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import warnings
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
UpperCAmelCase__ = logging.get_logger(__name__) # pylint: disable=invalid-name
def A ( _UpperCAmelCase : Union[List, PIL.Image.Image, torch.Tensor] ) -> Optional[Any]:
'''simple docstring'''
warnings.warn(
'The preprocess method is deprecated and will be removed in a future version. Please'
' use VaeImageProcessor.preprocess instead' , _UpperCAmelCase , )
if isinstance(_UpperCAmelCase , torch.Tensor ):
return image
elif isinstance(_UpperCAmelCase , PIL.Image.Image ):
_UpperCAmelCase = [image]
if isinstance(image[0] , PIL.Image.Image ):
_UpperCAmelCase , _UpperCAmelCase = image[0].size
_UpperCAmelCase , _UpperCAmelCase = (x - x % 8 for x in (w, h)) # resize to integer multiple of 8
_UpperCAmelCase = [np.array(i.resize((w, h) , resample=PIL_INTERPOLATION['lanczos'] ) )[None, :] for i in image]
_UpperCAmelCase = np.concatenate(_UpperCAmelCase , axis=0 )
_UpperCAmelCase = np.array(_UpperCAmelCase ).astype(np.floataa ) / 255.0
_UpperCAmelCase = image.transpose(0 , 3 , 1 , 2 )
_UpperCAmelCase = 2.0 * image - 1.0
_UpperCAmelCase = torch.from_numpy(_UpperCAmelCase )
elif isinstance(image[0] , torch.Tensor ):
_UpperCAmelCase = torch.cat(_UpperCAmelCase , dim=0 )
return image
def A ( _UpperCAmelCase : Union[List, PIL.Image.Image, torch.Tensor] ) -> List[Any]:
'''simple docstring'''
if isinstance(_UpperCAmelCase , torch.Tensor ):
return mask
elif isinstance(_UpperCAmelCase , PIL.Image.Image ):
_UpperCAmelCase = [mask]
if isinstance(mask[0] , PIL.Image.Image ):
_UpperCAmelCase , _UpperCAmelCase = mask[0].size
_UpperCAmelCase , _UpperCAmelCase = (x - x % 32 for x in (w, h)) # resize to integer multiple of 32
_UpperCAmelCase = [np.array(m.convert('L' ).resize((w, h) , resample=PIL_INTERPOLATION['nearest'] ) )[None, :] for m in mask]
_UpperCAmelCase = np.concatenate(_UpperCAmelCase , axis=0 )
_UpperCAmelCase = mask.astype(np.floataa ) / 255.0
_UpperCAmelCase = 0
_UpperCAmelCase = 1
_UpperCAmelCase = torch.from_numpy(_UpperCAmelCase )
elif isinstance(mask[0] , torch.Tensor ):
_UpperCAmelCase = torch.cat(_UpperCAmelCase , dim=0 )
return mask
class __lowerCAmelCase ( A ):
UpperCamelCase = 42
UpperCamelCase = 42
def __init__( self : Union[str, Any] , A : List[Any] , A : List[str]) -> Dict:
"""simple docstring"""
super().__init__()
self.register_modules(unet=A , scheduler=A)
@torch.no_grad()
def __call__( self : Tuple , A : Union[torch.Tensor, PIL.Image.Image] , A : Union[torch.Tensor, PIL.Image.Image] , A : int = 2_50 , A : float = 0.0 , A : int = 10 , A : int = 10 , A : Optional[Union[torch.Generator, List[torch.Generator]]] = None , A : Optional[str] = "pil" , A : bool = True , ) -> Union[ImagePipelineOutput, Tuple]:
"""simple docstring"""
_UpperCAmelCase = image
_UpperCAmelCase = _preprocess_image(A)
_UpperCAmelCase = original_image.to(device=self.device , dtype=self.unet.dtype)
_UpperCAmelCase = _preprocess_mask(A)
_UpperCAmelCase = mask_image.to(device=self.device , dtype=self.unet.dtype)
_UpperCAmelCase = original_image.shape[0]
# sample gaussian noise to begin the loop
if isinstance(A , A) and len(A) != batch_size:
raise ValueError(
F"You have passed a list of generators of length {len(A)}, but requested an effective batch"
F" size of {batch_size}. Make sure the batch size matches the length of the generators.")
_UpperCAmelCase = original_image.shape
_UpperCAmelCase = randn_tensor(A , generator=A , device=self.device , dtype=self.unet.dtype)
# set step values
self.scheduler.set_timesteps(A , A , A , self.device)
_UpperCAmelCase = eta
_UpperCAmelCase = self.scheduler.timesteps[0] + 1
_UpperCAmelCase = generator[0] if isinstance(A , A) else generator
for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
if t < t_last:
# predict the noise residual
_UpperCAmelCase = self.unet(A , A).sample
# compute previous image: x_t -> x_t-1
_UpperCAmelCase = self.scheduler.step(A , A , A , A , A , A).prev_sample
else:
# compute the reverse: x_t-1 -> x_t
_UpperCAmelCase = self.scheduler.undo_step(A , A , A)
_UpperCAmelCase = t
_UpperCAmelCase = (image / 2 + 0.5).clamp(0 , 1)
_UpperCAmelCase = image.cpu().permute(0 , 2 , 3 , 1).numpy()
if output_type == "pil":
_UpperCAmelCase = self.numpy_to_pil(A)
if not return_dict:
return (image,)
return ImagePipelineOutput(images=A)
| 339 |
import sys
from collections import defaultdict
class __lowerCAmelCase :
def __init__( self : int) -> str:
"""simple docstring"""
_UpperCAmelCase = []
def _lowerCamelCase ( self : Any , A : List[str]) -> int:
"""simple docstring"""
return self.node_position[vertex]
def _lowerCamelCase ( self : Optional[Any] , A : Optional[int] , A : str) -> List[str]:
"""simple docstring"""
_UpperCAmelCase = pos
def _lowerCamelCase ( self : Tuple , A : Tuple , A : Dict , A : List[str] , A : Optional[Any]) -> Dict:
"""simple docstring"""
if start > size // 2 - 1:
return
else:
if 2 * start + 2 >= size:
_UpperCAmelCase = 2 * start + 1
else:
if heap[2 * start + 1] < heap[2 * start + 2]:
_UpperCAmelCase = 2 * start + 1
else:
_UpperCAmelCase = 2 * start + 2
if heap[smallest_child] < heap[start]:
_UpperCAmelCase , _UpperCAmelCase = heap[smallest_child], positions[smallest_child]
_UpperCAmelCase , _UpperCAmelCase = (
heap[start],
positions[start],
)
_UpperCAmelCase , _UpperCAmelCase = temp, tempa
_UpperCAmelCase = self.get_position(positions[smallest_child])
self.set_position(
positions[smallest_child] , self.get_position(positions[start]))
self.set_position(positions[start] , A)
self.top_to_bottom(A , A , A , A)
def _lowerCamelCase ( self : Optional[int] , A : str , A : Optional[Any] , A : Optional[int] , A : str) -> Any:
"""simple docstring"""
_UpperCAmelCase = position[index]
while index != 0:
_UpperCAmelCase = int((index - 2) / 2) if index % 2 == 0 else int((index - 1) / 2)
if val < heap[parent]:
_UpperCAmelCase = heap[parent]
_UpperCAmelCase = position[parent]
self.set_position(position[parent] , A)
else:
_UpperCAmelCase = val
_UpperCAmelCase = temp
self.set_position(A , A)
break
_UpperCAmelCase = parent
else:
_UpperCAmelCase = val
_UpperCAmelCase = temp
self.set_position(A , 0)
def _lowerCamelCase ( self : Union[str, Any] , A : Optional[int] , A : Tuple) -> str:
"""simple docstring"""
_UpperCAmelCase = len(A) // 2 - 1
for i in range(A , -1 , -1):
self.top_to_bottom(A , A , len(A) , A)
def _lowerCamelCase ( self : Optional[int] , A : int , A : str) -> List[str]:
"""simple docstring"""
_UpperCAmelCase = positions[0]
_UpperCAmelCase = sys.maxsize
self.top_to_bottom(A , 0 , len(A) , A)
return temp
def A ( _UpperCAmelCase : int ) -> Any:
'''simple docstring'''
_UpperCAmelCase = Heap()
_UpperCAmelCase = [0] * len(_UpperCAmelCase )
_UpperCAmelCase = [-1] * len(_UpperCAmelCase ) # Neighboring Tree Vertex of selected vertex
# Minimum Distance of explored vertex with neighboring vertex of partial tree
# formed in graph
_UpperCAmelCase = [] # Heap of Distance of vertices from their neighboring vertex
_UpperCAmelCase = []
for vertex in range(len(_UpperCAmelCase ) ):
distance_tv.append(sys.maxsize )
positions.append(_UpperCAmelCase )
heap.node_position.append(_UpperCAmelCase )
_UpperCAmelCase = []
_UpperCAmelCase = 1
_UpperCAmelCase = sys.maxsize
for neighbor, distance in adjacency_list[0]:
_UpperCAmelCase = 0
_UpperCAmelCase = distance
heap.heapify(_UpperCAmelCase , _UpperCAmelCase )
for _ in range(1 , len(_UpperCAmelCase ) ):
_UpperCAmelCase = heap.delete_minimum(_UpperCAmelCase , _UpperCAmelCase )
if visited[vertex] == 0:
tree_edges.append((nbr_tv[vertex], vertex) )
_UpperCAmelCase = 1
for neighbor, distance in adjacency_list[vertex]:
if (
visited[neighbor] == 0
and distance < distance_tv[heap.get_position(_UpperCAmelCase )]
):
_UpperCAmelCase = distance
heap.bottom_to_top(
_UpperCAmelCase , heap.get_position(_UpperCAmelCase ) , _UpperCAmelCase , _UpperCAmelCase )
_UpperCAmelCase = vertex
return tree_edges
if __name__ == "__main__": # pragma: no cover
# < --------- Prims Algorithm --------- >
UpperCAmelCase__ = int(input("Enter number of edges: ").strip())
UpperCAmelCase__ = defaultdict(list)
for _ in range(edges_number):
UpperCAmelCase__ = [int(x) for x in input().strip().split()]
adjacency_list[edge[0]].append([edge[1], edge[2]])
adjacency_list[edge[1]].append([edge[0], edge[2]])
print(prisms_algorithm(adjacency_list))
| 339 | 1 |
from functools import lru_cache
def A ( _UpperCAmelCase : int ) -> set:
'''simple docstring'''
_UpperCAmelCase = 2
_UpperCAmelCase = set()
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.add(_UpperCAmelCase )
if n > 1:
factors.add(_UpperCAmelCase )
return factors
@lru_cache
def A ( _UpperCAmelCase : int ) -> int:
'''simple docstring'''
return len(unique_prime_factors(_UpperCAmelCase ) )
def A ( _UpperCAmelCase : list ) -> bool:
'''simple docstring'''
return len(set(_UpperCAmelCase ) ) in (0, 1)
def A ( _UpperCAmelCase : int ) -> list:
'''simple docstring'''
_UpperCAmelCase = 2
while True:
# Increment each value of a generated range
_UpperCAmelCase = [base + i for i in range(_UpperCAmelCase )]
# Run elements through out unique_prime_factors function
# Append our target number to the end.
_UpperCAmelCase = [upf_len(_UpperCAmelCase ) for x in group]
checker.append(_UpperCAmelCase )
# If all numbers in the list are equal, return the group variable.
if equality(_UpperCAmelCase ):
return group
# Increment our base variable by 1
base += 1
def A ( _UpperCAmelCase : int = 4 ) -> int:
'''simple docstring'''
_UpperCAmelCase = run(_UpperCAmelCase )
return results[0] if len(_UpperCAmelCase ) else None
if __name__ == "__main__":
print(solution())
| 339 |
import torch
from transformers import CamembertForMaskedLM, CamembertTokenizer
def A ( _UpperCAmelCase : str , _UpperCAmelCase : Any , _UpperCAmelCase : List[str] , _UpperCAmelCase : Optional[int]=5 ) -> List[Any]:
'''simple docstring'''
# Adapted from https://github.com/pytorch/fairseq/blob/master/fairseq/models/roberta/hub_interface.py
assert masked_input.count('<mask>' ) == 1
_UpperCAmelCase = torch.tensor(tokenizer.encode(_UpperCAmelCase , add_special_tokens=_UpperCAmelCase ) ).unsqueeze(0 ) # Batch size 1
_UpperCAmelCase = model(_UpperCAmelCase )[0] # The last hidden-state is the first element of the output tuple
_UpperCAmelCase = (input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
_UpperCAmelCase = logits[0, masked_index, :]
_UpperCAmelCase = logits.softmax(dim=0 )
_UpperCAmelCase , _UpperCAmelCase = prob.topk(k=_UpperCAmelCase , dim=0 )
_UpperCAmelCase = ' '.join(
[tokenizer.convert_ids_to_tokens(indices[i].item() ) for i in range(len(_UpperCAmelCase ) )] )
_UpperCAmelCase = tokenizer.mask_token
_UpperCAmelCase = []
for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(' ' ) ):
_UpperCAmelCase = predicted_token_bpe.replace('\u2581' , ' ' )
if " {0}".format(_UpperCAmelCase ) in masked_input:
topk_filled_outputs.append(
(
masked_input.replace(' {0}'.format(_UpperCAmelCase ) , _UpperCAmelCase ),
values[index].item(),
predicted_token,
) )
else:
topk_filled_outputs.append(
(
masked_input.replace(_UpperCAmelCase , _UpperCAmelCase ),
values[index].item(),
predicted_token,
) )
return topk_filled_outputs
UpperCAmelCase__ = CamembertTokenizer.from_pretrained("camembert-base")
UpperCAmelCase__ = CamembertForMaskedLM.from_pretrained("camembert-base")
model.eval()
UpperCAmelCase__ = "Le camembert est <mask> :)"
print(fill_mask(masked_input, model, tokenizer, topk=3))
| 339 | 1 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMultistepInverseScheduler,
DPMSolverMultistepScheduler,
StableDiffusionDiffEditPipeline,
UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device
from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class __lowerCAmelCase ( A , A , unittest.TestCase ):
UpperCamelCase = StableDiffusionDiffEditPipeline
UpperCamelCase = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''height''', '''width''', '''image'''} | {'''image_latents'''}
UpperCamelCase = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {'''image'''} | {'''image_latents'''}
UpperCamelCase = frozenset(
[] ) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
UpperCamelCase = frozenset([] )
def _lowerCamelCase ( self : List[str]) -> Optional[Any]:
"""simple docstring"""
torch.manual_seed(0)
_UpperCAmelCase = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') , up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D') , cross_attention_dim=32 , attention_head_dim=(2, 4) , use_linear_projection=A , )
_UpperCAmelCase = 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 , )
_UpperCAmelCase = DDIMInverseScheduler(
beta_start=0.0_0_0_8_5 , beta_end=0.0_1_2 , beta_schedule='scaled_linear' , clip_sample=A , set_alpha_to_zero=A , )
torch.manual_seed(0)
_UpperCAmelCase = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'] , up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'] , latent_channels=4 , sample_size=1_28 , )
torch.manual_seed(0)
_UpperCAmelCase = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=10_00 , hidden_act='gelu' , projection_dim=5_12 , )
_UpperCAmelCase = CLIPTextModel(A)
_UpperCAmelCase = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')
_UpperCAmelCase = {
'unet': unet,
'scheduler': scheduler,
'inverse_scheduler': inverse_scheduler,
'vae': vae,
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'safety_checker': None,
'feature_extractor': None,
}
return components
def _lowerCamelCase ( self : List[Any] , A : List[Any] , A : Tuple=0) -> str:
"""simple docstring"""
_UpperCAmelCase = floats_tensor((1, 16, 16) , rng=random.Random(A)).to(A)
_UpperCAmelCase = floats_tensor((1, 2, 4, 16, 16) , rng=random.Random(A)).to(A)
if str(A).startswith('mps'):
_UpperCAmelCase = torch.manual_seed(A)
else:
_UpperCAmelCase = torch.Generator(device=A).manual_seed(A)
_UpperCAmelCase = {
'prompt': 'a dog and a newt',
'mask_image': mask,
'image_latents': latents,
'generator': generator,
'num_inference_steps': 2,
'inpaint_strength': 1.0,
'guidance_scale': 6.0,
'output_type': 'numpy',
}
return inputs
def _lowerCamelCase ( self : Union[str, Any] , A : Dict , A : List[str]=0) -> str:
"""simple docstring"""
_UpperCAmelCase = floats_tensor((1, 3, 32, 32) , rng=random.Random(A)).to(A)
_UpperCAmelCase = image.cpu().permute(0 , 2 , 3 , 1)[0]
_UpperCAmelCase = Image.fromarray(np.uinta(A)).convert('RGB')
if str(A).startswith('mps'):
_UpperCAmelCase = torch.manual_seed(A)
else:
_UpperCAmelCase = torch.Generator(device=A).manual_seed(A)
_UpperCAmelCase = {
'image': image,
'source_prompt': 'a cat and a frog',
'target_prompt': 'a dog and a newt',
'generator': generator,
'num_inference_steps': 2,
'num_maps_per_mask': 2,
'mask_encode_strength': 1.0,
'guidance_scale': 6.0,
'output_type': 'numpy',
}
return inputs
def _lowerCamelCase ( self : int , A : Optional[int] , A : str=0) -> int:
"""simple docstring"""
_UpperCAmelCase = floats_tensor((1, 3, 32, 32) , rng=random.Random(A)).to(A)
_UpperCAmelCase = image.cpu().permute(0 , 2 , 3 , 1)[0]
_UpperCAmelCase = Image.fromarray(np.uinta(A)).convert('RGB')
if str(A).startswith('mps'):
_UpperCAmelCase = torch.manual_seed(A)
else:
_UpperCAmelCase = torch.Generator(device=A).manual_seed(A)
_UpperCAmelCase = {
'image': image,
'prompt': 'a cat and a frog',
'generator': generator,
'num_inference_steps': 2,
'inpaint_strength': 1.0,
'guidance_scale': 6.0,
'decode_latents': True,
'output_type': 'numpy',
}
return inputs
def _lowerCamelCase ( self : Optional[int]) -> int:
"""simple docstring"""
if not hasattr(self.pipeline_class , '_optional_components'):
return
_UpperCAmelCase = self.get_dummy_components()
_UpperCAmelCase = self.pipeline_class(**A)
pipe.to(A)
pipe.set_progress_bar_config(disable=A)
# set all optional components to None and update pipeline config accordingly
for optional_component in pipe._optional_components:
setattr(A , A , A)
pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components})
_UpperCAmelCase = self.get_dummy_inputs(A)
_UpperCAmelCase = pipe(**A)[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(A)
_UpperCAmelCase = self.pipeline_class.from_pretrained(A)
pipe_loaded.to(A)
pipe_loaded.set_progress_bar_config(disable=A)
for optional_component in pipe._optional_components:
self.assertTrue(
getattr(A , A) is None , F"`{optional_component}` did not stay set to None after loading." , )
_UpperCAmelCase = self.get_dummy_inputs(A)
_UpperCAmelCase = pipe_loaded(**A)[0]
_UpperCAmelCase = np.abs(output - output_loaded).max()
self.assertLess(A , 1E-4)
def _lowerCamelCase ( self : List[str]) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase = 'cpu'
_UpperCAmelCase = self.get_dummy_components()
_UpperCAmelCase = self.pipeline_class(**A)
pipe.to(A)
pipe.set_progress_bar_config(disable=A)
_UpperCAmelCase = self.get_dummy_mask_inputs(A)
_UpperCAmelCase = pipe.generate_mask(**A)
_UpperCAmelCase = mask[0, -3:, -3:]
self.assertEqual(mask.shape , (1, 16, 16))
_UpperCAmelCase = np.array([0] * 9)
_UpperCAmelCase = np.abs(mask_slice.flatten() - expected_slice).max()
self.assertLessEqual(A , 1E-3)
self.assertEqual(mask[0, -3, -4] , 0)
def _lowerCamelCase ( self : str) -> str:
"""simple docstring"""
_UpperCAmelCase = 'cpu'
_UpperCAmelCase = self.get_dummy_components()
_UpperCAmelCase = self.pipeline_class(**A)
pipe.to(A)
pipe.set_progress_bar_config(disable=A)
_UpperCAmelCase = self.get_dummy_inversion_inputs(A)
_UpperCAmelCase = pipe.invert(**A).images
_UpperCAmelCase = image[0, -1, -3:, -3:]
self.assertEqual(image.shape , (2, 32, 32, 3))
_UpperCAmelCase = np.array(
[0.5_1_5_0, 0.5_1_3_4, 0.5_0_4_3, 0.5_3_7_6, 0.4_6_9_4, 0.5_1_0_5_0, 0.5_0_1_5, 0.4_4_0_7, 0.4_7_9_9] , )
_UpperCAmelCase = np.abs(image_slice.flatten() - expected_slice).max()
self.assertLessEqual(A , 1E-3)
def _lowerCamelCase ( self : Optional[int]) -> Union[str, Any]:
"""simple docstring"""
super().test_inference_batch_single_identical(expected_max_diff=5E-3)
def _lowerCamelCase ( self : Dict) -> Dict:
"""simple docstring"""
_UpperCAmelCase = 'cpu'
_UpperCAmelCase = self.get_dummy_components()
_UpperCAmelCase = {'beta_start': 0.0_0_0_8_5, 'beta_end': 0.0_1_2, 'beta_schedule': 'scaled_linear'}
_UpperCAmelCase = DPMSolverMultistepScheduler(**A)
_UpperCAmelCase = DPMSolverMultistepInverseScheduler(**A)
_UpperCAmelCase = self.pipeline_class(**A)
pipe.to(A)
pipe.set_progress_bar_config(disable=A)
_UpperCAmelCase = self.get_dummy_inversion_inputs(A)
_UpperCAmelCase = pipe.invert(**A).images
_UpperCAmelCase = image[0, -1, -3:, -3:]
self.assertEqual(image.shape , (2, 32, 32, 3))
_UpperCAmelCase = np.array(
[0.5_1_5_0, 0.5_1_3_4, 0.5_0_4_3, 0.5_3_7_6, 0.4_6_9_4, 0.5_1_0_5_0, 0.5_0_1_5, 0.4_4_0_7, 0.4_7_9_9] , )
_UpperCAmelCase = np.abs(image_slice.flatten() - expected_slice).max()
self.assertLessEqual(A , 1E-3)
@require_torch_gpu
@slow
class __lowerCAmelCase ( unittest.TestCase ):
def _lowerCamelCase ( self : str) -> Dict:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@classmethod
def _lowerCamelCase ( cls : int) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png')
_UpperCAmelCase = raw_image.convert('RGB').resize((7_68, 7_68))
_UpperCAmelCase = raw_image
def _lowerCamelCase ( self : Optional[int]) -> List[str]:
"""simple docstring"""
_UpperCAmelCase = torch.manual_seed(0)
_UpperCAmelCase = StableDiffusionDiffEditPipeline.from_pretrained(
'stabilityai/stable-diffusion-2-1' , safety_checker=A , torch_dtype=torch.floataa)
_UpperCAmelCase = DDIMScheduler.from_config(pipe.scheduler.config)
_UpperCAmelCase = DDIMInverseScheduler.from_config(pipe.scheduler.config)
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=A)
_UpperCAmelCase = 'a bowl of fruit'
_UpperCAmelCase = 'a bowl of pears'
_UpperCAmelCase = pipe.generate_mask(
image=self.raw_image , source_prompt=A , target_prompt=A , generator=A , )
_UpperCAmelCase = pipe.invert(
prompt=A , image=self.raw_image , inpaint_strength=0.7 , generator=A).latents
_UpperCAmelCase = pipe(
prompt=A , mask_image=A , image_latents=A , generator=A , negative_prompt=A , inpaint_strength=0.7 , output_type='numpy' , ).images[0]
_UpperCAmelCase = (
np.array(
load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/diffedit/pears.png').resize((7_68, 7_68)))
/ 2_55
)
assert np.abs((expected_image - image).max()) < 5E-1
def _lowerCamelCase ( self : Union[str, Any]) -> Dict:
"""simple docstring"""
_UpperCAmelCase = torch.manual_seed(0)
_UpperCAmelCase = StableDiffusionDiffEditPipeline.from_pretrained(
'stabilityai/stable-diffusion-2-1' , safety_checker=A , torch_dtype=torch.floataa)
_UpperCAmelCase = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
_UpperCAmelCase = DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config)
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=A)
_UpperCAmelCase = 'a bowl of fruit'
_UpperCAmelCase = 'a bowl of pears'
_UpperCAmelCase = pipe.generate_mask(
image=self.raw_image , source_prompt=A , target_prompt=A , generator=A , )
_UpperCAmelCase = pipe.invert(
prompt=A , image=self.raw_image , inpaint_strength=0.7 , generator=A , num_inference_steps=25 , ).latents
_UpperCAmelCase = pipe(
prompt=A , mask_image=A , image_latents=A , generator=A , negative_prompt=A , inpaint_strength=0.7 , num_inference_steps=25 , output_type='numpy' , ).images[0]
_UpperCAmelCase = (
np.array(
load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/diffedit/pears.png').resize((7_68, 7_68)))
/ 2_55
)
assert np.abs((expected_image - image).max()) < 5E-1
| 339 |
import math
import unittest
def A ( _UpperCAmelCase : int ) -> bool:
'''simple docstring'''
assert isinstance(_UpperCAmelCase , _UpperCAmelCase ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(_UpperCAmelCase ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
class __lowerCAmelCase ( unittest.TestCase ):
def _lowerCamelCase ( self : Tuple) -> Union[str, Any]:
"""simple docstring"""
self.assertTrue(is_prime(2))
self.assertTrue(is_prime(3))
self.assertTrue(is_prime(5))
self.assertTrue(is_prime(7))
self.assertTrue(is_prime(11))
self.assertTrue(is_prime(13))
self.assertTrue(is_prime(17))
self.assertTrue(is_prime(19))
self.assertTrue(is_prime(23))
self.assertTrue(is_prime(29))
def _lowerCamelCase ( self : Optional[int]) -> Any:
"""simple docstring"""
with self.assertRaises(A):
is_prime(-19)
self.assertFalse(
is_prime(0) , 'Zero doesn\'t have any positive factors, primes must have exactly two.' , )
self.assertFalse(
is_prime(1) , 'One only has 1 positive factor, primes must have exactly two.' , )
self.assertFalse(is_prime(2 * 2))
self.assertFalse(is_prime(2 * 3))
self.assertFalse(is_prime(3 * 3))
self.assertFalse(is_prime(3 * 5))
self.assertFalse(is_prime(3 * 5 * 7))
if __name__ == "__main__":
unittest.main()
| 339 | 1 |
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
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
"google/mobilenet_v1_1.0_224": "https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json",
"google/mobilenet_v1_0.75_192": "https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json",
# See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}
class __lowerCAmelCase ( A ):
UpperCamelCase = '''mobilenet_v1'''
def __init__( self : Dict , A : List[str]=3 , A : str=2_24 , A : Dict=1.0 , A : Optional[Any]=8 , A : Tuple="relu6" , A : Tuple=True , A : Union[str, Any]=0.9_9_9 , A : List[Any]=0.0_2 , A : Optional[Any]=0.0_0_1 , **A : Optional[int] , ) -> str:
"""simple docstring"""
super().__init__(**A)
if depth_multiplier <= 0:
raise ValueError('depth_multiplier must be greater than zero.')
