': 'decoder.layers.10.shortcut.norm',
'decoder.model.12.convtr.norm': 'decoder.layers.12.norm',
'decoder.model.13.block.1.conv.norm': 'decoder.layers.13.block.1.norm',
'decoder.model.13.block.3.conv.norm': 'decoder.layers.13.block.3.norm',
'decoder.model.13.shortcut.conv.norm': 'decoder.layers.13.shortcut.norm',
'decoder.model.15.conv.norm': 'decoder.layers.15.norm',
}
lowercase__ = {
**MAPPING_QUANTIZER,
**MAPPING_ENCODER,
**MAPPING_DECODER,
}
lowercase__ = {
**MAPPING_QUANTIZER,
**MAPPING_ENCODER,
**MAPPING_ENCODER_48K,
**MAPPING_DECODER,
**MAPPING_DECODER_48K,
}
lowercase__ = []
lowercase__ = []
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->int:
for attribute in key.split('.' ):
a__: str = getattr(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
if weight_type is not None:
a__: List[str] = getattr(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ).shape
else:
a__: Optional[Any] = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
F'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
F' {value.shape} for {full_name}' )
if weight_type == "weight":
a__: str = value
elif weight_type == "weight_g":
a__: int = value
elif weight_type == "weight_v":
a__: Tuple = value
elif weight_type == "bias":
a__: Dict = value
elif weight_type == "running_mean":
a__: Any = value
elif weight_type == "running_var":
a__: Tuple = value
elif weight_type == "num_batches_tracked":
a__: List[str] = value
elif weight_type == "weight_ih_l0":
a__: List[Any] = value
elif weight_type == "weight_hh_l0":
a__: List[Any] = value
elif weight_type == "bias_ih_l0":
a__: List[Any] = value
elif weight_type == "bias_hh_l0":
a__: List[Any] = value
elif weight_type == "weight_ih_l1":
a__: int = value
elif weight_type == "weight_hh_l1":
a__: str = value
elif weight_type == "bias_ih_l1":
a__: Union[str, Any] = value
elif weight_type == "bias_hh_l1":
a__: Any = value
else:
a__: Union[str, Any] = value
logger.info(F'{key + ("." + weight_type if weight_type is not None else "")} was initialized from {full_name}.' )
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->Dict:
for key in ignore_keys:
if key.endswith('.*' ):
if name.startswith(key[:-1] ):
return True
elif ".*." in key:
a__ , a__: Optional[Any] = key.split('.*.' )
if prefix in name and suffix in name:
return True
elif key in name:
return True
return False
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->List[str]:
a__: List[Any] = []
if model_name == "encodec_24khz" or "encodec_32khz":
a__: Optional[int] = MAPPING_24K
elif model_name == "encodec_48khz":
a__: List[Any] = MAPPING_48K
else:
raise ValueError(F'Unsupported model: {model_name}' )
for name, value in orig_dict.items():
if should_ignore(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
logger.info(F'{name} was ignored' )
continue
a__: int = False
for key, mapped_key in MAPPING.items():
if "*" in key:
a__ , a__: str = key.split('.*.' )
if prefix in name and suffix in name:
a__: List[str] = suffix
if key in name:
# HACK otherwise .embed gets initialized with .embed_avg too
if key.endswith('embed' ) and name.endswith('embed_avg' ):
continue
a__: List[str] = True
if "*" in mapped_key:
a__: List[str] = name.split(_SCREAMING_SNAKE_CASE )[0].split('.' )[-2]
a__: str = mapped_key.replace('*' , _SCREAMING_SNAKE_CASE )
if "weight_g" in name:
a__: int = 'weight_g'
elif "weight_v" in name:
a__: Dict = 'weight_v'
elif "weight_ih_l0" in name:
a__: int = 'weight_ih_l0'
elif "weight_hh_l0" in name:
a__: Union[str, Any] = 'weight_hh_l0'
elif "bias_ih_l0" in name:
a__: Optional[Any] = 'bias_ih_l0'
elif "bias_hh_l0" in name:
a__: Optional[int] = 'bias_hh_l0'
elif "weight_ih_l1" in name:
a__: Dict = 'weight_ih_l1'
elif "weight_hh_l1" in name:
a__: Optional[Any] = 'weight_hh_l1'
elif "bias_ih_l1" in name:
a__: List[str] = 'bias_ih_l1'
elif "bias_hh_l1" in name:
a__: Optional[Any] = 'bias_hh_l1'
elif "bias" in name:
a__: List[str] = 'bias'
elif "weight" in name:
a__: Any = 'weight'
elif "running_mean" in name:
a__: Dict = 'running_mean'
elif "running_var" in name:
a__: Dict = 'running_var'
elif "num_batches_tracked" in name:
a__: Dict = 'num_batches_tracked'
else:
a__: List[str] = None
set_recursively(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
continue
if not is_used:
unused_weights.append(_SCREAMING_SNAKE_CASE )
logger.warning(F'Unused weights: {unused_weights}' )
@torch.no_grad()
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , ) ->int:
if config_path is not None:
a__: Dict = EncodecConfig.from_pretrained(_SCREAMING_SNAKE_CASE )
else:
a__: Tuple = EncodecConfig()
if model_name == "encodec_24khz":
pass # config is already correct
elif model_name == "encodec_32khz":
a__: Any = [8, 5, 4, 4]
a__: List[str] = [2.2]
a__: List[Any] = 64
a__: Dict = 32000
a__: Union[str, Any] = 2048
a__: Union[str, Any] = False
a__: Any = False
a__: Optional[Any] = False
elif model_name == "encodec_48khz":
a__: Optional[int] = [8, 5, 4, 2]
a__: Union[str, Any] = [3.0, 6.0, 12.0, 24.0]
a__: List[str] = 48000
a__: Tuple = 2
a__: Optional[Any] = False
a__: Optional[int] = 'time_group_norm'
a__: Union[str, Any] = True
a__: Dict = 1.0
a__: str = 0.01
else:
raise ValueError(F'Unknown model name: {model_name}' )
a__: Optional[int] = EncodecModel(_SCREAMING_SNAKE_CASE )
a__: List[str] = EncodecFeatureExtractor(
feature_size=config.audio_channels , sampling_rate=config.sampling_rate , chunk_length_s=config.chunk_length_s , overlap=config.overlap , )
feature_extractor.save_pretrained(_SCREAMING_SNAKE_CASE )
a__: int = torch.load(_SCREAMING_SNAKE_CASE )
if "best_state" in original_checkpoint:
# we might have a training state saved, in which case discard the yaml results and just retain the weights
a__: str = original_checkpoint['best_state']
recursively_load_weights(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
model.save_pretrained(_SCREAMING_SNAKE_CASE )
if repo_id:
print('Pushing to the hub...' )
feature_extractor.push_to_hub(_SCREAMING_SNAKE_CASE )
model.push_to_hub(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
lowercase__ = argparse.ArgumentParser()
parser.add_argument(
'--model',
default='encodec_24khz',
type=str,
help='The model to convert. Should be one of \'encodec_24khz\', \'encodec_32khz\', \'encodec_48khz\'.',
)
parser.add_argument('--checkpoint_path', required=True, default=None, type=str, help='Path to original checkpoint')
parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
parser.add_argument(
'--pytorch_dump_folder_path', required=True, default=None, type=str, help='Path to the output PyTorch model.'
)
parser.add_argument(
'--push_to_hub', default=None, type=str, help='Where to upload the converted model on the 🤗 hub.'
)
lowercase__ = parser.parse_args()
convert_checkpoint(
args.model,
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.config_path,
args.push_to_hub,
)
| 290 | 0 |
'''simple docstring'''
def __snake_case ( UpperCAmelCase_ : Union[str, Any] , UpperCAmelCase_ : Union[str, Any] , UpperCAmelCase_ : Tuple , UpperCAmelCase_ : Optional[Any] ):
global f # a global dp table for knapsack
if f[i][j] < 0:
if j < wt[i - 1]:
lowerCamelCase_ = mf_knapsack(i - 1 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
else:
lowerCamelCase_ = max(
mf_knapsack(i - 1 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) , mf_knapsack(i - 1 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , j - wt[i - 1] ) + val[i - 1] , )
lowerCamelCase_ = val
return f[i][j]
def __snake_case ( UpperCAmelCase_ : Any , UpperCAmelCase_ : Union[str, Any] , UpperCAmelCase_ : List[Any] , UpperCAmelCase_ : Optional[int] ):
lowerCamelCase_ = [[0] * (w + 1) for _ in range(n + 1 )]
for i in range(1 , n + 1 ):
for w_ in range(1 , w + 1 ):
if wt[i - 1] <= w_:
lowerCamelCase_ = max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]] , dp[i - 1][w_] )
else:
lowerCamelCase_ = dp[i - 1][w_]
return dp[n][w_], dp
def __snake_case ( UpperCAmelCase_ : int , UpperCAmelCase_ : Dict , UpperCAmelCase_ : Tuple ):
if not (isinstance(_SCREAMING_SNAKE_CASE , (list, tuple) ) and isinstance(_SCREAMING_SNAKE_CASE , (list, tuple) )):
raise ValueError(
"Both the weights and values vectors must be either lists or tuples" )
lowerCamelCase_ = len(_SCREAMING_SNAKE_CASE )
if num_items != len(_SCREAMING_SNAKE_CASE ):
lowerCamelCase_ = (
'The number of weights must be the same as the number of values.\n'
F'''But got {num_items} weights and {len(_SCREAMING_SNAKE_CASE )} values'''
)
raise ValueError(_SCREAMING_SNAKE_CASE )
for i in range(_SCREAMING_SNAKE_CASE ):
if not isinstance(wt[i] , _SCREAMING_SNAKE_CASE ):
lowerCamelCase_ = (
'All weights must be integers but got weight of '
F'''type {type(wt[i] )} at index {i}'''
)
raise TypeError(_SCREAMING_SNAKE_CASE )
lowerCamelCase_ = knapsack(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
lowerCamelCase_ = set()
_construct_solution(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
return optimal_val, example_optional_set
def __snake_case ( UpperCAmelCase_ : List[Any] , UpperCAmelCase_ : List[Any] , UpperCAmelCase_ : int , UpperCAmelCase_ : Any , UpperCAmelCase_ : Tuple ):
# for the current item i at a maximum weight j to be part of an optimal subset,
# the optimal value at (i, j) must be greater than the optimal value at (i-1, j).
# where i - 1 means considering only the previous items at the given maximum weight
if i > 0 and j > 0:
if dp[i - 1][j] == dp[i][j]:
_construct_solution(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , i - 1 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
else:
optimal_set.add(_SCREAMING_SNAKE_CASE )
_construct_solution(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , i - 1 , j - wt[i - 1] , _SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
a_ : List[Any] = [3, 2, 4, 4]
a_ : str = [4, 3, 2, 3]
a_ : Union[str, Any] = 4
a_ : Union[str, Any] = 6
a_ : List[str] = [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
a_ , a_ : Union[str, Any] = knapsack(w, wt, val, n)
print(optimal_solution)
print(mf_knapsack(n, wt, val, w)) # switched the n and w
# testing the dynamic programming problem with example
# the optimal subset for the above example are items 3 and 4
a_ , a_ : List[str] = knapsack_with_example_solution(w, wt, val)
assert optimal_solution == 8
assert optimal_subset == {3, 4}
print("""optimal_value = """, optimal_solution)
print("""An optimal subset corresponding to the optimal value""", optimal_subset)
| 55 | """simple docstring"""
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->Union[str, Any]:
if height >= 1:
move_tower(height - 1 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
move_disk(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
move_tower(height - 1 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->Union[str, Any]:
print('moving disk from' , _SCREAMING_SNAKE_CASE , 'to' , _SCREAMING_SNAKE_CASE )
def __a ( ) ->List[str]:
a__: Dict = int(input('Height of hanoi: ' ).strip() )
move_tower(_SCREAMING_SNAKE_CASE , 'A' , 'B' , 'C' )
if __name__ == "__main__":
main()
| 290 | 0 |
'''simple docstring'''
def _lowercase ( __A ,__A ):
'''simple docstring'''
while a != 0:
__UpperCamelCase = b % a, a
return b
def _lowercase ( __A ,__A ):
'''simple docstring'''
if gcd(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ) != 1:
__UpperCamelCase = f"mod inverse of {a!r} and {m!r} does not exist"
raise ValueError(_SCREAMING_SNAKE_CASE )
__UpperCamelCase = 1, 0, a
__UpperCamelCase = 0, 1, m
while va != 0:
__UpperCamelCase = ua // va
__UpperCamelCase = (ua - q * va), (ua - q * va), (ua - q * va), va, va, va
return ua % m
| 349 | """simple docstring"""
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = False ) ->str:
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
a__: Optional[int] = F'Expected string as input, found {type(_SCREAMING_SNAKE_CASE )}'
raise ValueError(_SCREAMING_SNAKE_CASE )
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
a__: List[str] = F'Expected boolean as use_pascal parameter, found {type(_SCREAMING_SNAKE_CASE )}'
raise ValueError(_SCREAMING_SNAKE_CASE )
a__: int = input_str.split('_' )
a__: List[str] = 0 if use_pascal else 1
a__: List[str] = words[start_index:]
a__: List[str] = [word[0].upper() + word[1:] for word in words_to_capitalize]
a__: List[str] = '' if use_pascal else words[0]
return "".join([initial_word, *capitalized_words] )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 290 | 0 |
'''simple docstring'''
import unittest
from datasets import load_dataset
from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class A ( __lowerCAmelCase , unittest.TestCase ):
lowerCamelCase : Any = None
lowerCamelCase : Dict = BloomTokenizerFast
lowerCamelCase : Any = BloomTokenizerFast
lowerCamelCase : int = True
lowerCamelCase : Union[str, Any] = False
lowerCamelCase : Tuple = """tokenizer_file"""
lowerCamelCase : Optional[int] = {"""bos_token""": """<s>""", """eos_token""": """</s>""", """unk_token""": """<unk>""", """pad_token""": """<pad>"""}
def A__ ( self ) -> List[str]:
'''simple docstring'''
super().setUp()
lowercase__ = BloomTokenizerFast.from_pretrained("""bigscience/tokenizer""" )
tokenizer.save_pretrained(self.tmpdirname )
def A__ ( self , **lowerCamelCase__ ) -> Optional[int]:
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return BloomTokenizerFast.from_pretrained(self.tmpdirname , **lowerCamelCase__ )
def A__ ( self ) -> List[Any]:
'''simple docstring'''
lowercase__ = self.get_rust_tokenizer()
lowercase__ = ['The quick brown fox</s>', 'jumps over the lazy dog</s>']
lowercase__ = [[2_175, 23_714, 73_173, 144_252, 2], [77, 132_619, 3_478, 368, 109_586, 35_433, 2]]
lowercase__ = tokenizer.batch_encode_plus(lowerCamelCase__ )['input_ids']
self.assertListEqual(lowerCamelCase__ , lowerCamelCase__ )
lowercase__ = tokenizer.batch_decode(lowerCamelCase__ )
self.assertListEqual(lowerCamelCase__ , lowerCamelCase__ )
def A__ ( self , lowerCamelCase__=6 ) -> Tuple:
'''simple docstring'''
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
lowercase__ = self.rust_tokenizer_class.from_pretrained(lowerCamelCase__ , **lowerCamelCase__ )
# tokenizer_r.pad_token = None # Hotfixing padding = None
# Simple input
lowercase__ = 'This is a simple input'
lowercase__ = ['This is a simple input 1', 'This is a simple input 2']
lowercase__ = ('This is a simple input', 'This is a pair')
lowercase__ = [
('This is a simple input 1', 'This is a simple input 2'),
('This is a simple pair 1', 'This is a simple pair 2'),
]
# Simple input tests
try:
tokenizer_r.encode(lowerCamelCase__ , max_length=lowerCamelCase__ )
tokenizer_r.encode_plus(lowerCamelCase__ , max_length=lowerCamelCase__ )
tokenizer_r.batch_encode_plus(lowerCamelCase__ , max_length=lowerCamelCase__ )
tokenizer_r.encode(lowerCamelCase__ , max_length=lowerCamelCase__ )
tokenizer_r.batch_encode_plus(lowerCamelCase__ , max_length=lowerCamelCase__ )
except ValueError:
self.fail("""Bloom Tokenizer should be able to deal with padding""" )
lowercase__ = None # Hotfixing padding = None
self.assertRaises(lowerCamelCase__ , tokenizer_r.encode , lowerCamelCase__ , max_length=lowerCamelCase__ , padding="""max_length""" )
# Simple input
self.assertRaises(lowerCamelCase__ , tokenizer_r.encode_plus , lowerCamelCase__ , max_length=lowerCamelCase__ , padding="""max_length""" )
# Simple input
self.assertRaises(
lowerCamelCase__ , tokenizer_r.batch_encode_plus , lowerCamelCase__ , max_length=lowerCamelCase__ , padding="""max_length""" , )
# Pair input
self.assertRaises(lowerCamelCase__ , tokenizer_r.encode , lowerCamelCase__ , max_length=lowerCamelCase__ , padding="""max_length""" )
# Pair input
self.assertRaises(lowerCamelCase__ , tokenizer_r.encode_plus , lowerCamelCase__ , max_length=lowerCamelCase__ , padding="""max_length""" )
# Pair input
self.assertRaises(
lowerCamelCase__ , tokenizer_r.batch_encode_plus , lowerCamelCase__ , max_length=lowerCamelCase__ , padding="""max_length""" , )
def A__ ( self ) -> List[str]:
'''simple docstring'''
lowercase__ = self.get_rust_tokenizer()
lowercase__ = load_dataset("""xnli""" , """all_languages""" , split="""test""" , streaming=lowerCamelCase__ )
lowercase__ = next(iter(lowerCamelCase__ ) )['premise'] # pick up one data
lowercase__ = list(sample_data.values() )
lowercase__ = list(map(tokenizer.encode , lowerCamelCase__ ) )
lowercase__ = [tokenizer.decode(lowerCamelCase__ , clean_up_tokenization_spaces=lowerCamelCase__ ) for x in output_tokens]
self.assertListEqual(lowerCamelCase__ , lowerCamelCase__ )
def A__ ( self ) -> List[str]:
'''simple docstring'''
self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map ) , 1 )
self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values() )[0] ) , 1 )
| 164 | """simple docstring"""
class __snake_case :
def __init__( self , lowercase , lowercase=None , lowercase=None) -> List[str]:
'''simple docstring'''
a__: Dict = data
a__: List[Any] = previous
a__: Any = next_node
def __str__( self) -> str:
'''simple docstring'''
return f'{self.data}'
def lowerCamelCase_ ( self) -> int:
'''simple docstring'''
return self.data
def lowerCamelCase_ ( self) -> Union[str, Any]:
'''simple docstring'''
return self.next
def lowerCamelCase_ ( self) -> Optional[Any]:
'''simple docstring'''
return self.previous
class __snake_case :
def __init__( self , lowercase) -> Dict:
'''simple docstring'''
a__: List[Any] = head
def __iter__( self) -> List[Any]:
'''simple docstring'''
return self
def lowerCamelCase_ ( self) -> Optional[Any]:
'''simple docstring'''
if not self.current:
raise StopIteration
else:
a__: Dict = self.current.get_data()
a__: Optional[Any] = self.current.get_next()
return value
class __snake_case :
def __init__( self) -> Dict:
'''simple docstring'''
a__: List[Any] = None # First node in list
a__: Optional[int] = None # Last node in list
def __str__( self) -> Optional[Any]:
'''simple docstring'''
a__: Dict = self.head
a__: Optional[Any] = []
while current is not None:
nodes.append(current.get_data())
a__: str = current.get_next()
return " ".join(str(lowercase) for node in nodes)
def __contains__( self , lowercase) -> Optional[int]:
'''simple docstring'''
a__: Optional[int] = self.head
while current:
if current.get_data() == value:
return True
a__: Dict = current.get_next()
return False
def __iter__( self) -> int:
'''simple docstring'''
return LinkedListIterator(self.head)
def lowerCamelCase_ ( self) -> List[Any]:
'''simple docstring'''
if self.head:
return self.head.get_data()
return None
def lowerCamelCase_ ( self) -> Optional[Any]:
'''simple docstring'''
if self.tail:
return self.tail.get_data()
return None
def lowerCamelCase_ ( self , lowercase) -> None:
'''simple docstring'''
if self.head is None:
a__: Optional[Any] = node
a__: Optional[Any] = node
else:
self.insert_before_node(self.head , lowercase)
def lowerCamelCase_ ( self , lowercase) -> None:
'''simple docstring'''
if self.head is None:
self.set_head(lowercase)
else:
self.insert_after_node(self.tail , lowercase)
def lowerCamelCase_ ( self , lowercase) -> None:
'''simple docstring'''
a__: Tuple = Node(lowercase)
if self.head is None:
self.set_head(lowercase)
else:
self.set_tail(lowercase)
def lowerCamelCase_ ( self , lowercase , lowercase) -> None:
'''simple docstring'''
a__: Union[str, Any] = node
a__: Optional[Any] = node.previous
if node.get_previous() is None:
a__: Tuple = node_to_insert
else:
a__: int = node_to_insert
a__: Optional[int] = node_to_insert
def lowerCamelCase_ ( self , lowercase , lowercase) -> None:
'''simple docstring'''
a__: Optional[int] = node
a__: Tuple = node.next
if node.get_next() is None:
a__: Optional[int] = node_to_insert
else:
a__: Any = node_to_insert
a__: str = node_to_insert
def lowerCamelCase_ ( self , lowercase , lowercase) -> None:
'''simple docstring'''
a__: Any = 1
a__: Tuple = Node(lowercase)
a__: Tuple = self.head
while node:
if current_position == position:
self.insert_before_node(lowercase , lowercase)
return
current_position += 1
a__: List[Any] = node.next
self.insert_after_node(self.tail , lowercase)
def lowerCamelCase_ ( self , lowercase) -> Node:
'''simple docstring'''
a__: Tuple = self.head
while node:
if node.get_data() == item:
return node
a__: List[str] = node.get_next()
raise Exception('Node not found')
def lowerCamelCase_ ( self , lowercase) -> Any:
'''simple docstring'''
if (node := self.get_node(lowercase)) is not None:
if node == self.head:
a__: Any = self.head.get_next()
if node == self.tail:
a__: List[Any] = self.tail.get_previous()
self.remove_node_pointers(lowercase)
@staticmethod
def lowerCamelCase_ ( lowercase) -> None:
'''simple docstring'''
if node.get_next():
a__: Any = node.previous
if node.get_previous():
a__: List[str] = node.next
a__: int = None
a__: Union[str, Any] = None
def lowerCamelCase_ ( self) -> str:
'''simple docstring'''
return self.head is None
def __a ( ) ->None:
pass
if __name__ == "__main__":
import doctest
doctest.testmod()
| 290 | 0 |
"""simple docstring"""
from collections.abc import Sequence
from queue import Queue
class a__ :
def __init__( self , _a , _a , _a , _a=None , _a=None ):
lowercase : Any = start
lowercase : Tuple = end
lowercase : List[Any] = val
lowercase : Any = (start + end) // 2
lowercase : Tuple = left
lowercase : Tuple = right
def __repr__( self ):
return f"""SegmentTreeNode(start={self.start}, end={self.end}, val={self.val})"""
class a__ :
def __init__( self , _a , _a ):
lowercase : Optional[Any] = collection
lowercase : int = function
if self.collection:
lowercase : Optional[Any] = self._build_tree(0 , len(_a ) - 1 )
def __magic_name__ ( self , _a , _a ):
self._update_tree(self.root , _a , _a )
def __magic_name__ ( self , _a , _a ):
return self._query_range(self.root , _a , _a )
def __magic_name__ ( self , _a , _a ):
if start == end:
return SegmentTreeNode(_a , _a , self.collection[start] )
lowercase : Any = (start + end) // 2
lowercase : List[Any] = self._build_tree(_a , _a )
lowercase : int = self._build_tree(mid + 1 , _a )
return SegmentTreeNode(_a , _a , self.fn(left.val , right.val ) , _a , _a )
def __magic_name__ ( self , _a , _a , _a ):
if node.start == i and node.end == i:
lowercase : Optional[Any] = val
return
if i <= node.mid:
self._update_tree(node.left , _a , _a )
else:
self._update_tree(node.right , _a , _a )
lowercase : str = self.fn(node.left.val , node.right.val )
def __magic_name__ ( self , _a , _a , _a ):
if node.start == i and node.end == j:
return node.val
if i <= node.mid:
if j <= node.mid:
# range in left child tree
return self._query_range(node.left , _a , _a )
else:
# range in left child tree and right child tree
return self.fn(
self._query_range(node.left , _a , node.mid ) , self._query_range(node.right , node.mid + 1 , _a ) , )
else:
# range in right child tree
return self._query_range(node.right , _a , _a )
def __magic_name__ ( self ):
if self.root is not None:
lowercase : int = Queue()
queue.put(self.root )
while not queue.empty():
lowercase : int = queue.get()
yield node
if node.left is not None:
queue.put(node.left )
if node.right is not None:
queue.put(node.right )
if __name__ == "__main__":
import operator
for fn in [operator.add, max, min]:
print("""*""" * 50)
_A : str = SegmentTree([2, 1, 5, 3, 4], fn)
for node in arr.traverse():
print(node)
print()
arr.update(1, 5)
for node in arr.traverse():
print(node)
print()
print(arr.query_range(3, 4)) # 7
print(arr.query_range(2, 2)) # 5
print(arr.query_range(1, 3)) # 13
print()
| 202 | """simple docstring"""
import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable
import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization import from_bytes, to_bytes
from flax.training import train_state
from flax.training.common_utils import shard
from tqdm.auto import tqdm
from transformers import BigBirdConfig, FlaxBigBirdForQuestionAnswering
from transformers.models.big_bird.modeling_flax_big_bird import FlaxBigBirdForQuestionAnsweringModule
class __snake_case ( __lowerCAmelCase ):
a__ = 42
a__ = jnp.floataa
a__ = True
def lowerCamelCase_ ( self) -> int:
'''simple docstring'''
super().setup()
a__: int = nn.Dense(5 , dtype=self.dtype)
def __call__( self , *lowercase , **lowercase) -> Dict:
'''simple docstring'''
a__: Dict = super().__call__(*lowercase , **lowercase)
a__: str = self.cls(outputs[2])
return outputs[:2] + (cls_out,)
class __snake_case ( __lowerCAmelCase ):
a__ = FlaxBigBirdForNaturalQuestionsModule
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->Optional[Any]:
def cross_entropy(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None ):
a__: Any = logits.shape[-1]
a__: List[Any] = (labels[..., None] == jnp.arange(_SCREAMING_SNAKE_CASE )[None]).astype('f4' )
a__: List[str] = jax.nn.log_softmax(_SCREAMING_SNAKE_CASE , axis=-1 )
a__: Dict = -jnp.sum(labels * logits , axis=-1 )
if reduction is not None:
a__: str = reduction(_SCREAMING_SNAKE_CASE )
return loss
a__: Tuple = partial(_SCREAMING_SNAKE_CASE , reduction=jnp.mean )
a__: List[str] = cross_entropy(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
a__: Union[str, Any] = cross_entropy(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
a__: Any = cross_entropy(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
return (start_loss + end_loss + pooled_loss) / 3
@dataclass
class __snake_case :
a__ = "google/bigbird-roberta-base"
a__ = 3000
a__ = 1_0500
a__ = 128
a__ = 3
a__ = 1
a__ = 5
# tx_args
a__ = 3e-5
a__ = 0.0
a__ = 2_0000
a__ = 0.0095
a__ = "bigbird-roberta-natural-questions"
a__ = "training-expt"
a__ = "data/nq-training.jsonl"
a__ = "data/nq-validation.jsonl"
def lowerCamelCase_ ( self) -> Optional[Any]:
'''simple docstring'''
os.makedirs(self.base_dir , exist_ok=lowercase)
a__: str = os.path.join(self.base_dir , self.save_dir)
a__: List[str] = self.batch_size_per_device * jax.device_count()
@dataclass
class __snake_case :
a__ = 42
a__ = 4096 # no dynamic padding on TPUs
def __call__( self , lowercase) -> List[Any]:
'''simple docstring'''
a__: int = self.collate_fn(lowercase)
a__: Optional[int] = jax.tree_util.tree_map(lowercase , lowercase)
return batch
def lowerCamelCase_ ( self , lowercase) -> Dict:
'''simple docstring'''
a__ , a__: Dict = self.fetch_inputs(features['input_ids'])
a__: List[Any] = {
'input_ids': jnp.array(lowercase , dtype=jnp.intaa),
'attention_mask': jnp.array(lowercase , dtype=jnp.intaa),
'start_labels': jnp.array(features['start_token'] , dtype=jnp.intaa),
'end_labels': jnp.array(features['end_token'] , dtype=jnp.intaa),
'pooled_labels': jnp.array(features['category'] , dtype=jnp.intaa),
}
return batch
def lowerCamelCase_ ( self , lowercase) -> List[str]:
'''simple docstring'''
a__: List[Any] = [self._fetch_inputs(lowercase) for ids in input_ids]
return zip(*lowercase)
def lowerCamelCase_ ( self , lowercase) -> Dict:
'''simple docstring'''
a__: Union[str, Any] = [1 for _ in range(len(lowercase))]
while len(lowercase) < self.max_length:
input_ids.append(self.pad_id)
attention_mask.append(0)
return input_ids, attention_mask
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None ) ->List[Any]:
if seed is not None:
a__: int = dataset.shuffle(seed=_SCREAMING_SNAKE_CASE )
for i in range(len(_SCREAMING_SNAKE_CASE ) // batch_size ):
a__: Union[str, Any] = dataset[i * batch_size : (i + 1) * batch_size]
yield dict(_SCREAMING_SNAKE_CASE )
@partial(jax.pmap , axis_name='batch' )
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ) ->Any:
def loss_fn(_SCREAMING_SNAKE_CASE ):
a__: str = model_inputs.pop('start_labels' )
a__: Dict = model_inputs.pop('end_labels' )
a__: Optional[int] = model_inputs.pop('pooled_labels' )
a__: Optional[Any] = state.apply_fn(**_SCREAMING_SNAKE_CASE , params=_SCREAMING_SNAKE_CASE , dropout_rng=_SCREAMING_SNAKE_CASE , train=_SCREAMING_SNAKE_CASE )
a__ , a__ , a__: Optional[int] = outputs
return state.loss_fn(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , )
a__ , a__: Union[str, Any] = jax.random.split(_SCREAMING_SNAKE_CASE )
a__: List[Any] = jax.value_and_grad(_SCREAMING_SNAKE_CASE )
a__ , a__: str = grad_fn(state.params )
a__: Optional[int] = jax.lax.pmean({'loss': loss} , axis_name='batch' )
a__: int = jax.lax.pmean(_SCREAMING_SNAKE_CASE , 'batch' )
a__: Union[str, Any] = state.apply_gradients(grads=_SCREAMING_SNAKE_CASE )
return state, metrics, new_drp_rng
@partial(jax.pmap , axis_name='batch' )
def __a ( _SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ) ->Optional[Any]:
a__: Optional[int] = model_inputs.pop('start_labels' )
a__: int = model_inputs.pop('end_labels' )
a__: Dict = model_inputs.pop('pooled_labels' )
a__: Union[str, Any] = state.apply_fn(**_SCREAMING_SNAKE_CASE , params=state.params , train=_SCREAMING_SNAKE_CASE )
a__ , a__ , a__: int = outputs
a__: Optional[int] = state.loss_fn(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
a__: Tuple = jax.lax.pmean({'loss': loss} , axis_name='batch' )
return metrics
class __snake_case ( train_state.TrainState ):
a__ = struct.field(pytree_node=__lowerCAmelCase )
@dataclass
class __snake_case :
a__ = 42
a__ = 42
a__ = 42
a__ = 42
a__ = 42
a__ = 42
a__ = None
def lowerCamelCase_ ( self , lowercase , lowercase , lowercase , lowercase=None) -> Optional[int]:
'''simple docstring'''
a__: Dict = model.params
a__: Any = TrainState.create(
apply_fn=model.__call__ , params=lowercase , tx=lowercase , loss_fn=lowercase , )
if ckpt_dir is not None:
a__ , a__ , a__ , a__ , a__: Any = restore_checkpoint(lowercase , lowercase)
a__: Any = {
'lr': args.lr,
'init_lr': args.init_lr,
'warmup_steps': args.warmup_steps,
'num_train_steps': num_train_steps,
'weight_decay': args.weight_decay,
}
a__ , a__: str = build_tx(**lowercase)
a__: Optional[Any] = train_state.TrainState(
step=lowercase , apply_fn=model.__call__ , params=lowercase , tx=lowercase , opt_state=lowercase , )
a__: int = args
a__: Union[str, Any] = data_collator
a__: Any = lr
a__: Dict = params
a__: Tuple = jax_utils.replicate(lowercase)
return state
def lowerCamelCase_ ( self , lowercase , lowercase , lowercase) -> int:
'''simple docstring'''
a__: int = self.args
a__: str = len(lowercase) // args.batch_size
a__: Tuple = jax.random.PRNGKey(0)
a__: List[Any] = jax.random.split(lowercase , jax.device_count())
for epoch in range(args.max_epochs):
a__: str = jnp.array(0 , dtype=jnp.floataa)
a__: Tuple = get_batched_dataset(lowercase , args.batch_size , seed=lowercase)
a__: Optional[int] = 0
for batch in tqdm(lowercase , total=lowercase , desc=f'Running EPOCH-{epoch}'):
a__: List[str] = self.data_collator(lowercase)
a__ , a__ , a__: int = self.train_step_fn(lowercase , lowercase , **lowercase)
running_loss += jax_utils.unreplicate(metrics['loss'])
i += 1
if i % args.logging_steps == 0:
a__: List[Any] = jax_utils.unreplicate(state.step)
a__: Tuple = running_loss.item() / i
a__: Optional[Any] = self.scheduler_fn(state_step - 1)
a__: List[Any] = self.evaluate(lowercase , lowercase)
a__: List[str] = {
'step': state_step.item(),
'eval_loss': eval_loss.item(),
'tr_loss': tr_loss,
'lr': lr.item(),
}
tqdm.write(str(lowercase))
self.logger.log(lowercase , commit=lowercase)
if i % args.save_steps == 0:
self.save_checkpoint(args.save_dir + f'-e{epoch}-s{i}' , state=lowercase)
def lowerCamelCase_ ( self , lowercase , lowercase) -> List[Any]:
'''simple docstring'''
a__: Tuple = get_batched_dataset(lowercase , self.args.batch_size)
a__: Dict = len(lowercase) // self.args.batch_size
a__: Tuple = jnp.array(0 , dtype=jnp.floataa)
a__: List[Any] = 0
for batch in tqdm(lowercase , total=lowercase , desc='Evaluating ... '):
a__: str = self.data_collator(lowercase)
a__: List[str] = self.val_step_fn(lowercase , **lowercase)
running_loss += jax_utils.unreplicate(metrics['loss'])
i += 1
return running_loss / i
def lowerCamelCase_ ( self , lowercase , lowercase) -> Any:
'''simple docstring'''
a__: List[Any] = jax_utils.unreplicate(lowercase)
print(f'SAVING CHECKPOINT IN {save_dir}' , end=' ... ')
self.model_save_fn(lowercase , params=state.params)
with open(os.path.join(lowercase , 'opt_state.msgpack') , 'wb') as f:
f.write(to_bytes(state.opt_state))
joblib.dump(self.args , os.path.join(lowercase , 'args.joblib'))
joblib.dump(self.data_collator , os.path.join(lowercase , 'data_collator.joblib'))
with open(os.path.join(lowercase , 'training_state.json') , 'w') as f:
json.dump({'step': state.step.item()} , lowercase)
print('DONE')
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->Optional[Any]:
print(F'RESTORING CHECKPOINT FROM {save_dir}' , end=' ... ' )
with open(os.path.join(_SCREAMING_SNAKE_CASE , 'flax_model.msgpack' ) , 'rb' ) as f:
a__: int = from_bytes(state.params , f.read() )
with open(os.path.join(_SCREAMING_SNAKE_CASE , 'opt_state.msgpack' ) , 'rb' ) as f:
a__: Optional[Any] = from_bytes(state.opt_state , f.read() )
a__: Optional[Any] = joblib.load(os.path.join(_SCREAMING_SNAKE_CASE , 'args.joblib' ) )
a__: int = joblib.load(os.path.join(_SCREAMING_SNAKE_CASE , 'data_collator.joblib' ) )
with open(os.path.join(_SCREAMING_SNAKE_CASE , 'training_state.json' ) , 'r' ) as f:
a__: Any = json.load(_SCREAMING_SNAKE_CASE )
a__: Optional[Any] = training_state['step']
print('DONE' )
return params, opt_state, step, args, data_collator
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->Optional[int]:
a__: str = num_train_steps - warmup_steps
a__: str = optax.linear_schedule(init_value=_SCREAMING_SNAKE_CASE , end_value=_SCREAMING_SNAKE_CASE , transition_steps=_SCREAMING_SNAKE_CASE )
a__: List[Any] = optax.linear_schedule(init_value=_SCREAMING_SNAKE_CASE , end_value=1e-7 , transition_steps=_SCREAMING_SNAKE_CASE )
a__: int = optax.join_schedules(schedules=[warmup_fn, decay_fn] , boundaries=[warmup_steps] )
return lr
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->Tuple:
def weight_decay_mask(_SCREAMING_SNAKE_CASE ):
a__: List[Any] = traverse_util.flatten_dict(_SCREAMING_SNAKE_CASE )
a__: List[str] = {k: (v[-1] != 'bias' and v[-2:] != ('LayerNorm', 'scale')) for k, v in params.items()}
return traverse_util.unflatten_dict(_SCREAMING_SNAKE_CASE )
a__: List[str] = scheduler_fn(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
a__: Any = optax.adamw(learning_rate=_SCREAMING_SNAKE_CASE , weight_decay=_SCREAMING_SNAKE_CASE , mask=_SCREAMING_SNAKE_CASE )
return tx, lr
| 290 | 0 |
'''simple docstring'''
import inspect
import unittest
from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_MAPPING,
SegformerForImageClassification,
SegformerForSemanticSegmentation,
SegformerModel,
)
from transformers.models.segformer.modeling_segformer import SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import SegformerImageProcessor
class A_ ( __lowerCAmelCase ):
def lowercase ( self : Tuple ):
_UpperCAmelCase = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(snake_case_ , "hidden_sizes" ) )
self.parent.assertTrue(hasattr(snake_case_ , "num_attention_heads" ) )
self.parent.assertTrue(hasattr(snake_case_ , "num_encoder_blocks" ) )
class A_ :
def __init__( self : List[str] , snake_case_ : Optional[Any] , snake_case_ : Tuple=1_3 , snake_case_ : Optional[int]=6_4 , snake_case_ : Dict=3 , snake_case_ : Dict=4 , snake_case_ : List[str]=[2, 2, 2, 2] , snake_case_ : int=[8, 4, 2, 1] , snake_case_ : int=[1_6, 3_2, 6_4, 1_2_8] , snake_case_ : Dict=[1, 4, 8, 1_6] , snake_case_ : Optional[Any]=[1, 2, 4, 8] , snake_case_ : Any=True , snake_case_ : Tuple=True , snake_case_ : List[str]="gelu" , snake_case_ : Dict=0.1 , snake_case_ : int=0.1 , snake_case_ : Optional[Any]=0.0_2 , snake_case_ : List[str]=3 , snake_case_ : Dict=None , ):
_UpperCAmelCase = parent
_UpperCAmelCase = batch_size
_UpperCAmelCase = image_size
_UpperCAmelCase = num_channels
_UpperCAmelCase = num_encoder_blocks
_UpperCAmelCase = sr_ratios
_UpperCAmelCase = depths
_UpperCAmelCase = hidden_sizes
_UpperCAmelCase = downsampling_rates
_UpperCAmelCase = num_attention_heads
_UpperCAmelCase = is_training
_UpperCAmelCase = use_labels
_UpperCAmelCase = hidden_act
_UpperCAmelCase = hidden_dropout_prob
_UpperCAmelCase = attention_probs_dropout_prob
_UpperCAmelCase = initializer_range
_UpperCAmelCase = num_labels
_UpperCAmelCase = scope
def lowercase ( self : int ):
_UpperCAmelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_UpperCAmelCase = None
if self.use_labels:
_UpperCAmelCase = ids_tensor([self.batch_size, self.image_size, self.image_size] , self.num_labels )
_UpperCAmelCase = self.get_config()
return config, pixel_values, labels
def lowercase ( self : int ):
return SegformerConfig(
image_size=self.image_size , num_channels=self.num_channels , num_encoder_blocks=self.num_encoder_blocks , depths=self.depths , hidden_sizes=self.hidden_sizes , num_attention_heads=self.num_attention_heads , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , initializer_range=self.initializer_range , )
def lowercase ( self : int , snake_case_ : str , snake_case_ : str , snake_case_ : Optional[int] ):
_UpperCAmelCase = SegformerModel(config=snake_case_ )
model.to(snake_case_ )
model.eval()
_UpperCAmelCase = model(snake_case_ )
_UpperCAmelCase = self.image_size // (self.downsampling_rates[-1] * 2)
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width) )
def lowercase ( self : Dict , snake_case_ : List[str] , snake_case_ : Any , snake_case_ : Optional[Any] ):
_UpperCAmelCase = self.num_labels
_UpperCAmelCase = SegformerForSemanticSegmentation(snake_case_ )
model.to(snake_case_ )
model.eval()
_UpperCAmelCase = model(snake_case_ )
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4) )
_UpperCAmelCase = model(snake_case_ , labels=snake_case_ )
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4) )
self.parent.assertGreater(result.loss , 0.0 )
def lowercase ( self : Dict , snake_case_ : int , snake_case_ : List[Any] , snake_case_ : List[str] ):
_UpperCAmelCase = 1
_UpperCAmelCase = SegformerForSemanticSegmentation(config=snake_case_ )
model.to(snake_case_ )
model.eval()
_UpperCAmelCase = torch.randint(0 , 1 , (self.batch_size, self.image_size, self.image_size) ).to(snake_case_ )
_UpperCAmelCase = model(snake_case_ , labels=snake_case_ )
self.parent.assertGreater(result.loss , 0.0 )
def lowercase ( self : Any ):
_UpperCAmelCase = self.prepare_config_and_inputs()
_UpperCAmelCase = config_and_inputs
_UpperCAmelCase = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class A_ ( __lowerCAmelCase , __lowerCAmelCase , unittest.TestCase ):
_lowerCamelCase : str = (
(
SegformerModel,
SegformerForSemanticSegmentation,
SegformerForImageClassification,
)
if is_torch_available()
else ()
)
_lowerCamelCase : Any = (
{
"""feature-extraction""": SegformerModel,
"""image-classification""": SegformerForImageClassification,
"""image-segmentation""": SegformerForSemanticSegmentation,
}
if is_torch_available()
else {}
)
_lowerCamelCase : int = True
_lowerCamelCase : int = False
_lowerCamelCase : List[Any] = False
_lowerCamelCase : List[Any] = False
def lowercase ( self : Optional[int] ):
_UpperCAmelCase = SegformerModelTester(self )
_UpperCAmelCase = SegformerConfigTester(self , config_class=snake_case_ )
def lowercase ( self : Any ):
self.config_tester.run_common_tests()
def lowercase ( self : Optional[Any] ):
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case_ )
def lowercase ( self : Dict ):
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_binary_image_segmentation(*snake_case_ )
def lowercase ( self : str ):
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_segmentation(*snake_case_ )
@unittest.skip("SegFormer does not use inputs_embeds" )
def lowercase ( self : Optional[Any] ):
pass
@unittest.skip("SegFormer does not have get_input_embeddings method and get_output_embeddings methods" )
def lowercase ( self : int ):
pass
def lowercase ( self : Tuple ):
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCAmelCase = model_class(snake_case_ )
_UpperCAmelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_UpperCAmelCase = [*signature.parameters.keys()]
_UpperCAmelCase = ['pixel_values']
self.assertListEqual(arg_names[:1] , snake_case_ )
def lowercase ( self : Tuple ):
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
_UpperCAmelCase = True
for model_class in self.all_model_classes:
_UpperCAmelCase = True
_UpperCAmelCase = False
_UpperCAmelCase = True
_UpperCAmelCase = model_class(snake_case_ )
model.to(snake_case_ )
model.eval()
with torch.no_grad():
_UpperCAmelCase = model(**self._prepare_for_class(snake_case_ , snake_case_ ) )
_UpperCAmelCase = outputs.attentions
_UpperCAmelCase = sum(self.model_tester.depths )
self.assertEqual(len(snake_case_ ) , snake_case_ )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
_UpperCAmelCase = True
_UpperCAmelCase = model_class(snake_case_ )
model.to(snake_case_ )
model.eval()
with torch.no_grad():
_UpperCAmelCase = model(**self._prepare_for_class(snake_case_ , snake_case_ ) )
_UpperCAmelCase = outputs.attentions
self.assertEqual(len(snake_case_ ) , snake_case_ )
# verify the first attentions (first block, first layer)
_UpperCAmelCase = (self.model_tester.image_size // 4) ** 2
_UpperCAmelCase = (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len] , )
# verify the last attentions (last block, last layer)
_UpperCAmelCase = (self.model_tester.image_size // 3_2) ** 2
_UpperCAmelCase = (self.model_tester.image_size // (3_2 * self.model_tester.sr_ratios[-1])) ** 2
self.assertListEqual(
list(attentions[-1].shape[-3:] ) , [self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len] , )
_UpperCAmelCase = len(snake_case_ )
# Check attention is always last and order is fine
_UpperCAmelCase = True
_UpperCAmelCase = True
_UpperCAmelCase = model_class(snake_case_ )
model.to(snake_case_ )
model.eval()
with torch.no_grad():
_UpperCAmelCase = model(**self._prepare_for_class(snake_case_ , snake_case_ ) )
self.assertEqual(out_len + 1 , len(snake_case_ ) )
_UpperCAmelCase = outputs.attentions
self.assertEqual(len(snake_case_ ) , snake_case_ )
# verify the first attentions (first block, first layer)
_UpperCAmelCase = (self.model_tester.image_size // 4) ** 2
_UpperCAmelCase = (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len] , )
def lowercase ( self : Optional[Any] ):
def check_hidden_states_output(snake_case_ : Tuple , snake_case_ : Dict , snake_case_ : Any ):
_UpperCAmelCase = model_class(snake_case_ )
model.to(snake_case_ )
model.eval()
with torch.no_grad():
_UpperCAmelCase = model(**self._prepare_for_class(snake_case_ , snake_case_ ) )
_UpperCAmelCase = outputs.hidden_states
_UpperCAmelCase = self.model_tester.num_encoder_blocks
self.assertEqual(len(snake_case_ ) , snake_case_ )
# verify the first hidden states (first block)
self.assertListEqual(
list(hidden_states[0].shape[-3:] ) , [
self.model_tester.hidden_sizes[0],
self.model_tester.image_size // 4,
self.model_tester.image_size // 4,
] , )
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCAmelCase = True
check_hidden_states_output(snake_case_ , snake_case_ , snake_case_ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_UpperCAmelCase = True
check_hidden_states_output(snake_case_ , snake_case_ , snake_case_ )
def lowercase ( self : List[str] ):
if not self.model_tester.is_training:
return
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
_UpperCAmelCase = True
for model_class in self.all_model_classes:
if model_class in get_values(snake_case_ ):
continue
_UpperCAmelCase = model_class(snake_case_ )
model.to(snake_case_ )
model.train()
_UpperCAmelCase = self._prepare_for_class(snake_case_ , snake_case_ , return_labels=snake_case_ )
_UpperCAmelCase = model(**snake_case_ ).loss
loss.backward()
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
def lowercase ( self : Optional[Any] ):
pass
@slow
def lowercase ( self : List[str] ):
for model_name in SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCAmelCase = SegformerModel.from_pretrained(snake_case_ )
self.assertIsNotNone(snake_case_ )
def UpperCAmelCase_ ( ) -> Optional[Any]:
'''simple docstring'''
_UpperCAmelCase = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
class A_ ( unittest.TestCase ):
@slow
def lowercase ( self : str ):
_UpperCAmelCase = SegformerImageProcessor(
image_scale=(5_1_2, 5_1_2) , keep_ratio=snake_case_ , align=snake_case_ , do_random_crop=snake_case_ )
_UpperCAmelCase = SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512" ).to(
snake_case_ )
_UpperCAmelCase = prepare_img()
_UpperCAmelCase = image_processor(images=snake_case_ , return_tensors="pt" )
_UpperCAmelCase = encoded_inputs.pixel_values.to(snake_case_ )
with torch.no_grad():
_UpperCAmelCase = model(snake_case_ )
_UpperCAmelCase = torch.Size((1, model.config.num_labels, 1_2_8, 1_2_8) )
self.assertEqual(outputs.logits.shape , snake_case_ )
_UpperCAmelCase = torch.tensor(
[
[[-4.6_3_1_0, -5.5_2_3_2, -6.2_3_5_6], [-5.1_9_2_1, -6.1_4_4_4, -6.5_9_9_6], [-5.4_4_2_4, -6.2_7_9_0, -6.7_5_7_4]],
[[-1_2.1_3_9_1, -1_3.3_1_2_2, -1_3.9_5_5_4], [-1_2.8_7_3_2, -1_3.9_3_5_2, -1_4.3_5_6_3], [-1_2.9_4_3_8, -1_3.8_2_2_6, -1_4.2_5_1_3]],
[[-1_2.5_1_3_4, -1_3.4_6_8_6, -1_4.4_9_1_5], [-1_2.8_6_6_9, -1_4.4_3_4_3, -1_4.7_7_5_8], [-1_3.2_5_2_3, -1_4.5_8_1_9, -1_5.0_6_9_4]],
] ).to(snake_case_ )
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3] , snake_case_ , atol=1e-4 ) )
@slow
def lowercase ( self : List[str] ):
_UpperCAmelCase = SegformerImageProcessor(
image_scale=(5_1_2, 5_1_2) , keep_ratio=snake_case_ , align=snake_case_ , do_random_crop=snake_case_ )
_UpperCAmelCase = SegformerForSemanticSegmentation.from_pretrained(
"nvidia/segformer-b1-finetuned-cityscapes-1024-1024" ).to(snake_case_ )
_UpperCAmelCase = prepare_img()
_UpperCAmelCase = image_processor(images=snake_case_ , return_tensors="pt" )
_UpperCAmelCase = encoded_inputs.pixel_values.to(snake_case_ )
with torch.no_grad():
_UpperCAmelCase = model(snake_case_ )
_UpperCAmelCase = torch.Size((1, model.config.num_labels, 1_2_8, 1_2_8) )
self.assertEqual(outputs.logits.shape , snake_case_ )
_UpperCAmelCase = torch.tensor(
[
[[-1_3.5_7_4_8, -1_3.9_1_1_1, -1_2.6_5_0_0], [-1_4.3_5_0_0, -1_5.3_6_8_3, -1_4.2_3_2_8], [-1_4.7_5_3_2, -1_6.0_4_2_4, -1_5.6_0_8_7]],
[[-1_7.1_6_5_1, -1_5.8_7_2_5, -1_2.9_6_5_3], [-1_7.2_5_8_0, -1_7.3_7_1_8, -1_4.8_2_2_3], [-1_6.6_0_5_8, -1_6.8_7_8_3, -1_6.7_4_5_2]],
[[-3.6_4_5_6, -3.0_2_0_9, -1.4_2_0_3], [-3.0_7_9_7, -3.1_9_5_9, -2.0_0_0_0], [-1.8_7_5_7, -1.9_2_1_7, -1.6_9_9_7]],
] ).to(snake_case_ )
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3] , snake_case_ , atol=1e-1 ) )
@slow
def lowercase ( self : Optional[int] ):
_UpperCAmelCase = SegformerImageProcessor(
image_scale=(5_1_2, 5_1_2) , keep_ratio=snake_case_ , align=snake_case_ , do_random_crop=snake_case_ )
_UpperCAmelCase = SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512" ).to(
snake_case_ )
_UpperCAmelCase = prepare_img()
_UpperCAmelCase = image_processor(images=snake_case_ , return_tensors="pt" )
_UpperCAmelCase = encoded_inputs.pixel_values.to(snake_case_ )
with torch.no_grad():
_UpperCAmelCase = model(snake_case_ )
_UpperCAmelCase = outputs.logits.detach().cpu()
_UpperCAmelCase = image_processor.post_process_semantic_segmentation(outputs=snake_case_ , target_sizes=[(5_0_0, 3_0_0)] )
_UpperCAmelCase = torch.Size((5_0_0, 3_0_0) )
self.assertEqual(segmentation[0].shape , snake_case_ )
_UpperCAmelCase = image_processor.post_process_semantic_segmentation(outputs=snake_case_ )
_UpperCAmelCase = torch.Size((1_2_8, 1_2_8) )
self.assertEqual(segmentation[0].shape , snake_case_ )
| 22 | """simple docstring"""
from typing import List, Optional, Tuple, Union
import PIL
import torch
from torchvision import transforms
from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor
lowercase__ = transforms.Compose(
[
transforms.Resize((256, 256)),
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5]),
]
)
def __a ( _SCREAMING_SNAKE_CASE ) ->Any:
if isinstance(_SCREAMING_SNAKE_CASE , torch.Tensor ):
return image
elif isinstance(_SCREAMING_SNAKE_CASE , PIL.Image.Image ):
a__: Optional[int] = [image]
a__: str = [trans(img.convert('RGB' ) ) for img in image]
a__: Any = torch.stack(_SCREAMING_SNAKE_CASE )
return image
class __snake_case ( __lowerCAmelCase ):
def __init__( self , lowercase , lowercase) -> Optional[int]:
'''simple docstring'''
super().__init__()
# make sure scheduler can always be converted to DDIM
a__: Dict = DDIMScheduler.from_config(scheduler.config)
self.register_modules(unet=lowercase , scheduler=lowercase)
def lowerCamelCase_ ( self , lowercase) -> int:
'''simple docstring'''
if strength < 0 or strength > 1:
raise ValueError(f'The value of strength should in [0.0, 1.0] but is {strength}')
def lowerCamelCase_ ( self , lowercase , lowercase , lowercase) -> Dict:
'''simple docstring'''
a__: int = min(int(num_inference_steps * strength) , lowercase)
a__: Any = max(num_inference_steps - init_timestep , 0)
a__: Union[str, Any] = self.scheduler.timesteps[t_start:]
return timesteps, num_inference_steps - t_start
def lowerCamelCase_ ( self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase=None) -> List[Any]:
'''simple docstring'''
if not isinstance(lowercase , (torch.Tensor, PIL.Image.Image, list)):
raise ValueError(
f'`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(lowercase)}')
a__: Tuple = image.to(device=lowercase , dtype=lowercase)
if isinstance(lowercase , lowercase) and len(lowercase) != batch_size:
raise ValueError(
f'You have passed a list of generators of length {len(lowercase)}, but requested an effective batch'
f' size of {batch_size}. Make sure the batch size matches the length of the generators.')