_UpperCAmelCase = num_channels
_UpperCAmelCase = image_size
_UpperCAmelCase = depth_multiplier
_UpperCAmelCase = min_depth
_UpperCAmelCase = hidden_act
_UpperCAmelCase = tf_padding
_UpperCAmelCase = classifier_dropout_prob
_UpperCAmelCase = initializer_range
_UpperCAmelCase = layer_norm_eps
class __lowerCAmelCase ( A ):
UpperCamelCase = version.parse('''1.11''' )
@property
def _lowerCamelCase ( self : List[str]) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
return OrderedDict([('pixel_values', {0: 'batch'})])
@property
def _lowerCamelCase ( self : Dict) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
if self.task == "image-classification":
return OrderedDict([('logits', {0: 'batch'})])
else:
return OrderedDict([('last_hidden_state', {0: 'batch'}), ('pooler_output', {0: 'batch'})])
@property
def _lowerCamelCase ( self : Union[str, Any]) -> float:
"""simple docstring"""
return 1E-4
| 339 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
UpperCAmelCase__ = "\\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"
UpperCAmelCase__ = "\\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"
UpperCAmelCase__ = "\\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 __lowerCAmelCase ( datasets.Metric ):
def _lowerCamelCase ( self : str) -> MetricInfo:
"""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 _lowerCamelCase ( self : Union[str, Any] , A : List[List[List[str]]] , A : List[List[str]] , A : int = 1 , A : int = 4 , ) -> Dict[str, float]:
"""simple docstring"""
return {
"google_bleu": gleu_score.corpus_gleu(
list_of_references=A , hypotheses=A , min_len=A , max_len=A)
}
| 339 | 1 |
UpperCAmelCase__ = "\n# Installazione di Transformers\n! pip install transformers datasets\n# Per installare dalla fonte invece dell'ultima versione rilasciata, commenta il comando sopra e\n# rimuovi la modalità commento al comando seguente.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"
UpperCAmelCase__ = [{"type": "code", "content": INSTALL_CONTENT}]
UpperCAmelCase__ = {
"{processor_class}": "FakeProcessorClass",
"{model_class}": "FakeModelClass",
"{object_class}": "FakeObjectClass",
}
| 339 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
if is_sentencepiece_available():
from ..ta.tokenization_ta import TaTokenizer
else:
from ...utils.dummy_sentencepiece_objects import TaTokenizer
UpperCAmelCase__ = TaTokenizer
if is_tokenizers_available():
from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
from ...utils.dummy_tokenizers_objects import TaTokenizerFast
UpperCAmelCase__ = TaTokenizerFast
UpperCAmelCase__ = {"configuration_mt5": ["MT5Config", "MT5OnnxConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ = [
"MT5EncoderModel",
"MT5ForConditionalGeneration",
"MT5ForQuestionAnswering",
"MT5Model",
"MT5PreTrainedModel",
"MT5Stack",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ = ["TFMT5EncoderModel", "TFMT5ForConditionalGeneration", "TFMT5Model"]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ = ["FlaxMT5EncoderModel", "FlaxMT5ForConditionalGeneration", "FlaxMT5Model"]
if TYPE_CHECKING:
from .configuration_mta import MTaConfig, MTaOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mta import (
MTaEncoderModel,
MTaForConditionalGeneration,
MTaForQuestionAnswering,
MTaModel,
MTaPreTrainedModel,
MTaStack,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel
else:
import sys
UpperCAmelCase__ = _LazyModule(
__name__,
globals()["__file__"],
_import_structure,
extra_objects={"MT5Tokenizer": MTaTokenizer, "MT5TokenizerFast": MTaTokenizerFast},
module_spec=__spec__,
)
| 339 | 1 |
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_available():
import os
# The slow tests are often failing with OOM error on GPU
# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
UpperCAmelCase__ = "platform"
import jax
import jax.numpy as jnp
from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
FlaxBlenderbotSmallForConditionalGeneration,
FlaxBlenderbotSmallModel,
shift_tokens_right,
)
def A ( _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : List[Any] , _UpperCAmelCase : Dict=None , _UpperCAmelCase : str=None , _UpperCAmelCase : Dict=None , _UpperCAmelCase : List[Any]=None , _UpperCAmelCase : List[Any]=None , _UpperCAmelCase : Any=None , ) -> str:
'''simple docstring'''
if attention_mask is None:
_UpperCAmelCase = np.where(input_ids != config.pad_token_id , 1 , 0 )
if decoder_attention_mask is None:
_UpperCAmelCase = np.where(decoder_input_ids != config.pad_token_id , 1 , 0 )
if head_mask is None:
_UpperCAmelCase = np.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
_UpperCAmelCase = np.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
_UpperCAmelCase = np.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": attention_mask,
}
class __lowerCAmelCase :
def __init__( self : int , A : Tuple , A : Union[str, Any]=13 , A : List[str]=7 , A : Union[str, Any]=True , A : List[str]=False , A : Any=99 , A : Tuple=16 , A : Optional[Any]=2 , A : Optional[Any]=4 , A : str=4 , A : Any="gelu" , A : Dict=0.1 , A : str=0.1 , A : Optional[Any]=32 , A : Any=2 , A : int=1 , A : Dict=0 , A : Any=0.0_2 , ) -> Any:
"""simple docstring"""
_UpperCAmelCase = parent
_UpperCAmelCase = batch_size
_UpperCAmelCase = seq_length
_UpperCAmelCase = is_training
_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 = eos_token_id
_UpperCAmelCase = pad_token_id
_UpperCAmelCase = bos_token_id
_UpperCAmelCase = initializer_range
def _lowerCamelCase ( self : int) -> List[str]:
"""simple docstring"""
_UpperCAmelCase = np.clip(ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size) , 3 , self.vocab_size)
_UpperCAmelCase = np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1) , dtype=np.intaa)) , -1)
_UpperCAmelCase = shift_tokens_right(A , 1 , 2)
_UpperCAmelCase = BlenderbotSmallConfig(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_id=self.eos_token_id , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , initializer_range=self.initializer_range , use_cache=A , )
_UpperCAmelCase = prepare_blenderbot_inputs_dict(A , A , A)
return config, inputs_dict
def _lowerCamelCase ( self : str) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase , _UpperCAmelCase = self.prepare_config_and_inputs()
return config, inputs_dict
def _lowerCamelCase ( self : Optional[int] , A : List[str] , A : int , A : Optional[int]) -> Union[str, Any]:
"""simple docstring"""
_UpperCAmelCase = 20
_UpperCAmelCase = model_class_name(A)
_UpperCAmelCase = model.encode(inputs_dict['input_ids'])
_UpperCAmelCase , _UpperCAmelCase = (
inputs_dict['decoder_input_ids'],
inputs_dict['decoder_attention_mask'],
)
_UpperCAmelCase = model.init_cache(decoder_input_ids.shape[0] , A , A)
_UpperCAmelCase = jnp.ones((decoder_input_ids.shape[0], max_decoder_length) , dtype='i4')
_UpperCAmelCase = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
_UpperCAmelCase = model.decode(
decoder_input_ids[:, :-1] , A , decoder_attention_mask=A , past_key_values=A , decoder_position_ids=A , )
_UpperCAmelCase = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype='i4')
_UpperCAmelCase = model.decode(
decoder_input_ids[:, -1:] , A , decoder_attention_mask=A , past_key_values=outputs_cache.past_key_values , decoder_position_ids=A , )
_UpperCAmelCase = model.decode(A , A)
_UpperCAmelCase = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
self.parent.assertTrue(diff < 1E-3 , msg=F"Max diff is {diff}")
def _lowerCamelCase ( self : Optional[Any] , A : Optional[Any] , A : str , A : str) -> Any:
"""simple docstring"""
_UpperCAmelCase = 20
_UpperCAmelCase = model_class_name(A)
_UpperCAmelCase = model.encode(inputs_dict['input_ids'])
_UpperCAmelCase , _UpperCAmelCase = (
inputs_dict['decoder_input_ids'],
inputs_dict['decoder_attention_mask'],
)
_UpperCAmelCase = jnp.concatenate(
[
decoder_attention_mask,
jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])),
] , axis=-1 , )
_UpperCAmelCase = model.init_cache(decoder_input_ids.shape[0] , A , A)
_UpperCAmelCase = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
_UpperCAmelCase = model.decode(
decoder_input_ids[:, :-1] , A , decoder_attention_mask=A , past_key_values=A , decoder_position_ids=A , )
_UpperCAmelCase = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype='i4')
_UpperCAmelCase = model.decode(
decoder_input_ids[:, -1:] , A , past_key_values=outputs_cache.past_key_values , decoder_attention_mask=A , decoder_position_ids=A , )
_UpperCAmelCase = model.decode(A , A , decoder_attention_mask=A)
_UpperCAmelCase = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
self.parent.assertTrue(diff < 1E-3 , msg=F"Max diff is {diff}")
@require_flax
class __lowerCAmelCase ( unittest.TestCase ):
UpperCamelCase = 9_9
def _lowerCamelCase ( self : str) -> str:
"""simple docstring"""
_UpperCAmelCase = np.array(
[
[71, 82, 18, 33, 46, 91, 2],
[68, 34, 26, 58, 30, 82, 2],
[5, 97, 17, 39, 94, 40, 2],
[76, 83, 94, 25, 70, 78, 2],
[87, 59, 41, 35, 48, 66, 2],
[55, 13, 16, 58, 5, 2, 1], # note padding
[64, 27, 31, 51, 12, 75, 2],
[52, 64, 86, 17, 83, 39, 2],
[48, 61, 9, 24, 71, 82, 2],
[26, 1, 60, 48, 22, 13, 2],
[21, 5, 62, 28, 14, 76, 2],
[45, 98, 37, 86, 59, 48, 2],
[70, 70, 50, 9, 28, 0, 2],
] , dtype=np.intaa , )
_UpperCAmelCase = input_ids.shape[0]
_UpperCAmelCase = BlenderbotSmallConfig(
vocab_size=self.vocab_size , d_model=24 , encoder_layers=2 , decoder_layers=2 , encoder_attention_heads=2 , decoder_attention_heads=2 , encoder_ffn_dim=32 , decoder_ffn_dim=32 , max_position_embeddings=48 , eos_token_id=2 , pad_token_id=1 , bos_token_id=0 , )
return config, input_ids, batch_size
def _lowerCamelCase ( self : int) -> List[str]:
"""simple docstring"""
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = self._get_config_and_data()
_UpperCAmelCase = FlaxBlenderbotSmallForConditionalGeneration(A)
_UpperCAmelCase = lm_model(input_ids=A)
_UpperCAmelCase = (batch_size, input_ids.shape[1], config.vocab_size)
self.assertEqual(outputs['logits'].shape , A)
def _lowerCamelCase ( self : List[Any]) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase = BlenderbotSmallConfig(
vocab_size=self.vocab_size , d_model=14 , encoder_layers=2 , decoder_layers=2 , encoder_attention_heads=2 , decoder_attention_heads=2 , encoder_ffn_dim=8 , decoder_ffn_dim=8 , max_position_embeddings=48 , )
_UpperCAmelCase = FlaxBlenderbotSmallForConditionalGeneration(A)
_UpperCAmelCase = np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]] , dtype=np.intaa)
_UpperCAmelCase = np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]] , dtype=np.intaa)
_UpperCAmelCase = lm_model(input_ids=A , decoder_input_ids=A)
_UpperCAmelCase = (*summary.shape, config.vocab_size)
self.assertEqual(outputs['logits'].shape , A)
def _lowerCamelCase ( self : Tuple) -> Union[str, Any]:
"""simple docstring"""
_UpperCAmelCase = np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]] , dtype=np.intaa)
_UpperCAmelCase = shift_tokens_right(A , 1 , 2)
_UpperCAmelCase = np.equal(A , 1).astype(np.floataa).sum()
_UpperCAmelCase = np.equal(A , 1).astype(np.floataa).sum()
self.assertEqual(shifted.shape , input_ids.shape)
self.assertEqual(A , n_pad_before - 1)
self.assertTrue(np.equal(shifted[:, 0] , 2).all())
@require_flax
class __lowerCAmelCase ( A , unittest.TestCase , A ):
UpperCamelCase = True
UpperCamelCase = (
(
FlaxBlenderbotSmallModel,
FlaxBlenderbotSmallForConditionalGeneration,
)
if is_flax_available()
else ()
)
UpperCamelCase = (FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()
def _lowerCamelCase ( self : Any) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase = FlaxBlenderbotSmallModelTester(self)
def _lowerCamelCase ( self : str) -> Tuple:
"""simple docstring"""
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward(A , A , A)
def _lowerCamelCase ( self : Union[str, Any]) -> Union[str, Any]:
"""simple docstring"""
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward_with_attn_mask(A , A , A)
def _lowerCamelCase ( self : Any) -> List[str]:
"""simple docstring"""
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
_UpperCAmelCase = self._prepare_for_class(A , A)
_UpperCAmelCase = model_class(A)
@jax.jit
def encode_jitted(A : Any , A : str=None , **A : str):
return model.encode(input_ids=A , attention_mask=A)
with self.subTest('JIT Enabled'):
_UpperCAmelCase = encode_jitted(**A).to_tuple()
with self.subTest('JIT Disabled'):
with jax.disable_jit():
_UpperCAmelCase = encode_jitted(**A).to_tuple()
self.assertEqual(len(A) , len(A))
for jitted_output, output in zip(A , A):
self.assertEqual(jitted_output.shape , output.shape)
def _lowerCamelCase ( self : List[str]) -> int:
"""simple docstring"""
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
_UpperCAmelCase = model_class(A)
_UpperCAmelCase = model.encode(inputs_dict['input_ids'] , inputs_dict['attention_mask'])
_UpperCAmelCase = {
'decoder_input_ids': inputs_dict['decoder_input_ids'],
'decoder_attention_mask': inputs_dict['decoder_attention_mask'],
'encoder_outputs': encoder_outputs,
}
@jax.jit
def decode_jitted(A : List[str] , A : int , A : str):
return model.decode(
decoder_input_ids=A , decoder_attention_mask=A , encoder_outputs=A , )
with self.subTest('JIT Enabled'):
_UpperCAmelCase = decode_jitted(**A).to_tuple()
with self.subTest('JIT Disabled'):
with jax.disable_jit():
_UpperCAmelCase = decode_jitted(**A).to_tuple()
self.assertEqual(len(A) , len(A))
for jitted_output, output in zip(A , A):
self.assertEqual(jitted_output.shape , output.shape)
@slow
def _lowerCamelCase ( self : List[str]) -> int:
"""simple docstring"""
for model_class_name in self.all_model_classes:
_UpperCAmelCase = model_class_name.from_pretrained('facebook/blenderbot_small-90M')
# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
_UpperCAmelCase = np.ones((1, 1)) * model.config.eos_token_id
_UpperCAmelCase = model(A)
self.assertIsNotNone(A)
| 339 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
"s-JoL/Open-Llama-V1": "https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json",
}
class __lowerCAmelCase ( A ):
UpperCamelCase = '''open-llama'''
def __init__( self : str , A : List[Any]=10_00_00 , A : Tuple=40_96 , A : Tuple=1_10_08 , A : List[str]=32 , A : Tuple=32 , A : Optional[Any]="silu" , A : int=20_48 , A : Optional[Any]=0.0_2 , A : Dict=1E-6 , A : Optional[Any]=True , A : List[Any]=0 , A : Dict=1 , A : int=2 , A : Dict=False , A : Optional[int]=True , A : List[Any]=0.1 , A : str=0.1 , A : Dict=True , A : Optional[Any]=True , A : Dict=None , **A : Union[str, Any] , ) -> Dict:
"""simple docstring"""
_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' , A)
_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=A , bos_token_id=A , eos_token_id=A , tie_word_embeddings=A , **A , )
def _lowerCamelCase ( self : List[str]) -> Union[str, Any]:
"""simple docstring"""
if self.rope_scaling is None:
return
if not isinstance(self.rope_scaling , A) or len(self.rope_scaling) != 2:
raise ValueError(
'`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '
F"got {self.rope_scaling}")
_UpperCAmelCase = self.rope_scaling.get('type' , A)
_UpperCAmelCase = self.rope_scaling.get('factor' , A)
if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
raise ValueError(
F"`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}")
if rope_scaling_factor is None or not isinstance(A , A) or rope_scaling_factor <= 1.0:
raise ValueError(F"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}")
| 339 | 1 |
def A ( _UpperCAmelCase : list ) -> list:
'''simple docstring'''
for i in range(len(_UpperCAmelCase ) - 1 , 0 , -1 ):
_UpperCAmelCase = False
for j in range(_UpperCAmelCase , 0 , -1 ):
if unsorted[j] < unsorted[j - 1]:
_UpperCAmelCase , _UpperCAmelCase = unsorted[j - 1], unsorted[j]
_UpperCAmelCase = True
for j in range(_UpperCAmelCase ):
if unsorted[j] > unsorted[j + 1]:
_UpperCAmelCase , _UpperCAmelCase = unsorted[j + 1], unsorted[j]
_UpperCAmelCase = True
if not swapped:
break
return unsorted
if __name__ == "__main__":
import doctest
doctest.testmod()
UpperCAmelCase__ = input("Enter numbers separated by a comma:\n").strip()
UpperCAmelCase__ = [int(item) for item in user_input.split(",")]
print(f"""{cocktail_shaker_sort(unsorted) = }""")
| 339 |
def A ( _UpperCAmelCase : str ) -> bool:
'''simple docstring'''
return credit_card_number.startswith(('34', '35', '37', '4', '5', '6') )
def A ( _UpperCAmelCase : str ) -> bool:
'''simple docstring'''
_UpperCAmelCase = credit_card_number
_UpperCAmelCase = 0
_UpperCAmelCase = len(_UpperCAmelCase ) - 2
for i in range(_UpperCAmelCase , -1 , -2 ):
# double the value of every second digit
_UpperCAmelCase = int(cc_number[i] )
digit *= 2
# If doubling of a number results in a two digit number
# i.e greater than 9(e.g., 6 × 2 = 12),
# then add the digits of the product (e.g., 12: 1 + 2 = 3, 15: 1 + 5 = 6),
# to get a single digit number.
if digit > 9:
digit %= 10
digit += 1
_UpperCAmelCase = cc_number[:i] + str(_UpperCAmelCase ) + cc_number[i + 1 :]
total += digit
# Sum up the remaining digits
for i in range(len(_UpperCAmelCase ) - 1 , -1 , -2 ):
total += int(cc_number[i] )
return total % 10 == 0
def A ( _UpperCAmelCase : str ) -> bool:
'''simple docstring'''
_UpperCAmelCase = F"{credit_card_number} is an invalid credit card number because"
if not credit_card_number.isdigit():
print(F"{error_message} it has nonnumerical characters." )
return False
if not 13 <= len(_UpperCAmelCase ) <= 16:
print(F"{error_message} of its length." )
return False
if not validate_initial_digits(_UpperCAmelCase ):
print(F"{error_message} of its first two digits." )
return False
if not luhn_validation(_UpperCAmelCase ):
print(F"{error_message} it fails the Luhn check." )
return False
print(F"{credit_card_number} is a valid credit card number." )
return True
if __name__ == "__main__":
import doctest
doctest.testmod()
validate_credit_card_number("4111111111111111")
validate_credit_card_number("32323")
| 339 | 1 |
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_head,
ftp_get,
ftp_head,
get_from_cache,
http_get,
http_head,
)
UpperCAmelCase__ = "\\n Text data.\n Second line of data."
UpperCAmelCase__ = "file"
@pytest.fixture(scope='session' )
def A ( _UpperCAmelCase : str ) -> Tuple:
'''simple docstring'''
_UpperCAmelCase = tmp_path_factory.mktemp('data' ) / (FILE_PATH + '.zstd')
_UpperCAmelCase = bytes(_UpperCAmelCase , 'utf-8' )
with zstd.open(_UpperCAmelCase , 'wb' ) as f:
f.write(_UpperCAmelCase )
return path
@pytest.fixture
def A ( _UpperCAmelCase : Union[str, Any] ) -> Dict:
'''simple docstring'''
with open(os.path.join(tmpfs.local_root_dir , _UpperCAmelCase ) , 'w' ) as f:
f.write(_UpperCAmelCase )
return FILE_PATH
@pytest.mark.parametrize('compression_format' , ['gzip', 'xz', 'zstd'] )
def A ( _UpperCAmelCase : List[str] , _UpperCAmelCase : Dict , _UpperCAmelCase : List[Any] , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : List[Any] , _UpperCAmelCase : Tuple ) -> str:
'''simple docstring'''
_UpperCAmelCase = {'gzip': gz_file, 'xz': xz_file, 'zstd': zstd_path}
_UpperCAmelCase = input_paths[compression_format]
_UpperCAmelCase = tmp_path / 'cache'
_UpperCAmelCase = DownloadConfig(cache_dir=_UpperCAmelCase , extract_compressed_file=_UpperCAmelCase )
_UpperCAmelCase = cached_path(_UpperCAmelCase , download_config=_UpperCAmelCase )
with open(_UpperCAmelCase ) as f:
_UpperCAmelCase = f.read()
with open(_UpperCAmelCase ) as f:
_UpperCAmelCase = f.read()
assert extracted_file_content == expected_file_content
@pytest.mark.parametrize('default_extracted' , [True, False] )
@pytest.mark.parametrize('default_cache_dir' , [True, False] )
def A ( _UpperCAmelCase : List[str] , _UpperCAmelCase : str , _UpperCAmelCase : Tuple , _UpperCAmelCase : List[Any] , _UpperCAmelCase : str ) -> Dict:
'''simple docstring'''
_UpperCAmelCase = 'custom_cache'
_UpperCAmelCase = 'custom_extracted_dir'
_UpperCAmelCase = tmp_path / 'custom_extracted_path'
if default_extracted:
_UpperCAmelCase = ('downloads' if default_cache_dir else custom_cache_dir, 'extracted')
else:
monkeypatch.setattr('datasets.config.EXTRACTED_DATASETS_DIR' , _UpperCAmelCase )
monkeypatch.setattr('datasets.config.EXTRACTED_DATASETS_PATH' , str(_UpperCAmelCase ) )
_UpperCAmelCase = custom_extracted_path.parts[-2:] if default_cache_dir else (custom_cache_dir, custom_extracted_dir)
_UpperCAmelCase = xz_file
_UpperCAmelCase = (
DownloadConfig(extract_compressed_file=_UpperCAmelCase )
if default_cache_dir
else DownloadConfig(cache_dir=tmp_path / custom_cache_dir , extract_compressed_file=_UpperCAmelCase )
)
_UpperCAmelCase = cached_path(_UpperCAmelCase , download_config=_UpperCAmelCase )
assert Path(_UpperCAmelCase ).parent.parts[-2:] == expected
def A ( _UpperCAmelCase : Tuple ) -> Optional[int]:
'''simple docstring'''
# absolute path
_UpperCAmelCase = str(Path(_UpperCAmelCase ).resolve() )
assert cached_path(_UpperCAmelCase ) == text_file
# relative path
_UpperCAmelCase = str(Path(_UpperCAmelCase ).resolve().relative_to(Path(os.getcwd() ) ) )
assert cached_path(_UpperCAmelCase ) == text_file
def A ( _UpperCAmelCase : List[str] ) -> Optional[int]:
'''simple docstring'''
# absolute path
_UpperCAmelCase = str(tmp_path.resolve() / '__missing_file__.txt' )
with pytest.raises(_UpperCAmelCase ):
cached_path(_UpperCAmelCase )
# relative path
_UpperCAmelCase = './__missing_file__.txt'
with pytest.raises(_UpperCAmelCase ):
cached_path(_UpperCAmelCase )
def A ( _UpperCAmelCase : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
_UpperCAmelCase = get_from_cache(F"tmp://{tmpfs_file}" )
with open(_UpperCAmelCase ) as f:
_UpperCAmelCase = f.read()
assert output_file_content == FILE_CONTENT
@patch('datasets.config.HF_DATASETS_OFFLINE' , _UpperCAmelCase )
def A ( ) -> Dict:
'''simple docstring'''
with pytest.raises(_UpperCAmelCase ):
cached_path('https://huggingface.co' )
@patch('datasets.config.HF_DATASETS_OFFLINE' , _UpperCAmelCase )
def A ( _UpperCAmelCase : Dict ) -> Union[str, Any]:
'''simple docstring'''
_UpperCAmelCase = tmp_path_factory.mktemp('data' ) / 'file.html'
with pytest.raises(_UpperCAmelCase ):
http_get('https://huggingface.co' , temp_file=_UpperCAmelCase )
with pytest.raises(_UpperCAmelCase ):
http_head('https://huggingface.co' )
@patch('datasets.config.HF_DATASETS_OFFLINE' , _UpperCAmelCase )
def A ( _UpperCAmelCase : Any ) -> int:
'''simple docstring'''
_UpperCAmelCase = tmp_path_factory.mktemp('data' ) / 'file.html'
with pytest.raises(_UpperCAmelCase ):
ftp_get('ftp://huggingface.co' , temp_file=_UpperCAmelCase )
with pytest.raises(_UpperCAmelCase ):
ftp_head('ftp://huggingface.co' )
@patch('datasets.config.HF_DATASETS_OFFLINE' , _UpperCAmelCase )
def A ( _UpperCAmelCase : Any ) -> int:
'''simple docstring'''
_UpperCAmelCase = tmp_path_factory.mktemp('data' ) / 'file.html'
with pytest.raises(_UpperCAmelCase ):
fsspec_get('s3://huggingface.co' , temp_file=_UpperCAmelCase )
with pytest.raises(_UpperCAmelCase ):
fsspec_head('s3://huggingface.co' )
| 339 |
from functools import reduce
UpperCAmelCase__ = (
"73167176531330624919225119674426574742355349194934"
"96983520312774506326239578318016984801869478851843"
"85861560789112949495459501737958331952853208805511"
"12540698747158523863050715693290963295227443043557"
"66896648950445244523161731856403098711121722383113"
"62229893423380308135336276614282806444486645238749"
"30358907296290491560440772390713810515859307960866"
"70172427121883998797908792274921901699720888093776"
"65727333001053367881220235421809751254540594752243"
"52584907711670556013604839586446706324415722155397"
"53697817977846174064955149290862569321978468622482"
"83972241375657056057490261407972968652414535100474"
"82166370484403199890008895243450658541227588666881"
"16427171479924442928230863465674813919123162824586"
"17866458359124566529476545682848912883142607690042"
"24219022671055626321111109370544217506941658960408"
"07198403850962455444362981230987879927244284909188"
"84580156166097919133875499200524063689912560717606"
"05886116467109405077541002256983155200055935729725"
"71636269561882670428252483600823257530420752963450"
)
def A ( _UpperCAmelCase : str = N ) -> int:
'''simple docstring'''
return max(
# mypy cannot properly interpret reduce
int(reduce(lambda _UpperCAmelCase , _UpperCAmelCase : str(int(_UpperCAmelCase ) * int(_UpperCAmelCase ) ) , n[i : i + 13] ) )
for i in range(len(_UpperCAmelCase ) - 12 ) )
if __name__ == "__main__":
print(f"""{solution() = }""")
| 339 | 1 |
from typing import Callable, List, Optional, Union
import PIL
import torch
from transformers import (
CLIPImageProcessor,
CLIPSegForImageSegmentation,
CLIPSegProcessor,
CLIPTextModel,
CLIPTokenizer,
)
from diffusers import DiffusionPipeline
from diffusers.configuration_utils import FrozenDict
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import deprecate, is_accelerate_available, logging
UpperCAmelCase__ = logging.get_logger(__name__) # pylint: disable=invalid-name
class __lowerCAmelCase ( A ):
def __init__( self : Union[str, Any] , A : CLIPSegForImageSegmentation , A : CLIPSegProcessor , A : AutoencoderKL , A : CLIPTextModel , A : CLIPTokenizer , A : UNetaDConditionModel , A : Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler] , A : StableDiffusionSafetyChecker , A : CLIPImageProcessor , ) -> int:
"""simple docstring"""
super().__init__()
if hasattr(scheduler.config , 'steps_offset') and scheduler.config.steps_offset != 1:
_UpperCAmelCase = (
F"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
F" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
'to update the config accordingly as leaving `steps_offset` might led to incorrect results'
' in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,'
' it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`'
' file'
)
deprecate('steps_offset!=1' , '1.0.0' , A , standard_warn=A)
_UpperCAmelCase = dict(scheduler.config)
_UpperCAmelCase = 1
_UpperCAmelCase = FrozenDict(A)
if hasattr(scheduler.config , 'skip_prk_steps') and scheduler.config.skip_prk_steps is False:
_UpperCAmelCase = (
F"The configuration file of this scheduler: {scheduler} has not set the configuration"
' `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make'
' sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead to'
' incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face'
' Hub, it would be very nice if you could open a Pull request for the'
' `scheduler/scheduler_config.json` file'
)
deprecate('skip_prk_steps not set' , '1.0.0' , A , standard_warn=A)
_UpperCAmelCase = dict(scheduler.config)
_UpperCAmelCase = True
_UpperCAmelCase = FrozenDict(A)
if safety_checker is None:
logger.warning(
F"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
' that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered'
' results in services or applications open to the public. Both the diffusers team and Hugging Face'
' strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling'
' it only for use-cases that involve analyzing network behavior or auditing its results. For more'
' information, please have a look at https://github.com/huggingface/diffusers/pull/254 .')
self.register_modules(
segmentation_model=A , segmentation_processor=A , vae=A , text_encoder=A , tokenizer=A , unet=A , scheduler=A , safety_checker=A , feature_extractor=A , )
def _lowerCamelCase ( self : Tuple , A : Optional[Union[str, int]] = "auto") -> str:
"""simple docstring"""
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
_UpperCAmelCase = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(A)
def _lowerCamelCase ( self : Union[str, Any]) -> Union[str, Any]:
"""simple docstring"""
self.enable_attention_slicing(A)
def _lowerCamelCase ( self : int) -> Union[str, Any]:
"""simple docstring"""
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError('Please install accelerate via `pip install accelerate`')
_UpperCAmelCase = torch.device('cuda')
for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.safety_checker]:
if cpu_offloaded_model is not None:
cpu_offload(A , A)
@property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
def _lowerCamelCase ( self : Any) -> Dict:
"""simple docstring"""
if self.device != torch.device('meta') or not hasattr(self.unet , '_hf_hook'):
return self.device
for module in self.unet.modules():
if (
hasattr(A , '_hf_hook')
and hasattr(module._hf_hook , 'execution_device')
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device)
return self.device
@torch.no_grad()
def __call__( self : Dict , A : Union[str, List[str]] , A : Union[torch.FloatTensor, PIL.Image.Image] , A : str , A : int = 5_12 , A : int = 5_12 , A : int = 50 , A : float = 7.5 , A : Optional[Union[str, List[str]]] = None , A : Optional[int] = 1 , A : float = 0.0 , A : Optional[torch.Generator] = None , A : Optional[torch.FloatTensor] = None , A : Optional[str] = "pil" , A : bool = True , A : Optional[Callable[[int, int, torch.FloatTensor], None]] = None , A : int = 1 , **A : Tuple , ) -> Union[str, Any]:
"""simple docstring"""
_UpperCAmelCase = self.segmentation_processor(
text=[text] , images=[image] , padding='max_length' , return_tensors='pt').to(self.device)
_UpperCAmelCase = self.segmentation_model(**A)
_UpperCAmelCase = torch.sigmoid(outputs.logits).cpu().detach().unsqueeze(-1).numpy()
_UpperCAmelCase = self.numpy_to_pil(A)[0].resize(image.size)
# Run inpainting pipeline with the generated mask
_UpperCAmelCase = StableDiffusionInpaintPipeline(
vae=self.vae , text_encoder=self.text_encoder , tokenizer=self.tokenizer , unet=self.unet , scheduler=self.scheduler , safety_checker=self.safety_checker , feature_extractor=self.feature_extractor , )
return inpainting_pipeline(
prompt=A , image=A , mask_image=A , height=A , width=A , num_inference_steps=A , guidance_scale=A , negative_prompt=A , num_images_per_prompt=A , eta=A , generator=A , latents=A , output_type=A , return_dict=A , callback=A , callback_steps=A , )
| 339 |
from __future__ import annotations
from collections.abc import Callable
UpperCAmelCase__ = list[list[float | int]]
def A ( _UpperCAmelCase : Matrix , _UpperCAmelCase : Matrix ) -> Matrix:
'''simple docstring'''
_UpperCAmelCase = len(_UpperCAmelCase )
_UpperCAmelCase = [[0 for _ in range(size + 1 )] for _ in range(_UpperCAmelCase )]
_UpperCAmelCase = 42
_UpperCAmelCase = 42
_UpperCAmelCase = 42
_UpperCAmelCase = 42
_UpperCAmelCase = 42
_UpperCAmelCase = 42
for row in range(_UpperCAmelCase ):
for col in range(_UpperCAmelCase ):
_UpperCAmelCase = matrix[row][col]
_UpperCAmelCase = vector[row][0]
_UpperCAmelCase = 0
_UpperCAmelCase = 0
while row < size and col < size:
# pivoting
_UpperCAmelCase = max((abs(augmented[rowa][col] ), rowa) for rowa in range(_UpperCAmelCase , _UpperCAmelCase ) )[
1
]
if augmented[pivot_row][col] == 0:
col += 1
continue
else:
_UpperCAmelCase , _UpperCAmelCase = augmented[pivot_row], augmented[row]
for rowa in range(row + 1 , _UpperCAmelCase ):
_UpperCAmelCase = augmented[rowa][col] / augmented[row][col]
_UpperCAmelCase = 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 ):
_UpperCAmelCase = 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 A ( _UpperCAmelCase : list[int] ) -> Callable[[int], int]:
'''simple docstring'''
_UpperCAmelCase = len(_UpperCAmelCase )
_UpperCAmelCase = [[0 for _ in range(_UpperCAmelCase )] for _ in range(_UpperCAmelCase )]
_UpperCAmelCase = [[0] for _ in range(_UpperCAmelCase )]
_UpperCAmelCase = 42
_UpperCAmelCase = 42
_UpperCAmelCase = 42
_UpperCAmelCase = 42
for x_val, y_val in enumerate(_UpperCAmelCase ):
for col in range(_UpperCAmelCase ):
_UpperCAmelCase = (x_val + 1) ** (size - col - 1)
_UpperCAmelCase = y_val
_UpperCAmelCase = 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 A ( _UpperCAmelCase : int ) -> 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 A ( _UpperCAmelCase : Callable[[int], int] = question_function , _UpperCAmelCase : int = 10 ) -> int:
'''simple docstring'''
_UpperCAmelCase = [func(_UpperCAmelCase ) for x_val in range(1 , order + 1 )]
_UpperCAmelCase = [
interpolate(data_points[:max_coeff] ) for max_coeff in range(1 , order + 1 )
]
_UpperCAmelCase = 0
_UpperCAmelCase = 42
_UpperCAmelCase = 42
for poly in polynomials:
_UpperCAmelCase = 1
while func(_UpperCAmelCase ) == poly(_UpperCAmelCase ):
x_val += 1
ret += poly(_UpperCAmelCase )
return ret
if __name__ == "__main__":
print(f"""{solution() = }""")
| 339 | 1 |
import numpy as np
from nltk.translate import meteor_score
import datasets
from datasets.config import importlib_metadata, version
UpperCAmelCase__ = version.parse(importlib_metadata.version("nltk"))
if NLTK_VERSION >= version.Version("3.6.4"):
from nltk import word_tokenize
UpperCAmelCase__ = "\\n@inproceedings{banarjee2005,\n title = {{METEOR}: An Automatic Metric for {MT} Evaluation with Improved Correlation with Human Judgments},\n author = {Banerjee, Satanjeev and Lavie, Alon},\n booktitle = {Proceedings of the {ACL} Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization},\n month = jun,\n year = {2005},\n address = {Ann Arbor, Michigan},\n publisher = {Association for Computational Linguistics},\n url = {https://www.aclweb.org/anthology/W05-0909},\n pages = {65--72},\n}\n"
UpperCAmelCase__ = "\\nMETEOR, an automatic metric for machine translation evaluation\nthat is based on a generalized concept of unigram matching between the\nmachine-produced translation and human-produced reference translations.\nUnigrams can be matched based on their surface forms, stemmed forms,\nand meanings; furthermore, METEOR can be easily extended to include more\nadvanced matching strategies. Once all generalized unigram matches\nbetween the two strings have been found, METEOR computes a score for\nthis matching using a combination of unigram-precision, unigram-recall, and\na measure of fragmentation that is designed to directly capture how\nwell-ordered the matched words in the machine translation are in relation\nto the reference.\n\nMETEOR gets an R correlation value of 0.347 with human evaluation on the Arabic\ndata and 0.331 on the Chinese data. This is shown to be an improvement on\nusing simply unigram-precision, unigram-recall and their harmonic F1\ncombination.\n"
UpperCAmelCase__ = "\nComputes METEOR score of translated segments against one or more references.\nArgs:\n predictions: list of predictions to score. Each prediction\n should be a string with tokens separated by spaces.\n references: list of reference for each prediction. Each\n reference should be a string with tokens separated by spaces.\n alpha: Parameter for controlling relative weights of precision and recall. default: 0.9\n beta: Parameter for controlling shape of penalty as a function of fragmentation. default: 3\n gamma: Relative weight assigned to fragmentation penalty. default: 0.5\nReturns:\n 'meteor': meteor score.\nExamples:\n\n >>> meteor = datasets.load_metric('meteor')\n >>> predictions = [\"It is a guide to action which ensures that the military always obeys the commands of the party\"]\n >>> references = [\"It is a guide to action that ensures that the military will forever heed Party commands\"]\n >>> results = meteor.compute(predictions=predictions, references=references)\n >>> print(round(results[\"meteor\"], 4))\n 0.6944\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __lowerCAmelCase ( datasets.Metric ):
def _lowerCamelCase ( self : List[Any]) -> List[Any]:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'predictions': datasets.Value('string' , id='sequence'),
'references': datasets.Value('string' , id='sequence'),
}) , codebase_urls=['https://github.com/nltk/nltk/blob/develop/nltk/translate/meteor_score.py'] , reference_urls=[
'https://www.nltk.org/api/nltk.translate.html#module-nltk.translate.meteor_score',
'https://en.wikipedia.org/wiki/METEOR',
] , )
def _lowerCamelCase ( self : Optional[Any] , A : List[str]) -> List[Any]:
"""simple docstring"""
import nltk
nltk.download('wordnet')
if NLTK_VERSION >= version.Version('3.6.5'):
nltk.download('punkt')
if NLTK_VERSION >= version.Version('3.6.6'):
nltk.download('omw-1.4')
def _lowerCamelCase ( self : Optional[Any] , A : Tuple , A : Optional[int] , A : List[Any]=0.9 , A : Optional[Any]=3 , A : Optional[int]=0.5) -> Any:
"""simple docstring"""
if NLTK_VERSION >= version.Version('3.6.5'):
_UpperCAmelCase = [
meteor_score.single_meteor_score(
word_tokenize(A) , word_tokenize(A) , alpha=A , beta=A , gamma=A)
for ref, pred in zip(A , A)
]
else:
_UpperCAmelCase = [
meteor_score.single_meteor_score(A , A , alpha=A , beta=A , gamma=A)
for ref, pred in zip(A , A)
]
return {"meteor": np.mean(A)}
| 339 |
from __future__ import annotations
def A ( _UpperCAmelCase : list[int] ) -> bool:
'''simple docstring'''
return len(set(_UpperCAmelCase ) ) == len(_UpperCAmelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 339 | 1 |
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class __lowerCAmelCase ( A , A ):
@register_to_config
def __init__( self : List[str] , A : int = 7_68 , ) -> Any:
"""simple docstring"""
super().__init__()
_UpperCAmelCase = nn.Parameter(torch.zeros(1 , A))
_UpperCAmelCase = nn.Parameter(torch.ones(1 , A))
def _lowerCamelCase ( self : int , A : Optional[Union[str, torch.device]] = None , A : Optional[torch.dtype] = None , ) -> List[str]:
"""simple docstring"""
_UpperCAmelCase = nn.Parameter(self.mean.to(A).to(A))
_UpperCAmelCase = nn.Parameter(self.std.to(A).to(A))
return self
def _lowerCamelCase ( self : Optional[Any] , A : Tuple) -> Any:
"""simple docstring"""
_UpperCAmelCase = (embeds - self.mean) * 1.0 / self.std
return embeds
def _lowerCamelCase ( self : List[Any] , A : List[Any]) -> str:
"""simple docstring"""
_UpperCAmelCase = (embeds * self.std) + self.mean
return embeds
| 339 |
import os
UpperCAmelCase__ = {"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1000}
def A ( _UpperCAmelCase : str ) -> int:
'''simple docstring'''
_UpperCAmelCase = 0
_UpperCAmelCase = 0
while index < len(_UpperCAmelCase ) - 1:
_UpperCAmelCase = SYMBOLS[numerals[index]]
_UpperCAmelCase = SYMBOLS[numerals[index + 1]]
if current_value < next_value:
total_value -= current_value
else:
total_value += current_value
index += 1
total_value += SYMBOLS[numerals[index]]
return total_value
def A ( _UpperCAmelCase : int ) -> str:
'''simple docstring'''
_UpperCAmelCase = ''
_UpperCAmelCase = num // 1_000
numerals += m_count * "M"
num %= 1_000
_UpperCAmelCase = num // 100
if c_count == 9:
numerals += "CM"
c_count -= 9
elif c_count == 4:
numerals += "CD"
c_count -= 4
if c_count >= 5:
numerals += "D"
c_count -= 5
numerals += c_count * "C"
num %= 100
_UpperCAmelCase = num // 10
if x_count == 9:
numerals += "XC"
x_count -= 9
elif x_count == 4:
numerals += "XL"
x_count -= 4
if x_count >= 5:
numerals += "L"
x_count -= 5
numerals += x_count * "X"
num %= 10
if num == 9:
numerals += "IX"
num -= 9
elif num == 4:
numerals += "IV"
num -= 4
if num >= 5:
numerals += "V"
num -= 5
numerals += num * "I"
return numerals
def A ( _UpperCAmelCase : str = "/p089_roman.txt" ) -> int:
'''simple docstring'''
_UpperCAmelCase = 0
with open(os.path.dirname(_UpperCAmelCase ) + roman_numerals_filename ) as filea:
_UpperCAmelCase = filea.readlines()
for line in lines:
_UpperCAmelCase = line.strip()
_UpperCAmelCase = parse_roman_numerals(_UpperCAmelCase )
_UpperCAmelCase = generate_roman_numerals(_UpperCAmelCase )
savings += len(_UpperCAmelCase ) - len(_UpperCAmelCase )
return savings
if __name__ == "__main__":
print(f"""{solution() = }""")
| 339 | 1 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {"vocab_file": "sentencepiece.bpe.model"}
UpperCAmelCase__ = {
"vocab_file": {
"camembert-base": "https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model",
}
}
UpperCAmelCase__ = {
"camembert-base": 512,
}
UpperCAmelCase__ = "▁"
class __lowerCAmelCase ( A ):
UpperCamelCase = VOCAB_FILES_NAMES
UpperCamelCase = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase = ['''input_ids''', '''attention_mask''']
def __init__( self : List[str] , A : List[str] , A : Tuple="<s>" , A : str="</s>" , A : Dict="</s>" , A : int="<s>" , A : str="<unk>" , A : Optional[int]="<pad>" , A : List[Any]="<mask>" , A : Optional[Any]=["<s>NOTUSED", "</s>NOTUSED"] , A : Optional[Dict[str, Any]] = None , **A : List[str] , ) -> None:
"""simple docstring"""
_UpperCAmelCase = AddedToken(A , lstrip=A , rstrip=A) if isinstance(A , A) else mask_token
_UpperCAmelCase = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=A , eos_token=A , unk_token=A , sep_token=A , cls_token=A , pad_token=A , mask_token=A , additional_special_tokens=A , sp_model_kwargs=self.sp_model_kwargs , **A , )
_UpperCAmelCase = spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.Load(str(A))
_UpperCAmelCase = vocab_file
# HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
# sentencepiece vocabulary (this is the case for <s> and </s>
_UpperCAmelCase = {'<s>NOTUSED': 0, '<pad>': 1, '</s>NOTUSED': 2, '<unk>': 3}
_UpperCAmelCase = len(self.fairseq_tokens_to_ids)
_UpperCAmelCase = len(self.sp_model) + len(self.fairseq_tokens_to_ids)
_UpperCAmelCase = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
def _lowerCamelCase ( self : Optional[int] , A : List[int] , A : Optional[List[int]] = None) -> List[int]:
"""simple docstring"""
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
_UpperCAmelCase = [self.cls_token_id]
_UpperCAmelCase = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def _lowerCamelCase ( self : Any , A : List[int] , A : Optional[List[int]] = None , A : bool = False) -> List[int]:
"""simple docstring"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=A , token_ids_a=A , already_has_special_tokens=A)
if token_ids_a is None:
return [1] + ([0] * len(A)) + [1]
return [1] + ([0] * len(A)) + [1, 1] + ([0] * len(A)) + [1]
def _lowerCamelCase ( self : Any , A : List[int] , A : Optional[List[int]] = None) -> List[int]:
"""simple docstring"""
_UpperCAmelCase = [self.sep_token_id]
_UpperCAmelCase = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep) * [0]
@property
def _lowerCamelCase ( self : int) -> int:
"""simple docstring"""
return len(self.fairseq_tokens_to_ids) + len(self.sp_model)
def _lowerCamelCase ( self : str) -> Dict:
"""simple docstring"""
_UpperCAmelCase = {self.convert_ids_to_tokens(A): i for i in range(self.vocab_size)}
vocab.update(self.added_tokens_encoder)
return vocab
def _lowerCamelCase ( self : int , A : str) -> List[str]:
"""simple docstring"""
return self.sp_model.encode(A , out_type=A)
def _lowerCamelCase ( self : Tuple , A : int) -> Any:
"""simple docstring"""
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
elif self.sp_model.PieceToId(A) == 0:
# Convert sentence piece unk token to fairseq unk token index
return self.unk_token_id
return self.fairseq_offset + self.sp_model.PieceToId(A)
def _lowerCamelCase ( self : Any , A : Optional[int]) -> Optional[Any]:
"""simple docstring"""
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(index - self.fairseq_offset)
def _lowerCamelCase ( self : List[Any] , A : str) -> Tuple:
"""simple docstring"""
_UpperCAmelCase = []
_UpperCAmelCase = ''
_UpperCAmelCase = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(A) + token
_UpperCAmelCase = True
_UpperCAmelCase = []
else:
current_sub_tokens.append(A)
_UpperCAmelCase = False
out_string += self.sp_model.decode(A)
return out_string.strip()
def __getstate__( self : str) -> Dict:
"""simple docstring"""
_UpperCAmelCase = self.__dict__.copy()
_UpperCAmelCase = None
return state
def __setstate__( self : Dict , A : Any) -> Union[str, Any]:
"""simple docstring"""
_UpperCAmelCase = d
# for backward compatibility
if not hasattr(self , 'sp_model_kwargs'):
_UpperCAmelCase = {}
_UpperCAmelCase = spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.Load(self.vocab_file)
def _lowerCamelCase ( self : Optional[Any] , A : str , A : Optional[str] = None) -> Tuple[str]:
"""simple docstring"""
if not os.path.isdir(A):
logger.error(F"Vocabulary path ({save_directory}) should be a directory")
return
_UpperCAmelCase = os.path.join(
A , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])
if os.path.abspath(self.vocab_file) != os.path.abspath(A) and os.path.isfile(self.vocab_file):
copyfile(self.vocab_file , A)
elif not os.path.isfile(self.vocab_file):
with open(A , 'wb') as fi:
_UpperCAmelCase = self.sp_model.serialized_model_proto()
fi.write(A)
return (out_vocab_file,)
| 339 |
import requests
from bsa import BeautifulSoup
def A ( _UpperCAmelCase : str , _UpperCAmelCase : dict ) -> str:
'''simple docstring'''
_UpperCAmelCase = BeautifulSoup(requests.get(_UpperCAmelCase , params=_UpperCAmelCase ).content , 'html.parser' )
_UpperCAmelCase = soup.find('div' , attrs={'class': 'gs_ri'} )
_UpperCAmelCase = div.find('div' , attrs={'class': 'gs_fl'} ).find_all('a' )
return anchors[2].get_text()
if __name__ == "__main__":
UpperCAmelCase__ = {
"title": (
"Precisely geometry controlled microsupercapacitors for ultrahigh areal "
"capacitance, volumetric capacitance, and energy density"
),
"journal": "Chem. Mater.",
"volume": 30,
"pages": "3979-3990",
"year": 2018,
"hl": "en",
}
print(get_citation("https://scholar.google.com/scholar_lookup", params=params))
| 339 | 1 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase__ = {
"configuration_mgp_str": ["MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP", "MgpstrConfig"],
"processing_mgp_str": ["MgpstrProcessor"],
"tokenization_mgp_str": ["MgpstrTokenizer"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ = [
"MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST",
"MgpstrModel",
"MgpstrPreTrainedModel",
"MgpstrForSceneTextRecognition",
]
if TYPE_CHECKING:
from .configuration_mgp_str import MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP, MgpstrConfig
from .processing_mgp_str import MgpstrProcessor
from .tokenization_mgp_str import MgpstrTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mgp_str import (
MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST,
MgpstrForSceneTextRecognition,
MgpstrModel,
MgpstrPreTrainedModel,
)
else:
import sys
UpperCAmelCase__ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 339 |
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.roformer.modeling_flax_roformer import (
FlaxRoFormerForMaskedLM,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerModel,
)
class __lowerCAmelCase ( unittest.TestCase ):
def __init__( self : Optional[Any] , A : Dict , A : Union[str, Any]=13 , A : Dict=7 , A : Dict=True , A : Tuple=True , A : Union[str, Any]=True , A : int=True , A : Optional[int]=99 , A : List[str]=32 , A : List[Any]=5 , A : int=4 , A : Any=37 , A : Optional[int]="gelu" , A : Optional[Any]=0.1 , A : Any=0.1 , A : Union[str, Any]=5_12 , A : int=16 , A : List[str]=2 , A : Union[str, Any]=0.0_2 , A : Union[str, Any]=4 , ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase = parent
_UpperCAmelCase = batch_size
_UpperCAmelCase = seq_length
_UpperCAmelCase = is_training
_UpperCAmelCase = use_attention_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_choices
def _lowerCamelCase ( self : Optional[Any]) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
_UpperCAmelCase = None
if self.use_attention_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 = RoFormerConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=A , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def _lowerCamelCase ( self : List[Any]) -> List[str]:
"""simple docstring"""
_UpperCAmelCase = self.prepare_config_and_inputs()
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = config_and_inputs
_UpperCAmelCase = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
return config, inputs_dict
@require_flax
class __lowerCAmelCase ( A , unittest.TestCase ):
UpperCamelCase = True
UpperCamelCase = (
(
FlaxRoFormerModel,
FlaxRoFormerForMaskedLM,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
)
if is_flax_available()
else ()
)
def _lowerCamelCase ( self : Optional[int]) -> Any:
"""simple docstring"""
_UpperCAmelCase = FlaxRoFormerModelTester(self)
@slow
def _lowerCamelCase ( self : List[Any]) -> Dict:
"""simple docstring"""
for model_class_name in self.all_model_classes:
_UpperCAmelCase = model_class_name.from_pretrained('junnyu/roformer_chinese_small' , from_pt=A)
_UpperCAmelCase = model(np.ones((1, 1)))
self.assertIsNotNone(A)
@require_flax
class __lowerCAmelCase ( unittest.TestCase ):
@slow
def _lowerCamelCase ( self : List[Any]) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase = FlaxRoFormerForMaskedLM.from_pretrained('junnyu/roformer_chinese_base')
_UpperCAmelCase = jnp.array([[0, 1, 2, 3, 4, 5]])
_UpperCAmelCase = model(A)[0]
_UpperCAmelCase = 5_00_00
_UpperCAmelCase = (1, 6, vocab_size)
self.assertEqual(output.shape , A)
_UpperCAmelCase = jnp.array(
[[[-0.1_2_0_5, -1.0_2_6_5, 0.2_9_2_2], [-1.5_1_3_4, 0.1_9_7_4, 0.1_5_1_9], [-5.0_1_3_5, -3.9_0_0_3, -0.8_4_0_4]]])
self.assertTrue(jnp.allclose(output[:, :3, :3] , A , atol=1E-4))
| 339 | 1 |
def A ( ) -> list[list[int]]:
'''simple docstring'''
return [list(range(1_000 - i , -1_000 - i , -1 ) ) for i in range(1_000 )]
UpperCAmelCase__ = generate_large_matrix()
UpperCAmelCase__ = (
[[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
[[3, 2], [1, 0]],
[[7, 7, 6]],
[[7, 7, 6], [-1, -2, -3]],
grid,
)
def A ( _UpperCAmelCase : list[list[int]] ) -> None:
'''simple docstring'''
assert all(row == sorted(_UpperCAmelCase , reverse=_UpperCAmelCase ) for row in grid )
assert all(list(_UpperCAmelCase ) == sorted(_UpperCAmelCase , reverse=_UpperCAmelCase ) for col in zip(*_UpperCAmelCase ) )
def A ( _UpperCAmelCase : list[int] ) -> int:
'''simple docstring'''
_UpperCAmelCase = 0
_UpperCAmelCase = len(_UpperCAmelCase ) - 1
# Edge cases such as no values or all numbers are negative.
if not array or array[0] < 0:
return 0
while right + 1 > left:
_UpperCAmelCase = (left + right) // 2
_UpperCAmelCase = array[mid]
# Num must be negative and the index must be greater than or equal to 0.
if num < 0 and array[mid - 1] >= 0:
return mid
if num >= 0:
_UpperCAmelCase = mid + 1
else:
_UpperCAmelCase = mid - 1
# No negative numbers so return the last index of the array + 1 which is the length.
return len(_UpperCAmelCase )
def A ( _UpperCAmelCase : list[list[int]] ) -> int:
'''simple docstring'''
_UpperCAmelCase = 0
_UpperCAmelCase = len(grid[0] )
for i in range(len(_UpperCAmelCase ) ):
_UpperCAmelCase = find_negative_index(grid[i][:bound] )
total += bound
return (len(_UpperCAmelCase ) * len(grid[0] )) - total
def A ( _UpperCAmelCase : list[list[int]] ) -> int:
'''simple docstring'''
return len([number for row in grid for number in row if number < 0] )
def A ( _UpperCAmelCase : list[list[int]] ) -> int:
'''simple docstring'''
_UpperCAmelCase = 0
for row in grid:
for i, number in enumerate(_UpperCAmelCase ):
if number < 0:
total += len(_UpperCAmelCase ) - i
break
return total
def A ( ) -> None:
'''simple docstring'''
from timeit import timeit
print('Running benchmarks' )
_UpperCAmelCase = (
'from __main__ import count_negatives_binary_search, '
'count_negatives_brute_force, count_negatives_brute_force_with_break, grid'
)
for func in (
"count_negatives_binary_search", # took 0.7727 seconds
"count_negatives_brute_force_with_break", # took 4.6505 seconds
"count_negatives_brute_force", # took 12.8160 seconds
):
_UpperCAmelCase = timeit(F"{func}(grid=grid)" , setup=_UpperCAmelCase , number=500 )
print(F"{func}() took {time:0.4f} seconds" )
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 339 |
UpperCAmelCase__ = {}
def A ( _UpperCAmelCase : int , _UpperCAmelCase : int , _UpperCAmelCase : int ) -> int:
'''simple docstring'''
# if we are absent twice, or late 3 consecutive days,
# no further prize strings are possible
if late == 3 or absent == 2:
return 0
# if we have no days left, and have not failed any other rules,
# we have a prize string
if days == 0:
return 1
# No easy solution, so now we need to do the recursive calculation
# First, check if the combination is already in the cache, and
# if yes, return the stored value from there since we already
# know the number of possible prize strings from this point on
_UpperCAmelCase = (days, absent, late)
if key in cache:
return cache[key]
# now we calculate the three possible ways that can unfold from
# this point on, depending on our attendance today
# 1) if we are late (but not absent), the "absent" counter stays as
# it is, but the "late" counter increases by one
_UpperCAmelCase = _calculate(days - 1 , _UpperCAmelCase , late + 1 )
# 2) if we are absent, the "absent" counter increases by 1, and the
# "late" counter resets to 0
_UpperCAmelCase = _calculate(days - 1 , absent + 1 , 0 )
# 3) if we are on time, this resets the "late" counter and keeps the
# absent counter
_UpperCAmelCase = _calculate(days - 1 , _UpperCAmelCase , 0 )
_UpperCAmelCase = state_late + state_absent + state_ontime
_UpperCAmelCase = prizestrings
return prizestrings
def A ( _UpperCAmelCase : int = 30 ) -> int:
'''simple docstring'''
return _calculate(_UpperCAmelCase , absent=0 , late=0 )
if __name__ == "__main__":
print(solution())
| 339 | 1 |
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer, DPRReaderTokenizer
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
UpperCAmelCase__ = {
"vocab_file": {
"facebook/dpr-ctx_encoder-single-nq-base": (
"https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt"
),
"facebook/dpr-ctx_encoder-multiset-base": (
"https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt"
),
},
"tokenizer_file": {
"facebook/dpr-ctx_encoder-single-nq-base": (
"https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json"
),
"facebook/dpr-ctx_encoder-multiset-base": (
"https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json"
),
},
}
UpperCAmelCase__ = {
"vocab_file": {
"facebook/dpr-question_encoder-single-nq-base": (
"https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt"
),
"facebook/dpr-question_encoder-multiset-base": (
"https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt"
),
},
"tokenizer_file": {
"facebook/dpr-question_encoder-single-nq-base": (
"https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json"
),
"facebook/dpr-question_encoder-multiset-base": (
"https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json"
),
},
}
UpperCAmelCase__ = {
"vocab_file": {
"facebook/dpr-reader-single-nq-base": (
"https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt"
),
"facebook/dpr-reader-multiset-base": (
"https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt"
),
},
"tokenizer_file": {
"facebook/dpr-reader-single-nq-base": (
"https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json"
),
"facebook/dpr-reader-multiset-base": (
"https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json"
),
},
}
UpperCAmelCase__ = {
"facebook/dpr-ctx_encoder-single-nq-base": 512,
"facebook/dpr-ctx_encoder-multiset-base": 512,
}
UpperCAmelCase__ = {
"facebook/dpr-question_encoder-single-nq-base": 512,
"facebook/dpr-question_encoder-multiset-base": 512,
}
UpperCAmelCase__ = {
"facebook/dpr-reader-single-nq-base": 512,
"facebook/dpr-reader-multiset-base": 512,
}
UpperCAmelCase__ = {
"facebook/dpr-ctx_encoder-single-nq-base": {"do_lower_case": True},
"facebook/dpr-ctx_encoder-multiset-base": {"do_lower_case": True},
}
UpperCAmelCase__ = {
"facebook/dpr-question_encoder-single-nq-base": {"do_lower_case": True},
"facebook/dpr-question_encoder-multiset-base": {"do_lower_case": True},
}
UpperCAmelCase__ = {
"facebook/dpr-reader-single-nq-base": {"do_lower_case": True},
"facebook/dpr-reader-multiset-base": {"do_lower_case": True},
}
class __lowerCAmelCase ( A ):
UpperCamelCase = VOCAB_FILES_NAMES
UpperCamelCase = CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase = CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase = CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
UpperCamelCase = DPRContextEncoderTokenizer
class __lowerCAmelCase ( A ):
UpperCamelCase = VOCAB_FILES_NAMES
UpperCamelCase = QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase = QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase = QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
UpperCamelCase = DPRQuestionEncoderTokenizer
UpperCAmelCase__ = collections.namedtuple(
"DPRSpanPrediction", ["span_score", "relevance_score", "doc_id", "start_index", "end_index", "text"]
)
UpperCAmelCase__ = collections.namedtuple("DPRReaderOutput", ["start_logits", "end_logits", "relevance_logits"])
UpperCAmelCase__ = r"\n Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.\n It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),\n using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`\n with the format:\n\n [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n\n Args:\n questions (`str` or `List[str]`):\n The questions to be encoded. You can specify one question for many passages. In this case, the question\n will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in\n `titles` or `texts`.\n titles (`str` or `List[str]`):\n The passages titles to be encoded. This can be a string or a list of strings if there are several passages.\n texts (`str` or `List[str]`):\n The passages texts to be encoded. This can be a string or a list of strings if there are several passages.\n padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):\n Activates and controls padding. Accepts the following values:\n\n - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence\n if provided).\n - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum\n acceptable input length for the model if that argument is not provided.\n - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different\n lengths).\n truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):\n Activates and controls truncation. Accepts the following values:\n\n - `True` or `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or to\n the maximum acceptable input length for the model if that argument is not provided. This will truncate\n token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch\n of pairs) is provided.\n - `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n acceptable input length for the model if that argument is not provided. This will only truncate the first\n sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n - `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n acceptable input length for the model if that argument is not provided. This will only truncate the\n second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n - `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths\n greater than the model maximum admissible input size).\n max_length (`int`, *optional*):\n Controls the maximum length to use by one of the truncation/padding parameters.\n\n If left unset or set to `None`, this will use the predefined model maximum length if a maximum length\n is required by one of the truncation/padding parameters. If the model has no specific maximum input\n length (like XLNet) truncation/padding to a maximum length will be deactivated.\n return_tensors (`str` or [`~utils.TensorType`], *optional*):\n If set, will return tensors instead of list of python integers. Acceptable values are:\n\n - `'tf'`: Return TensorFlow `tf.constant` objects.\n - `'pt'`: Return PyTorch `torch.Tensor` objects.\n - `'np'`: Return Numpy `np.ndarray` objects.\n return_attention_mask (`bool`, *optional*):\n Whether or not to return the attention mask. If not set, will return the attention mask according to the\n specific tokenizer's default, defined by the `return_outputs` attribute.\n\n [What are attention masks?](../glossary#attention-mask)\n\n Return:\n `Dict[str, List[List[int]]]`: A dictionary with the following keys:\n\n - `input_ids`: List of token ids to be fed to a model.\n - `attention_mask`: List of indices specifying which tokens should be attended to by the model.\n "
@add_start_docstrings(A )
class __lowerCAmelCase :
def __call__( self : Dict , A : int , A : Optional[str] = None , A : Optional[str] = None , A : Union[bool, str] = False , A : Union[bool, str] = False , A : Optional[int] = None , A : Optional[Union[str, TensorType]] = None , A : Optional[bool] = None , **A : int , ) -> BatchEncoding:
"""simple docstring"""
if titles is None and texts is None:
return super().__call__(
A , padding=A , truncation=A , max_length=A , return_tensors=A , return_attention_mask=A , **A , )
elif titles is None or texts is None:
_UpperCAmelCase = titles if texts is None else texts
return super().__call__(
A , A , padding=A , truncation=A , max_length=A , return_tensors=A , return_attention_mask=A , **A , )
_UpperCAmelCase = titles if not isinstance(A , A) else [titles]
_UpperCAmelCase = texts if not isinstance(A , A) else [texts]
_UpperCAmelCase = len(A)
_UpperCAmelCase = questions if not isinstance(A , A) else [questions] * n_passages
assert len(A) == len(
A), F"There should be as many titles than texts but got {len(A)} titles and {len(A)} texts."
_UpperCAmelCase = super().__call__(A , A , padding=A , truncation=A)['input_ids']
_UpperCAmelCase = super().__call__(A , add_special_tokens=A , padding=A , truncation=A)['input_ids']
_UpperCAmelCase = {
'input_ids': [
(encoded_question_and_title + encoded_text)[:max_length]
if max_length is not None and truncation
else encoded_question_and_title + encoded_text
for encoded_question_and_title, encoded_text in zip(A , A)
]
}
if return_attention_mask is not False:
_UpperCAmelCase = []
for input_ids in encoded_inputs["input_ids"]:
attention_mask.append([int(input_id != self.pad_token_id) for input_id in input_ids])
_UpperCAmelCase = attention_mask
return self.pad(A , padding=A , max_length=A , return_tensors=A)
def _lowerCamelCase ( self : str , A : BatchEncoding , A : DPRReaderOutput , A : int = 16 , A : int = 64 , A : int = 4 , ) -> List[DPRSpanPrediction]:
"""simple docstring"""
_UpperCAmelCase = reader_input['input_ids']
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = reader_output[:3]
_UpperCAmelCase = len(A)
_UpperCAmelCase = sorted(range(A) , reverse=A , key=relevance_logits.__getitem__)
_UpperCAmelCase = []
for doc_id in sorted_docs:
_UpperCAmelCase = list(input_ids[doc_id])
# assuming question & title information is at the beginning of the sequence
_UpperCAmelCase = sequence_ids.index(self.sep_token_id , 2) + 1 # second sep id
if sequence_ids[-1] == self.pad_token_id:
_UpperCAmelCase = sequence_ids.index(self.pad_token_id)
else:
_UpperCAmelCase = len(A)
_UpperCAmelCase = self._get_best_spans(
start_logits=start_logits[doc_id][passage_offset:sequence_len] , end_logits=end_logits[doc_id][passage_offset:sequence_len] , max_answer_length=A , top_spans=A , )
for start_index, end_index in best_spans:
start_index += passage_offset
end_index += passage_offset
nbest_spans_predictions.append(
DPRSpanPrediction(
span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index] , relevance_score=relevance_logits[doc_id] , doc_id=A , start_index=A , end_index=A , text=self.decode(sequence_ids[start_index : end_index + 1]) , ))
if len(A) >= num_spans:
break
return nbest_spans_predictions[:num_spans]
def _lowerCamelCase ( self : int , A : List[int] , A : List[int] , A : int , A : int , ) -> List[DPRSpanPrediction]:
"""simple docstring"""
_UpperCAmelCase = []
for start_index, start_score in enumerate(A):
for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length]):
scores.append(((start_index, start_index + answer_length), start_score + end_score))
_UpperCAmelCase = sorted(A , key=lambda A: x[1] , reverse=A)
_UpperCAmelCase = []
for (start_index, end_index), score in scores:
assert start_index <= end_index, F"Wrong span indices: [{start_index}:{end_index}]"
_UpperCAmelCase = end_index - start_index + 1
assert length <= max_answer_length, F"Span is too long: {length} > {max_answer_length}"
if any(
start_index <= prev_start_index <= prev_end_index <= end_index
or prev_start_index <= start_index <= end_index <= prev_end_index
for (prev_start_index, prev_end_index) in chosen_span_intervals):
continue
chosen_span_intervals.append((start_index, end_index))
if len(A) == top_spans:
break
return chosen_span_intervals
@add_end_docstrings(A )
class __lowerCAmelCase ( A , A ):
UpperCamelCase = VOCAB_FILES_NAMES
UpperCamelCase = READER_PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase = READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase = READER_PRETRAINED_INIT_CONFIGURATION
UpperCamelCase = ['''input_ids''', '''attention_mask''']
UpperCamelCase = DPRReaderTokenizer
| 339 |
import os
import sys
import unittest
UpperCAmelCase__ = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init # noqa: E402
# Align TRANSFORMERS_PATH in check_dummies with the current path
UpperCAmelCase__ = os.path.join(git_repo_path, "src", "diffusers")
class __lowerCAmelCase ( unittest.TestCase ):
def _lowerCamelCase ( self : Tuple) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase = find_backend(' if not is_torch_available():')
self.assertEqual(A , 'torch')
# backend_with_underscore = find_backend(" if not is_tensorflow_text_available():")
# self.assertEqual(backend_with_underscore, "tensorflow_text")
_UpperCAmelCase = find_backend(' if not (is_torch_available() and is_transformers_available()):')
self.assertEqual(A , 'torch_and_transformers')
# double_backend_with_underscore = find_backend(
# " if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
# )
# self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text")
_UpperCAmelCase = find_backend(
' if not (is_torch_available() and is_transformers_available() and is_onnx_available()):')
self.assertEqual(A , 'torch_and_transformers_and_onnx')
def _lowerCamelCase ( self : int) -> Dict:
"""simple docstring"""
_UpperCAmelCase = read_init()
# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
self.assertIn('torch' , A)
self.assertIn('torch_and_transformers' , A)
self.assertIn('flax_and_transformers' , A)
self.assertIn('torch_and_transformers_and_onnx' , A)
# Likewise, we can't assert on the exact content of a key
self.assertIn('UNet2DModel' , objects['torch'])
self.assertIn('FlaxUNet2DConditionModel' , objects['flax'])
self.assertIn('StableDiffusionPipeline' , objects['torch_and_transformers'])
self.assertIn('FlaxStableDiffusionPipeline' , objects['flax_and_transformers'])
self.assertIn('LMSDiscreteScheduler' , objects['torch_and_scipy'])
self.assertIn('OnnxStableDiffusionPipeline' , objects['torch_and_transformers_and_onnx'])
def _lowerCamelCase ( self : Union[str, Any]) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase = create_dummy_object('CONSTANT' , '\'torch\'')
self.assertEqual(A , '\nCONSTANT = None\n')
_UpperCAmelCase = create_dummy_object('function' , '\'torch\'')
self.assertEqual(
A , '\ndef function(*args, **kwargs):\n requires_backends(function, \'torch\')\n')
_UpperCAmelCase = '\nclass FakeClass(metaclass=DummyObject):\n _backends = \'torch\'\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, \'torch\')\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, \'torch\')\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, \'torch\')\n'
_UpperCAmelCase = create_dummy_object('FakeClass' , '\'torch\'')
self.assertEqual(A , A)
def _lowerCamelCase ( self : Dict) -> int:
"""simple docstring"""
_UpperCAmelCase = '# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n requires_backends(function, ["torch"])\n\n\nclass FakeClass(metaclass=DummyObject):\n _backends = ["torch"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, ["torch"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, ["torch"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, ["torch"])\n'
_UpperCAmelCase = create_dummy_files({'torch': ['CONSTANT', 'function', 'FakeClass']})
self.assertEqual(dummy_files['torch'] , A)
| 339 | 1 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool
class __lowerCAmelCase ( A ):
UpperCamelCase = '''philschmid/bart-large-cnn-samsum'''
UpperCamelCase = (
'''This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, '''
'''and returns a summary of the text.'''