a__: List[str] = init_latents.shape
a__: List[Any] = randn_tensor(lowercase , generator=lowercase , device=lowercase , dtype=lowercase)
# get latents
print('add noise to latents at timestep' , lowercase)
a__: int = self.scheduler.add_noise(lowercase , lowercase , lowercase)
a__: Dict = init_latents
return latents
@torch.no_grad()
def __call__( self , lowercase = None , lowercase = 0.8 , lowercase = 1 , lowercase = None , lowercase = 0.0 , lowercase = 50 , lowercase = None , lowercase = "pil" , lowercase = True , ) -> Union[ImagePipelineOutput, Tuple]:
'''simple docstring'''
self.check_inputs(lowercase)
# 2. Preprocess image
a__: Tuple = preprocess(lowercase)
# 3. set timesteps
self.scheduler.set_timesteps(lowercase , device=self.device)
a__ , a__: Union[str, Any] = self.get_timesteps(lowercase , lowercase , self.device)
a__: Optional[int] = timesteps[:1].repeat(lowercase)
# 4. Prepare latent variables
a__: Union[str, Any] = self.prepare_latents(lowercase , lowercase , lowercase , self.unet.dtype , self.device , lowercase)
a__: Optional[Any] = latents
# 5. Denoising loop
for t in self.progress_bar(lowercase):
# 1. predict noise model_output
a__: Dict = self.unet(lowercase , lowercase).sample
# 2. predict previous mean of image x_t-1 and add variance depending on eta
# eta corresponds to η in paper and should be between [0, 1]
# do x_t -> x_t-1
a__: Optional[Any] = self.scheduler.step(
lowercase , lowercase , lowercase , eta=lowercase , use_clipped_model_output=lowercase , generator=lowercase , ).prev_sample
a__: Union[str, Any] = (image / 2 + 0.5).clamp(0 , 1)
a__: Optional[int] = image.cpu().permute(0 , 2 , 3 , 1).numpy()
if output_type == "pil":
a__: Dict = self.numpy_to_pil(lowercase)
if not return_dict:
return (image, latent_timestep.item())
return ImagePipelineOutput(images=lowercase)
| 290 | 0 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class __UpperCAmelCase ( __lowerCAmelCase ):
'''simple docstring'''
__lowerCAmelCase = 42
__lowerCAmelCase = 42
def __init__(self : Tuple , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : List[str] ):
super().__init__()
self.register_modules(unet=_lowerCAmelCase , scheduler=_lowerCAmelCase )
@torch.no_grad()
def __call__(self : List[str] , _lowerCAmelCase : Any = 1 , _lowerCAmelCase : str = 2000 , _lowerCAmelCase : Optional[Any] = None , _lowerCAmelCase : Optional[int] = "pil" , _lowerCAmelCase : Union[str, Any] = True , **_lowerCAmelCase : Optional[Any] , ):
A = self.unet.config.sample_size
A = (batch_size, 3, img_size, img_size)
A = self.unet
A = randn_tensor(_lowerCAmelCase , generator=_lowerCAmelCase ) * self.scheduler.init_noise_sigma
A = sample.to(self.device )
self.scheduler.set_timesteps(_lowerCAmelCase )
self.scheduler.set_sigmas(_lowerCAmelCase )
for i, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
A = self.scheduler.sigmas[i] * torch.ones(shape[0] , device=self.device )
# correction step
for _ in range(self.scheduler.config.correct_steps ):
A = self.unet(_lowerCAmelCase , _lowerCAmelCase ).sample
A = self.scheduler.step_correct(_lowerCAmelCase , _lowerCAmelCase , generator=_lowerCAmelCase ).prev_sample
# prediction step
A = model(_lowerCAmelCase , _lowerCAmelCase ).sample
A = self.scheduler.step_pred(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , generator=_lowerCAmelCase )
A = output.prev_sample, output.prev_sample_mean
A = sample_mean.clamp(0 , 1 )
A = sample.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
A = self.numpy_to_pil(_lowerCAmelCase )
if not return_dict:
return (sample,)
return ImagePipelineOutput(images=_lowerCAmelCase )
| 258 | """simple docstring"""
import shutil
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_tf_cross_test,
require_tf,
require_torch,
require_torchvision,
require_vision,
)
from transformers.utils import is_tf_available, is_torch_available, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, SamImageProcessor, SamProcessor
if is_torch_available():
import torch
if is_tf_available():
import tensorflow as tf
@require_vision
@require_torchvision
class __snake_case ( unittest.TestCase ):
def lowerCamelCase_ ( self) -> Any:
'''simple docstring'''
a__: Optional[Any] = tempfile.mkdtemp()
a__: Optional[int] = SamImageProcessor()
a__: Tuple = SamProcessor(lowercase)
processor.save_pretrained(self.tmpdirname)
def lowerCamelCase_ ( self , **lowercase) -> List[Any]:
'''simple docstring'''
return AutoProcessor.from_pretrained(self.tmpdirname , **lowercase).image_processor
def lowerCamelCase_ ( self) -> Optional[int]:
'''simple docstring'''
shutil.rmtree(self.tmpdirname)
def lowerCamelCase_ ( self) -> int:
'''simple docstring'''
a__: Any = [np.random.randint(2_55 , size=(3, 30, 4_00) , dtype=np.uinta)]
a__: Optional[Any] = [Image.fromarray(np.moveaxis(lowercase , 0 , -1)) for x in image_inputs]
return image_inputs
def lowerCamelCase_ ( self) -> str:
'''simple docstring'''
a__: List[str] = SamProcessor(image_processor=self.get_image_processor())
processor.save_pretrained(self.tmpdirname)
a__: Optional[int] = self.get_image_processor(do_normalize=lowercase , padding_value=1.0)
a__: List[Any] = SamProcessor.from_pretrained(self.tmpdirname , do_normalize=lowercase , padding_value=1.0)
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string())
self.assertIsInstance(processor.image_processor , lowercase)
def lowerCamelCase_ ( self) -> Tuple:
'''simple docstring'''
a__: Union[str, Any] = self.get_image_processor()
a__: List[Any] = SamProcessor(image_processor=lowercase)
a__: Optional[int] = self.prepare_image_inputs()
a__: Optional[Any] = image_processor(lowercase , return_tensors='np')
a__: Tuple = processor(images=lowercase , return_tensors='np')
input_feat_extract.pop('original_sizes') # pop original_sizes as it is popped in the processor
input_feat_extract.pop('reshaped_input_sizes') # pop original_sizes as it is popped in the processor
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1e-2)
@require_torch
def lowerCamelCase_ ( self) -> Any:
'''simple docstring'''
a__: int = self.get_image_processor()
a__: List[str] = SamProcessor(image_processor=lowercase)
a__: Optional[Any] = [torch.ones((1, 3, 5, 5))]
a__: Union[str, Any] = [[17_64, 26_46]]
a__: Optional[Any] = [[6_83, 10_24]]
a__: int = processor.post_process_masks(lowercase , lowercase , lowercase)
self.assertEqual(masks[0].shape , (1, 3, 17_64, 26_46))
a__: Optional[int] = processor.post_process_masks(
lowercase , torch.tensor(lowercase) , torch.tensor(lowercase))
self.assertEqual(masks[0].shape , (1, 3, 17_64, 26_46))
# should also work with np
a__: Dict = [np.ones((1, 3, 5, 5))]
a__: Tuple = processor.post_process_masks(lowercase , np.array(lowercase) , np.array(lowercase))
self.assertEqual(masks[0].shape , (1, 3, 17_64, 26_46))
a__: Tuple = [[1, 0], [0, 1]]
with self.assertRaises(lowercase):
a__: List[Any] = processor.post_process_masks(lowercase , np.array(lowercase) , np.array(lowercase))
@require_vision
@require_tf
class __snake_case ( unittest.TestCase ):
def lowerCamelCase_ ( self) -> List[Any]:
'''simple docstring'''
a__: Optional[Any] = tempfile.mkdtemp()
a__: List[Any] = SamImageProcessor()
a__: Optional[int] = SamProcessor(lowercase)
processor.save_pretrained(self.tmpdirname)
def lowerCamelCase_ ( self , **lowercase) -> int:
'''simple docstring'''
return AutoProcessor.from_pretrained(self.tmpdirname , **lowercase).image_processor
def lowerCamelCase_ ( self) -> Tuple:
'''simple docstring'''
shutil.rmtree(self.tmpdirname)
def lowerCamelCase_ ( self) -> Tuple:
'''simple docstring'''
a__: Optional[Any] = [np.random.randint(2_55 , size=(3, 30, 4_00) , dtype=np.uinta)]
a__: Optional[int] = [Image.fromarray(np.moveaxis(lowercase , 0 , -1)) for x in image_inputs]
return image_inputs
def lowerCamelCase_ ( self) -> Any:
'''simple docstring'''
a__: List[str] = SamProcessor(image_processor=self.get_image_processor())
processor.save_pretrained(self.tmpdirname)
a__: Dict = self.get_image_processor(do_normalize=lowercase , padding_value=1.0)
a__: Union[str, Any] = SamProcessor.from_pretrained(self.tmpdirname , do_normalize=lowercase , padding_value=1.0)
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string())
self.assertIsInstance(processor.image_processor , lowercase)
def lowerCamelCase_ ( self) -> int:
'''simple docstring'''
a__: Optional[Any] = self.get_image_processor()
a__: str = SamProcessor(image_processor=lowercase)
a__: int = self.prepare_image_inputs()
a__: int = image_processor(lowercase , return_tensors='np')
a__: Dict = processor(images=lowercase , return_tensors='np')
input_feat_extract.pop('original_sizes') # pop original_sizes as it is popped in the processor
input_feat_extract.pop('reshaped_input_sizes') # pop reshaped_input_sizes as it is popped in the processor
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1e-2)
@require_tf
def lowerCamelCase_ ( self) -> Any:
'''simple docstring'''
a__: Tuple = self.get_image_processor()
a__: Any = SamProcessor(image_processor=lowercase)
a__: str = [tf.ones((1, 3, 5, 5))]
a__: List[Any] = [[17_64, 26_46]]
a__: List[Any] = [[6_83, 10_24]]
a__: List[Any] = processor.post_process_masks(lowercase , lowercase , lowercase , return_tensors='tf')
self.assertEqual(masks[0].shape , (1, 3, 17_64, 26_46))
a__: Tuple = processor.post_process_masks(
lowercase , tf.convert_to_tensor(lowercase) , tf.convert_to_tensor(lowercase) , return_tensors='tf' , )
self.assertEqual(masks[0].shape , (1, 3, 17_64, 26_46))
# should also work with np
a__: Optional[Any] = [np.ones((1, 3, 5, 5))]
a__: int = processor.post_process_masks(
lowercase , np.array(lowercase) , np.array(lowercase) , return_tensors='tf')
self.assertEqual(masks[0].shape , (1, 3, 17_64, 26_46))
a__: List[str] = [[1, 0], [0, 1]]
with self.assertRaises(tf.errors.InvalidArgumentError):
a__: Any = processor.post_process_masks(
lowercase , np.array(lowercase) , np.array(lowercase) , return_tensors='tf')
@require_vision
@require_torchvision
class __snake_case ( unittest.TestCase ):
def lowerCamelCase_ ( self) -> Dict:
'''simple docstring'''
a__: str = tempfile.mkdtemp()
a__: int = SamImageProcessor()
a__: Union[str, Any] = SamProcessor(lowercase)
processor.save_pretrained(self.tmpdirname)
def lowerCamelCase_ ( self , **lowercase) -> Optional[int]:
'''simple docstring'''
return AutoProcessor.from_pretrained(self.tmpdirname , **lowercase).image_processor
def lowerCamelCase_ ( self) -> Tuple:
'''simple docstring'''
shutil.rmtree(self.tmpdirname)
def lowerCamelCase_ ( self) -> Tuple:
'''simple docstring'''
a__: Any = [np.random.randint(2_55 , size=(3, 30, 4_00) , dtype=np.uinta)]
a__: Any = [Image.fromarray(np.moveaxis(lowercase , 0 , -1)) for x in image_inputs]
return image_inputs
@is_pt_tf_cross_test
def lowerCamelCase_ ( self) -> int:
'''simple docstring'''
a__: Optional[int] = self.get_image_processor()
a__: int = SamProcessor(image_processor=lowercase)
a__: int = np.random.randint(0 , 2 , size=(1, 3, 5, 5)).astype(np.floataa)
a__: Dict = [tf.convert_to_tensor(lowercase)]
a__: Union[str, Any] = [torch.tensor(lowercase)]
a__: List[Any] = [[17_64, 26_46]]
a__: Optional[Any] = [[6_83, 10_24]]
a__: Tuple = processor.post_process_masks(
lowercase , lowercase , lowercase , return_tensors='tf')
a__: str = processor.post_process_masks(
lowercase , lowercase , lowercase , return_tensors='pt')
self.assertTrue(np.all(tf_masks[0].numpy() == pt_masks[0].numpy()))
@is_pt_tf_cross_test
def lowerCamelCase_ ( self) -> str:
'''simple docstring'''
a__: Tuple = self.get_image_processor()
a__: Dict = SamProcessor(image_processor=lowercase)
a__: Any = self.prepare_image_inputs()
a__: List[Any] = image_processor(lowercase , return_tensors='pt')['pixel_values'].numpy()
a__: Tuple = processor(images=lowercase , return_tensors='pt')['pixel_values'].numpy()
a__: Any = image_processor(lowercase , return_tensors='tf')['pixel_values'].numpy()
a__: Any = processor(images=lowercase , return_tensors='tf')['pixel_values'].numpy()
self.assertTrue(np.allclose(lowercase , lowercase))
self.assertTrue(np.allclose(lowercase , lowercase))
self.assertTrue(np.allclose(lowercase , lowercase))
| 290 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__SCREAMING_SNAKE_CASE : Tuple = {
"""configuration_blenderbot""": [
"""BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""BlenderbotConfig""",
"""BlenderbotOnnxConfig""",
],
"""tokenization_blenderbot""": ["""BlenderbotTokenizer"""],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE : Optional[int] = ["""BlenderbotTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE : str = [
"""BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""BlenderbotForCausalLM""",
"""BlenderbotForConditionalGeneration""",
"""BlenderbotModel""",
"""BlenderbotPreTrainedModel""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE : List[str] = [
"""TFBlenderbotForConditionalGeneration""",
"""TFBlenderbotModel""",
"""TFBlenderbotPreTrainedModel""",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE : Dict = [
"""FlaxBlenderbotForConditionalGeneration""",
"""FlaxBlenderbotModel""",
"""FlaxBlenderbotPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_blenderbot import (
BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
BlenderbotConfig,
BlenderbotOnnxConfig,
)
from .tokenization_blenderbot import BlenderbotTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_blenderbot_fast import BlenderbotTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blenderbot import (
BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
BlenderbotForCausalLM,
BlenderbotForConditionalGeneration,
BlenderbotModel,
BlenderbotPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_blenderbot import (
TFBlenderbotForConditionalGeneration,
TFBlenderbotModel,
TFBlenderbotPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_blenderbot import (
FlaxBlenderbotForConditionalGeneration,
FlaxBlenderbotModel,
FlaxBlenderbotPreTrainedModel,
)
else:
import sys
__SCREAMING_SNAKE_CASE : Union[str, Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 31 | """simple docstring"""
from math import pow, sqrt
def __a ( *_SCREAMING_SNAKE_CASE ) ->bool:
a__: Union[str, Any] = len(_SCREAMING_SNAKE_CASE ) > 0 and all(value > 0.0 for value in values )
return result
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->float | ValueError:
return (
round(sqrt(molar_mass_a / molar_mass_a ) , 6 )
if validate(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
else ValueError('Input Error: Molar mass values must greater than 0.' )
)
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->float | ValueError:
return (
round(effusion_rate * sqrt(molar_mass_a / molar_mass_a ) , 6 )
if validate(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
else ValueError(
'Input Error: Molar mass and effusion rate values must greater than 0.' )
)
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->float | ValueError:
return (
round(effusion_rate / sqrt(molar_mass_a / molar_mass_a ) , 6 )
if validate(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
else ValueError(
'Input Error: Molar mass and effusion rate values must greater than 0.' )
)
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->float | ValueError:
return (
round(molar_mass / pow(effusion_rate_a / effusion_rate_a , 2 ) , 6 )
if validate(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
else ValueError(
'Input Error: Molar mass and effusion rate values must greater than 0.' )
)
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->float | ValueError:
return (
round(pow(effusion_rate_a / effusion_rate_a , 2 ) / molar_mass , 6 )
if validate(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
else ValueError(
'Input Error: Molar mass and effusion rate values must greater than 0.' )
)
| 290 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
lowerCAmelCase__ :Optional[int] = {
'''configuration_mega''': ['''MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MegaConfig''', '''MegaOnnxConfig'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ :List[str] = [
'''MEGA_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''MegaForCausalLM''',
'''MegaForMaskedLM''',
'''MegaForMultipleChoice''',
'''MegaForQuestionAnswering''',
'''MegaForSequenceClassification''',
'''MegaForTokenClassification''',
'''MegaModel''',
'''MegaPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_mega import MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP, MegaConfig, MegaOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mega import (
MEGA_PRETRAINED_MODEL_ARCHIVE_LIST,
MegaForCausalLM,
MegaForMaskedLM,
MegaForMultipleChoice,
MegaForQuestionAnswering,
MegaForSequenceClassification,
MegaForTokenClassification,
MegaModel,
MegaPreTrainedModel,
)
else:
import sys
lowerCAmelCase__ :Optional[Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 329 | """simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
'andreasmadsen/efficient_mlm_m0.40': (
'https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json'
),
}
class __snake_case ( __lowerCAmelCase ):
a__ = """roberta-prelayernorm"""
def __init__( self , lowercase=5_02_65 , lowercase=7_68 , lowercase=12 , lowercase=12 , lowercase=30_72 , lowercase="gelu" , lowercase=0.1 , lowercase=0.1 , lowercase=5_12 , lowercase=2 , lowercase=0.02 , lowercase=1e-12 , lowercase=1 , lowercase=0 , lowercase=2 , lowercase="absolute" , lowercase=True , lowercase=None , **lowercase , ) -> Any:
'''simple docstring'''
super().__init__(pad_token_id=lowercase , bos_token_id=lowercase , eos_token_id=lowercase , **lowercase)
a__: Union[str, Any] = vocab_size
a__: str = hidden_size
a__: Tuple = num_hidden_layers
a__: List[str] = num_attention_heads
a__: Dict = hidden_act
a__: int = intermediate_size
a__: Tuple = hidden_dropout_prob
a__: str = attention_probs_dropout_prob
a__: Tuple = max_position_embeddings
a__: Tuple = type_vocab_size
a__: Optional[Any] = initializer_range
a__: Tuple = layer_norm_eps
a__: Optional[int] = position_embedding_type
a__: Any = use_cache
a__: Dict = classifier_dropout
class __snake_case ( __lowerCAmelCase ):
@property
def lowerCamelCase_ ( self) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
if self.task == "multiple-choice":
a__: str = {0: 'batch', 1: 'choice', 2: 'sequence'}
else:
a__: Union[str, Any] = {0: 'batch', 1: 'sequence'}
return OrderedDict(
[
('input_ids', dynamic_axis),
('attention_mask', dynamic_axis),
])
| 290 | 0 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import (
DiffusionPipeline,
UnCLIPImageVariationPipeline,
UnCLIPScheduler,
UNetaDConditionModel,
UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps
from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class A_ ( __lowerCAmelCase , unittest.TestCase ):
'''simple docstring'''
UpperCAmelCase_ : Any = UnCLIPImageVariationPipeline
UpperCAmelCase_ : Optional[Any] = IMAGE_VARIATION_PARAMS - {"""height""", """width""", """guidance_scale"""}
UpperCAmelCase_ : Optional[int] = IMAGE_VARIATION_BATCH_PARAMS
UpperCAmelCase_ : Union[str, Any] = [
"""generator""",
"""return_dict""",
"""decoder_num_inference_steps""",
"""super_res_num_inference_steps""",
]
UpperCAmelCase_ : Tuple = False
@property
def UpperCAmelCase_ ( self : Tuple ) -> Union[str, Any]:
return 32
@property
def UpperCAmelCase_ ( self : int ) -> List[str]:
return 32
@property
def UpperCAmelCase_ ( self : Optional[Any] ) -> Optional[Any]:
return self.time_input_dim
@property
def UpperCAmelCase_ ( self : Tuple ) -> List[str]:
return self.time_input_dim * 4
@property
def UpperCAmelCase_ ( self : Dict ) -> Optional[int]:
return 100
@property
def UpperCAmelCase_ ( self : str ) -> Union[str, Any]:
UpperCAmelCase : Union[str, Any] = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
return tokenizer
@property
def UpperCAmelCase_ ( self : str ) -> str:
torch.manual_seed(0 )
UpperCAmelCase : Optional[int] = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=self.text_embedder_hidden_size , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_000 , )
return CLIPTextModelWithProjection(lowercase_ )
@property
def UpperCAmelCase_ ( self : Dict ) -> List[Any]:
torch.manual_seed(0 )
UpperCAmelCase : List[str] = CLIPVisionConfig(
hidden_size=self.text_embedder_hidden_size , projection_dim=self.text_embedder_hidden_size , num_hidden_layers=5 , num_attention_heads=4 , image_size=32 , intermediate_size=37 , patch_size=1 , )
return CLIPVisionModelWithProjection(lowercase_ )
@property
def UpperCAmelCase_ ( self : Any ) -> Any:
torch.manual_seed(0 )
UpperCAmelCase : List[Any] = {
'clip_embeddings_dim': self.text_embedder_hidden_size,
'time_embed_dim': self.time_embed_dim,
'cross_attention_dim': self.cross_attention_dim,
}
UpperCAmelCase : List[Any] = UnCLIPTextProjModel(**lowercase_ )
return model
@property
def UpperCAmelCase_ ( self : Union[str, Any] ) -> List[Any]:
torch.manual_seed(0 )
UpperCAmelCase : Any = {
'sample_size': 32,
# RGB in channels
'in_channels': 3,
# Out channels is double in channels because predicts mean and variance
'out_channels': 6,
'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,
'cross_attention_dim': self.cross_attention_dim,
'attention_head_dim': 4,
'resnet_time_scale_shift': 'scale_shift',
'class_embed_type': 'identity',
}
UpperCAmelCase : Optional[Any] = UNetaDConditionModel(**lowercase_ )
return model
@property
def UpperCAmelCase_ ( self : Union[str, Any] ) -> Dict:
return {
"sample_size": 64,
"layers_per_block": 1,
"down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
"up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
"block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
"in_channels": 6,
"out_channels": 3,
}
@property
def UpperCAmelCase_ ( self : int ) -> int:
torch.manual_seed(0 )
UpperCAmelCase : int = UNetaDModel(**self.dummy_super_res_kwargs )
return model
@property
def UpperCAmelCase_ ( self : List[str] ) -> Any:
torch.manual_seed(1 )
UpperCAmelCase : Any = UNetaDModel(**self.dummy_super_res_kwargs )
return model
def UpperCAmelCase_ ( self : Dict ) -> Optional[int]:
UpperCAmelCase : List[str] = self.dummy_decoder
UpperCAmelCase : str = self.dummy_text_proj
UpperCAmelCase : Dict = self.dummy_text_encoder
UpperCAmelCase : Dict = self.dummy_tokenizer
UpperCAmelCase : Optional[int] = self.dummy_super_res_first
UpperCAmelCase : Dict = self.dummy_super_res_last
UpperCAmelCase : Tuple = UnCLIPScheduler(
variance_type='learned_range' , prediction_type='epsilon' , num_train_timesteps=1_000 , )
UpperCAmelCase : Dict = UnCLIPScheduler(
variance_type='fixed_small_log' , prediction_type='epsilon' , num_train_timesteps=1_000 , )
UpperCAmelCase : Optional[int] = CLIPImageProcessor(crop_size=32 , size=32 )
UpperCAmelCase : int = self.dummy_image_encoder
return {
"decoder": decoder,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"text_proj": text_proj,
"feature_extractor": feature_extractor,
"image_encoder": image_encoder,
"super_res_first": super_res_first,
"super_res_last": super_res_last,
"decoder_scheduler": decoder_scheduler,
"super_res_scheduler": super_res_scheduler,
}
def UpperCAmelCase_ ( self : str , lowercase_ : Optional[Any] , lowercase_ : Any=0 , lowercase_ : List[str]=True ) -> Optional[int]:
UpperCAmelCase : Any = floats_tensor((1, 3, 32, 32) , rng=random.Random(lowercase_ ) ).to(lowercase_ )
if str(lowercase_ ).startswith('mps' ):
UpperCAmelCase : Dict = torch.manual_seed(lowercase_ )
else:
UpperCAmelCase : str = torch.Generator(device=lowercase_ ).manual_seed(lowercase_ )
if pil_image:
UpperCAmelCase : List[Any] = input_image * 0.5 + 0.5
UpperCAmelCase : Any = input_image.clamp(0 , 1 )
UpperCAmelCase : Tuple = input_image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
UpperCAmelCase : Tuple = DiffusionPipeline.numpy_to_pil(lowercase_ )[0]
return {
"image": input_image,
"generator": generator,
"decoder_num_inference_steps": 2,
"super_res_num_inference_steps": 2,
"output_type": "np",
}
def UpperCAmelCase_ ( self : int ) -> Optional[Any]:
UpperCAmelCase : Optional[Any] = 'cpu'
UpperCAmelCase : Optional[int] = self.get_dummy_components()
UpperCAmelCase : Dict = self.pipeline_class(**lowercase_ )
UpperCAmelCase : str = pipe.to(lowercase_ )
pipe.set_progress_bar_config(disable=lowercase_ )
UpperCAmelCase : Optional[Any] = self.get_dummy_inputs(lowercase_ , pil_image=lowercase_ )
UpperCAmelCase : str = pipe(**lowercase_ )
UpperCAmelCase : str = output.images
UpperCAmelCase : List[str] = self.get_dummy_inputs(lowercase_ , pil_image=lowercase_ )
UpperCAmelCase : Optional[int] = pipe(
**lowercase_ , return_dict=lowercase_ , )[0]
UpperCAmelCase : Optional[Any] = image[0, -3:, -3:, -1]
UpperCAmelCase : Union[str, Any] = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
UpperCAmelCase : List[str] = np.array(
[
0.9997,
0.0002,
0.9997,
0.9997,
0.9969,
0.0023,
0.9997,
0.9969,
0.9970,
] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
def UpperCAmelCase_ ( self : Any ) -> Optional[int]:
UpperCAmelCase : str = 'cpu'
UpperCAmelCase : int = self.get_dummy_components()
UpperCAmelCase : Union[str, Any] = self.pipeline_class(**lowercase_ )
UpperCAmelCase : int = pipe.to(lowercase_ )
pipe.set_progress_bar_config(disable=lowercase_ )
UpperCAmelCase : int = self.get_dummy_inputs(lowercase_ , pil_image=lowercase_ )
UpperCAmelCase : str = pipe(**lowercase_ )
UpperCAmelCase : Dict = output.images
UpperCAmelCase : List[str] = self.get_dummy_inputs(lowercase_ , pil_image=lowercase_ )
UpperCAmelCase : Any = pipe(
**lowercase_ , return_dict=lowercase_ , )[0]
UpperCAmelCase : str = image[0, -3:, -3:, -1]
UpperCAmelCase : Optional[Any] = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
UpperCAmelCase : str = np.array([0.9997, 0.0003, 0.9997, 0.9997, 0.9970, 0.0024, 0.9997, 0.9971, 0.9971] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
def UpperCAmelCase_ ( self : List[str] ) -> Optional[Any]:
UpperCAmelCase : Optional[int] = 'cpu'
UpperCAmelCase : List[str] = self.get_dummy_components()
UpperCAmelCase : Dict = self.pipeline_class(**lowercase_ )
UpperCAmelCase : Any = pipe.to(lowercase_ )
pipe.set_progress_bar_config(disable=lowercase_ )
UpperCAmelCase : Optional[Any] = self.get_dummy_inputs(lowercase_ , pil_image=lowercase_ )
UpperCAmelCase : str = [
pipeline_inputs['image'],
pipeline_inputs['image'],
]
UpperCAmelCase : Any = pipe(**lowercase_ )
UpperCAmelCase : str = output.images
UpperCAmelCase : Union[str, Any] = self.get_dummy_inputs(lowercase_ , pil_image=lowercase_ )
UpperCAmelCase : Optional[Any] = [
tuple_pipeline_inputs['image'],
tuple_pipeline_inputs['image'],
]
UpperCAmelCase : Tuple = pipe(
**lowercase_ , return_dict=lowercase_ , )[0]
UpperCAmelCase : Optional[Any] = image[0, -3:, -3:, -1]
UpperCAmelCase : Tuple = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (2, 64, 64, 3)
UpperCAmelCase : str = np.array(
[
0.9997,
0.9989,
0.0008,
0.0021,
0.9960,
0.0018,
0.0014,
0.0002,
0.9933,
] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
def UpperCAmelCase_ ( self : str ) -> Dict:
UpperCAmelCase : Optional[Any] = torch.device('cpu' )
class A_ :
'''simple docstring'''
UpperCAmelCase_ : List[Any] = 1
UpperCAmelCase : Optional[int] = self.get_dummy_components()
UpperCAmelCase : List[str] = self.pipeline_class(**lowercase_ )
UpperCAmelCase : List[Any] = pipe.to(lowercase_ )
pipe.set_progress_bar_config(disable=lowercase_ )
UpperCAmelCase : Tuple = torch.Generator(device=lowercase_ ).manual_seed(0 )
UpperCAmelCase : Any = pipe.decoder.dtype
UpperCAmelCase : Dict = 1
UpperCAmelCase : int = (
batch_size,
pipe.decoder.config.in_channels,
pipe.decoder.config.sample_size,
pipe.decoder.config.sample_size,
)
UpperCAmelCase : List[Any] = pipe.prepare_latents(
lowercase_ , dtype=lowercase_ , device=lowercase_ , generator=lowercase_ , latents=lowercase_ , scheduler=DummyScheduler() )
UpperCAmelCase : str = (
batch_size,
pipe.super_res_first.config.in_channels // 2,
pipe.super_res_first.config.sample_size,
pipe.super_res_first.config.sample_size,
)
UpperCAmelCase : Tuple = pipe.prepare_latents(
lowercase_ , dtype=lowercase_ , device=lowercase_ , generator=lowercase_ , latents=lowercase_ , scheduler=DummyScheduler() )
UpperCAmelCase : Dict = self.get_dummy_inputs(lowercase_ , pil_image=lowercase_ )
UpperCAmelCase : Optional[Any] = pipe(
**lowercase_ , decoder_latents=lowercase_ , super_res_latents=lowercase_ ).images
UpperCAmelCase : int = self.get_dummy_inputs(lowercase_ , pil_image=lowercase_ )
# Don't pass image, instead pass embedding
UpperCAmelCase : Tuple = pipeline_inputs.pop('image' )
UpperCAmelCase : int = pipe.image_encoder(lowercase_ ).image_embeds
UpperCAmelCase : Union[str, Any] = pipe(
**lowercase_ , decoder_latents=lowercase_ , super_res_latents=lowercase_ , image_embeddings=lowercase_ , ).images
# make sure passing text embeddings manually is identical
assert np.abs(img_out_a - img_out_a ).max() < 1E-4
@skip_mps
def UpperCAmelCase_ ( self : Optional[Any] ) -> int:
UpperCAmelCase : List[Any] = torch_device == 'cpu'
# Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
UpperCAmelCase : Any = 1E-2
self._test_attention_slicing_forward_pass(
test_max_difference=lowercase_ , expected_max_diff=lowercase_ )
@skip_mps
def UpperCAmelCase_ ( self : Tuple ) -> Dict:
UpperCAmelCase : Any = torch_device == 'cpu'
UpperCAmelCase : Any = True
UpperCAmelCase : str = [
'decoder_num_inference_steps',
'super_res_num_inference_steps',
]
self._test_inference_batch_single_identical(
test_max_difference=lowercase_ , relax_max_difference=lowercase_ , additional_params_copy_to_batched_inputs=lowercase_ , )
def UpperCAmelCase_ ( self : Any ) -> Any:
UpperCAmelCase : int = [
'decoder_num_inference_steps',
'super_res_num_inference_steps',
]
if torch_device == "mps":
# TODO: MPS errors with larger batch sizes
UpperCAmelCase : Union[str, Any] = [2, 3]
self._test_inference_batch_consistent(
batch_sizes=lowercase_ , additional_params_copy_to_batched_inputs=lowercase_ , )
else:
self._test_inference_batch_consistent(
additional_params_copy_to_batched_inputs=lowercase_ )
@skip_mps
def UpperCAmelCase_ ( self : Optional[Any] ) -> Union[str, Any]:
return super().test_dict_tuple_outputs_equivalent()
@skip_mps
def UpperCAmelCase_ ( self : Any ) -> int:
return super().test_save_load_local()
@skip_mps
def UpperCAmelCase_ ( self : Dict ) -> Optional[Any]:
return super().test_save_load_optional_components()
@slow
@require_torch_gpu
class A_ ( unittest.TestCase ):
'''simple docstring'''
def UpperCAmelCase_ ( self : str ) -> int:
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCAmelCase_ ( self : Tuple ) -> List[str]:
UpperCAmelCase : int = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png' )
UpperCAmelCase : int = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/unclip/karlo_v1_alpha_cat_variation_fp16.npy' )
UpperCAmelCase : Optional[Any] = UnCLIPImageVariationPipeline.from_pretrained(
'kakaobrain/karlo-v1-alpha-image-variations' , torch_dtype=torch.floataa )
UpperCAmelCase : Dict = pipeline.to(lowercase_ )
pipeline.set_progress_bar_config(disable=lowercase_ )
UpperCAmelCase : Dict = torch.Generator(device='cpu' ).manual_seed(0 )
UpperCAmelCase : Union[str, Any] = pipeline(
lowercase_ , generator=lowercase_ , output_type='np' , )
UpperCAmelCase : int = output.images[0]
assert image.shape == (256, 256, 3)
assert_mean_pixel_difference(lowercase_ , lowercase_ , 15 )
| 151 | """simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
'MIT/ast-finetuned-audioset-10-10-0.4593': (
'https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve/main/config.json'
),
}
class __snake_case ( __lowerCAmelCase ):
a__ = """audio-spectrogram-transformer"""
def __init__( self , lowercase=7_68 , lowercase=12 , lowercase=12 , lowercase=30_72 , lowercase="gelu" , lowercase=0.0 , lowercase=0.0 , lowercase=0.02 , lowercase=1e-12 , lowercase=16 , lowercase=True , lowercase=10 , lowercase=10 , lowercase=10_24 , lowercase=1_28 , **lowercase , ) -> str:
'''simple docstring'''
super().__init__(**lowercase)
a__: Any = hidden_size
a__: int = num_hidden_layers
a__: Union[str, Any] = num_attention_heads
a__: Any = intermediate_size
a__: Union[str, Any] = hidden_act
a__: int = hidden_dropout_prob
a__: str = attention_probs_dropout_prob
a__: str = initializer_range
a__: Tuple = layer_norm_eps
a__: Any = patch_size
a__: int = qkv_bias
a__: Optional[Any] = frequency_stride
a__: int = time_stride
a__: List[str] = max_length
a__: Tuple = num_mel_bins
| 290 | 0 |
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 A_ ( __lowerCAmelCase , unittest.TestCase ):
_lowercase : Optional[Any] = KandinskyInpaintPipeline
_lowercase : int = ['prompt', 'image_embeds', 'negative_image_embeds', 'image', 'mask_image']
_lowercase : List[Any] = [
'prompt',
'negative_prompt',
'image_embeds',
'negative_image_embeds',
'image',
'mask_image',
]
_lowercase : Any = [
'generator',
'height',
'width',
'latents',
'guidance_scale',
'negative_prompt',
'num_inference_steps',
'return_dict',
'guidance_scale',
'num_images_per_prompt',
'output_type',
'return_dict',
]
_lowercase : str = False
@property
def UpperCAmelCase ( self : Optional[Any] ) -> Optional[int]:
return 3_2
@property
def UpperCAmelCase ( self : Union[str, Any] ) -> Tuple:
return 3_2
@property
def UpperCAmelCase ( self : Dict ) -> Dict:
return self.time_input_dim
@property
def UpperCAmelCase ( self : Tuple ) -> Dict:
return self.time_input_dim * 4
@property
def UpperCAmelCase ( self : List[Any] ) -> List[Any]:
return 1_0_0
@property
def UpperCAmelCase ( self : int ) -> List[Any]:
__lowerCAmelCase: Optional[int] = XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base' )
return tokenizer
@property
def UpperCAmelCase ( self : Optional[Any] ) -> Any:
torch.manual_seed(0 )
__lowerCAmelCase: Dict = 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 , )
__lowerCAmelCase: Optional[Any] = MultilingualCLIP(UpperCAmelCase )
__lowerCAmelCase: int = text_encoder.eval()
return text_encoder
@property
def UpperCAmelCase ( self : Optional[Any] ) -> List[str]:
torch.manual_seed(0 )
__lowerCAmelCase: Any = {
'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,
}
__lowerCAmelCase: str = UNetaDConditionModel(**UpperCAmelCase )
return model
@property
def UpperCAmelCase ( self : Optional[Any] ) -> Union[str, Any]:
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 UpperCAmelCase ( self : Dict ) -> List[Any]:
torch.manual_seed(0 )
__lowerCAmelCase: Any = VQModel(**self.dummy_movq_kwargs )
return model
def UpperCAmelCase ( self : Optional[Any] ) -> Any:
__lowerCAmelCase: Dict = self.dummy_text_encoder
__lowerCAmelCase: int = self.dummy_tokenizer
__lowerCAmelCase: str = self.dummy_unet
__lowerCAmelCase: Any = self.dummy_movq
__lowerCAmelCase: Tuple = DDIMScheduler(
num_train_timesteps=1_0_0_0 , beta_schedule='linear' , beta_start=0.00085 , beta_end=0.012 , clip_sample=UpperCAmelCase , set_alpha_to_one=UpperCAmelCase , steps_offset=1 , prediction_type='epsilon' , thresholding=UpperCAmelCase , )
__lowerCAmelCase: Tuple = {
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'unet': unet,
'scheduler': scheduler,
'movq': movq,
}
return components
def UpperCAmelCase ( self : Union[str, Any] , UpperCAmelCase : List[Any] , UpperCAmelCase : List[str]=0 ) -> Any:
__lowerCAmelCase: List[Any] = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(UpperCAmelCase ) ).to(UpperCAmelCase )
__lowerCAmelCase: int = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(seed + 1 ) ).to(UpperCAmelCase )
# create init_image
__lowerCAmelCase: Optional[int] = floats_tensor((1, 3, 6_4, 6_4) , rng=random.Random(UpperCAmelCase ) ).to(UpperCAmelCase )
__lowerCAmelCase: int = image.cpu().permute(0 , 2 , 3 , 1 )[0]
__lowerCAmelCase: Optional[int] = Image.fromarray(np.uinta(UpperCAmelCase ) ).convert('RGB' ).resize((2_5_6, 2_5_6) )
# create mask
__lowerCAmelCase: Tuple = np.ones((6_4, 6_4) , dtype=np.floataa )
__lowerCAmelCase: Optional[Any] = 0
if str(UpperCAmelCase ).startswith('mps' ):
__lowerCAmelCase: str = torch.manual_seed(UpperCAmelCase )
else:
__lowerCAmelCase: Dict = torch.Generator(device=UpperCAmelCase ).manual_seed(UpperCAmelCase )
__lowerCAmelCase: Optional[int] = {
'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 UpperCAmelCase ( self : Dict ) -> str:
__lowerCAmelCase: Optional[Any] = 'cpu'
__lowerCAmelCase: List[Any] = self.get_dummy_components()
__lowerCAmelCase: Optional[Any] = self.pipeline_class(**UpperCAmelCase )
__lowerCAmelCase: str = pipe.to(UpperCAmelCase )
pipe.set_progress_bar_config(disable=UpperCAmelCase )
__lowerCAmelCase: Optional[int] = pipe(**self.get_dummy_inputs(UpperCAmelCase ) )
__lowerCAmelCase: List[str] = output.images
__lowerCAmelCase: int = pipe(
**self.get_dummy_inputs(UpperCAmelCase ) , return_dict=UpperCAmelCase , )[0]
__lowerCAmelCase: Optional[Any] = image[0, -3:, -3:, -1]
__lowerCAmelCase: List[Any] = image_from_tuple[0, -3:, -3:, -1]
print(F'''image.shape {image.shape}''' )
assert image.shape == (1, 6_4, 6_4, 3)
__lowerCAmelCase: str = np.array(
[0.8326919, 0.73790467, 0.20918581, 0.9309612, 0.5511791, 0.43713328, 0.5513321, 0.49922934, 0.59497786] )
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 UpperCAmelCase ( self : int ) -> str:
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
@slow
@require_torch_gpu
class A_ ( unittest.TestCase ):
def UpperCAmelCase ( self : Tuple ) -> Optional[Any]:
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCAmelCase ( self : Union[str, Any] ) -> Dict:
__lowerCAmelCase: List[Any] = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy' )
__lowerCAmelCase: int = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png' )
__lowerCAmelCase: Union[str, Any] = np.ones((7_6_8, 7_6_8) , dtype=np.floataa )
__lowerCAmelCase: int = 0
__lowerCAmelCase: Optional[int] = 'a hat'
__lowerCAmelCase: int = KandinskyPriorPipeline.from_pretrained(
'kandinsky-community/kandinsky-2-1-prior' , torch_dtype=torch.floataa )
pipe_prior.to(UpperCAmelCase )
__lowerCAmelCase: Any = KandinskyInpaintPipeline.from_pretrained(
'kandinsky-community/kandinsky-2-1-inpaint' , torch_dtype=torch.floataa )
__lowerCAmelCase: Optional[Any] = pipeline.to(UpperCAmelCase )
pipeline.set_progress_bar_config(disable=UpperCAmelCase )
__lowerCAmelCase: Dict = torch.Generator(device='cpu' ).manual_seed(0 )
__lowerCAmelCase: Optional[Any] = pipe_prior(
UpperCAmelCase , generator=UpperCAmelCase , num_inference_steps=5 , negative_prompt='' , ).to_tuple()
__lowerCAmelCase: List[str] = pipeline(
UpperCAmelCase , image=UpperCAmelCase , mask_image=UpperCAmelCase , image_embeds=UpperCAmelCase , negative_image_embeds=UpperCAmelCase , generator=UpperCAmelCase , num_inference_steps=1_0_0 , height=7_6_8 , width=7_6_8 , output_type='np' , )
__lowerCAmelCase: str = output.images[0]
assert image.shape == (7_6_8, 7_6_8, 3)
assert_mean_pixel_difference(UpperCAmelCase , UpperCAmelCase )
| 322 | """simple docstring"""
import unittest
from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
lowercase__ = get_tests_dir('fixtures/test_sentencepiece.model')
lowercase__ = get_tests_dir('fixtures/test_sentencepiece_bpe.model')
lowercase__ = 'pt' if is_torch_available() else 'tf'
@require_sentencepiece
@require_tokenizers
class __snake_case ( __lowerCAmelCase , unittest.TestCase ):
a__ = CamembertTokenizer
a__ = CamembertTokenizerFast
a__ = True
a__ = True
def lowerCamelCase_ ( self) -> Union[str, Any]:
'''simple docstring'''
super().setUp()
# We have a SentencePiece fixture for testing
a__: Tuple = CamembertTokenizer(lowercase)
tokenizer.save_pretrained(self.tmpdirname)
def lowerCamelCase_ ( self) -> List[str]:
'''simple docstring'''
a__: Optional[Any] = '<pad>'
a__: List[Any] = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowercase) , lowercase)
self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowercase) , lowercase)
def lowerCamelCase_ ( self) -> Any:
'''simple docstring'''
a__: str = list(self.get_tokenizer().get_vocab().keys())
self.assertEqual(vocab_keys[0] , '<s>NOTUSED')
self.assertEqual(vocab_keys[1] , '<pad>')
self.assertEqual(vocab_keys[-1] , '<mask>')
self.assertEqual(len(lowercase) , 10_04)
def lowerCamelCase_ ( self) -> Any:
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size , 10_05)
def lowerCamelCase_ ( self) -> Optional[Any]:
'''simple docstring'''
a__: Optional[Any] = CamembertTokenizer(lowercase)
tokenizer.save_pretrained(self.tmpdirname)
a__: List[Any] = CamembertTokenizerFast.from_pretrained(self.tmpdirname)
a__: Dict = 'I was born in 92000, and this is falsé.'
a__: Optional[int] = tokenizer.encode(lowercase)
a__: Any = rust_tokenizer.encode(lowercase)
self.assertListEqual(lowercase , lowercase)
a__: Optional[Any] = tokenizer.encode(lowercase , add_special_tokens=lowercase)
a__: str = rust_tokenizer.encode(lowercase , add_special_tokens=lowercase)
self.assertListEqual(lowercase , lowercase)
# <unk> tokens are not the same for `rust` than for `slow`.
# Because spm gives back raw token instead of `unk` in EncodeAsPieces
# tokens = tokenizer.tokenize(sequence)
a__: Tuple = tokenizer.convert_ids_to_tokens(lowercase)
a__: Tuple = rust_tokenizer.tokenize(lowercase)
self.assertListEqual(lowercase , lowercase)
def lowerCamelCase_ ( self) -> Dict:
'''simple docstring'''
if not self.test_rust_tokenizer:
return
a__: Dict = self.get_tokenizer()
a__: str = self.get_rust_tokenizer()
a__: int = 'I was born in 92000, and this is falsé.'
a__: Optional[Any] = tokenizer.tokenize(lowercase)
a__: List[Any] = rust_tokenizer.tokenize(lowercase)
self.assertListEqual(lowercase , lowercase)
a__: str = tokenizer.encode(lowercase , add_special_tokens=lowercase)
a__: str = rust_tokenizer.encode(lowercase , add_special_tokens=lowercase)
self.assertListEqual(lowercase , lowercase)
a__: Tuple = self.get_rust_tokenizer()
a__: Union[str, Any] = tokenizer.encode(lowercase)
a__: List[Any] = rust_tokenizer.encode(lowercase)
self.assertListEqual(lowercase , lowercase)
@slow
def lowerCamelCase_ ( self) -> Optional[Any]:
'''simple docstring'''
a__: Union[str, Any] = {'input_ids': [[5, 54, 71_96, 2_97, 30, 23, 7_76, 18, 11, 32_15, 37_05, 82_52, 22, 31_64, 11_81, 21_16, 29, 16, 8_13, 25, 7_91, 33_14, 20, 34_46, 38, 2_75_75, 1_20, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 4_68, 17, 11, 90_88, 20, 15_17, 8, 2_28_04, 1_88_18, 10, 38, 6_29, 6_07, 6_07, 1_42, 19, 71_96, 8_67, 56, 1_03_26, 24, 22_67, 20, 4_16, 50_72, 1_56_12, 2_33, 7_34, 7, 23_99, 27, 16, 30_15, 16_49, 7, 24, 20, 43_38, 23_99, 27, 13, 34_00, 14, 13, 61_89, 8, 9_30, 9, 6]], '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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501
# fmt: on
# camembert is a french model. So we also use french texts.
a__: int = [
'Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '
'utilisé principalement dans le domaine du traitement automatique des langues (TAL).',
'À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '
'pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '
'telles que la traduction et la synthèse de texte.',
]
self.tokenizer_integration_test_util(
expected_encoding=lowercase , model_name='camembert-base' , revision='3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf' , sequences=lowercase , )
| 290 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowercase__ : Any = {
'''configuration_mvp''': ['''MVP_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MvpConfig''', '''MvpOnnxConfig'''],
'''tokenization_mvp''': ['''MvpTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase__ : Any = ['''MvpTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase__ : List[str] = [
'''MVP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''MvpForCausalLM''',
'''MvpForConditionalGeneration''',
'''MvpForQuestionAnswering''',
'''MvpForSequenceClassification''',
'''MvpModel''',
'''MvpPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
from .tokenization_mvp import MvpTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mvp_fast import MvpTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mvp import (
MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
MvpForCausalLM,
MvpForConditionalGeneration,
MvpForQuestionAnswering,
MvpForSequenceClassification,
MvpModel,
MvpPreTrainedModel,
)
else:
import sys
lowercase__ : Any = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 264 | """simple docstring"""
def __a ( _SCREAMING_SNAKE_CASE = 1000000 ) ->int:
a__: int = limit + 1
a__: Optional[int] = [0] * limit
for first_term in range(1 , _SCREAMING_SNAKE_CASE ):
for n in range(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
a__: List[Any] = first_term + n / first_term
if common_difference % 4: # d must be divisble by 4
continue
else:
common_difference /= 4
if (
first_term > common_difference
and first_term < 4 * common_difference
): # since x,y,z are positive integers
frequency[n] += 1 # so z>0 and a>d ,also 4d<a
a__: Any = sum(1 for x in frequency[1:limit] if x == 10 )
return count
if __name__ == "__main__":
print(f"{solution() = }")
| 290 | 0 |
'''simple docstring'''
from math import pow, sqrt
def __snake_case ( *UpperCAmelCase_ : Tuple ):
lowerCamelCase_ = len(_SCREAMING_SNAKE_CASE ) > 0 and all(value > 0.0 for value in values )
return result
def __snake_case ( UpperCAmelCase_ : Tuple , UpperCAmelCase_ : Union[str, Any] ):
return (
round(sqrt(molar_mass_a / molar_mass_a ) , 6 )
if validate(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
else ValueError("Input Error: Molar mass values must greater than 0." )
)
def __snake_case ( UpperCAmelCase_ : Any , UpperCAmelCase_ : Tuple , UpperCAmelCase_ : Optional[Any] ):
return (
round(effusion_rate * sqrt(molar_mass_a / molar_mass_a ) , 6 )
if validate(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
else ValueError(
"Input Error: Molar mass and effusion rate values must greater than 0." )
)
def __snake_case ( UpperCAmelCase_ : Union[str, Any] , UpperCAmelCase_ : List[Any] , UpperCAmelCase_ : List[Any] ):
return (
round(effusion_rate / sqrt(molar_mass_a / molar_mass_a ) , 6 )
if validate(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
else ValueError(
"Input Error: Molar mass and effusion rate values must greater than 0." )
)
def __snake_case ( UpperCAmelCase_ : List[str] , UpperCAmelCase_ : Optional[int] , UpperCAmelCase_ : str ):
return (
round(molar_mass / pow(effusion_rate_a / effusion_rate_a , 2 ) , 6 )
if validate(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
else ValueError(
"Input Error: Molar mass and effusion rate values must greater than 0." )
)
def __snake_case ( UpperCAmelCase_ : Union[str, Any] , UpperCAmelCase_ : str , UpperCAmelCase_ : Union[str, Any] ):
return (
round(pow(effusion_rate_a / effusion_rate_a , 2 ) / molar_mass , 6 )
if validate(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
else ValueError(
"Input Error: Molar mass and effusion rate values must greater than 0." )
)
| 55 | """simple docstring"""
import os
from typing import Dict, List, Tuple, TypeVar, Union
lowercase__ = TypeVar('T')
lowercase__ = Union[List[T], Tuple[T, ...]]
lowercase__ = Union[T, List[T], Dict[str, T]]
lowercase__ = Union[str, bytes, os.PathLike]
| 290 | 0 |
'''simple docstring'''
import unittest
from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
else:
class UpperCAmelCase__ :
@staticmethod
def __lowerCamelCase ( *lowercase , **lowercase ) -> int:
pass
@is_pipeline_test
@require_torch
@require_vision
class UpperCAmelCase__ ( unittest.TestCase):
__SCREAMING_SNAKE_CASE = MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING
def __lowerCamelCase ( self , lowercase , lowercase , lowercase ) -> Optional[int]:
__UpperCamelCase = pipeline("""visual-question-answering""" , model="""hf-internal-testing/tiny-vilt-random-vqa""" )
__UpperCamelCase = [
{
'image': Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" ),
'question': 'How many cats are there?',
},
{
'image': './tests/fixtures/tests_samples/COCO/000000039769.png',
'question': 'How many cats are there?',
},
]
return vqa_pipeline, examples
def __lowerCamelCase ( self , lowercase , lowercase ) -> Optional[Any]:
__UpperCamelCase = vqa_pipeline(lowercase , top_k=1 )
self.assertEqual(
lowercase , [
[{"""score""": ANY(lowercase ), """answer""": ANY(lowercase )}],
[{"""score""": ANY(lowercase ), """answer""": ANY(lowercase )}],
] , )
@require_torch
def __lowerCamelCase ( self ) -> int:
__UpperCamelCase = pipeline("""visual-question-answering""" , model="""hf-internal-testing/tiny-vilt-random-vqa""" )
__UpperCamelCase = './tests/fixtures/tests_samples/COCO/000000039769.png'
__UpperCamelCase = 'How many cats are there?'
__UpperCamelCase = vqa_pipeline(image=lowercase , question="""How many cats are there?""" , top_k=2 )
self.assertEqual(
lowercase , [{"""score""": ANY(lowercase ), """answer""": ANY(lowercase )}, {"""score""": ANY(lowercase ), """answer""": ANY(lowercase )}] )
__UpperCamelCase = vqa_pipeline({"""image""": image, """question""": question} , top_k=2 )
self.assertEqual(
lowercase , [{"""score""": ANY(lowercase ), """answer""": ANY(lowercase )}, {"""score""": ANY(lowercase ), """answer""": ANY(lowercase )}] )
@slow
@require_torch
def __lowerCamelCase ( self ) -> Dict:
__UpperCamelCase = pipeline("""visual-question-answering""" , model="""dandelin/vilt-b32-finetuned-vqa""" )
__UpperCamelCase = './tests/fixtures/tests_samples/COCO/000000039769.png'
__UpperCamelCase = 'How many cats are there?'
__UpperCamelCase = vqa_pipeline(image=lowercase , question=lowercase , top_k=2 )
self.assertEqual(
nested_simplify(lowercase , decimals=4 ) , [{"""score""": 0.8_799, """answer""": """2"""}, {"""score""": 0.296, """answer""": """1"""}] )
__UpperCamelCase = vqa_pipeline({"""image""": image, """question""": question} , top_k=2 )
self.assertEqual(
nested_simplify(lowercase , decimals=4 ) , [{"""score""": 0.8_799, """answer""": """2"""}, {"""score""": 0.296, """answer""": """1"""}] )
__UpperCamelCase = vqa_pipeline(
[{"""image""": image, """question""": question}, {"""image""": image, """question""": question}] , top_k=2 )
self.assertEqual(
nested_simplify(lowercase , decimals=4 ) , [[{"""score""": 0.8_799, """answer""": """2"""}, {"""score""": 0.296, """answer""": """1"""}]] * 2 , )
@require_tf
@unittest.skip("""Visual question answering not implemented in TF""" )
def __lowerCamelCase ( self ) -> Optional[Any]:
pass
| 349 | """simple docstring"""
from math import pi, sqrt, tan
def __a ( _SCREAMING_SNAKE_CASE ) ->float:
if side_length < 0:
raise ValueError('surface_area_cube() only accepts non-negative values' )
return 6 * side_length**2
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->float:
if length < 0 or breadth < 0 or height < 0:
raise ValueError('surface_area_cuboid() only accepts non-negative values' )
return 2 * ((length * breadth) + (breadth * height) + (length * height))
def __a ( _SCREAMING_SNAKE_CASE ) ->float:
if radius < 0:
raise ValueError('surface_area_sphere() only accepts non-negative values' )
return 4 * pi * radius**2
def __a ( _SCREAMING_SNAKE_CASE ) ->float:
if radius < 0:
raise ValueError('surface_area_hemisphere() only accepts non-negative values' )
return 3 * pi * radius**2
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->float:
if radius < 0 or height < 0:
raise ValueError('surface_area_cone() only accepts non-negative values' )
return pi * radius * (radius + (height**2 + radius**2) ** 0.5)
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->float:
if radius_a < 0 or radius_a < 0 or height < 0:
raise ValueError(
'surface_area_conical_frustum() only accepts non-negative values' )
a__: List[Any] = (height**2 + (radius_a - radius_a) ** 2) ** 0.5
return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->float:
if radius < 0 or height < 0:
raise ValueError('surface_area_cylinder() only accepts non-negative values' )
return 2 * pi * radius * (height + radius)
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->float:
if torus_radius < 0 or tube_radius < 0:
raise ValueError('surface_area_torus() only accepts non-negative values' )
if torus_radius < tube_radius:
raise ValueError(
'surface_area_torus() does not support spindle or self intersecting tori' )
return 4 * pow(_SCREAMING_SNAKE_CASE , 2 ) * torus_radius * tube_radius
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->float:
if length < 0 or width < 0:
raise ValueError('area_rectangle() only accepts non-negative values' )
return length * width
def __a ( _SCREAMING_SNAKE_CASE ) ->float:
if side_length < 0:
raise ValueError('area_square() only accepts non-negative values' )
return side_length**2
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->float:
if base < 0 or height < 0:
raise ValueError('area_triangle() only accepts non-negative values' )
return (base * height) / 2
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->float:
if sidea < 0 or sidea < 0 or sidea < 0:
raise ValueError('area_triangle_three_sides() only accepts non-negative values' )
elif sidea + sidea < sidea or sidea + sidea < sidea or sidea + sidea < sidea:
raise ValueError('Given three sides do not form a triangle' )
a__: int = (sidea + sidea + sidea) / 2
a__: Tuple = sqrt(
semi_perimeter
* (semi_perimeter - sidea)
* (semi_perimeter - sidea)
* (semi_perimeter - sidea) )
return area
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->float:
if base < 0 or height < 0:
raise ValueError('area_parallelogram() only accepts non-negative values' )
return base * height
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->float:
if basea < 0 or basea < 0 or height < 0:
raise ValueError('area_trapezium() only accepts non-negative values' )
return 1 / 2 * (basea + basea) * height
def __a ( _SCREAMING_SNAKE_CASE ) ->float:
if radius < 0:
raise ValueError('area_circle() only accepts non-negative values' )
return pi * radius**2
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->float:
if radius_x < 0 or radius_y < 0:
raise ValueError('area_ellipse() only accepts non-negative values' )
return pi * radius_x * radius_y
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->float:
if diagonal_a < 0 or diagonal_a < 0:
raise ValueError('area_rhombus() only accepts non-negative values' )
return 1 / 2 * diagonal_a * diagonal_a
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->float:
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) or sides < 3:
raise ValueError(
'area_reg_polygon() only accepts integers greater than or \
equal to three as number of sides' )
elif length < 0:
raise ValueError(
'area_reg_polygon() only accepts non-negative values as \
length of a side' )
return (sides * length**2) / (4 * tan(pi / sides ))
return (sides * length**2) / (4 * tan(pi / sides ))
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True) # verbose so we can see methods missing tests
print('[DEMO] Areas of various geometric shapes: \n')
print(f"Rectangle: {area_rectangle(10, 20) = }")
print(f"Square: {area_square(10) = }")
print(f"Triangle: {area_triangle(10, 10) = }")
print(f"Triangle: {area_triangle_three_sides(5, 12, 13) = }")
print(f"Parallelogram: {area_parallelogram(10, 20) = }")
print(f"Rhombus: {area_rhombus(10, 20) = }")
print(f"Trapezium: {area_trapezium(10, 20, 30) = }")
print(f"Circle: {area_circle(20) = }")
print(f"Ellipse: {area_ellipse(10, 20) = }")
print('\nSurface Areas of various geometric shapes: \n')
print(f"Cube: {surface_area_cube(20) = }")
print(f"Cuboid: {surface_area_cuboid(10, 20, 30) = }")
print(f"Sphere: {surface_area_sphere(20) = }")
print(f"Hemisphere: {surface_area_hemisphere(20) = }")
print(f"Cone: {surface_area_cone(10, 20) = }")
print(f"Conical Frustum: {surface_area_conical_frustum(10, 20, 30) = }")
print(f"Cylinder: {surface_area_cylinder(10, 20) = }")
print(f"Torus: {surface_area_torus(20, 10) = }")
print(f"Equilateral Triangle: {area_reg_polygon(3, 10) = }")
print(f"Square: {area_reg_polygon(4, 10) = }")
print(f"Reqular Pentagon: {area_reg_polygon(5, 10) = }")
| 290 | 0 |
'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
__A = logging.get_logger(__name__)
def _A ( lowercase__ ):
lowercase__ = OrderedDict()
for key, value in state_dict.items():
if key.startswith("""module.encoder""" ):
lowercase__ = key.replace("""module.encoder""" , """glpn.encoder""" )
if key.startswith("""module.decoder""" ):
lowercase__ = key.replace("""module.decoder""" , """decoder.stages""" )
if "patch_embed" in key:
# replace for example patch_embed1 by patch_embeddings.0
lowercase__ = key[key.find("""patch_embed""" ) + len("""patch_embed""" )]
lowercase__ = key.replace(f'''patch_embed{idx}''' , f'''patch_embeddings.{int(_SCREAMING_SNAKE_CASE )-1}''' )
if "norm" in key:
lowercase__ = key.replace("""norm""" , """layer_norm""" )
if "glpn.encoder.layer_norm" in key:
# replace for example layer_norm1 by layer_norm.0
lowercase__ = key[key.find("""glpn.encoder.layer_norm""" ) + len("""glpn.encoder.layer_norm""" )]
lowercase__ = key.replace(f'''layer_norm{idx}''' , f'''layer_norm.{int(_SCREAMING_SNAKE_CASE )-1}''' )
if "layer_norm1" in key:
lowercase__ = key.replace("""layer_norm1""" , """layer_norm_1""" )
if "layer_norm2" in key:
lowercase__ = key.replace("""layer_norm2""" , """layer_norm_2""" )
if "block" in key:
# replace for example block1 by block.0
lowercase__ = key[key.find("""block""" ) + len("""block""" )]
lowercase__ = key.replace(f'''block{idx}''' , f'''block.{int(_SCREAMING_SNAKE_CASE )-1}''' )
if "attn.q" in key:
lowercase__ = key.replace("""attn.q""" , """attention.self.query""" )
if "attn.proj" in key:
lowercase__ = key.replace("""attn.proj""" , """attention.output.dense""" )
if "attn" in key:
lowercase__ = key.replace("""attn""" , """attention.self""" )
if "fc1" in key:
lowercase__ = key.replace("""fc1""" , """dense1""" )
if "fc2" in key:
lowercase__ = key.replace("""fc2""" , """dense2""" )
if "linear_pred" in key:
lowercase__ = key.replace("""linear_pred""" , """classifier""" )
if "linear_fuse" in key:
lowercase__ = key.replace("""linear_fuse.conv""" , """linear_fuse""" )
lowercase__ = key.replace("""linear_fuse.bn""" , """batch_norm""" )
if "linear_c" in key:
# replace for example linear_c4 by linear_c.3
lowercase__ = key[key.find("""linear_c""" ) + len("""linear_c""" )]
lowercase__ = key.replace(f'''linear_c{idx}''' , f'''linear_c.{int(_SCREAMING_SNAKE_CASE )-1}''' )
if "bot_conv" in key:
lowercase__ = key.replace("""bot_conv""" , """0.convolution""" )
if "skip_conv1" in key:
lowercase__ = key.replace("""skip_conv1""" , """1.convolution""" )
if "skip_conv2" in key:
lowercase__ = key.replace("""skip_conv2""" , """2.convolution""" )
if "fusion1" in key:
lowercase__ = key.replace("""fusion1""" , """1.fusion""" )
if "fusion2" in key:
lowercase__ = key.replace("""fusion2""" , """2.fusion""" )
if "fusion3" in key:
lowercase__ = key.replace("""fusion3""" , """3.fusion""" )
if "fusion" in key and "conv" in key:
lowercase__ = key.replace("""conv""" , """convolutional_layer""" )
if key.startswith("""module.last_layer_depth""" ):
lowercase__ = key.replace("""module.last_layer_depth""" , """head.head""" )
lowercase__ = value
return new_state_dict
def _A ( lowercase__ , lowercase__ ):
# for each of the encoder blocks:
for i in range(config.num_encoder_blocks ):
for j in range(config.depths[i] ):
# read in weights + bias of keys and values (which is a single matrix in the original implementation)
lowercase__ = state_dict.pop(f'''glpn.encoder.block.{i}.{j}.attention.self.kv.weight''' )
lowercase__ = state_dict.pop(f'''glpn.encoder.block.{i}.{j}.attention.self.kv.bias''' )
# next, add keys and values (in that order) to the state dict
lowercase__ = kv_weight[
: config.hidden_sizes[i], :
]
lowercase__ = kv_bias[: config.hidden_sizes[i]]
lowercase__ = kv_weight[
config.hidden_sizes[i] :, :
]
lowercase__ = kv_bias[config.hidden_sizes[i] :]
def _A ( ):
lowercase__ = 'http://images.cocodataset.org/val2017/000000039769.jpg'
lowercase__ = Image.open(requests.get(_SCREAMING_SNAKE_CASE , stream=_SCREAMING_SNAKE_CASE ).raw )
return image
@torch.no_grad()
def _A ( lowercase__ , lowercase__ , lowercase__=False , lowercase__=None ):
lowercase__ = GLPNConfig(hidden_sizes=[64, 128, 320, 512] , decoder_hidden_size=64 , depths=[3, 8, 27, 3] )
# load image processor (only resize + rescale)
lowercase__ = GLPNImageProcessor()
# prepare image
lowercase__ = prepare_img()
lowercase__ = image_processor(images=_SCREAMING_SNAKE_CASE , return_tensors="""pt""" ).pixel_values
logger.info("""Converting model...""" )
# load original state dict
lowercase__ = torch.load(_SCREAMING_SNAKE_CASE , map_location=torch.device("""cpu""" ) )
# rename keys
lowercase__ = rename_keys(_SCREAMING_SNAKE_CASE )
# key and value matrices need special treatment
read_in_k_v(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# create HuggingFace model and load state dict
lowercase__ = GLPNForDepthEstimation(_SCREAMING_SNAKE_CASE )
model.load_state_dict(_SCREAMING_SNAKE_CASE )
model.eval()
# forward pass
lowercase__ = model(_SCREAMING_SNAKE_CASE )
lowercase__ = outputs.predicted_depth
# verify output
if model_name is not None:
if "nyu" in model_name:
lowercase__ = torch.tensor(
[[4.4_1_4_7, 4.0_8_7_3, 4.0_6_7_3], [3.7_8_9_0, 3.2_8_8_1, 3.1_5_2_5], [3.7_6_7_4, 3.5_4_2_3, 3.4_9_1_3]] )
elif "kitti" in model_name:
lowercase__ = torch.tensor(
[[3.4_2_9_1, 2.7_8_6_5, 2.5_1_5_1], [3.2_8_4_1, 2.7_0_2_1, 2.3_5_0_2], [3.1_1_4_7, 2.4_6_2_5, 2.2_4_8_1]] )
else:
raise ValueError(f'''Unknown model name: {model_name}''' )
lowercase__ = torch.Size([1, 480, 640] )
assert predicted_depth.shape == expected_shape
assert torch.allclose(predicted_depth[0, :3, :3] , _SCREAMING_SNAKE_CASE , atol=1e-4 )
print("""Looks ok!""" )
# finally, push to hub if required
if push_to_hub:
logger.info("""Pushing model and image processor to the hub...""" )
model.push_to_hub(
repo_path_or_name=Path(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) , organization="""nielsr""" , commit_message="""Add model""" , use_temp_dir=_SCREAMING_SNAKE_CASE , )
image_processor.push_to_hub(
repo_path_or_name=Path(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) , organization="""nielsr""" , commit_message="""Add image processor""" , use_temp_dir=_SCREAMING_SNAKE_CASE , )
if __name__ == "__main__":
__A = argparse.ArgumentParser()
parser.add_argument(
"--checkpoint_path",
default=None,
type=str,
help="Path to the original PyTorch checkpoint (.pth file).",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
)
parser.add_argument(
"--push_to_hub", action="store_true", help="Whether to upload the model to the HuggingFace hub."
)
parser.add_argument(
"--model_name",
default="glpn-kitti",
type=str,
help="Name of the model in case you\'re pushing to the hub.",
)
__A = parser.parse_args()
convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)
| 164 | """simple docstring"""
import itertools
import random
import unittest
import numpy as np
from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
lowercase__ = random.Random()
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=1.0 , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None ) ->Optional[int]:
if rng is None:
a__: Any = global_rng
a__: int = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
class __snake_case ( unittest.TestCase ):
def __init__( self , lowercase , lowercase=7 , lowercase=4_00 , lowercase=20_00 , lowercase=1 , lowercase=0.0 , lowercase=1_60_00 , lowercase=True , lowercase=True , ) -> Union[str, Any]:
'''simple docstring'''
a__: Tuple = parent
a__: Optional[int] = batch_size
a__: Optional[Any] = min_seq_length
a__: Optional[int] = max_seq_length
a__: Tuple = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
a__: Dict = feature_size
a__: Any = padding_value
a__: Optional[Any] = sampling_rate
a__: Optional[Any] = return_attention_mask
a__: str = do_normalize
def lowerCamelCase_ ( self) -> Dict:
'''simple docstring'''
return {
"feature_size": self.feature_size,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"return_attention_mask": self.return_attention_mask,
"do_normalize": self.do_normalize,
}
def lowerCamelCase_ ( self , lowercase=False , lowercase=False) -> Tuple:
'''simple docstring'''
def _flatten(lowercase):
return list(itertools.chain(*lowercase))
if equal_length:
a__: Dict = floats_list((self.batch_size, self.max_seq_length))
else:
# make sure that inputs increase in size
a__: List[Any] = [
_flatten(floats_list((x, self.feature_size)))
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff)
]
if numpify:
a__: str = [np.asarray(lowercase) for x in speech_inputs]
return speech_inputs
class __snake_case ( __lowerCAmelCase , unittest.TestCase ):
a__ = WavaVecaFeatureExtractor
def lowerCamelCase_ ( self) -> Tuple:
'''simple docstring'''
a__: Optional[int] = WavaVecaFeatureExtractionTester(self)
def lowerCamelCase_ ( self , lowercase) -> List[Any]:
'''simple docstring'''
self.assertTrue(np.all(np.mean(lowercase , axis=0) < 1e-3))
self.assertTrue(np.all(np.abs(np.var(lowercase , axis=0) - 1) < 1e-3))
def lowerCamelCase_ ( self) -> List[str]:
'''simple docstring'''
a__: List[Any] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
# create three inputs of length 800, 1000, and 1200
a__: Optional[Any] = [floats_list((1, x))[0] for x in range(8_00 , 14_00 , 2_00)]
a__: List[str] = [np.asarray(lowercase) for speech_input in speech_inputs]
# Test not batched input
a__: Optional[Any] = feat_extract(speech_inputs[0] , return_tensors='np').input_values
a__: Dict = feat_extract(np_speech_inputs[0] , return_tensors='np').input_values
self.assertTrue(np.allclose(lowercase , lowercase , atol=1e-3))
# Test batched
a__: Dict = feat_extract(lowercase , return_tensors='np').input_values
a__: int = feat_extract(lowercase , return_tensors='np').input_values
for enc_seq_a, enc_seq_a in zip(lowercase , lowercase):
self.assertTrue(np.allclose(lowercase , lowercase , atol=1e-3))
# Test 2-D numpy arrays are batched.
a__: int = [floats_list((1, x))[0] for x in (8_00, 8_00, 8_00)]
a__: Union[str, Any] = np.asarray(lowercase)
a__: int = feat_extract(lowercase , return_tensors='np').input_values
a__: Any = feat_extract(lowercase , return_tensors='np').input_values
for enc_seq_a, enc_seq_a in zip(lowercase , lowercase):
self.assertTrue(np.allclose(lowercase , lowercase , atol=1e-3))
def lowerCamelCase_ ( self) -> List[Any]:
'''simple docstring'''
a__: Tuple = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
a__: List[Any] = [floats_list((1, x))[0] for x in range(8_00 , 14_00 , 2_00)]
a__: Optional[int] = ['longest', 'max_length', 'do_not_pad']
a__: List[Any] = [None, 16_00, None]
for max_length, padding in zip(lowercase , lowercase):
a__: Dict = feat_extract(lowercase , padding=lowercase , max_length=lowercase , return_tensors='np')
a__: Union[str, Any] = processed.input_values
self._check_zero_mean_unit_variance(input_values[0][:8_00])
self.assertTrue(input_values[0][8_00:].sum() < 1e-6)
self._check_zero_mean_unit_variance(input_values[1][:10_00])
self.assertTrue(input_values[0][10_00:].sum() < 1e-6)
self._check_zero_mean_unit_variance(input_values[2][:12_00])
def lowerCamelCase_ ( self) -> Optional[int]:
'''simple docstring'''
a__: str = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
a__: Optional[int] = range(8_00 , 14_00 , 2_00)
a__: List[str] = [floats_list((1, x))[0] for x in lengths]
a__: Tuple = ['longest', 'max_length', 'do_not_pad']
a__: Dict = [None, 16_00, None]
for max_length, padding in zip(lowercase , lowercase):
a__: int = feat_extract(lowercase , max_length=lowercase , padding=lowercase)
a__: Any = processed.input_values
self._check_zero_mean_unit_variance(input_values[0][:8_00])
self._check_zero_mean_unit_variance(input_values[1][:10_00])
self._check_zero_mean_unit_variance(input_values[2][:12_00])
def lowerCamelCase_ ( self) -> List[str]:
'''simple docstring'''
a__: Any = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
a__: Any = [floats_list((1, x))[0] for x in range(8_00 , 14_00 , 2_00)]
a__: Dict = feat_extract(
lowercase , truncation=lowercase , max_length=10_00 , padding='max_length' , return_tensors='np')
a__: int = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :8_00])
self._check_zero_mean_unit_variance(input_values[1])
self._check_zero_mean_unit_variance(input_values[2])
def lowerCamelCase_ ( self) -> Dict:
'''simple docstring'''
a__: Tuple = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
a__: int = [floats_list((1, x))[0] for x in range(8_00 , 14_00 , 2_00)]
a__: str = feat_extract(
lowercase , truncation=lowercase , max_length=10_00 , padding='longest' , return_tensors='np')
a__: Any = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :8_00])
self._check_zero_mean_unit_variance(input_values[1, :10_00])
self._check_zero_mean_unit_variance(input_values[2])
# make sure that if max_length < longest -> then pad to max_length
self.assertTrue(input_values.shape == (3, 10_00))
a__: Dict = [floats_list((1, x))[0] for x in range(8_00 , 14_00 , 2_00)]
a__: Tuple = feat_extract(
lowercase , truncation=lowercase , max_length=20_00 , padding='longest' , return_tensors='np')
a__: str = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :8_00])
self._check_zero_mean_unit_variance(input_values[1, :10_00])
self._check_zero_mean_unit_variance(input_values[2])
# make sure that if max_length > longest -> then pad to longest
self.assertTrue(input_values.shape == (3, 12_00))
@require_torch
def lowerCamelCase_ ( self) -> Tuple:
'''simple docstring'''
import torch
a__: Optional[int] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
a__: Tuple = np.random.rand(1_00).astype(np.floataa)
a__: Tuple = np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
a__: Any = feature_extractor.pad([{'input_values': inputs}] , return_tensors='np')
self.assertTrue(np_processed.input_values.dtype == np.floataa)
a__: Optional[Any] = feature_extractor.pad([{'input_values': inputs}] , return_tensors='pt')
self.assertTrue(pt_processed.input_values.dtype == torch.floataa)
@slow
@require_torch
def lowerCamelCase_ ( self) -> Optional[Any]:
'''simple docstring'''
for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
a__: str = WavaVecaConfig.from_pretrained(lowercase)
a__: str = WavaVecaFeatureExtractor.from_pretrained(lowercase)
# only "layer" feature extraction norm should make use of
# attention_mask
self.assertEqual(feat_extract.return_attention_mask , config.feat_extract_norm == 'layer')
| 290 | 0 |
"""simple docstring"""
import argparse
import json
import os
import re
from collections import OrderedDict
from os.path import basename, dirname
import fairseq
import torch
from fairseq import hub_utils
from fairseq.data.dictionary import Dictionary
from transformers import FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging
logging.set_verbosity_warning()
_A : Optional[Any] = 2
# based on the results of a search on a range of `num_beams`, `length_penalty` and `early_stopping`
# values against wmt19 test data to obtain the best BLEU scores, we will use the following defaults:
#
# * `num_beams`: 5 (higher scores better, but requires more memory/is slower, can be adjusted by users)
# * `early_stopping`: `False` consistently scored better
# * `length_penalty` varied, so will assign the best one depending on the model
_A : Any = {
# fairseq:
"""wmt19-ru-en""": {"""length_penalty""": 1.1},
"""wmt19-en-ru""": {"""length_penalty""": 1.1_5},
"""wmt19-en-de""": {"""length_penalty""": 1.0},
"""wmt19-de-en""": {"""length_penalty""": 1.1},
# allenai:
"""wmt16-en-de-dist-12-1""": {"""length_penalty""": 0.6},
"""wmt16-en-de-dist-6-1""": {"""length_penalty""": 0.6},
"""wmt16-en-de-12-1""": {"""length_penalty""": 0.8},
"""wmt19-de-en-6-6-base""": {"""length_penalty""": 0.6},
"""wmt19-de-en-6-6-big""": {"""length_penalty""": 0.6},
}
# this remaps the different models to their organization names
_A : Optional[Any] = {}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
_A : Dict = """facebook"""
for m in [
"wmt16-en-de-dist-12-1",
"wmt16-en-de-dist-6-1",
"wmt16-en-de-12-1",
"wmt19-de-en-6-6-base",
"wmt19-de-en-6-6-big",
]:
_A : Any = """allenai"""
def __magic_name__ ( __snake_case : Dict ) -> str:
# (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up,
# e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7}
lowercase : Union[str, Any] = dict((re.sub(r"@@$" , "" , _SCREAMING_SNAKE_CASE ), v) if k.endswith("@@" ) else (re.sub(r"$" , "</w>" , _SCREAMING_SNAKE_CASE ), v) for k, v in d.items() )
lowercase : List[str] = '<s> <pad> </s> <unk>'.split()
# restore the special tokens
for k in keep_keys:
del da[f"""{k}</w>"""]
lowercase : Any = d[k] # restore
return da
def __magic_name__ ( __snake_case : Optional[int] , __snake_case : Dict ) -> Optional[int]:
# prep
assert os.path.exists(_SCREAMING_SNAKE_CASE )
os.makedirs(_SCREAMING_SNAKE_CASE , exist_ok=_SCREAMING_SNAKE_CASE )
print(f"""Writing results to {pytorch_dump_folder_path}""" )
# handle various types of models
lowercase : Optional[int] = basename(_SCREAMING_SNAKE_CASE )
lowercase : str = dirname(_SCREAMING_SNAKE_CASE )
lowercase : Optional[Any] = fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
lowercase : List[Any] = cls.hub_models()
lowercase : str = {'bpe': 'fastbpe', 'tokenizer': 'moses'}
lowercase : int = '.'
# note: since the model dump is old, fairseq has upgraded its model some
# time later, and it does a whole lot of rewrites and splits on the saved
# weights, therefore we can't use torch.load() directly on the model file.
# see: upgrade_state_dict(state_dict) in fairseq_model.py
print(f"""using checkpoint {checkpoint_file}""" )
lowercase : List[str] = hub_utils.from_pretrained(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , archive_map=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
lowercase : int = vars(chkpt["args"]["model"] )
lowercase : int = args['source_lang']
lowercase : Optional[Any] = args['target_lang']
lowercase : Optional[int] = dirname(_SCREAMING_SNAKE_CASE )
lowercase : Optional[int] = basename(_SCREAMING_SNAKE_CASE )
# dicts
lowercase : Union[str, Any] = os.path.join(_SCREAMING_SNAKE_CASE , f"""dict.{src_lang}.txt""" )
lowercase : Optional[int] = os.path.join(_SCREAMING_SNAKE_CASE , f"""dict.{tgt_lang}.txt""" )
lowercase : int = Dictionary.load(_SCREAMING_SNAKE_CASE )
lowercase : Tuple = rewrite_dict_keys(src_dict.indices )
lowercase : Optional[int] = len(_SCREAMING_SNAKE_CASE )
lowercase : Union[str, Any] = os.path.join(_SCREAMING_SNAKE_CASE , "vocab-src.json" )
print(f"""Generating {src_vocab_file} of {src_vocab_size} of {src_lang} records""" )
with open(_SCREAMING_SNAKE_CASE , "w" , encoding="utf-8" ) as f:
f.write(json.dumps(_SCREAMING_SNAKE_CASE , ensure_ascii=_SCREAMING_SNAKE_CASE , indent=_SCREAMING_SNAKE_CASE ) )
# detect whether this is a do_lower_case situation, which can be derived by checking whether we
# have at least one uppercase letter in the source vocab
lowercase : str = True
for k in src_vocab.keys():
if not k.islower():
lowercase : int = False
break
lowercase : List[str] = Dictionary.load(_SCREAMING_SNAKE_CASE )
lowercase : int = rewrite_dict_keys(tgt_dict.indices )
lowercase : int = len(_SCREAMING_SNAKE_CASE )
lowercase : Tuple = os.path.join(_SCREAMING_SNAKE_CASE , "vocab-tgt.json" )
print(f"""Generating {tgt_vocab_file} of {tgt_vocab_size} of {tgt_lang} records""" )
with open(_SCREAMING_SNAKE_CASE , "w" , encoding="utf-8" ) as f:
f.write(json.dumps(_SCREAMING_SNAKE_CASE , ensure_ascii=_SCREAMING_SNAKE_CASE , indent=_SCREAMING_SNAKE_CASE ) )
# merges_file (bpecodes)
lowercase : Dict = os.path.join(_SCREAMING_SNAKE_CASE , VOCAB_FILES_NAMES["merges_file"] )
for fn in ["bpecodes", "code"]: # older fairseq called the merges file "code"
lowercase : Union[str, Any] = os.path.join(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
if os.path.exists(_SCREAMING_SNAKE_CASE ):
break
with open(_SCREAMING_SNAKE_CASE , encoding="utf-8" ) as fin:
lowercase : Dict = fin.read()
lowercase : List[Any] = re.sub(r" \d+$" , "" , _SCREAMING_SNAKE_CASE , 0 , re.M ) # remove frequency number
print(f"""Generating {merges_file}""" )
with open(_SCREAMING_SNAKE_CASE , "w" , encoding="utf-8" ) as fout:
fout.write(_SCREAMING_SNAKE_CASE )
# model config
lowercase : List[Any] = os.path.join(_SCREAMING_SNAKE_CASE , "config.json" )
# validate bpe/tokenizer config, as currently it's hardcoded to moses+fastbpe -
# may have to modify the tokenizer if a different type is used by a future model
assert args["bpe"] == "fastbpe", f"""need to extend tokenizer to support bpe={args["bpe"]}"""
assert args["tokenizer"] == "moses", f"""need to extend tokenizer to support bpe={args["tokenizer"]}"""
lowercase : Any = {
'architectures': ['FSMTForConditionalGeneration'],
'model_type': 'fsmt',
'activation_dropout': args['activation_dropout'],
'activation_function': 'relu',
'attention_dropout': args['attention_dropout'],
'd_model': args['decoder_embed_dim'],
'dropout': args['dropout'],
'init_std': 0.02,
'max_position_embeddings': args['max_source_positions'],
'num_hidden_layers': args['encoder_layers'],
'src_vocab_size': src_vocab_size,
'tgt_vocab_size': tgt_vocab_size,
'langs': [src_lang, tgt_lang],
'encoder_attention_heads': args['encoder_attention_heads'],
'encoder_ffn_dim': args['encoder_ffn_embed_dim'],
'encoder_layerdrop': args['encoder_layerdrop'],
'encoder_layers': args['encoder_layers'],
'decoder_attention_heads': args['decoder_attention_heads'],
'decoder_ffn_dim': args['decoder_ffn_embed_dim'],
'decoder_layerdrop': args['decoder_layerdrop'],
'decoder_layers': args['decoder_layers'],
'bos_token_id': 0,
'pad_token_id': 1,
'eos_token_id': 2,
'is_encoder_decoder': True,
'scale_embedding': not args['no_scale_embedding'],
'tie_word_embeddings': args['share_all_embeddings'],
}
# good hparam defaults to start with
lowercase : Optional[Any] = 5
lowercase : Union[str, Any] = False
if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
lowercase : str = best_score_hparams[model_dir]['length_penalty']
else:
lowercase : Tuple = 1.0
print(f"""Generating {fsmt_model_config_file}""" )
with open(_SCREAMING_SNAKE_CASE , "w" , encoding="utf-8" ) as f:
f.write(json.dumps(_SCREAMING_SNAKE_CASE , ensure_ascii=_SCREAMING_SNAKE_CASE , indent=_SCREAMING_SNAKE_CASE ) )
# tokenizer config
lowercase : List[Any] = os.path.join(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
lowercase : List[Any] = {
'langs': [src_lang, tgt_lang],
'model_max_length': 1024,
'do_lower_case': do_lower_case,
}
print(f"""Generating {fsmt_tokenizer_config_file}""" )
with open(_SCREAMING_SNAKE_CASE , "w" , encoding="utf-8" ) as f:
f.write(json.dumps(_SCREAMING_SNAKE_CASE , ensure_ascii=_SCREAMING_SNAKE_CASE , indent=_SCREAMING_SNAKE_CASE ) )
# model
lowercase : Union[str, Any] = chkpt['models'][0]
lowercase : str = model.state_dict()
# rename keys to start with 'model.'
lowercase : Optional[int] = OrderedDict(("model." + k, v) for k, v in model_state_dict.items() )
# remove unneeded keys
lowercase : Union[str, Any] = [
'model.model',
'model.encoder.version',
'model.decoder.version',
'model.encoder_embed_tokens.weight',
'model.decoder_embed_tokens.weight',
'model.encoder.embed_positions._float_tensor',
'model.decoder.embed_positions._float_tensor',
]
for k in ignore_keys:
model_state_dict.pop(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
lowercase : Dict = FSMTConfig.from_pretrained(_SCREAMING_SNAKE_CASE )
lowercase : int = FSMTForConditionalGeneration(_SCREAMING_SNAKE_CASE )
# check that it loads ok
model_new.load_state_dict(_SCREAMING_SNAKE_CASE , strict=_SCREAMING_SNAKE_CASE )
# save
lowercase : Union[str, Any] = os.path.join(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
print(f"""Generating {pytorch_weights_dump_path}""" )
torch.save(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
print("Conversion is done!" )
print("\nLast step is to upload the files to s3" )
print(f"""cd {data_root}""" )
print(f"""transformers-cli upload {model_dir}""" )
if __name__ == "__main__":
_A : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--fsmt_checkpoint_path""",
default=None,
type=str,
required=True,
help=(
"""Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,"""
""" bpecodes, etc."""
),
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
)
_A : Any = parser.parse_args()
convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)
| 202 | """simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
'edbeeching/decision-transformer-gym-hopper-medium': (
'https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json'
),
# See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}
class __snake_case ( __lowerCAmelCase ):
a__ = """decision_transformer"""
a__ = ["""past_key_values"""]
a__ = {
"""max_position_embeddings""": """n_positions""",
"""num_attention_heads""": """n_head""",
"""num_hidden_layers""": """n_layer""",
}
def __init__( self , lowercase=17 , lowercase=4 , lowercase=1_28 , lowercase=40_96 , lowercase=True , lowercase=1 , lowercase=10_24 , lowercase=3 , lowercase=1 , lowercase=None , lowercase="relu" , lowercase=0.1 , lowercase=0.1 , lowercase=0.1 , lowercase=1e-5 , lowercase=0.02 , lowercase=True , lowercase=True , lowercase=5_02_56 , lowercase=5_02_56 , lowercase=False , lowercase=False , **lowercase , ) -> Tuple:
'''simple docstring'''
a__: List[str] = state_dim
a__: int = act_dim
a__: List[Any] = hidden_size
a__: List[str] = max_ep_len
a__: List[Any] = action_tanh
a__: Optional[Any] = vocab_size
a__: Tuple = n_positions
a__: Dict = n_layer
a__: Optional[int] = n_head
a__: Optional[int] = n_inner
a__: Any = activation_function
a__: Union[str, Any] = resid_pdrop
a__: Any = embd_pdrop
a__: Any = attn_pdrop
a__: List[Any] = layer_norm_epsilon
a__: Optional[Any] = initializer_range
a__: Any = scale_attn_weights
a__: Dict = use_cache
a__: Optional[int] = scale_attn_by_inverse_layer_idx
a__: List[str] = reorder_and_upcast_attn
a__: Any = bos_token_id
a__: int = eos_token_id
super().__init__(bos_token_id=lowercase , eos_token_id=lowercase , **lowercase)
| 290 | 0 |
'''simple docstring'''
from collections.abc import Sequence
def UpperCAmelCase_ ( __lowercase : List[Any] = None ) -> int:
'''simple docstring'''
if nums is None or not nums:
raise ValueError("Input sequence should not be empty" )
_UpperCAmelCase = nums[0]
for i in range(1 , len(_SCREAMING_SNAKE_CASE ) ):
_UpperCAmelCase = nums[i]
_UpperCAmelCase = max(_SCREAMING_SNAKE_CASE , ans + num , _SCREAMING_SNAKE_CASE )
return ans
if __name__ == "__main__":
import doctest
doctest.testmod()
# Try on a sample input from the user
__SCREAMING_SNAKE_CASE :int = int(input('''Enter number of elements : ''').strip())
__SCREAMING_SNAKE_CASE :Any = list(map(int, input('''\nEnter the numbers : ''').strip().split()))[:n]
print(max_subsequence_sum(array))
| 22 | """simple docstring"""
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->int:
while a != 0:
a__ , a__: List[str] = b % a, a
return b
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->int:
if gcd(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) != 1:
a__: Dict = F'mod inverse of {a!r} and {m!r} does not exist'
raise ValueError(_SCREAMING_SNAKE_CASE )
a__ , a__ , a__: Union[str, Any] = 1, 0, a
a__ , a__ , a__: Any = 0, 1, m
while va != 0:
a__: int = ua // va
a__ , a__ , a__ , a__ , a__ , a__: Any = (ua - q * va), (ua - q * va), (ua - q * va), va, va, va
return ua % m
| 290 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_lowerCamelCase : Optional[int] = {
'configuration_bridgetower': [
'BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'BridgeTowerConfig',
'BridgeTowerTextConfig',
'BridgeTowerVisionConfig',
],
'processing_bridgetower': ['BridgeTowerProcessor'],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase : Tuple = ['BridgeTowerImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase : Optional[int] = [
'BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST',
'BridgeTowerForContrastiveLearning',
'BridgeTowerForImageAndTextRetrieval',
'BridgeTowerForMaskedLM',
'BridgeTowerModel',
'BridgeTowerPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_bridgetower import (
BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP,
BridgeTowerConfig,
BridgeTowerTextConfig,
BridgeTowerVisionConfig,
)
from .processing_bridgetower import BridgeTowerProcessor
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_bridgetower import BridgeTowerImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_bridgetower import (
BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST,
BridgeTowerForContrastiveLearning,
BridgeTowerForImageAndTextRetrieval,
BridgeTowerForMaskedLM,
BridgeTowerModel,
BridgeTowerPreTrainedModel,
)
else:
import sys
_lowerCamelCase : int = _LazyModule(__name__, globals()['__file__'], _import_structure)
| 258 | """simple docstring"""
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
lowercase__ = logging.getLogger(__name__)
class __snake_case :
def __init__( self) -> Optional[int]:
'''simple docstring'''
a__: Optional[Any] = False
def lowerCamelCase_ ( self , lowercase , lowercase , lowercase , lowercase) -> str:
'''simple docstring'''
if not self.initialized:
a__: Optional[int] = RagRetriever(
lowercase , question_encoder_tokenizer=lowercase , generator_tokenizer=lowercase , index=lowercase , init_retrieval=lowercase , )
a__: Optional[int] = True
def lowerCamelCase_ ( self) -> int:
'''simple docstring'''
self.retriever.index.init_index()
def lowerCamelCase_ ( self , lowercase , lowercase) -> Union[str, Any]:
'''simple docstring'''
a__ , a__: str = self.retriever._main_retrieve(lowercase , lowercase)
return doc_ids, retrieved_doc_embeds
class __snake_case ( __lowerCAmelCase ):
def __init__( self , lowercase , lowercase , lowercase , lowercase , lowercase=None) -> int:
'''simple docstring'''
if index is not None and index.is_initialized() and len(lowercase) > 0:
raise ValueError(
'When using Ray for distributed fine-tuning, '
'you\'ll need to provide the paths instead, '
'as the dataset and the index are loaded '
'separately. More info in examples/rag/use_own_knowledge_dataset.py ')
super().__init__(
lowercase , question_encoder_tokenizer=lowercase , generator_tokenizer=lowercase , index=lowercase , init_retrieval=lowercase , )
a__: Any = retrieval_workers
if len(self.retrieval_workers) > 0:
ray.get(
[
worker.create_rag_retriever.remote(lowercase , lowercase , lowercase , lowercase)
for worker in self.retrieval_workers
])
def lowerCamelCase_ ( self) -> Any:
'''simple docstring'''
logger.info('initializing retrieval')
if len(self.retrieval_workers) > 0:
ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers])
else:
# Non-distributed training. Load index into this same process.
self.index.init_index()
def lowerCamelCase_ ( self , lowercase , lowercase) -> Union[str, Any]:
'''simple docstring'''
if len(self.retrieval_workers) > 0:
# Select a random retrieval actor.
a__: int = self.retrieval_workers[random.randint(0 , len(self.retrieval_workers) - 1)]
a__ , a__: List[Any] = ray.get(random_worker.retrieve.remote(lowercase , lowercase))
else:
a__ , a__: Dict = self._main_retrieve(lowercase , lowercase)
return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(lowercase)
@classmethod
def lowerCamelCase_ ( cls , lowercase , lowercase=None , **lowercase) -> Tuple:
'''simple docstring'''
return super(lowercase , cls).get_tokenizers(lowercase , lowercase , **lowercase)
@classmethod
def lowerCamelCase_ ( cls , lowercase , lowercase , lowercase=None , **lowercase) -> Union[str, Any]:
'''simple docstring'''
a__: Optional[int] = kwargs.pop('config' , lowercase) or RagConfig.from_pretrained(lowercase , **lowercase)
a__: Union[str, Any] = RagTokenizer.from_pretrained(lowercase , config=lowercase)
a__: int = rag_tokenizer.question_encoder
a__: Any = rag_tokenizer.generator
if indexed_dataset is not None:
a__: List[Any] = 'custom'
a__: Optional[Any] = CustomHFIndex(config.retrieval_vector_size , lowercase)
else:
a__: Dict = cls._build_index(lowercase)
return cls(
lowercase , question_encoder_tokenizer=lowercase , generator_tokenizer=lowercase , retrieval_workers=lowercase , index=lowercase , )
| 290 | 0 |
'''simple docstring'''
import time
from contextlib import contextmanager
from pathlib import Path
import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder
__SCREAMING_SNAKE_CASE : Dict = """__DUMMY_TRANSFORMERS_USER__"""
__SCREAMING_SNAKE_CASE : Any = """Dummy User"""
__SCREAMING_SNAKE_CASE : Dict = """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"""
__SCREAMING_SNAKE_CASE : Optional[Any] = """https://hub-ci.huggingface.co"""
__SCREAMING_SNAKE_CASE : List[str] = CI_HUB_ENDPOINT + """/datasets/{repo_id}/resolve/{revision}/{path}"""
__SCREAMING_SNAKE_CASE : Any = CI_HUB_ENDPOINT + """/{repo_id}/resolve/{revision}/{filename}"""
__SCREAMING_SNAKE_CASE : str = Path("""~/.huggingface/hub_ci_token""").expanduser()
@pytest.fixture
def UpperCamelCase_ ( _UpperCAmelCase : Dict ) -> List[str]:
"""simple docstring"""
monkeypatch.setattr(
"huggingface_hub.file_download.HUGGINGFACE_CO_URL_TEMPLATE" , _SCREAMING_SNAKE_CASE )
@pytest.fixture
def UpperCamelCase_ ( _UpperCAmelCase : Any ) -> str:
"""simple docstring"""
monkeypatch.setattr("datasets.config.HF_ENDPOINT" , _SCREAMING_SNAKE_CASE )
monkeypatch.setattr("datasets.config.HUB_DATASETS_URL" , _SCREAMING_SNAKE_CASE )
@pytest.fixture
def UpperCamelCase_ ( _UpperCAmelCase : Optional[Any] ) -> str:
"""simple docstring"""
monkeypatch.setattr("huggingface_hub.hf_api.HfFolder.path_token" , _SCREAMING_SNAKE_CASE )
@pytest.fixture
def UpperCamelCase_ ( _UpperCAmelCase : Tuple , _UpperCAmelCase : Any ) -> Union[str, Any]:
"""simple docstring"""
HfFolder.save_token(_SCREAMING_SNAKE_CASE )
yield
HfFolder.delete_token()
@pytest.fixture(scope="session" )
def UpperCamelCase_ ( ) -> Optional[int]:
"""simple docstring"""
return HfApi(endpoint=_SCREAMING_SNAKE_CASE )
@pytest.fixture(scope="session" )
def UpperCamelCase_ ( _UpperCAmelCase : List[str] ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase : Union[str, Any] = HfFolder.get_token()
HfFolder.save_token(_SCREAMING_SNAKE_CASE )
yield CI_HUB_USER_TOKEN
if previous_token is not None:
HfFolder.save_token(_SCREAMING_SNAKE_CASE )
@pytest.fixture
def UpperCamelCase_ ( _UpperCAmelCase : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
def _cleanup_repo(_UpperCAmelCase : Optional[int] ):
hf_api.delete_repo(_SCREAMING_SNAKE_CASE , token=_SCREAMING_SNAKE_CASE , repo_type="dataset" )
return _cleanup_repo
@pytest.fixture
def UpperCamelCase_ ( _UpperCAmelCase : Dict ) -> List[str]:
"""simple docstring"""
@contextmanager
def _temporary_repo(_UpperCAmelCase : Union[str, Any] ):
try:
yield repo_id
finally:
cleanup_repo(_SCREAMING_SNAKE_CASE )
return _temporary_repo
@pytest.fixture(scope="session" )
def UpperCamelCase_ ( _UpperCAmelCase : str , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Tuple ) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase : List[Any] = F"""repo_txt_data-{int(time.time() * 10e3 )}"""
_UpperCAmelCase : str = F"""{CI_HUB_USER}/{repo_name}"""
hf_api.create_repo(_SCREAMING_SNAKE_CASE , token=_SCREAMING_SNAKE_CASE , repo_type="dataset" , private=_SCREAMING_SNAKE_CASE )
hf_api.upload_file(
token=_SCREAMING_SNAKE_CASE , path_or_fileobj=str(_SCREAMING_SNAKE_CASE ) , path_in_repo="data/text_data.txt" , repo_id=_SCREAMING_SNAKE_CASE , repo_type="dataset" , )
yield repo_id
try:
hf_api.delete_repo(_SCREAMING_SNAKE_CASE , token=_SCREAMING_SNAKE_CASE , repo_type="dataset" )
except (requests.exceptions.HTTPError, ValueError): # catch http error and token invalid error
pass
@pytest.fixture()
def UpperCamelCase_ ( _UpperCAmelCase : List[str] , _UpperCAmelCase : int , _UpperCAmelCase : int ) -> Any:
"""simple docstring"""
return hf_private_dataset_repo_txt_data_
@pytest.fixture(scope="session" )
def UpperCamelCase_ ( _UpperCAmelCase : List[str] , _UpperCAmelCase : int , _UpperCAmelCase : Any ) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase : Optional[Any] = F"""repo_zipped_txt_data-{int(time.time() * 10e3 )}"""
_UpperCAmelCase : Any = F"""{CI_HUB_USER}/{repo_name}"""
hf_api.create_repo(_SCREAMING_SNAKE_CASE , token=_SCREAMING_SNAKE_CASE , repo_type="dataset" , private=_SCREAMING_SNAKE_CASE )
hf_api.upload_file(
token=_SCREAMING_SNAKE_CASE , path_or_fileobj=str(_SCREAMING_SNAKE_CASE ) , path_in_repo="data.zip" , repo_id=_SCREAMING_SNAKE_CASE , repo_type="dataset" , )
yield repo_id
try:
hf_api.delete_repo(_SCREAMING_SNAKE_CASE , token=_SCREAMING_SNAKE_CASE , repo_type="dataset" )
except (requests.exceptions.HTTPError, ValueError): # catch http error and token invalid error
pass
@pytest.fixture()
def UpperCamelCase_ ( _UpperCAmelCase : List[Any] , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Tuple ) -> Tuple:
"""simple docstring"""
return hf_private_dataset_repo_zipped_txt_data_
@pytest.fixture(scope="session" )
def UpperCamelCase_ ( _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : int , _UpperCAmelCase : Any ) -> List[str]:
"""simple docstring"""
_UpperCAmelCase : str = F"""repo_zipped_img_data-{int(time.time() * 10e3 )}"""
_UpperCAmelCase : List[Any] = F"""{CI_HUB_USER}/{repo_name}"""
hf_api.create_repo(_SCREAMING_SNAKE_CASE , token=_SCREAMING_SNAKE_CASE , repo_type="dataset" , private=_SCREAMING_SNAKE_CASE )
hf_api.upload_file(
token=_SCREAMING_SNAKE_CASE , path_or_fileobj=str(_SCREAMING_SNAKE_CASE ) , path_in_repo="data.zip" , repo_id=_SCREAMING_SNAKE_CASE , repo_type="dataset" , )
yield repo_id
try:
hf_api.delete_repo(_SCREAMING_SNAKE_CASE , token=_SCREAMING_SNAKE_CASE , repo_type="dataset" )
except (requests.exceptions.HTTPError, ValueError): # catch http error and token invalid error
pass
@pytest.fixture()
def UpperCamelCase_ ( _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Dict , _UpperCAmelCase : Union[str, Any] ) -> Dict:
"""simple docstring"""
return hf_private_dataset_repo_zipped_img_data_
| 31 | """simple docstring"""
import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter
import requests
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None ) ->Optional[int]:
a__: int = None
if token is not None:
a__: Tuple = {'Accept': 'application/vnd.github+json', 'Authorization': F'Bearer {token}'}
a__: Optional[Any] = F'https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100'
a__: str = requests.get(_SCREAMING_SNAKE_CASE , headers=_SCREAMING_SNAKE_CASE ).json()
a__: str = {}
try:
job_links.update({job['name']: job['html_url'] for job in result['jobs']} )
a__: int = math.ceil((result['total_count'] - 100) / 100 )
for i in range(_SCREAMING_SNAKE_CASE ):
a__: Dict = requests.get(url + F'&page={i + 2}' , headers=_SCREAMING_SNAKE_CASE ).json()
job_links.update({job['name']: job['html_url'] for job in result['jobs']} )
return job_links
except Exception:
print(F'Unknown error, could not fetch links:\n{traceback.format_exc()}' )
return {}
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None ) ->Dict:
a__: Dict = None
if token is not None:
a__: List[str] = {'Accept': 'application/vnd.github+json', 'Authorization': F'Bearer {token}'}
a__: Dict = F'https://api.github.com/repos/huggingface/transformers/actions/runs/{worflow_run_id}/artifacts?per_page=100'
a__: Union[str, Any] = requests.get(_SCREAMING_SNAKE_CASE , headers=_SCREAMING_SNAKE_CASE ).json()
a__: List[Any] = {}
try:
artifacts.update({artifact['name']: artifact['archive_download_url'] for artifact in result['artifacts']} )
a__: Dict = math.ceil((result['total_count'] - 100) / 100 )
for i in range(_SCREAMING_SNAKE_CASE ):
a__: Optional[int] = requests.get(url + F'&page={i + 2}' , headers=_SCREAMING_SNAKE_CASE ).json()
artifacts.update({artifact['name']: artifact['archive_download_url'] for artifact in result['artifacts']} )
return artifacts
except Exception:
print(F'Unknown error, could not fetch links:\n{traceback.format_exc()}' )
return {}
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->str:
a__: List[Any] = None
if token is not None:
a__: Optional[int] = {'Accept': 'application/vnd.github+json', 'Authorization': F'Bearer {token}'}
a__: Union[str, Any] = requests.get(_SCREAMING_SNAKE_CASE , headers=_SCREAMING_SNAKE_CASE , allow_redirects=_SCREAMING_SNAKE_CASE )
a__: Optional[Any] = result.headers['Location']
a__: Optional[int] = requests.get(_SCREAMING_SNAKE_CASE , allow_redirects=_SCREAMING_SNAKE_CASE )
a__: int = os.path.join(_SCREAMING_SNAKE_CASE , F'{artifact_name}.zip' )
with open(_SCREAMING_SNAKE_CASE , 'wb' ) as fp:
fp.write(response.content )
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None ) ->List[Any]:
a__: List[Any] = []
a__: Optional[Any] = []
a__: List[Any] = None
with zipfile.ZipFile(_SCREAMING_SNAKE_CASE ) as z:
for filename in z.namelist():
if not os.path.isdir(_SCREAMING_SNAKE_CASE ):
# read the file
if filename in ["failures_line.txt", "summary_short.txt", "job_name.txt"]:
with z.open(_SCREAMING_SNAKE_CASE ) as f:
for line in f:
a__: Optional[int] = line.decode('UTF-8' ).strip()
if filename == "failures_line.txt":
try:
# `error_line` is the place where `error` occurs
a__: Union[str, Any] = line[: line.index(': ' )]
a__: Union[str, Any] = line[line.index(': ' ) + len(': ' ) :]
errors.append([error_line, error] )
except Exception:
# skip un-related lines
pass
elif filename == "summary_short.txt" and line.startswith('FAILED ' ):
# `test` is the test method that failed
a__: Optional[int] = line[len('FAILED ' ) :]
failed_tests.append(_SCREAMING_SNAKE_CASE )
elif filename == "job_name.txt":
a__: Union[str, Any] = line
if len(_SCREAMING_SNAKE_CASE ) != len(_SCREAMING_SNAKE_CASE ):
raise ValueError(
F'`errors` and `failed_tests` should have the same number of elements. Got {len(_SCREAMING_SNAKE_CASE )} for `errors` '
F'and {len(_SCREAMING_SNAKE_CASE )} for `failed_tests` instead. The test reports in {artifact_zip_path} have some'
' problem.' )
a__: Tuple = None
if job_name and job_links:
a__: Dict = job_links.get(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# A list with elements of the form (line of error, error, failed test)
a__: int = [x + [y] + [job_link] for x, y in zip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )]
return result
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None ) ->str:
a__: int = []
a__: Optional[int] = [os.path.join(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) for p in os.listdir(_SCREAMING_SNAKE_CASE ) if p.endswith('.zip' )]
for p in paths:
errors.extend(get_errors_from_single_artifact(_SCREAMING_SNAKE_CASE , job_links=_SCREAMING_SNAKE_CASE ) )
return errors
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None ) ->Any:
a__: str = Counter()
counter.update([x[1] for x in logs] )
a__: int = counter.most_common()
a__: Any = {}
for error, count in counts:
if error_filter is None or error not in error_filter:
a__: List[str] = {'count': count, 'failed_tests': [(x[2], x[0]) for x in logs if x[1] == error]}
a__: Optional[Any] = dict(sorted(r.items() , key=lambda _SCREAMING_SNAKE_CASE : item[1]["count"] , reverse=_SCREAMING_SNAKE_CASE ) )
return r
def __a ( _SCREAMING_SNAKE_CASE ) ->Union[str, Any]:
a__: List[str] = test.split('::' )[0]
if test.startswith('tests/models/' ):
a__: Dict = test.split('/' )[2]
else:
a__: Any = None
return test
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None ) ->List[str]:
a__: int = [(x[0], x[1], get_model(x[2] )) for x in logs]
a__: List[Any] = [x for x in logs if x[2] is not None]
a__: Optional[Any] = {x[2] for x in logs}
a__: Dict = {}
for test in tests:
a__: Union[str, Any] = Counter()
# count by errors in `test`
counter.update([x[1] for x in logs if x[2] == test] )
a__: Union[str, Any] = counter.most_common()
a__: List[str] = {error: count for error, count in counts if (error_filter is None or error not in error_filter)}
a__: List[Any] = sum(error_counts.values() )
if n_errors > 0:
a__: Any = {'count': n_errors, 'errors': error_counts}
a__: Optional[int] = dict(sorted(r.items() , key=lambda _SCREAMING_SNAKE_CASE : item[1]["count"] , reverse=_SCREAMING_SNAKE_CASE ) )
return r
def __a ( _SCREAMING_SNAKE_CASE ) ->Union[str, Any]:
a__: Any = '| no. | error | status |'
a__: Any = '|-:|:-|:-|'
a__: str = [header, sep]
for error in reduced_by_error:
a__: int = reduced_by_error[error]['count']
a__: Tuple = F'| {count} | {error[:100]} | |'
lines.append(_SCREAMING_SNAKE_CASE )
return "\n".join(_SCREAMING_SNAKE_CASE )
def __a ( _SCREAMING_SNAKE_CASE ) ->str:
a__: List[str] = '| model | no. of errors | major error | count |'
a__: str = '|-:|-:|-:|-:|'
a__: int = [header, sep]
for model in reduced_by_model:
a__: Tuple = reduced_by_model[model]['count']
a__ , a__: Dict = list(reduced_by_model[model]['errors'].items() )[0]
a__: Dict = F'| {model} | {count} | {error[:60]} | {_count} |'
lines.append(_SCREAMING_SNAKE_CASE )
return "\n".join(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
lowercase__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument('--workflow_run_id', type=str, required=True, help='A GitHub Actions workflow run id.')
parser.add_argument(
'--output_dir',
type=str,
required=True,
help='Where to store the downloaded artifacts and other result files.',
)
parser.add_argument('--token', default=None, type=str, help='A token that has actions:read permission.')
lowercase__ = parser.parse_args()
os.makedirs(args.output_dir, exist_ok=True)
lowercase__ = get_job_links(args.workflow_run_id, token=args.token)
lowercase__ = {}
# To deal with `workflow_call` event, where a job name is the combination of the job names in the caller and callee.
# For example, `PyTorch 1.11 / Model tests (models/albert, single-gpu)`.
if _job_links:
for k, v in _job_links.items():
# This is how GitHub actions combine job names.
if " / " in k:
lowercase__ = k.find(' / ')
lowercase__ = k[index + len(' / ') :]
lowercase__ = v
with open(os.path.join(args.output_dir, 'job_links.json'), 'w', encoding='UTF-8') as fp:
json.dump(job_links, fp, ensure_ascii=False, indent=4)
lowercase__ = get_artifacts_links(args.workflow_run_id, token=args.token)
with open(os.path.join(args.output_dir, 'artifacts.json'), 'w', encoding='UTF-8') as fp:
json.dump(artifacts, fp, ensure_ascii=False, indent=4)
for idx, (name, url) in enumerate(artifacts.items()):
download_artifact(name, url, args.output_dir, args.token)
# Be gentle to GitHub
time.sleep(1)
lowercase__ = get_all_errors(args.output_dir, job_links=job_links)
# `e[1]` is the error
lowercase__ = Counter()
counter.update([e[1] for e in errors])
# print the top 30 most common test errors
lowercase__ = counter.most_common(30)
for item in most_common:
print(item)
with open(os.path.join(args.output_dir, 'errors.json'), 'w', encoding='UTF-8') as fp:
json.dump(errors, fp, ensure_ascii=False, indent=4)
lowercase__ = reduce_by_error(errors)
lowercase__ = reduce_by_model(errors)
lowercase__ = make_github_table(reduced_by_error)
lowercase__ = make_github_table_per_model(reduced_by_model)
with open(os.path.join(args.output_dir, 'reduced_by_error.txt'), 'w', encoding='UTF-8') as fp:
fp.write(sa)
with open(os.path.join(args.output_dir, 'reduced_by_model.txt'), 'w', encoding='UTF-8') as fp:
fp.write(sa)
| 290 | 0 |
import unittest
from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
lowerCAmelCase__ :Optional[int] = get_tests_dir('''fixtures/test_sentencepiece.model''')
lowerCAmelCase__ :int = get_tests_dir('''fixtures/test_sentencepiece_bpe.model''')
lowerCAmelCase__ :str = '''pt''' if is_torch_available() else '''tf'''
@require_sentencepiece
@require_tokenizers
class __a ( __lowerCAmelCase , unittest.TestCase ):
_a : int = CamembertTokenizer
_a : List[str] = CamembertTokenizerFast
_a : Union[str, Any] = True
_a : Tuple = True
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
"""simple docstring"""
super().setUp()
# We have a SentencePiece fixture for testing
_UpperCAmelCase = CamembertTokenizer(_SCREAMING_SNAKE_CASE )
tokenizer.save_pretrained(self.tmpdirname )
def UpperCAmelCase__ ( self ) -> List[str]:
"""simple docstring"""
_UpperCAmelCase = '<pad>'
_UpperCAmelCase = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE )
def UpperCAmelCase__ ( self ) -> Any:
"""simple docstring"""
_UpperCAmelCase = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '<s>NOTUSED' )
self.assertEqual(vocab_keys[1] , '<pad>' )
self.assertEqual(vocab_keys[-1] , '<mask>' )
self.assertEqual(len(_SCREAMING_SNAKE_CASE ) , 1004 )
def UpperCAmelCase__ ( self ) -> Any:
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size , 1005 )
def UpperCAmelCase__ ( self ) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase = CamembertTokenizer(_SCREAMING_SNAKE_CASE )
tokenizer.save_pretrained(self.tmpdirname )
_UpperCAmelCase = CamembertTokenizerFast.from_pretrained(self.tmpdirname )
_UpperCAmelCase = 'I was born in 92000, and this is falsé.'
_UpperCAmelCase = tokenizer.encode(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = rust_tokenizer.encode(_SCREAMING_SNAKE_CASE )
self.assertListEqual(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
_UpperCAmelCase = tokenizer.encode(_SCREAMING_SNAKE_CASE , add_special_tokens=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = rust_tokenizer.encode(_SCREAMING_SNAKE_CASE , add_special_tokens=_SCREAMING_SNAKE_CASE )
self.assertListEqual(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# <unk> tokens are not the same for `rust` than for `slow`.
# Because spm gives back raw token instead of `unk` in EncodeAsPieces
# tokens = tokenizer.tokenize(sequence)
_UpperCAmelCase = tokenizer.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = rust_tokenizer.tokenize(_SCREAMING_SNAKE_CASE )
self.assertListEqual(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def UpperCAmelCase__ ( self ) -> Dict:
"""simple docstring"""
if not self.test_rust_tokenizer:
return
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = self.get_rust_tokenizer()
_UpperCAmelCase = 'I was born in 92000, and this is falsé.'