)
UpperCamelCase = '''summarizer'''
UpperCamelCase = AutoTokenizer
UpperCamelCase = AutoModelForSeqaSeqLM
UpperCamelCase = ['''text''']
UpperCamelCase = ['''text''']
def _lowerCamelCase ( self : Any , A : Optional[Any]) -> str:
"""simple docstring"""
return self.pre_processor(A , return_tensors='pt' , truncation=A)
def _lowerCamelCase ( self : str , A : List[Any]) -> Optional[Any]:
"""simple docstring"""
return self.model.generate(**A)[0]
def _lowerCamelCase ( self : Tuple , A : str) -> List[Any]:
"""simple docstring"""
return self.pre_processor.decode(A , skip_special_tokens=A , clean_up_tokenization_spaces=A)
| 339 |
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartForSequenceClassification,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
TapexTokenizer,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version
from transformers.utils.versions import require_version
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.17.0.dev0")
require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")
UpperCAmelCase__ = logging.getLogger(__name__)
@dataclass
class __lowerCAmelCase :
UpperCamelCase = field(
default='''tab_fact''' , metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''} )
UpperCamelCase = field(
default='''tab_fact''' , metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''} , )
UpperCamelCase = field(
default=1_0_2_4 , metadata={
'''help''': (
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
} , )
UpperCamelCase = field(
default=A , metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''} )
UpperCamelCase = field(
default=A , metadata={
'''help''': (
'''Whether to pad all samples to `max_seq_length`. '''
'''If False, will pad the samples dynamically when batching to the maximum length in the batch.'''
)
} , )
UpperCamelCase = field(
default=A , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of training examples to this '''
'''value if set.'''
)
} , )
UpperCamelCase = field(
default=A , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
'''value if set.'''
)
} , )
UpperCamelCase = field(
default=A , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of prediction examples to this '''
'''value if set.'''
)
} , )
UpperCamelCase = field(
default=A , metadata={'''help''': '''A csv or a json file containing the training data.'''} )
UpperCamelCase = field(
default=A , metadata={'''help''': '''A csv or a json file containing the validation data.'''} )
UpperCamelCase = field(default=A , metadata={'''help''': '''A csv or a json file containing the test data.'''} )
def _lowerCamelCase ( self : str) -> List[Any]:
"""simple docstring"""
if self.dataset_name is not None:
pass
elif self.train_file is None or self.validation_file is None:
raise ValueError('Need either a GLUE task, a training/validation file or a dataset name.')
else:
_UpperCAmelCase = self.train_file.split('.')[-1]
assert train_extension in ["csv", "json"], "`train_file` should be a csv or a json file."
_UpperCAmelCase = self.validation_file.split('.')[-1]
assert (
validation_extension == train_extension
), "`validation_file` should have the same extension (csv or json) as `train_file`."
@dataclass
class __lowerCAmelCase :
UpperCamelCase = field(
default=A , metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} )
UpperCamelCase = field(
default=A , metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
UpperCamelCase = field(
default=A , metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} )
UpperCamelCase = field(
default=A , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''} , )
UpperCamelCase = field(
default=A , metadata={'''help''': '''Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'''} , )
UpperCamelCase = field(
default='''main''' , metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''} , )
UpperCamelCase = field(
default=A , metadata={
'''help''': (
'''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
'''with private models).'''
)
} , )
def A ( ) -> Optional[int]:
'''simple docstring'''
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
_UpperCAmelCase = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith('.json' ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = parser.parse_args_into_dataclasses()
# Setup logging
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s' , datefmt='%m/%d/%Y %H:%M:%S' , handlers=[logging.StreamHandler(sys.stdout )] , )
_UpperCAmelCase = training_args.get_process_log_level()
logger.setLevel(_UpperCAmelCase )
datasets.utils.logging.set_verbosity(_UpperCAmelCase )
transformers.utils.logging.set_verbosity(_UpperCAmelCase )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
F"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
+ F"distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}" )
logger.info(F"Training/evaluation parameters {training_args}" )
# Detecting last checkpoint.
_UpperCAmelCase = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
_UpperCAmelCase = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
F"Output directory ({training_args.output_dir}) already exists and is not empty. "
'Use --overwrite_output_dir to overcome.' )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
F"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.' )
# Set seed before initializing model.
set_seed(training_args.seed )
# Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
# or specify a GLUE benchmark task (the dataset will be downloaded automatically from the datasets Hub).
#
# For JSON files, this script will use the `question` column for the input question and `table` column for the corresponding table.
#
# If the CSVs/JSONs contain only one non-label column, the script does single sentence classification on this
# single column. You can easily tweak this behavior (see below)
#
# In distributed training, the load_dataset function guarantee that only one local process can concurrently
# download the dataset.
if data_args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
_UpperCAmelCase = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , cache_dir=model_args.cache_dir )
else:
# Loading a dataset from your local files.
# CSV/JSON training and evaluation files are needed.
_UpperCAmelCase = {'train': data_args.train_file, 'validation': data_args.validation_file}
# Get the test dataset: you can provide your own CSV/JSON test file (see below)
# when you use `do_predict` without specifying a GLUE benchmark task.
if training_args.do_predict:
if data_args.test_file is not None:
_UpperCAmelCase = data_args.train_file.split('.' )[-1]
_UpperCAmelCase = data_args.test_file.split('.' )[-1]
assert (
test_extension == train_extension
), "`test_file` should have the same extension (csv or json) as `train_file`."
_UpperCAmelCase = data_args.test_file
else:
raise ValueError('Need either a GLUE task or a test file for `do_predict`.' )
for key in data_files.keys():
logger.info(F"load a local file for {key}: {data_files[key]}" )
if data_args.train_file.endswith('.csv' ):
# Loading a dataset from local csv files
_UpperCAmelCase = load_dataset('csv' , data_files=_UpperCAmelCase , cache_dir=model_args.cache_dir )
else:
# Loading a dataset from local json files
_UpperCAmelCase = load_dataset('json' , data_files=_UpperCAmelCase , cache_dir=model_args.cache_dir )
# See more about loading any type of standard or custom dataset at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Labels
_UpperCAmelCase = raw_datasets['train'].features['label'].names
_UpperCAmelCase = len(_UpperCAmelCase )
# Load pretrained model and tokenizer
#
# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
_UpperCAmelCase = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=_UpperCAmelCase , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# load tapex tokenizer
_UpperCAmelCase = TapexTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast_tokenizer , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , add_prefix_space=_UpperCAmelCase , )
_UpperCAmelCase = BartForSequenceClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool('.ckpt' in model_args.model_name_or_path ) , config=_UpperCAmelCase , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# Padding strategy
if data_args.pad_to_max_length:
_UpperCAmelCase = 'max_length'
else:
# We will pad later, dynamically at batch creation, to the max sequence length in each batch
_UpperCAmelCase = False
# Some models have set the order of the labels to use, so let's make sure we do use it.
_UpperCAmelCase = {'Refused': 0, 'Entailed': 1}
_UpperCAmelCase = {0: 'Refused', 1: 'Entailed'}
if data_args.max_seq_length > tokenizer.model_max_length:
logger.warning(
F"The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the"
F"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}." )
_UpperCAmelCase = min(data_args.max_seq_length , tokenizer.model_max_length )
def preprocess_tabfact_function(_UpperCAmelCase : Union[str, Any] ):
# Tokenize the texts
def _convert_table_text_to_pandas(_UpperCAmelCase : Dict ):
_UpperCAmelCase = [_table_row.split('#' ) for _table_row in _table_text.strip('\n' ).split('\n' )]
_UpperCAmelCase = pd.DataFrame.from_records(_table_content[1:] , columns=_table_content[0] )
return _table_pd
_UpperCAmelCase = examples['statement']
_UpperCAmelCase = list(map(_convert_table_text_to_pandas , examples['table_text'] ) )
_UpperCAmelCase = tokenizer(_UpperCAmelCase , _UpperCAmelCase , padding=_UpperCAmelCase , max_length=_UpperCAmelCase , truncation=_UpperCAmelCase )
_UpperCAmelCase = examples['label']
return result
with training_args.main_process_first(desc='dataset map pre-processing' ):
_UpperCAmelCase = raw_datasets.map(
_UpperCAmelCase , batched=_UpperCAmelCase , load_from_cache_file=not data_args.overwrite_cache , desc='Running tokenizer on dataset' , )
if training_args.do_train:
if "train" not in raw_datasets:
raise ValueError('--do_train requires a train dataset' )
_UpperCAmelCase = raw_datasets['train']
if data_args.max_train_samples is not None:
_UpperCAmelCase = train_dataset.select(range(data_args.max_train_samples ) )
if training_args.do_eval:
if "validation" not in raw_datasets and "validation_matched" not in raw_datasets:
raise ValueError('--do_eval requires a validation dataset' )
_UpperCAmelCase = raw_datasets['validation']
if data_args.max_eval_samples is not None:
_UpperCAmelCase = eval_dataset.select(range(data_args.max_eval_samples ) )
if training_args.do_predict or data_args.test_file is not None:
if "test" not in raw_datasets and "test_matched" not in raw_datasets:
raise ValueError('--do_predict requires a test dataset' )
_UpperCAmelCase = raw_datasets['test']
if data_args.max_predict_samples is not None:
_UpperCAmelCase = predict_dataset.select(range(data_args.max_predict_samples ) )
# Log a few random samples from the training set:
if training_args.do_train:
for index in random.sample(range(len(_UpperCAmelCase ) ) , 3 ):
logger.info(F"Sample {index} of the training set: {train_dataset[index]}." )
# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
# predictions and label_ids field) and has to return a dictionary string to float.
def compute_metrics(_UpperCAmelCase : EvalPrediction ):
_UpperCAmelCase = p.predictions[0] if isinstance(p.predictions , _UpperCAmelCase ) else p.predictions
_UpperCAmelCase = np.argmax(_UpperCAmelCase , axis=1 )
return {"accuracy": (preds == p.label_ids).astype(np.floataa ).mean().item()}
# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
if data_args.pad_to_max_length:
_UpperCAmelCase = default_data_collator
elif training_args.fpaa:
_UpperCAmelCase = DataCollatorWithPadding(_UpperCAmelCase , pad_to_multiple_of=8 )
else:
_UpperCAmelCase = None
# Initialize our Trainer
_UpperCAmelCase = Trainer(
model=_UpperCAmelCase , args=_UpperCAmelCase , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , compute_metrics=_UpperCAmelCase , tokenizer=_UpperCAmelCase , data_collator=_UpperCAmelCase , )
# Training
if training_args.do_train:
_UpperCAmelCase = None
if training_args.resume_from_checkpoint is not None:
_UpperCAmelCase = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
_UpperCAmelCase = last_checkpoint
_UpperCAmelCase = trainer.train(resume_from_checkpoint=_UpperCAmelCase )
_UpperCAmelCase = train_result.metrics
_UpperCAmelCase = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(_UpperCAmelCase )
)
_UpperCAmelCase = min(_UpperCAmelCase , len(_UpperCAmelCase ) )
trainer.save_model() # Saves the tokenizer too for easy upload
trainer.log_metrics('train' , _UpperCAmelCase )
trainer.save_metrics('train' , _UpperCAmelCase )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info('*** Evaluate ***' )
_UpperCAmelCase = trainer.evaluate(eval_dataset=_UpperCAmelCase )
_UpperCAmelCase = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(_UpperCAmelCase )
_UpperCAmelCase = min(_UpperCAmelCase , len(_UpperCAmelCase ) )
trainer.log_metrics('eval' , _UpperCAmelCase )
trainer.save_metrics('eval' , _UpperCAmelCase )
if training_args.do_predict:
logger.info('*** Predict ***' )
# Removing the `label` columns because it contains -1 and Trainer won't like that.
_UpperCAmelCase = predict_dataset.remove_columns('label' )
_UpperCAmelCase = trainer.predict(_UpperCAmelCase , metric_key_prefix='predict' ).predictions
_UpperCAmelCase = np.argmax(_UpperCAmelCase , axis=1 )
_UpperCAmelCase = os.path.join(training_args.output_dir , 'predict_results_tabfact.txt' )
if trainer.is_world_process_zero():
with open(_UpperCAmelCase , 'w' ) as writer:
logger.info('***** Predict Results *****' )
writer.write('index\tprediction\n' )
for index, item in enumerate(_UpperCAmelCase ):
_UpperCAmelCase = label_list[item]
writer.write(F"{index}\t{item}\n" )
_UpperCAmelCase = {'finetuned_from': model_args.model_name_or_path, 'tasks': 'text-classification'}
if training_args.push_to_hub:
trainer.push_to_hub(**_UpperCAmelCase )
else:
trainer.create_model_card(**_UpperCAmelCase )
def A ( _UpperCAmelCase : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 339 | 1 |
import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
if is_onnx_available():
import onnxruntime as ort
class __lowerCAmelCase ( A , unittest.TestCase ):
# FIXME: add fast tests
pass
@nightly
@require_onnxruntime
@require_torch_gpu
class __lowerCAmelCase ( unittest.TestCase ):
@property
def _lowerCamelCase ( self : List[Any]) -> Union[str, Any]:
"""simple docstring"""
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def _lowerCamelCase ( self : List[str]) -> List[str]:
"""simple docstring"""
_UpperCAmelCase = ort.SessionOptions()
_UpperCAmelCase = False
return options
def _lowerCamelCase ( self : str) -> str:
"""simple docstring"""
_UpperCAmelCase = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/in_paint/overture-creations-5sI6fQgYIuo.png')
_UpperCAmelCase = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/in_paint/overture-creations-5sI6fQgYIuo_mask.png')
_UpperCAmelCase = OnnxStableDiffusionInpaintPipeline.from_pretrained(
'runwayml/stable-diffusion-inpainting' , revision='onnx' , safety_checker=A , feature_extractor=A , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=A)
_UpperCAmelCase = 'A red cat sitting on a park bench'
_UpperCAmelCase = np.random.RandomState(0)
_UpperCAmelCase = pipe(
prompt=A , image=A , mask_image=A , guidance_scale=7.5 , num_inference_steps=10 , generator=A , output_type='np' , )
_UpperCAmelCase = output.images
_UpperCAmelCase = images[0, 2_55:2_58, 2_55:2_58, -1]
assert images.shape == (1, 5_12, 5_12, 3)
_UpperCAmelCase = np.array([0.2_5_1_4, 0.3_0_0_7, 0.3_5_1_7, 0.1_7_9_0, 0.2_3_8_2, 0.3_1_6_7, 0.1_9_4_4, 0.2_2_7_3, 0.2_4_6_4])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-3
def _lowerCamelCase ( self : int) -> str:
"""simple docstring"""
_UpperCAmelCase = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/in_paint/overture-creations-5sI6fQgYIuo.png')
_UpperCAmelCase = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/in_paint/overture-creations-5sI6fQgYIuo_mask.png')
_UpperCAmelCase = LMSDiscreteScheduler.from_pretrained(
'runwayml/stable-diffusion-inpainting' , subfolder='scheduler' , revision='onnx')
_UpperCAmelCase = OnnxStableDiffusionInpaintPipeline.from_pretrained(
'runwayml/stable-diffusion-inpainting' , revision='onnx' , scheduler=A , safety_checker=A , feature_extractor=A , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=A)
_UpperCAmelCase = 'A red cat sitting on a park bench'
_UpperCAmelCase = np.random.RandomState(0)
_UpperCAmelCase = pipe(
prompt=A , image=A , mask_image=A , guidance_scale=7.5 , num_inference_steps=20 , generator=A , output_type='np' , )
_UpperCAmelCase = output.images
_UpperCAmelCase = images[0, 2_55:2_58, 2_55:2_58, -1]
assert images.shape == (1, 5_12, 5_12, 3)
_UpperCAmelCase = np.array([0.0_0_8_6, 0.0_0_7_7, 0.0_0_8_3, 0.0_0_9_3, 0.0_1_0_7, 0.0_1_3_9, 0.0_0_9_4, 0.0_0_9_7, 0.0_1_2_5])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-3
| 339 |
# This code is adapted from OpenAI's release
# https://github.com/openai/human-eval/blob/master/human_eval/execution.py
import contextlib
import faulthandler
import io
import multiprocessing
import os
import platform
import signal
import tempfile
def A ( _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Dict , _UpperCAmelCase : Dict , _UpperCAmelCase : Dict ) -> Any:
'''simple docstring'''
_UpperCAmelCase = multiprocessing.Manager()
_UpperCAmelCase = manager.list()
_UpperCAmelCase = multiprocessing.Process(target=_UpperCAmelCase , args=(check_program, result, timeout) )
p.start()
p.join(timeout=timeout + 1 )
if p.is_alive():
p.kill()
if not result:
result.append('timed out' )
return {
"task_id": task_id,
"passed": result[0] == "passed",
"result": result[0],
"completion_id": completion_id,
}
def A ( _UpperCAmelCase : str , _UpperCAmelCase : List[str] , _UpperCAmelCase : Dict ) -> Optional[int]:
'''simple docstring'''
with create_tempdir():
# These system calls are needed when cleaning up tempdir.
import os
import shutil
_UpperCAmelCase = shutil.rmtree
_UpperCAmelCase = os.rmdir
_UpperCAmelCase = os.chdir
# Disable functionalities that can make destructive changes to the test.
reliability_guard()
# Run program.
try:
_UpperCAmelCase = {}
with swallow_io():
with time_limit(_UpperCAmelCase ):
exec(_UpperCAmelCase , _UpperCAmelCase )
result.append('passed' )
except TimeoutException:
result.append('timed out' )
except BaseException as e:
result.append(F"failed: {e}" )
# Needed for cleaning up.
_UpperCAmelCase = rmtree
_UpperCAmelCase = rmdir
_UpperCAmelCase = chdir
@contextlib.contextmanager
def A ( _UpperCAmelCase : Union[str, Any] ) -> Any:
'''simple docstring'''
def signal_handler(_UpperCAmelCase : List[Any] , _UpperCAmelCase : Dict ):
raise TimeoutException('Timed out!' )
signal.setitimer(signal.ITIMER_REAL , _UpperCAmelCase )
signal.signal(signal.SIGALRM , _UpperCAmelCase )
try:
yield
finally:
signal.setitimer(signal.ITIMER_REAL , 0 )
@contextlib.contextmanager
def A ( ) -> Optional[int]:
'''simple docstring'''
_UpperCAmelCase = WriteOnlyStringIO()
with contextlib.redirect_stdout(_UpperCAmelCase ):
with contextlib.redirect_stderr(_UpperCAmelCase ):
with redirect_stdin(_UpperCAmelCase ):
yield
@contextlib.contextmanager
def A ( ) -> Any:
'''simple docstring'''
with tempfile.TemporaryDirectory() as dirname:
with chdir(_UpperCAmelCase ):
yield dirname
class __lowerCAmelCase ( A ):
pass
class __lowerCAmelCase ( io.StringIO ):
def _lowerCamelCase ( self : Tuple , *A : str , **A : Any) -> Any:
"""simple docstring"""
raise OSError
def _lowerCamelCase ( self : List[str] , *A : Optional[Any] , **A : Optional[Any]) -> Optional[int]:
"""simple docstring"""
raise OSError
def _lowerCamelCase ( self : str , *A : List[str] , **A : List[Any]) -> Union[str, Any]:
"""simple docstring"""
raise OSError
def _lowerCamelCase ( self : Union[str, Any] , *A : Optional[Any] , **A : List[str]) -> Optional[int]:
"""simple docstring"""
return False
class __lowerCAmelCase ( contextlib._RedirectStream ): # type: ignore
UpperCamelCase = '''stdin'''
@contextlib.contextmanager
def A ( _UpperCAmelCase : List[Any] ) -> Dict:
'''simple docstring'''
if root == ".":
yield
return
_UpperCAmelCase = os.getcwd()
os.chdir(_UpperCAmelCase )
try:
yield
except BaseException as exc:
raise exc
finally:
os.chdir(_UpperCAmelCase )
def A ( _UpperCAmelCase : List[str]=None ) -> Any:
'''simple docstring'''
if maximum_memory_bytes is not None:
import resource
resource.setrlimit(resource.RLIMIT_AS , (maximum_memory_bytes, maximum_memory_bytes) )
resource.setrlimit(resource.RLIMIT_DATA , (maximum_memory_bytes, maximum_memory_bytes) )
if not platform.uname().system == "Darwin":
resource.setrlimit(resource.RLIMIT_STACK , (maximum_memory_bytes, maximum_memory_bytes) )
faulthandler.disable()
import builtins
_UpperCAmelCase = None
_UpperCAmelCase = None
import os
_UpperCAmelCase = '1'
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
import shutil
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
import subprocess
_UpperCAmelCase = None # type: ignore
_UpperCAmelCase = None
import sys
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
| 339 | 1 |
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.modeling_flax_pytorch_utils import (
convert_pytorch_state_dict_to_flax,
load_flax_weights_in_pytorch_model,
)
from transformers.models.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel
if is_torch_available():
import torch
class __lowerCAmelCase :
def __init__( self : Optional[int] , A : Tuple , A : Tuple=14 , A : Dict=7 , A : Any=True , A : str=True , A : Tuple=False , A : Optional[int]=True , A : Tuple=99 , A : List[str]=32 , A : List[str]=4 , A : Dict=4 , A : List[Any]=4 , A : List[str]=37 , A : Union[str, Any]="gelu" , A : Dict=0.1 , A : Tuple=0.1 , A : int=5_12 , A : Optional[Any]=0.0_2 , ) -> Optional[Any]:
"""simple docstring"""
_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 = rotary_dim
_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 = initializer_range
_UpperCAmelCase = None
_UpperCAmelCase = vocab_size - 1
_UpperCAmelCase = vocab_size - 1
_UpperCAmelCase = vocab_size - 1
def _lowerCamelCase ( self : Optional[Any]) -> str:
"""simple docstring"""
_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 = GPTJConfig(
vocab_size=self.vocab_size , n_embd=self.hidden_size , n_layer=self.num_hidden_layers , n_head=self.num_attention_heads , n_positions=self.max_position_embeddings , use_cache=A , bos_token_id=self.bos_token_id , eos_token_id=self.eos_token_id , pad_token_id=self.pad_token_id , rotary_dim=self.rotary_dim , )
return (config, input_ids, input_mask)
def _lowerCamelCase ( self : Optional[int]) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase = self.prepare_config_and_inputs()
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = config_and_inputs
_UpperCAmelCase = {'input_ids': input_ids, 'attention_mask': attention_mask}
return config, inputs_dict
def _lowerCamelCase ( self : Tuple , A : List[str] , A : str , A : Any , A : Any) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase = 20
_UpperCAmelCase = model_class_name(A)
_UpperCAmelCase = model.init_cache(input_ids.shape[0] , A)
_UpperCAmelCase = jnp.ones((input_ids.shape[0], max_decoder_length) , dtype='i4')
_UpperCAmelCase = jnp.broadcast_to(
jnp.arange(input_ids.shape[-1] - 1)[None, :] , (input_ids.shape[0], input_ids.shape[-1] - 1))
_UpperCAmelCase = model(
input_ids[:, :-1] , attention_mask=A , past_key_values=A , position_ids=A , )
_UpperCAmelCase = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] , dtype='i4')
_UpperCAmelCase = model(
input_ids[:, -1:] , attention_mask=A , past_key_values=outputs_cache.past_key_values , position_ids=A , )
_UpperCAmelCase = model(A)
_UpperCAmelCase = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
self.parent.assertTrue(diff < 1E-3 , msg=F"Max diff is {diff}")
def _lowerCamelCase ( self : Dict , A : List[Any] , A : Optional[Any] , A : Union[str, Any] , A : Optional[Any]) -> int:
"""simple docstring"""
_UpperCAmelCase = 20
_UpperCAmelCase = model_class_name(A)
_UpperCAmelCase = jnp.concatenate(
[attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1]))] , axis=-1 , )
_UpperCAmelCase = model.init_cache(input_ids.shape[0] , A)
_UpperCAmelCase = jnp.broadcast_to(
jnp.arange(input_ids.shape[-1] - 1)[None, :] , (input_ids.shape[0], input_ids.shape[-1] - 1))
_UpperCAmelCase = model(
input_ids[:, :-1] , attention_mask=A , past_key_values=A , position_ids=A , )
_UpperCAmelCase = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] , dtype='i4')
_UpperCAmelCase = model(
input_ids[:, -1:] , past_key_values=outputs_cache.past_key_values , attention_mask=A , position_ids=A , )
_UpperCAmelCase = model(A , attention_mask=A)
_UpperCAmelCase = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
self.parent.assertTrue(diff < 1E-3 , msg=F"Max diff is {diff}")
@require_flax
class __lowerCAmelCase ( A , A , unittest.TestCase ):
UpperCamelCase = (FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
UpperCamelCase = (FlaxGPTJForCausalLM,) if is_flax_available() else ()
def _lowerCamelCase ( self : List[Any]) -> Dict:
"""simple docstring"""
_UpperCAmelCase = FlaxGPTJModelTester(self)
def _lowerCamelCase ( self : Optional[Any]) -> Optional[Any]:
"""simple docstring"""
for model_class_name in self.all_model_classes:
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_use_cache_forward(A , A , A , A)
def _lowerCamelCase ( self : Union[str, Any]) -> Any:
"""simple docstring"""
for model_class_name in self.all_model_classes:
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_use_cache_forward_with_attn_mask(
A , A , A , A)
@tooslow
def _lowerCamelCase ( self : str) -> Union[str, Any]:
"""simple docstring"""
_UpperCAmelCase = GPTaTokenizer.from_pretrained('gpt2' , pad_token='<|endoftext|>' , padding_side='left')
_UpperCAmelCase = tokenizer(['Hello this is a long string', 'Hey'] , return_tensors='np' , padding=A , truncation=A)
_UpperCAmelCase = FlaxGPTJForCausalLM.from_pretrained('EleutherAI/gpt-j-6B')
_UpperCAmelCase = False
_UpperCAmelCase = model.config.eos_token_id
_UpperCAmelCase = jax.jit(model.generate)
_UpperCAmelCase = jit_generate(
inputs['input_ids'] , attention_mask=inputs['attention_mask'] , pad_token_id=tokenizer.pad_token_id).sequences
_UpperCAmelCase = tokenizer.batch_decode(A , skip_special_tokens=A)
_UpperCAmelCase = [
'Hello this is a long string of text.\n\nI\'m trying to get the text of the',
'Hey, I\'m a little late to the party. I\'m going to',
]
self.assertListEqual(A , A)
@is_pt_flax_cross_test
def _lowerCamelCase ( self : List[str]) -> str:
"""simple docstring"""
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
# prepare inputs
_UpperCAmelCase = self._prepare_for_class(A , A)
_UpperCAmelCase = {k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}
# load corresponding PyTorch class
_UpperCAmelCase = model_class.__name__[4:] # Skip the "Flax" at the beginning
_UpperCAmelCase = getattr(A , A)
_UpperCAmelCase , _UpperCAmelCase = pt_inputs['input_ids'].shape
_UpperCAmelCase = np.random.randint(0 , seq_length - 1 , size=(batch_size,))
for batch_idx, start_index in enumerate(A):
_UpperCAmelCase = 0
_UpperCAmelCase = 1
_UpperCAmelCase = 0
_UpperCAmelCase = 1
_UpperCAmelCase = pt_model_class(A).eval()
_UpperCAmelCase = model_class(A , dtype=jnp.floataa)
_UpperCAmelCase = convert_pytorch_state_dict_to_flax(pt_model.state_dict() , A)
_UpperCAmelCase = fx_state
with torch.no_grad():
_UpperCAmelCase = pt_model(**A).to_tuple()
_UpperCAmelCase = fx_model(**A).to_tuple()
self.assertEqual(len(A) , len(A) , 'Output lengths differ between Flax and PyTorch')
for fx_output, pt_output in zip(A , A):
self.assert_almost_equals(fx_output[:, -1] , pt_output[:, -1].numpy() , 4E-2)
with tempfile.TemporaryDirectory() as tmpdirname:
pt_model.save_pretrained(A)
_UpperCAmelCase = model_class.from_pretrained(A , from_pt=A)
_UpperCAmelCase = fx_model_loaded(**A).to_tuple()
self.assertEqual(
len(A) , len(A) , 'Output lengths differ between Flax and PyTorch')
for fx_output_loaded, pt_output in zip(A , A):
self.assert_almost_equals(fx_output_loaded[:, -1] , pt_output[:, -1].numpy() , 4E-2)
@is_pt_flax_cross_test
def _lowerCamelCase ( self : Optional[int]) -> int:
"""simple docstring"""
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
# prepare inputs
_UpperCAmelCase = self._prepare_for_class(A , A)
_UpperCAmelCase = {k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}
# load corresponding PyTorch class
_UpperCAmelCase = model_class.__name__[4:] # Skip the "Flax" at the beginning
_UpperCAmelCase = getattr(A , A)
_UpperCAmelCase = pt_model_class(A).eval()
_UpperCAmelCase = model_class(A , dtype=jnp.floataa)
_UpperCAmelCase = load_flax_weights_in_pytorch_model(A , fx_model.params)
_UpperCAmelCase , _UpperCAmelCase = pt_inputs['input_ids'].shape
_UpperCAmelCase = np.random.randint(0 , seq_length - 1 , size=(batch_size,))
for batch_idx, start_index in enumerate(A):
_UpperCAmelCase = 0
_UpperCAmelCase = 1
_UpperCAmelCase = 0
_UpperCAmelCase = 1
# make sure weights are tied in PyTorch
pt_model.tie_weights()
with torch.no_grad():
_UpperCAmelCase = pt_model(**A).to_tuple()
_UpperCAmelCase = fx_model(**A).to_tuple()
self.assertEqual(len(A) , len(A) , 'Output lengths differ between Flax and PyTorch')
for fx_output, pt_output in zip(A , A):
self.assert_almost_equals(fx_output[:, -1] , pt_output[:, -1].numpy() , 4E-2)
with tempfile.TemporaryDirectory() as tmpdirname:
fx_model.save_pretrained(A)
_UpperCAmelCase = pt_model_class.from_pretrained(A , from_flax=A)
with torch.no_grad():
_UpperCAmelCase = pt_model_loaded(**A).to_tuple()
self.assertEqual(
len(A) , len(A) , 'Output lengths differ between Flax and PyTorch')
for fx_output, pt_output in zip(A , A):
self.assert_almost_equals(fx_output[:, -1] , pt_output[:, -1].numpy() , 4E-2)
@tooslow
def _lowerCamelCase ( self : Dict) -> Optional[int]:
"""simple docstring"""
for model_class_name in self.all_model_classes:
_UpperCAmelCase = model_class_name.from_pretrained('EleutherAI/gpt-j-6B')
_UpperCAmelCase = model(np.ones((1, 1)))
self.assertIsNotNone(A)
| 339 |
import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock
import torch
from ..state import AcceleratorState, PartialState
from ..utils import (
gather,
is_bnb_available,
is_comet_ml_available,
is_datasets_available,
is_deepspeed_available,
is_mps_available,
is_safetensors_available,
is_tensorboard_available,
is_torch_version,
is_tpu_available,
is_transformers_available,
is_wandb_available,
is_xpu_available,
)
def A ( _UpperCAmelCase : Tuple , _UpperCAmelCase : Union[str, Any]=False ) -> str:
'''simple docstring'''
try:
_UpperCAmelCase = os.environ[key]
except KeyError:
# KEY isn't set, default to `default`.
_UpperCAmelCase = default
else:
# KEY is set, convert it to True or False.
try:
_UpperCAmelCase = strtobool(_UpperCAmelCase )
except ValueError:
# More values are supported, but let's keep the message simple.
raise ValueError(F"If set, {key} must be yes or no." )
return _value
UpperCAmelCase__ = parse_flag_from_env("RUN_SLOW", default=False)
def A ( _UpperCAmelCase : List[str] ) -> List[str]:
'''simple docstring'''
return unittest.skip('Test was skipped' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Dict ) -> str:
'''simple docstring'''
return unittest.skipUnless(_run_slow_tests , 'test is slow' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Any ) -> str:
'''simple docstring'''
return unittest.skipUnless(not torch.cuda.is_available() , 'test requires only a CPU' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Dict ) -> Dict:
'''simple docstring'''
return unittest.skipUnless(torch.cuda.is_available() , 'test requires a GPU' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Optional[Any] ) -> List[Any]:
'''simple docstring'''
return unittest.skipUnless(is_xpu_available() , 'test requires a XPU' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Optional[int] ) -> List[str]:
'''simple docstring'''
return unittest.skipUnless(is_mps_available() , 'test requires a `mps` backend support in `torch`' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
return unittest.skipUnless(
is_transformers_available() and is_datasets_available() , 'test requires the Hugging Face suite' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : str ) -> str:
'''simple docstring'''
return unittest.skipUnless(is_bnb_available() , 'test requires the bitsandbytes library' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
return unittest.skipUnless(is_tpu_available() , 'test requires TPU' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Optional[Any] ) -> str:
'''simple docstring'''
return unittest.skipUnless(torch.cuda.device_count() == 1 , 'test requires a GPU' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Tuple ) -> int:
'''simple docstring'''
return unittest.skipUnless(torch.xpu.device_count() == 1 , 'test requires a XPU' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Any ) -> Optional[int]:
'''simple docstring'''
return unittest.skipUnless(torch.cuda.device_count() > 1 , 'test requires multiple GPUs' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Tuple ) -> Any:
'''simple docstring'''
return unittest.skipUnless(torch.xpu.device_count() > 1 , 'test requires multiple XPUs' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Any ) -> Optional[int]:
'''simple docstring'''
return unittest.skipUnless(is_safetensors_available() , 'test requires safetensors' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : List[Any] ) -> Dict:
'''simple docstring'''
return unittest.skipUnless(is_deepspeed_available() , 'test requires DeepSpeed' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Optional[int] ) -> str:
'''simple docstring'''
return unittest.skipUnless(is_torch_version('>=' , '1.12.0' ) , 'test requires torch version >= 1.12.0' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Any=None , _UpperCAmelCase : List[Any]=None ) -> Dict:
'''simple docstring'''
if test_case is None:
return partial(_UpperCAmelCase , version=_UpperCAmelCase )
return unittest.skipUnless(is_torch_version('>=' , _UpperCAmelCase ) , F"test requires torch version >= {version}" )(_UpperCAmelCase )
def A ( _UpperCAmelCase : List[str] ) -> int:
'''simple docstring'''
return unittest.skipUnless(is_tensorboard_available() , 'test requires Tensorboard' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Union[str, Any] ) -> Union[str, Any]:
'''simple docstring'''
return unittest.skipUnless(is_wandb_available() , 'test requires wandb' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : List[str] ) -> Optional[int]:
'''simple docstring'''
return unittest.skipUnless(is_comet_ml_available() , 'test requires comet_ml' )(_UpperCAmelCase )
UpperCAmelCase__ = (
any([is_wandb_available(), is_tensorboard_available()]) and not is_comet_ml_available()
)
def A ( _UpperCAmelCase : List[str] ) -> Any:
'''simple docstring'''
return unittest.skipUnless(
_atleast_one_tracker_available , 'test requires at least one tracker to be available and for `comet_ml` to not be installed' , )(_UpperCAmelCase )
class __lowerCAmelCase ( unittest.TestCase ):
UpperCamelCase = True
@classmethod
def _lowerCamelCase ( cls : List[Any]) -> Tuple:
"""simple docstring"""
_UpperCAmelCase = tempfile.mkdtemp()
@classmethod
def _lowerCamelCase ( cls : Union[str, Any]) -> str:
"""simple docstring"""
if os.path.exists(cls.tmpdir):
shutil.rmtree(cls.tmpdir)
def _lowerCamelCase ( self : List[str]) -> List[Any]:
"""simple docstring"""
if self.clear_on_setup:
for path in Path(self.tmpdir).glob('**/*'):
if path.is_file():
path.unlink()
elif path.is_dir():
shutil.rmtree(A)
class __lowerCAmelCase ( unittest.TestCase ):
def _lowerCamelCase ( self : Dict) -> Tuple:
"""simple docstring"""
super().tearDown()
# Reset the state of the AcceleratorState singleton.
AcceleratorState._reset_state()
PartialState._reset_state()
class __lowerCAmelCase ( unittest.TestCase ):
def _lowerCamelCase ( self : Optional[int] , A : Union[mock.Mock, List[mock.Mock]]) -> Tuple:
"""simple docstring"""
_UpperCAmelCase = mocks if isinstance(A , (tuple, list)) else [mocks]
for m in self.mocks:
m.start()
self.addCleanup(m.stop)
def A ( _UpperCAmelCase : List[Any] ) -> int:
'''simple docstring'''
_UpperCAmelCase = AcceleratorState()
_UpperCAmelCase = tensor[None].clone().to(state.device )
_UpperCAmelCase = gather(_UpperCAmelCase ).cpu()
_UpperCAmelCase = tensor[0].cpu()
for i in range(tensors.shape[0] ):
if not torch.equal(tensors[i] , _UpperCAmelCase ):
return False
return True
class __lowerCAmelCase :
def __init__( self : Optional[Any] , A : Union[str, Any] , A : Optional[int] , A : str) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase = returncode
_UpperCAmelCase = stdout
_UpperCAmelCase = stderr
async def A ( _UpperCAmelCase : str , _UpperCAmelCase : Optional[int] ) -> Optional[Any]:
'''simple docstring'''
while True:
_UpperCAmelCase = await stream.readline()
if line:
callback(_UpperCAmelCase )
else:
break
async def A ( _UpperCAmelCase : Optional[int] , _UpperCAmelCase : List[str]=None , _UpperCAmelCase : str=None , _UpperCAmelCase : str=None , _UpperCAmelCase : Dict=False , _UpperCAmelCase : Union[str, Any]=False ) -> _RunOutput:
'''simple docstring'''
if echo:
print('\nRunning: ' , ' '.join(_UpperCAmelCase ) )
_UpperCAmelCase = await asyncio.create_subprocess_exec(
cmd[0] , *cmd[1:] , stdin=_UpperCAmelCase , stdout=asyncio.subprocess.PIPE , stderr=asyncio.subprocess.PIPE , env=_UpperCAmelCase , )
# note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe
# https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait
#
# If it starts hanging, will need to switch to the following code. The problem is that no data
# will be seen until it's done and if it hangs for example there will be no debug info.
# out, err = await p.communicate()
# return _RunOutput(p.returncode, out, err)
_UpperCAmelCase = []
_UpperCAmelCase = []
def tee(_UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : List[Any] , _UpperCAmelCase : str , _UpperCAmelCase : str="" ):
_UpperCAmelCase = line.decode('utf-8' ).rstrip()
sink.append(_UpperCAmelCase )
if not quiet:
print(_UpperCAmelCase , _UpperCAmelCase , file=_UpperCAmelCase )
# XXX: the timeout doesn't seem to make any difference here
await asyncio.wait(
[
asyncio.create_task(_read_stream(p.stdout , lambda _UpperCAmelCase : tee(_UpperCAmelCase , _UpperCAmelCase , sys.stdout , label='stdout:' ) ) ),
asyncio.create_task(_read_stream(p.stderr , lambda _UpperCAmelCase : tee(_UpperCAmelCase , _UpperCAmelCase , sys.stderr , label='stderr:' ) ) ),
] , timeout=_UpperCAmelCase , )
return _RunOutput(await p.wait() , _UpperCAmelCase , _UpperCAmelCase )
def A ( _UpperCAmelCase : str , _UpperCAmelCase : Dict=None , _UpperCAmelCase : str=None , _UpperCAmelCase : str=180 , _UpperCAmelCase : List[Any]=False , _UpperCAmelCase : List[Any]=True ) -> _RunOutput:
'''simple docstring'''
_UpperCAmelCase = asyncio.get_event_loop()
_UpperCAmelCase = loop.run_until_complete(
_stream_subprocess(_UpperCAmelCase , env=_UpperCAmelCase , stdin=_UpperCAmelCase , timeout=_UpperCAmelCase , quiet=_UpperCAmelCase , echo=_UpperCAmelCase ) )
_UpperCAmelCase = ' '.join(_UpperCAmelCase )
if result.returncode > 0:
_UpperCAmelCase = '\n'.join(result.stderr )
raise RuntimeError(
F"'{cmd_str}' failed with returncode {result.returncode}\n\n"
F"The combined stderr from workers follows:\n{stderr}" )
return result
class __lowerCAmelCase ( A ):
pass
def A ( _UpperCAmelCase : List[str] , _UpperCAmelCase : str=False ) -> Tuple:
'''simple docstring'''
try:
_UpperCAmelCase = subprocess.check_output(_UpperCAmelCase , stderr=subprocess.STDOUT )
if return_stdout:
if hasattr(_UpperCAmelCase , 'decode' ):
_UpperCAmelCase = output.decode('utf-8' )
return output
except subprocess.CalledProcessError as e:
raise SubprocessCallException(
F"Command `{' '.join(_UpperCAmelCase )}` failed with the following error:\n\n{e.output.decode()}" ) from e
| 339 | 1 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_nllb import NllbTokenizer
else:
UpperCAmelCase__ = None
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}
UpperCAmelCase__ = {
"vocab_file": {
"facebook/nllb-200-distilled-600M": (
"https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/sentencepiece.bpe.model"
),
},
"tokenizer_file": {
"facebook/nllb-200-distilled-600M": (
"https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/tokenizer.json"
),
},
}
UpperCAmelCase__ = {
"facebook/nllb-large-en-ro": 1024,
"facebook/nllb-200-distilled-600M": 1024,
}
# fmt: off
UpperCAmelCase__ = ["ace_Arab", "ace_Latn", "acm_Arab", "acq_Arab", "aeb_Arab", "afr_Latn", "ajp_Arab", "aka_Latn", "amh_Ethi", "apc_Arab", "arb_Arab", "ars_Arab", "ary_Arab", "arz_Arab", "asm_Beng", "ast_Latn", "awa_Deva", "ayr_Latn", "azb_Arab", "azj_Latn", "bak_Cyrl", "bam_Latn", "ban_Latn", "bel_Cyrl", "bem_Latn", "ben_Beng", "bho_Deva", "bjn_Arab", "bjn_Latn", "bod_Tibt", "bos_Latn", "bug_Latn", "bul_Cyrl", "cat_Latn", "ceb_Latn", "ces_Latn", "cjk_Latn", "ckb_Arab", "crh_Latn", "cym_Latn", "dan_Latn", "deu_Latn", "dik_Latn", "dyu_Latn", "dzo_Tibt", "ell_Grek", "eng_Latn", "epo_Latn", "est_Latn", "eus_Latn", "ewe_Latn", "fao_Latn", "pes_Arab", "fij_Latn", "fin_Latn", "fon_Latn", "fra_Latn", "fur_Latn", "fuv_Latn", "gla_Latn", "gle_Latn", "glg_Latn", "grn_Latn", "guj_Gujr", "hat_Latn", "hau_Latn", "heb_Hebr", "hin_Deva", "hne_Deva", "hrv_Latn", "hun_Latn", "hye_Armn", "ibo_Latn", "ilo_Latn", "ind_Latn", "isl_Latn", "ita_Latn", "jav_Latn", "jpn_Jpan", "kab_Latn", "kac_Latn", "kam_Latn", "kan_Knda", "kas_Arab", "kas_Deva", "kat_Geor", "knc_Arab", "knc_Latn", "kaz_Cyrl", "kbp_Latn", "kea_Latn", "khm_Khmr", "kik_Latn", "kin_Latn", "kir_Cyrl", "kmb_Latn", "kon_Latn", "kor_Hang", "kmr_Latn", "lao_Laoo", "lvs_Latn", "lij_Latn", "lim_Latn", "lin_Latn", "lit_Latn", "lmo_Latn", "ltg_Latn", "ltz_Latn", "lua_Latn", "lug_Latn", "luo_Latn", "lus_Latn", "mag_Deva", "mai_Deva", "mal_Mlym", "mar_Deva", "min_Latn", "mkd_Cyrl", "plt_Latn", "mlt_Latn", "mni_Beng", "khk_Cyrl", "mos_Latn", "mri_Latn", "zsm_Latn", "mya_Mymr", "nld_Latn", "nno_Latn", "nob_Latn", "npi_Deva", "nso_Latn", "nus_Latn", "nya_Latn", "oci_Latn", "gaz_Latn", "ory_Orya", "pag_Latn", "pan_Guru", "pap_Latn", "pol_Latn", "por_Latn", "prs_Arab", "pbt_Arab", "quy_Latn", "ron_Latn", "run_Latn", "rus_Cyrl", "sag_Latn", "san_Deva", "sat_Beng", "scn_Latn", "shn_Mymr", "sin_Sinh", "slk_Latn", "slv_Latn", "smo_Latn", "sna_Latn", "snd_Arab", "som_Latn", "sot_Latn", "spa_Latn", "als_Latn", "srd_Latn", "srp_Cyrl", "ssw_Latn", "sun_Latn", "swe_Latn", "swh_Latn", "szl_Latn", "tam_Taml", "tat_Cyrl", "tel_Telu", "tgk_Cyrl", "tgl_Latn", "tha_Thai", "tir_Ethi", "taq_Latn", "taq_Tfng", "tpi_Latn", "tsn_Latn", "tso_Latn", "tuk_Latn", "tum_Latn", "tur_Latn", "twi_Latn", "tzm_Tfng", "uig_Arab", "ukr_Cyrl", "umb_Latn", "urd_Arab", "uzn_Latn", "vec_Latn", "vie_Latn", "war_Latn", "wol_Latn", "xho_Latn", "ydd_Hebr", "yor_Latn", "yue_Hant", "zho_Hans", "zho_Hant", "zul_Latn"]
class __lowerCAmelCase ( A ):
UpperCamelCase = VOCAB_FILES_NAMES
UpperCamelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase = ['''input_ids''', '''attention_mask''']
UpperCamelCase = NllbTokenizer
UpperCamelCase = []
UpperCamelCase = []
def __init__( self : Any , A : Dict=None , A : str=None , A : Optional[Any]="<s>" , A : Optional[int]="</s>" , A : Tuple="</s>" , A : Union[str, Any]="<s>" , A : Union[str, Any]="<unk>" , A : Union[str, Any]="<pad>" , A : Union[str, Any]="<mask>" , A : List[str]=None , A : List[str]=None , A : Optional[Any]=None , A : List[str]=False , **A : Optional[Any] , ) -> int:
"""simple docstring"""
_UpperCAmelCase = AddedToken(A , lstrip=A , rstrip=A) if isinstance(A , A) else mask_token
_UpperCAmelCase = legacy_behaviour
super().__init__(
vocab_file=A , tokenizer_file=A , bos_token=A , eos_token=A , sep_token=A , cls_token=A , unk_token=A , pad_token=A , mask_token=A , src_lang=A , tgt_lang=A , additional_special_tokens=A , legacy_behaviour=A , **A , )
_UpperCAmelCase = vocab_file
_UpperCAmelCase = False if not self.vocab_file else True
_UpperCAmelCase = FAIRSEQ_LANGUAGE_CODES.copy()
if additional_special_tokens is not None:
# Only add those special tokens if they are not already there.
_additional_special_tokens.extend(
[t for t in additional_special_tokens if t not in _additional_special_tokens])
self.add_special_tokens({'additional_special_tokens': _additional_special_tokens})
_UpperCAmelCase = {
lang_code: self.convert_tokens_to_ids(A) for lang_code in FAIRSEQ_LANGUAGE_CODES
}
_UpperCAmelCase = src_lang if src_lang is not None else 'eng_Latn'
_UpperCAmelCase = self.convert_tokens_to_ids(self._src_lang)
_UpperCAmelCase = tgt_lang
self.set_src_lang_special_tokens(self._src_lang)
@property
def _lowerCamelCase ( self : Optional[int]) -> str:
"""simple docstring"""
return self._src_lang
@src_lang.setter
def _lowerCamelCase ( self : str , A : str) -> None:
"""simple docstring"""
_UpperCAmelCase = new_src_lang
self.set_src_lang_special_tokens(self._src_lang)
def _lowerCamelCase ( self : List[str] , A : List[int] , A : Optional[List[int]] = None) -> List[int]:
"""simple docstring"""
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + self.suffix_tokens
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
def _lowerCamelCase ( self : Optional[int] , A : List[int] , A : Optional[List[int]] = None) -> List[int]:
"""simple docstring"""
_UpperCAmelCase = [self.sep_token_id]
_UpperCAmelCase = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep) * [0]
def _lowerCamelCase ( self : Optional[Any] , A : Tuple , A : str , A : Optional[str] , A : Optional[str] , **A : int) -> str:
"""simple docstring"""
if src_lang is None or tgt_lang is None:
raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model')
_UpperCAmelCase = src_lang
_UpperCAmelCase = self(A , add_special_tokens=A , return_tensors=A , **A)
_UpperCAmelCase = self.convert_tokens_to_ids(A)
_UpperCAmelCase = tgt_lang_id
return inputs
def _lowerCamelCase ( self : Union[str, Any] , A : List[str] , A : str = "eng_Latn" , A : Optional[List[str]] = None , A : str = "fra_Latn" , **A : Union[str, Any] , ) -> BatchEncoding:
"""simple docstring"""
_UpperCAmelCase = src_lang
_UpperCAmelCase = tgt_lang
return super().prepare_seqaseq_batch(A , A , **A)
def _lowerCamelCase ( self : List[Any]) -> Optional[Any]:
"""simple docstring"""
return self.set_src_lang_special_tokens(self.src_lang)
def _lowerCamelCase ( self : Dict) -> Union[str, Any]:
"""simple docstring"""
return self.set_tgt_lang_special_tokens(self.tgt_lang)
def _lowerCamelCase ( self : int , A : Dict) -> None:
"""simple docstring"""
_UpperCAmelCase = self.convert_tokens_to_ids(A)
if self.legacy_behaviour:
_UpperCAmelCase = []
_UpperCAmelCase = [self.eos_token_id, self.cur_lang_code]
else:
_UpperCAmelCase = [self.cur_lang_code]
_UpperCAmelCase = [self.eos_token_id]
_UpperCAmelCase = self.convert_ids_to_tokens(self.prefix_tokens)
_UpperCAmelCase = self.convert_ids_to_tokens(self.suffix_tokens)
_UpperCAmelCase = processors.TemplateProcessing(
single=prefix_tokens_str + ['$A'] + suffix_tokens_str , pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens)) , )
def _lowerCamelCase ( self : Tuple , A : str) -> None:
"""simple docstring"""
_UpperCAmelCase = self.convert_tokens_to_ids(A)
if self.legacy_behaviour:
_UpperCAmelCase = []
_UpperCAmelCase = [self.eos_token_id, self.cur_lang_code]
else:
_UpperCAmelCase = [self.cur_lang_code]
_UpperCAmelCase = [self.eos_token_id]
_UpperCAmelCase = self.convert_ids_to_tokens(self.prefix_tokens)
_UpperCAmelCase = self.convert_ids_to_tokens(self.suffix_tokens)
_UpperCAmelCase = processors.TemplateProcessing(
single=prefix_tokens_str + ['$A'] + suffix_tokens_str , pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens)) , )
def _lowerCamelCase ( self : str , A : str , A : Optional[str] = None) -> Tuple[str]:
"""simple docstring"""
if not self.can_save_slow_tokenizer:
raise ValueError(
'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
'tokenizer.')
if not os.path.isdir(A):
logger.error(F"Vocabulary path ({save_directory}) should be a directory.")
return
_UpperCAmelCase = os.path.join(
A , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])
if os.path.abspath(self.vocab_file) != os.path.abspath(A):
copyfile(self.vocab_file , A)
return (out_vocab_file,)
| 339 |
from __future__ import annotations
UpperCAmelCase__ = list[list[int]]
# assigning initial values to the grid
UpperCAmelCase__ = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5],
[0, 5, 0, 0, 9, 0, 6, 0, 0],
[1, 3, 0, 0, 0, 0, 2, 5, 0],
[0, 0, 0, 0, 0, 0, 0, 7, 4],
[0, 0, 5, 2, 0, 6, 3, 0, 0],
]
# a grid with no solution
UpperCAmelCase__ = [
[5, 0, 6, 5, 0, 8, 4, 0, 3],
[5, 2, 0, 0, 0, 0, 0, 0, 2],
[1, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5],
[0, 5, 0, 0, 9, 0, 6, 0, 0],
[1, 3, 0, 0, 0, 0, 2, 5, 0],
[0, 0, 0, 0, 0, 0, 0, 7, 4],
[0, 0, 5, 2, 0, 6, 3, 0, 0],
]
def A ( _UpperCAmelCase : Matrix , _UpperCAmelCase : int , _UpperCAmelCase : int , _UpperCAmelCase : int ) -> bool:
'''simple docstring'''
for i in range(9 ):
if grid[row][i] == n or grid[i][column] == n:
return False
for i in range(3 ):
for j in range(3 ):
if grid[(row - row % 3) + i][(column - column % 3) + j] == n:
return False
return True
def A ( _UpperCAmelCase : Matrix ) -> tuple[int, int] | None:
'''simple docstring'''
for i in range(9 ):
for j in range(9 ):
if grid[i][j] == 0:
return i, j
return None
def A ( _UpperCAmelCase : Matrix ) -> Matrix | None:
'''simple docstring'''
if location := find_empty_location(_UpperCAmelCase ):
_UpperCAmelCase , _UpperCAmelCase = location
else:
# If the location is ``None``, then the grid is solved.
return grid
for digit in range(1 , 10 ):
if is_safe(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
_UpperCAmelCase = digit
if sudoku(_UpperCAmelCase ) is not None:
return grid
_UpperCAmelCase = 0
return None
def A ( _UpperCAmelCase : Matrix ) -> None:
'''simple docstring'''
for row in grid:
for cell in row:
print(_UpperCAmelCase , end=' ' )
print()
if __name__ == "__main__":
# make a copy of grid so that you can compare with the unmodified grid
for example_grid in (initial_grid, no_solution):
print("\nExample grid:\n" + "=" * 20)
print_solution(example_grid)
print("\nExample grid solution:")
UpperCAmelCase__ = sudoku(example_grid)
if solution is not None:
print_solution(solution)
else:
print("Cannot find a solution.")
| 339 | 1 |
import numpy as np
def A ( _UpperCAmelCase : np.ndarray , _UpperCAmelCase : np.ndarray , _UpperCAmelCase : float = 1E-12 , _UpperCAmelCase : int = 100 , ) -> tuple[float, np.ndarray]:
'''simple docstring'''
assert np.shape(_UpperCAmelCase )[0] == np.shape(_UpperCAmelCase )[1]
# Ensure proper dimensionality.
assert np.shape(_UpperCAmelCase )[0] == np.shape(_UpperCAmelCase )[0]
# Ensure inputs are either both complex or both real
assert np.iscomplexobj(_UpperCAmelCase ) == np.iscomplexobj(_UpperCAmelCase )
_UpperCAmelCase = np.iscomplexobj(_UpperCAmelCase )
if is_complex:
# Ensure complex input_matrix is Hermitian
assert np.array_equal(_UpperCAmelCase , input_matrix.conj().T )
# Set convergence to False. Will define convergence when we exceed max_iterations
# or when we have small changes from one iteration to next.
_UpperCAmelCase = False
_UpperCAmelCase = 0
_UpperCAmelCase = 0
_UpperCAmelCase = 1E12
while not convergence:
# Multiple matrix by the vector.
_UpperCAmelCase = np.dot(_UpperCAmelCase , _UpperCAmelCase )
# Normalize the resulting output vector.
_UpperCAmelCase = w / np.linalg.norm(_UpperCAmelCase )
# Find rayleigh quotient
# (faster than usual b/c we know vector is normalized already)
_UpperCAmelCase = vector.conj().T if is_complex else vector.T
_UpperCAmelCase = np.dot(_UpperCAmelCase , np.dot(_UpperCAmelCase , _UpperCAmelCase ) )
# Check convergence.
_UpperCAmelCase = np.abs(lambda_ - lambda_previous ) / lambda_
iterations += 1
if error <= error_tol or iterations >= max_iterations:
_UpperCAmelCase = True
_UpperCAmelCase = lambda_
if is_complex:
_UpperCAmelCase = np.real(lambda_ )
return lambda_, vector
def A ( ) -> None:
'''simple docstring'''
_UpperCAmelCase = np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]] )
_UpperCAmelCase = np.array([41, 4, 20] )
_UpperCAmelCase = real_input_matrix.astype(np.complexaaa )
_UpperCAmelCase = np.triu(1J * complex_input_matrix , 1 )
complex_input_matrix += imag_matrix
complex_input_matrix += -1 * imag_matrix.T
_UpperCAmelCase = np.array([41, 4, 20] ).astype(np.complexaaa )
for problem_type in ["real", "complex"]:
if problem_type == "real":
_UpperCAmelCase = real_input_matrix
_UpperCAmelCase = real_vector
elif problem_type == "complex":
_UpperCAmelCase = complex_input_matrix
_UpperCAmelCase = complex_vector
# Our implementation.
_UpperCAmelCase , _UpperCAmelCase = power_iteration(_UpperCAmelCase , _UpperCAmelCase )
# Numpy implementation.
# Get eigenvalues and eigenvectors using built-in numpy
# eigh (eigh used for symmetric or hermetian matrices).
_UpperCAmelCase , _UpperCAmelCase = np.linalg.eigh(_UpperCAmelCase )
# Last eigenvalue is the maximum one.
_UpperCAmelCase = eigen_values[-1]
# Last column in this matrix is eigenvector corresponding to largest eigenvalue.
_UpperCAmelCase = eigen_vectors[:, -1]