_UpperCAmelCase = tokenizer.tokenize(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = rust_tokenizer.tokenize(_SCREAMING_SNAKE_CASE )
self.assertListEqual(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
_UpperCAmelCase = tokenizer.encode(_SCREAMING_SNAKE_CASE , add_special_tokens=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = rust_tokenizer.encode(_SCREAMING_SNAKE_CASE , add_special_tokens=_SCREAMING_SNAKE_CASE )
self.assertListEqual(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
_UpperCAmelCase = self.get_rust_tokenizer()
_UpperCAmelCase = tokenizer.encode(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = rust_tokenizer.encode(_SCREAMING_SNAKE_CASE )
self.assertListEqual(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
@slow
def UpperCAmelCase__ ( self ) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase = {'input_ids': [[5, 54, 7196, 297, 30, 23, 776, 18, 11, 3215, 3705, 8252, 22, 3164, 1181, 2116, 29, 16, 813, 25, 791, 3314, 20, 3446, 38, 27575, 120, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 468, 17, 11, 9088, 20, 1517, 8, 22804, 18818, 10, 38, 629, 607, 607, 142, 19, 7196, 867, 56, 10326, 24, 2267, 20, 416, 5072, 15612, 233, 734, 7, 2399, 27, 16, 3015, 1649, 7, 24, 20, 4338, 2399, 27, 13, 3400, 14, 13, 6189, 8, 930, 9, 6]], '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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501
# fmt: on
# camembert is a french model. So we also use french texts.
_UpperCAmelCase = [
'Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '
'utilisé principalement dans le domaine du traitement automatique des langues (TAL).',
'À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '
'pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '
'telles que la traduction et la synthèse de texte.',
]
self.tokenizer_integration_test_util(
expected_encoding=_SCREAMING_SNAKE_CASE , model_name='camembert-base' , revision='3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf' , sequences=_SCREAMING_SNAKE_CASE , )
| 329 | """simple docstring"""
import math
def __a ( _SCREAMING_SNAKE_CASE ) ->bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(_SCREAMING_SNAKE_CASE ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def __a ( _SCREAMING_SNAKE_CASE = 0.1 ) ->int:
a__: str = 3
a__: Optional[Any] = 3
while primes / (2 * j - 1) >= ratio:
for i in range(j * j + j + 1 , (j + 2) * (j + 2) , j + 1 ):
primes += is_prime(_SCREAMING_SNAKE_CASE )
j += 2
return j
if __name__ == "__main__":
import doctest
doctest.testmod()
| 290 | 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
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
"hustvl/yolos-small": "https://huggingface.co/hustvl/yolos-small/resolve/main/config.json",
# See all YOLOS models at https://huggingface.co/models?filter=yolos
}
class A_ ( __lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase_ : Dict = """yolos"""
def __init__( self : Optional[Any] , lowercase_ : Any=768 , lowercase_ : int=12 , lowercase_ : Tuple=12 , lowercase_ : List[str]=3_072 , lowercase_ : Union[str, Any]="gelu" , lowercase_ : List[str]=0.0 , lowercase_ : List[Any]=0.0 , lowercase_ : List[Any]=0.02 , lowercase_ : Optional[int]=1E-12 , lowercase_ : List[Any]=[512, 864] , lowercase_ : str=16 , lowercase_ : Optional[int]=3 , lowercase_ : List[str]=True , lowercase_ : Optional[int]=100 , lowercase_ : str=True , lowercase_ : int=False , lowercase_ : Optional[Any]=1 , lowercase_ : Dict=5 , lowercase_ : Tuple=2 , lowercase_ : str=5 , lowercase_ : int=2 , lowercase_ : int=0.1 , **lowercase_ : List[str] , ) -> List[Any]:
super().__init__(**lowercase_ )
UpperCAmelCase : Optional[int] = hidden_size
UpperCAmelCase : List[str] = num_hidden_layers
UpperCAmelCase : Optional[Any] = num_attention_heads
UpperCAmelCase : List[str] = intermediate_size
UpperCAmelCase : Tuple = hidden_act
UpperCAmelCase : Any = hidden_dropout_prob
UpperCAmelCase : Dict = attention_probs_dropout_prob
UpperCAmelCase : List[Any] = initializer_range
UpperCAmelCase : int = layer_norm_eps
UpperCAmelCase : str = image_size
UpperCAmelCase : Optional[Any] = patch_size
UpperCAmelCase : Dict = num_channels
UpperCAmelCase : Optional[Any] = qkv_bias
UpperCAmelCase : List[str] = num_detection_tokens
UpperCAmelCase : Tuple = use_mid_position_embeddings
UpperCAmelCase : Tuple = auxiliary_loss
# Hungarian matcher
UpperCAmelCase : Optional[int] = class_cost
UpperCAmelCase : Tuple = bbox_cost
UpperCAmelCase : Optional[int] = giou_cost
# Loss coefficients
UpperCAmelCase : Union[str, Any] = bbox_loss_coefficient
UpperCAmelCase : List[str] = giou_loss_coefficient
UpperCAmelCase : Optional[int] = eos_coefficient
class A_ ( __lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase_ : Optional[int] = version.parse("""1.11""" )
@property
def UpperCAmelCase_ ( self : Any ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
] )
@property
def UpperCAmelCase_ ( self : Optional[int] ) -> float:
return 1E-4
@property
def UpperCAmelCase_ ( self : List[str] ) -> int:
return 12
| 151 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import _LazyModule
lowercase__ = {'processing_wav2vec2_with_lm': ['Wav2Vec2ProcessorWithLM']}
if TYPE_CHECKING:
from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM
else:
import sys
lowercase__ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 290 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class A_ ( __lowerCAmelCase , unittest.TestCase ):
_lowercase : str = KandinskyVaaControlnetPipeline
_lowercase : Union[str, Any] = ['image_embeds', 'negative_image_embeds', 'hint']
_lowercase : str = ['image_embeds', 'negative_image_embeds', 'hint']
_lowercase : Optional[Any] = [
'generator',
'height',
'width',
'latents',
'guidance_scale',
'num_inference_steps',
'return_dict',
'guidance_scale',
'num_images_per_prompt',
'output_type',
'return_dict',
]
_lowercase : int = False
@property
def UpperCAmelCase ( self : Union[str, Any] ) -> int:
return 3_2
@property
def UpperCAmelCase ( self : Any ) -> int:
return 3_2
@property
def UpperCAmelCase ( self : Union[str, Any] ) -> List[str]:
return self.time_input_dim
@property
def UpperCAmelCase ( self : List[Any] ) -> List[Any]:
return self.time_input_dim * 4
@property
def UpperCAmelCase ( self : List[Any] ) -> Optional[Any]:
return 1_0_0
@property
def UpperCAmelCase ( self : Optional[int] ) -> str:
torch.manual_seed(0 )
__lowerCAmelCase: Union[str, Any] = {
'in_channels': 8,
# Out channels is double in channels because predicts mean and variance
'out_channels': 8,
'addition_embed_type': 'image_hint',
'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
'layers_per_block': 1,
'encoder_hid_dim': self.text_embedder_hidden_size,
'encoder_hid_dim_type': 'image_proj',
'cross_attention_dim': self.cross_attention_dim,
'attention_head_dim': 4,
'resnet_time_scale_shift': 'scale_shift',
'class_embed_type': None,
}
__lowerCAmelCase: Optional[int] = UNetaDConditionModel(**UpperCAmelCase )
return model
@property
def UpperCAmelCase ( self : Dict ) -> Optional[Any]:
return {
"block_out_channels": [3_2, 3_2, 6_4, 6_4],
"down_block_types": [
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"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", "UpDecoderBlock2D", "UpDecoderBlock2D"],
"vq_embed_dim": 4,
}
@property
def UpperCAmelCase ( self : List[Any] ) -> Tuple:
torch.manual_seed(0 )
__lowerCAmelCase: str = VQModel(**self.dummy_movq_kwargs )
return model
def UpperCAmelCase ( self : Any ) -> Union[str, Any]:
__lowerCAmelCase: str = self.dummy_unet
__lowerCAmelCase: List[Any] = self.dummy_movq
__lowerCAmelCase: str = DDIMScheduler(
num_train_timesteps=1_0_0_0 , beta_schedule='linear' , beta_start=0.00085 , beta_end=0.012 , clip_sample=UpperCAmelCase , set_alpha_to_one=UpperCAmelCase , steps_offset=1 , prediction_type='epsilon' , thresholding=UpperCAmelCase , )
__lowerCAmelCase: Union[str, Any] = {
'unet': unet,
'scheduler': scheduler,
'movq': movq,
}
return components
def UpperCAmelCase ( self : List[str] , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : List[Any]=0 ) -> List[str]:
__lowerCAmelCase: Optional[Any] = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(UpperCAmelCase ) ).to(UpperCAmelCase )
__lowerCAmelCase: Union[str, Any] = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(seed + 1 ) ).to(
UpperCAmelCase )
# create hint
__lowerCAmelCase: Union[str, Any] = floats_tensor((1, 3, 6_4, 6_4) , rng=random.Random(UpperCAmelCase ) ).to(UpperCAmelCase )
if str(UpperCAmelCase ).startswith('mps' ):
__lowerCAmelCase: List[str] = torch.manual_seed(UpperCAmelCase )
else:
__lowerCAmelCase: Union[str, Any] = torch.Generator(device=UpperCAmelCase ).manual_seed(UpperCAmelCase )
__lowerCAmelCase: Union[str, Any] = {
'image_embeds': image_embeds,
'negative_image_embeds': negative_image_embeds,
'hint': hint,
'generator': generator,
'height': 6_4,
'width': 6_4,
'guidance_scale': 4.0,
'num_inference_steps': 2,
'output_type': 'np',
}
return inputs
def UpperCAmelCase ( self : Any ) -> Optional[Any]:
__lowerCAmelCase: str = 'cpu'
__lowerCAmelCase: int = self.get_dummy_components()
__lowerCAmelCase: Optional[int] = self.pipeline_class(**UpperCAmelCase )
__lowerCAmelCase: Any = pipe.to(UpperCAmelCase )
pipe.set_progress_bar_config(disable=UpperCAmelCase )
__lowerCAmelCase: Optional[Any] = pipe(**self.get_dummy_inputs(UpperCAmelCase ) )
__lowerCAmelCase: Any = output.images
__lowerCAmelCase: Any = pipe(
**self.get_dummy_inputs(UpperCAmelCase ) , return_dict=UpperCAmelCase , )[0]
__lowerCAmelCase: Optional[Any] = image[0, -3:, -3:, -1]
__lowerCAmelCase: Optional[int] = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 6_4, 6_4, 3)
__lowerCAmelCase: List[Any] = np.array(
[0.6959826, 0.868279, 0.7558092, 0.68769467, 0.85805804, 0.65977496, 0.44885302, 0.5959111, 0.4251595] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
), F''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
), F''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''
@slow
@require_torch_gpu
class A_ ( unittest.TestCase ):
def UpperCAmelCase ( self : str ) -> Optional[Any]:
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCAmelCase ( self : Optional[int] ) -> List[Any]:
__lowerCAmelCase: List[str] = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy' )
__lowerCAmelCase: Any = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/kandinskyv22/hint_image_cat.png' )
__lowerCAmelCase: int = torch.from_numpy(np.array(UpperCAmelCase ) ).float() / 255.0
__lowerCAmelCase: Tuple = hint.permute(2 , 0 , 1 ).unsqueeze(0 )
__lowerCAmelCase: List[str] = KandinskyVaaPriorPipeline.from_pretrained(
'kandinsky-community/kandinsky-2-2-prior' , torch_dtype=torch.floataa )
pipe_prior.to(UpperCAmelCase )
__lowerCAmelCase: Tuple = KandinskyVaaControlnetPipeline.from_pretrained(
'kandinsky-community/kandinsky-2-2-controlnet-depth' , torch_dtype=torch.floataa )
__lowerCAmelCase: Union[str, Any] = pipeline.to(UpperCAmelCase )
pipeline.set_progress_bar_config(disable=UpperCAmelCase )
__lowerCAmelCase: Union[str, Any] = 'A robot, 4k photo'
__lowerCAmelCase: List[str] = torch.Generator(device='cuda' ).manual_seed(0 )
__lowerCAmelCase: Union[str, Any] = pipe_prior(
UpperCAmelCase , generator=UpperCAmelCase , num_inference_steps=5 , negative_prompt='' , ).to_tuple()
__lowerCAmelCase: Optional[Any] = torch.Generator(device='cuda' ).manual_seed(0 )
__lowerCAmelCase: Optional[int] = pipeline(
image_embeds=UpperCAmelCase , negative_image_embeds=UpperCAmelCase , hint=UpperCAmelCase , generator=UpperCAmelCase , num_inference_steps=1_0_0 , output_type='np' , )
__lowerCAmelCase: Dict = output.images[0]
assert image.shape == (5_1_2, 5_1_2, 3)
assert_mean_pixel_difference(UpperCAmelCase , UpperCAmelCase )
| 322 | """simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, 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 __snake_case ( __lowerCAmelCase , unittest.TestCase ):
a__ = KandinskyInpaintPipeline
a__ = ["""prompt""", """image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
a__ = [
"""prompt""",
"""negative_prompt""",
"""image_embeds""",
"""negative_image_embeds""",
"""image""",
"""mask_image""",
]
a__ = [
"""generator""",
"""height""",
"""width""",
"""latents""",
"""guidance_scale""",
"""negative_prompt""",
"""num_inference_steps""",
"""return_dict""",
"""guidance_scale""",
"""num_images_per_prompt""",
"""output_type""",
"""return_dict""",
]
a__ = False
@property
def lowerCamelCase_ ( self) -> Optional[int]:
'''simple docstring'''
return 32
@property
def lowerCamelCase_ ( self) -> Tuple:
'''simple docstring'''
return 32
@property
def lowerCamelCase_ ( self) -> Dict:
'''simple docstring'''
return self.time_input_dim
@property
def lowerCamelCase_ ( self) -> Dict:
'''simple docstring'''
return self.time_input_dim * 4
@property
def lowerCamelCase_ ( self) -> List[Any]:
'''simple docstring'''
return 1_00
@property
def lowerCamelCase_ ( self) -> List[Any]:
'''simple docstring'''
a__: Optional[int] = XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base')
return tokenizer
@property
def lowerCamelCase_ ( self) -> Any:
'''simple docstring'''
torch.manual_seed(0)
a__: Dict = MCLIPConfig(
numDims=self.cross_attention_dim , transformerDimensions=self.text_embedder_hidden_size , hidden_size=self.text_embedder_hidden_size , intermediate_size=37 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=10_05 , )
a__: Optional[Any] = MultilingualCLIP(lowercase)
a__: int = text_encoder.eval()
return text_encoder
@property
def lowerCamelCase_ ( self) -> List[str]:
'''simple docstring'''
torch.manual_seed(0)
a__: Any = {
'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__: str = UNetaDConditionModel(**lowercase)
return model
@property
def lowerCamelCase_ ( self) -> Union[str, Any]:
'''simple docstring'''
return {
"block_out_channels": [32, 64],
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def lowerCamelCase_ ( self) -> List[Any]:
'''simple docstring'''
torch.manual_seed(0)
a__: Any = VQModel(**self.dummy_movq_kwargs)
return model
def lowerCamelCase_ ( self) -> Any:
'''simple docstring'''
a__: Dict = self.dummy_text_encoder
a__: int = self.dummy_tokenizer
a__: str = self.dummy_unet
a__: Any = self.dummy_movq
a__: Tuple = DDIMScheduler(
num_train_timesteps=10_00 , beta_schedule='linear' , beta_start=0.00085 , beta_end=0.012 , clip_sample=lowercase , set_alpha_to_one=lowercase , steps_offset=1 , prediction_type='epsilon' , thresholding=lowercase , )
a__: Tuple = {
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'unet': unet,
'scheduler': scheduler,
'movq': movq,
}
return components
def lowerCamelCase_ ( self , lowercase , lowercase=0) -> Any:
'''simple docstring'''
a__: List[Any] = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(lowercase)).to(lowercase)
a__: int = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(seed + 1)).to(lowercase)
# create init_image
a__: Optional[int] = floats_tensor((1, 3, 64, 64) , rng=random.Random(lowercase)).to(lowercase)
a__: int = image.cpu().permute(0 , 2 , 3 , 1)[0]
a__: Optional[int] = Image.fromarray(np.uinta(lowercase)).convert('RGB').resize((2_56, 2_56))
# create mask
a__: Tuple = np.ones((64, 64) , dtype=np.floataa)
a__: Optional[Any] = 0
if str(lowercase).startswith('mps'):
a__: str = torch.manual_seed(lowercase)
else:
a__: Dict = torch.Generator(device=lowercase).manual_seed(lowercase)
a__: Optional[int] = {
'prompt': 'horse',
'image': init_image,
'mask_image': mask,
'image_embeds': image_embeds,
'negative_image_embeds': negative_image_embeds,
'generator': generator,
'height': 64,
'width': 64,
'num_inference_steps': 2,
'guidance_scale': 4.0,
'output_type': 'np',
}
return inputs
def lowerCamelCase_ ( self) -> str:
'''simple docstring'''
a__: Optional[Any] = 'cpu'
a__: List[Any] = self.get_dummy_components()
a__: Optional[Any] = self.pipeline_class(**lowercase)
a__: str = pipe.to(lowercase)
pipe.set_progress_bar_config(disable=lowercase)
a__: Optional[int] = pipe(**self.get_dummy_inputs(lowercase))
a__: List[str] = output.images
a__: int = pipe(
**self.get_dummy_inputs(lowercase) , return_dict=lowercase , )[0]
a__: Optional[Any] = image[0, -3:, -3:, -1]
a__: List[Any] = image_from_tuple[0, -3:, -3:, -1]
print(f'image.shape {image.shape}')
assert image.shape == (1, 64, 64, 3)
a__: str = np.array(
[0.8326919, 0.73790467, 0.20918581, 0.9309612, 0.5511791, 0.43713328, 0.5513321, 0.49922934, 0.59497786])
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 lowerCamelCase_ ( self) -> str:
'''simple docstring'''
super().test_inference_batch_single_identical(expected_max_diff=3e-3)
@slow
@require_torch_gpu
class __snake_case ( unittest.TestCase ):
def lowerCamelCase_ ( self) -> Optional[Any]:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase_ ( self) -> Dict:
'''simple docstring'''
a__: List[Any] = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy')
a__: int = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png')
a__: Union[str, Any] = np.ones((7_68, 7_68) , dtype=np.floataa)
a__: int = 0
a__: Optional[int] = 'a hat'
a__: int = KandinskyPriorPipeline.from_pretrained(
'kandinsky-community/kandinsky-2-1-prior' , torch_dtype=torch.floataa)
pipe_prior.to(lowercase)
a__: Any = KandinskyInpaintPipeline.from_pretrained(
'kandinsky-community/kandinsky-2-1-inpaint' , torch_dtype=torch.floataa)
a__: Optional[Any] = pipeline.to(lowercase)
pipeline.set_progress_bar_config(disable=lowercase)
a__: Dict = torch.Generator(device='cpu').manual_seed(0)
a__ , a__: Optional[Any] = pipe_prior(
lowercase , generator=lowercase , num_inference_steps=5 , negative_prompt='' , ).to_tuple()
a__: List[str] = pipeline(
lowercase , image=lowercase , mask_image=lowercase , image_embeds=lowercase , negative_image_embeds=lowercase , generator=lowercase , num_inference_steps=1_00 , height=7_68 , width=7_68 , output_type='np' , )
a__: str = output.images[0]
assert image.shape == (7_68, 7_68, 3)
assert_mean_pixel_difference(lowercase , lowercase)
| 290 | 0 |
"""simple docstring"""
import unittest
from knapsack import knapsack as k
class _UpperCAmelCase ( unittest.TestCase):
def _snake_case ( self : Tuple ):
snake_case_ : Optional[Any] = 0
snake_case_ : Optional[int] = [0]
snake_case_ : Any = [0]
snake_case_ : int = len(lowercase_ )
self.assertEqual(k.knapsack(lowercase_ , lowercase_ , lowercase_ , lowercase_ ) , 0 )
snake_case_ : Tuple = [60]
snake_case_ : Union[str, Any] = [10]
snake_case_ : Tuple = len(lowercase_ )
self.assertEqual(k.knapsack(lowercase_ , lowercase_ , lowercase_ , lowercase_ ) , 0 )
def _snake_case ( self : Optional[Any] ):
snake_case_ : Tuple = 3
snake_case_ : List[Any] = [1, 2, 3]
snake_case_ : Any = [3, 2, 1]
snake_case_ : List[Any] = len(lowercase_ )
self.assertEqual(k.knapsack(lowercase_ , lowercase_ , lowercase_ , lowercase_ ) , 5 )
def _snake_case ( self : List[Any] ):
snake_case_ : str = 50
snake_case_ : List[str] = [60, 100, 120]
snake_case_ : Union[str, Any] = [10, 20, 30]
snake_case_ : str = len(lowercase_ )
self.assertEqual(k.knapsack(lowercase_ , lowercase_ , lowercase_ , lowercase_ ) , 220 )
if __name__ == "__main__":
unittest.main()
| 264 | """simple docstring"""
import argparse
import torch
from transformers import (
EncodecConfig,
EncodecFeatureExtractor,
EncodecModel,
logging,
)
# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/main/compression_state_dict.bin
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_48khz-7e698e3e.th
logging.set_verbosity_info()
lowercase__ = logging.get_logger('transformers.models.encodec')
lowercase__ = {
'quantizer.vq.layers.*._codebook.inited': 'quantizer.layers.*.codebook.inited',
'quantizer.vq.layers.*._codebook.cluster_size': 'quantizer.layers.*.codebook.cluster_size',
'quantizer.vq.layers.*._codebook.embed': 'quantizer.layers.*.codebook.embed',
'quantizer.vq.layers.*._codebook.embed_avg': 'quantizer.layers.*.codebook.embed_avg',
}
lowercase__ = {
'encoder.model.0.conv.conv': 'encoder.layers.0.conv',
'encoder.model.1.block.1.conv.conv': 'encoder.layers.1.block.1.conv',
'encoder.model.1.block.3.conv.conv': 'encoder.layers.1.block.3.conv',
'encoder.model.1.shortcut.conv.conv': 'encoder.layers.1.shortcut.conv',
'encoder.model.3.conv.conv': 'encoder.layers.3.conv',
'encoder.model.4.block.1.conv.conv': 'encoder.layers.4.block.1.conv',
'encoder.model.4.block.3.conv.conv': 'encoder.layers.4.block.3.conv',
'encoder.model.4.shortcut.conv.conv': 'encoder.layers.4.shortcut.conv',
'encoder.model.6.conv.conv': 'encoder.layers.6.conv',
'encoder.model.7.block.1.conv.conv': 'encoder.layers.7.block.1.conv',
'encoder.model.7.block.3.conv.conv': 'encoder.layers.7.block.3.conv',
'encoder.model.7.shortcut.conv.conv': 'encoder.layers.7.shortcut.conv',
'encoder.model.9.conv.conv': 'encoder.layers.9.conv',
'encoder.model.10.block.1.conv.conv': 'encoder.layers.10.block.1.conv',
'encoder.model.10.block.3.conv.conv': 'encoder.layers.10.block.3.conv',
'encoder.model.10.shortcut.conv.conv': 'encoder.layers.10.shortcut.conv',
'encoder.model.12.conv.conv': 'encoder.layers.12.conv',
'encoder.model.13.lstm': 'encoder.layers.13.lstm',
'encoder.model.15.conv.conv': 'encoder.layers.15.conv',
}
lowercase__ = {
'encoder.model.0.conv.norm': 'encoder.layers.0.norm',
'encoder.model.1.block.1.conv.norm': 'encoder.layers.1.block.1.norm',
'encoder.model.1.block.3.conv.norm': 'encoder.layers.1.block.3.norm',
'encoder.model.1.shortcut.conv.norm': 'encoder.layers.1.shortcut.norm',
'encoder.model.3.conv.norm': 'encoder.layers.3.norm',
'encoder.model.4.block.1.conv.norm': 'encoder.layers.4.block.1.norm',
'encoder.model.4.block.3.conv.norm': 'encoder.layers.4.block.3.norm',
'encoder.model.4.shortcut.conv.norm': 'encoder.layers.4.shortcut.norm',
'encoder.model.6.conv.norm': 'encoder.layers.6.norm',
'encoder.model.7.block.1.conv.norm': 'encoder.layers.7.block.1.norm',
'encoder.model.7.block.3.conv.norm': 'encoder.layers.7.block.3.norm',
'encoder.model.7.shortcut.conv.norm': 'encoder.layers.7.shortcut.norm',
'encoder.model.9.conv.norm': 'encoder.layers.9.norm',
'encoder.model.10.block.1.conv.norm': 'encoder.layers.10.block.1.norm',
'encoder.model.10.block.3.conv.norm': 'encoder.layers.10.block.3.norm',
'encoder.model.10.shortcut.conv.norm': 'encoder.layers.10.shortcut.norm',
'encoder.model.12.conv.norm': 'encoder.layers.12.norm',
'encoder.model.15.conv.norm': 'encoder.layers.15.norm',
}
lowercase__ = {
'decoder.model.0.conv.conv': 'decoder.layers.0.conv',
'decoder.model.1.lstm': 'decoder.layers.1.lstm',
'decoder.model.3.convtr.convtr': 'decoder.layers.3.conv',
'decoder.model.4.block.1.conv.conv': 'decoder.layers.4.block.1.conv',
'decoder.model.4.block.3.conv.conv': 'decoder.layers.4.block.3.conv',
'decoder.model.4.shortcut.conv.conv': 'decoder.layers.4.shortcut.conv',
'decoder.model.6.convtr.convtr': 'decoder.layers.6.conv',
'decoder.model.7.block.1.conv.conv': 'decoder.layers.7.block.1.conv',
'decoder.model.7.block.3.conv.conv': 'decoder.layers.7.block.3.conv',
'decoder.model.7.shortcut.conv.conv': 'decoder.layers.7.shortcut.conv',
'decoder.model.9.convtr.convtr': 'decoder.layers.9.conv',
'decoder.model.10.block.1.conv.conv': 'decoder.layers.10.block.1.conv',
'decoder.model.10.block.3.conv.conv': 'decoder.layers.10.block.3.conv',
'decoder.model.10.shortcut.conv.conv': 'decoder.layers.10.shortcut.conv',
'decoder.model.12.convtr.convtr': 'decoder.layers.12.conv',
'decoder.model.13.block.1.conv.conv': 'decoder.layers.13.block.1.conv',
'decoder.model.13.block.3.conv.conv': 'decoder.layers.13.block.3.conv',
'decoder.model.13.shortcut.conv.conv': 'decoder.layers.13.shortcut.conv',
'decoder.model.15.conv.conv': 'decoder.layers.15.conv',
}
lowercase__ = {
'decoder.model.0.conv.norm': 'decoder.layers.0.norm',
'decoder.model.3.convtr.norm': 'decoder.layers.3.norm',
'decoder.model.4.block.1.conv.norm': 'decoder.layers.4.block.1.norm',
'decoder.model.4.block.3.conv.norm': 'decoder.layers.4.block.3.norm',
'decoder.model.4.shortcut.conv.norm': 'decoder.layers.4.shortcut.norm',
'decoder.model.6.convtr.norm': 'decoder.layers.6.norm',
'decoder.model.7.block.1.conv.norm': 'decoder.layers.7.block.1.norm',
'decoder.model.7.block.3.conv.norm': 'decoder.layers.7.block.3.norm',
'decoder.model.7.shortcut.conv.norm': 'decoder.layers.7.shortcut.norm',
'decoder.model.9.convtr.norm': 'decoder.layers.9.norm',
'decoder.model.10.block.1.conv.norm': 'decoder.layers.10.block.1.norm',
'decoder.model.10.block.3.conv.norm': 'decoder.layers.10.block.3.norm',
'decoder.model.10.shortcut.conv.norm': 'decoder.layers.10.shortcut.norm',
'decoder.model.12.convtr.norm': 'decoder.layers.12.norm',
'decoder.model.13.block.1.conv.norm': 'decoder.layers.13.block.1.norm',
'decoder.model.13.block.3.conv.norm': 'decoder.layers.13.block.3.norm',
'decoder.model.13.shortcut.conv.norm': 'decoder.layers.13.shortcut.norm',
'decoder.model.15.conv.norm': 'decoder.layers.15.norm',
}
lowercase__ = {
**MAPPING_QUANTIZER,
**MAPPING_ENCODER,
**MAPPING_DECODER,
}
lowercase__ = {
**MAPPING_QUANTIZER,
**MAPPING_ENCODER,
**MAPPING_ENCODER_48K,
**MAPPING_DECODER,
**MAPPING_DECODER_48K,
}
lowercase__ = []
lowercase__ = []
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->int:
for attribute in key.split('.' ):
a__: str = getattr(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
if weight_type is not None:
a__: List[str] = getattr(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ).shape
else:
a__: Optional[Any] = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
F'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
F' {value.shape} for {full_name}' )
if weight_type == "weight":
a__: str = value
elif weight_type == "weight_g":
a__: int = value
elif weight_type == "weight_v":
a__: Tuple = value
elif weight_type == "bias":
a__: Dict = value
elif weight_type == "running_mean":
a__: Any = value
elif weight_type == "running_var":
a__: Tuple = value
elif weight_type == "num_batches_tracked":
a__: List[str] = value
elif weight_type == "weight_ih_l0":
a__: List[Any] = value
elif weight_type == "weight_hh_l0":
a__: List[Any] = value
elif weight_type == "bias_ih_l0":
a__: List[Any] = value
elif weight_type == "bias_hh_l0":
a__: List[Any] = value
elif weight_type == "weight_ih_l1":
a__: int = value
elif weight_type == "weight_hh_l1":
a__: str = value
elif weight_type == "bias_ih_l1":
a__: Union[str, Any] = value
elif weight_type == "bias_hh_l1":
a__: Any = value
else:
a__: Union[str, Any] = value
logger.info(F'{key + ("." + weight_type if weight_type is not None else "")} was initialized from {full_name}.' )
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->Dict:
for key in ignore_keys:
if key.endswith('.*' ):
if name.startswith(key[:-1] ):
return True
elif ".*." in key:
a__ , a__: Optional[Any] = key.split('.*.' )
if prefix in name and suffix in name:
return True
elif key in name:
return True
return False
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->List[str]:
a__: List[Any] = []
if model_name == "encodec_24khz" or "encodec_32khz":
a__: Optional[int] = MAPPING_24K
elif model_name == "encodec_48khz":
a__: List[Any] = MAPPING_48K
else:
raise ValueError(F'Unsupported model: {model_name}' )
for name, value in orig_dict.items():
if should_ignore(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
logger.info(F'{name} was ignored' )
continue
a__: int = False
for key, mapped_key in MAPPING.items():
if "*" in key:
a__ , a__: str = key.split('.*.' )
if prefix in name and suffix in name:
a__: List[str] = suffix
if key in name:
# HACK otherwise .embed gets initialized with .embed_avg too
if key.endswith('embed' ) and name.endswith('embed_avg' ):
continue
a__: List[str] = True
if "*" in mapped_key:
a__: List[str] = name.split(_SCREAMING_SNAKE_CASE )[0].split('.' )[-2]
a__: str = mapped_key.replace('*' , _SCREAMING_SNAKE_CASE )
if "weight_g" in name:
a__: int = 'weight_g'
elif "weight_v" in name:
a__: Dict = 'weight_v'
elif "weight_ih_l0" in name:
a__: int = 'weight_ih_l0'
elif "weight_hh_l0" in name:
a__: Union[str, Any] = 'weight_hh_l0'
elif "bias_ih_l0" in name:
a__: Optional[Any] = 'bias_ih_l0'
elif "bias_hh_l0" in name:
a__: Optional[int] = 'bias_hh_l0'
elif "weight_ih_l1" in name:
a__: Dict = 'weight_ih_l1'
elif "weight_hh_l1" in name:
a__: Optional[Any] = 'weight_hh_l1'
elif "bias_ih_l1" in name:
a__: List[str] = 'bias_ih_l1'
elif "bias_hh_l1" in name:
a__: Optional[Any] = 'bias_hh_l1'
elif "bias" in name:
a__: List[str] = 'bias'
elif "weight" in name:
a__: Any = 'weight'
elif "running_mean" in name:
a__: Dict = 'running_mean'
elif "running_var" in name:
a__: Dict = 'running_var'
elif "num_batches_tracked" in name:
a__: Dict = 'num_batches_tracked'
else:
a__: List[str] = None
set_recursively(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
continue
if not is_used:
unused_weights.append(_SCREAMING_SNAKE_CASE )
logger.warning(F'Unused weights: {unused_weights}' )
@torch.no_grad()
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , ) ->int:
if config_path is not None:
a__: Dict = EncodecConfig.from_pretrained(_SCREAMING_SNAKE_CASE )
else:
a__: Tuple = EncodecConfig()
if model_name == "encodec_24khz":
pass # config is already correct
elif model_name == "encodec_32khz":
a__: Any = [8, 5, 4, 4]
a__: List[str] = [2.2]
a__: List[Any] = 64
a__: Dict = 32000
a__: Union[str, Any] = 2048
a__: Union[str, Any] = False
a__: Any = False
a__: Optional[Any] = False
elif model_name == "encodec_48khz":
a__: Optional[int] = [8, 5, 4, 2]
a__: Union[str, Any] = [3.0, 6.0, 12.0, 24.0]
a__: List[str] = 48000
a__: Tuple = 2
a__: Optional[Any] = False
a__: Optional[int] = 'time_group_norm'
a__: Union[str, Any] = True
a__: Dict = 1.0
a__: str = 0.01
else:
raise ValueError(F'Unknown model name: {model_name}' )
a__: Optional[int] = EncodecModel(_SCREAMING_SNAKE_CASE )
a__: List[str] = EncodecFeatureExtractor(
feature_size=config.audio_channels , sampling_rate=config.sampling_rate , chunk_length_s=config.chunk_length_s , overlap=config.overlap , )
feature_extractor.save_pretrained(_SCREAMING_SNAKE_CASE )
a__: int = torch.load(_SCREAMING_SNAKE_CASE )
if "best_state" in original_checkpoint:
# we might have a training state saved, in which case discard the yaml results and just retain the weights
a__: str = original_checkpoint['best_state']
recursively_load_weights(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
model.save_pretrained(_SCREAMING_SNAKE_CASE )
if repo_id:
print('Pushing to the hub...' )
feature_extractor.push_to_hub(_SCREAMING_SNAKE_CASE )
model.push_to_hub(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
lowercase__ = argparse.ArgumentParser()
parser.add_argument(
'--model',
default='encodec_24khz',
type=str,
help='The model to convert. Should be one of \'encodec_24khz\', \'encodec_32khz\', \'encodec_48khz\'.',
)
parser.add_argument('--checkpoint_path', required=True, default=None, type=str, help='Path to original checkpoint')
parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
parser.add_argument(
'--pytorch_dump_folder_path', required=True, default=None, type=str, help='Path to the output PyTorch model.'
)
parser.add_argument(
'--push_to_hub', default=None, type=str, help='Where to upload the converted model on the 🤗 hub.'
)
lowercase__ = parser.parse_args()
convert_checkpoint(
args.model,
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.config_path,
args.push_to_hub,
)
| 290 | 0 |
'''simple docstring'''
import argparse
import torch
from transformers import (
EncodecConfig,
EncodecFeatureExtractor,
EncodecModel,
logging,
)
# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/main/compression_state_dict.bin
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_48khz-7e698e3e.th
logging.set_verbosity_info()
a_ : List[str] = logging.get_logger("""transformers.models.encodec""")
a_ : List[Any] = {
"""quantizer.vq.layers.*._codebook.inited""": """quantizer.layers.*.codebook.inited""",
"""quantizer.vq.layers.*._codebook.cluster_size""": """quantizer.layers.*.codebook.cluster_size""",
"""quantizer.vq.layers.*._codebook.embed""": """quantizer.layers.*.codebook.embed""",
"""quantizer.vq.layers.*._codebook.embed_avg""": """quantizer.layers.*.codebook.embed_avg""",
}
a_ : int = {
"""encoder.model.0.conv.conv""": """encoder.layers.0.conv""",
"""encoder.model.1.block.1.conv.conv""": """encoder.layers.1.block.1.conv""",
"""encoder.model.1.block.3.conv.conv""": """encoder.layers.1.block.3.conv""",
"""encoder.model.1.shortcut.conv.conv""": """encoder.layers.1.shortcut.conv""",
"""encoder.model.3.conv.conv""": """encoder.layers.3.conv""",
"""encoder.model.4.block.1.conv.conv""": """encoder.layers.4.block.1.conv""",
"""encoder.model.4.block.3.conv.conv""": """encoder.layers.4.block.3.conv""",
"""encoder.model.4.shortcut.conv.conv""": """encoder.layers.4.shortcut.conv""",
"""encoder.model.6.conv.conv""": """encoder.layers.6.conv""",
"""encoder.model.7.block.1.conv.conv""": """encoder.layers.7.block.1.conv""",
"""encoder.model.7.block.3.conv.conv""": """encoder.layers.7.block.3.conv""",
"""encoder.model.7.shortcut.conv.conv""": """encoder.layers.7.shortcut.conv""",
"""encoder.model.9.conv.conv""": """encoder.layers.9.conv""",
"""encoder.model.10.block.1.conv.conv""": """encoder.layers.10.block.1.conv""",
"""encoder.model.10.block.3.conv.conv""": """encoder.layers.10.block.3.conv""",
"""encoder.model.10.shortcut.conv.conv""": """encoder.layers.10.shortcut.conv""",
"""encoder.model.12.conv.conv""": """encoder.layers.12.conv""",
"""encoder.model.13.lstm""": """encoder.layers.13.lstm""",
"""encoder.model.15.conv.conv""": """encoder.layers.15.conv""",
}
a_ : Tuple = {
"""encoder.model.0.conv.norm""": """encoder.layers.0.norm""",
"""encoder.model.1.block.1.conv.norm""": """encoder.layers.1.block.1.norm""",
"""encoder.model.1.block.3.conv.norm""": """encoder.layers.1.block.3.norm""",
"""encoder.model.1.shortcut.conv.norm""": """encoder.layers.1.shortcut.norm""",
"""encoder.model.3.conv.norm""": """encoder.layers.3.norm""",
"""encoder.model.4.block.1.conv.norm""": """encoder.layers.4.block.1.norm""",
"""encoder.model.4.block.3.conv.norm""": """encoder.layers.4.block.3.norm""",
"""encoder.model.4.shortcut.conv.norm""": """encoder.layers.4.shortcut.norm""",
"""encoder.model.6.conv.norm""": """encoder.layers.6.norm""",
"""encoder.model.7.block.1.conv.norm""": """encoder.layers.7.block.1.norm""",
"""encoder.model.7.block.3.conv.norm""": """encoder.layers.7.block.3.norm""",
"""encoder.model.7.shortcut.conv.norm""": """encoder.layers.7.shortcut.norm""",
"""encoder.model.9.conv.norm""": """encoder.layers.9.norm""",
"""encoder.model.10.block.1.conv.norm""": """encoder.layers.10.block.1.norm""",
"""encoder.model.10.block.3.conv.norm""": """encoder.layers.10.block.3.norm""",
"""encoder.model.10.shortcut.conv.norm""": """encoder.layers.10.shortcut.norm""",
"""encoder.model.12.conv.norm""": """encoder.layers.12.norm""",
"""encoder.model.15.conv.norm""": """encoder.layers.15.norm""",
}
a_ : Dict = {
"""decoder.model.0.conv.conv""": """decoder.layers.0.conv""",
"""decoder.model.1.lstm""": """decoder.layers.1.lstm""",
"""decoder.model.3.convtr.convtr""": """decoder.layers.3.conv""",
"""decoder.model.4.block.1.conv.conv""": """decoder.layers.4.block.1.conv""",
"""decoder.model.4.block.3.conv.conv""": """decoder.layers.4.block.3.conv""",
"""decoder.model.4.shortcut.conv.conv""": """decoder.layers.4.shortcut.conv""",
"""decoder.model.6.convtr.convtr""": """decoder.layers.6.conv""",
"""decoder.model.7.block.1.conv.conv""": """decoder.layers.7.block.1.conv""",
"""decoder.model.7.block.3.conv.conv""": """decoder.layers.7.block.3.conv""",
"""decoder.model.7.shortcut.conv.conv""": """decoder.layers.7.shortcut.conv""",
"""decoder.model.9.convtr.convtr""": """decoder.layers.9.conv""",
"""decoder.model.10.block.1.conv.conv""": """decoder.layers.10.block.1.conv""",
"""decoder.model.10.block.3.conv.conv""": """decoder.layers.10.block.3.conv""",
"""decoder.model.10.shortcut.conv.conv""": """decoder.layers.10.shortcut.conv""",
"""decoder.model.12.convtr.convtr""": """decoder.layers.12.conv""",
"""decoder.model.13.block.1.conv.conv""": """decoder.layers.13.block.1.conv""",
"""decoder.model.13.block.3.conv.conv""": """decoder.layers.13.block.3.conv""",
"""decoder.model.13.shortcut.conv.conv""": """decoder.layers.13.shortcut.conv""",
"""decoder.model.15.conv.conv""": """decoder.layers.15.conv""",
}
a_ : Union[str, Any] = {
"""decoder.model.0.conv.norm""": """decoder.layers.0.norm""",
"""decoder.model.3.convtr.norm""": """decoder.layers.3.norm""",
"""decoder.model.4.block.1.conv.norm""": """decoder.layers.4.block.1.norm""",
"""decoder.model.4.block.3.conv.norm""": """decoder.layers.4.block.3.norm""",
"""decoder.model.4.shortcut.conv.norm""": """decoder.layers.4.shortcut.norm""",
"""decoder.model.6.convtr.norm""": """decoder.layers.6.norm""",
"""decoder.model.7.block.1.conv.norm""": """decoder.layers.7.block.1.norm""",
"""decoder.model.7.block.3.conv.norm""": """decoder.layers.7.block.3.norm""",
"""decoder.model.7.shortcut.conv.norm""": """decoder.layers.7.shortcut.norm""",
"""decoder.model.9.convtr.norm""": """decoder.layers.9.norm""",
"""decoder.model.10.block.1.conv.norm""": """decoder.layers.10.block.1.norm""",
"""decoder.model.10.block.3.conv.norm""": """decoder.layers.10.block.3.norm""",
"""decoder.model.10.shortcut.conv.norm""": """decoder.layers.10.shortcut.norm""",
"""decoder.model.12.convtr.norm""": """decoder.layers.12.norm""",
"""decoder.model.13.block.1.conv.norm""": """decoder.layers.13.block.1.norm""",
"""decoder.model.13.block.3.conv.norm""": """decoder.layers.13.block.3.norm""",
"""decoder.model.13.shortcut.conv.norm""": """decoder.layers.13.shortcut.norm""",
"""decoder.model.15.conv.norm""": """decoder.layers.15.norm""",
}
a_ : Any = {
**MAPPING_QUANTIZER,
**MAPPING_ENCODER,
**MAPPING_DECODER,
}
a_ : str = {
**MAPPING_QUANTIZER,
**MAPPING_ENCODER,
**MAPPING_ENCODER_48K,
**MAPPING_DECODER,
**MAPPING_DECODER_48K,
}
a_ : int = []
a_ : str = []
def __snake_case ( UpperCAmelCase_ : int , UpperCAmelCase_ : str , UpperCAmelCase_ : Any , UpperCAmelCase_ : Optional[int] , UpperCAmelCase_ : Optional[int] ):
for attribute in key.split("." ):
lowerCamelCase_ = getattr(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
if weight_type is not None:
lowerCamelCase_ = getattr(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ).shape
else:
lowerCamelCase_ = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
F'''Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'''
F''' {value.shape} for {full_name}''' )
if weight_type == "weight":
lowerCamelCase_ = value
elif weight_type == "weight_g":
lowerCamelCase_ = value
elif weight_type == "weight_v":
lowerCamelCase_ = value
elif weight_type == "bias":
lowerCamelCase_ = value
elif weight_type == "running_mean":
lowerCamelCase_ = value
elif weight_type == "running_var":
lowerCamelCase_ = value
elif weight_type == "num_batches_tracked":
lowerCamelCase_ = value
elif weight_type == "weight_ih_l0":
lowerCamelCase_ = value
elif weight_type == "weight_hh_l0":
lowerCamelCase_ = value
elif weight_type == "bias_ih_l0":
lowerCamelCase_ = value
elif weight_type == "bias_hh_l0":
lowerCamelCase_ = value
elif weight_type == "weight_ih_l1":
lowerCamelCase_ = value
elif weight_type == "weight_hh_l1":
lowerCamelCase_ = value
elif weight_type == "bias_ih_l1":
lowerCamelCase_ = value
elif weight_type == "bias_hh_l1":
lowerCamelCase_ = value
else:
lowerCamelCase_ = value
logger.info(F'''{key + ("." + weight_type if weight_type is not None else "")} was initialized from {full_name}.''' )
def __snake_case ( UpperCAmelCase_ : Optional[Any] , UpperCAmelCase_ : Union[str, Any] ):
for key in ignore_keys:
if key.endswith(".*" ):
if name.startswith(key[:-1] ):
return True
elif ".*." in key:
lowerCamelCase_ = key.split(".*." )
if prefix in name and suffix in name:
return True
elif key in name:
return True
return False
def __snake_case ( UpperCAmelCase_ : int , UpperCAmelCase_ : str , UpperCAmelCase_ : List[Any] ):
lowerCamelCase_ = []
if model_name == "encodec_24khz" or "encodec_32khz":
lowerCamelCase_ = MAPPING_24K
elif model_name == "encodec_48khz":
lowerCamelCase_ = MAPPING_48K
else:
raise ValueError(F'''Unsupported model: {model_name}''' )
for name, value in orig_dict.items():
if should_ignore(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
logger.info(F'''{name} was ignored''' )
continue
lowerCamelCase_ = False
for key, mapped_key in MAPPING.items():
if "*" in key:
lowerCamelCase_ = key.split(".*." )
if prefix in name and suffix in name:
lowerCamelCase_ = suffix
if key in name:
# HACK otherwise .embed gets initialized with .embed_avg too
if key.endswith("embed" ) and name.endswith("embed_avg" ):
continue
lowerCamelCase_ = True
if "*" in mapped_key:
lowerCamelCase_ = name.split(_SCREAMING_SNAKE_CASE )[0].split("." )[-2]
lowerCamelCase_ = mapped_key.replace("*" , _SCREAMING_SNAKE_CASE )
if "weight_g" in name:
lowerCamelCase_ = 'weight_g'
elif "weight_v" in name:
lowerCamelCase_ = 'weight_v'
elif "weight_ih_l0" in name:
lowerCamelCase_ = 'weight_ih_l0'
elif "weight_hh_l0" in name:
lowerCamelCase_ = 'weight_hh_l0'
elif "bias_ih_l0" in name:
lowerCamelCase_ = 'bias_ih_l0'
elif "bias_hh_l0" in name:
lowerCamelCase_ = 'bias_hh_l0'
elif "weight_ih_l1" in name:
lowerCamelCase_ = 'weight_ih_l1'
elif "weight_hh_l1" in name:
lowerCamelCase_ = 'weight_hh_l1'
elif "bias_ih_l1" in name:
lowerCamelCase_ = 'bias_ih_l1'
elif "bias_hh_l1" in name:
lowerCamelCase_ = 'bias_hh_l1'
elif "bias" in name:
lowerCamelCase_ = 'bias'
elif "weight" in name:
lowerCamelCase_ = 'weight'
elif "running_mean" in name:
lowerCamelCase_ = 'running_mean'
elif "running_var" in name:
lowerCamelCase_ = 'running_var'
elif "num_batches_tracked" in name:
lowerCamelCase_ = 'num_batches_tracked'
else:
lowerCamelCase_ = None
set_recursively(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
continue
if not is_used:
unused_weights.append(_SCREAMING_SNAKE_CASE )
logger.warning(F'''Unused weights: {unused_weights}''' )
@torch.no_grad()
def __snake_case ( UpperCAmelCase_ : Dict , UpperCAmelCase_ : Tuple , UpperCAmelCase_ : Optional[Any] , UpperCAmelCase_ : str=None , UpperCAmelCase_ : int=None , ):
if config_path is not None:
lowerCamelCase_ = EncodecConfig.from_pretrained(_SCREAMING_SNAKE_CASE )
else:
lowerCamelCase_ = EncodecConfig()
if model_name == "encodec_24khz":
pass # config is already correct
elif model_name == "encodec_32khz":
lowerCamelCase_ = [8, 5, 4, 4]
lowerCamelCase_ = [2.2]
lowerCamelCase_ = 64
lowerCamelCase_ = 32000
lowerCamelCase_ = 2048
lowerCamelCase_ = False
lowerCamelCase_ = False
lowerCamelCase_ = False
elif model_name == "encodec_48khz":
lowerCamelCase_ = [8, 5, 4, 2]
lowerCamelCase_ = [3.0, 6.0, 12.0, 24.0]
lowerCamelCase_ = 48000
lowerCamelCase_ = 2
lowerCamelCase_ = False
lowerCamelCase_ = 'time_group_norm'
lowerCamelCase_ = True
lowerCamelCase_ = 1.0
lowerCamelCase_ = 0.01
else:
raise ValueError(F'''Unknown model name: {model_name}''' )
lowerCamelCase_ = EncodecModel(_SCREAMING_SNAKE_CASE )
lowerCamelCase_ = EncodecFeatureExtractor(
feature_size=config.audio_channels , sampling_rate=config.sampling_rate , chunk_length_s=config.chunk_length_s , overlap=config.overlap , )
feature_extractor.save_pretrained(_SCREAMING_SNAKE_CASE )
lowerCamelCase_ = torch.load(_SCREAMING_SNAKE_CASE )
if "best_state" in original_checkpoint:
# we might have a training state saved, in which case discard the yaml results and just retain the weights
lowerCamelCase_ = original_checkpoint['best_state']
recursively_load_weights(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
model.save_pretrained(_SCREAMING_SNAKE_CASE )
if repo_id:
print("Pushing to the hub..." )
feature_extractor.push_to_hub(_SCREAMING_SNAKE_CASE )
model.push_to_hub(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
a_ : List[Any] = argparse.ArgumentParser()
parser.add_argument(
"""--model""",
default="""encodec_24khz""",
type=str,
help="""The model to convert. Should be one of \'encodec_24khz\', \'encodec_32khz\', \'encodec_48khz\'.""",
)
parser.add_argument("""--checkpoint_path""", required=True, default=None, type=str, help="""Path to original checkpoint""")
parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
parser.add_argument(
"""--pytorch_dump_folder_path""", required=True, default=None, type=str, help="""Path to the output PyTorch model."""
)
parser.add_argument(
"""--push_to_hub""", default=None, type=str, help="""Where to upload the converted model on the 🤗 hub."""
)
a_ : Optional[int] = parser.parse_args()
convert_checkpoint(
args.model,
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.config_path,
args.push_to_hub,
)
| 55 | """simple docstring"""
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->Union[str, Any]:
if height >= 1:
move_tower(height - 1 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
move_disk(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
move_tower(height - 1 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->Union[str, Any]:
print('moving disk from' , _SCREAMING_SNAKE_CASE , 'to' , _SCREAMING_SNAKE_CASE )
def __a ( ) ->List[str]:
a__: Dict = int(input('Height of hanoi: ' ).strip() )
move_tower(_SCREAMING_SNAKE_CASE , 'A' , 'B' , 'C' )
if __name__ == "__main__":
main()
| 290 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__ : Dict = logging.get_logger(__name__)
a__ : List[Any] = {
'alibaba-damo/mgp-str-base': 'https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json',
}
class UpperCAmelCase__ ( __lowerCAmelCase):
__SCREAMING_SNAKE_CASE = '''mgp-str'''
def __init__( self , lowercase=[3_2, 1_2_8] , lowercase=4 , lowercase=3 , lowercase=2_7 , lowercase=3_8 , lowercase=5_0_2_5_7 , lowercase=3_0_5_2_2 , lowercase=7_6_8 , lowercase=1_2 , lowercase=1_2 , lowercase=4.0 , lowercase=True , lowercase=False , lowercase=1E-5 , lowercase=0.0 , lowercase=0.0 , lowercase=0.0 , lowercase=False , lowercase=0.02 , **lowercase , ) -> Optional[int]:
super().__init__(**lowercase )
__UpperCamelCase = image_size
__UpperCamelCase = patch_size
__UpperCamelCase = num_channels
__UpperCamelCase = max_token_length
__UpperCamelCase = num_character_labels
__UpperCamelCase = num_bpe_labels
__UpperCamelCase = num_wordpiece_labels
__UpperCamelCase = hidden_size
__UpperCamelCase = num_hidden_layers
__UpperCamelCase = num_attention_heads
__UpperCamelCase = mlp_ratio
__UpperCamelCase = distilled
__UpperCamelCase = layer_norm_eps
__UpperCamelCase = drop_rate
__UpperCamelCase = qkv_bias
__UpperCamelCase = attn_drop_rate
__UpperCamelCase = drop_path_rate
__UpperCamelCase = output_aa_attentions
__UpperCamelCase = initializer_range
| 349 | """simple docstring"""
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = False ) ->str:
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
a__: Optional[int] = F'Expected string as input, found {type(_SCREAMING_SNAKE_CASE )}'
raise ValueError(_SCREAMING_SNAKE_CASE )
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
a__: List[str] = F'Expected boolean as use_pascal parameter, found {type(_SCREAMING_SNAKE_CASE )}'
raise ValueError(_SCREAMING_SNAKE_CASE )
a__: int = input_str.split('_' )
a__: List[str] = 0 if use_pascal else 1
a__: List[str] = words[start_index:]
a__: List[str] = [word[0].upper() + word[1:] for word in words_to_capitalize]
a__: List[str] = '' if use_pascal else words[0]
return "".join([initial_word, *capitalized_words] )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 290 | 0 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging
logging.set_verbosity_info()
__A = logging.get_logger(__name__)
def _A ( lowercase__ ):
lowercase__ = ASTConfig()
if "10-10" in model_name:
pass
elif "speech-commands" in model_name:
lowercase__ = 128
elif "12-12" in model_name:
lowercase__ = 12
lowercase__ = 12
elif "14-14" in model_name:
lowercase__ = 14
lowercase__ = 14
elif "16-16" in model_name:
lowercase__ = 16
lowercase__ = 16
else:
raise ValueError("""Model not supported""" )
lowercase__ = 'huggingface/label-files'
if "speech-commands" in model_name:
lowercase__ = 35
lowercase__ = 'speech-commands-v2-id2label.json'
else:
lowercase__ = 527
lowercase__ = 'audioset-id2label.json'
lowercase__ = json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , repo_type="""dataset""" ) , """r""" ) )
lowercase__ = {int(_SCREAMING_SNAKE_CASE ): v for k, v in idalabel.items()}
lowercase__ = idalabel
lowercase__ = {v: k for k, v in idalabel.items()}
return config
def _A ( lowercase__ ):
if "module.v" in name:
lowercase__ = name.replace("""module.v""" , """audio_spectrogram_transformer""" )
if "cls_token" in name:
lowercase__ = name.replace("""cls_token""" , """embeddings.cls_token""" )
if "dist_token" in name:
lowercase__ = name.replace("""dist_token""" , """embeddings.distillation_token""" )
if "pos_embed" in name:
lowercase__ = name.replace("""pos_embed""" , """embeddings.position_embeddings""" )
if "patch_embed.proj" in name:
lowercase__ = name.replace("""patch_embed.proj""" , """embeddings.patch_embeddings.projection""" )
# transformer blocks
if "blocks" in name:
lowercase__ = name.replace("""blocks""" , """encoder.layer""" )
if "attn.proj" in name:
lowercase__ = name.replace("""attn.proj""" , """attention.output.dense""" )
if "attn" in name:
lowercase__ = name.replace("""attn""" , """attention.self""" )
if "norm1" in name:
lowercase__ = name.replace("""norm1""" , """layernorm_before""" )
if "norm2" in name:
lowercase__ = name.replace("""norm2""" , """layernorm_after""" )
if "mlp.fc1" in name:
lowercase__ = name.replace("""mlp.fc1""" , """intermediate.dense""" )
if "mlp.fc2" in name:
lowercase__ = name.replace("""mlp.fc2""" , """output.dense""" )
# final layernorm
if "audio_spectrogram_transformer.norm" in name:
lowercase__ = name.replace("""audio_spectrogram_transformer.norm""" , """audio_spectrogram_transformer.layernorm""" )
# classifier head
if "module.mlp_head.0" in name:
lowercase__ = name.replace("""module.mlp_head.0""" , """classifier.layernorm""" )
if "module.mlp_head.1" in name:
lowercase__ = name.replace("""module.mlp_head.1""" , """classifier.dense""" )
return name
def _A ( lowercase__ , lowercase__ ):
for key in orig_state_dict.copy().keys():
lowercase__ = orig_state_dict.pop(_SCREAMING_SNAKE_CASE )
if "qkv" in key:
lowercase__ = key.split(""".""" )
lowercase__ = int(key_split[3] )
lowercase__ = config.hidden_size
if "weight" in key:
lowercase__ = val[:dim, :]
lowercase__ = val[dim : dim * 2, :]
lowercase__ = val[-dim:, :]
else:
lowercase__ = val[:dim]
lowercase__ = val[dim : dim * 2]
lowercase__ = val[-dim:]
else:
lowercase__ = val
return orig_state_dict
def _A ( lowercase__ ):
lowercase__ = [
'module.v.head.weight',
'module.v.head.bias',
'module.v.head_dist.weight',
'module.v.head_dist.bias',
]
for k in ignore_keys:
state_dict.pop(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
@torch.no_grad()
def _A ( lowercase__ , lowercase__ , lowercase__=False ):
lowercase__ = get_audio_spectrogram_transformer_config(_SCREAMING_SNAKE_CASE )
lowercase__ = {
'ast-finetuned-audioset-10-10-0.4593': (
'https://www.dropbox.com/s/ca0b1v2nlxzyeb4/audioset_10_10_0.4593.pth?dl=1'
),
'ast-finetuned-audioset-10-10-0.450': (
'https://www.dropbox.com/s/1tv0hovue1bxupk/audioset_10_10_0.4495.pth?dl=1'
),
'ast-finetuned-audioset-10-10-0.448': (
'https://www.dropbox.com/s/6u5sikl4b9wo4u5/audioset_10_10_0.4483.pth?dl=1'
),
'ast-finetuned-audioset-10-10-0.448-v2': (
'https://www.dropbox.com/s/kt6i0v9fvfm1mbq/audioset_10_10_0.4475.pth?dl=1'
),
'ast-finetuned-audioset-12-12-0.447': (
'https://www.dropbox.com/s/snfhx3tizr4nuc8/audioset_12_12_0.4467.pth?dl=1'
),
'ast-finetuned-audioset-14-14-0.443': (
'https://www.dropbox.com/s/z18s6pemtnxm4k7/audioset_14_14_0.4431.pth?dl=1'
),
'ast-finetuned-audioset-16-16-0.442': (
'https://www.dropbox.com/s/mdsa4t1xmcimia6/audioset_16_16_0.4422.pth?dl=1'
),
'ast-finetuned-speech-commands-v2': (
'https://www.dropbox.com/s/q0tbqpwv44pquwy/speechcommands_10_10_0.9812.pth?dl=1'
),
}
# load original state_dict
lowercase__ = model_name_to_url[model_name]
lowercase__ = torch.hub.load_state_dict_from_url(_SCREAMING_SNAKE_CASE , map_location="""cpu""" )
# remove some keys
remove_keys(_SCREAMING_SNAKE_CASE )
# rename some keys
lowercase__ = convert_state_dict(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# load 🤗 model
lowercase__ = ASTForAudioClassification(_SCREAMING_SNAKE_CASE )
model.eval()
model.load_state_dict(_SCREAMING_SNAKE_CASE )
# verify outputs on dummy input
# source: https://github.com/YuanGongND/ast/blob/79e873b8a54d0a3b330dd522584ff2b9926cd581/src/run.py#L62
lowercase__ = -4.2_6_7_7_3_9_3 if 'speech-commands' not in model_name else -6.8_4_5_9_7_8
lowercase__ = 4.5_6_8_9_9_7_4 if 'speech-commands' not in model_name else 5.5_6_5_4_5_2_6
lowercase__ = 1024 if 'speech-commands' not in model_name else 128
lowercase__ = ASTFeatureExtractor(mean=_SCREAMING_SNAKE_CASE , std=_SCREAMING_SNAKE_CASE , max_length=_SCREAMING_SNAKE_CASE )
if "speech-commands" in model_name:
lowercase__ = load_dataset("""speech_commands""" , """v0.02""" , split="""validation""" )
lowercase__ = dataset[0]['audio']['array']
else:
lowercase__ = hf_hub_download(
repo_id="""nielsr/audio-spectogram-transformer-checkpoint""" , filename="""sample_audio.flac""" , repo_type="""dataset""" , )
lowercase__ = torchaudio.load(_SCREAMING_SNAKE_CASE )
lowercase__ = waveform.squeeze().numpy()
lowercase__ = feature_extractor(_SCREAMING_SNAKE_CASE , sampling_rate=16000 , return_tensors="""pt""" )
# forward pass
lowercase__ = model(**_SCREAMING_SNAKE_CASE )
lowercase__ = outputs.logits
if model_name == "ast-finetuned-audioset-10-10-0.4593":
lowercase__ = torch.tensor([-0.8_7_6_0, -7.0_0_4_2, -8.6_6_0_2] )
elif model_name == "ast-finetuned-audioset-10-10-0.450":
lowercase__ = torch.tensor([-1.1_9_8_6, -7.0_9_0_3, -8.2_7_1_8] )
elif model_name == "ast-finetuned-audioset-10-10-0.448":
lowercase__ = torch.tensor([-2.6_1_2_8, -8.0_0_8_0, -9.4_3_4_4] )
elif model_name == "ast-finetuned-audioset-10-10-0.448-v2":
lowercase__ = torch.tensor([-1.5_0_8_0, -7.4_5_3_4, -8.8_9_1_7] )
elif model_name == "ast-finetuned-audioset-12-12-0.447":
lowercase__ = torch.tensor([-0.5_0_5_0, -6.5_8_3_3, -8.0_8_4_3] )
elif model_name == "ast-finetuned-audioset-14-14-0.443":
lowercase__ = torch.tensor([-0.3_8_2_6, -7.0_3_3_6, -8.2_4_1_3] )
elif model_name == "ast-finetuned-audioset-16-16-0.442":
lowercase__ = torch.tensor([-1.2_1_1_3, -6.9_1_0_1, -8.3_4_7_0] )
elif model_name == "ast-finetuned-speech-commands-v2":
lowercase__ = torch.tensor([6.1_5_8_9, -8.0_5_6_6, -8.7_9_8_4] )
else:
raise ValueError("""Unknown model name""" )
if not torch.allclose(logits[0, :3] , _SCREAMING_SNAKE_CASE , atol=1e-4 ):
raise ValueError("""Logits don\'t match""" )
print("""Looks ok!""" )
if pytorch_dump_folder_path is not None:
Path(_SCREAMING_SNAKE_CASE ).mkdir(exist_ok=_SCREAMING_SNAKE_CASE )
print(f'''Saving model {model_name} to {pytorch_dump_folder_path}''' )
model.save_pretrained(_SCREAMING_SNAKE_CASE )
print(f'''Saving feature extractor to {pytorch_dump_folder_path}''' )
feature_extractor.save_pretrained(_SCREAMING_SNAKE_CASE )
if push_to_hub:
print("""Pushing model and feature extractor to the hub...""" )
model.push_to_hub(f'''MIT/{model_name}''' )
feature_extractor.push_to_hub(f'''MIT/{model_name}''' )
if __name__ == "__main__":
__A = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default="ast-finetuned-audioset-10-10-0.4593",
type=str,
help="Name of the Audio Spectrogram Transformer model you\'d like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
)
parser.add_argument(
"--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
)
__A = parser.parse_args()
convert_audio_spectrogram_transformer_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 164 | """simple docstring"""
class __snake_case :
def __init__( self , lowercase , lowercase=None , lowercase=None) -> List[str]:
'''simple docstring'''
a__: Dict = data
a__: List[Any] = previous
a__: Any = next_node
def __str__( self) -> str:
'''simple docstring'''
return f'{self.data}'
def lowerCamelCase_ ( self) -> int:
'''simple docstring'''
return self.data
def lowerCamelCase_ ( self) -> Union[str, Any]:
'''simple docstring'''
return self.next
def lowerCamelCase_ ( self) -> Optional[Any]:
'''simple docstring'''
return self.previous
class __snake_case :
def __init__( self , lowercase) -> Dict:
'''simple docstring'''
a__: List[Any] = head
def __iter__( self) -> List[Any]:
'''simple docstring'''
return self
def lowerCamelCase_ ( self) -> Optional[Any]:
'''simple docstring'''
if not self.current:
raise StopIteration
else:
a__: Dict = self.current.get_data()
a__: Optional[Any] = self.current.get_next()
return value
class __snake_case :
def __init__( self) -> Dict:
'''simple docstring'''
a__: List[Any] = None # First node in list
a__: Optional[int] = None # Last node in list
def __str__( self) -> Optional[Any]:
'''simple docstring'''
a__: Dict = self.head
a__: Optional[Any] = []
while current is not None:
nodes.append(current.get_data())
a__: str = current.get_next()
return " ".join(str(lowercase) for node in nodes)
def __contains__( self , lowercase) -> Optional[int]:
'''simple docstring'''
a__: Optional[int] = self.head
while current:
if current.get_data() == value:
return True
a__: Dict = current.get_next()
return False
def __iter__( self) -> int:
'''simple docstring'''
return LinkedListIterator(self.head)
def lowerCamelCase_ ( self) -> List[Any]:
'''simple docstring'''
if self.head:
return self.head.get_data()
return None
def lowerCamelCase_ ( self) -> Optional[Any]:
'''simple docstring'''
if self.tail:
return self.tail.get_data()
return None
def lowerCamelCase_ ( self , lowercase) -> None:
'''simple docstring'''
if self.head is None:
a__: Optional[Any] = node
a__: Optional[Any] = node
else:
self.insert_before_node(self.head , lowercase)
def lowerCamelCase_ ( self , lowercase) -> None:
'''simple docstring'''
if self.head is None:
self.set_head(lowercase)
else:
self.insert_after_node(self.tail , lowercase)
def lowerCamelCase_ ( self , lowercase) -> None:
'''simple docstring'''
a__: Tuple = Node(lowercase)
if self.head is None:
self.set_head(lowercase)
else:
self.set_tail(lowercase)
def lowerCamelCase_ ( self , lowercase , lowercase) -> None:
'''simple docstring'''
a__: Union[str, Any] = node
a__: Optional[Any] = node.previous
if node.get_previous() is None:
a__: Tuple = node_to_insert
else:
a__: int = node_to_insert
a__: Optional[int] = node_to_insert
def lowerCamelCase_ ( self , lowercase , lowercase) -> None:
'''simple docstring'''
a__: Optional[int] = node
a__: Tuple = node.next
if node.get_next() is None:
a__: Optional[int] = node_to_insert
else:
a__: Any = node_to_insert
a__: str = node_to_insert
def lowerCamelCase_ ( self , lowercase , lowercase) -> None:
'''simple docstring'''
a__: Any = 1
a__: Tuple = Node(lowercase)
a__: Tuple = self.head
while node:
if current_position == position:
self.insert_before_node(lowercase , lowercase)
return
current_position += 1
a__: List[Any] = node.next
self.insert_after_node(self.tail , lowercase)
def lowerCamelCase_ ( self , lowercase) -> Node:
'''simple docstring'''
a__: Tuple = self.head
while node:
if node.get_data() == item:
return node
a__: List[str] = node.get_next()
raise Exception('Node not found')
def lowerCamelCase_ ( self , lowercase) -> Any:
'''simple docstring'''
if (node := self.get_node(lowercase)) is not None:
if node == self.head:
a__: Any = self.head.get_next()
if node == self.tail:
a__: List[Any] = self.tail.get_previous()
self.remove_node_pointers(lowercase)
@staticmethod
def lowerCamelCase_ ( lowercase) -> None:
'''simple docstring'''
if node.get_next():
a__: Any = node.previous
if node.get_previous():
a__: List[str] = node.next
a__: int = None
a__: Union[str, Any] = None
def lowerCamelCase_ ( self) -> str:
'''simple docstring'''
return self.head is None
def __a ( ) ->None:
pass
if __name__ == "__main__":
import doctest
doctest.testmod()
| 290 | 0 |
"""simple docstring"""
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_detectrona,
require_pytesseract,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
from transformers.image_utils import load_image
else:
class a__ :
@staticmethod
def __magic_name__ ( *_a , **_a ):
pass
def __magic_name__ ( __snake_case : List[Any] ) -> int:
return None
# This is a pinned image from a specific revision of a document question answering space, hosted by HuggingFace,
# so we can expect it to be available.
_A : List[Any] = (
"""https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png"""
)
@is_pipeline_test
@require_torch
@require_vision
class a__ ( unittest.TestCase ):
__lowerCAmelCase = MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING
@require_pytesseract
@require_vision
def __magic_name__ ( self , _a , _a , _a ):
lowercase : List[str] = pipeline(
"document-question-answering" , model=_a , tokenizer=_a , image_processor=_a )
lowercase : List[str] = INVOICE_URL
lowercase : Dict = list(zip(*apply_tesseract(load_image(_a ) , _a , "" ) ) )
lowercase : str = 'What is the placebo?'