# Check our implementation and numpy gives close answers.
assert np.abs(eigen_value - eigen_value_max ) <= 1E-6
# Take absolute values element wise of each eigenvector.
# as they are only unique to a minus sign.
assert np.linalg.norm(np.abs(_UpperCAmelCase ) - np.abs(_UpperCAmelCase ) ) <= 1E-6
if __name__ == "__main__":
import doctest
doctest.testmod()
test_power_iteration()
| 339 |
import numpy as np
from nltk.translate import meteor_score
import datasets
from datasets.config import importlib_metadata, version
UpperCAmelCase__ = version.parse(importlib_metadata.version("nltk"))
if NLTK_VERSION >= version.Version("3.6.4"):
from nltk import word_tokenize
UpperCAmelCase__ = "\\n@inproceedings{banarjee2005,\n title = {{METEOR}: An Automatic Metric for {MT} Evaluation with Improved Correlation with Human Judgments},\n author = {Banerjee, Satanjeev and Lavie, Alon},\n booktitle = {Proceedings of the {ACL} Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization},\n month = jun,\n year = {2005},\n address = {Ann Arbor, Michigan},\n publisher = {Association for Computational Linguistics},\n url = {https://www.aclweb.org/anthology/W05-0909},\n pages = {65--72},\n}\n"
UpperCAmelCase__ = "\\nMETEOR, an automatic metric for machine translation evaluation\nthat is based on a generalized concept of unigram matching between the\nmachine-produced translation and human-produced reference translations.\nUnigrams can be matched based on their surface forms, stemmed forms,\nand meanings; furthermore, METEOR can be easily extended to include more\nadvanced matching strategies. Once all generalized unigram matches\nbetween the two strings have been found, METEOR computes a score for\nthis matching using a combination of unigram-precision, unigram-recall, and\na measure of fragmentation that is designed to directly capture how\nwell-ordered the matched words in the machine translation are in relation\nto the reference.\n\nMETEOR gets an R correlation value of 0.347 with human evaluation on the Arabic\ndata and 0.331 on the Chinese data. This is shown to be an improvement on\nusing simply unigram-precision, unigram-recall and their harmonic F1\ncombination.\n"
UpperCAmelCase__ = "\nComputes METEOR score of translated segments against one or more references.\nArgs:\n predictions: list of predictions to score. Each prediction\n should be a string with tokens separated by spaces.\n references: list of reference for each prediction. Each\n reference should be a string with tokens separated by spaces.\n alpha: Parameter for controlling relative weights of precision and recall. default: 0.9\n beta: Parameter for controlling shape of penalty as a function of fragmentation. default: 3\n gamma: Relative weight assigned to fragmentation penalty. default: 0.5\nReturns:\n 'meteor': meteor score.\nExamples:\n\n >>> meteor = datasets.load_metric('meteor')\n >>> predictions = [\"It is a guide to action which ensures that the military always obeys the commands of the party\"]\n >>> references = [\"It is a guide to action that ensures that the military will forever heed Party commands\"]\n >>> results = meteor.compute(predictions=predictions, references=references)\n >>> print(round(results[\"meteor\"], 4))\n 0.6944\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __lowerCAmelCase ( datasets.Metric ):
def _lowerCamelCase ( self : List[Any]) -> List[Any]:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'predictions': datasets.Value('string' , id='sequence'),
'references': datasets.Value('string' , id='sequence'),
}) , codebase_urls=['https://github.com/nltk/nltk/blob/develop/nltk/translate/meteor_score.py'] , reference_urls=[
'https://www.nltk.org/api/nltk.translate.html#module-nltk.translate.meteor_score',
'https://en.wikipedia.org/wiki/METEOR',
] , )
def _lowerCamelCase ( self : Optional[Any] , A : List[str]) -> List[Any]:
"""simple docstring"""
import nltk
nltk.download('wordnet')
if NLTK_VERSION >= version.Version('3.6.5'):
nltk.download('punkt')
if NLTK_VERSION >= version.Version('3.6.6'):
nltk.download('omw-1.4')
def _lowerCamelCase ( self : Optional[Any] , A : Tuple , A : Optional[int] , A : List[Any]=0.9 , A : Optional[Any]=3 , A : Optional[int]=0.5) -> Any:
"""simple docstring"""
if NLTK_VERSION >= version.Version('3.6.5'):
_UpperCAmelCase = [
meteor_score.single_meteor_score(
word_tokenize(A) , word_tokenize(A) , alpha=A , beta=A , gamma=A)
for ref, pred in zip(A , A)
]
else:
_UpperCAmelCase = [
meteor_score.single_meteor_score(A , A , alpha=A , beta=A , gamma=A)
for ref, pred in zip(A , A)
]
return {"meteor": np.mean(A)}
| 339 | 1 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.25.0")):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import (
VersatileDiffusionDualGuidedPipeline,
VersatileDiffusionImageVariationPipeline,
VersatileDiffusionPipeline,
VersatileDiffusionTextToImagePipeline,
)
else:
from .modeling_text_unet import UNetFlatConditionModel
from .pipeline_versatile_diffusion import VersatileDiffusionPipeline
from .pipeline_versatile_diffusion_dual_guided import VersatileDiffusionDualGuidedPipeline
from .pipeline_versatile_diffusion_image_variation import VersatileDiffusionImageVariationPipeline
from .pipeline_versatile_diffusion_text_to_image import VersatileDiffusionTextToImagePipeline
| 339 |
import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
UpperCAmelCase__ = {
"tiny.en": "https://openaipublic.azureedge.net/main/whisper/models/d3dd57d32accea0b295c96e26691aa14d8822fac7d9d27d5dc00b4ca2826dd03/tiny.en.pt",
"tiny": "https://openaipublic.azureedge.net/main/whisper/models/65147644a518d12f04e32d6f3b26facc3f8dd46e5390956a9424a650c0ce22b9/tiny.pt",
"base.en": "https://openaipublic.azureedge.net/main/whisper/models/25a8566e1d0c1e2231d1c762132cd20e0f96a85d16145c3a00adf5d1ac670ead/base.en.pt",
"base": "https://openaipublic.azureedge.net/main/whisper/models/ed3a0b6b1c0edf879ad9b11b1af5a0e6ab5db9205f891f668f8b0e6c6326e34e/base.pt",
"small.en": "https://openaipublic.azureedge.net/main/whisper/models/f953ad0fd29cacd07d5a9eda5624af0f6bcf2258be67c92b79389873d91e0872/small.en.pt",
"small": "https://openaipublic.azureedge.net/main/whisper/models/9ecf779972d90ba49c06d968637d720dd632c55bbf19d441fb42bf17a411e794/small.pt",
"medium.en": "https://openaipublic.azureedge.net/main/whisper/models/d7440d1dc186f76616474e0ff0b3b6b879abc9d1a4926b7adfa41db2d497ab4f/medium.en.pt",
"medium": "https://openaipublic.azureedge.net/main/whisper/models/345ae4da62f9b3d59415adc60127b97c714f32e89e936602e85993674d08dcb1/medium.pt",
"large": "https://openaipublic.azureedge.net/main/whisper/models/e4b87e7e0bf463eb8e6956e646f1e277e901512310def2c24bf0e11bd3c28e9a/large.pt",
"large-v2": "https://openaipublic.azureedge.net/main/whisper/models/81f7c96c852ee8fc832187b0132e569d6c3065a3252ed18e56effd0b6a73e524/large-v2.pt",
}
def A ( _UpperCAmelCase : Optional[int] ) -> str:
'''simple docstring'''
_UpperCAmelCase = ['layers', 'blocks']
for k in ignore_keys:
state_dict.pop(_UpperCAmelCase , _UpperCAmelCase )
UpperCAmelCase__ = {
"blocks": "layers",
"mlp.0": "fc1",
"mlp.2": "fc2",
"mlp_ln": "final_layer_norm",
".attn.query": ".self_attn.q_proj",
".attn.key": ".self_attn.k_proj",
".attn.value": ".self_attn.v_proj",
".attn_ln": ".self_attn_layer_norm",
".attn.out": ".self_attn.out_proj",
".cross_attn.query": ".encoder_attn.q_proj",
".cross_attn.key": ".encoder_attn.k_proj",
".cross_attn.value": ".encoder_attn.v_proj",
".cross_attn_ln": ".encoder_attn_layer_norm",
".cross_attn.out": ".encoder_attn.out_proj",
"decoder.ln.": "decoder.layer_norm.",
"encoder.ln.": "encoder.layer_norm.",
"token_embedding": "embed_tokens",
"encoder.positional_embedding": "encoder.embed_positions.weight",
"decoder.positional_embedding": "decoder.embed_positions.weight",
"ln_post": "layer_norm",
}
def A ( _UpperCAmelCase : Dict ) -> Optional[int]:
'''simple docstring'''
_UpperCAmelCase = list(s_dict.keys() )
for key in keys:
_UpperCAmelCase = key
for k, v in WHISPER_MAPPING.items():
if k in key:
_UpperCAmelCase = new_key.replace(_UpperCAmelCase , _UpperCAmelCase )
print(F"{key} -> {new_key}" )
_UpperCAmelCase = s_dict.pop(_UpperCAmelCase )
return s_dict
def A ( _UpperCAmelCase : List[Any] ) -> Union[str, Any]:
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase = emb.weight.shape
_UpperCAmelCase = nn.Linear(_UpperCAmelCase , _UpperCAmelCase , bias=_UpperCAmelCase )
_UpperCAmelCase = emb.weight.data
return lin_layer
def A ( _UpperCAmelCase : str , _UpperCAmelCase : str ) -> bytes:
'''simple docstring'''
os.makedirs(_UpperCAmelCase , exist_ok=_UpperCAmelCase )
_UpperCAmelCase = os.path.basename(_UpperCAmelCase )
_UpperCAmelCase = url.split('/' )[-2]
_UpperCAmelCase = os.path.join(_UpperCAmelCase , _UpperCAmelCase )
if os.path.exists(_UpperCAmelCase ) and not os.path.isfile(_UpperCAmelCase ):
raise RuntimeError(F"{download_target} exists and is not a regular file" )
if os.path.isfile(_UpperCAmelCase ):
_UpperCAmelCase = open(_UpperCAmelCase , 'rb' ).read()
if hashlib.shaaaa(_UpperCAmelCase ).hexdigest() == expected_shaaaa:
return model_bytes
else:
warnings.warn(F"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file" )
with urllib.request.urlopen(_UpperCAmelCase ) as source, open(_UpperCAmelCase , 'wb' ) as output:
with tqdm(
total=int(source.info().get('Content-Length' ) ) , ncols=80 , unit='iB' , unit_scale=_UpperCAmelCase , unit_divisor=1_024 ) as loop:
while True:
_UpperCAmelCase = source.read(8_192 )
if not buffer:
break
output.write(_UpperCAmelCase )
loop.update(len(_UpperCAmelCase ) )
_UpperCAmelCase = open(_UpperCAmelCase , 'rb' ).read()
if hashlib.shaaaa(_UpperCAmelCase ).hexdigest() != expected_shaaaa:
raise RuntimeError(
'Model has been downloaded but the SHA256 checksum does not not match. Please retry loading the model.' )
return model_bytes
def A ( _UpperCAmelCase : List[Any] , _UpperCAmelCase : Any ) -> Optional[int]:
'''simple docstring'''
if ".pt" not in checkpoint_path:
_UpperCAmelCase = _download(_MODELS[checkpoint_path] )
else:
_UpperCAmelCase = torch.load(_UpperCAmelCase , map_location='cpu' )
_UpperCAmelCase = original_checkpoint['dims']
_UpperCAmelCase = original_checkpoint['model_state_dict']
_UpperCAmelCase = state_dict['decoder.token_embedding.weight']
remove_ignore_keys_(_UpperCAmelCase )
rename_keys(_UpperCAmelCase )
_UpperCAmelCase = True
_UpperCAmelCase = state_dict['decoder.layers.0.fc1.weight'].shape[0]
_UpperCAmelCase = WhisperConfig(
vocab_size=dimensions['n_vocab'] , encoder_ffn_dim=_UpperCAmelCase , decoder_ffn_dim=_UpperCAmelCase , num_mel_bins=dimensions['n_mels'] , d_model=dimensions['n_audio_state'] , max_target_positions=dimensions['n_text_ctx'] , encoder_layers=dimensions['n_audio_layer'] , encoder_attention_heads=dimensions['n_audio_head'] , decoder_layers=dimensions['n_text_layer'] , decoder_attention_heads=dimensions['n_text_state'] , max_source_positions=dimensions['n_audio_ctx'] , )
_UpperCAmelCase = WhisperForConditionalGeneration(_UpperCAmelCase )
_UpperCAmelCase , _UpperCAmelCase = model.model.load_state_dict(_UpperCAmelCase , strict=_UpperCAmelCase )
if len(_UpperCAmelCase ) > 0 and not set(_UpperCAmelCase ) <= {
"encoder.embed_positions.weights",
"decoder.embed_positions.weights",
}:
raise ValueError(
'Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights` are allowed to be missing,'
F" but all the following weights are missing {missing}" )
if tie_embeds:
_UpperCAmelCase = make_linear_from_emb(model.model.decoder.embed_tokens )
else:
_UpperCAmelCase = proj_out_weights
model.save_pretrained(_UpperCAmelCase )
if __name__ == "__main__":
UpperCAmelCase__ = argparse.ArgumentParser()
# # Required parameters
parser.add_argument("--checkpoint_path", type=str, help="Patht to the downloaded checkpoints")
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
UpperCAmelCase__ = parser.parse_args()
convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)
| 339 | 1 |
def A ( _UpperCAmelCase : int = 50 ) -> int:
'''simple docstring'''
_UpperCAmelCase = [[0] * 3 for _ in range(length + 1 )]
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
different_colour_ways_number[row_length][tile_length - 2] += (
different_colour_ways_number[row_length - tile_start - tile_length][
tile_length - 2
]
+ 1
)
return sum(different_colour_ways_number[length] )
if __name__ == "__main__":
print(f"""{solution() = }""")
| 339 |
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
UpperCAmelCase__ = datasets.utils.logging.get_logger(__name__)
class __lowerCAmelCase ( folder_based_builder.FolderBasedBuilderConfig ):
UpperCamelCase = None
UpperCamelCase = None
class __lowerCAmelCase ( folder_based_builder.FolderBasedBuilder ):
UpperCamelCase = datasets.Audio()
UpperCamelCase = '''audio'''
UpperCamelCase = AudioFolderConfig
UpperCamelCase = 42 # definition at the bottom of the script
UpperCamelCase = AudioClassification(audio_column='''audio''' , label_column='''label''' )
UpperCAmelCase__ = [
".aiff",
".au",
".avr",
".caf",
".flac",
".htk",
".svx",
".mat4",
".mat5",
".mpc2k",
".ogg",
".paf",
".pvf",
".raw",
".rf64",
".sd2",
".sds",
".ircam",
".voc",
".w64",
".wav",
".nist",
".wavex",
".wve",
".xi",
".mp3",
".opus",
]
UpperCAmelCase__ = AUDIO_EXTENSIONS
| 339 | 1 |
import warnings
from ...utils import logging
from .image_processing_flava import FlavaImageProcessor
UpperCAmelCase__ = logging.get_logger(__name__)
class __lowerCAmelCase ( A ):
def __init__( self : List[str] , *A : int , **A : int) -> None:
"""simple docstring"""
warnings.warn(
'The class FlavaFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
' use FlavaImageProcessor instead.' , A , )
super().__init__(*A , **A)
| 339 |
import sys
from collections import defaultdict
class __lowerCAmelCase :
def __init__( self : int) -> str:
"""simple docstring"""
_UpperCAmelCase = []
def _lowerCamelCase ( self : Any , A : List[str]) -> int:
"""simple docstring"""
return self.node_position[vertex]
def _lowerCamelCase ( self : Optional[Any] , A : Optional[int] , A : str) -> List[str]:
"""simple docstring"""
_UpperCAmelCase = pos
def _lowerCamelCase ( self : Tuple , A : Tuple , A : Dict , A : List[str] , A : Optional[Any]) -> Dict:
"""simple docstring"""
if start > size // 2 - 1:
return
else:
if 2 * start + 2 >= size:
_UpperCAmelCase = 2 * start + 1
else:
if heap[2 * start + 1] < heap[2 * start + 2]:
_UpperCAmelCase = 2 * start + 1
else:
_UpperCAmelCase = 2 * start + 2
if heap[smallest_child] < heap[start]:
_UpperCAmelCase , _UpperCAmelCase = heap[smallest_child], positions[smallest_child]
_UpperCAmelCase , _UpperCAmelCase = (
heap[start],
positions[start],
)
_UpperCAmelCase , _UpperCAmelCase = temp, tempa
_UpperCAmelCase = self.get_position(positions[smallest_child])
self.set_position(
positions[smallest_child] , self.get_position(positions[start]))
self.set_position(positions[start] , A)
self.top_to_bottom(A , A , A , A)
def _lowerCamelCase ( self : Optional[int] , A : str , A : Optional[Any] , A : Optional[int] , A : str) -> Any:
"""simple docstring"""
_UpperCAmelCase = position[index]
while index != 0:
_UpperCAmelCase = int((index - 2) / 2) if index % 2 == 0 else int((index - 1) / 2)
if val < heap[parent]:
_UpperCAmelCase = heap[parent]
_UpperCAmelCase = position[parent]
self.set_position(position[parent] , A)
else:
_UpperCAmelCase = val
_UpperCAmelCase = temp
self.set_position(A , A)
break
_UpperCAmelCase = parent
else:
_UpperCAmelCase = val
_UpperCAmelCase = temp
self.set_position(A , 0)
def _lowerCamelCase ( self : Union[str, Any] , A : Optional[int] , A : Tuple) -> str:
"""simple docstring"""
_UpperCAmelCase = len(A) // 2 - 1
for i in range(A , -1 , -1):
self.top_to_bottom(A , A , len(A) , A)
def _lowerCamelCase ( self : Optional[int] , A : int , A : str) -> List[str]:
"""simple docstring"""
_UpperCAmelCase = positions[0]
_UpperCAmelCase = sys.maxsize
self.top_to_bottom(A , 0 , len(A) , A)
return temp
def A ( _UpperCAmelCase : int ) -> Any:
'''simple docstring'''
_UpperCAmelCase = Heap()
_UpperCAmelCase = [0] * len(_UpperCAmelCase )
_UpperCAmelCase = [-1] * len(_UpperCAmelCase ) # Neighboring Tree Vertex of selected vertex
# Minimum Distance of explored vertex with neighboring vertex of partial tree
# formed in graph
_UpperCAmelCase = [] # Heap of Distance of vertices from their neighboring vertex
_UpperCAmelCase = []
for vertex in range(len(_UpperCAmelCase ) ):
distance_tv.append(sys.maxsize )
positions.append(_UpperCAmelCase )
heap.node_position.append(_UpperCAmelCase )
_UpperCAmelCase = []
_UpperCAmelCase = 1
_UpperCAmelCase = sys.maxsize
for neighbor, distance in adjacency_list[0]:
_UpperCAmelCase = 0
_UpperCAmelCase = distance
heap.heapify(_UpperCAmelCase , _UpperCAmelCase )
for _ in range(1 , len(_UpperCAmelCase ) ):
_UpperCAmelCase = heap.delete_minimum(_UpperCAmelCase , _UpperCAmelCase )
if visited[vertex] == 0:
tree_edges.append((nbr_tv[vertex], vertex) )
_UpperCAmelCase = 1
for neighbor, distance in adjacency_list[vertex]:
if (
visited[neighbor] == 0
and distance < distance_tv[heap.get_position(_UpperCAmelCase )]
):
_UpperCAmelCase = distance
heap.bottom_to_top(
_UpperCAmelCase , heap.get_position(_UpperCAmelCase ) , _UpperCAmelCase , _UpperCAmelCase )
_UpperCAmelCase = vertex
return tree_edges
if __name__ == "__main__": # pragma: no cover
# < --------- Prims Algorithm --------- >
UpperCAmelCase__ = int(input("Enter number of edges: ").strip())
UpperCAmelCase__ = defaultdict(list)
for _ in range(edges_number):
UpperCAmelCase__ = [int(x) for x in input().strip().split()]
adjacency_list[edge[0]].append([edge[1], edge[2]])
adjacency_list[edge[1]].append([edge[0], edge[2]])
print(prisms_algorithm(adjacency_list))
| 339 | 1 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate
@dataclass(frozen=A )
class __lowerCAmelCase ( A ):
UpperCamelCase = field(default='''audio-classification''' , metadata={'''include_in_asdict_even_if_is_default''': True} )
UpperCamelCase = Features({'''audio''': Audio()} )
UpperCamelCase = Features({'''labels''': ClassLabel} )
UpperCamelCase = "audio"
UpperCamelCase = "labels"
def _lowerCamelCase ( self : Optional[Any] , A : List[Any]) -> Optional[Any]:
"""simple docstring"""
if self.label_column not in features:
raise ValueError(F"Column {self.label_column} is not present in features.")
if not isinstance(features[self.label_column] , A):
raise ValueError(F"Column {self.label_column} is not a ClassLabel.")
_UpperCAmelCase = copy.deepcopy(self)
_UpperCAmelCase = self.label_schema.copy()
_UpperCAmelCase = features[self.label_column]
_UpperCAmelCase = label_schema
return task_template
@property
def _lowerCamelCase ( self : int) -> Dict[str, str]:
"""simple docstring"""
return {
self.audio_column: "audio",
self.label_column: "labels",
}
| 339 |
import torch
from transformers import CamembertForMaskedLM, CamembertTokenizer
def A ( _UpperCAmelCase : str , _UpperCAmelCase : Any , _UpperCAmelCase : List[str] , _UpperCAmelCase : Optional[int]=5 ) -> List[Any]:
'''simple docstring'''
# Adapted from https://github.com/pytorch/fairseq/blob/master/fairseq/models/roberta/hub_interface.py
assert masked_input.count('<mask>' ) == 1
_UpperCAmelCase = torch.tensor(tokenizer.encode(_UpperCAmelCase , add_special_tokens=_UpperCAmelCase ) ).unsqueeze(0 ) # Batch size 1
_UpperCAmelCase = model(_UpperCAmelCase )[0] # The last hidden-state is the first element of the output tuple
_UpperCAmelCase = (input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
_UpperCAmelCase = logits[0, masked_index, :]
_UpperCAmelCase = logits.softmax(dim=0 )
_UpperCAmelCase , _UpperCAmelCase = prob.topk(k=_UpperCAmelCase , dim=0 )
_UpperCAmelCase = ' '.join(
[tokenizer.convert_ids_to_tokens(indices[i].item() ) for i in range(len(_UpperCAmelCase ) )] )
_UpperCAmelCase = tokenizer.mask_token
_UpperCAmelCase = []
for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(' ' ) ):
_UpperCAmelCase = predicted_token_bpe.replace('\u2581' , ' ' )
if " {0}".format(_UpperCAmelCase ) in masked_input:
topk_filled_outputs.append(
(
masked_input.replace(' {0}'.format(_UpperCAmelCase ) , _UpperCAmelCase ),
values[index].item(),
predicted_token,
) )
else:
topk_filled_outputs.append(
(
masked_input.replace(_UpperCAmelCase , _UpperCAmelCase ),
values[index].item(),
predicted_token,
) )
return topk_filled_outputs
UpperCAmelCase__ = CamembertTokenizer.from_pretrained("camembert-base")
UpperCAmelCase__ = CamembertForMaskedLM.from_pretrained("camembert-base")
model.eval()
UpperCAmelCase__ = "Le camembert est <mask> :)"
print(fill_mask(masked_input, model, tokenizer, topk=3))
| 339 | 1 |
from __future__ import annotations
def A ( _UpperCAmelCase : list[int] ) -> bool:
'''simple docstring'''
return len(set(_UpperCAmelCase ) ) == len(_UpperCAmelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 339 |
import math
import unittest
def A ( _UpperCAmelCase : int ) -> bool:
'''simple docstring'''
assert isinstance(_UpperCAmelCase , _UpperCAmelCase ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(_UpperCAmelCase ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
class __lowerCAmelCase ( unittest.TestCase ):
def _lowerCamelCase ( self : Tuple) -> Union[str, Any]:
"""simple docstring"""
self.assertTrue(is_prime(2))
self.assertTrue(is_prime(3))
self.assertTrue(is_prime(5))
self.assertTrue(is_prime(7))
self.assertTrue(is_prime(11))
self.assertTrue(is_prime(13))
self.assertTrue(is_prime(17))
self.assertTrue(is_prime(19))
self.assertTrue(is_prime(23))
self.assertTrue(is_prime(29))
def _lowerCamelCase ( self : Optional[int]) -> Any:
"""simple docstring"""
with self.assertRaises(A):
is_prime(-19)
self.assertFalse(
is_prime(0) , 'Zero doesn\'t have any positive factors, primes must have exactly two.' , )
self.assertFalse(
is_prime(1) , 'One only has 1 positive factor, primes must have exactly two.' , )
self.assertFalse(is_prime(2 * 2))
self.assertFalse(is_prime(2 * 3))
self.assertFalse(is_prime(3 * 3))
self.assertFalse(is_prime(3 * 5))
self.assertFalse(is_prime(3 * 5 * 7))
if __name__ == "__main__":
unittest.main()
| 339 | 1 |
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 __lowerCAmelCase ( A ):
UpperCamelCase = 42
UpperCamelCase = jnp.floataa
UpperCamelCase = True
def _lowerCamelCase ( self : Optional[int]) -> Optional[Any]:
"""simple docstring"""
super().setup()
_UpperCAmelCase = nn.Dense(5 , dtype=self.dtype)
def __call__( self : Dict , *A : Union[str, Any] , **A : List[Any]) -> Dict:
"""simple docstring"""
_UpperCAmelCase = super().__call__(*A , **A)
_UpperCAmelCase = self.cls(outputs[2])
return outputs[:2] + (cls_out,)
class __lowerCAmelCase ( A ):
UpperCamelCase = FlaxBigBirdForNaturalQuestionsModule
def A ( _UpperCAmelCase : int , _UpperCAmelCase : Any , _UpperCAmelCase : str , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : List[Any] ) -> int:
'''simple docstring'''
def cross_entropy(_UpperCAmelCase : int , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : str=None ):
_UpperCAmelCase = logits.shape[-1]
_UpperCAmelCase = (labels[..., None] == jnp.arange(_UpperCAmelCase )[None]).astype('f4' )
_UpperCAmelCase = jax.nn.log_softmax(_UpperCAmelCase , axis=-1 )
_UpperCAmelCase = -jnp.sum(labels * logits , axis=-1 )
if reduction is not None:
_UpperCAmelCase = reduction(_UpperCAmelCase )
return loss
_UpperCAmelCase = partial(_UpperCAmelCase , reduction=jnp.mean )
_UpperCAmelCase = cross_entropy(_UpperCAmelCase , _UpperCAmelCase )
_UpperCAmelCase = cross_entropy(_UpperCAmelCase , _UpperCAmelCase )
_UpperCAmelCase = cross_entropy(_UpperCAmelCase , _UpperCAmelCase )
return (start_loss + end_loss + pooled_loss) / 3
@dataclass
class __lowerCAmelCase :
UpperCamelCase = "google/bigbird-roberta-base"
UpperCamelCase = 3_0_0_0
UpperCamelCase = 1_0_5_0_0
UpperCamelCase = 1_2_8
UpperCamelCase = 3
UpperCamelCase = 1
UpperCamelCase = 5
# tx_args
UpperCamelCase = 3e-5
UpperCamelCase = 0.0
UpperCamelCase = 2_0_0_0_0
UpperCamelCase = 0.0_095
UpperCamelCase = "bigbird-roberta-natural-questions"
UpperCamelCase = "training-expt"
UpperCamelCase = "data/nq-training.jsonl"
UpperCamelCase = "data/nq-validation.jsonl"
def _lowerCamelCase ( self : Tuple) -> Union[str, Any]:
"""simple docstring"""
os.makedirs(self.base_dir , exist_ok=A)
_UpperCAmelCase = os.path.join(self.base_dir , self.save_dir)
_UpperCAmelCase = self.batch_size_per_device * jax.device_count()
@dataclass
class __lowerCAmelCase :
UpperCamelCase = 42
UpperCamelCase = 4_0_9_6 # no dynamic padding on TPUs
def __call__( self : List[str] , A : Any) -> str:
"""simple docstring"""
_UpperCAmelCase = self.collate_fn(A)
_UpperCAmelCase = jax.tree_util.tree_map(A , A)
return batch
def _lowerCamelCase ( self : Union[str, Any] , A : Union[str, Any]) -> List[str]:
"""simple docstring"""
_UpperCAmelCase , _UpperCAmelCase = self.fetch_inputs(features['input_ids'])
_UpperCAmelCase = {
'input_ids': jnp.array(A , dtype=jnp.intaa),
'attention_mask': jnp.array(A , 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 : Tuple , A : list) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase = [self._fetch_inputs(A) for ids in input_ids]
return zip(*A)
def _lowerCamelCase ( self : Optional[Any] , A : list) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase = [1 for _ in range(len(A))]
while len(A) < self.max_length:
input_ids.append(self.pad_id)
attention_mask.append(0)
return input_ids, attention_mask
def A ( _UpperCAmelCase : Dict , _UpperCAmelCase : List[str] , _UpperCAmelCase : Optional[int]=None ) -> int:
'''simple docstring'''
if seed is not None:
_UpperCAmelCase = dataset.shuffle(seed=_UpperCAmelCase )
for i in range(len(_UpperCAmelCase ) // batch_size ):
_UpperCAmelCase = dataset[i * batch_size : (i + 1) * batch_size]
yield dict(_UpperCAmelCase )
@partial(jax.pmap , axis_name='batch' )
def A ( _UpperCAmelCase : Dict , _UpperCAmelCase : Any , **_UpperCAmelCase : Optional[Any] ) -> List[Any]:
'''simple docstring'''
def loss_fn(_UpperCAmelCase : Any ):
_UpperCAmelCase = model_inputs.pop('start_labels' )
_UpperCAmelCase = model_inputs.pop('end_labels' )
_UpperCAmelCase = model_inputs.pop('pooled_labels' )
_UpperCAmelCase = state.apply_fn(**_UpperCAmelCase , params=_UpperCAmelCase , dropout_rng=_UpperCAmelCase , train=_UpperCAmelCase )
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = outputs
return state.loss_fn(
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , )
_UpperCAmelCase , _UpperCAmelCase = jax.random.split(_UpperCAmelCase )
_UpperCAmelCase = jax.value_and_grad(_UpperCAmelCase )
_UpperCAmelCase , _UpperCAmelCase = grad_fn(state.params )
_UpperCAmelCase = jax.lax.pmean({'loss': loss} , axis_name='batch' )
_UpperCAmelCase = jax.lax.pmean(_UpperCAmelCase , 'batch' )
_UpperCAmelCase = state.apply_gradients(grads=_UpperCAmelCase )
return state, metrics, new_drp_rng
@partial(jax.pmap , axis_name='batch' )
def A ( _UpperCAmelCase : str , **_UpperCAmelCase : int ) -> Optional[int]:
'''simple docstring'''
_UpperCAmelCase = model_inputs.pop('start_labels' )
_UpperCAmelCase = model_inputs.pop('end_labels' )
_UpperCAmelCase = model_inputs.pop('pooled_labels' )
_UpperCAmelCase = state.apply_fn(**_UpperCAmelCase , params=state.params , train=_UpperCAmelCase )
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = outputs
_UpperCAmelCase = state.loss_fn(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
_UpperCAmelCase = jax.lax.pmean({'loss': loss} , axis_name='batch' )
return metrics
class __lowerCAmelCase ( train_state.TrainState ):
UpperCamelCase = struct.field(pytree_node=A )
@dataclass
class __lowerCAmelCase :
UpperCamelCase = 42
UpperCamelCase = 42
UpperCamelCase = 42
UpperCamelCase = 42
UpperCamelCase = 42
UpperCamelCase = 42
UpperCamelCase = None
def _lowerCamelCase ( self : Any , A : List[str] , A : Union[str, Any] , A : str , A : Optional[int]=None) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase = model.params
_UpperCAmelCase = TrainState.create(
apply_fn=model.__call__ , params=A , tx=A , loss_fn=A , )
if ckpt_dir is not None:
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = restore_checkpoint(A , A)
_UpperCAmelCase = {
'lr': args.lr,
'init_lr': args.init_lr,
'warmup_steps': args.warmup_steps,
'num_train_steps': num_train_steps,
'weight_decay': args.weight_decay,
}
_UpperCAmelCase , _UpperCAmelCase = build_tx(**A)
_UpperCAmelCase = train_state.TrainState(
step=A , apply_fn=model.__call__ , params=A , tx=A , opt_state=A , )
_UpperCAmelCase = args
_UpperCAmelCase = data_collator
_UpperCAmelCase = lr
_UpperCAmelCase = params
_UpperCAmelCase = jax_utils.replicate(A)
return state
def _lowerCamelCase ( self : Optional[Any] , A : Dict , A : Dict , A : Dict) -> str:
"""simple docstring"""
_UpperCAmelCase = self.args
_UpperCAmelCase = len(A) // args.batch_size
_UpperCAmelCase = jax.random.PRNGKey(0)
_UpperCAmelCase = jax.random.split(A , jax.device_count())
for epoch in range(args.max_epochs):
_UpperCAmelCase = jnp.array(0 , dtype=jnp.floataa)
_UpperCAmelCase = get_batched_dataset(A , args.batch_size , seed=A)
_UpperCAmelCase = 0
for batch in tqdm(A , total=A , desc=F"Running EPOCH-{epoch}"):
_UpperCAmelCase = self.data_collator(A)
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = self.train_step_fn(A , A , **A)