lowercase : List[Any] = [
{
'image': load_image(_a ),
'question': question,
},
{
'image': image,
'question': question,
},
{
'image': image,
'question': question,
'word_boxes': word_boxes,
},
]
return dqa_pipeline, examples
def __magic_name__ ( self , _a , _a ):
lowercase : Optional[Any] = dqa_pipeline(_a , top_k=2 )
self.assertEqual(
_a , [
[
{"score": ANY(_a ), "answer": ANY(_a ), "start": ANY(_a ), "end": ANY(_a )},
{"score": ANY(_a ), "answer": ANY(_a ), "start": ANY(_a ), "end": ANY(_a )},
]
]
* 3 , )
@require_torch
@require_detectrona
@require_pytesseract
def __magic_name__ ( self ):
lowercase : Dict = pipeline("document-question-answering" , model="hf-internal-testing/tiny-random-layoutlmv2" )
lowercase : List[Any] = INVOICE_URL
lowercase : List[str] = 'How many cats are there?'
lowercase : Union[str, Any] = [
{'score': 0.0_0_0_1, 'answer': 'oy 2312/2019', 'start': 38, 'end': 39},
{'score': 0.0_0_0_1, 'answer': 'oy 2312/2019 DUE', 'start': 38, 'end': 40},
]
lowercase : List[str] = dqa_pipeline(image=_a , question=_a , top_k=2 )
self.assertEqual(nested_simplify(_a , decimals=4 ) , _a )
lowercase : Union[str, Any] = dqa_pipeline({"image": image, "question": question} , top_k=2 )
self.assertEqual(nested_simplify(_a , decimals=4 ) , _a )
# This image does not detect ANY text in it, meaning layoutlmv2 should fail.
# Empty answer probably
lowercase : int = './tests/fixtures/tests_samples/COCO/000000039769.png'
lowercase : Dict = dqa_pipeline(image=_a , question=_a , top_k=2 )
self.assertEqual(_a , [] )
# We can optionnally pass directly the words and bounding boxes
lowercase : Optional[int] = './tests/fixtures/tests_samples/COCO/000000039769.png'
lowercase : int = []
lowercase : Any = []
lowercase : Tuple = dqa_pipeline(image=_a , question=_a , words=_a , boxes=_a , top_k=2 )
self.assertEqual(_a , [] )
@slow
@require_torch
@require_detectrona
@require_pytesseract
def __magic_name__ ( self ):
lowercase : int = pipeline(
"document-question-answering" , model="tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa" , revision="9977165" , )
lowercase : int = INVOICE_URL
lowercase : Dict = 'What is the invoice number?'
lowercase : Tuple = dqa_pipeline(image=_a , question=_a , top_k=2 )
self.assertEqual(
nested_simplify(_a , decimals=4 ) , [
{"score": 0.9_9_4_4, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0_0_0_9, "answer": "us-001", "start": 16, "end": 16},
] , )
lowercase : Optional[Any] = dqa_pipeline({"image": image, "question": question} , top_k=2 )
self.assertEqual(
nested_simplify(_a , decimals=4 ) , [
{"score": 0.9_9_4_4, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0_0_0_9, "answer": "us-001", "start": 16, "end": 16},
] , )
lowercase : int = dqa_pipeline(
[{"image": image, "question": question}, {"image": image, "question": question}] , top_k=2 )
self.assertEqual(
nested_simplify(_a , decimals=4 ) , [
[
{"score": 0.9_9_4_4, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0_0_0_9, "answer": "us-001", "start": 16, "end": 16},
],
]
* 2 , )
@slow
@require_torch
@require_detectrona
@require_pytesseract
def __magic_name__ ( self ):
lowercase : Any = pipeline(
"document-question-answering" , model="tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa" , revision="9977165" , max_seq_len=50 , )
lowercase : Union[str, Any] = INVOICE_URL
lowercase : List[str] = 'What is the invoice number?'
lowercase : Optional[int] = dqa_pipeline(image=_a , question=_a , top_k=2 )
self.assertEqual(
nested_simplify(_a , decimals=4 ) , [
{"score": 0.9_9_7_4, "answer": "1110212019", "start": 23, "end": 23},
{"score": 0.9_9_4_8, "answer": "us-001", "start": 16, "end": 16},
] , )
lowercase : Optional[int] = dqa_pipeline({"image": image, "question": question} , top_k=2 )
self.assertEqual(
nested_simplify(_a , decimals=4 ) , [
{"score": 0.9_9_7_4, "answer": "1110212019", "start": 23, "end": 23},
{"score": 0.9_9_4_8, "answer": "us-001", "start": 16, "end": 16},
] , )
lowercase : Optional[Any] = dqa_pipeline(
[{"image": image, "question": question}, {"image": image, "question": question}] , top_k=2 )
self.assertEqual(
nested_simplify(_a , decimals=4 ) , [
[
{"score": 0.9_9_7_4, "answer": "1110212019", "start": 23, "end": 23},
{"score": 0.9_9_4_8, "answer": "us-001", "start": 16, "end": 16},
]
]
* 2 , )
@slow
@require_torch
@require_pytesseract
@require_vision
def __magic_name__ ( self ):
lowercase : int = AutoTokenizer.from_pretrained(
"impira/layoutlm-document-qa" , revision="3dc6de3" , add_prefix_space=_a )
lowercase : Union[str, Any] = pipeline(
"document-question-answering" , model="impira/layoutlm-document-qa" , tokenizer=_a , revision="3dc6de3" , )
lowercase : str = INVOICE_URL
lowercase : Union[str, Any] = 'What is the invoice number?'
lowercase : Dict = dqa_pipeline(image=_a , question=_a , top_k=2 )
self.assertEqual(
nested_simplify(_a , decimals=4 ) , [
{"score": 0.4_2_5_1, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0_8_1_9, "answer": "1110212019", "start": 23, "end": 23},
] , )
lowercase : str = dqa_pipeline({"image": image, "question": question} , top_k=2 )
self.assertEqual(
nested_simplify(_a , decimals=4 ) , [
{"score": 0.4_2_5_1, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0_8_1_9, "answer": "1110212019", "start": 23, "end": 23},
] , )
lowercase : List[str] = dqa_pipeline(
[{"image": image, "question": question}, {"image": image, "question": question}] , top_k=2 )
self.assertEqual(
nested_simplify(_a , decimals=4 ) , [
[
{"score": 0.4_2_5_1, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0_8_1_9, "answer": "1110212019", "start": 23, "end": 23},
]
]
* 2 , )
lowercase : List[str] = list(zip(*apply_tesseract(load_image(_a ) , _a , "" ) ) )
# This model should also work if `image` is set to None
lowercase : Any = dqa_pipeline({"image": None, "word_boxes": word_boxes, "question": question} , top_k=2 )
self.assertEqual(
nested_simplify(_a , decimals=4 ) , [
{"score": 0.4_2_5_1, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0_8_1_9, "answer": "1110212019", "start": 23, "end": 23},
] , )
@slow
@require_torch
@require_pytesseract
@require_vision
def __magic_name__ ( self ):
lowercase : Union[str, Any] = AutoTokenizer.from_pretrained(
"impira/layoutlm-document-qa" , revision="3dc6de3" , add_prefix_space=_a )
lowercase : List[Any] = pipeline(
"document-question-answering" , model="impira/layoutlm-document-qa" , tokenizer=_a , revision="3dc6de3" , max_seq_len=50 , )
lowercase : int = INVOICE_URL
lowercase : Any = 'What is the invoice number?'
lowercase : Union[str, Any] = dqa_pipeline(image=_a , question=_a , top_k=2 )
self.assertEqual(
nested_simplify(_a , decimals=4 ) , [
{"score": 0.9_9_9_9, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.9_9_9_8, "answer": "us-001", "start": 16, "end": 16},
] , )
lowercase : List[str] = dqa_pipeline(
[{"image": image, "question": question}, {"image": image, "question": question}] , top_k=2 )
self.assertEqual(
nested_simplify(_a , decimals=4 ) , [
[
{"score": 0.9_9_9_9, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.9_9_9_8, "answer": "us-001", "start": 16, "end": 16},
]
]
* 2 , )
lowercase : Any = list(zip(*apply_tesseract(load_image(_a ) , _a , "" ) ) )
# This model should also work if `image` is set to None
lowercase : Dict = dqa_pipeline({"image": None, "word_boxes": word_boxes, "question": question} , top_k=2 )
self.assertEqual(
nested_simplify(_a , decimals=4 ) , [
{"score": 0.9_9_9_9, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.9_9_9_8, "answer": "us-001", "start": 16, "end": 16},
] , )
@slow
@require_torch
def __magic_name__ ( self ):
lowercase : Optional[Any] = pipeline(
"document-question-answering" , model="naver-clova-ix/donut-base-finetuned-docvqa" , tokenizer=AutoTokenizer.from_pretrained("naver-clova-ix/donut-base-finetuned-docvqa" ) , feature_extractor="naver-clova-ix/donut-base-finetuned-docvqa" , )
lowercase : Optional[int] = INVOICE_URL
lowercase : Tuple = 'What is the invoice number?'
lowercase : str = dqa_pipeline(image=_a , question=_a , top_k=2 )
self.assertEqual(nested_simplify(_a , decimals=4 ) , [{"answer": "us-001"}] )
@require_tf
@unittest.skip("Document question answering not implemented in TF" )
def __magic_name__ ( self ):
pass
| 202 | """simple docstring"""
import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable
import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization import from_bytes, to_bytes
from flax.training import train_state
from flax.training.common_utils import shard
from tqdm.auto import tqdm
from transformers import BigBirdConfig, FlaxBigBirdForQuestionAnswering
from transformers.models.big_bird.modeling_flax_big_bird import FlaxBigBirdForQuestionAnsweringModule
class __snake_case ( __lowerCAmelCase ):
a__ = 42
a__ = jnp.floataa
a__ = True
def lowerCamelCase_ ( self) -> int:
'''simple docstring'''
super().setup()
a__: int = nn.Dense(5 , dtype=self.dtype)
def __call__( self , *lowercase , **lowercase) -> Dict:
'''simple docstring'''
a__: Dict = super().__call__(*lowercase , **lowercase)
a__: str = self.cls(outputs[2])
return outputs[:2] + (cls_out,)
class __snake_case ( __lowerCAmelCase ):
a__ = FlaxBigBirdForNaturalQuestionsModule
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->Optional[Any]:
def cross_entropy(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None ):
a__: Any = logits.shape[-1]
a__: List[Any] = (labels[..., None] == jnp.arange(_SCREAMING_SNAKE_CASE )[None]).astype('f4' )
a__: List[str] = jax.nn.log_softmax(_SCREAMING_SNAKE_CASE , axis=-1 )
a__: Dict = -jnp.sum(labels * logits , axis=-1 )
if reduction is not None:
a__: str = reduction(_SCREAMING_SNAKE_CASE )
return loss
a__: Tuple = partial(_SCREAMING_SNAKE_CASE , reduction=jnp.mean )
a__: List[str] = cross_entropy(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
a__: Union[str, Any] = cross_entropy(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
a__: Any = cross_entropy(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
return (start_loss + end_loss + pooled_loss) / 3
@dataclass
class __snake_case :
a__ = "google/bigbird-roberta-base"
a__ = 3000
a__ = 1_0500
a__ = 128
a__ = 3
a__ = 1
a__ = 5
# tx_args
a__ = 3e-5
a__ = 0.0
a__ = 2_0000
a__ = 0.0095
a__ = "bigbird-roberta-natural-questions"
a__ = "training-expt"
a__ = "data/nq-training.jsonl"
a__ = "data/nq-validation.jsonl"
def lowerCamelCase_ ( self) -> Optional[Any]:
'''simple docstring'''
os.makedirs(self.base_dir , exist_ok=lowercase)
a__: str = os.path.join(self.base_dir , self.save_dir)
a__: List[str] = self.batch_size_per_device * jax.device_count()
@dataclass
class __snake_case :
a__ = 42
a__ = 4096 # no dynamic padding on TPUs
def __call__( self , lowercase) -> List[Any]:
'''simple docstring'''
a__: int = self.collate_fn(lowercase)
a__: Optional[int] = jax.tree_util.tree_map(lowercase , lowercase)
return batch
def lowerCamelCase_ ( self , lowercase) -> Dict:
'''simple docstring'''
a__ , a__: Dict = self.fetch_inputs(features['input_ids'])
a__: List[Any] = {
'input_ids': jnp.array(lowercase , dtype=jnp.intaa),
'attention_mask': jnp.array(lowercase , dtype=jnp.intaa),
'start_labels': jnp.array(features['start_token'] , dtype=jnp.intaa),
'end_labels': jnp.array(features['end_token'] , dtype=jnp.intaa),
'pooled_labels': jnp.array(features['category'] , dtype=jnp.intaa),
}
return batch
def lowerCamelCase_ ( self , lowercase) -> List[str]:
'''simple docstring'''
a__: List[Any] = [self._fetch_inputs(lowercase) for ids in input_ids]
return zip(*lowercase)
def lowerCamelCase_ ( self , lowercase) -> Dict:
'''simple docstring'''
a__: Union[str, Any] = [1 for _ in range(len(lowercase))]
while len(lowercase) < self.max_length:
input_ids.append(self.pad_id)
attention_mask.append(0)
return input_ids, attention_mask
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None ) ->List[Any]:
if seed is not None:
a__: int = dataset.shuffle(seed=_SCREAMING_SNAKE_CASE )
for i in range(len(_SCREAMING_SNAKE_CASE ) // batch_size ):
a__: Union[str, Any] = dataset[i * batch_size : (i + 1) * batch_size]
yield dict(_SCREAMING_SNAKE_CASE )
@partial(jax.pmap , axis_name='batch' )
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ) ->Any:
def loss_fn(_SCREAMING_SNAKE_CASE ):
a__: str = model_inputs.pop('start_labels' )
a__: Dict = model_inputs.pop('end_labels' )
a__: Optional[int] = model_inputs.pop('pooled_labels' )
a__: Optional[Any] = state.apply_fn(**_SCREAMING_SNAKE_CASE , params=_SCREAMING_SNAKE_CASE , dropout_rng=_SCREAMING_SNAKE_CASE , train=_SCREAMING_SNAKE_CASE )
a__ , a__ , a__: Optional[int] = outputs
return state.loss_fn(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , )
a__ , a__: Union[str, Any] = jax.random.split(_SCREAMING_SNAKE_CASE )
a__: List[Any] = jax.value_and_grad(_SCREAMING_SNAKE_CASE )
a__ , a__: str = grad_fn(state.params )
a__: Optional[int] = jax.lax.pmean({'loss': loss} , axis_name='batch' )
a__: int = jax.lax.pmean(_SCREAMING_SNAKE_CASE , 'batch' )
a__: Union[str, Any] = state.apply_gradients(grads=_SCREAMING_SNAKE_CASE )
return state, metrics, new_drp_rng
@partial(jax.pmap , axis_name='batch' )
def __a ( _SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ) ->Optional[Any]:
a__: Optional[int] = model_inputs.pop('start_labels' )
a__: int = model_inputs.pop('end_labels' )
a__: Dict = model_inputs.pop('pooled_labels' )
a__: Union[str, Any] = state.apply_fn(**_SCREAMING_SNAKE_CASE , params=state.params , train=_SCREAMING_SNAKE_CASE )
a__ , a__ , a__: int = outputs
a__: Optional[int] = state.loss_fn(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
a__: Tuple = jax.lax.pmean({'loss': loss} , axis_name='batch' )
return metrics
class __snake_case ( train_state.TrainState ):
a__ = struct.field(pytree_node=__lowerCAmelCase )
@dataclass
class __snake_case :
a__ = 42
a__ = 42
a__ = 42
a__ = 42
a__ = 42
a__ = 42
a__ = None
def lowerCamelCase_ ( self , lowercase , lowercase , lowercase , lowercase=None) -> Optional[int]:
'''simple docstring'''
a__: Dict = model.params
a__: Any = TrainState.create(
apply_fn=model.__call__ , params=lowercase , tx=lowercase , loss_fn=lowercase , )
if ckpt_dir is not None:
a__ , a__ , a__ , a__ , a__: Any = restore_checkpoint(lowercase , lowercase)
a__: Any = {
'lr': args.lr,
'init_lr': args.init_lr,
'warmup_steps': args.warmup_steps,
'num_train_steps': num_train_steps,
'weight_decay': args.weight_decay,
}
a__ , a__: str = build_tx(**lowercase)
a__: Optional[Any] = train_state.TrainState(
step=lowercase , apply_fn=model.__call__ , params=lowercase , tx=lowercase , opt_state=lowercase , )
a__: int = args
a__: Union[str, Any] = data_collator
a__: Any = lr
a__: Dict = params
a__: Tuple = jax_utils.replicate(lowercase)
return state
def lowerCamelCase_ ( self , lowercase , lowercase , lowercase) -> int:
'''simple docstring'''
a__: int = self.args
a__: str = len(lowercase) // args.batch_size
a__: Tuple = jax.random.PRNGKey(0)
a__: List[Any] = jax.random.split(lowercase , jax.device_count())
for epoch in range(args.max_epochs):
a__: str = jnp.array(0 , dtype=jnp.floataa)
a__: Tuple = get_batched_dataset(lowercase , args.batch_size , seed=lowercase)
a__: Optional[int] = 0
for batch in tqdm(lowercase , total=lowercase , desc=f'Running EPOCH-{epoch}'):
a__: List[str] = self.data_collator(lowercase)
a__ , a__ , a__: int = self.train_step_fn(lowercase , lowercase , **lowercase)
running_loss += jax_utils.unreplicate(metrics['loss'])
i += 1
if i % args.logging_steps == 0:
a__: List[Any] = jax_utils.unreplicate(state.step)
a__: Tuple = running_loss.item() / i
a__: Optional[Any] = self.scheduler_fn(state_step - 1)
a__: List[Any] = self.evaluate(lowercase , lowercase)
a__: List[str] = {
'step': state_step.item(),
'eval_loss': eval_loss.item(),
'tr_loss': tr_loss,
'lr': lr.item(),
}
tqdm.write(str(lowercase))
self.logger.log(lowercase , commit=lowercase)
if i % args.save_steps == 0:
self.save_checkpoint(args.save_dir + f'-e{epoch}-s{i}' , state=lowercase)
def lowerCamelCase_ ( self , lowercase , lowercase) -> List[Any]:
'''simple docstring'''
a__: Tuple = get_batched_dataset(lowercase , self.args.batch_size)
a__: Dict = len(lowercase) // self.args.batch_size
a__: Tuple = jnp.array(0 , dtype=jnp.floataa)
a__: List[Any] = 0
for batch in tqdm(lowercase , total=lowercase , desc='Evaluating ... '):
a__: str = self.data_collator(lowercase)
a__: List[str] = self.val_step_fn(lowercase , **lowercase)
running_loss += jax_utils.unreplicate(metrics['loss'])
i += 1
return running_loss / i
def lowerCamelCase_ ( self , lowercase , lowercase) -> Any:
'''simple docstring'''
a__: List[Any] = jax_utils.unreplicate(lowercase)
print(f'SAVING CHECKPOINT IN {save_dir}' , end=' ... ')
self.model_save_fn(lowercase , params=state.params)
with open(os.path.join(lowercase , 'opt_state.msgpack') , 'wb') as f:
f.write(to_bytes(state.opt_state))
joblib.dump(self.args , os.path.join(lowercase , 'args.joblib'))
joblib.dump(self.data_collator , os.path.join(lowercase , 'data_collator.joblib'))
with open(os.path.join(lowercase , 'training_state.json') , 'w') as f:
json.dump({'step': state.step.item()} , lowercase)
print('DONE')
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->Optional[Any]:
print(F'RESTORING CHECKPOINT FROM {save_dir}' , end=' ... ' )
with open(os.path.join(_SCREAMING_SNAKE_CASE , 'flax_model.msgpack' ) , 'rb' ) as f:
a__: int = from_bytes(state.params , f.read() )
with open(os.path.join(_SCREAMING_SNAKE_CASE , 'opt_state.msgpack' ) , 'rb' ) as f:
a__: Optional[Any] = from_bytes(state.opt_state , f.read() )
a__: Optional[Any] = joblib.load(os.path.join(_SCREAMING_SNAKE_CASE , 'args.joblib' ) )
a__: int = joblib.load(os.path.join(_SCREAMING_SNAKE_CASE , 'data_collator.joblib' ) )
with open(os.path.join(_SCREAMING_SNAKE_CASE , 'training_state.json' ) , 'r' ) as f:
a__: Any = json.load(_SCREAMING_SNAKE_CASE )
a__: Optional[Any] = training_state['step']
print('DONE' )
return params, opt_state, step, args, data_collator
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->Optional[int]:
a__: str = num_train_steps - warmup_steps
a__: str = optax.linear_schedule(init_value=_SCREAMING_SNAKE_CASE , end_value=_SCREAMING_SNAKE_CASE , transition_steps=_SCREAMING_SNAKE_CASE )
a__: List[Any] = optax.linear_schedule(init_value=_SCREAMING_SNAKE_CASE , end_value=1e-7 , transition_steps=_SCREAMING_SNAKE_CASE )
a__: int = optax.join_schedules(schedules=[warmup_fn, decay_fn] , boundaries=[warmup_steps] )
return lr
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->Tuple:
def weight_decay_mask(_SCREAMING_SNAKE_CASE ):
a__: List[Any] = traverse_util.flatten_dict(_SCREAMING_SNAKE_CASE )
a__: List[str] = {k: (v[-1] != 'bias' and v[-2:] != ('LayerNorm', 'scale')) for k, v in params.items()}
return traverse_util.unflatten_dict(_SCREAMING_SNAKE_CASE )
a__: List[str] = scheduler_fn(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
a__: Any = optax.adamw(learning_rate=_SCREAMING_SNAKE_CASE , weight_decay=_SCREAMING_SNAKE_CASE , mask=_SCREAMING_SNAKE_CASE )
return tx, lr
| 290 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFFunnelBaseModel,
TFFunnelForMaskedLM,
TFFunnelForMultipleChoice,
TFFunnelForPreTraining,
TFFunnelForQuestionAnswering,
TFFunnelForSequenceClassification,
TFFunnelForTokenClassification,
TFFunnelModel,
)
class A_ :
def __init__( self : List[Any] , snake_case_ : List[str] , snake_case_ : Tuple=1_3 , snake_case_ : Any=7 , snake_case_ : Optional[int]=True , snake_case_ : Optional[int]=True , snake_case_ : int=True , snake_case_ : List[Any]=True , snake_case_ : List[Any]=9_9 , snake_case_ : Optional[Any]=[1, 1, 2] , snake_case_ : Any=1 , snake_case_ : List[Any]=3_2 , snake_case_ : str=4 , snake_case_ : Union[str, Any]=8 , snake_case_ : str=3_7 , snake_case_ : Union[str, Any]="gelu_new" , snake_case_ : Optional[Any]=0.1 , snake_case_ : Optional[Any]=0.1 , snake_case_ : str=0.0 , snake_case_ : int=5_1_2 , snake_case_ : Tuple=3 , snake_case_ : str=0.0_2 , snake_case_ : Dict=3 , snake_case_ : str=4 , snake_case_ : Dict=None , snake_case_ : str=False , ):
_UpperCAmelCase = parent
_UpperCAmelCase = batch_size
_UpperCAmelCase = seq_length
_UpperCAmelCase = is_training
_UpperCAmelCase = use_input_mask
_UpperCAmelCase = use_token_type_ids
_UpperCAmelCase = use_labels
_UpperCAmelCase = vocab_size
_UpperCAmelCase = block_sizes
_UpperCAmelCase = num_decoder_layers
_UpperCAmelCase = d_model
_UpperCAmelCase = n_head
_UpperCAmelCase = d_head
_UpperCAmelCase = d_inner
_UpperCAmelCase = hidden_act
_UpperCAmelCase = hidden_dropout
_UpperCAmelCase = attention_dropout
_UpperCAmelCase = activation_dropout
_UpperCAmelCase = max_position_embeddings
_UpperCAmelCase = type_vocab_size
_UpperCAmelCase = 2
_UpperCAmelCase = num_labels
_UpperCAmelCase = num_choices
_UpperCAmelCase = scope
_UpperCAmelCase = initializer_std
# Used in the tests to check the size of the first attention layer
_UpperCAmelCase = n_head
# Used in the tests to check the size of the first hidden state
_UpperCAmelCase = self.d_model
# Used in the tests to check the number of output hidden states/attentions
_UpperCAmelCase = sum(self.block_sizes ) + (0 if base else self.num_decoder_layers)
# FunnelModel adds two hidden layers: input embeddings and the sum of the upsampled encoder hidden state with
# the last hidden state of the first block (which is the first hidden state of the decoder).
if not base:
_UpperCAmelCase = self.num_hidden_layers + 2
def lowercase ( self : Tuple ):
_UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_UpperCAmelCase = None
if self.use_input_mask:
_UpperCAmelCase = random_attention_mask([self.batch_size, self.seq_length] )
_UpperCAmelCase = None
if self.use_token_type_ids:
_UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
if self.use_labels:
_UpperCAmelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_UpperCAmelCase = ids_tensor([self.batch_size] , self.num_choices )
_UpperCAmelCase = FunnelConfig(
vocab_size=self.vocab_size , block_sizes=self.block_sizes , num_decoder_layers=self.num_decoder_layers , d_model=self.d_model , n_head=self.n_head , d_head=self.d_head , d_inner=self.d_inner , hidden_act=self.hidden_act , hidden_dropout=self.hidden_dropout , attention_dropout=self.attention_dropout , activation_dropout=self.activation_dropout , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_std=self.initializer_std , )
return (
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
)
def lowercase ( self : List[Any] , snake_case_ : Optional[Any] , snake_case_ : List[str] , snake_case_ : List[str] , snake_case_ : Union[str, Any] , snake_case_ : Dict , snake_case_ : Union[str, Any] , snake_case_ : str , ):
_UpperCAmelCase = TFFunnelModel(config=snake_case_ )
_UpperCAmelCase = {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
_UpperCAmelCase = model(snake_case_ )
_UpperCAmelCase = [input_ids, input_mask]
_UpperCAmelCase = model(snake_case_ )
_UpperCAmelCase = model(snake_case_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.d_model) )
_UpperCAmelCase = False
_UpperCAmelCase = TFFunnelModel(config=snake_case_ )
_UpperCAmelCase = model(snake_case_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.d_model) )
_UpperCAmelCase = False
_UpperCAmelCase = TFFunnelModel(config=snake_case_ )
_UpperCAmelCase = model(snake_case_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.d_model) )
def lowercase ( self : List[Any] , snake_case_ : Optional[int] , snake_case_ : Tuple , snake_case_ : Dict , snake_case_ : Any , snake_case_ : Optional[int] , snake_case_ : int , snake_case_ : Optional[Any] , ):
_UpperCAmelCase = TFFunnelBaseModel(config=snake_case_ )
_UpperCAmelCase = {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
_UpperCAmelCase = model(snake_case_ )
_UpperCAmelCase = [input_ids, input_mask]
_UpperCAmelCase = model(snake_case_ )
_UpperCAmelCase = model(snake_case_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, 2, self.d_model) )
_UpperCAmelCase = False
_UpperCAmelCase = TFFunnelBaseModel(config=snake_case_ )
_UpperCAmelCase = model(snake_case_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, 3, self.d_model) )
_UpperCAmelCase = False
_UpperCAmelCase = TFFunnelBaseModel(config=snake_case_ )
_UpperCAmelCase = model(snake_case_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, 2, self.d_model) )
def lowercase ( self : Dict , snake_case_ : Any , snake_case_ : int , snake_case_ : List[str] , snake_case_ : Union[str, Any] , snake_case_ : List[Any] , snake_case_ : Tuple , snake_case_ : List[str] , ):
_UpperCAmelCase = TFFunnelForPreTraining(config=snake_case_ )
_UpperCAmelCase = {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
_UpperCAmelCase = model(snake_case_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length) )
def lowercase ( self : Dict , snake_case_ : Union[str, Any] , snake_case_ : List[str] , snake_case_ : int , snake_case_ : int , snake_case_ : Dict , snake_case_ : List[Any] , snake_case_ : Optional[int] , ):
_UpperCAmelCase = TFFunnelForMaskedLM(config=snake_case_ )
_UpperCAmelCase = {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
_UpperCAmelCase = model(snake_case_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def lowercase ( self : int , snake_case_ : Tuple , snake_case_ : Optional[int] , snake_case_ : Optional[Any] , snake_case_ : int , snake_case_ : Any , snake_case_ : Optional[Any] , snake_case_ : Dict , ):
_UpperCAmelCase = self.num_labels
_UpperCAmelCase = TFFunnelForSequenceClassification(config=snake_case_ )
_UpperCAmelCase = {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
_UpperCAmelCase = model(snake_case_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowercase ( self : str , snake_case_ : int , snake_case_ : Optional[int] , snake_case_ : Optional[Any] , snake_case_ : str , snake_case_ : str , snake_case_ : Tuple , snake_case_ : Dict , ):
_UpperCAmelCase = self.num_choices
_UpperCAmelCase = TFFunnelForMultipleChoice(config=snake_case_ )
_UpperCAmelCase = tf.tile(tf.expand_dims(snake_case_ , 1 ) , (1, self.num_choices, 1) )
_UpperCAmelCase = tf.tile(tf.expand_dims(snake_case_ , 1 ) , (1, self.num_choices, 1) )
_UpperCAmelCase = tf.tile(tf.expand_dims(snake_case_ , 1 ) , (1, self.num_choices, 1) )
_UpperCAmelCase = {
'input_ids': multiple_choice_inputs_ids,
'attention_mask': multiple_choice_input_mask,
'token_type_ids': multiple_choice_token_type_ids,
}
_UpperCAmelCase = model(snake_case_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def lowercase ( self : Optional[Any] , snake_case_ : str , snake_case_ : Union[str, Any] , snake_case_ : Optional[int] , snake_case_ : Optional[Any] , snake_case_ : Any , snake_case_ : Any , snake_case_ : int , ):
_UpperCAmelCase = self.num_labels
_UpperCAmelCase = TFFunnelForTokenClassification(config=snake_case_ )
_UpperCAmelCase = {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
_UpperCAmelCase = model(snake_case_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def lowercase ( self : Optional[int] , snake_case_ : Dict , snake_case_ : Optional[Any] , snake_case_ : Optional[Any] , snake_case_ : int , snake_case_ : int , snake_case_ : Optional[int] , snake_case_ : str , ):
_UpperCAmelCase = TFFunnelForQuestionAnswering(config=snake_case_ )
_UpperCAmelCase = {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
_UpperCAmelCase = model(snake_case_ )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def lowercase ( self : Optional[Any] ):
_UpperCAmelCase = self.prepare_config_and_inputs()
(
_UpperCAmelCase
) = config_and_inputs
_UpperCAmelCase = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_tf
class A_ ( __lowerCAmelCase , __lowerCAmelCase , unittest.TestCase ):
_lowerCamelCase : Union[str, Any] = (
(
TFFunnelModel,
TFFunnelForMaskedLM,
TFFunnelForPreTraining,
TFFunnelForQuestionAnswering,
TFFunnelForTokenClassification,
)
if is_tf_available()
else ()
)
_lowerCamelCase : int = (
{
"""feature-extraction""": (TFFunnelBaseModel, TFFunnelModel),
"""fill-mask""": TFFunnelForMaskedLM,
"""question-answering""": TFFunnelForQuestionAnswering,
"""text-classification""": TFFunnelForSequenceClassification,
"""token-classification""": TFFunnelForTokenClassification,
"""zero-shot""": TFFunnelForSequenceClassification,
}
if is_tf_available()
else {}
)
_lowerCamelCase : Union[str, Any] = False
_lowerCamelCase : Union[str, Any] = False
def lowercase ( self : Optional[int] ):
_UpperCAmelCase = TFFunnelModelTester(self )
_UpperCAmelCase = ConfigTester(self , config_class=snake_case_ )
def lowercase ( self : Union[str, Any] ):
self.config_tester.run_common_tests()
def lowercase ( self : Dict ):
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case_ )
def lowercase ( self : Union[str, Any] ):
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*snake_case_ )
def lowercase ( self : str ):
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*snake_case_ )
def lowercase ( self : Optional[int] ):
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*snake_case_ )
def lowercase ( self : Union[str, Any] ):
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*snake_case_ )
@require_tf
class A_ ( __lowerCAmelCase , unittest.TestCase ):
_lowerCamelCase : Tuple = (
(TFFunnelBaseModel, TFFunnelForMultipleChoice, TFFunnelForSequenceClassification) if is_tf_available() else ()
)
_lowerCamelCase : Optional[Any] = False
_lowerCamelCase : Optional[Any] = False
def lowercase ( self : Optional[Any] ):
_UpperCAmelCase = TFFunnelModelTester(self , base=snake_case_ )
_UpperCAmelCase = ConfigTester(self , config_class=snake_case_ )
def lowercase ( self : str ):
self.config_tester.run_common_tests()
def lowercase ( self : Optional[int] ):
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_base_model(*snake_case_ )
def lowercase ( self : List[str] ):
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*snake_case_ )
def lowercase ( self : List[Any] ):
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*snake_case_ )
| 22 | """simple docstring"""
from typing import List, Optional, Tuple, Union
import PIL
import torch
from torchvision import transforms
from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor
lowercase__ = transforms.Compose(
[
transforms.Resize((256, 256)),
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5]),
]
)
def __a ( _SCREAMING_SNAKE_CASE ) ->Any:
if isinstance(_SCREAMING_SNAKE_CASE , torch.Tensor ):
return image
elif isinstance(_SCREAMING_SNAKE_CASE , PIL.Image.Image ):
a__: Optional[int] = [image]
a__: str = [trans(img.convert('RGB' ) ) for img in image]
a__: Any = torch.stack(_SCREAMING_SNAKE_CASE )
return image
class __snake_case ( __lowerCAmelCase ):
def __init__( self , lowercase , lowercase) -> Optional[int]:
'''simple docstring'''
super().__init__()
# make sure scheduler can always be converted to DDIM
a__: Dict = DDIMScheduler.from_config(scheduler.config)
self.register_modules(unet=lowercase , scheduler=lowercase)
def lowerCamelCase_ ( self , lowercase) -> int:
'''simple docstring'''
if strength < 0 or strength > 1:
raise ValueError(f'The value of strength should in [0.0, 1.0] but is {strength}')
def lowerCamelCase_ ( self , lowercase , lowercase , lowercase) -> Dict:
'''simple docstring'''
a__: int = min(int(num_inference_steps * strength) , lowercase)
a__: Any = max(num_inference_steps - init_timestep , 0)
a__: Union[str, Any] = self.scheduler.timesteps[t_start:]
return timesteps, num_inference_steps - t_start
def lowerCamelCase_ ( self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase=None) -> List[Any]:
'''simple docstring'''
if not isinstance(lowercase , (torch.Tensor, PIL.Image.Image, list)):
raise ValueError(
f'`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(lowercase)}')
a__: Tuple = image.to(device=lowercase , dtype=lowercase)
if isinstance(lowercase , lowercase) and len(lowercase) != batch_size:
raise ValueError(
f'You have passed a list of generators of length {len(lowercase)}, but requested an effective batch'
f' size of {batch_size}. Make sure the batch size matches the length of the generators.')
a__: List[str] = init_latents.shape
a__: List[Any] = randn_tensor(lowercase , generator=lowercase , device=lowercase , dtype=lowercase)
# get latents
print('add noise to latents at timestep' , lowercase)
a__: int = self.scheduler.add_noise(lowercase , lowercase , lowercase)
a__: Dict = init_latents
return latents
@torch.no_grad()
def __call__( self , lowercase = None , lowercase = 0.8 , lowercase = 1 , lowercase = None , lowercase = 0.0 , lowercase = 50 , lowercase = None , lowercase = "pil" , lowercase = True , ) -> Union[ImagePipelineOutput, Tuple]:
'''simple docstring'''
self.check_inputs(lowercase)
# 2. Preprocess image
a__: Tuple = preprocess(lowercase)
# 3. set timesteps
self.scheduler.set_timesteps(lowercase , device=self.device)
a__ , a__: Union[str, Any] = self.get_timesteps(lowercase , lowercase , self.device)
a__: Optional[int] = timesteps[:1].repeat(lowercase)
# 4. Prepare latent variables
a__: Union[str, Any] = self.prepare_latents(lowercase , lowercase , lowercase , self.unet.dtype , self.device , lowercase)
a__: Optional[Any] = latents
# 5. Denoising loop
for t in self.progress_bar(lowercase):
# 1. predict noise model_output
a__: Dict = self.unet(lowercase , lowercase).sample
# 2. predict previous mean of image x_t-1 and add variance depending on eta
# eta corresponds to η in paper and should be between [0, 1]
# do x_t -> x_t-1
a__: Optional[Any] = self.scheduler.step(
lowercase , lowercase , lowercase , eta=lowercase , use_clipped_model_output=lowercase , generator=lowercase , ).prev_sample
a__: Union[str, Any] = (image / 2 + 0.5).clamp(0 , 1)
a__: Optional[int] = image.cpu().permute(0 , 2 , 3 , 1).numpy()
if output_type == "pil":
a__: Dict = self.numpy_to_pil(lowercase)
if not return_dict:
return (image, latent_timestep.item())
return ImagePipelineOutput(images=lowercase)
| 290 | 0 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
class __UpperCAmelCase ( __lowerCAmelCase ):
'''simple docstring'''
__lowerCAmelCase = '''SpeechT5FeatureExtractor'''
__lowerCAmelCase = '''SpeechT5Tokenizer'''
def __init__(self : Union[str, Any] , _lowerCAmelCase : str , _lowerCAmelCase : int ):
super().__init__(_lowerCAmelCase , _lowerCAmelCase )
def __call__(self : List[str] , *_lowerCAmelCase : Any , **_lowerCAmelCase : Optional[Any] ):
A = kwargs.pop("""audio""" , _lowerCAmelCase )
A = kwargs.pop("""text""" , _lowerCAmelCase )
A = kwargs.pop("""text_target""" , _lowerCAmelCase )
A = kwargs.pop("""audio_target""" , _lowerCAmelCase )
A = kwargs.pop("""sampling_rate""" , _lowerCAmelCase )
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:
A = self.feature_extractor(_lowerCAmelCase , *_lowerCAmelCase , sampling_rate=_lowerCAmelCase , **_lowerCAmelCase )
elif text is not None:
A = self.tokenizer(_lowerCAmelCase , **_lowerCAmelCase )
else:
A = None
if audio_target is not None:
A = self.feature_extractor(audio_target=_lowerCAmelCase , *_lowerCAmelCase , sampling_rate=_lowerCAmelCase , **_lowerCAmelCase )
A = targets['input_values']
elif text_target is not None:
A = self.tokenizer(_lowerCAmelCase , **_lowerCAmelCase )
A = targets['input_ids']
else:
A = None
if inputs is None:
return targets
if targets is not None:
A = labels
A = targets.get("""attention_mask""" )
if decoder_attention_mask is not None:
A = decoder_attention_mask
return inputs
def A (self : Any , *_lowerCAmelCase : Union[str, Any] , **_lowerCAmelCase : Optional[Any] ):
A = kwargs.pop("""input_values""" , _lowerCAmelCase )
A = kwargs.pop("""input_ids""" , _lowerCAmelCase )
A = kwargs.pop("""labels""" , _lowerCAmelCase )
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:
A = self.feature_extractor.pad(_lowerCAmelCase , *_lowerCAmelCase , **_lowerCAmelCase )
elif input_ids is not None:
A = self.tokenizer.pad(_lowerCAmelCase , **_lowerCAmelCase )
else:
A = None
if labels is not None:
if "input_ids" in labels or (isinstance(_lowerCAmelCase , _lowerCAmelCase ) and "input_ids" in labels[0]):
A = self.tokenizer.pad(_lowerCAmelCase , **_lowerCAmelCase )
A = targets['input_ids']
else:
A = self.feature_extractor.feature_size
A = self.feature_extractor.num_mel_bins
A = self.feature_extractor.pad(_lowerCAmelCase , *_lowerCAmelCase , **_lowerCAmelCase )
A = feature_size_hack
A = targets['input_values']
else:
A = None
if inputs is None:
return targets
if targets is not None:
A = labels
A = targets.get("""attention_mask""" )
if decoder_attention_mask is not None:
A = decoder_attention_mask
return inputs
def A (self : Union[str, Any] , *_lowerCAmelCase : str , **_lowerCAmelCase : Any ):
return self.tokenizer.batch_decode(*_lowerCAmelCase , **_lowerCAmelCase )
def A (self : int , *_lowerCAmelCase : Dict , **_lowerCAmelCase : Optional[Any] ):
return self.tokenizer.decode(*_lowerCAmelCase , **_lowerCAmelCase )
| 258 | """simple docstring"""
import shutil
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_tf_cross_test,
require_tf,
require_torch,
require_torchvision,
require_vision,
)
from transformers.utils import is_tf_available, is_torch_available, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, SamImageProcessor, SamProcessor
if is_torch_available():
import torch
if is_tf_available():
import tensorflow as tf
@require_vision
@require_torchvision
class __snake_case ( unittest.TestCase ):
def lowerCamelCase_ ( self) -> Any:
'''simple docstring'''
a__: Optional[Any] = tempfile.mkdtemp()
a__: Optional[int] = SamImageProcessor()
a__: Tuple = SamProcessor(lowercase)
processor.save_pretrained(self.tmpdirname)
def lowerCamelCase_ ( self , **lowercase) -> List[Any]:
'''simple docstring'''
return AutoProcessor.from_pretrained(self.tmpdirname , **lowercase).image_processor
def lowerCamelCase_ ( self) -> Optional[int]:
'''simple docstring'''
shutil.rmtree(self.tmpdirname)
def lowerCamelCase_ ( self) -> int:
'''simple docstring'''
a__: Any = [np.random.randint(2_55 , size=(3, 30, 4_00) , dtype=np.uinta)]
a__: Optional[Any] = [Image.fromarray(np.moveaxis(lowercase , 0 , -1)) for x in image_inputs]
return image_inputs
def lowerCamelCase_ ( self) -> str:
'''simple docstring'''
a__: List[str] = SamProcessor(image_processor=self.get_image_processor())
processor.save_pretrained(self.tmpdirname)
a__: Optional[int] = self.get_image_processor(do_normalize=lowercase , padding_value=1.0)
a__: List[Any] = SamProcessor.from_pretrained(self.tmpdirname , do_normalize=lowercase , padding_value=1.0)
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string())
self.assertIsInstance(processor.image_processor , lowercase)
def lowerCamelCase_ ( self) -> Tuple:
'''simple docstring'''
a__: Union[str, Any] = self.get_image_processor()
a__: List[Any] = SamProcessor(image_processor=lowercase)
a__: Optional[int] = self.prepare_image_inputs()
a__: Optional[Any] = image_processor(lowercase , return_tensors='np')
a__: Tuple = processor(images=lowercase , return_tensors='np')
input_feat_extract.pop('original_sizes') # pop original_sizes as it is popped in the processor
input_feat_extract.pop('reshaped_input_sizes') # pop original_sizes as it is popped in the processor
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1e-2)
@require_torch
def lowerCamelCase_ ( self) -> Any:
'''simple docstring'''
a__: int = self.get_image_processor()
a__: List[str] = SamProcessor(image_processor=lowercase)
a__: Optional[Any] = [torch.ones((1, 3, 5, 5))]
a__: Union[str, Any] = [[17_64, 26_46]]
a__: Optional[Any] = [[6_83, 10_24]]
a__: int = processor.post_process_masks(lowercase , lowercase , lowercase)
self.assertEqual(masks[0].shape , (1, 3, 17_64, 26_46))
a__: Optional[int] = processor.post_process_masks(
lowercase , torch.tensor(lowercase) , torch.tensor(lowercase))
self.assertEqual(masks[0].shape , (1, 3, 17_64, 26_46))
# should also work with np
a__: Dict = [np.ones((1, 3, 5, 5))]
a__: Tuple = processor.post_process_masks(lowercase , np.array(lowercase) , np.array(lowercase))
self.assertEqual(masks[0].shape , (1, 3, 17_64, 26_46))
a__: Tuple = [[1, 0], [0, 1]]
with self.assertRaises(lowercase):
a__: List[Any] = processor.post_process_masks(lowercase , np.array(lowercase) , np.array(lowercase))
@require_vision
@require_tf
class __snake_case ( unittest.TestCase ):
def lowerCamelCase_ ( self) -> List[Any]:
'''simple docstring'''
a__: Optional[Any] = tempfile.mkdtemp()
a__: List[Any] = SamImageProcessor()
a__: Optional[int] = SamProcessor(lowercase)
processor.save_pretrained(self.tmpdirname)
def lowerCamelCase_ ( self , **lowercase) -> int:
'''simple docstring'''
return AutoProcessor.from_pretrained(self.tmpdirname , **lowercase).image_processor
def lowerCamelCase_ ( self) -> Tuple:
'''simple docstring'''
shutil.rmtree(self.tmpdirname)
def lowerCamelCase_ ( self) -> Tuple:
'''simple docstring'''
a__: Optional[Any] = [np.random.randint(2_55 , size=(3, 30, 4_00) , dtype=np.uinta)]
a__: Optional[int] = [Image.fromarray(np.moveaxis(lowercase , 0 , -1)) for x in image_inputs]
return image_inputs
def lowerCamelCase_ ( self) -> Any:
'''simple docstring'''
a__: List[str] = SamProcessor(image_processor=self.get_image_processor())
processor.save_pretrained(self.tmpdirname)
a__: Dict = self.get_image_processor(do_normalize=lowercase , padding_value=1.0)
a__: Union[str, Any] = SamProcessor.from_pretrained(self.tmpdirname , do_normalize=lowercase , padding_value=1.0)
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string())
self.assertIsInstance(processor.image_processor , lowercase)
def lowerCamelCase_ ( self) -> int:
'''simple docstring'''
a__: Optional[Any] = self.get_image_processor()
a__: str = SamProcessor(image_processor=lowercase)
a__: int = self.prepare_image_inputs()
a__: int = image_processor(lowercase , return_tensors='np')
a__: Dict = processor(images=lowercase , return_tensors='np')
input_feat_extract.pop('original_sizes') # pop original_sizes as it is popped in the processor
input_feat_extract.pop('reshaped_input_sizes') # pop reshaped_input_sizes as it is popped in the processor
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1e-2)
@require_tf
def lowerCamelCase_ ( self) -> Any:
'''simple docstring'''
a__: Tuple = self.get_image_processor()
a__: Any = SamProcessor(image_processor=lowercase)
a__: str = [tf.ones((1, 3, 5, 5))]
a__: List[Any] = [[17_64, 26_46]]
a__: List[Any] = [[6_83, 10_24]]
a__: List[Any] = processor.post_process_masks(lowercase , lowercase , lowercase , return_tensors='tf')
self.assertEqual(masks[0].shape , (1, 3, 17_64, 26_46))
a__: Tuple = processor.post_process_masks(
lowercase , tf.convert_to_tensor(lowercase) , tf.convert_to_tensor(lowercase) , return_tensors='tf' , )
self.assertEqual(masks[0].shape , (1, 3, 17_64, 26_46))
# should also work with np
a__: Optional[Any] = [np.ones((1, 3, 5, 5))]
a__: int = processor.post_process_masks(
lowercase , np.array(lowercase) , np.array(lowercase) , return_tensors='tf')
self.assertEqual(masks[0].shape , (1, 3, 17_64, 26_46))
a__: List[str] = [[1, 0], [0, 1]]
with self.assertRaises(tf.errors.InvalidArgumentError):
a__: Any = processor.post_process_masks(
lowercase , np.array(lowercase) , np.array(lowercase) , return_tensors='tf')
@require_vision
@require_torchvision
class __snake_case ( unittest.TestCase ):
def lowerCamelCase_ ( self) -> Dict:
'''simple docstring'''
a__: str = tempfile.mkdtemp()
a__: int = SamImageProcessor()
a__: Union[str, Any] = SamProcessor(lowercase)
processor.save_pretrained(self.tmpdirname)
def lowerCamelCase_ ( self , **lowercase) -> Optional[int]:
'''simple docstring'''
return AutoProcessor.from_pretrained(self.tmpdirname , **lowercase).image_processor
def lowerCamelCase_ ( self) -> Tuple:
'''simple docstring'''
shutil.rmtree(self.tmpdirname)
def lowerCamelCase_ ( self) -> Tuple:
'''simple docstring'''
a__: Any = [np.random.randint(2_55 , size=(3, 30, 4_00) , dtype=np.uinta)]
a__: Any = [Image.fromarray(np.moveaxis(lowercase , 0 , -1)) for x in image_inputs]
return image_inputs
@is_pt_tf_cross_test
def lowerCamelCase_ ( self) -> int:
'''simple docstring'''
a__: Optional[int] = self.get_image_processor()
a__: int = SamProcessor(image_processor=lowercase)
a__: int = np.random.randint(0 , 2 , size=(1, 3, 5, 5)).astype(np.floataa)
a__: Dict = [tf.convert_to_tensor(lowercase)]
a__: Union[str, Any] = [torch.tensor(lowercase)]
a__: List[Any] = [[17_64, 26_46]]
a__: Optional[Any] = [[6_83, 10_24]]
a__: Tuple = processor.post_process_masks(
lowercase , lowercase , lowercase , return_tensors='tf')
a__: str = processor.post_process_masks(
lowercase , lowercase , lowercase , return_tensors='pt')
self.assertTrue(np.all(tf_masks[0].numpy() == pt_masks[0].numpy()))
@is_pt_tf_cross_test
def lowerCamelCase_ ( self) -> str:
'''simple docstring'''
a__: Tuple = self.get_image_processor()
a__: Dict = SamProcessor(image_processor=lowercase)
a__: Any = self.prepare_image_inputs()
a__: List[Any] = image_processor(lowercase , return_tensors='pt')['pixel_values'].numpy()
a__: Tuple = processor(images=lowercase , return_tensors='pt')['pixel_values'].numpy()
a__: Any = image_processor(lowercase , return_tensors='tf')['pixel_values'].numpy()
a__: Any = processor(images=lowercase , return_tensors='tf')['pixel_values'].numpy()
self.assertTrue(np.allclose(lowercase , lowercase))
self.assertTrue(np.allclose(lowercase , lowercase))
self.assertTrue(np.allclose(lowercase , lowercase))
| 290 | 0 |
'''simple docstring'''
import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transformers import CLIPModel, CLIPTokenizerFast
from utils import get_device, get_timestamp, show_pil
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self : Union[str, Any] , A : Dict = "cpu" , A : Union[str, Any] = "openai/clip-vit-large-patch14" ):
_UpperCAmelCase : List[str] = device
_UpperCAmelCase : str = CLIPTokenizerFast.from_pretrained(A )
_UpperCAmelCase : int = [0.48_145_466, 0.4_578_275, 0.40_821_073]
_UpperCAmelCase : Union[str, Any] = [0.26_862_954, 0.26_130_258, 0.27_577_711]
_UpperCAmelCase : List[str] = torchvision.transforms.Normalize(self.image_mean , self.image_std )
_UpperCAmelCase : Optional[int] = torchvision.transforms.Resize(224 )
_UpperCAmelCase : Optional[Any] = torchvision.transforms.CenterCrop(224 )
def _A ( self : Any , A : Tuple ):
_UpperCAmelCase : str = self.resize(A )
_UpperCAmelCase : str = self.center_crop(A )
_UpperCAmelCase : Dict = self.normalize(A )
return images
def __call__( self : str , A : List[str]=None , A : Any=None , **A : int ):
_UpperCAmelCase : Optional[int] = self.tokenizer(text=A , **A )
_UpperCAmelCase : Tuple = self.preprocess_img(A )
_UpperCAmelCase : List[str] = {key: value.to(self.device ) for (key, value) in encoding.items()}
return encoding
class lowerCamelCase_ (nn.Module ):
'''simple docstring'''
def __init__( self : Union[str, Any] , A : Dict=10 , A : Union[str, Any]=0.01 , A : Optional[int]=None , A : List[str]=None , A : Any=None , A : Any=None , A : Optional[Any]=None , A : Tuple=None , A : Optional[int]=False , A : Any=True , A : List[str]="image" , A : int=True , A : Union[str, Any]=False , A : List[str]=False , A : List[Any]=False , ):
super().__init__()
_UpperCAmelCase : Dict = None
_UpperCAmelCase : Any = device if device else get_device()
if vqgan:
_UpperCAmelCase : Optional[int] = vqgan
else:
_UpperCAmelCase : Union[str, Any] = load_vqgan(self.device , conf_path=A , ckpt_path=A )
self.vqgan.eval()
if clip:
_UpperCAmelCase : Optional[int] = clip
else:
_UpperCAmelCase : str = CLIPModel.from_pretrained("openai/clip-vit-base-patch32" )
self.clip.to(self.device )
_UpperCAmelCase : Tuple = ProcessorGradientFlow(device=self.device )
_UpperCAmelCase : int = iterations
_UpperCAmelCase : Any = lr
_UpperCAmelCase : Optional[Any] = log
_UpperCAmelCase : Dict = make_grid
_UpperCAmelCase : int = return_val
_UpperCAmelCase : Optional[int] = quantize
_UpperCAmelCase : List[str] = self.vqgan.decoder.z_shape
def _A ( self : Union[str, Any] , A : Optional[int]=None , A : List[Any]=None , A : List[str]=5 , A : Any=True ):
_UpperCAmelCase : Dict = []
if output_path is None:
_UpperCAmelCase : int = './animation.gif'
if input_path is None:
_UpperCAmelCase : Union[str, Any] = self.save_path
_UpperCAmelCase : Any = sorted(glob(input_path + "/*" ) )
if not len(A ):
raise ValueError(
"No images found in save path, aborting (did you pass save_intermediate=True to the generate"
" function?)" )
if len(A ) == 1:
print("Only one image found in save path, (did you pass save_intermediate=True to the generate function?)" )
_UpperCAmelCase : Dict = total_duration / len(A )
_UpperCAmelCase : Optional[Any] = [frame_duration] * len(A )
if extend_frames:
_UpperCAmelCase : Any = 1.5
_UpperCAmelCase : Any = 3
for file_name in paths:
if file_name.endswith(".png" ):
images.append(imageio.imread(A ) )
imageio.mimsave(A , A , duration=A )
print(F"""gif saved to {output_path}""" )
def _A ( self : str , A : str=None , A : List[str]=None ):
if not (path or img):
raise ValueError("Input either path or tensor" )
if img is not None:
raise NotImplementedError
_UpperCAmelCase : List[Any] = preprocess(Image.open(A ) , target_image_size=256 ).to(self.device )
_UpperCAmelCase : Union[str, Any] = preprocess_vqgan(A )
_UpperCAmelCase : List[Any] = self.vqgan.encode(A )
return z
def _A ( self : Optional[int] , A : List[Any] ):
_UpperCAmelCase : int = self.latent.detach().requires_grad_()
_UpperCAmelCase : str = base_latent + transform_vector
if self.quantize:
_UpperCAmelCase : int = self.vqgan.quantize(A )
else:
_UpperCAmelCase : List[str] = trans_latent
return self.vqgan.decode(A )
def _A ( self : Optional[int] , A : Any , A : List[Any] , A : Tuple=None ):
_UpperCAmelCase : List[Any] = self.clip_preprocessor(text=A , images=A , return_tensors="pt" , padding=A )
_UpperCAmelCase : Dict = self.clip(**A )
_UpperCAmelCase : Any = clip_outputs.logits_per_image
if weights is not None:
_UpperCAmelCase : Tuple = similarity_logits * weights
return similarity_logits.sum()
def _A ( self : Union[str, Any] , A : int , A : List[Any] , A : str ):
_UpperCAmelCase : Dict = self._get_clip_similarity(pos_prompts["prompts"] , A , weights=(1 / pos_prompts["weights"]) )
if neg_prompts:
_UpperCAmelCase : Optional[Any] = self._get_clip_similarity(neg_prompts["prompts"] , A , weights=neg_prompts["weights"] )
else:
_UpperCAmelCase : List[Any] = torch.tensor([1] , device=self.device )
_UpperCAmelCase : Dict = -torch.log(A ) + torch.log(A )
return loss
def _A ( self : Optional[int] , A : Optional[Any] , A : str , A : int ):
_UpperCAmelCase : Union[str, Any] = torch.randn_like(self.latent , requires_grad=A , device=self.device )
_UpperCAmelCase : int = torch.optim.Adam([vector] , lr=self.lr )
for i in range(self.iterations ):
optim.zero_grad()
_UpperCAmelCase : List[Any] = self._add_vector(A )
_UpperCAmelCase : Dict = loop_post_process(A )
_UpperCAmelCase : List[str] = self._get_CLIP_loss(A , A , A )
print("CLIP loss" , A )
if self.log:
wandb.log({"CLIP Loss": clip_loss} )
clip_loss.backward(retain_graph=A )
optim.step()
if self.return_val == "image":
yield custom_to_pil(transformed_img[0] )
else:
yield vector
def _A ( self : Any , A : List[Any] , A : int , A : str ):
wandb.init(reinit=A , project="face-editor" )
wandb.config.update({"Positive Prompts": positive_prompts} )
wandb.config.update({"Negative Prompts": negative_prompts} )
wandb.config.update({"lr": self.lr, "iterations": self.iterations} )
if image_path:
_UpperCAmelCase : Any = Image.open(A )
_UpperCAmelCase : Union[str, Any] = image.resize((256, 256) )
wandb.log("Original Image" , wandb.Image(A ) )
def _A ( self : List[Any] , A : Tuple ):
if not prompts:
return []
_UpperCAmelCase : str = []
_UpperCAmelCase : Optional[Any] = []
if isinstance(A , A ):
_UpperCAmelCase : Optional[Any] = [prompt.strip() for prompt in prompts.split("|" )]
for prompt in prompts:
if isinstance(A , (tuple, list) ):
_UpperCAmelCase : int = prompt[0]
_UpperCAmelCase : Union[str, Any] = float(prompt[1] )
elif ":" in prompt:
_UpperCAmelCase : str = prompt.split(":" )
_UpperCAmelCase : Union[str, Any] = float(A )
else:
_UpperCAmelCase : str = prompt
_UpperCAmelCase : List[str] = 1.0
processed_prompts.append(A )
weights.append(A )
return {
"prompts": processed_prompts,
"weights": torch.tensor(A , device=self.device ),
}
def _A ( self : Union[str, Any] , A : List[str] , A : Union[str, Any]=None , A : Optional[int]=None , A : int=True , A : List[Any]=False , A : Any=True , A : Union[str, Any]=True , A : Dict=None , ):
if image_path:
_UpperCAmelCase : Union[str, Any] = self._get_latent(A )
else:
_UpperCAmelCase : List[Any] = torch.randn(self.latent_dim , device=self.device )
if self.log:
self._init_logging(A , A , A )
assert pos_prompts, "You must provide at least one positive prompt."
_UpperCAmelCase : List[Any] = self.process_prompts(A )
_UpperCAmelCase : Dict = self.process_prompts(A )
if save_final and save_path is None:
_UpperCAmelCase : Optional[int] = os.path.join("./outputs/" , "_".join(pos_prompts["prompts"] ) )
if not os.path.exists(A ):
os.makedirs(A )
else:
_UpperCAmelCase : List[Any] = save_path + '_' + get_timestamp()
os.makedirs(A )
_UpperCAmelCase : Tuple = save_path
_UpperCAmelCase : int = self.vqgan.decode(self.latent )[0]
if show_intermediate:
print("Original Image" )
show_pil(custom_to_pil(A ) )
_UpperCAmelCase : str = loop_post_process(A )
for iter, transformed_img in enumerate(self._optimize_CLIP(A , A , A ) ):
if show_intermediate:
show_pil(A )
if save_intermediate:
transformed_img.save(os.path.join(self.save_path , F"""iter_{iter:03d}.png""" ) )
if self.log:
wandb.log({"Image": wandb.Image(A )} )
if show_final:
show_pil(A )
if save_final:
transformed_img.save(os.path.join(self.save_path , F"""iter_{iter:03d}_final.png""" ) )
| 31 | """simple docstring"""
from math import pow, sqrt
def __a ( *_SCREAMING_SNAKE_CASE ) ->bool:
a__: Union[str, Any] = len(_SCREAMING_SNAKE_CASE ) > 0 and all(value > 0.0 for value in values )
return result
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->float | ValueError:
return (
round(sqrt(molar_mass_a / molar_mass_a ) , 6 )
if validate(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
else ValueError('Input Error: Molar mass values must greater than 0.' )
)
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->float | ValueError:
return (
round(effusion_rate * sqrt(molar_mass_a / molar_mass_a ) , 6 )
if validate(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
else ValueError(
'Input Error: Molar mass and effusion rate values must greater than 0.' )
)
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->float | ValueError:
return (
round(effusion_rate / sqrt(molar_mass_a / molar_mass_a ) , 6 )
if validate(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
else ValueError(
'Input Error: Molar mass and effusion rate values must greater than 0.' )
)
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->float | ValueError:
return (
round(molar_mass / pow(effusion_rate_a / effusion_rate_a , 2 ) , 6 )
if validate(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
else ValueError(
'Input Error: Molar mass and effusion rate values must greater than 0.' )
)
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->float | ValueError:
return (
round(pow(effusion_rate_a / effusion_rate_a , 2 ) / molar_mass , 6 )
if validate(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
else ValueError(
'Input Error: Molar mass and effusion rate values must greater than 0.' )
)
| 290 | 0 |
import argparse
import fairseq
import torch
from torch import nn
from transformers import (
MBartaaTokenizer,
MBartConfig,
MBartForCausalLM,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaModel,
logging,
)
logging.set_verbosity_info()
lowerCAmelCase__ :List[Any] = logging.get_logger(__name__)
lowerCAmelCase__ :Optional[Any] = {
'''post_extract_proj''': '''feature_projection.projection''',
'''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
'''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
'''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
'''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
'''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
'''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
'''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
'''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
'''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
'''encoder.layer_norm''': '''encoder.layer_norm''',
'''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
'''quantizer.weight_proj''': '''quantizer.weight_proj''',
'''quantizer.vars''': '''quantizer.codevectors''',
'''project_q''': '''project_q''',
'''final_proj''': '''project_hid''',
'''w2v_encoder.proj''': '''lm_head''',
'''mask_emb''': '''masked_spec_embed''',
}
lowerCAmelCase__ :Optional[int] = [
'''lm_head''',
'''quantizer.weight_proj''',
'''quantizer.codevectors''',
'''project_q''',
'''project_hid''',
]
def lowerCAmelCase__ ( a__: int , a__: List[str] , a__: Tuple , a__: Optional[Any] , a__: List[Any] ) -> List[Any]:
'''simple docstring'''
for attribute in key.split('.' ):
_UpperCAmelCase = getattr(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
if weight_type is not None:
_UpperCAmelCase = getattr(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ).shape
else:
_UpperCAmelCase = hf_pointer.shape
assert hf_shape == value.shape, (
F'''Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'''
F''' {value.shape} for {full_name}'''
)
if weight_type == "weight":
_UpperCAmelCase = value
elif weight_type == "weight_g":
_UpperCAmelCase = value
elif weight_type == "weight_v":
_UpperCAmelCase = value
elif weight_type == "bias":
_UpperCAmelCase = value
else:
_UpperCAmelCase = value
logger.info(F'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.''' )
def lowerCAmelCase__ ( a__: Optional[Any] , a__: Dict ) -> List[str]:
'''simple docstring'''
_UpperCAmelCase = []
_UpperCAmelCase = fairseq_model.state_dict()
_UpperCAmelCase = hf_model.feature_extractor
_UpperCAmelCase = hf_model.adapter
for name, value in fairseq_dict.items():
_UpperCAmelCase = False
if "conv_layers" in name:
load_conv_layer(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , hf_model.config.feat_extract_norm == 'group' , )
_UpperCAmelCase = True
elif any(x in name for x in ['adaptor', 'w2v_encoder.proj.', 'w2v_proj_ln.'] ):
load_adapter(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
_UpperCAmelCase = True
else:
for key, mapped_key in MAPPING.items():
if key in name or key.split('w2v_model.' )[-1] == name.split('.' )[0]:
_UpperCAmelCase = True
if "*" in mapped_key:
_UpperCAmelCase = name.split(_SCREAMING_SNAKE_CASE )[0].split('.' )[-2]
_UpperCAmelCase = mapped_key.replace('*' , _SCREAMING_SNAKE_CASE )
if "weight_g" in name:
_UpperCAmelCase = 'weight_g'
elif "weight_v" in name:
_UpperCAmelCase = 'weight_v'
elif "bias" in name:
_UpperCAmelCase = 'bias'
elif "weight" in name:
_UpperCAmelCase = 'weight'
else:
_UpperCAmelCase = None
set_recursively(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
continue
if not is_used:
unused_weights.append(_SCREAMING_SNAKE_CASE )
logger.warning(F'''Unused weights: {unused_weights}''' )
def lowerCAmelCase__ ( a__: Optional[Any] , a__: Optional[Any] , a__: Dict , a__: Optional[Any] , a__: Any ) -> Optional[int]:
'''simple docstring'''
_UpperCAmelCase = full_name.split('conv_layers.' )[-1]
_UpperCAmelCase = name.split('.' )
_UpperCAmelCase = int(items[0] )
_UpperCAmelCase = int(items[1] )
if type_id == 0:
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
)
_UpperCAmelCase = value
logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
)
_UpperCAmelCase = value
logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
F'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
" found."
)
_UpperCAmelCase = value
logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
)
_UpperCAmelCase = value
logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
else:
unused_weights.append(_SCREAMING_SNAKE_CASE )
def lowerCAmelCase__ ( a__: str , a__: Optional[Any] , a__: Any , a__: Dict ) -> str:
'''simple docstring'''
_UpperCAmelCase = full_name.split('adaptor.' )[-1]
_UpperCAmelCase = name.split('.' )
if items[1].isdigit():
_UpperCAmelCase = int(items[1] )
else:
_UpperCAmelCase = None
if "adaptor" not in full_name:
if "proj_ln" in full_name:
# has to be layer norm
if "bias" in name:
assert (
value.shape == adapter.proj_layer_norm.bias.data.shape
), F'''{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found.'''
_UpperCAmelCase = value
logger.info(F'''Adapter proj layer norm bias was initialized from {full_name}.''' )
if "weight" in name:
assert (
value.shape == adapter.proj_layer_norm.weight.data.shape
), F'''{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found.'''
_UpperCAmelCase = value
else:
# has to be projection layer
if "bias" in name:
assert (
value.shape == adapter.proj.bias.data.shape
), F'''{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found.'''
_UpperCAmelCase = value
logger.info(F'''Adapter proj layer bias was initialized from {full_name}.''' )
if "weight" in name:
assert (
value.shape == adapter.proj.weight.data.shape
), F'''{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found.'''
_UpperCAmelCase = value
logger.info(F'''Adapter proj layer weight was initialized from {full_name}.''' )
elif isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
if "bias" in name:
assert (
value.shape == adapter.layers[layer_id].conv.bias.data.shape
), F'''{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found.'''
_UpperCAmelCase = value
logger.info(F'''Adapter layer {layer_id} bias was initialized from {full_name}.''' )
elif "weight" in name:
assert (
value.shape == adapter.layers[layer_id].conv.weight.data.shape
), F'''{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found.'''
_UpperCAmelCase = value
logger.info(F'''Adapter layer {layer_id} bias was initialized from {full_name}.''' )
else:
unused_weights.append(_SCREAMING_SNAKE_CASE )
def lowerCAmelCase__ ( a__: Optional[int] ) -> Union[str, Any]:
'''simple docstring'''
_UpperCAmelCase = emb.weight.shape
_UpperCAmelCase = nn.Linear(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , bias=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = emb.weight.data
return lin_layer
@torch.no_grad()
def lowerCAmelCase__ ( a__: int , a__: List[Any] , a__: Optional[Any] , a__: Union[str, Any] , a__: Any , a__: Tuple , a__: Dict , a__: str , a__: Optional[int] , a__: Optional[Any] , a__: int , ) -> int:
'''simple docstring'''
_UpperCAmelCase = WavaVecaConfig.from_pretrained(
_SCREAMING_SNAKE_CASE , add_adapter=_SCREAMING_SNAKE_CASE , adapter_stride=_SCREAMING_SNAKE_CASE , adapter_kernel_size=_SCREAMING_SNAKE_CASE , use_auth_token=_SCREAMING_SNAKE_CASE , output_hidden_size=_SCREAMING_SNAKE_CASE , )
_UpperCAmelCase = MBartConfig.from_pretrained(_SCREAMING_SNAKE_CASE )
# load model
_UpperCAmelCase = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={
'config_yaml': config_yaml_path,
'data': '/'.join(dict_path.split('/' )[:-1] ),
'w2v_path': checkpoint_path,
'load_pretrained_decoder_from': None,
} , )
_UpperCAmelCase = model[0].eval()
# load feature extractor
_UpperCAmelCase = WavaVecaFeatureExtractor.from_pretrained(_SCREAMING_SNAKE_CASE , use_auth_token=_SCREAMING_SNAKE_CASE )
# set weights for wav2vec2 encoder
_UpperCAmelCase = WavaVecaModel(_SCREAMING_SNAKE_CASE )
recursively_load_weights_wavaveca(model.encoder , _SCREAMING_SNAKE_CASE )
# load decoder weights
_UpperCAmelCase = MBartForCausalLM(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict() , strict=_SCREAMING_SNAKE_CASE )
logger.warning(F'''The following keys are missing when loading the decoder weights: {missing_keys}''' )
logger.warning(F'''The following keys are unexpected when loading the decoder weights: {unexpected_keys}''' )
_UpperCAmelCase = SpeechEncoderDecoderModel(encoder=_SCREAMING_SNAKE_CASE , decoder=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = False
_UpperCAmelCase = MBartaaTokenizer(_SCREAMING_SNAKE_CASE )
tokenizer.save_pretrained(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = hf_wavavec.config.to_dict()
_UpperCAmelCase = tokenizer.pad_token_id
_UpperCAmelCase = tokenizer.bos_token_id
_UpperCAmelCase = tokenizer.eos_token_id
_UpperCAmelCase = 'mbart50'
_UpperCAmelCase = 'wav2vec2'
_UpperCAmelCase = tokenizer.eos_token_id
_UpperCAmelCase = 2_5_0_0_0_4
_UpperCAmelCase = tokenizer.eos_token_id
_UpperCAmelCase = SpeechEncoderDecoderConfig.from_dict(_SCREAMING_SNAKE_CASE )
hf_wavavec.save_pretrained(_SCREAMING_SNAKE_CASE )
feature_extractor.save_pretrained(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
lowerCAmelCase__ :Dict = argparse.ArgumentParser()
parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
parser.add_argument('''--config_yaml_path''', default=None, type=str, help='''Path to yaml file of fine-tuned model''')
parser.add_argument(
'''--encoder_config_path''',
default='''facebook/wav2vec2-xls-r-1b''',
type=str,
help='''Path to hf encoder wav2vec2 checkpoint config''',
)
parser.add_argument(
'''--decoder_config_path''',
default='''facebook/mbart-large-50-one-to-many-mmt''',
type=str,
help='''Path to hf decoder checkpoint config''',
)
parser.add_argument('''--add_adapter''', default=True, type=bool, help='''whethere to add model adapter layers''')
parser.add_argument('''--adapter_stride''', default=2, type=int, help='''stride of adapter layers''')
parser.add_argument('''--adapter_kernel_size''', default=3, type=int, help='''kernel size of adapter layers''')
parser.add_argument('''--encoder_output_dim''', default=1_0_2_4, type=int, help='''encoder output dim''')
parser.add_argument('''--start_token_id''', default=2_5_0_0_0_4, type=int, help='''`decoder_start_token_id` of model config''')
lowerCAmelCase__ :List[str] = parser.parse_args()
convert_wavaveca_checkpoint(
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.dict_path,
args.config_yaml_path,
encoder_config_path=args.encoder_config_path,
decoder_config_path=args.decoder_config_path,
add_adapter=args.add_adapter,
adapter_kernel_size=args.adapter_kernel_size,
adapter_stride=args.adapter_stride,
decoder_start_token_id=args.start_token_id,
encoder_output_dim=args.encoder_output_dim,
)
| 329 | """simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
'andreasmadsen/efficient_mlm_m0.40': (
'https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json'
),
}
class __snake_case ( __lowerCAmelCase ):
a__ = """roberta-prelayernorm"""
def __init__( self , lowercase=5_02_65 , lowercase=7_68 , lowercase=12 , lowercase=12 , lowercase=30_72 , lowercase="gelu" , lowercase=0.1 , lowercase=0.1 , lowercase=5_12 , lowercase=2 , lowercase=0.02 , lowercase=1e-12 , lowercase=1 , lowercase=0 , lowercase=2 , lowercase="absolute" , lowercase=True , lowercase=None , **lowercase , ) -> Any:
'''simple docstring'''
super().__init__(pad_token_id=lowercase , bos_token_id=lowercase , eos_token_id=lowercase , **lowercase)
a__: Union[str, Any] = vocab_size
a__: str = hidden_size
a__: Tuple = num_hidden_layers
a__: List[str] = num_attention_heads
a__: Dict = hidden_act
a__: int = intermediate_size
a__: Tuple = hidden_dropout_prob
a__: str = attention_probs_dropout_prob
a__: Tuple = max_position_embeddings
a__: Tuple = type_vocab_size
a__: Optional[Any] = initializer_range
a__: Tuple = layer_norm_eps
a__: Optional[int] = position_embedding_type
a__: Any = use_cache
a__: Dict = classifier_dropout
class __snake_case ( __lowerCAmelCase ):
@property
def lowerCamelCase_ ( self) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
if self.task == "multiple-choice":
a__: str = {0: 'batch', 1: 'choice', 2: 'sequence'}
else:
a__: Union[str, Any] = {0: 'batch', 1: 'sequence'}
return OrderedDict(
[
('input_ids', dynamic_axis),
('attention_mask', dynamic_axis),
])
| 290 | 0 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
class A_ ( __lowerCAmelCase , unittest.TestCase ):
'''simple docstring'''
UpperCAmelCase_ : Dict = ShapEImgaImgPipeline
UpperCAmelCase_ : Optional[Any] = ["""image"""]
UpperCAmelCase_ : str = ["""image"""]
UpperCAmelCase_ : Dict = [
"""num_images_per_prompt""",
"""num_inference_steps""",
"""generator""",
"""latents""",
"""guidance_scale""",
"""frame_size""",
"""output_type""",
"""return_dict""",
]
UpperCAmelCase_ : str = False
@property
def UpperCAmelCase_ ( self : List[str] ) -> str:
return 32
@property
def UpperCAmelCase_ ( self : Optional[int] ) -> List[Any]:
return 32
@property
def UpperCAmelCase_ ( self : List[Any] ) -> Any:
return self.time_input_dim * 4
@property
def UpperCAmelCase_ ( self : List[Any] ) -> List[Any]:
return 8
@property
def UpperCAmelCase_ ( self : Optional[int] ) -> Optional[int]:
torch.manual_seed(0 )
UpperCAmelCase : str = CLIPVisionConfig(
hidden_size=self.text_embedder_hidden_size , image_size=64 , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , num_attention_heads=4 , num_channels=3 , num_hidden_layers=5 , patch_size=1 , )
UpperCAmelCase : List[str] = CLIPVisionModel(lowercase_ )
return model
@property
def UpperCAmelCase_ ( self : Any ) -> Union[str, Any]:
UpperCAmelCase : Optional[Any] = CLIPImageProcessor(
crop_size=224 , do_center_crop=lowercase_ , do_normalize=lowercase_ , do_resize=lowercase_ , image_mean=[0.4814_5466, 0.457_8275, 0.4082_1073] , image_std=[0.2686_2954, 0.2613_0258, 0.2757_7711] , resample=3 , size=224 , )
return image_processor
@property
def UpperCAmelCase_ ( self : List[str] ) -> Optional[Any]:
torch.manual_seed(0 )
UpperCAmelCase : Optional[int] = {
'num_attention_heads': 2,
'attention_head_dim': 16,
'embedding_dim': self.time_input_dim,
'num_embeddings': 32,
'embedding_proj_dim': self.text_embedder_hidden_size,
'time_embed_dim': self.time_embed_dim,
'num_layers': 1,
'clip_embed_dim': self.time_input_dim * 2,
'additional_embeddings': 0,
'time_embed_act_fn': 'gelu',
'norm_in_type': 'layer',
'embedding_proj_norm_type': 'layer',
'encoder_hid_proj_type': None,
'added_emb_type': None,
}
UpperCAmelCase : Optional[Any] = PriorTransformer(**lowercase_ )
return model
@property
def UpperCAmelCase_ ( self : Union[str, Any] ) -> Optional[Any]:
torch.manual_seed(0 )
UpperCAmelCase : List[Any] = {
'param_shapes': (
(self.renderer_dim, 93),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
),
'd_latent': self.time_input_dim,
'd_hidden': self.renderer_dim,
'n_output': 12,
'background': (
0.1,
0.1,
0.1,
),
}
UpperCAmelCase : Optional[Any] = ShapERenderer(**lowercase_ )
return model
def UpperCAmelCase_ ( self : Tuple ) -> List[str]:
UpperCAmelCase : List[Any] = self.dummy_prior
UpperCAmelCase : Optional[Any] = self.dummy_image_encoder
UpperCAmelCase : str = self.dummy_image_processor
UpperCAmelCase : Tuple = self.dummy_renderer
UpperCAmelCase : int = HeunDiscreteScheduler(
beta_schedule='exp' , num_train_timesteps=1_024 , prediction_type='sample' , use_karras_sigmas=lowercase_ , clip_sample=lowercase_ , clip_sample_range=1.0 , )
UpperCAmelCase : str = {
'prior': prior,
'image_encoder': image_encoder,
'image_processor': image_processor,
'renderer': renderer,
'scheduler': scheduler,
}
return components
def UpperCAmelCase_ ( self : Any , lowercase_ : List[str] , lowercase_ : Optional[Any]=0 ) -> int:
UpperCAmelCase : Optional[Any] = floats_tensor((1, 3, 64, 64) , rng=random.Random(lowercase_ ) ).to(lowercase_ )
if str(lowercase_ ).startswith('mps' ):
UpperCAmelCase : Any = torch.manual_seed(lowercase_ )
else:
UpperCAmelCase : Optional[Any] = torch.Generator(device=lowercase_ ).manual_seed(lowercase_ )
UpperCAmelCase : Dict = {
'image': input_image,
'generator': generator,
'num_inference_steps': 1,
'frame_size': 32,
'output_type': 'np',
}
return inputs
def UpperCAmelCase_ ( self : Optional[Any] ) -> str:
UpperCAmelCase : Dict = 'cpu'
UpperCAmelCase : Dict = self.get_dummy_components()
UpperCAmelCase : str = self.pipeline_class(**lowercase_ )
UpperCAmelCase : Tuple = pipe.to(lowercase_ )
pipe.set_progress_bar_config(disable=lowercase_ )
UpperCAmelCase : Any = pipe(**self.get_dummy_inputs(lowercase_ ) )
UpperCAmelCase : List[str] = output.images[0]
UpperCAmelCase : Tuple = image[0, -3:, -3:, -1]
assert image.shape == (20, 32, 32, 3)
UpperCAmelCase : int = np.array(
[
0.0003_9216,
0.0003_9216,
0.0003_9216,
0.0003_9216,
0.0003_9216,
0.0003_9216,
0.0003_9216,
0.0003_9216,
0.0003_9216,
] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def UpperCAmelCase_ ( self : List[str] ) -> List[str]:
self._test_inference_batch_consistent(batch_sizes=[1, 2] )
def UpperCAmelCase_ ( self : Dict ) -> List[str]:
UpperCAmelCase : str = torch_device == 'cpu'
UpperCAmelCase : Dict = True
self._test_inference_batch_single_identical(
batch_size=2 , test_max_difference=lowercase_ , relax_max_difference=lowercase_ , )
def UpperCAmelCase_ ( self : int ) -> Tuple:
UpperCAmelCase : Tuple = self.get_dummy_components()
UpperCAmelCase : List[Any] = self.pipeline_class(**lowercase_ )
UpperCAmelCase : List[str] = pipe.to(lowercase_ )
pipe.set_progress_bar_config(disable=lowercase_ )
UpperCAmelCase : Dict = 1
UpperCAmelCase : Tuple = 2
UpperCAmelCase : Tuple = self.get_dummy_inputs(lowercase_ )
for key in inputs.keys():
if key in self.batch_params:
UpperCAmelCase : str = batch_size * [inputs[key]]
UpperCAmelCase : List[str] = pipe(**lowercase_ , num_images_per_prompt=lowercase_ )[0]
assert images.shape[0] == batch_size * num_images_per_prompt
@slow
@require_torch_gpu
class A_ ( unittest.TestCase ):
'''simple docstring'''
def UpperCAmelCase_ ( self : List[Any] ) -> Dict:
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCAmelCase_ ( self : Tuple ) -> str:
UpperCAmelCase : Optional[Any] = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/shap_e/corgi.png' )
UpperCAmelCase : Any = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/shap_e/test_shap_e_img2img_out.npy' )
UpperCAmelCase : List[str] = ShapEImgaImgPipeline.from_pretrained('openai/shap-e-img2img' )
UpperCAmelCase : List[str] = pipe.to(lowercase_ )
pipe.set_progress_bar_config(disable=lowercase_ )
UpperCAmelCase : Tuple = torch.Generator(device=lowercase_ ).manual_seed(0 )
UpperCAmelCase : Optional[Any] = pipe(
lowercase_ , generator=lowercase_ , guidance_scale=3.0 , num_inference_steps=64 , frame_size=64 , output_type='np' , ).images[0]
assert images.shape == (20, 64, 64, 3)
assert_mean_pixel_difference(lowercase_ , lowercase_ )
| 151 | """simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
'MIT/ast-finetuned-audioset-10-10-0.4593': (
'https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve/main/config.json'
),
}
class __snake_case ( __lowerCAmelCase ):
a__ = """audio-spectrogram-transformer"""
def __init__( self , lowercase=7_68 , lowercase=12 , lowercase=12 , lowercase=30_72 , lowercase="gelu" , lowercase=0.0 , lowercase=0.0 , lowercase=0.02 , lowercase=1e-12 , lowercase=16 , lowercase=True , lowercase=10 , lowercase=10 , lowercase=10_24 , lowercase=1_28 , **lowercase , ) -> str:
'''simple docstring'''
super().__init__(**lowercase)
a__: Any = hidden_size
a__: int = num_hidden_layers
a__: Union[str, Any] = num_attention_heads
a__: Any = intermediate_size
a__: Union[str, Any] = hidden_act
a__: int = hidden_dropout_prob
a__: str = attention_probs_dropout_prob
a__: str = initializer_range
a__: Tuple = layer_norm_eps
a__: Any = patch_size
a__: int = qkv_bias
a__: Optional[Any] = frequency_stride
a__: int = time_stride
a__: List[str] = max_length
a__: Tuple = num_mel_bins
| 290 | 0 |
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 322 | """simple docstring"""
import unittest
from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
lowercase__ = get_tests_dir('fixtures/test_sentencepiece.model')
lowercase__ = get_tests_dir('fixtures/test_sentencepiece_bpe.model')
lowercase__ = 'pt' if is_torch_available() else 'tf'
@require_sentencepiece
@require_tokenizers
class __snake_case ( __lowerCAmelCase , unittest.TestCase ):
a__ = CamembertTokenizer
a__ = CamembertTokenizerFast
a__ = True
a__ = True
def lowerCamelCase_ ( self) -> Union[str, Any]:
'''simple docstring'''
super().setUp()
# We have a SentencePiece fixture for testing
a__: Tuple = CamembertTokenizer(lowercase)
tokenizer.save_pretrained(self.tmpdirname)
def lowerCamelCase_ ( self) -> List[str]:
'''simple docstring'''
a__: Optional[Any] = '<pad>'
a__: List[Any] = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowercase) , lowercase)
self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowercase) , lowercase)
def lowerCamelCase_ ( self) -> Any:
'''simple docstring'''
a__: str = list(self.get_tokenizer().get_vocab().keys())
self.assertEqual(vocab_keys[0] , '<s>NOTUSED')
self.assertEqual(vocab_keys[1] , '<pad>')
self.assertEqual(vocab_keys[-1] , '<mask>')
self.assertEqual(len(lowercase) , 10_04)
def lowerCamelCase_ ( self) -> Any:
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size , 10_05)
def lowerCamelCase_ ( self) -> Optional[Any]:
'''simple docstring'''
a__: Optional[Any] = CamembertTokenizer(lowercase)
tokenizer.save_pretrained(self.tmpdirname)
a__: List[Any] = CamembertTokenizerFast.from_pretrained(self.tmpdirname)
a__: Dict = 'I was born in 92000, and this is falsé.'
a__: Optional[int] = tokenizer.encode(lowercase)
a__: Any = rust_tokenizer.encode(lowercase)
self.assertListEqual(lowercase , lowercase)
a__: Optional[Any] = tokenizer.encode(lowercase , add_special_tokens=lowercase)
a__: str = rust_tokenizer.encode(lowercase , add_special_tokens=lowercase)
self.assertListEqual(lowercase , lowercase)
# <unk> tokens are not the same for `rust` than for `slow`.
# Because spm gives back raw token instead of `unk` in EncodeAsPieces
# tokens = tokenizer.tokenize(sequence)
a__: Tuple = tokenizer.convert_ids_to_tokens(lowercase)
a__: Tuple = rust_tokenizer.tokenize(lowercase)
self.assertListEqual(lowercase , lowercase)
def lowerCamelCase_ ( self) -> Dict:
'''simple docstring'''
if not self.test_rust_tokenizer:
return
a__: Dict = self.get_tokenizer()
a__: str = self.get_rust_tokenizer()
a__: int = 'I was born in 92000, and this is falsé.'
a__: Optional[Any] = tokenizer.tokenize(lowercase)
a__: List[Any] = rust_tokenizer.tokenize(lowercase)
self.assertListEqual(lowercase , lowercase)
a__: str = tokenizer.encode(lowercase , add_special_tokens=lowercase)
a__: str = rust_tokenizer.encode(lowercase , add_special_tokens=lowercase)
self.assertListEqual(lowercase , lowercase)
a__: Tuple = self.get_rust_tokenizer()
a__: Union[str, Any] = tokenizer.encode(lowercase)
a__: List[Any] = rust_tokenizer.encode(lowercase)
self.assertListEqual(lowercase , lowercase)
@slow
def lowerCamelCase_ ( self) -> Optional[Any]:
'''simple docstring'''
a__: Union[str, Any] = {'input_ids': [[5, 54, 71_96, 2_97, 30, 23, 7_76, 18, 11, 32_15, 37_05, 82_52, 22, 31_64, 11_81, 21_16, 29, 16, 8_13, 25, 7_91, 33_14, 20, 34_46, 38, 2_75_75, 1_20, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 4_68, 17, 11, 90_88, 20, 15_17, 8, 2_28_04, 1_88_18, 10, 38, 6_29, 6_07, 6_07, 1_42, 19, 71_96, 8_67, 56, 1_03_26, 24, 22_67, 20, 4_16, 50_72, 1_56_12, 2_33, 7_34, 7, 23_99, 27, 16, 30_15, 16_49, 7, 24, 20, 43_38, 23_99, 27, 13, 34_00, 14, 13, 61_89, 8, 9_30, 9, 6]], '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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501
# fmt: on
# camembert is a french model. So we also use french texts.
a__: int = [
'Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '
'utilisé principalement dans le domaine du traitement automatique des langues (TAL).',
'À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '
'pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '
'telles que la traduction et la synthèse de texte.',
]
self.tokenizer_integration_test_util(
expected_encoding=lowercase , model_name='camembert-base' , revision='3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf' , sequences=lowercase , )
| 290 | 0 |
"""simple docstring"""
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler
lowercase__ : Optional[int] = 16
lowercase__ : Optional[int] = 32
def __lowercase ( _a , _a = 16 , _a = "bert-base-cased" ):
snake_case_ : Optional[int] = AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE )
snake_case_ : str = load_dataset('''glue''' , '''mrpc''' )
def tokenize_function(_a ):
# max_length=None => use the model max length (it's actually the default)
snake_case_ : Optional[Any] = tokenizer(examples['''sentence1'''] , examples['''sentence2'''] , truncation=_SCREAMING_SNAKE_CASE , max_length=_SCREAMING_SNAKE_CASE )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
snake_case_ : Optional[Any] = datasets.map(
_SCREAMING_SNAKE_CASE , batched=_SCREAMING_SNAKE_CASE , remove_columns=['''idx''', '''sentence1''', '''sentence2'''] , load_from_cache_file=_SCREAMING_SNAKE_CASE )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
snake_case_ : Optional[Any] = tokenized_datasets.rename_column('''label''' , '''labels''' )
def collate_fn(_a ):
# On TPU it's best to pad everything to the same length or training will be very slow.
if accelerator.distributed_type == DistributedType.TPU:
return tokenizer.pad(_SCREAMING_SNAKE_CASE , padding='''max_length''' , max_length=128 , return_tensors='''pt''' )
return tokenizer.pad(_SCREAMING_SNAKE_CASE , padding='''longest''' , return_tensors='''pt''' )
# Instantiate dataloaders.
snake_case_ : Any = DataLoader(
tokenized_datasets['''train'''] , shuffle=_SCREAMING_SNAKE_CASE , collate_fn=_SCREAMING_SNAKE_CASE , batch_size=_SCREAMING_SNAKE_CASE )
snake_case_ : Optional[int] = DataLoader(
tokenized_datasets['''validation'''] , shuffle=_SCREAMING_SNAKE_CASE , collate_fn=_SCREAMING_SNAKE_CASE , batch_size=_SCREAMING_SNAKE_CASE )
return train_dataloader, eval_dataloader
def __lowercase ( _a , _a ):
# Initialize accelerator
snake_case_ : List[str] = Accelerator()
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
snake_case_ : int = config['lr']
snake_case_ : str = int(config['''num_epochs'''] )
snake_case_ : str = int(config['''seed'''] )
snake_case_ : Tuple = int(config['''batch_size'''] )
snake_case_ : List[str] = args.model_name_or_path
set_seed(_SCREAMING_SNAKE_CASE )
snake_case_ : List[str] = get_dataloaders(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
snake_case_ : str = AutoModelForSequenceClassification.from_pretrained(_SCREAMING_SNAKE_CASE , return_dict=_SCREAMING_SNAKE_CASE )
# Instantiate optimizer
snake_case_ : Optional[int] = (
AdamW
if accelerator.state.deepspeed_plugin is None
or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
else DummyOptim
)
snake_case_ : Optional[int] = optimizer_cls(params=model.parameters() , lr=_SCREAMING_SNAKE_CASE )
if accelerator.state.deepspeed_plugin is not None:
snake_case_ : Dict = accelerator.state.deepspeed_plugin.deepspeed_config[
'gradient_accumulation_steps'
]
else:
snake_case_ : str = 1
snake_case_ : Union[str, Any] = (len(_SCREAMING_SNAKE_CASE ) * num_epochs) // gradient_accumulation_steps
# Instantiate scheduler
if (
accelerator.state.deepspeed_plugin is None
or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
):
snake_case_ : Tuple = get_linear_schedule_with_warmup(
optimizer=_SCREAMING_SNAKE_CASE , num_warmup_steps=0 , num_training_steps=_SCREAMING_SNAKE_CASE , )
else:
snake_case_ : Tuple = DummyScheduler(_SCREAMING_SNAKE_CASE , total_num_steps=_SCREAMING_SNAKE_CASE , 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.
snake_case_ : Union[str, Any] = accelerator.prepare(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# We need to keep track of how many total steps we have iterated over
snake_case_ : List[str] = 0
# We also need to keep track of the stating epoch so files are named properly
snake_case_ : Optional[int] = 0
# Now we train the model
snake_case_ : str = evaluate.load('''glue''' , '''mrpc''' )
snake_case_ : Optional[Any] = 0
snake_case_ : str = {}
for epoch in range(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
model.train()
for step, batch in enumerate(_SCREAMING_SNAKE_CASE ):
snake_case_ : List[Any] = model(**_SCREAMING_SNAKE_CASE )
snake_case_ : Optional[Any] = outputs.loss
snake_case_ : str = loss / gradient_accumulation_steps
accelerator.backward(_SCREAMING_SNAKE_CASE )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
overall_step += 1
model.eval()
snake_case_ : List[str] = 0
for step, batch in enumerate(_SCREAMING_SNAKE_CASE ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
snake_case_ : int = model(**_SCREAMING_SNAKE_CASE )
snake_case_ : Optional[int] = outputs.logits.argmax(dim=-1 )
# It is slightly faster to call this once, than multiple times
snake_case_ : List[str] = accelerator.gather(
(predictions, batch['''labels''']) ) # If we are in a multiprocess environment, the last batch has duplicates
if accelerator.use_distributed:
if step == len(_SCREAMING_SNAKE_CASE ) - 1:
snake_case_ : Any = predictions[: len(eval_dataloader.dataset ) - samples_seen]
snake_case_ : Union[str, Any] = references[: len(eval_dataloader.dataset ) - samples_seen]
else:
samples_seen += references.shape[0]
metric.add_batch(
predictions=_SCREAMING_SNAKE_CASE , references=_SCREAMING_SNAKE_CASE , )
snake_case_ : Union[str, Any] = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(f"epoch {epoch}:" , _SCREAMING_SNAKE_CASE )
snake_case_ : Union[str, Any] = eval_metric['accuracy']
if best_performance < eval_metric["accuracy"]:
snake_case_ : Optional[Any] = eval_metric['accuracy']
if args.performance_lower_bound is not None:
assert (
args.performance_lower_bound <= best_performance
), f"Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}"
accelerator.wait_for_everyone()
if accelerator.is_main_process:
with open(os.path.join(args.output_dir , '''all_results.json''' ) , '''w''' ) as f:
json.dump(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def __lowercase ( ):
snake_case_ : str = argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.''' )
parser.add_argument(
'''--model_name_or_path''' , type=_SCREAMING_SNAKE_CASE , default='''bert-base-cased''' , help='''Path to pretrained model or model identifier from huggingface.co/models.''' , required=_SCREAMING_SNAKE_CASE , )
parser.add_argument(
'''--output_dir''' , type=_SCREAMING_SNAKE_CASE , default='''.''' , help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.''' , )
parser.add_argument(
'''--performance_lower_bound''' , type=_SCREAMING_SNAKE_CASE , default=_SCREAMING_SNAKE_CASE , help='''Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.''' , )
parser.add_argument(
'''--num_epochs''' , type=_SCREAMING_SNAKE_CASE , default=3 , help='''Number of train epochs.''' , )
snake_case_ : List[Any] = parser.parse_args()
snake_case_ : Any = {'lr': 2E-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}
training_function(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
main()
| 264 | """simple docstring"""
def __a ( _SCREAMING_SNAKE_CASE = 1000000 ) ->int:
a__: int = limit + 1
a__: Optional[int] = [0] * limit
for first_term in range(1 , _SCREAMING_SNAKE_CASE ):
for n in range(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
a__: List[Any] = first_term + n / first_term
if common_difference % 4: # d must be divisble by 4
continue
else:
common_difference /= 4
if (
first_term > common_difference
and first_term < 4 * common_difference
): # since x,y,z are positive integers
frequency[n] += 1 # so z>0 and a>d ,also 4d<a
a__: Any = sum(1 for x in frequency[1:limit] if x == 10 )
return count
if __name__ == "__main__":
print(f"{solution() = }")
| 290 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a_ : Optional[Any] = logging.get_logger(__name__)
a_ : Dict = {
"""facebook/xmod-base""": """https://huggingface.co/facebook/xmod-base/resolve/main/config.json""",
"""facebook/xmod-large-prenorm""": """https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json""",
"""facebook/xmod-base-13-125k""": """https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json""",
"""facebook/xmod-base-30-125k""": """https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json""",
"""facebook/xmod-base-30-195k""": """https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json""",
"""facebook/xmod-base-60-125k""": """https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json""",
"""facebook/xmod-base-60-265k""": """https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json""",
"""facebook/xmod-base-75-125k""": """https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json""",
"""facebook/xmod-base-75-269k""": """https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json""",
}
class snake_case ( __lowerCAmelCase ):
"""simple docstring"""
_lowerCamelCase = "xmod"
def __init__( self , UpperCamelCase=3_0522 , UpperCamelCase=768 , UpperCamelCase=12 , UpperCamelCase=12 , UpperCamelCase=3072 , UpperCamelCase="gelu" , UpperCamelCase=0.1 , UpperCamelCase=0.1 , UpperCamelCase=512 , UpperCamelCase=2 , UpperCamelCase=0.02 , UpperCamelCase=1e-12 , UpperCamelCase=1 , UpperCamelCase=0 , UpperCamelCase=2 , UpperCamelCase="absolute" , UpperCamelCase=True , UpperCamelCase=None , UpperCamelCase=False , UpperCamelCase=2 , UpperCamelCase=False , UpperCamelCase=True , UpperCamelCase=True , UpperCamelCase=("en_XX",) , UpperCamelCase=None , **UpperCamelCase , ):
"""simple docstring"""
super().__init__(pad_token_id=UpperCamelCase , bos_token_id=UpperCamelCase , eos_token_id=UpperCamelCase , **UpperCamelCase )
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_ = position_embedding_type
lowerCamelCase_ = use_cache
lowerCamelCase_ = classifier_dropout
lowerCamelCase_ = pre_norm
lowerCamelCase_ = adapter_reduction_factor
lowerCamelCase_ = adapter_layer_norm
lowerCamelCase_ = adapter_reuse_layer_norm
lowerCamelCase_ = ln_before_adapter
lowerCamelCase_ = list(UpperCamelCase )
lowerCamelCase_ = default_language
class snake_case ( __lowerCAmelCase ):
"""simple docstring"""
@property
def snake_case ( self ):
"""simple docstring"""
if self.task == "multiple-choice":
lowerCamelCase_ = {0: 'batch', 1: 'choice', 2: 'sequence'}
else:
lowerCamelCase_ = {0: 'batch', 1: 'sequence'}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
] )
| 55 | """simple docstring"""
import os
from typing import Dict, List, Tuple, TypeVar, Union
lowercase__ = TypeVar('T')
lowercase__ = Union[List[T], Tuple[T, ...]]
lowercase__ = Union[T, List[T], Dict[str, T]]
lowercase__ = Union[str, bytes, os.PathLike]
| 290 | 0 |
'''simple docstring'''
from typing import Union
import fire
import torch
from tqdm import tqdm
def _lowercase ( __A ,__A = "cpu" ,__A = None ):
'''simple docstring'''
__UpperCamelCase = torch.load(_SCREAMING_SNAKE_CASE ,map_location=_SCREAMING_SNAKE_CASE )
for k, v in tqdm(state_dict.items() ):
if not isinstance(_SCREAMING_SNAKE_CASE ,torch.Tensor ):
raise TypeError("""FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin""" )
__UpperCamelCase = v.half()
if save_path is None: # overwrite src_path
__UpperCamelCase = src_path
torch.save(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
fire.Fire(convert)
| 349 | """simple docstring"""
from math import pi, sqrt, tan
def __a ( _SCREAMING_SNAKE_CASE ) ->float:
if side_length < 0:
raise ValueError('surface_area_cube() only accepts non-negative values' )
return 6 * side_length**2
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->float:
if length < 0 or breadth < 0 or height < 0:
raise ValueError('surface_area_cuboid() only accepts non-negative values' )
return 2 * ((length * breadth) + (breadth * height) + (length * height))
def __a ( _SCREAMING_SNAKE_CASE ) ->float:
if radius < 0:
raise ValueError('surface_area_sphere() only accepts non-negative values' )
return 4 * pi * radius**2
def __a ( _SCREAMING_SNAKE_CASE ) ->float:
if radius < 0:
raise ValueError('surface_area_hemisphere() only accepts non-negative values' )
return 3 * pi * radius**2
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->float:
if radius < 0 or height < 0:
raise ValueError('surface_area_cone() only accepts non-negative values' )
return pi * radius * (radius + (height**2 + radius**2) ** 0.5)
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->float:
if radius_a < 0 or radius_a < 0 or height < 0:
raise ValueError(
'surface_area_conical_frustum() only accepts non-negative values' )
a__: List[Any] = (height**2 + (radius_a - radius_a) ** 2) ** 0.5
return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->float:
if radius < 0 or height < 0:
raise ValueError('surface_area_cylinder() only accepts non-negative values' )
return 2 * pi * radius * (height + radius)
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->float:
if torus_radius < 0 or tube_radius < 0:
raise ValueError('surface_area_torus() only accepts non-negative values' )
if torus_radius < tube_radius:
raise ValueError(
'surface_area_torus() does not support spindle or self intersecting tori' )
return 4 * pow(_SCREAMING_SNAKE_CASE , 2 ) * torus_radius * tube_radius
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->float:
if length < 0 or width < 0:
raise ValueError('area_rectangle() only accepts non-negative values' )
return length * width
def __a ( _SCREAMING_SNAKE_CASE ) ->float:
if side_length < 0:
raise ValueError('area_square() only accepts non-negative values' )
return side_length**2
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->float:
if base < 0 or height < 0:
raise ValueError('area_triangle() only accepts non-negative values' )
return (base * height) / 2
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->float:
if sidea < 0 or sidea < 0 or sidea < 0:
raise ValueError('area_triangle_three_sides() only accepts non-negative values' )
elif sidea + sidea < sidea or sidea + sidea < sidea or sidea + sidea < sidea:
raise ValueError('Given three sides do not form a triangle' )
a__: int = (sidea + sidea + sidea) / 2
a__: Tuple = sqrt(
semi_perimeter
* (semi_perimeter - sidea)
* (semi_perimeter - sidea)
* (semi_perimeter - sidea) )
return area
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->float:
if base < 0 or height < 0:
raise ValueError('area_parallelogram() only accepts non-negative values' )
return base * height
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->float:
if basea < 0 or basea < 0 or height < 0:
raise ValueError('area_trapezium() only accepts non-negative values' )
return 1 / 2 * (basea + basea) * height
def __a ( _SCREAMING_SNAKE_CASE ) ->float:
if radius < 0:
raise ValueError('area_circle() only accepts non-negative values' )
return pi * radius**2
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->float:
if radius_x < 0 or radius_y < 0:
raise ValueError('area_ellipse() only accepts non-negative values' )
return pi * radius_x * radius_y
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->float:
if diagonal_a < 0 or diagonal_a < 0:
raise ValueError('area_rhombus() only accepts non-negative values' )
return 1 / 2 * diagonal_a * diagonal_a
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->float:
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) or sides < 3:
raise ValueError(
'area_reg_polygon() only accepts integers greater than or \
equal to three as number of sides' )
elif length < 0:
raise ValueError(
'area_reg_polygon() only accepts non-negative values as \
length of a side' )
return (sides * length**2) / (4 * tan(pi / sides ))
return (sides * length**2) / (4 * tan(pi / sides ))
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True) # verbose so we can see methods missing tests
print('[DEMO] Areas of various geometric shapes: \n')
print(f"Rectangle: {area_rectangle(10, 20) = }")
print(f"Square: {area_square(10) = }")
print(f"Triangle: {area_triangle(10, 10) = }")
print(f"Triangle: {area_triangle_three_sides(5, 12, 13) = }")
print(f"Parallelogram: {area_parallelogram(10, 20) = }")
print(f"Rhombus: {area_rhombus(10, 20) = }")
print(f"Trapezium: {area_trapezium(10, 20, 30) = }")
print(f"Circle: {area_circle(20) = }")
print(f"Ellipse: {area_ellipse(10, 20) = }")
print('\nSurface Areas of various geometric shapes: \n')
print(f"Cube: {surface_area_cube(20) = }")
print(f"Cuboid: {surface_area_cuboid(10, 20, 30) = }")
print(f"Sphere: {surface_area_sphere(20) = }")
print(f"Hemisphere: {surface_area_hemisphere(20) = }")
print(f"Cone: {surface_area_cone(10, 20) = }")
print(f"Conical Frustum: {surface_area_conical_frustum(10, 20, 30) = }")
print(f"Cylinder: {surface_area_cylinder(10, 20) = }")
print(f"Torus: {surface_area_torus(20, 10) = }")
print(f"Equilateral Triangle: {area_reg_polygon(3, 10) = }")
print(f"Square: {area_reg_polygon(4, 10) = }")
print(f"Reqular Pentagon: {area_reg_polygon(5, 10) = }")
| 290 | 0 |
'''simple docstring'''
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
__A = _symbol_database.Default()
__A = _descriptor_pool.Default().AddSerializedFile(
B"\n\x19sentencepiece_model.proto\x12\rsentencepiece\"\x80\x0c\n\x0bTrainerSpec\x12\r\n\x05input\x18\x01 \x03(\t\x12\x14\n\x0cinput_format\x18\x07 \x01(\t\x12\x14\n\x0cmodel_prefix\x18\x02 \x01(\t\x12\x41\n\nmodel_type\x18\x03 \x01(\x0e\x32$.sentencepiece.TrainerSpec.ModelType:\x07UNIGRAM\x12\x18\n\nvocab_size\x18\x04 \x01(\x05:\x04\x38\x30\x30\x30\x12\x17\n\x0f\x61\x63\x63\x65pt_language\x18\x05 \x03(\t\x12 \n\x15self_test_sample_size\x18\x06 \x01(\x05:\x01\x30\x12*\n\x1b\x65nable_differential_privacy\x18\x32 \x01(\x08:\x05\x66\x61lse\x12+\n differential_privacy_noise_level\x18\x33 \x01(\x02:\x01\x30\x12\x32\n\'differential_privacy_clipping_threshold\x18\x34 \x01(\x04:\x01\x30\x12\"\n\x12\x63haracter_coverage\x18\n \x01(\x02:\x06\x30.9995\x12\x1e\n\x13input_sentence_size\x18\x0b \x01(\x04:\x01\x30\x12$\n\x16shuffle_input_sentence\x18\x13 \x01(\x08:\x04true\x12 \n\x14mining_sentence_size\x18\x0c \x01(\x05\x42\x02\x18\x01\x12\"\n\x16training_sentence_size\x18\r \x01(\x05\x42\x02\x18\x01\x12(\n\x17seed_sentencepiece_size\x18\x0e \x01(\x05:\x07\x31\x30\x30\x30\x30\x30\x30\x12\x1e\n\x10shrinking_factor\x18\x0f \x01(\x02:\x04\x30.75\x12!\n\x13max_sentence_length\x18\x12 \x01(\x05:\x04\x34\x31\x39\x32\x12\x17\n\x0bnum_threads\x18\x10 \x01(\x05:\x02\x31\x36\x12\x1d\n\x12num_sub_iterations\x18\x11 \x01(\x05:\x01\x32\x12$\n\x18max_sentencepiece_length\x18\x14 \x01(\x05:\x02\x31\x36\x12%\n\x17split_by_unicode_script\x18\x15 \x01(\x08:\x04true\x12\x1d\n\x0fsplit_by_number\x18\x17 \x01(\x08:\x04true\x12!\n\x13split_by_whitespace\x18\x16 \x01(\x08:\x04true\x12)\n\x1atreat_whitespace_as_suffix\x18\x18 \x01(\x08:\x05\x66\x61lse\x12+\n\x1c\x61llow_whitespace_only_pieces\x18\x1a \x01(\x08:\x05\x66\x61lse\x12\x1b\n\x0csplit_digits\x18\x19 \x01(\x08:\x05\x66\x61lse\x12#\n\x19pretokenization_delimiter\x18\x35 \x01(\t:\x00\x12\x17\n\x0f\x63ontrol_symbols\x18\x1e \x03(\t\x12\x1c\n\x14user_defined_symbols\x18\x1f \x03(\t\x12\x16\n\x0erequired_chars\x18$ \x01(\t\x12\x1c\n\rbyte_fallback\x18# \x01(\x08:\x05\x66\x61lse\x12+\n\x1dvocabulary_output_piece_score\x18 \x01(\x08:\x04true\x12\x1e\n\x10hard_vocab_limit\x18! \x01(\x08:\x04true\x12\x1c\n\ruse_all_vocab\x18\" \x01(\x08:\x05\x66\x61lse\x12\x11\n\x06unk_id\x18( \x01(\x05:\x01\x30\x12\x11\n\x06\x62os_id\x18) \x01(\x05:\x01\x31\x12\x11\n\x06\x65os_id\x18* \x01(\x05:\x01\x32\x12\x12\n\x06pad_id\x18+ \x01(\x05:\x02-1\x12\x18\n\tunk_piece\x18- \x01(\t:\x05<unk>\x12\x16\n\tbos_piece\x18. \x01(\t:\x03<s>\x12\x17\n\teos_piece\x18/ \x01(\t:\x04</s>\x12\x18\n\tpad_piece\x18\x30 \x01(\t:\x05<pad>\x12\x1a\n\x0bunk_surface\x18, \x01(\t:\x05 \xe2\x81\x87 \x12+\n\x1ctrain_extremely_large_corpus\x18\x31 \x01(\x08:\x05\x66\x61lse\"5\n\tModelType\x12\x0b\n\x07UNIGRAM\x10\x01\x12\x07\n\x03\x42PE\x10\x02\x12\x08\n\x04WORD\x10\x03\x12\x08\n\x04\x43HAR\x10\x04*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\"\xd1\x01\n\x0eNormalizerSpec\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x1c\n\x14precompiled_charsmap\x18\x02 \x01(\x0c\x12\x1e\n\x10\x61\x64\x64_dummy_prefix\x18\x03 \x01(\x08:\x04true\x12&\n\x18remove_extra_whitespaces\x18\x04 \x01(\x08:\x04true\x12 \n\x12\x65scape_whitespaces\x18\x05 \x01(\x08:\x04true\x12\x1e\n\x16normalization_rule_tsv\x18\x06 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\"y\n\x0cSelfTestData\x12\x33\n\x07samples\x18\x01 \x03(\x0b\x32\".sentencepiece.SelfTestData.Sample\x1a)\n\x06Sample\x12\r\n\x05input\x18\x01 \x01(\t\x12\x10\n\x08\x65xpected\x18\x02 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\"\xfe\x03\n\nModelProto\x12\x37\n\x06pieces\x18\x01 \x03(\x0b\x32\'.sentencepiece.ModelProto.SentencePiece\x12\x30\n\x0ctrainer_spec\x18\x02 \x01(\x0b\x32\x1a.sentencepiece.TrainerSpec\x12\x36\n\x0fnormalizer_spec\x18\x03 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x12\x33\n\x0eself_test_data\x18\x04 \x01(\x0b\x32\x1b.sentencepiece.SelfTestData\x12\x38\n\x11\x64\x65normalizer_spec\x18\x05 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x1a\xd2\x01\n\rSentencePiece\x12\r\n\x05piece\x18\x01 \x01(\t\x12\r\n\x05score\x18\x02 \x01(\x02\x12\x42\n\x04type\x18\x03 \x01(\x0e\x32,.sentencepiece.ModelProto.SentencePiece.Type:\x06NORMAL\"T\n\x04Type\x12\n\n\x06NORMAL\x10\x01\x12\x0b\n\x07UNKNOWN\x10\x02\x12\x0b\n\x07\x43ONTROL\x10\x03\x12\x10\n\x0cUSER_DEFINED\x10\x04\x12\x08\n\x04\x42YTE\x10\x06\x12\n\n\x06UNUSED\x10\x05*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\x42\x02H\x03"
)
__A = globals()
_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, "sentencepiece_model_pb2", _globals)
if _descriptor._USE_C_DESCRIPTORS is False:
__A = None
__A = B"H\003"
# (generated by protobuf compiler, but `_TRAINERSPEC` is not defined)
# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._options = None
# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._serialized_options = b"\030\001"
# _TRAINERSPEC.fields_by_name["training_sentence_size"]._options = None
# _TRAINERSPEC.fields_by_name["training_sentence_size"]._serialized_options = b"\030\001"
__A = 45
__A = 1_581
__A = 1_517
__A = 1_570
__A = 1_584
__A = 1_793
__A = 1_795
__A = 1_916
__A = 1_864
__A = 1_905
__A = 1_919
__A = 2_429
__A = 2_208
__A = 2_418
__A = 2_323
__A = 2_407
# @@protoc_insertion_point(module_scope)
| 164 | """simple docstring"""
import itertools
import random
import unittest
import numpy as np
from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
lowercase__ = random.Random()
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=1.0 , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None ) ->Optional[int]:
if rng is None:
a__: Any = global_rng
a__: int = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
class __snake_case ( unittest.TestCase ):
def __init__( self , lowercase , lowercase=7 , lowercase=4_00 , lowercase=20_00 , lowercase=1 , lowercase=0.0 , lowercase=1_60_00 , lowercase=True , lowercase=True , ) -> Union[str, Any]:
'''simple docstring'''
a__: Tuple = parent
a__: Optional[int] = batch_size
a__: Optional[Any] = min_seq_length
a__: Optional[int] = max_seq_length
a__: Tuple = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
a__: Dict = feature_size
a__: Any = padding_value
a__: Optional[Any] = sampling_rate
a__: Optional[Any] = return_attention_mask
a__: str = do_normalize
def lowerCamelCase_ ( self) -> Dict:
'''simple docstring'''
return {
"feature_size": self.feature_size,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"return_attention_mask": self.return_attention_mask,
"do_normalize": self.do_normalize,
}
def lowerCamelCase_ ( self , lowercase=False , lowercase=False) -> Tuple:
'''simple docstring'''
def _flatten(lowercase):
return list(itertools.chain(*lowercase))
if equal_length:
a__: Dict = floats_list((self.batch_size, self.max_seq_length))
else:
# make sure that inputs increase in size
a__: List[Any] = [
_flatten(floats_list((x, self.feature_size)))
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff)
]
if numpify:
a__: str = [np.asarray(lowercase) for x in speech_inputs]
return speech_inputs
class __snake_case ( __lowerCAmelCase , unittest.TestCase ):
a__ = WavaVecaFeatureExtractor
def lowerCamelCase_ ( self) -> Tuple:
'''simple docstring'''
a__: Optional[int] = WavaVecaFeatureExtractionTester(self)
def lowerCamelCase_ ( self , lowercase) -> List[Any]:
'''simple docstring'''
self.assertTrue(np.all(np.mean(lowercase , axis=0) < 1e-3))
self.assertTrue(np.all(np.abs(np.var(lowercase , axis=0) - 1) < 1e-3))
def lowerCamelCase_ ( self) -> List[str]:
'''simple docstring'''
a__: List[Any] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
# create three inputs of length 800, 1000, and 1200
a__: Optional[Any] = [floats_list((1, x))[0] for x in range(8_00 , 14_00 , 2_00)]
a__: List[str] = [np.asarray(lowercase) for speech_input in speech_inputs]
# Test not batched input
a__: Optional[Any] = feat_extract(speech_inputs[0] , return_tensors='np').input_values
a__: Dict = feat_extract(np_speech_inputs[0] , return_tensors='np').input_values
self.assertTrue(np.allclose(lowercase , lowercase , atol=1e-3))
# Test batched
a__: Dict = feat_extract(lowercase , return_tensors='np').input_values
a__: int = feat_extract(lowercase , return_tensors='np').input_values
for enc_seq_a, enc_seq_a in zip(lowercase , lowercase):
self.assertTrue(np.allclose(lowercase , lowercase , atol=1e-3))