running_loss += jax_utils.unreplicate(metrics['loss'])
i += 1
if i % args.logging_steps == 0:
_UpperCAmelCase = jax_utils.unreplicate(state.step)
_UpperCAmelCase = running_loss.item() / i
_UpperCAmelCase = self.scheduler_fn(state_step - 1)
_UpperCAmelCase = self.evaluate(A , A)
_UpperCAmelCase = {
'step': state_step.item(),
'eval_loss': eval_loss.item(),
'tr_loss': tr_loss,
'lr': lr.item(),
}
tqdm.write(str(A))
self.logger.log(A , commit=A)
if i % args.save_steps == 0:
self.save_checkpoint(args.save_dir + F"-e{epoch}-s{i}" , state=A)
def _lowerCamelCase ( self : int , A : Tuple , A : List[str]) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase = get_batched_dataset(A , self.args.batch_size)
_UpperCAmelCase = len(A) // self.args.batch_size
_UpperCAmelCase = jnp.array(0 , dtype=jnp.floataa)
_UpperCAmelCase = 0
for batch in tqdm(A , total=A , desc='Evaluating ... '):
_UpperCAmelCase = self.data_collator(A)
_UpperCAmelCase = self.val_step_fn(A , **A)
running_loss += jax_utils.unreplicate(metrics['loss'])
i += 1
return running_loss / i
def _lowerCamelCase ( self : Optional[Any] , A : List[Any] , A : List[Any]) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase = jax_utils.unreplicate(A)
print(F"SAVING CHECKPOINT IN {save_dir}" , end=' ... ')
self.model_save_fn(A , params=state.params)
with open(os.path.join(A , 'opt_state.msgpack') , 'wb') as f:
f.write(to_bytes(state.opt_state))
joblib.dump(self.args , os.path.join(A , 'args.joblib'))
joblib.dump(self.data_collator , os.path.join(A , 'data_collator.joblib'))
with open(os.path.join(A , 'training_state.json') , 'w') as f:
json.dump({'step': state.step.item()} , A)
print('DONE')
def A ( _UpperCAmelCase : List[Any] , _UpperCAmelCase : Optional[Any] ) -> List[Any]:
'''simple docstring'''
print(F"RESTORING CHECKPOINT FROM {save_dir}" , end=' ... ' )
with open(os.path.join(_UpperCAmelCase , 'flax_model.msgpack' ) , 'rb' ) as f:
_UpperCAmelCase = from_bytes(state.params , f.read() )
with open(os.path.join(_UpperCAmelCase , 'opt_state.msgpack' ) , 'rb' ) as f:
_UpperCAmelCase = from_bytes(state.opt_state , f.read() )
_UpperCAmelCase = joblib.load(os.path.join(_UpperCAmelCase , 'args.joblib' ) )
_UpperCAmelCase = joblib.load(os.path.join(_UpperCAmelCase , 'data_collator.joblib' ) )
with open(os.path.join(_UpperCAmelCase , 'training_state.json' ) , 'r' ) as f:
_UpperCAmelCase = json.load(_UpperCAmelCase )
_UpperCAmelCase = training_state['step']
print('DONE' )
return params, opt_state, step, args, data_collator
def A ( _UpperCAmelCase : List[str] , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : Optional[Any] ) -> Any:
'''simple docstring'''
_UpperCAmelCase = num_train_steps - warmup_steps
_UpperCAmelCase = optax.linear_schedule(init_value=_UpperCAmelCase , end_value=_UpperCAmelCase , transition_steps=_UpperCAmelCase )
_UpperCAmelCase = optax.linear_schedule(init_value=_UpperCAmelCase , end_value=1E-7 , transition_steps=_UpperCAmelCase )
_UpperCAmelCase = optax.join_schedules(schedules=[warmup_fn, decay_fn] , boundaries=[warmup_steps] )
return lr
def A ( _UpperCAmelCase : int , _UpperCAmelCase : Tuple , _UpperCAmelCase : Any , _UpperCAmelCase : int , _UpperCAmelCase : Optional[int] ) -> List[str]:
'''simple docstring'''
def weight_decay_mask(_UpperCAmelCase : Tuple ):
_UpperCAmelCase = traverse_util.flatten_dict(_UpperCAmelCase )
_UpperCAmelCase = {k: (v[-1] != 'bias' and v[-2:] != ('LayerNorm', 'scale')) for k, v in params.items()}
return traverse_util.unflatten_dict(_UpperCAmelCase )
_UpperCAmelCase = scheduler_fn(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
_UpperCAmelCase = optax.adamw(learning_rate=_UpperCAmelCase , weight_decay=_UpperCAmelCase , mask=_UpperCAmelCase )
return tx, lr
| 339 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
UpperCAmelCase__ = "\\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"
UpperCAmelCase__ = "\\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"
UpperCAmelCase__ = "\\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 __lowerCAmelCase ( datasets.Metric ):
def _lowerCamelCase ( self : str) -> MetricInfo:
"""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 _lowerCamelCase ( self : Union[str, Any] , A : List[List[List[str]]] , A : List[List[str]] , A : int = 1 , A : int = 4 , ) -> Dict[str, float]:
"""simple docstring"""
return {
"google_bleu": gleu_score.corpus_gleu(
list_of_references=A , hypotheses=A , min_len=A , max_len=A)
}
| 339 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {"ctrl": "https://huggingface.co/ctrl/resolve/main/config.json"}
class __lowerCAmelCase ( A ):
UpperCamelCase = '''ctrl'''
UpperCamelCase = ['''past_key_values''']
UpperCamelCase = {
'''max_position_embeddings''': '''n_positions''',
'''hidden_size''': '''n_embd''',
'''num_attention_heads''': '''n_head''',
'''num_hidden_layers''': '''n_layer''',
}
def __init__( self : Optional[Any] , A : Tuple=24_65_34 , A : str=2_56 , A : Any=12_80 , A : Optional[int]=81_92 , A : Optional[Any]=48 , A : Optional[Any]=16 , A : List[Any]=0.1 , A : Union[str, Any]=0.1 , A : List[str]=1E-6 , A : List[str]=0.0_2 , A : Optional[int]=True , **A : Union[str, Any] , ) -> Any:
"""simple docstring"""
_UpperCAmelCase = vocab_size
_UpperCAmelCase = n_positions
_UpperCAmelCase = n_embd
_UpperCAmelCase = n_layer
_UpperCAmelCase = n_head
_UpperCAmelCase = dff
_UpperCAmelCase = resid_pdrop
_UpperCAmelCase = embd_pdrop
_UpperCAmelCase = layer_norm_epsilon
_UpperCAmelCase = initializer_range
_UpperCAmelCase = use_cache
super().__init__(**A)
| 339 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
if is_sentencepiece_available():
from ..ta.tokenization_ta import TaTokenizer
else:
from ...utils.dummy_sentencepiece_objects import TaTokenizer
UpperCAmelCase__ = TaTokenizer
if is_tokenizers_available():
from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
from ...utils.dummy_tokenizers_objects import TaTokenizerFast
UpperCAmelCase__ = TaTokenizerFast
UpperCAmelCase__ = {"configuration_mt5": ["MT5Config", "MT5OnnxConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ = [
"MT5EncoderModel",
"MT5ForConditionalGeneration",
"MT5ForQuestionAnswering",
"MT5Model",
"MT5PreTrainedModel",
"MT5Stack",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ = ["TFMT5EncoderModel", "TFMT5ForConditionalGeneration", "TFMT5Model"]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ = ["FlaxMT5EncoderModel", "FlaxMT5ForConditionalGeneration", "FlaxMT5Model"]
if TYPE_CHECKING:
from .configuration_mta import MTaConfig, MTaOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mta import (
MTaEncoderModel,
MTaForConditionalGeneration,
MTaForQuestionAnswering,
MTaModel,
MTaPreTrainedModel,
MTaStack,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel
else:
import sys
UpperCAmelCase__ = _LazyModule(
__name__,
globals()["__file__"],
_import_structure,
extra_objects={"MT5Tokenizer": MTaTokenizer, "MT5TokenizerFast": MTaTokenizerFast},
module_spec=__spec__,
)
| 339 | 1 |
def A ( _UpperCAmelCase : int ) -> int:
'''simple docstring'''
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
_UpperCAmelCase = 1
_UpperCAmelCase = 1
while repunit:
_UpperCAmelCase = (10 * repunit + 1) % divisor
repunit_index += 1
return repunit_index
def A ( _UpperCAmelCase : int = 1_000_000 ) -> int:
'''simple docstring'''
_UpperCAmelCase = limit - 1
if divisor % 2 == 0:
divisor += 1
while least_divisible_repunit(_UpperCAmelCase ) <= limit:
divisor += 2
return divisor
if __name__ == "__main__":
print(f"""{solution() = }""")
| 339 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
"s-JoL/Open-Llama-V1": "https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json",
}
class __lowerCAmelCase ( A ):
UpperCamelCase = '''open-llama'''
def __init__( self : str , A : List[Any]=10_00_00 , A : Tuple=40_96 , A : Tuple=1_10_08 , A : List[str]=32 , A : Tuple=32 , A : Optional[Any]="silu" , A : int=20_48 , A : Optional[Any]=0.0_2 , A : Dict=1E-6 , A : Optional[Any]=True , A : List[Any]=0 , A : Dict=1 , A : int=2 , A : Dict=False , A : Optional[int]=True , A : List[Any]=0.1 , A : str=0.1 , A : Dict=True , A : Optional[Any]=True , A : Dict=None , **A : Union[str, Any] , ) -> Dict:
"""simple docstring"""
_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' , A)
_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=A , bos_token_id=A , eos_token_id=A , tie_word_embeddings=A , **A , )
def _lowerCamelCase ( self : List[str]) -> Union[str, Any]:
"""simple docstring"""
if self.rope_scaling is None:
return
if not isinstance(self.rope_scaling , A) or len(self.rope_scaling) != 2:
raise ValueError(
'`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '
F"got {self.rope_scaling}")
_UpperCAmelCase = self.rope_scaling.get('type' , A)
_UpperCAmelCase = self.rope_scaling.get('factor' , A)
if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
raise ValueError(
F"`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}")
if rope_scaling_factor is None or not isinstance(A , A) or rope_scaling_factor <= 1.0:
raise ValueError(F"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}")
| 339 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
UpperCAmelCase__ = {
"vocab_file": {
"squeezebert/squeezebert-uncased": (
"https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt"
),
"squeezebert/squeezebert-mnli": "https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt",
"squeezebert/squeezebert-mnli-headless": (
"https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt"
),
},
"tokenizer_file": {
"squeezebert/squeezebert-uncased": (
"https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json"
),
"squeezebert/squeezebert-mnli": (
"https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json"
),
"squeezebert/squeezebert-mnli-headless": (
"https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json"
),
},
}
UpperCAmelCase__ = {
"squeezebert/squeezebert-uncased": 512,
"squeezebert/squeezebert-mnli": 512,
"squeezebert/squeezebert-mnli-headless": 512,
}
UpperCAmelCase__ = {
"squeezebert/squeezebert-uncased": {"do_lower_case": True},
"squeezebert/squeezebert-mnli": {"do_lower_case": True},
"squeezebert/squeezebert-mnli-headless": {"do_lower_case": True},
}
class __lowerCAmelCase ( A ):
UpperCamelCase = VOCAB_FILES_NAMES
UpperCamelCase = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase = PRETRAINED_INIT_CONFIGURATION
UpperCamelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase = SqueezeBertTokenizer
def __init__( self : List[str] , A : Dict=None , A : int=None , A : List[str]=True , A : Any="[UNK]" , A : int="[SEP]" , A : List[str]="[PAD]" , A : Union[str, Any]="[CLS]" , A : Optional[Any]="[MASK]" , A : Union[str, Any]=True , A : Union[str, Any]=None , **A : str , ) -> List[Any]:
"""simple docstring"""
super().__init__(
A , tokenizer_file=A , do_lower_case=A , unk_token=A , sep_token=A , pad_token=A , cls_token=A , mask_token=A , tokenize_chinese_chars=A , strip_accents=A , **A , )
_UpperCAmelCase = json.loads(self.backend_tokenizer.normalizer.__getstate__())
if (
normalizer_state.get('lowercase' , A) != do_lower_case
or normalizer_state.get('strip_accents' , A) != strip_accents
or normalizer_state.get('handle_chinese_chars' , A) != tokenize_chinese_chars
):
_UpperCAmelCase = getattr(A , normalizer_state.pop('type'))
_UpperCAmelCase = do_lower_case
_UpperCAmelCase = strip_accents
_UpperCAmelCase = tokenize_chinese_chars
_UpperCAmelCase = normalizer_class(**A)
_UpperCAmelCase = do_lower_case
def _lowerCamelCase ( self : List[str] , A : Any , A : List[Any]=None) -> Union[str, Any]:
"""simple docstring"""
_UpperCAmelCase = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def _lowerCamelCase ( self : List[Any] , A : List[int] , A : Optional[List[int]] = None) -> List[int]:
"""simple docstring"""
_UpperCAmelCase = [self.sep_token_id]
_UpperCAmelCase = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep) * [0]
return len(cls + token_ids_a + sep) * [0] + len(token_ids_a + sep) * [1]
def _lowerCamelCase ( self : Optional[Any] , A : str , A : Optional[str] = None) -> Tuple[str]:
"""simple docstring"""
_UpperCAmelCase = self._tokenizer.model.save(A , name=A)
return tuple(A)
| 339 |
def A ( _UpperCAmelCase : str ) -> bool:
'''simple docstring'''
return credit_card_number.startswith(('34', '35', '37', '4', '5', '6') )
def A ( _UpperCAmelCase : str ) -> bool:
'''simple docstring'''
_UpperCAmelCase = credit_card_number
_UpperCAmelCase = 0
_UpperCAmelCase = len(_UpperCAmelCase ) - 2
for i in range(_UpperCAmelCase , -1 , -2 ):
# double the value of every second digit
_UpperCAmelCase = int(cc_number[i] )
digit *= 2
# If doubling of a number results in a two digit number
# i.e greater than 9(e.g., 6 × 2 = 12),
# then add the digits of the product (e.g., 12: 1 + 2 = 3, 15: 1 + 5 = 6),
# to get a single digit number.
if digit > 9:
digit %= 10
digit += 1
_UpperCAmelCase = cc_number[:i] + str(_UpperCAmelCase ) + cc_number[i + 1 :]
total += digit
# Sum up the remaining digits
for i in range(len(_UpperCAmelCase ) - 1 , -1 , -2 ):
total += int(cc_number[i] )
return total % 10 == 0
def A ( _UpperCAmelCase : str ) -> bool:
'''simple docstring'''
_UpperCAmelCase = F"{credit_card_number} is an invalid credit card number because"
if not credit_card_number.isdigit():
print(F"{error_message} it has nonnumerical characters." )
return False
if not 13 <= len(_UpperCAmelCase ) <= 16:
print(F"{error_message} of its length." )
return False
if not validate_initial_digits(_UpperCAmelCase ):
print(F"{error_message} of its first two digits." )
return False
if not luhn_validation(_UpperCAmelCase ):
print(F"{error_message} it fails the Luhn check." )
return False
print(F"{credit_card_number} is a valid credit card number." )
return True
if __name__ == "__main__":
import doctest
doctest.testmod()
validate_credit_card_number("4111111111111111")
validate_credit_card_number("32323")
| 339 | 1 |
from datetime import datetime as dt
import os
from github import Github
UpperCAmelCase__ = [
"good first issue",
"good second issue",
"good difficult issue",
"feature request",
"new model",
"wip",
]
def A ( ) -> Union[str, Any]:
'''simple docstring'''
_UpperCAmelCase = Github(os.environ['GITHUB_TOKEN'] )
_UpperCAmelCase = g.get_repo('huggingface/transformers' )
_UpperCAmelCase = repo.get_issues(state='open' )
for issue in open_issues:
_UpperCAmelCase = sorted([comment for comment in issue.get_comments()] , key=lambda _UpperCAmelCase : i.created_at , reverse=_UpperCAmelCase )
_UpperCAmelCase = comments[0] if len(_UpperCAmelCase ) > 0 else None
if (
last_comment is not None
and last_comment.user.login == "github-actions[bot]"
and (dt.utcnow() - issue.updated_at).days > 7
and (dt.utcnow() - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# print(f"Would close issue {issue.number} since it has been 7 days of inactivity since bot mention.")
issue.edit(state='closed' )
elif (
(dt.utcnow() - issue.updated_at).days > 23
and (dt.utcnow() - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# print(f"Would add stale comment to {issue.number}")
issue.create_comment(
'This issue has been automatically marked as stale because it has not had '
'recent activity. If you think this still needs to be addressed '
'please comment on this thread.\n\nPlease note that issues that do not follow the '
'[contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) '
'are likely to be ignored.' )
if __name__ == "__main__":
main()
| 339 |
from functools import reduce
UpperCAmelCase__ = (
"73167176531330624919225119674426574742355349194934"
"96983520312774506326239578318016984801869478851843"
"85861560789112949495459501737958331952853208805511"
"12540698747158523863050715693290963295227443043557"
"66896648950445244523161731856403098711121722383113"
"62229893423380308135336276614282806444486645238749"
"30358907296290491560440772390713810515859307960866"
"70172427121883998797908792274921901699720888093776"
"65727333001053367881220235421809751254540594752243"
"52584907711670556013604839586446706324415722155397"
"53697817977846174064955149290862569321978468622482"
"83972241375657056057490261407972968652414535100474"
"82166370484403199890008895243450658541227588666881"
"16427171479924442928230863465674813919123162824586"
"17866458359124566529476545682848912883142607690042"
"24219022671055626321111109370544217506941658960408"
"07198403850962455444362981230987879927244284909188"
"84580156166097919133875499200524063689912560717606"
"05886116467109405077541002256983155200055935729725"
"71636269561882670428252483600823257530420752963450"
)
def A ( _UpperCAmelCase : str = N ) -> int:
'''simple docstring'''
return max(
# mypy cannot properly interpret reduce
int(reduce(lambda _UpperCAmelCase , _UpperCAmelCase : str(int(_UpperCAmelCase ) * int(_UpperCAmelCase ) ) , n[i : i + 13] ) )
for i in range(len(_UpperCAmelCase ) - 12 ) )
if __name__ == "__main__":
print(f"""{solution() = }""")
| 339 | 1 |
from manim import *
class __lowerCAmelCase ( A ):
def _lowerCamelCase ( self : Optional[Any]) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase = Rectangle(height=0.5 , width=0.5)
_UpperCAmelCase = Rectangle(height=0.4_6 , width=0.4_6).set_stroke(width=0)
_UpperCAmelCase = [mem.copy() for i in range(6)]
_UpperCAmelCase = [mem.copy() for i in range(6)]
_UpperCAmelCase = VGroup(*A).arrange(A , buff=0)
_UpperCAmelCase = VGroup(*A).arrange(A , buff=0)
_UpperCAmelCase = VGroup(A , A).arrange(A , buff=0)
_UpperCAmelCase = Text('CPU' , font_size=24)
_UpperCAmelCase = Group(A , A).arrange(A , buff=0.5 , aligned_edge=A)
cpu.move_to([-2.5, -0.5, 0])
self.add(A)
_UpperCAmelCase = [mem.copy() for i in range(1)]
_UpperCAmelCase = VGroup(*A).arrange(A , buff=0)
_UpperCAmelCase = Text('GPU' , font_size=24)
_UpperCAmelCase = Group(A , A).arrange(A , buff=0.5 , aligned_edge=A)
gpu.align_to(A , A)
gpu.set_x(gpu.get_x() - 1)
self.add(A)
_UpperCAmelCase = [mem.copy() for i in range(6)]
_UpperCAmelCase = VGroup(*A).arrange(A , buff=0)
_UpperCAmelCase = Text('Model' , font_size=24)
_UpperCAmelCase = Group(A , A).arrange(A , buff=0.5 , aligned_edge=A)
model.move_to([3, -1.0, 0])
self.play(
Create(A , run_time=1) , Create(A , run_time=1) , Create(A , run_time=1) , )
_UpperCAmelCase = MarkupText(
F"First, an empty model skeleton is loaded\ninto <span fgcolor='{YELLOW}'>memory</span> without using much RAM." , font_size=24 , )
_UpperCAmelCase = Square(side_length=2.2)
key.move_to([-5, 2, 0])
_UpperCAmelCase = MarkupText(
F"<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model" , font_size=18 , )
key_text.move_to([-5, 2.4, 0])
step_a.move_to([2, 2, 0])
self.play(Write(A , run_time=2.5) , Write(A) , Write(A))
self.add(A)
_UpperCAmelCase = []
_UpperCAmelCase = []
_UpperCAmelCase = []
for i, rect in enumerate(A):
_UpperCAmelCase = Rectangle(height=0.4_6 , width=0.4_6).set_stroke(width=0.0).set_fill(A , opacity=0.7)
cpu_target.move_to(A)
cpu_target.generate_target()
_UpperCAmelCase = 0.4_6 / 4
_UpperCAmelCase = 0.4_6 / 3
if i == 0:
cpu_target.target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT) , buff=0.0_2 , direction=A)
cpu_target.target.set_x(cpu_target.target.get_x() + 0.1)
elif i == 3:
cpu_target.target.next_to(cpu_targs[0].target , direction=A , buff=0.0)
else:
cpu_target.target.next_to(cpu_targs[i - 1].target , direction=A , buff=0.0)
cpu_targs.append(A)
first_animations.append(rect.animate(run_time=0.5).set_stroke(A))
second_animations.append(MoveToTarget(A , run_time=1.5))
self.play(*A)
self.play(*A)
self.wait()
| 339 |
from __future__ import annotations
from collections.abc import Callable
UpperCAmelCase__ = list[list[float | int]]
def A ( _UpperCAmelCase : Matrix , _UpperCAmelCase : Matrix ) -> Matrix:
'''simple docstring'''
_UpperCAmelCase = len(_UpperCAmelCase )
_UpperCAmelCase = [[0 for _ in range(size + 1 )] for _ in range(_UpperCAmelCase )]
_UpperCAmelCase = 42
_UpperCAmelCase = 42
_UpperCAmelCase = 42
_UpperCAmelCase = 42
_UpperCAmelCase = 42
_UpperCAmelCase = 42
for row in range(_UpperCAmelCase ):
for col in range(_UpperCAmelCase ):
_UpperCAmelCase = matrix[row][col]
_UpperCAmelCase = vector[row][0]
_UpperCAmelCase = 0
_UpperCAmelCase = 0
while row < size and col < size:
# pivoting
_UpperCAmelCase = max((abs(augmented[rowa][col] ), rowa) for rowa in range(_UpperCAmelCase , _UpperCAmelCase ) )[
1
]
if augmented[pivot_row][col] == 0:
col += 1
continue
else:
_UpperCAmelCase , _UpperCAmelCase = augmented[pivot_row], augmented[row]
for rowa in range(row + 1 , _UpperCAmelCase ):
_UpperCAmelCase = augmented[rowa][col] / augmented[row][col]
_UpperCAmelCase = 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 ):
_UpperCAmelCase = 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 A ( _UpperCAmelCase : list[int] ) -> Callable[[int], int]:
'''simple docstring'''
_UpperCAmelCase = len(_UpperCAmelCase )
_UpperCAmelCase = [[0 for _ in range(_UpperCAmelCase )] for _ in range(_UpperCAmelCase )]
_UpperCAmelCase = [[0] for _ in range(_UpperCAmelCase )]
_UpperCAmelCase = 42
_UpperCAmelCase = 42
_UpperCAmelCase = 42
_UpperCAmelCase = 42
for x_val, y_val in enumerate(_UpperCAmelCase ):
for col in range(_UpperCAmelCase ):
_UpperCAmelCase = (x_val + 1) ** (size - col - 1)
_UpperCAmelCase = y_val
_UpperCAmelCase = 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 A ( _UpperCAmelCase : int ) -> 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 A ( _UpperCAmelCase : Callable[[int], int] = question_function , _UpperCAmelCase : int = 10 ) -> int:
'''simple docstring'''
_UpperCAmelCase = [func(_UpperCAmelCase ) for x_val in range(1 , order + 1 )]
_UpperCAmelCase = [
interpolate(data_points[:max_coeff] ) for max_coeff in range(1 , order + 1 )
]
_UpperCAmelCase = 0
_UpperCAmelCase = 42
_UpperCAmelCase = 42
for poly in polynomials:
_UpperCAmelCase = 1
while func(_UpperCAmelCase ) == poly(_UpperCAmelCase ):
x_val += 1
ret += poly(_UpperCAmelCase )
return ret
if __name__ == "__main__":
print(f"""{solution() = }""")
| 339 | 1 |
import shutil
import tempfile
import unittest
from unittest.mock import patch
from transformers import (
DefaultFlowCallback,
IntervalStrategy,
PrinterCallback,
ProgressCallback,
Trainer,
TrainerCallback,
TrainingArguments,
is_torch_available,
)
from transformers.testing_utils import require_torch
if is_torch_available():
from transformers.trainer import DEFAULT_CALLBACKS
from .test_trainer import RegressionDataset, RegressionModelConfig, RegressionPreTrainedModel
class __lowerCAmelCase ( A ):
def __init__( self : Dict) -> Tuple:
"""simple docstring"""
_UpperCAmelCase = []
def _lowerCamelCase ( self : str , A : str , A : Optional[int] , A : List[Any] , **A : str) -> Optional[Any]:
"""simple docstring"""
self.events.append('on_init_end')
def _lowerCamelCase ( self : str , A : int , A : Tuple , A : List[str] , **A : Union[str, Any]) -> int:
"""simple docstring"""
self.events.append('on_train_begin')
def _lowerCamelCase ( self : Dict , A : Any , A : Dict , A : Optional[int] , **A : str) -> List[str]:
"""simple docstring"""
self.events.append('on_train_end')
def _lowerCamelCase ( self : Union[str, Any] , A : Union[str, Any] , A : Optional[Any] , A : Any , **A : str) -> Optional[int]:
"""simple docstring"""
self.events.append('on_epoch_begin')
def _lowerCamelCase ( self : List[str] , A : Optional[int] , A : Optional[int] , A : int , **A : Optional[int]) -> str:
"""simple docstring"""
self.events.append('on_epoch_end')
def _lowerCamelCase ( self : Union[str, Any] , A : Tuple , A : Optional[int] , A : Union[str, Any] , **A : List[Any]) -> Union[str, Any]:
"""simple docstring"""
self.events.append('on_step_begin')
def _lowerCamelCase ( self : Optional[int] , A : Tuple , A : List[str] , A : int , **A : str) -> Dict:
"""simple docstring"""
self.events.append('on_step_end')
def _lowerCamelCase ( self : Union[str, Any] , A : Optional[Any] , A : Tuple , A : Optional[int] , **A : Dict) -> Tuple:
"""simple docstring"""
self.events.append('on_evaluate')
def _lowerCamelCase ( self : Optional[int] , A : List[str] , A : str , A : str , **A : Optional[int]) -> int:
"""simple docstring"""
self.events.append('on_predict')
def _lowerCamelCase ( self : Optional[Any] , A : List[str] , A : List[str] , A : Tuple , **A : Any) -> Union[str, Any]:
"""simple docstring"""
self.events.append('on_save')
def _lowerCamelCase ( self : Any , A : Union[str, Any] , A : Any , A : Dict , **A : int) -> Optional[int]:
"""simple docstring"""
self.events.append('on_log')
def _lowerCamelCase ( self : Union[str, Any] , A : Union[str, Any] , A : List[Any] , A : Dict , **A : Optional[Any]) -> List[Any]:
"""simple docstring"""
self.events.append('on_prediction_step')
@require_torch
class __lowerCAmelCase ( unittest.TestCase ):
def _lowerCamelCase ( self : Dict) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase = tempfile.mkdtemp()
def _lowerCamelCase ( self : Union[str, Any]) -> Union[str, Any]:
"""simple docstring"""
shutil.rmtree(self.output_dir)
def _lowerCamelCase ( self : Any , A : int=0 , A : Tuple=0 , A : str=64 , A : str=64 , A : Optional[Any]=None , A : Tuple=False , **A : str) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase = RegressionDataset(length=A)
_UpperCAmelCase = RegressionDataset(length=A)
_UpperCAmelCase = RegressionModelConfig(a=A , b=A)
_UpperCAmelCase = RegressionPreTrainedModel(A)
_UpperCAmelCase = TrainingArguments(self.output_dir , disable_tqdm=A , report_to=[] , **A)
return Trainer(
A , A , train_dataset=A , eval_dataset=A , callbacks=A , )
def _lowerCamelCase ( self : List[Any] , A : Union[str, Any] , A : Dict) -> Optional[int]:
"""simple docstring"""
self.assertEqual(len(A) , len(A))
# Order doesn't matter
_UpperCAmelCase = sorted(A , key=lambda A: cb.__name__ if isinstance(A , A) else cb.__class__.__name__)
_UpperCAmelCase = sorted(A , key=lambda A: cb.__name__ if isinstance(A , A) else cb.__class__.__name__)
for cba, cba in zip(A , A):
if isinstance(A , A) and isinstance(A , A):
self.assertEqual(A , A)
elif isinstance(A , A) and not isinstance(A , A):
self.assertEqual(A , cba.__class__)
elif not isinstance(A , A) and isinstance(A , A):
self.assertEqual(cba.__class__ , A)
else:
self.assertEqual(A , A)
def _lowerCamelCase ( self : int , A : Optional[int]) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase = ['on_init_end', 'on_train_begin']
_UpperCAmelCase = 0
_UpperCAmelCase = len(trainer.get_eval_dataloader())
_UpperCAmelCase = ['on_prediction_step'] * len(trainer.get_eval_dataloader()) + ['on_log', 'on_evaluate']
for _ in range(trainer.state.num_train_epochs):
expected_events.append('on_epoch_begin')
for _ in range(A):
step += 1
expected_events += ["on_step_begin", "on_step_end"]
if step % trainer.args.logging_steps == 0:
expected_events.append('on_log')
if trainer.args.evaluation_strategy == IntervalStrategy.STEPS and step % trainer.args.eval_steps == 0:
expected_events += evaluation_events.copy()
if step % trainer.args.save_steps == 0:
expected_events.append('on_save')
expected_events.append('on_epoch_end')
if trainer.args.evaluation_strategy == IntervalStrategy.EPOCH:
expected_events += evaluation_events.copy()
expected_events += ["on_log", "on_train_end"]
return expected_events
def _lowerCamelCase ( self : Dict) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase = self.get_trainer()
_UpperCAmelCase = DEFAULT_CALLBACKS.copy() + [ProgressCallback]
self.check_callbacks_equality(trainer.callback_handler.callbacks , A)
# Callbacks passed at init are added to the default callbacks
_UpperCAmelCase = self.get_trainer(callbacks=[MyTestTrainerCallback])
expected_callbacks.append(A)
self.check_callbacks_equality(trainer.callback_handler.callbacks , A)
# TrainingArguments.disable_tqdm controls if use ProgressCallback or PrinterCallback
_UpperCAmelCase = self.get_trainer(disable_tqdm=A)
_UpperCAmelCase = DEFAULT_CALLBACKS.copy() + [PrinterCallback]
self.check_callbacks_equality(trainer.callback_handler.callbacks , A)
def _lowerCamelCase ( self : List[str]) -> List[str]:
"""simple docstring"""
_UpperCAmelCase = DEFAULT_CALLBACKS.copy() + [ProgressCallback]
_UpperCAmelCase = self.get_trainer()
# We can add, pop, or remove by class name
trainer.remove_callback(A)
expected_callbacks.remove(A)
self.check_callbacks_equality(trainer.callback_handler.callbacks , A)
_UpperCAmelCase = self.get_trainer()
_UpperCAmelCase = trainer.pop_callback(A)
self.assertEqual(cb.__class__ , A)
self.check_callbacks_equality(trainer.callback_handler.callbacks , A)
trainer.add_callback(A)
expected_callbacks.insert(0 , A)
self.check_callbacks_equality(trainer.callback_handler.callbacks , A)
# We can also add, pop, or remove by instance
_UpperCAmelCase = self.get_trainer()
_UpperCAmelCase = trainer.callback_handler.callbacks[0]
trainer.remove_callback(A)
expected_callbacks.remove(A)
self.check_callbacks_equality(trainer.callback_handler.callbacks , A)
_UpperCAmelCase = self.get_trainer()
_UpperCAmelCase = trainer.callback_handler.callbacks[0]
_UpperCAmelCase = trainer.pop_callback(A)
self.assertEqual(A , A)
self.check_callbacks_equality(trainer.callback_handler.callbacks , A)
trainer.add_callback(A)
expected_callbacks.insert(0 , A)
self.check_callbacks_equality(trainer.callback_handler.callbacks , A)