# Test 2-D numpy arrays are batched.
a__: int = [floats_list((1, x))[0] for x in (8_00, 8_00, 8_00)]
a__: Union[str, Any] = np.asarray(lowercase)
a__: int = feat_extract(lowercase , return_tensors='np').input_values
a__: Any = feat_extract(lowercase , return_tensors='np').input_values
for enc_seq_a, enc_seq_a in zip(lowercase , lowercase):
self.assertTrue(np.allclose(lowercase , lowercase , atol=1e-3))
def lowerCamelCase_ ( self) -> List[Any]:
'''simple docstring'''
a__: Tuple = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
a__: List[Any] = [floats_list((1, x))[0] for x in range(8_00 , 14_00 , 2_00)]
a__: Optional[int] = ['longest', 'max_length', 'do_not_pad']
a__: List[Any] = [None, 16_00, None]
for max_length, padding in zip(lowercase , lowercase):
a__: Dict = feat_extract(lowercase , padding=lowercase , max_length=lowercase , return_tensors='np')
a__: Union[str, Any] = processed.input_values
self._check_zero_mean_unit_variance(input_values[0][:8_00])
self.assertTrue(input_values[0][8_00:].sum() < 1e-6)
self._check_zero_mean_unit_variance(input_values[1][:10_00])
self.assertTrue(input_values[0][10_00:].sum() < 1e-6)
self._check_zero_mean_unit_variance(input_values[2][:12_00])
def lowerCamelCase_ ( self) -> Optional[int]:
'''simple docstring'''
a__: str = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
a__: Optional[int] = range(8_00 , 14_00 , 2_00)
a__: List[str] = [floats_list((1, x))[0] for x in lengths]
a__: Tuple = ['longest', 'max_length', 'do_not_pad']
a__: Dict = [None, 16_00, None]
for max_length, padding in zip(lowercase , lowercase):
a__: int = feat_extract(lowercase , max_length=lowercase , padding=lowercase)
a__: Any = processed.input_values
self._check_zero_mean_unit_variance(input_values[0][:8_00])
self._check_zero_mean_unit_variance(input_values[1][:10_00])
self._check_zero_mean_unit_variance(input_values[2][:12_00])
def lowerCamelCase_ ( self) -> List[str]:
'''simple docstring'''
a__: Any = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
a__: Any = [floats_list((1, x))[0] for x in range(8_00 , 14_00 , 2_00)]
a__: Dict = feat_extract(
lowercase , truncation=lowercase , max_length=10_00 , padding='max_length' , return_tensors='np')
a__: int = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :8_00])
self._check_zero_mean_unit_variance(input_values[1])
self._check_zero_mean_unit_variance(input_values[2])
def lowerCamelCase_ ( self) -> Dict:
'''simple docstring'''
a__: Tuple = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
a__: int = [floats_list((1, x))[0] for x in range(8_00 , 14_00 , 2_00)]
a__: str = feat_extract(
lowercase , truncation=lowercase , max_length=10_00 , padding='longest' , return_tensors='np')
a__: Any = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :8_00])
self._check_zero_mean_unit_variance(input_values[1, :10_00])
self._check_zero_mean_unit_variance(input_values[2])
# make sure that if max_length < longest -> then pad to max_length
self.assertTrue(input_values.shape == (3, 10_00))
a__: Dict = [floats_list((1, x))[0] for x in range(8_00 , 14_00 , 2_00)]
a__: Tuple = feat_extract(
lowercase , truncation=lowercase , max_length=20_00 , padding='longest' , return_tensors='np')
a__: str = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :8_00])
self._check_zero_mean_unit_variance(input_values[1, :10_00])
self._check_zero_mean_unit_variance(input_values[2])
# make sure that if max_length > longest -> then pad to longest
self.assertTrue(input_values.shape == (3, 12_00))
@require_torch
def lowerCamelCase_ ( self) -> Tuple:
'''simple docstring'''
import torch
a__: Optional[int] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
a__: Tuple = np.random.rand(1_00).astype(np.floataa)
a__: Tuple = np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
a__: Any = feature_extractor.pad([{'input_values': inputs}] , return_tensors='np')
self.assertTrue(np_processed.input_values.dtype == np.floataa)
a__: Optional[Any] = feature_extractor.pad([{'input_values': inputs}] , return_tensors='pt')
self.assertTrue(pt_processed.input_values.dtype == torch.floataa)
@slow
@require_torch
def lowerCamelCase_ ( self) -> Optional[Any]:
'''simple docstring'''
for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
a__: str = WavaVecaConfig.from_pretrained(lowercase)
a__: str = WavaVecaFeatureExtractor.from_pretrained(lowercase)
# only "layer" feature extraction norm should make use of
# attention_mask
self.assertEqual(feat_extract.return_attention_mask , config.feat_extract_norm == 'layer')
| 290 | 0 |
"""simple docstring"""
import math
def __magic_name__ ( __snake_case : Tuple ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(_SCREAMING_SNAKE_CASE ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def __magic_name__ ( __snake_case : Union[str, Any] = 0.1 ) -> int:
lowercase : str = 3
lowercase : Optional[Any] = 3
while primes / (2 * j - 1) >= ratio:
for i in range(j * j + j + 1 , (j + 2) * (j + 2) , j + 1 ):
primes += is_prime(_SCREAMING_SNAKE_CASE )
j += 2
return j
if __name__ == "__main__":
import doctest
doctest.testmod()
| 202 | """simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
'edbeeching/decision-transformer-gym-hopper-medium': (
'https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json'
),
# See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}
class __snake_case ( __lowerCAmelCase ):
a__ = """decision_transformer"""
a__ = ["""past_key_values"""]
a__ = {
"""max_position_embeddings""": """n_positions""",
"""num_attention_heads""": """n_head""",
"""num_hidden_layers""": """n_layer""",
}
def __init__( self , lowercase=17 , lowercase=4 , lowercase=1_28 , lowercase=40_96 , lowercase=True , lowercase=1 , lowercase=10_24 , lowercase=3 , lowercase=1 , lowercase=None , lowercase="relu" , lowercase=0.1 , lowercase=0.1 , lowercase=0.1 , lowercase=1e-5 , lowercase=0.02 , lowercase=True , lowercase=True , lowercase=5_02_56 , lowercase=5_02_56 , lowercase=False , lowercase=False , **lowercase , ) -> Tuple:
'''simple docstring'''
a__: List[str] = state_dim
a__: int = act_dim
a__: List[Any] = hidden_size
a__: List[str] = max_ep_len
a__: List[Any] = action_tanh
a__: Optional[Any] = vocab_size
a__: Tuple = n_positions
a__: Dict = n_layer
a__: Optional[int] = n_head
a__: Optional[int] = n_inner
a__: Any = activation_function
a__: Union[str, Any] = resid_pdrop
a__: Any = embd_pdrop
a__: Any = attn_pdrop
a__: List[Any] = layer_norm_epsilon
a__: Optional[Any] = initializer_range
a__: Any = scale_attn_weights
a__: Dict = use_cache
a__: Optional[int] = scale_attn_by_inverse_layer_idx
a__: List[str] = reorder_and_upcast_attn
a__: Any = bos_token_id
a__: int = eos_token_id
super().__init__(bos_token_id=lowercase , eos_token_id=lowercase , **lowercase)
| 290 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE :Tuple = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE :List[str] = {
'''s-JoL/Open-Llama-V1''': '''https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json''',
}
class A_ ( __lowerCAmelCase ):
_lowerCamelCase : Any = """open-llama"""
def __init__( self : Any , snake_case_ : Any=1_0_0_0_0_0 , snake_case_ : Union[str, Any]=4_0_9_6 , snake_case_ : str=1_1_0_0_8 , snake_case_ : int=3_2 , snake_case_ : Optional[int]=3_2 , snake_case_ : int="silu" , snake_case_ : Optional[int]=2_0_4_8 , snake_case_ : Optional[Any]=0.0_2 , snake_case_ : Tuple=1e-6 , snake_case_ : int=True , snake_case_ : Dict=0 , snake_case_ : Optional[Any]=1 , snake_case_ : Optional[int]=2 , snake_case_ : Union[str, Any]=False , snake_case_ : str=True , snake_case_ : List[Any]=0.1 , snake_case_ : List[str]=0.1 , snake_case_ : int=True , snake_case_ : List[Any]=True , snake_case_ : List[str]=None , **snake_case_ : str , ):
_UpperCAmelCase = vocab_size
_UpperCAmelCase = max_position_embeddings
_UpperCAmelCase = hidden_size
_UpperCAmelCase = intermediate_size
_UpperCAmelCase = num_hidden_layers
_UpperCAmelCase = num_attention_heads
_UpperCAmelCase = hidden_act
_UpperCAmelCase = initializer_range
_UpperCAmelCase = rms_norm_eps
_UpperCAmelCase = use_cache
_UpperCAmelCase = kwargs.pop(
"use_memorry_efficient_attention" , snake_case_ )
_UpperCAmelCase = hidden_dropout_prob
_UpperCAmelCase = attention_dropout_prob
_UpperCAmelCase = use_stable_embedding
_UpperCAmelCase = shared_input_output_embedding
_UpperCAmelCase = rope_scaling
self._rope_scaling_validation()
super().__init__(
pad_token_id=snake_case_ , bos_token_id=snake_case_ , eos_token_id=snake_case_ , tie_word_embeddings=snake_case_ , **snake_case_ , )
def lowercase ( self : List[str] ):
if self.rope_scaling is None:
return
if not isinstance(self.rope_scaling , snake_case_ ) 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}' )
_UpperCAmelCase = self.rope_scaling.get("type" , snake_case_ )
_UpperCAmelCase = self.rope_scaling.get("factor" , snake_case_ )
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(snake_case_ , snake_case_ ) or rope_scaling_factor <= 1.0:
raise ValueError(f'`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}' )
| 22 | """simple docstring"""
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->int:
while a != 0:
a__ , a__: List[str] = b % a, a
return b
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->int:
if gcd(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) != 1:
a__: Dict = F'mod inverse of {a!r} and {m!r} does not exist'
raise ValueError(_SCREAMING_SNAKE_CASE )
a__ , a__ , a__: Union[str, Any] = 1, 0, a
a__ , a__ , a__: Any = 0, 1, m
while va != 0:
a__: int = ua // va
a__ , a__ , a__ , a__ , a__ , a__: Any = (ua - q * va), (ua - q * va), (ua - q * va), va, va, va
return ua % m
| 290 | 0 |
'''simple docstring'''
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class __UpperCAmelCase ( __lowerCAmelCase ):
'''simple docstring'''
__lowerCAmelCase = DistilBertTokenizer
__lowerCAmelCase = DistilBertTokenizerFast
__lowerCAmelCase = True
@slow
def A (self : Any ):
A = DistilBertTokenizer.from_pretrained("""distilbert-base-uncased""" )
A = tokenizer.encode("""sequence builders""" , add_special_tokens=_lowerCAmelCase )
A = tokenizer.encode("""multi-sequence build""" , add_special_tokens=_lowerCAmelCase )
A = tokenizer.build_inputs_with_special_tokens(_lowerCAmelCase )
A = tokenizer.build_inputs_with_special_tokens(_lowerCAmelCase , _lowerCAmelCase )
assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
tokenizer.sep_token_id
]
| 258 | """simple docstring"""
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
lowercase__ = logging.getLogger(__name__)
class __snake_case :
def __init__( self) -> Optional[int]:
'''simple docstring'''
a__: Optional[Any] = False
def lowerCamelCase_ ( self , lowercase , lowercase , lowercase , lowercase) -> str:
'''simple docstring'''
if not self.initialized:
a__: Optional[int] = RagRetriever(
lowercase , question_encoder_tokenizer=lowercase , generator_tokenizer=lowercase , index=lowercase , init_retrieval=lowercase , )
a__: Optional[int] = True
def lowerCamelCase_ ( self) -> int:
'''simple docstring'''
self.retriever.index.init_index()
def lowerCamelCase_ ( self , lowercase , lowercase) -> Union[str, Any]:
'''simple docstring'''
a__ , a__: str = self.retriever._main_retrieve(lowercase , lowercase)
return doc_ids, retrieved_doc_embeds
class __snake_case ( __lowerCAmelCase ):
def __init__( self , lowercase , lowercase , lowercase , lowercase , lowercase=None) -> int:
'''simple docstring'''
if index is not None and index.is_initialized() and len(lowercase) > 0:
raise ValueError(
'When using Ray for distributed fine-tuning, '
'you\'ll need to provide the paths instead, '
'as the dataset and the index are loaded '
'separately. More info in examples/rag/use_own_knowledge_dataset.py ')
super().__init__(
lowercase , question_encoder_tokenizer=lowercase , generator_tokenizer=lowercase , index=lowercase , init_retrieval=lowercase , )
a__: Any = retrieval_workers
if len(self.retrieval_workers) > 0:
ray.get(
[
worker.create_rag_retriever.remote(lowercase , lowercase , lowercase , lowercase)
for worker in self.retrieval_workers
])
def lowerCamelCase_ ( self) -> Any:
'''simple docstring'''
logger.info('initializing retrieval')
if len(self.retrieval_workers) > 0:
ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers])
else:
# Non-distributed training. Load index into this same process.
self.index.init_index()
def lowerCamelCase_ ( self , lowercase , lowercase) -> Union[str, Any]:
'''simple docstring'''
if len(self.retrieval_workers) > 0:
# Select a random retrieval actor.
a__: int = self.retrieval_workers[random.randint(0 , len(self.retrieval_workers) - 1)]
a__ , a__: List[Any] = ray.get(random_worker.retrieve.remote(lowercase , lowercase))
else:
a__ , a__: Dict = self._main_retrieve(lowercase , lowercase)
return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(lowercase)
@classmethod
def lowerCamelCase_ ( cls , lowercase , lowercase=None , **lowercase) -> Tuple:
'''simple docstring'''
return super(lowercase , cls).get_tokenizers(lowercase , lowercase , **lowercase)
@classmethod
def lowerCamelCase_ ( cls , lowercase , lowercase , lowercase=None , **lowercase) -> Union[str, Any]:
'''simple docstring'''
a__: Optional[int] = kwargs.pop('config' , lowercase) or RagConfig.from_pretrained(lowercase , **lowercase)
a__: Union[str, Any] = RagTokenizer.from_pretrained(lowercase , config=lowercase)
a__: int = rag_tokenizer.question_encoder
a__: Any = rag_tokenizer.generator
if indexed_dataset is not None:
a__: List[Any] = 'custom'
a__: Optional[Any] = CustomHFIndex(config.retrieval_vector_size , lowercase)
else:
a__: Dict = cls._build_index(lowercase)
return cls(
lowercase , question_encoder_tokenizer=lowercase , generator_tokenizer=lowercase , retrieval_workers=lowercase , index=lowercase , )
| 290 | 0 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class lowerCamelCase_ (metaclass=__lowerCAmelCase ):
'''simple docstring'''
__UpperCamelCase: Tuple = ["onnx"]
def __init__( self : Optional[int] , *A : List[Any] , **A : Dict ):
requires_backends(self , ["onnx"] )
@classmethod
def _A ( cls : str , *A : Any , **A : Tuple ):
requires_backends(cls , ["onnx"] )
@classmethod
def _A ( cls : Any , *A : Dict , **A : List[Any] ):
requires_backends(cls , ["onnx"] )
| 31 | """simple docstring"""
import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter
import requests
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None ) ->Optional[int]:
a__: int = None
if token is not None:
a__: Tuple = {'Accept': 'application/vnd.github+json', 'Authorization': F'Bearer {token}'}
a__: Optional[Any] = F'https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100'
a__: str = requests.get(_SCREAMING_SNAKE_CASE , headers=_SCREAMING_SNAKE_CASE ).json()
a__: str = {}
try:
job_links.update({job['name']: job['html_url'] for job in result['jobs']} )
a__: int = math.ceil((result['total_count'] - 100) / 100 )
for i in range(_SCREAMING_SNAKE_CASE ):
a__: Dict = requests.get(url + F'&page={i + 2}' , headers=_SCREAMING_SNAKE_CASE ).json()
job_links.update({job['name']: job['html_url'] for job in result['jobs']} )
return job_links
except Exception:
print(F'Unknown error, could not fetch links:\n{traceback.format_exc()}' )
return {}
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None ) ->Dict:
a__: Dict = None
if token is not None:
a__: List[str] = {'Accept': 'application/vnd.github+json', 'Authorization': F'Bearer {token}'}
a__: Dict = F'https://api.github.com/repos/huggingface/transformers/actions/runs/{worflow_run_id}/artifacts?per_page=100'
a__: Union[str, Any] = requests.get(_SCREAMING_SNAKE_CASE , headers=_SCREAMING_SNAKE_CASE ).json()
a__: List[Any] = {}
try:
artifacts.update({artifact['name']: artifact['archive_download_url'] for artifact in result['artifacts']} )
a__: Dict = math.ceil((result['total_count'] - 100) / 100 )
for i in range(_SCREAMING_SNAKE_CASE ):
a__: Optional[int] = requests.get(url + F'&page={i + 2}' , headers=_SCREAMING_SNAKE_CASE ).json()
artifacts.update({artifact['name']: artifact['archive_download_url'] for artifact in result['artifacts']} )
return artifacts
except Exception:
print(F'Unknown error, could not fetch links:\n{traceback.format_exc()}' )
return {}
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->str:
a__: List[Any] = None
if token is not None:
a__: Optional[int] = {'Accept': 'application/vnd.github+json', 'Authorization': F'Bearer {token}'}
a__: Union[str, Any] = requests.get(_SCREAMING_SNAKE_CASE , headers=_SCREAMING_SNAKE_CASE , allow_redirects=_SCREAMING_SNAKE_CASE )
a__: Optional[Any] = result.headers['Location']
a__: Optional[int] = requests.get(_SCREAMING_SNAKE_CASE , allow_redirects=_SCREAMING_SNAKE_CASE )
a__: int = os.path.join(_SCREAMING_SNAKE_CASE , F'{artifact_name}.zip' )
with open(_SCREAMING_SNAKE_CASE , 'wb' ) as fp:
fp.write(response.content )
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None ) ->List[Any]:
a__: List[Any] = []
a__: Optional[Any] = []
a__: List[Any] = None
with zipfile.ZipFile(_SCREAMING_SNAKE_CASE ) as z:
for filename in z.namelist():
if not os.path.isdir(_SCREAMING_SNAKE_CASE ):
# read the file
if filename in ["failures_line.txt", "summary_short.txt", "job_name.txt"]:
with z.open(_SCREAMING_SNAKE_CASE ) as f:
for line in f:
a__: Optional[int] = line.decode('UTF-8' ).strip()
if filename == "failures_line.txt":
try:
# `error_line` is the place where `error` occurs
a__: Union[str, Any] = line[: line.index(': ' )]
a__: Union[str, Any] = line[line.index(': ' ) + len(': ' ) :]
errors.append([error_line, error] )
except Exception:
# skip un-related lines
pass
elif filename == "summary_short.txt" and line.startswith('FAILED ' ):
# `test` is the test method that failed
a__: Optional[int] = line[len('FAILED ' ) :]
failed_tests.append(_SCREAMING_SNAKE_CASE )
elif filename == "job_name.txt":
a__: Union[str, Any] = line
if len(_SCREAMING_SNAKE_CASE ) != len(_SCREAMING_SNAKE_CASE ):
raise ValueError(
F'`errors` and `failed_tests` should have the same number of elements. Got {len(_SCREAMING_SNAKE_CASE )} for `errors` '
F'and {len(_SCREAMING_SNAKE_CASE )} for `failed_tests` instead. The test reports in {artifact_zip_path} have some'
' problem.' )
a__: Tuple = None
if job_name and job_links:
a__: Dict = job_links.get(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# A list with elements of the form (line of error, error, failed test)
a__: int = [x + [y] + [job_link] for x, y in zip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )]
return result
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None ) ->str:
a__: int = []
a__: Optional[int] = [os.path.join(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) for p in os.listdir(_SCREAMING_SNAKE_CASE ) if p.endswith('.zip' )]
for p in paths:
errors.extend(get_errors_from_single_artifact(_SCREAMING_SNAKE_CASE , job_links=_SCREAMING_SNAKE_CASE ) )
return errors
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None ) ->Any:
a__: str = Counter()
counter.update([x[1] for x in logs] )
a__: int = counter.most_common()
a__: Any = {}
for error, count in counts:
if error_filter is None or error not in error_filter:
a__: List[str] = {'count': count, 'failed_tests': [(x[2], x[0]) for x in logs if x[1] == error]}
a__: Optional[Any] = dict(sorted(r.items() , key=lambda _SCREAMING_SNAKE_CASE : item[1]["count"] , reverse=_SCREAMING_SNAKE_CASE ) )
return r
def __a ( _SCREAMING_SNAKE_CASE ) ->Union[str, Any]:
a__: List[str] = test.split('::' )[0]
if test.startswith('tests/models/' ):
a__: Dict = test.split('/' )[2]
else:
a__: Any = None
return test
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None ) ->List[str]:
a__: int = [(x[0], x[1], get_model(x[2] )) for x in logs]
a__: List[Any] = [x for x in logs if x[2] is not None]
a__: Optional[Any] = {x[2] for x in logs}
a__: Dict = {}
for test in tests:
a__: Union[str, Any] = Counter()
# count by errors in `test`
counter.update([x[1] for x in logs if x[2] == test] )
a__: Union[str, Any] = counter.most_common()
a__: List[str] = {error: count for error, count in counts if (error_filter is None or error not in error_filter)}
a__: List[Any] = sum(error_counts.values() )
if n_errors > 0:
a__: Any = {'count': n_errors, 'errors': error_counts}
a__: Optional[int] = dict(sorted(r.items() , key=lambda _SCREAMING_SNAKE_CASE : item[1]["count"] , reverse=_SCREAMING_SNAKE_CASE ) )
return r
def __a ( _SCREAMING_SNAKE_CASE ) ->Union[str, Any]:
a__: Any = '| no. | error | status |'
a__: Any = '|-:|:-|:-|'
a__: str = [header, sep]
for error in reduced_by_error:
a__: int = reduced_by_error[error]['count']
a__: Tuple = F'| {count} | {error[:100]} | |'
lines.append(_SCREAMING_SNAKE_CASE )
return "\n".join(_SCREAMING_SNAKE_CASE )
def __a ( _SCREAMING_SNAKE_CASE ) ->str:
a__: List[str] = '| model | no. of errors | major error | count |'
a__: str = '|-:|-:|-:|-:|'
a__: int = [header, sep]
for model in reduced_by_model:
a__: Tuple = reduced_by_model[model]['count']
a__ , a__: Dict = list(reduced_by_model[model]['errors'].items() )[0]
a__: Dict = F'| {model} | {count} | {error[:60]} | {_count} |'
lines.append(_SCREAMING_SNAKE_CASE )
return "\n".join(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
lowercase__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument('--workflow_run_id', type=str, required=True, help='A GitHub Actions workflow run id.')
parser.add_argument(
'--output_dir',
type=str,
required=True,
help='Where to store the downloaded artifacts and other result files.',
)
parser.add_argument('--token', default=None, type=str, help='A token that has actions:read permission.')
lowercase__ = parser.parse_args()
os.makedirs(args.output_dir, exist_ok=True)
lowercase__ = get_job_links(args.workflow_run_id, token=args.token)
lowercase__ = {}
# To deal with `workflow_call` event, where a job name is the combination of the job names in the caller and callee.
# For example, `PyTorch 1.11 / Model tests (models/albert, single-gpu)`.
if _job_links:
for k, v in _job_links.items():
# This is how GitHub actions combine job names.
if " / " in k:
lowercase__ = k.find(' / ')
lowercase__ = k[index + len(' / ') :]
lowercase__ = v
with open(os.path.join(args.output_dir, 'job_links.json'), 'w', encoding='UTF-8') as fp:
json.dump(job_links, fp, ensure_ascii=False, indent=4)
lowercase__ = get_artifacts_links(args.workflow_run_id, token=args.token)
with open(os.path.join(args.output_dir, 'artifacts.json'), 'w', encoding='UTF-8') as fp:
json.dump(artifacts, fp, ensure_ascii=False, indent=4)
for idx, (name, url) in enumerate(artifacts.items()):
download_artifact(name, url, args.output_dir, args.token)
# Be gentle to GitHub
time.sleep(1)
lowercase__ = get_all_errors(args.output_dir, job_links=job_links)
# `e[1]` is the error
lowercase__ = Counter()
counter.update([e[1] for e in errors])
# print the top 30 most common test errors
lowercase__ = counter.most_common(30)
for item in most_common:
print(item)
with open(os.path.join(args.output_dir, 'errors.json'), 'w', encoding='UTF-8') as fp:
json.dump(errors, fp, ensure_ascii=False, indent=4)
lowercase__ = reduce_by_error(errors)
lowercase__ = reduce_by_model(errors)
lowercase__ = make_github_table(reduced_by_error)
lowercase__ = make_github_table_per_model(reduced_by_model)
with open(os.path.join(args.output_dir, 'reduced_by_error.txt'), 'w', encoding='UTF-8') as fp:
fp.write(sa)
with open(os.path.join(args.output_dir, 'reduced_by_model.txt'), 'w', encoding='UTF-8') as fp:
fp.write(sa)
| 290 | 0 |
def lowerCAmelCase__ ( a__: List[Any] = 4_0_0_0_0_0_0 ) -> int:
'''simple docstring'''
_UpperCAmelCase = []
_UpperCAmelCase = 0, 1
while b <= n:
if b % 2 == 0:
even_fibs.append(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = b, a + b
return sum(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
print(f'''{solution() = }''')
| 329 | """simple docstring"""
import math
def __a ( _SCREAMING_SNAKE_CASE ) ->bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(_SCREAMING_SNAKE_CASE ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def __a ( _SCREAMING_SNAKE_CASE = 0.1 ) ->int:
a__: str = 3
a__: Optional[Any] = 3
while primes / (2 * j - 1) >= ratio:
for i in range(j * j + j + 1 , (j + 2) * (j + 2) , j + 1 ):
primes += is_prime(_SCREAMING_SNAKE_CASE )
j += 2
return j
if __name__ == "__main__":
import doctest
doctest.testmod()
| 290 | 0 |
'''simple docstring'''
def UpperCamelCase( UpperCAmelCase_ ):
if a < 0:
raise ValueError('Input value must be a positive integer' )
elif isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
raise TypeError('Input value must be a \'int\' type' )
return bin(_SCREAMING_SNAKE_CASE ).count('1' )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 151 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import _LazyModule
lowercase__ = {'processing_wav2vec2_with_lm': ['Wav2Vec2ProcessorWithLM']}
if TYPE_CHECKING:
from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM
else:
import sys
lowercase__ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 290 | 0 |
import unittest
import numpy as np
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from transformers.models.bert.modeling_flax_bert import (
FlaxBertForMaskedLM,
FlaxBertForMultipleChoice,
FlaxBertForNextSentencePrediction,
FlaxBertForPreTraining,
FlaxBertForQuestionAnswering,
FlaxBertForSequenceClassification,
FlaxBertForTokenClassification,
FlaxBertModel,
)
class A_ ( unittest.TestCase ):
def __init__( self : Optional[int] , UpperCAmelCase : List[Any] , UpperCAmelCase : Tuple=1_3 , UpperCAmelCase : Optional[int]=7 , UpperCAmelCase : Optional[int]=True , UpperCAmelCase : Optional[Any]=True , UpperCAmelCase : int=True , UpperCAmelCase : Dict=True , UpperCAmelCase : Any=9_9 , UpperCAmelCase : Dict=3_2 , UpperCAmelCase : Optional[Any]=5 , UpperCAmelCase : Tuple=4 , UpperCAmelCase : List[Any]=3_7 , UpperCAmelCase : Dict="gelu" , UpperCAmelCase : Tuple=0.1 , UpperCAmelCase : str=0.1 , UpperCAmelCase : str=5_1_2 , UpperCAmelCase : List[str]=1_6 , UpperCAmelCase : Union[str, Any]=2 , UpperCAmelCase : Optional[int]=0.02 , UpperCAmelCase : int=4 , ) -> List[Any]:
__lowerCAmelCase: int = parent
__lowerCAmelCase: Optional[int] = batch_size
__lowerCAmelCase: str = seq_length
__lowerCAmelCase: Union[str, Any] = is_training
__lowerCAmelCase: Tuple = use_attention_mask
__lowerCAmelCase: Any = use_token_type_ids
__lowerCAmelCase: Tuple = use_labels
__lowerCAmelCase: int = vocab_size
__lowerCAmelCase: Any = hidden_size
__lowerCAmelCase: Tuple = num_hidden_layers
__lowerCAmelCase: Union[str, Any] = num_attention_heads
__lowerCAmelCase: Union[str, Any] = intermediate_size
__lowerCAmelCase: Tuple = hidden_act
__lowerCAmelCase: List[str] = hidden_dropout_prob
__lowerCAmelCase: Union[str, Any] = attention_probs_dropout_prob
__lowerCAmelCase: Union[str, Any] = max_position_embeddings
__lowerCAmelCase: Optional[int] = type_vocab_size
__lowerCAmelCase: Tuple = type_sequence_label_size
__lowerCAmelCase: Optional[Any] = initializer_range
__lowerCAmelCase: Any = num_choices
def UpperCAmelCase ( self : Tuple ) -> Optional[int]:
__lowerCAmelCase: Tuple = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__lowerCAmelCase: List[Any] = None
if self.use_attention_mask:
__lowerCAmelCase: Union[str, Any] = random_attention_mask([self.batch_size, self.seq_length] )
__lowerCAmelCase: List[Any] = None
if self.use_token_type_ids:
__lowerCAmelCase: Any = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__lowerCAmelCase: Tuple = BertConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=UpperCAmelCase , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def UpperCAmelCase ( self : List[str] ) -> str:
__lowerCAmelCase: Optional[int] = self.prepare_config_and_inputs()
__lowerCAmelCase: Optional[int] = config_and_inputs
__lowerCAmelCase: List[str] = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
return config, inputs_dict
def UpperCAmelCase ( self : Optional[int] ) -> Any:
__lowerCAmelCase: Optional[Any] = self.prepare_config_and_inputs()
__lowerCAmelCase: List[str] = config_and_inputs
__lowerCAmelCase: Dict = True
__lowerCAmelCase: Optional[Any] = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
__lowerCAmelCase: Any = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
attention_mask,
encoder_hidden_states,
encoder_attention_mask,
)
@require_flax
class A_ ( __lowerCAmelCase , unittest.TestCase ):
_lowercase : Optional[Any] = True
_lowercase : str = (
(
FlaxBertModel,
FlaxBertForPreTraining,
FlaxBertForMaskedLM,
FlaxBertForMultipleChoice,
FlaxBertForQuestionAnswering,
FlaxBertForNextSentencePrediction,
FlaxBertForSequenceClassification,
FlaxBertForTokenClassification,
FlaxBertForQuestionAnswering,
)
if is_flax_available()
else ()
)
def UpperCAmelCase ( self : Dict ) -> int:
__lowerCAmelCase: Tuple = FlaxBertModelTester(self )
@slow
def UpperCAmelCase ( self : Optional[int] ) -> Union[str, Any]:
__lowerCAmelCase: Any = FlaxBertModel.from_pretrained('bert-base-cased' )
__lowerCAmelCase: str = model(np.ones((1, 1) ) )
self.assertIsNotNone(UpperCAmelCase )
| 322 | """simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, 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 __snake_case ( __lowerCAmelCase , unittest.TestCase ):
a__ = KandinskyInpaintPipeline
a__ = ["""prompt""", """image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
a__ = [
"""prompt""",
"""negative_prompt""",
"""image_embeds""",
"""negative_image_embeds""",
"""image""",
"""mask_image""",
]
a__ = [
"""generator""",
"""height""",
"""width""",
"""latents""",
"""guidance_scale""",
"""negative_prompt""",
"""num_inference_steps""",
"""return_dict""",
"""guidance_scale""",
"""num_images_per_prompt""",
"""output_type""",
"""return_dict""",
]
a__ = False
@property
def lowerCamelCase_ ( self) -> Optional[int]:
'''simple docstring'''
return 32
@property
def lowerCamelCase_ ( self) -> Tuple:
'''simple docstring'''
return 32
@property
def lowerCamelCase_ ( self) -> Dict:
'''simple docstring'''
return self.time_input_dim
@property
def lowerCamelCase_ ( self) -> Dict:
'''simple docstring'''
return self.time_input_dim * 4
@property
def lowerCamelCase_ ( self) -> List[Any]:
'''simple docstring'''
return 1_00
@property
def lowerCamelCase_ ( self) -> List[Any]:
'''simple docstring'''
a__: Optional[int] = XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base')
return tokenizer
@property
def lowerCamelCase_ ( self) -> Any:
'''simple docstring'''
torch.manual_seed(0)
a__: Dict = MCLIPConfig(
numDims=self.cross_attention_dim , transformerDimensions=self.text_embedder_hidden_size , hidden_size=self.text_embedder_hidden_size , intermediate_size=37 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=10_05 , )
a__: Optional[Any] = MultilingualCLIP(lowercase)
a__: int = text_encoder.eval()
return text_encoder
@property
def lowerCamelCase_ ( self) -> List[str]:
'''simple docstring'''
torch.manual_seed(0)
a__: Any = {
'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__: str = UNetaDConditionModel(**lowercase)
return model
@property
def lowerCamelCase_ ( self) -> Union[str, Any]:
'''simple docstring'''
return {
"block_out_channels": [32, 64],
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def lowerCamelCase_ ( self) -> List[Any]:
'''simple docstring'''
torch.manual_seed(0)
a__: Any = VQModel(**self.dummy_movq_kwargs)
return model
def lowerCamelCase_ ( self) -> Any:
'''simple docstring'''
a__: Dict = self.dummy_text_encoder
a__: int = self.dummy_tokenizer
a__: str = self.dummy_unet
a__: Any = self.dummy_movq
a__: Tuple = DDIMScheduler(
num_train_timesteps=10_00 , beta_schedule='linear' , beta_start=0.00085 , beta_end=0.012 , clip_sample=lowercase , set_alpha_to_one=lowercase , steps_offset=1 , prediction_type='epsilon' , thresholding=lowercase , )
a__: Tuple = {
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'unet': unet,
'scheduler': scheduler,
'movq': movq,
}
return components
def lowerCamelCase_ ( self , lowercase , lowercase=0) -> Any:
'''simple docstring'''
a__: List[Any] = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(lowercase)).to(lowercase)
a__: int = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(seed + 1)).to(lowercase)
# create init_image
a__: Optional[int] = floats_tensor((1, 3, 64, 64) , rng=random.Random(lowercase)).to(lowercase)
a__: int = image.cpu().permute(0 , 2 , 3 , 1)[0]
a__: Optional[int] = Image.fromarray(np.uinta(lowercase)).convert('RGB').resize((2_56, 2_56))
# create mask
a__: Tuple = np.ones((64, 64) , dtype=np.floataa)
a__: Optional[Any] = 0
if str(lowercase).startswith('mps'):
a__: str = torch.manual_seed(lowercase)
else:
a__: Dict = torch.Generator(device=lowercase).manual_seed(lowercase)
a__: Optional[int] = {
'prompt': 'horse',
'image': init_image,
'mask_image': mask,
'image_embeds': image_embeds,
'negative_image_embeds': negative_image_embeds,
'generator': generator,
'height': 64,
'width': 64,
'num_inference_steps': 2,
'guidance_scale': 4.0,
'output_type': 'np',
}
return inputs
def lowerCamelCase_ ( self) -> str:
'''simple docstring'''
a__: Optional[Any] = 'cpu'
a__: List[Any] = self.get_dummy_components()
a__: Optional[Any] = self.pipeline_class(**lowercase)
a__: str = pipe.to(lowercase)
pipe.set_progress_bar_config(disable=lowercase)
a__: Optional[int] = pipe(**self.get_dummy_inputs(lowercase))
a__: List[str] = output.images
a__: int = pipe(
**self.get_dummy_inputs(lowercase) , return_dict=lowercase , )[0]
a__: Optional[Any] = image[0, -3:, -3:, -1]
a__: List[Any] = image_from_tuple[0, -3:, -3:, -1]
print(f'image.shape {image.shape}')
assert image.shape == (1, 64, 64, 3)
a__: str = np.array(
[0.8326919, 0.73790467, 0.20918581, 0.9309612, 0.5511791, 0.43713328, 0.5513321, 0.49922934, 0.59497786])
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 lowerCamelCase_ ( self) -> str:
'''simple docstring'''
super().test_inference_batch_single_identical(expected_max_diff=3e-3)
@slow
@require_torch_gpu
class __snake_case ( unittest.TestCase ):
def lowerCamelCase_ ( self) -> Optional[Any]:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase_ ( self) -> Dict:
'''simple docstring'''
a__: List[Any] = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy')
a__: int = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png')
a__: Union[str, Any] = np.ones((7_68, 7_68) , dtype=np.floataa)
a__: int = 0
a__: Optional[int] = 'a hat'
a__: int = KandinskyPriorPipeline.from_pretrained(
'kandinsky-community/kandinsky-2-1-prior' , torch_dtype=torch.floataa)
pipe_prior.to(lowercase)
a__: Any = KandinskyInpaintPipeline.from_pretrained(
'kandinsky-community/kandinsky-2-1-inpaint' , torch_dtype=torch.floataa)
a__: Optional[Any] = pipeline.to(lowercase)
pipeline.set_progress_bar_config(disable=lowercase)
a__: Dict = torch.Generator(device='cpu').manual_seed(0)
a__ , a__: Optional[Any] = pipe_prior(
lowercase , generator=lowercase , num_inference_steps=5 , negative_prompt='' , ).to_tuple()
a__: List[str] = pipeline(
lowercase , image=lowercase , mask_image=lowercase , image_embeds=lowercase , negative_image_embeds=lowercase , generator=lowercase , num_inference_steps=1_00 , height=7_68 , width=7_68 , output_type='np' , )
a__: str = output.images[0]
assert image.shape == (7_68, 7_68, 3)
assert_mean_pixel_difference(lowercase , lowercase)
| 290 | 0 |
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import ShapEPipeline
else:
from .camera import create_pan_cameras
from .pipeline_shap_e import ShapEPipeline
from .pipeline_shap_e_img2img import ShapEImgaImgPipeline
from .renderer import (
BoundingBoxVolume,
ImportanceRaySampler,
MLPNeRFModelOutput,
MLPNeRSTFModel,
ShapEParamsProjModel,
ShapERenderer,
StratifiedRaySampler,
VoidNeRFModel,
)
| 264 | """simple docstring"""
import argparse
import torch
from transformers import (
EncodecConfig,
EncodecFeatureExtractor,
EncodecModel,
logging,
)
# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/main/compression_state_dict.bin
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_48khz-7e698e3e.th
logging.set_verbosity_info()
lowercase__ = logging.get_logger('transformers.models.encodec')
lowercase__ = {
'quantizer.vq.layers.*._codebook.inited': 'quantizer.layers.*.codebook.inited',
'quantizer.vq.layers.*._codebook.cluster_size': 'quantizer.layers.*.codebook.cluster_size',
'quantizer.vq.layers.*._codebook.embed': 'quantizer.layers.*.codebook.embed',
'quantizer.vq.layers.*._codebook.embed_avg': 'quantizer.layers.*.codebook.embed_avg',
}
lowercase__ = {
'encoder.model.0.conv.conv': 'encoder.layers.0.conv',
'encoder.model.1.block.1.conv.conv': 'encoder.layers.1.block.1.conv',
'encoder.model.1.block.3.conv.conv': 'encoder.layers.1.block.3.conv',
'encoder.model.1.shortcut.conv.conv': 'encoder.layers.1.shortcut.conv',
'encoder.model.3.conv.conv': 'encoder.layers.3.conv',
'encoder.model.4.block.1.conv.conv': 'encoder.layers.4.block.1.conv',
'encoder.model.4.block.3.conv.conv': 'encoder.layers.4.block.3.conv',
'encoder.model.4.shortcut.conv.conv': 'encoder.layers.4.shortcut.conv',
'encoder.model.6.conv.conv': 'encoder.layers.6.conv',
'encoder.model.7.block.1.conv.conv': 'encoder.layers.7.block.1.conv',
'encoder.model.7.block.3.conv.conv': 'encoder.layers.7.block.3.conv',
'encoder.model.7.shortcut.conv.conv': 'encoder.layers.7.shortcut.conv',
'encoder.model.9.conv.conv': 'encoder.layers.9.conv',
'encoder.model.10.block.1.conv.conv': 'encoder.layers.10.block.1.conv',
'encoder.model.10.block.3.conv.conv': 'encoder.layers.10.block.3.conv',
'encoder.model.10.shortcut.conv.conv': 'encoder.layers.10.shortcut.conv',
'encoder.model.12.conv.conv': 'encoder.layers.12.conv',
'encoder.model.13.lstm': 'encoder.layers.13.lstm',
'encoder.model.15.conv.conv': 'encoder.layers.15.conv',
}
lowercase__ = {
'encoder.model.0.conv.norm': 'encoder.layers.0.norm',
'encoder.model.1.block.1.conv.norm': 'encoder.layers.1.block.1.norm',
'encoder.model.1.block.3.conv.norm': 'encoder.layers.1.block.3.norm',
'encoder.model.1.shortcut.conv.norm': 'encoder.layers.1.shortcut.norm',
'encoder.model.3.conv.norm': 'encoder.layers.3.norm',
'encoder.model.4.block.1.conv.norm': 'encoder.layers.4.block.1.norm',
'encoder.model.4.block.3.conv.norm': 'encoder.layers.4.block.3.norm',
'encoder.model.4.shortcut.conv.norm': 'encoder.layers.4.shortcut.norm',
'encoder.model.6.conv.norm': 'encoder.layers.6.norm',
'encoder.model.7.block.1.conv.norm': 'encoder.layers.7.block.1.norm',
'encoder.model.7.block.3.conv.norm': 'encoder.layers.7.block.3.norm',
'encoder.model.7.shortcut.conv.norm': 'encoder.layers.7.shortcut.norm',
'encoder.model.9.conv.norm': 'encoder.layers.9.norm',
'encoder.model.10.block.1.conv.norm': 'encoder.layers.10.block.1.norm',
'encoder.model.10.block.3.conv.norm': 'encoder.layers.10.block.3.norm',
'encoder.model.10.shortcut.conv.norm': 'encoder.layers.10.shortcut.norm',
'encoder.model.12.conv.norm': 'encoder.layers.12.norm',
'encoder.model.15.conv.norm': 'encoder.layers.15.norm',
}
lowercase__ = {
'decoder.model.0.conv.conv': 'decoder.layers.0.conv',
'decoder.model.1.lstm': 'decoder.layers.1.lstm',
'decoder.model.3.convtr.convtr': 'decoder.layers.3.conv',
'decoder.model.4.block.1.conv.conv': 'decoder.layers.4.block.1.conv',
'decoder.model.4.block.3.conv.conv': 'decoder.layers.4.block.3.conv',
'decoder.model.4.shortcut.conv.conv': 'decoder.layers.4.shortcut.conv',
'decoder.model.6.convtr.convtr': 'decoder.layers.6.conv',
'decoder.model.7.block.1.conv.conv': 'decoder.layers.7.block.1.conv',
'decoder.model.7.block.3.conv.conv': 'decoder.layers.7.block.3.conv',
'decoder.model.7.shortcut.conv.conv': 'decoder.layers.7.shortcut.conv',
'decoder.model.9.convtr.convtr': 'decoder.layers.9.conv',
'decoder.model.10.block.1.conv.conv': 'decoder.layers.10.block.1.conv',
'decoder.model.10.block.3.conv.conv': 'decoder.layers.10.block.3.conv',
'decoder.model.10.shortcut.conv.conv': 'decoder.layers.10.shortcut.conv',
'decoder.model.12.convtr.convtr': 'decoder.layers.12.conv',
'decoder.model.13.block.1.conv.conv': 'decoder.layers.13.block.1.conv',
'decoder.model.13.block.3.conv.conv': 'decoder.layers.13.block.3.conv',
'decoder.model.13.shortcut.conv.conv': 'decoder.layers.13.shortcut.conv',
'decoder.model.15.conv.conv': 'decoder.layers.15.conv',
}
lowercase__ = {
'decoder.model.0.conv.norm': 'decoder.layers.0.norm',
'decoder.model.3.convtr.norm': 'decoder.layers.3.norm',
'decoder.model.4.block.1.conv.norm': 'decoder.layers.4.block.1.norm',
'decoder.model.4.block.3.conv.norm': 'decoder.layers.4.block.3.norm',
'decoder.model.4.shortcut.conv.norm': 'decoder.layers.4.shortcut.norm',
'decoder.model.6.convtr.norm': 'decoder.layers.6.norm',
'decoder.model.7.block.1.conv.norm': 'decoder.layers.7.block.1.norm',
'decoder.model.7.block.3.conv.norm': 'decoder.layers.7.block.3.norm',
'decoder.model.7.shortcut.conv.norm': 'decoder.layers.7.shortcut.norm',
'decoder.model.9.convtr.norm': 'decoder.layers.9.norm',
'decoder.model.10.block.1.conv.norm': 'decoder.layers.10.block.1.norm',
'decoder.model.10.block.3.conv.norm': 'decoder.layers.10.block.3.norm',
'decoder.model.10.shortcut.conv.norm': 'decoder.layers.10.shortcut.norm',
'decoder.model.12.convtr.norm': 'decoder.layers.12.norm',
'decoder.model.13.block.1.conv.norm': 'decoder.layers.13.block.1.norm',
'decoder.model.13.block.3.conv.norm': 'decoder.layers.13.block.3.norm',
'decoder.model.13.shortcut.conv.norm': 'decoder.layers.13.shortcut.norm',
'decoder.model.15.conv.norm': 'decoder.layers.15.norm',
}
lowercase__ = {
**MAPPING_QUANTIZER,
**MAPPING_ENCODER,
**MAPPING_DECODER,
}
lowercase__ = {
**MAPPING_QUANTIZER,
**MAPPING_ENCODER,
**MAPPING_ENCODER_48K,
**MAPPING_DECODER,
**MAPPING_DECODER_48K,
}
lowercase__ = []
lowercase__ = []
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->int:
for attribute in key.split('.' ):
a__: str = getattr(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
if weight_type is not None:
a__: List[str] = getattr(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ).shape
else:
a__: Optional[Any] = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
F'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
F' {value.shape} for {full_name}' )
if weight_type == "weight":
a__: str = value
elif weight_type == "weight_g":
a__: int = value
elif weight_type == "weight_v":
a__: Tuple = value
elif weight_type == "bias":
a__: Dict = value
elif weight_type == "running_mean":
a__: Any = value
elif weight_type == "running_var":
a__: Tuple = value
elif weight_type == "num_batches_tracked":
a__: List[str] = value
elif weight_type == "weight_ih_l0":
a__: List[Any] = value
elif weight_type == "weight_hh_l0":
a__: List[Any] = value
elif weight_type == "bias_ih_l0":
a__: List[Any] = value
elif weight_type == "bias_hh_l0":
a__: List[Any] = value
elif weight_type == "weight_ih_l1":
a__: int = value
elif weight_type == "weight_hh_l1":
a__: str = value
elif weight_type == "bias_ih_l1":
a__: Union[str, Any] = value
elif weight_type == "bias_hh_l1":
a__: Any = value
else:
a__: Union[str, Any] = value
logger.info(F'{key + ("." + weight_type if weight_type is not None else "")} was initialized from {full_name}.' )
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->Dict:
for key in ignore_keys:
if key.endswith('.*' ):
if name.startswith(key[:-1] ):
return True
elif ".*." in key:
a__ , a__: Optional[Any] = key.split('.*.' )
if prefix in name and suffix in name:
return True
elif key in name:
return True
return False
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->List[str]:
a__: List[Any] = []
if model_name == "encodec_24khz" or "encodec_32khz":
a__: Optional[int] = MAPPING_24K
elif model_name == "encodec_48khz":
a__: List[Any] = MAPPING_48K
else:
raise ValueError(F'Unsupported model: {model_name}' )
for name, value in orig_dict.items():
if should_ignore(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
logger.info(F'{name} was ignored' )
continue
a__: int = False
for key, mapped_key in MAPPING.items():
if "*" in key:
a__ , a__: str = key.split('.*.' )
if prefix in name and suffix in name:
a__: List[str] = suffix
if key in name:
# HACK otherwise .embed gets initialized with .embed_avg too
if key.endswith('embed' ) and name.endswith('embed_avg' ):
continue
a__: List[str] = True
if "*" in mapped_key:
a__: List[str] = name.split(_SCREAMING_SNAKE_CASE )[0].split('.' )[-2]
a__: str = mapped_key.replace('*' , _SCREAMING_SNAKE_CASE )
if "weight_g" in name:
a__: int = 'weight_g'
elif "weight_v" in name:
a__: Dict = 'weight_v'
elif "weight_ih_l0" in name:
a__: int = 'weight_ih_l0'
elif "weight_hh_l0" in name:
a__: Union[str, Any] = 'weight_hh_l0'
elif "bias_ih_l0" in name:
a__: Optional[Any] = 'bias_ih_l0'
elif "bias_hh_l0" in name:
a__: Optional[int] = 'bias_hh_l0'
elif "weight_ih_l1" in name:
a__: Dict = 'weight_ih_l1'
elif "weight_hh_l1" in name:
a__: Optional[Any] = 'weight_hh_l1'
elif "bias_ih_l1" in name:
a__: List[str] = 'bias_ih_l1'
elif "bias_hh_l1" in name:
a__: Optional[Any] = 'bias_hh_l1'
elif "bias" in name:
a__: List[str] = 'bias'
elif "weight" in name:
a__: Any = 'weight'
elif "running_mean" in name:
a__: Dict = 'running_mean'
elif "running_var" in name:
a__: Dict = 'running_var'
elif "num_batches_tracked" in name:
a__: Dict = 'num_batches_tracked'
else:
a__: List[str] = None
set_recursively(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
continue
if not is_used:
unused_weights.append(_SCREAMING_SNAKE_CASE )
logger.warning(F'Unused weights: {unused_weights}' )
@torch.no_grad()
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , ) ->int:
if config_path is not None:
a__: Dict = EncodecConfig.from_pretrained(_SCREAMING_SNAKE_CASE )
else:
a__: Tuple = EncodecConfig()
if model_name == "encodec_24khz":
pass # config is already correct
elif model_name == "encodec_32khz":
a__: Any = [8, 5, 4, 4]
a__: List[str] = [2.2]
a__: List[Any] = 64
a__: Dict = 32000
a__: Union[str, Any] = 2048
a__: Union[str, Any] = False
a__: Any = False
a__: Optional[Any] = False
elif model_name == "encodec_48khz":
a__: Optional[int] = [8, 5, 4, 2]
a__: Union[str, Any] = [3.0, 6.0, 12.0, 24.0]
a__: List[str] = 48000
a__: Tuple = 2
a__: Optional[Any] = False
a__: Optional[int] = 'time_group_norm'
a__: Union[str, Any] = True
a__: Dict = 1.0
a__: str = 0.01
else:
raise ValueError(F'Unknown model name: {model_name}' )
a__: Optional[int] = EncodecModel(_SCREAMING_SNAKE_CASE )
a__: List[str] = EncodecFeatureExtractor(
feature_size=config.audio_channels , sampling_rate=config.sampling_rate , chunk_length_s=config.chunk_length_s , overlap=config.overlap , )
feature_extractor.save_pretrained(_SCREAMING_SNAKE_CASE )
a__: int = torch.load(_SCREAMING_SNAKE_CASE )
if "best_state" in original_checkpoint:
# we might have a training state saved, in which case discard the yaml results and just retain the weights
a__: str = original_checkpoint['best_state']
recursively_load_weights(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
model.save_pretrained(_SCREAMING_SNAKE_CASE )
if repo_id:
print('Pushing to the hub...' )
feature_extractor.push_to_hub(_SCREAMING_SNAKE_CASE )
model.push_to_hub(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
lowercase__ = argparse.ArgumentParser()
parser.add_argument(
'--model',
default='encodec_24khz',
type=str,
help='The model to convert. Should be one of \'encodec_24khz\', \'encodec_32khz\', \'encodec_48khz\'.',
)
parser.add_argument('--checkpoint_path', required=True, default=None, type=str, help='Path to original checkpoint')
parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
parser.add_argument(
'--pytorch_dump_folder_path', required=True, default=None, type=str, help='Path to the output PyTorch model.'
)
parser.add_argument(
'--push_to_hub', default=None, type=str, help='Where to upload the converted model on the 🤗 hub.'
)
lowercase__ = parser.parse_args()
convert_checkpoint(
args.model,
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.config_path,
args.push_to_hub,
)
| 290 | 0 |
'''simple docstring'''
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
a_ : Tuple = """\\n@misc{wu2016googles,\n title={Google\'s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n author={Yonghui Wu and Mike Schuster and Zhifeng Chen and Quoc V. Le and Mohammad Norouzi and Wolfgang Macherey\n and Maxim Krikun and Yuan Cao and Qin Gao and Klaus Macherey and Jeff Klingner and Apurva Shah and Melvin\n Johnson and Xiaobing Liu and Łukasz Kaiser and Stephan Gouws and Yoshikiyo Kato and Taku Kudo and Hideto\n Kazawa and Keith Stevens and George Kurian and Nishant Patil and Wei Wang and Cliff Young and\n Jason Smith and Jason Riesa and Alex Rudnick and Oriol Vinyals and Greg Corrado and Macduff Hughes\n and Jeffrey Dean},\n year={2016},\n eprint={1609.08144},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n}\n"""
a_ : Tuple = """\\nThe BLEU score has some undesirable properties when used for single\nsentences, as it was designed to be a corpus measure. We therefore\nuse a slightly different score for our RL experiments which we call\nthe \'GLEU score\'. For the GLEU score, we record all sub-sequences of\n1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then\ncompute a recall, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the target (ground truth) sequence,\nand a precision, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the generated output sequence. Then\nGLEU score is simply the minimum of recall and precision. This GLEU\nscore\'s range is always between 0 (no matches) and 1 (all match) and\nit is symmetrical when switching output and target. According to\nour experiments, GLEU score correlates quite well with the BLEU\nmetric on a corpus level but does not have its drawbacks for our per\nsentence reward objective.\n"""
a_ : Dict = """\\nComputes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.\nInstead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching\ntokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.\n\nArgs:\n predictions (list of str): list of translations to score.\n Each translation should be tokenized into a list of tokens.\n references (list of list of str): list of lists of references for each translation.\n Each reference should be tokenized into a list of tokens.\n min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.\n max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.\n\nReturns:\n \'google_bleu\': google_bleu score\n\nExamples:\n Example 1:\n >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n ... \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n ... \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n ... \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n\n >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n ... \'interested\', \'in\', \'world\', \'history\']\n >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n ... \'because\', \'he\', \'read\', \'the\', \'book\']\n\n >>> list_of_references = [[ref1a], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric(\"google_bleu\")\n >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n >>> print(round(results[\"google_bleu\"], 2))\n 0.44\n\n Example 2:\n >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n ... \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n ... \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n ... \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',\n ... \'heed\', \'the\', \'cat\', \'commands\']\n >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',\n ... \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',\n ... \'of\', \'the\', \'cat\']\n\n >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n ... \'interested\', \'in\', \'world\', \'history\']\n >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n ... \'because\', \'he\', \'read\', \'the\', \'book\']\n\n >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric(\"google_bleu\")\n >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n >>> print(round(results[\"google_bleu\"], 2))\n 0.61\n\n Example 3:\n >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n ... \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n ... \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n ... \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',\n ... \'heed\', \'the\', \'cat\', \'commands\']\n >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',\n ... \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',\n ... \'of\', \'the\', \'cat\']\n\n >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n ... \'interested\', \'in\', \'world\', \'history\']\n >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n ... \'because\', \'he\', \'read\', \'the\', \'book\']\n\n >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric(\"google_bleu\")\n >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)\n >>> print(round(results[\"google_bleu\"], 2))\n 0.53\n\n Example 4:\n >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n ... \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n ... \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n ... \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',\n ... \'heed\', \'the\', \'cat\', \'commands\']\n >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',\n ... \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',\n ... \'of\', \'the\', \'cat\']\n\n >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n ... \'interested\', \'in\', \'world\', \'history\']\n >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n ... \'because\', \'he\', \'read\', \'the\', \'book\']\n\n >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric(\"google_bleu\")\n >>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)\n >>> print(round(results[\"google_bleu\"], 2))\n 0.4\n"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class snake_case ( datasets.Metric ):
"""simple docstring"""
def snake_case ( self ):
"""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" ),
} ) , )
def snake_case ( self , UpperCamelCase , UpperCamelCase , UpperCamelCase = 1 , UpperCamelCase = 4 , ):
"""simple docstring"""
return {
"google_bleu": gleu_score.corpus_gleu(
list_of_references=UpperCamelCase , hypotheses=UpperCamelCase , min_len=UpperCamelCase , max_len=UpperCamelCase )
}
| 55 | """simple docstring"""
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->Union[str, Any]:
if height >= 1:
move_tower(height - 1 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
move_disk(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
move_tower(height - 1 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->Union[str, Any]:
print('moving disk from' , _SCREAMING_SNAKE_CASE , 'to' , _SCREAMING_SNAKE_CASE )
def __a ( ) ->List[str]:
a__: Dict = int(input('Height of hanoi: ' ).strip() )
move_tower(_SCREAMING_SNAKE_CASE , 'A' , 'B' , 'C' )
if __name__ == "__main__":
main()
| 290 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
import numpy as np
from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers.models.layoutlm.modeling_tf_layoutlm import (
TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFLayoutLMForMaskedLM,
TFLayoutLMForQuestionAnswering,
TFLayoutLMForSequenceClassification,
TFLayoutLMForTokenClassification,
TFLayoutLMModel,
)
class UpperCAmelCase__ :
def __init__( self , lowercase , lowercase=1_3 , lowercase=7 , lowercase=True , lowercase=True , lowercase=True , lowercase=True , lowercase=9_9 , lowercase=3_2 , lowercase=2 , lowercase=4 , lowercase=3_7 , lowercase="gelu" , lowercase=0.1 , lowercase=0.1 , lowercase=5_1_2 , lowercase=1_6 , lowercase=2 , lowercase=0.02 , lowercase=3 , lowercase=4 , lowercase=None , lowercase=1_0_0_0 , ) -> Any:
__UpperCamelCase = parent
__UpperCamelCase = batch_size
__UpperCamelCase = seq_length
__UpperCamelCase = is_training
__UpperCamelCase = use_input_mask
__UpperCamelCase = use_token_type_ids
__UpperCamelCase = use_labels
__UpperCamelCase = vocab_size
__UpperCamelCase = hidden_size
__UpperCamelCase = num_hidden_layers
__UpperCamelCase = num_attention_heads
__UpperCamelCase = intermediate_size
__UpperCamelCase = hidden_act
__UpperCamelCase = hidden_dropout_prob
__UpperCamelCase = attention_probs_dropout_prob
__UpperCamelCase = max_position_embeddings
__UpperCamelCase = type_vocab_size
__UpperCamelCase = type_sequence_label_size
__UpperCamelCase = initializer_range
__UpperCamelCase = num_labels
__UpperCamelCase = num_choices
__UpperCamelCase = scope
__UpperCamelCase = range_bbox
def __lowerCamelCase ( self ) -> Union[str, Any]:
__UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
# convert bbox to numpy since TF does not support item assignment
__UpperCamelCase = ids_tensor([self.batch_size, self.seq_length, 4] , self.range_bbox ).numpy()
# Ensure that bbox is legal
for i in range(bbox.shape[0] ):
for j in range(bbox.shape[1] ):
if bbox[i, j, 3] < bbox[i, j, 1]:
__UpperCamelCase = bbox[i, j, 3]
__UpperCamelCase = bbox[i, j, 1]
__UpperCamelCase = t
if bbox[i, j, 2] < bbox[i, j, 0]:
__UpperCamelCase = bbox[i, j, 2]
__UpperCamelCase = bbox[i, j, 0]
__UpperCamelCase = t
__UpperCamelCase = tf.convert_to_tensor(lowercase )
__UpperCamelCase = None
if self.use_input_mask:
__UpperCamelCase = random_attention_mask([self.batch_size, self.seq_length] )
__UpperCamelCase = None
if self.use_token_type_ids:
__UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__UpperCamelCase = None
__UpperCamelCase = None
__UpperCamelCase = None
if self.use_labels:
__UpperCamelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__UpperCamelCase = ids_tensor([self.batch_size] , self.num_choices )
__UpperCamelCase = LayoutLMConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , )
return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def __lowerCamelCase ( self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase ) -> Union[str, Any]:
__UpperCamelCase = TFLayoutLMModel(config=lowercase )
__UpperCamelCase = model(lowercase , lowercase , attention_mask=lowercase , token_type_ids=lowercase )
__UpperCamelCase = model(lowercase , lowercase , token_type_ids=lowercase )
__UpperCamelCase = model(lowercase , lowercase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def __lowerCamelCase ( self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase ) -> Optional[int]:
__UpperCamelCase = TFLayoutLMForMaskedLM(config=lowercase )
__UpperCamelCase = model(lowercase , lowercase , attention_mask=lowercase , token_type_ids=lowercase , labels=lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __lowerCamelCase ( self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase ) -> int:
__UpperCamelCase = self.num_labels
__UpperCamelCase = TFLayoutLMForSequenceClassification(config=lowercase )
__UpperCamelCase = model(lowercase , lowercase , attention_mask=lowercase , token_type_ids=lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __lowerCamelCase ( self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase ) -> Union[str, Any]:
__UpperCamelCase = self.num_labels
__UpperCamelCase = TFLayoutLMForTokenClassification(config=lowercase )
__UpperCamelCase = model(lowercase , lowercase , attention_mask=lowercase , token_type_ids=lowercase , labels=lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __lowerCamelCase ( self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase ) -> Tuple:
__UpperCamelCase = TFLayoutLMForQuestionAnswering(config=lowercase )
__UpperCamelCase = model(lowercase , lowercase , attention_mask=lowercase , token_type_ids=lowercase )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def __lowerCamelCase ( self ) -> Any:
__UpperCamelCase = self.prepare_config_and_inputs()
(
__UpperCamelCase
) = config_and_inputs
__UpperCamelCase = {
'input_ids': input_ids,
'bbox': bbox,
'token_type_ids': token_type_ids,
'attention_mask': input_mask,
}
return config, inputs_dict
@require_tf
class UpperCAmelCase__ ( __lowerCAmelCase , __lowerCAmelCase , unittest.TestCase):
__SCREAMING_SNAKE_CASE = (
(
TFLayoutLMModel,
TFLayoutLMForMaskedLM,
TFLayoutLMForTokenClassification,
TFLayoutLMForSequenceClassification,
TFLayoutLMForQuestionAnswering,
)
if is_tf_available()
else ()
)
__SCREAMING_SNAKE_CASE = (
{
'''feature-extraction''': TFLayoutLMModel,
'''fill-mask''': TFLayoutLMForMaskedLM,
'''text-classification''': TFLayoutLMForSequenceClassification,
'''token-classification''': TFLayoutLMForTokenClassification,
'''zero-shot''': TFLayoutLMForSequenceClassification,
}
if is_tf_available()
else {}
)
__SCREAMING_SNAKE_CASE = False
__SCREAMING_SNAKE_CASE = True
__SCREAMING_SNAKE_CASE = 1_0
def __lowerCamelCase ( self ) -> str:
__UpperCamelCase = TFLayoutLMModelTester(self )
__UpperCamelCase = ConfigTester(self , config_class=lowercase , hidden_size=3_7 )
def __lowerCamelCase ( self ) -> Union[str, Any]:
self.config_tester.run_common_tests()
def __lowerCamelCase ( self ) -> Any:
__UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowercase )
def __lowerCamelCase ( self ) -> int:
__UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*lowercase )
def __lowerCamelCase ( self ) -> Any:
__UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*lowercase )
def __lowerCamelCase ( self ) -> str:
__UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*lowercase )
def __lowerCamelCase ( self ) -> Any:
__UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*lowercase )
@slow
def __lowerCamelCase ( self ) -> Optional[int]:
for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__UpperCamelCase = TFLayoutLMModel.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
@unittest.skip("""Onnx compliancy broke with TF 2.10""" )
def __lowerCamelCase ( self ) -> Optional[int]:
pass
def _lowercase ( ):
'''simple docstring'''
__UpperCamelCase = tf.convert_to_tensor([[101,1_019,1_014,1_016,1_037,12_849,4_747,1_004,14_246,2_278,5_439,4_524,5_002,2_930,2_193,2_930,4_341,3_208,1_005,1_055,2_171,2_848,11_300,3_531,102],[101,4_070,4_034,7_020,1_024,3_058,1_015,1_013,2_861,1_013,6_070,19_274,2_772,6_205,27_814,16_147,16_147,4_343,2_047,10_283,10_969,14_389,1_012,2_338,102]] ) # noqa: E231
__UpperCamelCase = tf.convert_to_tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],] ) # noqa: E231
__UpperCamelCase = tf.convert_to_tensor([[[0,0,0,0],[423,237,440,251],[427,272,441,287],[419,115,437,129],[961,885,992,912],[256,38,330,58],[256,38,330,58],[336,42,353,57],[360,39,401,56],[360,39,401,56],[411,39,471,59],[479,41,528,59],[533,39,630,60],[67,113,134,131],[141,115,209,132],[68,149,133,166],[141,149,187,164],[195,148,287,165],[195,148,287,165],[195,148,287,165],[295,148,349,165],[441,149,492,166],[497,149,546,164],[64,201,125,218],[1_000,1_000,1_000,1_000]],[[0,0,0,0],[662,150,754,166],[665,199,742,211],[519,213,554,228],[519,213,554,228],[134,433,187,454],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[314,469,376,482],[504,684,582,706],[941,825,973,900],[941,825,973,900],[941,825,973,900],[941,825,973,900],[610,749,652,765],[130,659,168,672],[176,657,237,672],[238,657,312,672],[443,653,628,672],[443,653,628,672],[716,301,825,317],[1_000,1_000,1_000,1_000]]] ) # noqa: E231
__UpperCamelCase = tf.convert_to_tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]] ) # noqa: E231
# these are sequence labels (i.e. at the token level)
__UpperCamelCase = tf.convert_to_tensor([[-100,10,10,10,9,1,-100,7,7,-100,7,7,4,2,5,2,8,8,-100,-100,5,0,3,2,-100],[-100,12,12,12,-100,12,10,-100,-100,-100,-100,10,12,9,-100,-100,-100,10,10,10,9,12,-100,10,-100]] ) # noqa: E231
# fmt: on
return input_ids, attention_mask, bbox, token_type_ids, labels
@require_tf
class UpperCAmelCase__ ( unittest.TestCase):
@slow
def __lowerCamelCase ( self ) -> List[str]:
__UpperCamelCase = TFLayoutLMModel.from_pretrained("""microsoft/layoutlm-base-uncased""" )
__UpperCamelCase = prepare_layoutlm_batch_inputs()
# forward pass
__UpperCamelCase = model(input_ids=lowercase , bbox=lowercase , attention_mask=lowercase , token_type_ids=lowercase )
# test the sequence output on [0, :3, :3]
__UpperCamelCase = tf.convert_to_tensor(
[[0.1_785, -0.1_947, -0.0_425], [-0.3_254, -0.2_807, 0.2_553], [-0.5_391, -0.3_322, 0.3_364]] , )
self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3] , lowercase , atol=1E-3 ) )
# test the pooled output on [1, :3]
__UpperCamelCase = tf.convert_to_tensor([-0.6_580, -0.0_214, 0.8_552] )
self.assertTrue(np.allclose(outputs.pooler_output[1, :3] , lowercase , atol=1E-3 ) )
@slow
def __lowerCamelCase ( self ) -> List[str]:
__UpperCamelCase = TFLayoutLMForSequenceClassification.from_pretrained("""microsoft/layoutlm-base-uncased""" , num_labels=2 )
__UpperCamelCase = prepare_layoutlm_batch_inputs()
# forward pass
__UpperCamelCase = model(
input_ids=lowercase , bbox=lowercase , attention_mask=lowercase , token_type_ids=lowercase , labels=tf.convert_to_tensor([1, 1] ) , )
# test whether we get a loss as a scalar
__UpperCamelCase = outputs.loss
__UpperCamelCase = (2,)
self.assertEqual(loss.shape , lowercase )
# test the shape of the logits
__UpperCamelCase = outputs.logits
__UpperCamelCase = (2, 2)
self.assertEqual(logits.shape , lowercase )
@slow
def __lowerCamelCase ( self ) -> List[Any]:
__UpperCamelCase = TFLayoutLMForTokenClassification.from_pretrained("""microsoft/layoutlm-base-uncased""" , num_labels=1_3 )
__UpperCamelCase = prepare_layoutlm_batch_inputs()
# forward pass
__UpperCamelCase = model(
input_ids=lowercase , bbox=lowercase , attention_mask=lowercase , token_type_ids=lowercase , labels=lowercase )
# test the shape of the logits
__UpperCamelCase = outputs.logits
__UpperCamelCase = tf.convert_to_tensor((2, 2_5, 1_3) )
self.assertEqual(logits.shape , lowercase )
@slow
def __lowerCamelCase ( self ) -> Dict:
__UpperCamelCase = TFLayoutLMForQuestionAnswering.from_pretrained("""microsoft/layoutlm-base-uncased""" )
__UpperCamelCase = prepare_layoutlm_batch_inputs()
# forward pass
__UpperCamelCase = model(input_ids=lowercase , bbox=lowercase , attention_mask=lowercase , token_type_ids=lowercase )
# test the shape of the logits
__UpperCamelCase = tf.convert_to_tensor((2, 2_5) )
self.assertEqual(outputs.start_logits.shape , lowercase )
self.assertEqual(outputs.end_logits.shape , lowercase )
| 349 | """simple docstring"""
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = False ) ->str:
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
a__: Optional[int] = F'Expected string as input, found {type(_SCREAMING_SNAKE_CASE )}'
raise ValueError(_SCREAMING_SNAKE_CASE )
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
a__: List[str] = F'Expected boolean as use_pascal parameter, found {type(_SCREAMING_SNAKE_CASE )}'
raise ValueError(_SCREAMING_SNAKE_CASE )
a__: int = input_str.split('_' )
a__: List[str] = 0 if use_pascal else 1
a__: List[str] = words[start_index:]
a__: List[str] = [word[0].upper() + word[1:] for word in words_to_capitalize]
a__: List[str] = '' if use_pascal else words[0]
return "".join([initial_word, *capitalized_words] )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 290 | 0 |
'''simple docstring'''
__A = "Input must be a string of 8 numbers plus letter"
__A = "TRWAGMYFPDXBNJZSQVHLCKE"
def _A ( lowercase__ ):
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
lowercase__ = f'''Expected string as input, found {type(_SCREAMING_SNAKE_CASE ).__name__}'''
raise TypeError(_SCREAMING_SNAKE_CASE )
lowercase__ = spanish_id.replace("""-""" , """""" ).upper()
if len(_SCREAMING_SNAKE_CASE ) != 9:
raise ValueError(_SCREAMING_SNAKE_CASE )
try:
lowercase__ = int(spanish_id_clean[0:8] )
lowercase__ = spanish_id_clean[8]
except ValueError as ex:
raise ValueError(_SCREAMING_SNAKE_CASE ) from ex
if letter.isdigit():
raise ValueError(_SCREAMING_SNAKE_CASE )
return letter == LOOKUP_LETTERS[number % 23]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 164 | """simple docstring"""
class __snake_case :
def __init__( self , lowercase , lowercase=None , lowercase=None) -> List[str]:
'''simple docstring'''
a__: Dict = data
a__: List[Any] = previous
a__: Any = next_node
def __str__( self) -> str:
'''simple docstring'''
return f'{self.data}'
def lowerCamelCase_ ( self) -> int:
'''simple docstring'''
return self.data
def lowerCamelCase_ ( self) -> Union[str, Any]:
'''simple docstring'''
return self.next
def lowerCamelCase_ ( self) -> Optional[Any]:
'''simple docstring'''
return self.previous
class __snake_case :
def __init__( self , lowercase) -> Dict:
'''simple docstring'''
a__: List[Any] = head
def __iter__( self) -> List[Any]:
'''simple docstring'''
return self
def lowerCamelCase_ ( self) -> Optional[Any]:
'''simple docstring'''
if not self.current:
raise StopIteration
else:
a__: Dict = self.current.get_data()
a__: Optional[Any] = self.current.get_next()
return value
class __snake_case :
def __init__( self) -> Dict:
'''simple docstring'''
a__: List[Any] = None # First node in list
a__: Optional[int] = None # Last node in list
def __str__( self) -> Optional[Any]:
'''simple docstring'''
a__: Dict = self.head
a__: Optional[Any] = []
while current is not None:
nodes.append(current.get_data())
a__: str = current.get_next()
return " ".join(str(lowercase) for node in nodes)
def __contains__( self , lowercase) -> Optional[int]:
'''simple docstring'''
a__: Optional[int] = self.head
while current:
if current.get_data() == value:
return True
a__: Dict = current.get_next()
return False
def __iter__( self) -> int:
'''simple docstring'''
return LinkedListIterator(self.head)
def lowerCamelCase_ ( self) -> List[Any]:
'''simple docstring'''
if self.head:
return self.head.get_data()
return None
def lowerCamelCase_ ( self) -> Optional[Any]:
'''simple docstring'''
if self.tail:
return self.tail.get_data()
return None
def lowerCamelCase_ ( self , lowercase) -> None:
'''simple docstring'''
if self.head is None:
a__: Optional[Any] = node
a__: Optional[Any] = node
else:
self.insert_before_node(self.head , lowercase)
def lowerCamelCase_ ( self , lowercase) -> None:
'''simple docstring'''
if self.head is None:
self.set_head(lowercase)
else:
self.insert_after_node(self.tail , lowercase)
def lowerCamelCase_ ( self , lowercase) -> None:
'''simple docstring'''
a__: Tuple = Node(lowercase)
if self.head is None:
self.set_head(lowercase)
else:
self.set_tail(lowercase)
def lowerCamelCase_ ( self , lowercase , lowercase) -> None:
'''simple docstring'''
a__: Union[str, Any] = node
a__: Optional[Any] = node.previous
if node.get_previous() is None:
a__: Tuple = node_to_insert
else:
a__: int = node_to_insert
a__: Optional[int] = node_to_insert
def lowerCamelCase_ ( self , lowercase , lowercase) -> None:
'''simple docstring'''
a__: Optional[int] = node
a__: Tuple = node.next
if node.get_next() is None:
a__: Optional[int] = node_to_insert
else:
a__: Any = node_to_insert
a__: str = node_to_insert
def lowerCamelCase_ ( self , lowercase , lowercase) -> None:
'''simple docstring'''
a__: Any = 1
a__: Tuple = Node(lowercase)
a__: Tuple = self.head
while node:
if current_position == position:
self.insert_before_node(lowercase , lowercase)
return
current_position += 1
a__: List[Any] = node.next
self.insert_after_node(self.tail , lowercase)
def lowerCamelCase_ ( self , lowercase) -> Node:
'''simple docstring'''
a__: Tuple = self.head
while node:
if node.get_data() == item:
return node
a__: List[str] = node.get_next()
raise Exception('Node not found')
def lowerCamelCase_ ( self , lowercase) -> Any:
'''simple docstring'''
if (node := self.get_node(lowercase)) is not None:
if node == self.head:
a__: Any = self.head.get_next()
if node == self.tail:
a__: List[Any] = self.tail.get_previous()
self.remove_node_pointers(lowercase)
@staticmethod
def lowerCamelCase_ ( lowercase) -> None:
'''simple docstring'''
if node.get_next():
a__: Any = node.previous
if node.get_previous():
a__: List[str] = node.next
a__: int = None
a__: Union[str, Any] = None
def lowerCamelCase_ ( self) -> str:
'''simple docstring'''
return self.head is None
def __a ( ) ->None:
pass
if __name__ == "__main__":
import doctest
doctest.testmod()
| 290 | 0 |
"""simple docstring"""
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class a__ ( __lowerCAmelCase ):
__lowerCAmelCase = (DEISMultistepScheduler,)
__lowerCAmelCase = (("""num_inference_steps""", 25),)
def __magic_name__ ( self , **_a ):
lowercase : Dict = {
'num_train_timesteps': 1_000,
'beta_start': 0.0_0_0_1,
'beta_end': 0.0_2,
'beta_schedule': 'linear',
'solver_order': 2,
}
config.update(**_a )
return config
def __magic_name__ ( self , _a=0 , **_a ):
lowercase : List[Any] = dict(self.forward_default_kwargs )
lowercase : int = kwargs.pop("num_inference_steps" , _a )
lowercase : Union[str, Any] = self.dummy_sample
lowercase : Optional[int] = 0.1 * sample
lowercase : Optional[int] = [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
for scheduler_class in self.scheduler_classes:
lowercase : str = self.get_scheduler_config(**_a )
lowercase : Optional[int] = scheduler_class(**_a )
scheduler.set_timesteps(_a )
# copy over dummy past residuals
lowercase : Any = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(_a )
lowercase : Dict = scheduler_class.from_pretrained(_a )
new_scheduler.set_timesteps(_a )
# copy over dummy past residuals
lowercase : Optional[Any] = dummy_past_residuals[: new_scheduler.config.solver_order]
lowercase : Dict = sample, sample
for t in range(_a , time_step + scheduler.config.solver_order + 1 ):
lowercase : List[Any] = scheduler.step(_a , _a , _a , **_a ).prev_sample
lowercase : Optional[int] = new_scheduler.step(_a , _a , _a , **_a ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def __magic_name__ ( self ):
pass
def __magic_name__ ( self , _a=0 , **_a ):
lowercase : int = dict(self.forward_default_kwargs )
lowercase : List[Any] = kwargs.pop("num_inference_steps" , _a )
lowercase : List[Any] = self.dummy_sample
lowercase : Dict = 0.1 * sample
lowercase : Optional[Any] = [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
for scheduler_class in self.scheduler_classes:
lowercase : Dict = self.get_scheduler_config()
lowercase : Dict = scheduler_class(**_a )
scheduler.set_timesteps(_a )
# copy over dummy past residuals (must be after setting timesteps)
lowercase : List[Any] = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(_a )
lowercase : Union[str, Any] = scheduler_class.from_pretrained(_a )
# copy over dummy past residuals
new_scheduler.set_timesteps(_a )
# copy over dummy past residual (must be after setting timesteps)
lowercase : List[str] = dummy_past_residuals[: new_scheduler.config.solver_order]
lowercase : Optional[int] = scheduler.step(_a , _a , _a , **_a ).prev_sample
lowercase : Any = new_scheduler.step(_a , _a , _a , **_a ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def __magic_name__ ( self , _a=None , **_a ):
if scheduler is None:
lowercase : Union[str, Any] = self.scheduler_classes[0]
lowercase : str = self.get_scheduler_config(**_a )
lowercase : Any = scheduler_class(**_a )
lowercase : Any = self.scheduler_classes[0]
lowercase : Union[str, Any] = self.get_scheduler_config(**_a )
lowercase : Any = scheduler_class(**_a )
lowercase : Union[str, Any] = 10
lowercase : Optional[Any] = self.dummy_model()
lowercase : Any = self.dummy_sample_deter
scheduler.set_timesteps(_a )
for i, t in enumerate(scheduler.timesteps ):
lowercase : Optional[int] = model(_a , _a )
lowercase : List[Any] = scheduler.step(_a , _a , _a ).prev_sample
return sample
def __magic_name__ ( self ):
lowercase : Optional[Any] = dict(self.forward_default_kwargs )
lowercase : Optional[int] = kwargs.pop("num_inference_steps" , _a )
for scheduler_class in self.scheduler_classes:
lowercase : List[str] = self.get_scheduler_config()
lowercase : List[str] = scheduler_class(**_a )
lowercase : Union[str, Any] = self.dummy_sample
lowercase : Union[str, Any] = 0.1 * sample
if num_inference_steps is not None and hasattr(_a , "set_timesteps" ):
scheduler.set_timesteps(_a )
elif num_inference_steps is not None and not hasattr(_a , "set_timesteps" ):
lowercase : str = num_inference_steps
# copy over dummy past residuals (must be done after set_timesteps)
lowercase : Optional[int] = [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
lowercase : Optional[int] = dummy_past_residuals[: scheduler.config.solver_order]
lowercase : Optional[int] = scheduler.timesteps[5]
lowercase : List[Any] = scheduler.timesteps[6]
lowercase : Optional[int] = scheduler.step(_a , _a , _a , **_a ).prev_sample
lowercase : List[Any] = scheduler.step(_a , _a , _a , **_a ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
def __magic_name__ ( self ):
lowercase : str = DEISMultistepScheduler(**self.get_scheduler_config() )
lowercase : List[Any] = self.full_loop(scheduler=_a )
lowercase : str = torch.mean(torch.abs(_a ) )
assert abs(result_mean.item() - 0.2_3_9_1_6 ) < 1E-3
lowercase : Any = DPMSolverSinglestepScheduler.from_config(scheduler.config )
lowercase : str = DPMSolverMultistepScheduler.from_config(scheduler.config )
lowercase : str = UniPCMultistepScheduler.from_config(scheduler.config )
lowercase : Tuple = DEISMultistepScheduler.from_config(scheduler.config )
lowercase : str = self.full_loop(scheduler=_a )
lowercase : List[str] = torch.mean(torch.abs(_a ) )
assert abs(result_mean.item() - 0.2_3_9_1_6 ) < 1E-3
def __magic_name__ ( self ):
for timesteps in [25, 50, 100, 999, 1_000]:
self.check_over_configs(num_train_timesteps=_a )
def __magic_name__ ( self ):
self.check_over_configs(thresholding=_a )
for order in [1, 2, 3]:
for solver_type in ["logrho"]:
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
thresholding=_a , prediction_type=_a , sample_max_value=_a , algorithm_type="deis" , solver_order=_a , solver_type=_a , )
def __magic_name__ ( self ):
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=_a )
def __magic_name__ ( self ):
for algorithm_type in ["deis"]:
for solver_type in ["logrho"]:
for order in [1, 2, 3]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
solver_order=_a , solver_type=_a , prediction_type=_a , algorithm_type=_a , )
lowercase : str = self.full_loop(
solver_order=_a , solver_type=_a , prediction_type=_a , algorithm_type=_a , )
assert not torch.isnan(_a ).any(), "Samples have nan numbers"
def __magic_name__ ( self ):
self.check_over_configs(lower_order_final=_a )
self.check_over_configs(lower_order_final=_a )
def __magic_name__ ( self ):
for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
self.check_over_forward(num_inference_steps=_a , time_step=0 )
def __magic_name__ ( self ):
lowercase : Tuple = self.full_loop()
lowercase : Tuple = torch.mean(torch.abs(_a ) )
assert abs(result_mean.item() - 0.2_3_9_1_6 ) < 1E-3
def __magic_name__ ( self ):
lowercase : Dict = self.full_loop(prediction_type="v_prediction" )
lowercase : Any = torch.mean(torch.abs(_a ) )
assert abs(result_mean.item() - 0.0_9_1 ) < 1E-3
def __magic_name__ ( self ):
lowercase : Optional[Any] = self.scheduler_classes[0]
lowercase : List[str] = self.get_scheduler_config(thresholding=_a , dynamic_thresholding_ratio=0 )
lowercase : Any = scheduler_class(**_a )
lowercase : Tuple = 10
lowercase : Dict = self.dummy_model()
lowercase : Dict = self.dummy_sample_deter.half()
scheduler.set_timesteps(_a )
for i, t in enumerate(scheduler.timesteps ):
lowercase : Any = model(_a , _a )
lowercase : Any = scheduler.step(_a , _a , _a ).prev_sample
assert sample.dtype == torch.floataa
| 202 | """simple docstring"""
import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable
import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization import from_bytes, to_bytes
from flax.training import train_state
from flax.training.common_utils import shard
from tqdm.auto import tqdm
from transformers import BigBirdConfig, FlaxBigBirdForQuestionAnswering
from transformers.models.big_bird.modeling_flax_big_bird import FlaxBigBirdForQuestionAnsweringModule
class __snake_case ( __lowerCAmelCase ):
a__ = 42
a__ = jnp.floataa
a__ = True
def lowerCamelCase_ ( self) -> int:
'''simple docstring'''
super().setup()
a__: int = nn.Dense(5 , dtype=self.dtype)
def __call__( self , *lowercase , **lowercase) -> Dict:
'''simple docstring'''
a__: Dict = super().__call__(*lowercase , **lowercase)
a__: str = self.cls(outputs[2])
return outputs[:2] + (cls_out,)
class __snake_case ( __lowerCAmelCase ):
a__ = FlaxBigBirdForNaturalQuestionsModule
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->Optional[Any]:
def cross_entropy(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None ):
a__: Any = logits.shape[-1]
a__: List[Any] = (labels[..., None] == jnp.arange(_SCREAMING_SNAKE_CASE )[None]).astype('f4' )
a__: List[str] = jax.nn.log_softmax(_SCREAMING_SNAKE_CASE , axis=-1 )
a__: Dict = -jnp.sum(labels * logits , axis=-1 )
if reduction is not None:
a__: str = reduction(_SCREAMING_SNAKE_CASE )
return loss
a__: Tuple = partial(_SCREAMING_SNAKE_CASE , reduction=jnp.mean )
a__: List[str] = cross_entropy(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
a__: Union[str, Any] = cross_entropy(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
a__: Any = cross_entropy(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
return (start_loss + end_loss + pooled_loss) / 3
@dataclass
class __snake_case :
a__ = "google/bigbird-roberta-base"
a__ = 3000
a__ = 1_0500
a__ = 128
a__ = 3
a__ = 1
a__ = 5
# tx_args
a__ = 3e-5
a__ = 0.0
a__ = 2_0000
a__ = 0.0095
a__ = "bigbird-roberta-natural-questions"
a__ = "training-expt"
a__ = "data/nq-training.jsonl"
a__ = "data/nq-validation.jsonl"
def lowerCamelCase_ ( self) -> Optional[Any]:
'''simple docstring'''
os.makedirs(self.base_dir , exist_ok=lowercase)
a__: str = os.path.join(self.base_dir , self.save_dir)
a__: List[str] = self.batch_size_per_device * jax.device_count()
@dataclass
class __snake_case :
a__ = 42
a__ = 4096 # no dynamic padding on TPUs
def __call__( self , lowercase) -> List[Any]:
'''simple docstring'''
a__: int = self.collate_fn(lowercase)
a__: Optional[int] = jax.tree_util.tree_map(lowercase , lowercase)
return batch
def lowerCamelCase_ ( self , lowercase) -> Dict:
'''simple docstring'''
a__ , a__: Dict = self.fetch_inputs(features['input_ids'])
a__: List[Any] = {
'input_ids': jnp.array(lowercase , dtype=jnp.intaa),
'attention_mask': jnp.array(lowercase , dtype=jnp.intaa),
'start_labels': jnp.array(features['start_token'] , dtype=jnp.intaa),
'end_labels': jnp.array(features['end_token'] , dtype=jnp.intaa),
'pooled_labels': jnp.array(features['category'] , dtype=jnp.intaa),
}
return batch
def lowerCamelCase_ ( self , lowercase) -> List[str]:
'''simple docstring'''
a__: List[Any] = [self._fetch_inputs(lowercase) for ids in input_ids]
return zip(*lowercase)
def lowerCamelCase_ ( self , lowercase) -> Dict:
'''simple docstring'''
a__: Union[str, Any] = [1 for _ in range(len(lowercase))]
while len(lowercase) < self.max_length:
input_ids.append(self.pad_id)
attention_mask.append(0)
return input_ids, attention_mask
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None ) ->List[Any]:
if seed is not None:
a__: int = dataset.shuffle(seed=_SCREAMING_SNAKE_CASE )
for i in range(len(_SCREAMING_SNAKE_CASE ) // batch_size ):
a__: Union[str, Any] = dataset[i * batch_size : (i + 1) * batch_size]
yield dict(_SCREAMING_SNAKE_CASE )
@partial(jax.pmap , axis_name='batch' )
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ) ->Any:
def loss_fn(_SCREAMING_SNAKE_CASE ):
a__: str = model_inputs.pop('start_labels' )
a__: Dict = model_inputs.pop('end_labels' )
a__: Optional[int] = model_inputs.pop('pooled_labels' )
a__: Optional[Any] = state.apply_fn(**_SCREAMING_SNAKE_CASE , params=_SCREAMING_SNAKE_CASE , dropout_rng=_SCREAMING_SNAKE_CASE , train=_SCREAMING_SNAKE_CASE )
a__ , a__ , a__: Optional[int] = outputs
return state.loss_fn(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , )
a__ , a__: Union[str, Any] = jax.random.split(_SCREAMING_SNAKE_CASE )
a__: List[Any] = jax.value_and_grad(_SCREAMING_SNAKE_CASE )
a__ , a__: str = grad_fn(state.params )
a__: Optional[int] = jax.lax.pmean({'loss': loss} , axis_name='batch' )
a__: int = jax.lax.pmean(_SCREAMING_SNAKE_CASE , 'batch' )
a__: Union[str, Any] = state.apply_gradients(grads=_SCREAMING_SNAKE_CASE )
return state, metrics, new_drp_rng
@partial(jax.pmap , axis_name='batch' )
def __a ( _SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ) ->Optional[Any]:
a__: Optional[int] = model_inputs.pop('start_labels' )
a__: int = model_inputs.pop('end_labels' )
a__: Dict = model_inputs.pop('pooled_labels' )
a__: Union[str, Any] = state.apply_fn(**_SCREAMING_SNAKE_CASE , params=state.params , train=_SCREAMING_SNAKE_CASE )
a__ , a__ , a__: int = outputs
a__: Optional[int] = state.loss_fn(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
a__: Tuple = jax.lax.pmean({'loss': loss} , axis_name='batch' )
return metrics
class __snake_case ( train_state.TrainState ):
a__ = struct.field(pytree_node=__lowerCAmelCase )
@dataclass
class __snake_case :
a__ = 42
a__ = 42
a__ = 42
a__ = 42
a__ = 42
a__ = 42
a__ = None
def lowerCamelCase_ ( self , lowercase , lowercase , lowercase , lowercase=None) -> Optional[int]:
'''simple docstring'''
a__: Dict = model.params
a__: Any = TrainState.create(
apply_fn=model.__call__ , params=lowercase , tx=lowercase , loss_fn=lowercase , )
if ckpt_dir is not None:
a__ , a__ , a__ , a__ , a__: Any = restore_checkpoint(lowercase , lowercase)
a__: Any = {
'lr': args.lr,
'init_lr': args.init_lr,
'warmup_steps': args.warmup_steps,
'num_train_steps': num_train_steps,
'weight_decay': args.weight_decay,
}
a__ , a__: str = build_tx(**lowercase)
a__: Optional[Any] = train_state.TrainState(
step=lowercase , apply_fn=model.__call__ , params=lowercase , tx=lowercase , opt_state=lowercase , )
a__: int = args
a__: Union[str, Any] = data_collator
a__: Any = lr
a__: Dict = params
a__: Tuple = jax_utils.replicate(lowercase)
return state
def lowerCamelCase_ ( self , lowercase , lowercase , lowercase) -> int:
'''simple docstring'''
a__: int = self.args
a__: str = len(lowercase) // args.batch_size
a__: Tuple = jax.random.PRNGKey(0)
a__: List[Any] = jax.random.split(lowercase , jax.device_count())
for epoch in range(args.max_epochs):
a__: str = jnp.array(0 , dtype=jnp.floataa)
a__: Tuple = get_batched_dataset(lowercase , args.batch_size , seed=lowercase)
a__: Optional[int] = 0
for batch in tqdm(lowercase , total=lowercase , desc=f'Running EPOCH-{epoch}'):
a__: List[str] = self.data_collator(lowercase)
a__ , a__ , a__: int = self.train_step_fn(lowercase , lowercase , **lowercase)
running_loss += jax_utils.unreplicate(metrics['loss'])
i += 1
if i % args.logging_steps == 0:
a__: List[Any] = jax_utils.unreplicate(state.step)
a__: Tuple = running_loss.item() / i
a__: Optional[Any] = self.scheduler_fn(state_step - 1)
a__: List[Any] = self.evaluate(lowercase , lowercase)
a__: List[str] = {
'step': state_step.item(),
'eval_loss': eval_loss.item(),
'tr_loss': tr_loss,
'lr': lr.item(),
}
tqdm.write(str(lowercase))
self.logger.log(lowercase , commit=lowercase)
if i % args.save_steps == 0:
self.save_checkpoint(args.save_dir + f'-e{epoch}-s{i}' , state=lowercase)
def lowerCamelCase_ ( self , lowercase , lowercase) -> List[Any]:
'''simple docstring'''
a__: Tuple = get_batched_dataset(lowercase , self.args.batch_size)
a__: Dict = len(lowercase) // self.args.batch_size
a__: Tuple = jnp.array(0 , dtype=jnp.floataa)
a__: List[Any] = 0
for batch in tqdm(lowercase , total=lowercase , desc='Evaluating ... '):
a__: str = self.data_collator(lowercase)
a__: List[str] = self.val_step_fn(lowercase , **lowercase)
running_loss += jax_utils.unreplicate(metrics['loss'])
i += 1
return running_loss / i
def lowerCamelCase_ ( self , lowercase , lowercase) -> Any:
'''simple docstring'''
a__: List[Any] = jax_utils.unreplicate(lowercase)
print(f'SAVING CHECKPOINT IN {save_dir}' , end=' ... ')
self.model_save_fn(lowercase , params=state.params)
with open(os.path.join(lowercase , 'opt_state.msgpack') , 'wb') as f:
f.write(to_bytes(state.opt_state))
joblib.dump(self.args , os.path.join(lowercase , 'args.joblib'))
joblib.dump(self.data_collator , os.path.join(lowercase , 'data_collator.joblib'))
with open(os.path.join(lowercase , 'training_state.json') , 'w') as f:
json.dump({'step': state.step.item()} , lowercase)
print('DONE')
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->Optional[Any]:
print(F'RESTORING CHECKPOINT FROM {save_dir}' , end=' ... ' )
with open(os.path.join(_SCREAMING_SNAKE_CASE , 'flax_model.msgpack' ) , 'rb' ) as f:
a__: int = from_bytes(state.params , f.read() )
with open(os.path.join(_SCREAMING_SNAKE_CASE , 'opt_state.msgpack' ) , 'rb' ) as f:
a__: Optional[Any] = from_bytes(state.opt_state , f.read() )
a__: Optional[Any] = joblib.load(os.path.join(_SCREAMING_SNAKE_CASE , 'args.joblib' ) )
a__: int = joblib.load(os.path.join(_SCREAMING_SNAKE_CASE , 'data_collator.joblib' ) )
with open(os.path.join(_SCREAMING_SNAKE_CASE , 'training_state.json' ) , 'r' ) as f:
a__: Any = json.load(_SCREAMING_SNAKE_CASE )
a__: Optional[Any] = training_state['step']
print('DONE' )
return params, opt_state, step, args, data_collator
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->Optional[int]:
a__: str = num_train_steps - warmup_steps
a__: str = optax.linear_schedule(init_value=_SCREAMING_SNAKE_CASE , end_value=_SCREAMING_SNAKE_CASE , transition_steps=_SCREAMING_SNAKE_CASE )
a__: List[Any] = optax.linear_schedule(init_value=_SCREAMING_SNAKE_CASE , end_value=1e-7 , transition_steps=_SCREAMING_SNAKE_CASE )
a__: int = optax.join_schedules(schedules=[warmup_fn, decay_fn] , boundaries=[warmup_steps] )
return lr
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->Tuple:
def weight_decay_mask(_SCREAMING_SNAKE_CASE ):
a__: List[Any] = traverse_util.flatten_dict(_SCREAMING_SNAKE_CASE )
a__: List[str] = {k: (v[-1] != 'bias' and v[-2:] != ('LayerNorm', 'scale')) for k, v in params.items()}
return traverse_util.unflatten_dict(_SCREAMING_SNAKE_CASE )
a__: List[str] = scheduler_fn(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
a__: Any = optax.adamw(learning_rate=_SCREAMING_SNAKE_CASE , weight_decay=_SCREAMING_SNAKE_CASE , mask=_SCREAMING_SNAKE_CASE )
return tx, lr
| 290 | 0 |
'''simple docstring'''
from math import sqrt
def UpperCAmelCase_ ( __lowercase : List[str] ) -> int:
'''simple docstring'''
_UpperCAmelCase = 0
for i in range(1 , int(sqrt(_SCREAMING_SNAKE_CASE ) + 1 ) ):
if n % i == 0 and i != sqrt(_SCREAMING_SNAKE_CASE ):
total += i + n // i
elif i == sqrt(_SCREAMING_SNAKE_CASE ):
total += i
return total - n
def UpperCAmelCase_ ( __lowercase : List[Any] = 1_0000 ) -> int:
'''simple docstring'''
_UpperCAmelCase = sum(
i
for i in range(1 , _SCREAMING_SNAKE_CASE )
if sum_of_divisors(sum_of_divisors(_SCREAMING_SNAKE_CASE ) ) == i and sum_of_divisors(_SCREAMING_SNAKE_CASE ) != i )
return total
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 22 | """simple docstring"""
from typing import List, Optional, Tuple, Union
import PIL
import torch
from torchvision import transforms
from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor
lowercase__ = transforms.Compose(
[
transforms.Resize((256, 256)),
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5]),
]
)
def __a ( _SCREAMING_SNAKE_CASE ) ->Any:
if isinstance(_SCREAMING_SNAKE_CASE , torch.Tensor ):
return image
elif isinstance(_SCREAMING_SNAKE_CASE , PIL.Image.Image ):
a__: Optional[int] = [image]
a__: str = [trans(img.convert('RGB' ) ) for img in image]
a__: Any = torch.stack(_SCREAMING_SNAKE_CASE )
return image
class __snake_case ( __lowerCAmelCase ):
def __init__( self , lowercase , lowercase) -> Optional[int]:
'''simple docstring'''
super().__init__()
# make sure scheduler can always be converted to DDIM
a__: Dict = DDIMScheduler.from_config(scheduler.config)
self.register_modules(unet=lowercase , scheduler=lowercase)
def lowerCamelCase_ ( self , lowercase) -> int:
'''simple docstring'''
if strength < 0 or strength > 1:
raise ValueError(f'The value of strength should in [0.0, 1.0] but is {strength}')
def lowerCamelCase_ ( self , lowercase , lowercase , lowercase) -> Dict:
'''simple docstring'''
a__: int = min(int(num_inference_steps * strength) , lowercase)
a__: Any = max(num_inference_steps - init_timestep , 0)
a__: Union[str, Any] = self.scheduler.timesteps[t_start:]
return timesteps, num_inference_steps - t_start
def lowerCamelCase_ ( self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase=None) -> List[Any]:
'''simple docstring'''
if not isinstance(lowercase , (torch.Tensor, PIL.Image.Image, list)):
raise ValueError(
f'`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(lowercase)}')
a__: Tuple = image.to(device=lowercase , dtype=lowercase)
if isinstance(lowercase , lowercase) and len(lowercase) != batch_size:
raise ValueError(
f'You have passed a list of generators of length {len(lowercase)}, but requested an effective batch'
f' size of {batch_size}. Make sure the batch size matches the length of the generators.')
a__: List[str] = init_latents.shape
a__: List[Any] = randn_tensor(lowercase , generator=lowercase , device=lowercase , dtype=lowercase)
# get latents
print('add noise to latents at timestep' , lowercase)
a__: int = self.scheduler.add_noise(lowercase , lowercase , lowercase)
a__: Dict = init_latents
return latents
@torch.no_grad()
def __call__( self , lowercase = None , lowercase = 0.8 , lowercase = 1 , lowercase = None , lowercase = 0.0 , lowercase = 50 , lowercase = None , lowercase = "pil" , lowercase = True , ) -> Union[ImagePipelineOutput, Tuple]:
'''simple docstring'''
self.check_inputs(lowercase)
# 2. Preprocess image
a__: Tuple = preprocess(lowercase)
# 3. set timesteps
self.scheduler.set_timesteps(lowercase , device=self.device)
a__ , a__: Union[str, Any] = self.get_timesteps(lowercase , lowercase , self.device)
a__: Optional[int] = timesteps[:1].repeat(lowercase)
# 4. Prepare latent variables
a__: Union[str, Any] = self.prepare_latents(lowercase , lowercase , lowercase , self.unet.dtype , self.device , lowercase)
a__: Optional[Any] = latents
# 5. Denoising loop
for t in self.progress_bar(lowercase):
# 1. predict noise model_output
a__: Dict = self.unet(lowercase , lowercase).sample
# 2. predict previous mean of image x_t-1 and add variance depending on eta
# eta corresponds to η in paper and should be between [0, 1]
# do x_t -> x_t-1
a__: Optional[Any] = self.scheduler.step(
lowercase , lowercase , lowercase , eta=lowercase , use_clipped_model_output=lowercase , generator=lowercase , ).prev_sample
a__: Union[str, Any] = (image / 2 + 0.5).clamp(0 , 1)
a__: Optional[int] = image.cpu().permute(0 , 2 , 3 , 1).numpy()
if output_type == "pil":
a__: Dict = self.numpy_to_pil(lowercase)
if not return_dict:
return (image, latent_timestep.item())
return ImagePipelineOutput(images=lowercase)
| 290 | 0 |
'''simple docstring'''
class __UpperCAmelCase :
'''simple docstring'''
def __init__(self : Union[str, Any] , _lowerCAmelCase : int ):
A = arr.split(""",""" )
def A (self : Dict ):
A = [int(self.array[0] )] * len(self.array )
A = [int(self.array[0] )] * len(self.array )
for i in range(1 , len(self.array ) ):
A = max(
int(self.array[i] ) + sum_value[i - 1] , int(self.array[i] ) )
A = max(sum_value[i] , rear[i - 1] )
return rear[len(self.array ) - 1]
if __name__ == "__main__":
_lowerCamelCase : Optional[Any] = input('please input some numbers:')
_lowerCamelCase : str = SubArray(whole_array)
_lowerCamelCase : Any = array.solve_sub_array()
print(('the results is:', re))
| 258 | """simple docstring"""
import shutil
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_tf_cross_test,
require_tf,
require_torch,
require_torchvision,
require_vision,
)
from transformers.utils import is_tf_available, is_torch_available, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, SamImageProcessor, SamProcessor
if is_torch_available():
import torch
if is_tf_available():
import tensorflow as tf
@require_vision
@require_torchvision
class __snake_case ( unittest.TestCase ):
def lowerCamelCase_ ( self) -> Any:
'''simple docstring'''
a__: Optional[Any] = tempfile.mkdtemp()
a__: Optional[int] = SamImageProcessor()
a__: Tuple = SamProcessor(lowercase)
processor.save_pretrained(self.tmpdirname)
def lowerCamelCase_ ( self , **lowercase) -> List[Any]:
'''simple docstring'''
return AutoProcessor.from_pretrained(self.tmpdirname , **lowercase).image_processor
def lowerCamelCase_ ( self) -> Optional[int]:
'''simple docstring'''
shutil.rmtree(self.tmpdirname)
def lowerCamelCase_ ( self) -> int:
'''simple docstring'''
a__: Any = [np.random.randint(2_55 , size=(3, 30, 4_00) , dtype=np.uinta)]
a__: Optional[Any] = [Image.fromarray(np.moveaxis(lowercase , 0 , -1)) for x in image_inputs]
return image_inputs
def lowerCamelCase_ ( self) -> str:
'''simple docstring'''
a__: List[str] = SamProcessor(image_processor=self.get_image_processor())
processor.save_pretrained(self.tmpdirname)
a__: Optional[int] = self.get_image_processor(do_normalize=lowercase , padding_value=1.0)
a__: List[Any] = SamProcessor.from_pretrained(self.tmpdirname , do_normalize=lowercase , padding_value=1.0)
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string())
self.assertIsInstance(processor.image_processor , lowercase)
def lowerCamelCase_ ( self) -> Tuple:
'''simple docstring'''
a__: Union[str, Any] = self.get_image_processor()
a__: List[Any] = SamProcessor(image_processor=lowercase)
a__: Optional[int] = self.prepare_image_inputs()
a__: Optional[Any] = image_processor(lowercase , return_tensors='np')
a__: Tuple = processor(images=lowercase , return_tensors='np')
input_feat_extract.pop('original_sizes') # pop original_sizes as it is popped in the processor
input_feat_extract.pop('reshaped_input_sizes') # pop original_sizes as it is popped in the processor
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1e-2)
@require_torch
def lowerCamelCase_ ( self) -> Any:
'''simple docstring'''
a__: int = self.get_image_processor()
a__: List[str] = SamProcessor(image_processor=lowercase)
a__: Optional[Any] = [torch.ones((1, 3, 5, 5))]
a__: Union[str, Any] = [[17_64, 26_46]]
a__: Optional[Any] = [[6_83, 10_24]]
a__: int = processor.post_process_masks(lowercase , lowercase , lowercase)
self.assertEqual(masks[0].shape , (1, 3, 17_64, 26_46))
a__: Optional[int] = processor.post_process_masks(
lowercase , torch.tensor(lowercase) , torch.tensor(lowercase))
self.assertEqual(masks[0].shape , (1, 3, 17_64, 26_46))
# should also work with np
a__: Dict = [np.ones((1, 3, 5, 5))]
a__: Tuple = processor.post_process_masks(lowercase , np.array(lowercase) , np.array(lowercase))
self.assertEqual(masks[0].shape , (1, 3, 17_64, 26_46))
a__: Tuple = [[1, 0], [0, 1]]
with self.assertRaises(lowercase):
a__: List[Any] = processor.post_process_masks(lowercase , np.array(lowercase) , np.array(lowercase))
@require_vision
@require_tf
class __snake_case ( unittest.TestCase ):
def lowerCamelCase_ ( self) -> List[Any]:
'''simple docstring'''
a__: Optional[Any] = tempfile.mkdtemp()
a__: List[Any] = SamImageProcessor()
a__: Optional[int] = SamProcessor(lowercase)
processor.save_pretrained(self.tmpdirname)
def lowerCamelCase_ ( self , **lowercase) -> int:
'''simple docstring'''
return AutoProcessor.from_pretrained(self.tmpdirname , **lowercase).image_processor
def lowerCamelCase_ ( self) -> Tuple:
'''simple docstring'''
shutil.rmtree(self.tmpdirname)
def lowerCamelCase_ ( self) -> Tuple:
'''simple docstring'''
a__: Optional[Any] = [np.random.randint(2_55 , size=(3, 30, 4_00) , dtype=np.uinta)]
a__: Optional[int] = [Image.fromarray(np.moveaxis(lowercase , 0 , -1)) for x in image_inputs]
return image_inputs
def lowerCamelCase_ ( self) -> Any:
'''simple docstring'''
a__: List[str] = SamProcessor(image_processor=self.get_image_processor())
processor.save_pretrained(self.tmpdirname)
a__: Dict = self.get_image_processor(do_normalize=lowercase , padding_value=1.0)
a__: Union[str, Any] = SamProcessor.from_pretrained(self.tmpdirname , do_normalize=lowercase , padding_value=1.0)
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string())
self.assertIsInstance(processor.image_processor , lowercase)
def lowerCamelCase_ ( self) -> int:
'''simple docstring'''
a__: Optional[Any] = self.get_image_processor()
a__: str = SamProcessor(image_processor=lowercase)
a__: int = self.prepare_image_inputs()
a__: int = image_processor(lowercase , return_tensors='np')
a__: Dict = processor(images=lowercase , return_tensors='np')
input_feat_extract.pop('original_sizes') # pop original_sizes as it is popped in the processor
input_feat_extract.pop('reshaped_input_sizes') # pop reshaped_input_sizes as it is popped in the processor
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1e-2)
@require_tf
def lowerCamelCase_ ( self) -> Any:
'''simple docstring'''
a__: Tuple = self.get_image_processor()
a__: Any = SamProcessor(image_processor=lowercase)
a__: str = [tf.ones((1, 3, 5, 5))]
a__: List[Any] = [[17_64, 26_46]]
a__: List[Any] = [[6_83, 10_24]]
a__: List[Any] = processor.post_process_masks(lowercase , lowercase , lowercase , return_tensors='tf')
self.assertEqual(masks[0].shape , (1, 3, 17_64, 26_46))
a__: Tuple = processor.post_process_masks(
lowercase , tf.convert_to_tensor(lowercase) , tf.convert_to_tensor(lowercase) , return_tensors='tf' , )
self.assertEqual(masks[0].shape , (1, 3, 17_64, 26_46))
# should also work with np
a__: Optional[Any] = [np.ones((1, 3, 5, 5))]
a__: int = processor.post_process_masks(
lowercase , np.array(lowercase) , np.array(lowercase) , return_tensors='tf')
self.assertEqual(masks[0].shape , (1, 3, 17_64, 26_46))
a__: List[str] = [[1, 0], [0, 1]]
with self.assertRaises(tf.errors.InvalidArgumentError):
a__: Any = processor.post_process_masks(
lowercase , np.array(lowercase) , np.array(lowercase) , return_tensors='tf')
@require_vision
@require_torchvision
class __snake_case ( unittest.TestCase ):
def lowerCamelCase_ ( self) -> Dict:
'''simple docstring'''
a__: str = tempfile.mkdtemp()
a__: int = SamImageProcessor()
a__: Union[str, Any] = SamProcessor(lowercase)
processor.save_pretrained(self.tmpdirname)
def lowerCamelCase_ ( self , **lowercase) -> Optional[int]:
'''simple docstring'''
return AutoProcessor.from_pretrained(self.tmpdirname , **lowercase).image_processor
def lowerCamelCase_ ( self) -> Tuple:
'''simple docstring'''
shutil.rmtree(self.tmpdirname)
def lowerCamelCase_ ( self) -> Tuple:
'''simple docstring'''
a__: Any = [np.random.randint(2_55 , size=(3, 30, 4_00) , dtype=np.uinta)]
a__: Any = [Image.fromarray(np.moveaxis(lowercase , 0 , -1)) for x in image_inputs]
return image_inputs
@is_pt_tf_cross_test
def lowerCamelCase_ ( self) -> int:
'''simple docstring'''
a__: Optional[int] = self.get_image_processor()
a__: int = SamProcessor(image_processor=lowercase)
a__: int = np.random.randint(0 , 2 , size=(1, 3, 5, 5)).astype(np.floataa)
a__: Dict = [tf.convert_to_tensor(lowercase)]
a__: Union[str, Any] = [torch.tensor(lowercase)]
a__: List[Any] = [[17_64, 26_46]]
a__: Optional[Any] = [[6_83, 10_24]]
a__: Tuple = processor.post_process_masks(
lowercase , lowercase , lowercase , return_tensors='tf')
a__: str = processor.post_process_masks(
lowercase , lowercase , lowercase , return_tensors='pt')
self.assertTrue(np.all(tf_masks[0].numpy() == pt_masks[0].numpy()))
@is_pt_tf_cross_test
def lowerCamelCase_ ( self) -> str:
'''simple docstring'''
a__: Tuple = self.get_image_processor()
a__: Dict = SamProcessor(image_processor=lowercase)
a__: Any = self.prepare_image_inputs()
a__: List[Any] = image_processor(lowercase , return_tensors='pt')['pixel_values'].numpy()
a__: Tuple = processor(images=lowercase , return_tensors='pt')['pixel_values'].numpy()
a__: Any = image_processor(lowercase , return_tensors='tf')['pixel_values'].numpy()
a__: Any = processor(images=lowercase , return_tensors='tf')['pixel_values'].numpy()
self.assertTrue(np.allclose(lowercase , lowercase))
self.assertTrue(np.allclose(lowercase , lowercase))
self.assertTrue(np.allclose(lowercase , lowercase))
| 290 | 0 |
'''simple docstring'''
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class lowerCamelCase_ (unittest.TestCase , __lowerCAmelCase ):
'''simple docstring'''
def _A ( self : Union[str, Any] ):
_UpperCAmelCase : Any = load_tool("text-classification" )
self.tool.setup()
_UpperCAmelCase : str = load_tool("text-classification" , remote=A )
def _A ( self : Any ):
_UpperCAmelCase : Any = self.tool("That\'s quite cool" , ["positive", "negative"] )
self.assertEqual(A , "positive" )
def _A ( self : Union[str, Any] ):
_UpperCAmelCase : List[Any] = self.remote_tool("That\'s quite cool" , ["positive", "negative"] )
self.assertEqual(A , "positive" )
def _A ( self : Optional[Any] ):
_UpperCAmelCase : Optional[int] = self.tool(text="That\'s quite cool" , labels=["positive", "negative"] )
self.assertEqual(A , "positive" )
def _A ( self : Tuple ):
_UpperCAmelCase : Optional[Any] = self.remote_tool(text="That\'s quite cool" , labels=["positive", "negative"] )
self.assertEqual(A , "positive" )
| 31 | """simple docstring"""
from math import pow, sqrt
def __a ( *_SCREAMING_SNAKE_CASE ) ->bool:
a__: Union[str, Any] = len(_SCREAMING_SNAKE_CASE ) > 0 and all(value > 0.0 for value in values )
return result
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->float | ValueError:
return (
round(sqrt(molar_mass_a / molar_mass_a ) , 6 )
if validate(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
else ValueError('Input Error: Molar mass values must greater than 0.' )
)
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->float | ValueError:
return (
round(effusion_rate * sqrt(molar_mass_a / molar_mass_a ) , 6 )
if validate(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
else ValueError(
'Input Error: Molar mass and effusion rate values must greater than 0.' )
)
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->float | ValueError:
return (
round(effusion_rate / sqrt(molar_mass_a / molar_mass_a ) , 6 )
if validate(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
else ValueError(
'Input Error: Molar mass and effusion rate values must greater than 0.' )
)
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->float | ValueError:
return (
round(molar_mass / pow(effusion_rate_a / effusion_rate_a , 2 ) , 6 )
if validate(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
else ValueError(
'Input Error: Molar mass and effusion rate values must greater than 0.' )
)
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->float | ValueError:
return (
round(pow(effusion_rate_a / effusion_rate_a , 2 ) / molar_mass , 6 )
if validate(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
else ValueError(
'Input Error: Molar mass and effusion rate values must greater than 0.' )
)
| 290 | 0 |
from __future__ import annotations
def lowerCAmelCase__ ( a__: Tuple , a__: str , a__: Optional[Any] ) -> dict[str, float]:
'''simple docstring'''
if (voltage, current, resistance).count(0 ) != 1:
raise ValueError('One and only one argument must be 0' )
if resistance < 0:
raise ValueError('Resistance cannot be negative' )
if voltage == 0:
return {"voltage": float(current * resistance )}
elif current == 0:
return {"current": voltage / resistance}
elif resistance == 0:
return {"resistance": voltage / current}
else:
raise ValueError('Exactly one argument must be 0' )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 329 | """simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
'andreasmadsen/efficient_mlm_m0.40': (
'https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json'
),
}
class __snake_case ( __lowerCAmelCase ):
a__ = """roberta-prelayernorm"""
def __init__( self , lowercase=5_02_65 , lowercase=7_68 , lowercase=12 , lowercase=12 , lowercase=30_72 , lowercase="gelu" , lowercase=0.1 , lowercase=0.1 , lowercase=5_12 , lowercase=2 , lowercase=0.02 , lowercase=1e-12 , lowercase=1 , lowercase=0 , lowercase=2 , lowercase="absolute" , lowercase=True , lowercase=None , **lowercase , ) -> Any:
'''simple docstring'''
super().__init__(pad_token_id=lowercase , bos_token_id=lowercase , eos_token_id=lowercase , **lowercase)
a__: Union[str, Any] = vocab_size
a__: str = hidden_size
a__: Tuple = num_hidden_layers
a__: List[str] = num_attention_heads
a__: Dict = hidden_act
a__: int = intermediate_size
a__: Tuple = hidden_dropout_prob
a__: str = attention_probs_dropout_prob
a__: Tuple = max_position_embeddings
a__: Tuple = type_vocab_size
a__: Optional[Any] = initializer_range
a__: Tuple = layer_norm_eps
a__: Optional[int] = position_embedding_type
a__: Any = use_cache
a__: Dict = classifier_dropout
class __snake_case ( __lowerCAmelCase ):
@property
def lowerCamelCase_ ( self) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
if self.task == "multiple-choice":
a__: str = {0: 'batch', 1: 'choice', 2: 'sequence'}
else:
a__: Union[str, Any] = {0: 'batch', 1: 'sequence'}
return OrderedDict(
[
('input_ids', dynamic_axis),
('attention_mask', dynamic_axis),
])
| 290 | 0 |
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