def _lowerCamelCase ( self : List[Any]) -> Any:
"""simple docstring"""
import warnings
# XXX: for now ignore scatter_gather warnings in this test since it's not relevant to what's being tested
warnings.simplefilter(action='ignore' , category=A)
_UpperCAmelCase = self.get_trainer(callbacks=[MyTestTrainerCallback])
trainer.train()
_UpperCAmelCase = trainer.callback_handler.callbacks[-2].events
self.assertEqual(A , self.get_expected_events(A))
# Independent log/save/eval
_UpperCAmelCase = self.get_trainer(callbacks=[MyTestTrainerCallback] , logging_steps=5)
trainer.train()
_UpperCAmelCase = trainer.callback_handler.callbacks[-2].events
self.assertEqual(A , self.get_expected_events(A))
_UpperCAmelCase = self.get_trainer(callbacks=[MyTestTrainerCallback] , save_steps=5)
trainer.train()
_UpperCAmelCase = trainer.callback_handler.callbacks[-2].events
self.assertEqual(A , self.get_expected_events(A))
_UpperCAmelCase = self.get_trainer(callbacks=[MyTestTrainerCallback] , eval_steps=5 , evaluation_strategy='steps')
trainer.train()
_UpperCAmelCase = trainer.callback_handler.callbacks[-2].events
self.assertEqual(A , self.get_expected_events(A))
_UpperCAmelCase = self.get_trainer(callbacks=[MyTestTrainerCallback] , evaluation_strategy='epoch')
trainer.train()
_UpperCAmelCase = trainer.callback_handler.callbacks[-2].events
self.assertEqual(A , self.get_expected_events(A))
# A bit of everything
_UpperCAmelCase = self.get_trainer(
callbacks=[MyTestTrainerCallback] , logging_steps=3 , save_steps=10 , eval_steps=5 , evaluation_strategy='steps' , )
trainer.train()
_UpperCAmelCase = trainer.callback_handler.callbacks[-2].events
self.assertEqual(A , self.get_expected_events(A))
# warning should be emitted for duplicated callbacks
with patch('transformers.trainer_callback.logger.warning') as warn_mock:
_UpperCAmelCase = self.get_trainer(
callbacks=[MyTestTrainerCallback, MyTestTrainerCallback] , )
assert str(A) in warn_mock.call_args[0][0]
| 339 |
from __future__ import annotations
def A ( _UpperCAmelCase : list[int] ) -> bool:
'''simple docstring'''
return len(set(_UpperCAmelCase ) ) == len(_UpperCAmelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 339 | 1 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
# Register SEW's fairseq modules
from sew_asapp import tasks # noqa: F401
from transformers import (
SEWConfig,
SEWForCTC,
SEWModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
"post_extract_proj": "feature_projection",
"encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
"self_attn.k_proj": "encoder.layers.*.attention.k_proj",
"self_attn.v_proj": "encoder.layers.*.attention.v_proj",
"self_attn.q_proj": "encoder.layers.*.attention.q_proj",
"self_attn.out_proj": "encoder.layers.*.attention.out_proj",
"self_attn_layer_norm": "encoder.layers.*.layer_norm",
"fc1": "encoder.layers.*.feed_forward.intermediate_dense",
"fc2": "encoder.layers.*.feed_forward.output_dense",
"final_layer_norm": "encoder.layers.*.final_layer_norm",
"encoder.upsample.0": "encoder.upsample.projection",
"encoder.layer_norm": "encoder.layer_norm",
"w2v_model.layer_norm": "layer_norm",
"w2v_encoder.proj": "lm_head",
"mask_emb": "masked_spec_embed",
}
def A ( _UpperCAmelCase : Dict , _UpperCAmelCase : Any , _UpperCAmelCase : int , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : int ) -> int:
'''simple docstring'''
for attribute in key.split('.' ):
_UpperCAmelCase = getattr(_UpperCAmelCase , _UpperCAmelCase )
if weight_type is not None:
_UpperCAmelCase = getattr(_UpperCAmelCase , _UpperCAmelCase ).shape
else:
_UpperCAmelCase = hf_pointer.shape
assert hf_shape == value.shape, (
F"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"
F" {value.shape} for {full_name}"
)
if weight_type == "weight":
_UpperCAmelCase = value
elif weight_type == "weight_g":
_UpperCAmelCase = value
elif weight_type == "weight_v":
_UpperCAmelCase = value
elif weight_type == "bias":
_UpperCAmelCase = value
else:
_UpperCAmelCase = value
logger.info(F"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}." )
def A ( _UpperCAmelCase : List[str] , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Optional[int] ) -> List[str]:
'''simple docstring'''
_UpperCAmelCase = []
_UpperCAmelCase = fairseq_model.state_dict()
_UpperCAmelCase = hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor
for name, value in fairseq_dict.items():
_UpperCAmelCase = False
if "conv_layers" in name:
load_conv_layer(
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , hf_model.config.feat_extract_norm == 'group' , )
_UpperCAmelCase = True
else:
for key, mapped_key in MAPPING.items():
_UpperCAmelCase = 'sew.' + mapped_key if (is_finetuned and mapped_key != 'lm_head') else mapped_key
if key in name or key.split('w2v_model.' )[-1] == name.split('.' )[0]:
_UpperCAmelCase = True
if "*" in mapped_key:
_UpperCAmelCase = name.split(_UpperCAmelCase )[0].split('.' )[-2]
_UpperCAmelCase = mapped_key.replace('*' , _UpperCAmelCase )
if "weight_g" in name:
_UpperCAmelCase = 'weight_g'
elif "weight_v" in name:
_UpperCAmelCase = 'weight_v'
elif "weight" in name:
_UpperCAmelCase = 'weight'
elif "bias" in name:
_UpperCAmelCase = 'bias'
else:
_UpperCAmelCase = None
set_recursively(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
continue
if not is_used:
unused_weights.append(_UpperCAmelCase )
logger.warning(F"Unused weights: {unused_weights}" )
def A ( _UpperCAmelCase : Any , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : List[str] , _UpperCAmelCase : Tuple , _UpperCAmelCase : Union[str, Any] ) -> Optional[Any]:
'''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(_UpperCAmelCase )
def A ( _UpperCAmelCase : int , _UpperCAmelCase : Union[str, Any] ) -> List[str]:
'''simple docstring'''
_UpperCAmelCase = SEWConfig()
if is_finetuned:
_UpperCAmelCase = model.wav_encoder.wav_model.cfg
else:
_UpperCAmelCase = model.cfg
_UpperCAmelCase = fs_config.conv_bias
_UpperCAmelCase = eval(fs_config.conv_feature_layers )
_UpperCAmelCase = [x[0] for x in conv_layers]
_UpperCAmelCase = [x[1] for x in conv_layers]
_UpperCAmelCase = [x[2] for x in conv_layers]
_UpperCAmelCase = 'gelu'
_UpperCAmelCase = 'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
_UpperCAmelCase = 0.0
_UpperCAmelCase = fs_config.activation_fn.name
_UpperCAmelCase = fs_config.encoder_embed_dim
_UpperCAmelCase = 0.02
_UpperCAmelCase = fs_config.encoder_ffn_embed_dim
_UpperCAmelCase = 1E-5
_UpperCAmelCase = fs_config.encoder_layerdrop
_UpperCAmelCase = fs_config.encoder_attention_heads
_UpperCAmelCase = fs_config.conv_pos_groups
_UpperCAmelCase = fs_config.conv_pos
_UpperCAmelCase = len(_UpperCAmelCase )
_UpperCAmelCase = fs_config.encoder_layers
_UpperCAmelCase = fs_config.squeeze_factor
# take care of any params that are overridden by the Wav2VecCtc model
if is_finetuned:
_UpperCAmelCase = model.cfg
_UpperCAmelCase = fs_config.final_dropout
_UpperCAmelCase = fs_config.layerdrop
_UpperCAmelCase = fs_config.activation_dropout
_UpperCAmelCase = fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
_UpperCAmelCase = fs_config.attention_dropout
_UpperCAmelCase = fs_config.dropout_input
_UpperCAmelCase = fs_config.dropout
_UpperCAmelCase = fs_config.mask_channel_length
_UpperCAmelCase = fs_config.mask_channel_prob
_UpperCAmelCase = fs_config.mask_length
_UpperCAmelCase = fs_config.mask_prob
_UpperCAmelCase = 'Wav2Vec2FeatureExtractor'
_UpperCAmelCase = 'Wav2Vec2CTCTokenizer'
return config
@torch.no_grad()
def A ( _UpperCAmelCase : int , _UpperCAmelCase : str , _UpperCAmelCase : int=None , _UpperCAmelCase : Optional[Any]=None , _UpperCAmelCase : Any=True ) -> str:
'''simple docstring'''
if is_finetuned:
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={'data': '/'.join(dict_path.split('/' )[:-1] )} )
else:
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] )
if config_path is not None:
_UpperCAmelCase = SEWConfig.from_pretrained(_UpperCAmelCase )
else:
_UpperCAmelCase = convert_config(model[0] , _UpperCAmelCase )
_UpperCAmelCase = model[0].eval()
_UpperCAmelCase = True if config.feat_extract_norm == 'layer' else False
_UpperCAmelCase = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=16_000 , padding_value=0 , do_normalize=_UpperCAmelCase , return_attention_mask=_UpperCAmelCase , )
if is_finetuned:
if dict_path:
_UpperCAmelCase = Dictionary.load(_UpperCAmelCase )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
_UpperCAmelCase = target_dict.pad_index
_UpperCAmelCase = target_dict.bos_index
_UpperCAmelCase = target_dict.pad_index
_UpperCAmelCase = target_dict.bos_index
_UpperCAmelCase = target_dict.eos_index
_UpperCAmelCase = len(target_dict.symbols )
_UpperCAmelCase = os.path.join(_UpperCAmelCase , 'vocab.json' )
if not os.path.isdir(_UpperCAmelCase ):
logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(_UpperCAmelCase ) )
return
os.makedirs(_UpperCAmelCase , exist_ok=_UpperCAmelCase )
with open(_UpperCAmelCase , 'w' , encoding='utf-8' ) as vocab_handle:
json.dump(target_dict.indices , _UpperCAmelCase )
_UpperCAmelCase = WavaVecaCTCTokenizer(
_UpperCAmelCase , unk_token=target_dict.unk_word , pad_token=target_dict.pad_word , bos_token=target_dict.bos_word , eos_token=target_dict.eos_word , word_delimiter_token='|' , do_lower_case=_UpperCAmelCase , )
_UpperCAmelCase = WavaVecaProcessor(feature_extractor=_UpperCAmelCase , tokenizer=_UpperCAmelCase )
processor.save_pretrained(_UpperCAmelCase )
_UpperCAmelCase = SEWForCTC(_UpperCAmelCase )
else:
_UpperCAmelCase = SEWModel(_UpperCAmelCase )
feature_extractor.save_pretrained(_UpperCAmelCase )
recursively_load_weights(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
hf_model.save_pretrained(_UpperCAmelCase )
if __name__ == "__main__":
UpperCAmelCase__ = argparse.ArgumentParser()
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
parser.add_argument(
"--is_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
)
UpperCAmelCase__ = parser.parse_args()
convert_sew_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
)
| 339 |
import os
UpperCAmelCase__ = {"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1000}
def A ( _UpperCAmelCase : str ) -> int:
'''simple docstring'''
_UpperCAmelCase = 0
_UpperCAmelCase = 0
while index < len(_UpperCAmelCase ) - 1:
_UpperCAmelCase = SYMBOLS[numerals[index]]
_UpperCAmelCase = SYMBOLS[numerals[index + 1]]
if current_value < next_value:
total_value -= current_value
else:
total_value += current_value
index += 1
total_value += SYMBOLS[numerals[index]]
return total_value
def A ( _UpperCAmelCase : int ) -> str:
'''simple docstring'''
_UpperCAmelCase = ''
_UpperCAmelCase = num // 1_000
numerals += m_count * "M"
num %= 1_000
_UpperCAmelCase = num // 100
if c_count == 9:
numerals += "CM"
c_count -= 9
elif c_count == 4:
numerals += "CD"
c_count -= 4
if c_count >= 5:
numerals += "D"
c_count -= 5
numerals += c_count * "C"
num %= 100
_UpperCAmelCase = num // 10
if x_count == 9:
numerals += "XC"
x_count -= 9
elif x_count == 4:
numerals += "XL"
x_count -= 4
if x_count >= 5:
numerals += "L"
x_count -= 5
numerals += x_count * "X"
num %= 10
if num == 9:
numerals += "IX"
num -= 9
elif num == 4:
numerals += "IV"
num -= 4
if num >= 5:
numerals += "V"
num -= 5
numerals += num * "I"
return numerals
def A ( _UpperCAmelCase : str = "/p089_roman.txt" ) -> int:
'''simple docstring'''
_UpperCAmelCase = 0
with open(os.path.dirname(_UpperCAmelCase ) + roman_numerals_filename ) as filea:
_UpperCAmelCase = filea.readlines()
for line in lines:
_UpperCAmelCase = line.strip()
_UpperCAmelCase = parse_roman_numerals(_UpperCAmelCase )
_UpperCAmelCase = generate_roman_numerals(_UpperCAmelCase )
savings += len(_UpperCAmelCase ) - len(_UpperCAmelCase )
return savings
if __name__ == "__main__":
print(f"""{solution() = }""")
| 339 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
if is_sentencepiece_available():
from ..ta.tokenization_ta import TaTokenizer
else:
from ...utils.dummy_sentencepiece_objects import TaTokenizer
UpperCAmelCase__ = TaTokenizer
if is_tokenizers_available():
from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
from ...utils.dummy_tokenizers_objects import TaTokenizerFast
UpperCAmelCase__ = TaTokenizerFast
UpperCAmelCase__ = {"configuration_mt5": ["MT5Config", "MT5OnnxConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ = [
"MT5EncoderModel",
"MT5ForConditionalGeneration",
"MT5ForQuestionAnswering",
"MT5Model",
"MT5PreTrainedModel",
"MT5Stack",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ = ["TFMT5EncoderModel", "TFMT5ForConditionalGeneration", "TFMT5Model"]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ = ["FlaxMT5EncoderModel", "FlaxMT5ForConditionalGeneration", "FlaxMT5Model"]
if TYPE_CHECKING:
from .configuration_mta import MTaConfig, MTaOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mta import (
MTaEncoderModel,
MTaForConditionalGeneration,
MTaForQuestionAnswering,
MTaModel,
MTaPreTrainedModel,
MTaStack,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel
else:
import sys
UpperCAmelCase__ = _LazyModule(
__name__,
globals()["__file__"],
_import_structure,
extra_objects={"MT5Tokenizer": MTaTokenizer, "MT5TokenizerFast": MTaTokenizerFast},
module_spec=__spec__,
)
| 339 |
import requests
from bsa import BeautifulSoup
def A ( _UpperCAmelCase : str , _UpperCAmelCase : dict ) -> str:
'''simple docstring'''
_UpperCAmelCase = BeautifulSoup(requests.get(_UpperCAmelCase , params=_UpperCAmelCase ).content , 'html.parser' )
_UpperCAmelCase = soup.find('div' , attrs={'class': 'gs_ri'} )
_UpperCAmelCase = div.find('div' , attrs={'class': 'gs_fl'} ).find_all('a' )
return anchors[2].get_text()
if __name__ == "__main__":
UpperCAmelCase__ = {
"title": (
"Precisely geometry controlled microsupercapacitors for ultrahigh areal "
"capacitance, volumetric capacitance, and energy density"
),
"journal": "Chem. Mater.",
"volume": 30,
"pages": "3979-3990",
"year": 2018,
"hl": "en",
}
print(get_citation("https://scholar.google.com/scholar_lookup", params=params))
| 339 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
UpperCAmelCase__ = {
"configuration_roberta_prelayernorm": [
"ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP",
"RobertaPreLayerNormConfig",
"RobertaPreLayerNormOnnxConfig",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ = [
"ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST",
"RobertaPreLayerNormForCausalLM",
"RobertaPreLayerNormForMaskedLM",
"RobertaPreLayerNormForMultipleChoice",
"RobertaPreLayerNormForQuestionAnswering",
"RobertaPreLayerNormForSequenceClassification",
"RobertaPreLayerNormForTokenClassification",
"RobertaPreLayerNormModel",
"RobertaPreLayerNormPreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ = [
"TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFRobertaPreLayerNormForCausalLM",
"TFRobertaPreLayerNormForMaskedLM",
"TFRobertaPreLayerNormForMultipleChoice",
"TFRobertaPreLayerNormForQuestionAnswering",
"TFRobertaPreLayerNormForSequenceClassification",
"TFRobertaPreLayerNormForTokenClassification",
"TFRobertaPreLayerNormMainLayer",
"TFRobertaPreLayerNormModel",
"TFRobertaPreLayerNormPreTrainedModel",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ = [
"FlaxRobertaPreLayerNormForCausalLM",
"FlaxRobertaPreLayerNormForMaskedLM",
"FlaxRobertaPreLayerNormForMultipleChoice",
"FlaxRobertaPreLayerNormForQuestionAnswering",
"FlaxRobertaPreLayerNormForSequenceClassification",
"FlaxRobertaPreLayerNormForTokenClassification",
"FlaxRobertaPreLayerNormModel",
"FlaxRobertaPreLayerNormPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_roberta_prelayernorm import (
ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
RobertaPreLayerNormConfig,
RobertaPreLayerNormOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roberta_prelayernorm import (
ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
RobertaPreLayerNormForCausalLM,
RobertaPreLayerNormForMaskedLM,
RobertaPreLayerNormForMultipleChoice,
RobertaPreLayerNormForQuestionAnswering,
RobertaPreLayerNormForSequenceClassification,
RobertaPreLayerNormForTokenClassification,
RobertaPreLayerNormModel,
RobertaPreLayerNormPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_roberta_prelayernorm import (
TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRobertaPreLayerNormForCausalLM,
TFRobertaPreLayerNormForMaskedLM,
TFRobertaPreLayerNormForMultipleChoice,
TFRobertaPreLayerNormForQuestionAnswering,
TFRobertaPreLayerNormForSequenceClassification,
TFRobertaPreLayerNormForTokenClassification,
TFRobertaPreLayerNormMainLayer,
TFRobertaPreLayerNormModel,
TFRobertaPreLayerNormPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_roberta_prelayernorm import (
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormModel,
FlaxRobertaPreLayerNormPreTrainedModel,
)
else:
import sys
UpperCAmelCase__ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 339 |
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.roformer.modeling_flax_roformer import (
FlaxRoFormerForMaskedLM,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerModel,
)
class __lowerCAmelCase ( unittest.TestCase ):
def __init__( self : Optional[Any] , A : Dict , A : Union[str, Any]=13 , A : Dict=7 , A : Dict=True , A : Tuple=True , A : Union[str, Any]=True , A : int=True , A : Optional[int]=99 , A : List[str]=32 , A : List[Any]=5 , A : int=4 , A : Any=37 , A : Optional[int]="gelu" , A : Optional[Any]=0.1 , A : Any=0.1 , A : Union[str, Any]=5_12 , A : int=16 , A : List[str]=2 , A : Union[str, Any]=0.0_2 , A : Union[str, Any]=4 , ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase = parent
_UpperCAmelCase = batch_size
_UpperCAmelCase = seq_length
_UpperCAmelCase = is_training
_UpperCAmelCase = use_attention_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_choices
def _lowerCamelCase ( self : Optional[Any]) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
_UpperCAmelCase = None
if self.use_attention_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 = RoFormerConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=A , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def _lowerCamelCase ( self : List[Any]) -> List[str]:
"""simple docstring"""
_UpperCAmelCase = self.prepare_config_and_inputs()
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = config_and_inputs
_UpperCAmelCase = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
return config, inputs_dict
@require_flax
class __lowerCAmelCase ( A , unittest.TestCase ):
UpperCamelCase = True
UpperCamelCase = (
(
FlaxRoFormerModel,
FlaxRoFormerForMaskedLM,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
)
if is_flax_available()
else ()
)
def _lowerCamelCase ( self : Optional[int]) -> Any:
"""simple docstring"""
_UpperCAmelCase = FlaxRoFormerModelTester(self)
@slow
def _lowerCamelCase ( self : List[Any]) -> Dict:
"""simple docstring"""
for model_class_name in self.all_model_classes:
_UpperCAmelCase = model_class_name.from_pretrained('junnyu/roformer_chinese_small' , from_pt=A)
_UpperCAmelCase = model(np.ones((1, 1)))
self.assertIsNotNone(A)
@require_flax
class __lowerCAmelCase ( unittest.TestCase ):
@slow
def _lowerCamelCase ( self : List[Any]) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase = FlaxRoFormerForMaskedLM.from_pretrained('junnyu/roformer_chinese_base')
_UpperCAmelCase = jnp.array([[0, 1, 2, 3, 4, 5]])
_UpperCAmelCase = model(A)[0]
_UpperCAmelCase = 5_00_00
_UpperCAmelCase = (1, 6, vocab_size)
self.assertEqual(output.shape , A)
_UpperCAmelCase = jnp.array(
[[[-0.1_2_0_5, -1.0_2_6_5, 0.2_9_2_2], [-1.5_1_3_4, 0.1_9_7_4, 0.1_5_1_9], [-5.0_1_3_5, -3.9_0_0_3, -0.8_4_0_4]]])
self.assertTrue(jnp.allclose(output[:, :3, :3] , A , atol=1E-4))
| 339 | 1 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase__ = logging.get_logger(__name__)
def A ( _UpperCAmelCase : List[str] ) -> List[str]:
'''simple docstring'''
_UpperCAmelCase = 'huggingface/label-files'
_UpperCAmelCase = 'imagenet-1k-id2label.json'
_UpperCAmelCase = json.load(open(hf_hub_download(_UpperCAmelCase , _UpperCAmelCase , repo_type='dataset' ) , 'r' ) )
_UpperCAmelCase = {int(_UpperCAmelCase ): v for k, v in idalabel.items()}
_UpperCAmelCase = {v: k for k, v in idalabel.items()}
_UpperCAmelCase = 'std_conv' if 'bit' in model_name else False
# note that when using BiT as backbone for ViT-hybrid checkpoints,
# one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
# config.conv_layer = "std_conv_same"
_UpperCAmelCase = BitConfig(
conv_layer=_UpperCAmelCase , num_labels=1_000 , idalabel=_UpperCAmelCase , labelaid=_UpperCAmelCase , )
return config
def A ( _UpperCAmelCase : Union[str, Any] ) -> str:
'''simple docstring'''
if "stem.conv" in name:
_UpperCAmelCase = name.replace('stem.conv' , 'bit.embedder.convolution' )
if "blocks" in name:
_UpperCAmelCase = name.replace('blocks' , 'layers' )
if "head.fc" in name:
_UpperCAmelCase = name.replace('head.fc' , 'classifier.1' )
if name.startswith('norm' ):
_UpperCAmelCase = 'bit.' + name
if "bit" not in name and "classifier" not in name:
_UpperCAmelCase = 'bit.encoder.' + name
return name
def A ( ) -> int:
'''simple docstring'''
_UpperCAmelCase = 'http://images.cocodataset.org/val2017/000000039769.jpg'
_UpperCAmelCase = Image.open(requests.get(_UpperCAmelCase , stream=_UpperCAmelCase ).raw )
return im
@torch.no_grad()
def A ( _UpperCAmelCase : List[Any] , _UpperCAmelCase : Dict , _UpperCAmelCase : Optional[int]=False ) -> Tuple:
'''simple docstring'''
_UpperCAmelCase = get_config(_UpperCAmelCase )
# load original model from timm
_UpperCAmelCase = create_model(_UpperCAmelCase , pretrained=_UpperCAmelCase )
timm_model.eval()
# load state_dict of original model
_UpperCAmelCase = timm_model.state_dict()
for key in state_dict.copy().keys():
_UpperCAmelCase = state_dict.pop(_UpperCAmelCase )
_UpperCAmelCase = val.squeeze() if 'head' in key else val
# load HuggingFace model
_UpperCAmelCase = BitForImageClassification(_UpperCAmelCase )
model.eval()
model.load_state_dict(_UpperCAmelCase )
# create image processor
_UpperCAmelCase = create_transform(**resolve_data_config({} , model=_UpperCAmelCase ) )
_UpperCAmelCase = transform.transforms
_UpperCAmelCase = {
'bilinear': PILImageResampling.BILINEAR,
'bicubic': PILImageResampling.BICUBIC,
'nearest': PILImageResampling.NEAREST,
}
_UpperCAmelCase = BitImageProcessor(
do_resize=_UpperCAmelCase , size={'shortest_edge': timm_transforms[0].size} , resample=pillow_resamplings[timm_transforms[0].interpolation.value] , do_center_crop=_UpperCAmelCase , crop_size={'height': timm_transforms[1].size[0], 'width': timm_transforms[1].size[1]} , do_normalize=_UpperCAmelCase , image_mean=timm_transforms[-1].mean.tolist() , image_std=timm_transforms[-1].std.tolist() , )
_UpperCAmelCase = prepare_img()
_UpperCAmelCase = transform(_UpperCAmelCase ).unsqueeze(0 )
_UpperCAmelCase = processor(_UpperCAmelCase , return_tensors='pt' ).pixel_values
# verify pixel values
assert torch.allclose(_UpperCAmelCase , _UpperCAmelCase )
# verify logits
with torch.no_grad():
_UpperCAmelCase = model(_UpperCAmelCase )
_UpperCAmelCase = outputs.logits
print('Logits:' , logits[0, :3] )
print('Predicted class:' , model.config.idalabel[logits.argmax(-1 ).item()] )
_UpperCAmelCase = timm_model(_UpperCAmelCase )
assert timm_logits.shape == outputs.logits.shape
assert torch.allclose(_UpperCAmelCase , outputs.logits , atol=1E-3 )
print('Looks ok!' )
if pytorch_dump_folder_path is not None:
Path(_UpperCAmelCase ).mkdir(exist_ok=_UpperCAmelCase )
print(F"Saving model {model_name} and processor to {pytorch_dump_folder_path}" )
model.save_pretrained(_UpperCAmelCase )
processor.save_pretrained(_UpperCAmelCase )
if push_to_hub:
print(F"Pushing model {model_name} and processor to the hub" )
model.push_to_hub(F"ybelkada/{model_name}" )
processor.push_to_hub(F"ybelkada/{model_name}" )
if __name__ == "__main__":
UpperCAmelCase__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default="resnetv2_50x1_bitm",
type=str,
help="Name of the BiT timm model you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
)
parser.add_argument(
"--push_to_hub",
action="store_true",
help="Whether to push the model to the hub.",
)
UpperCAmelCase__ = parser.parse_args()
convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 339 |
UpperCAmelCase__ = {}
def A ( _UpperCAmelCase : int , _UpperCAmelCase : int , _UpperCAmelCase : int ) -> int:
'''simple docstring'''
# if we are absent twice, or late 3 consecutive days,
# no further prize strings are possible
if late == 3 or absent == 2:
return 0
# if we have no days left, and have not failed any other rules,
# we have a prize string
if days == 0:
return 1
# No easy solution, so now we need to do the recursive calculation
# First, check if the combination is already in the cache, and
# if yes, return the stored value from there since we already
# know the number of possible prize strings from this point on
_UpperCAmelCase = (days, absent, late)
if key in cache:
return cache[key]
# now we calculate the three possible ways that can unfold from
# this point on, depending on our attendance today
# 1) if we are late (but not absent), the "absent" counter stays as
# it is, but the "late" counter increases by one
_UpperCAmelCase = _calculate(days - 1 , _UpperCAmelCase , late + 1 )
# 2) if we are absent, the "absent" counter increases by 1, and the
# "late" counter resets to 0
_UpperCAmelCase = _calculate(days - 1 , absent + 1 , 0 )
# 3) if we are on time, this resets the "late" counter and keeps the
# absent counter
_UpperCAmelCase = _calculate(days - 1 , _UpperCAmelCase , 0 )
_UpperCAmelCase = state_late + state_absent + state_ontime
_UpperCAmelCase = prizestrings
return prizestrings
def A ( _UpperCAmelCase : int = 30 ) -> int:
'''simple docstring'''
return _calculate(_UpperCAmelCase , absent=0 , late=0 )
if __name__ == "__main__":
print(solution())
| 339 | 1 |
from random import shuffle
import tensorflow as tf
from numpy import array
def A ( _UpperCAmelCase : List[Any] , _UpperCAmelCase : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
_UpperCAmelCase = int(_UpperCAmelCase )
assert noofclusters < len(_UpperCAmelCase )
# Find out the dimensionality
_UpperCAmelCase = len(vectors[0] )
# Will help select random centroids from among the available vectors
_UpperCAmelCase = list(range(len(_UpperCAmelCase ) ) )
shuffle(_UpperCAmelCase )
# GRAPH OF COMPUTATION
# We initialize a new graph and set it as the default during each run
# of this algorithm. This ensures that as this function is called
# multiple times, the default graph doesn't keep getting crowded with
# unused ops and Variables from previous function calls.
_UpperCAmelCase = tf.Graph()
with graph.as_default():
# SESSION OF COMPUTATION
_UpperCAmelCase = tf.Session()
##CONSTRUCTING THE ELEMENTS OF COMPUTATION
##First lets ensure we have a Variable vector for each centroid,
##initialized to one of the vectors from the available data points
_UpperCAmelCase = [
tf.Variable(vectors[vector_indices[i]] ) for i in range(_UpperCAmelCase )
]
##These nodes will assign the centroid Variables the appropriate
##values
_UpperCAmelCase = tf.placeholder('float64' , [dim] )
_UpperCAmelCase = []
for centroid in centroids:
cent_assigns.append(tf.assign(_UpperCAmelCase , _UpperCAmelCase ) )
##Variables for cluster assignments of individual vectors(initialized
##to 0 at first)
_UpperCAmelCase = [tf.Variable(0 ) for i in range(len(_UpperCAmelCase ) )]
##These nodes will assign an assignment Variable the appropriate
##value
_UpperCAmelCase = tf.placeholder('int32' )
_UpperCAmelCase = []
for assignment in assignments:
cluster_assigns.append(tf.assign(_UpperCAmelCase , _UpperCAmelCase ) )
##Now lets construct the node that will compute the mean
# The placeholder for the input
_UpperCAmelCase = tf.placeholder('float' , [None, dim] )
# The Node/op takes the input and computes a mean along the 0th
# dimension, i.e. the list of input vectors
_UpperCAmelCase = tf.reduce_mean(_UpperCAmelCase , 0 )
##Node for computing Euclidean distances
# Placeholders for input
_UpperCAmelCase = tf.placeholder('float' , [dim] )
_UpperCAmelCase = tf.placeholder('float' , [dim] )
_UpperCAmelCase = tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(_UpperCAmelCase , _UpperCAmelCase ) , 2 ) ) )
##This node will figure out which cluster to assign a vector to,
##based on Euclidean distances of the vector from the centroids.
# Placeholder for input
_UpperCAmelCase = tf.placeholder('float' , [noofclusters] )
_UpperCAmelCase = tf.argmin(_UpperCAmelCase , 0 )
##INITIALIZING STATE VARIABLES
##This will help initialization of all Variables defined with respect
##to the graph. The Variable-initializer should be defined after
##all the Variables have been constructed, so that each of them
##will be included in the initialization.
_UpperCAmelCase = tf.initialize_all_variables()
# Initialize all variables
sess.run(_UpperCAmelCase )
##CLUSTERING ITERATIONS
# Now perform the Expectation-Maximization steps of K-Means clustering
# iterations. To keep things simple, we will only do a set number of
# iterations, instead of using a Stopping Criterion.
_UpperCAmelCase = 100
for _ in range(_UpperCAmelCase ):
##EXPECTATION STEP
##Based on the centroid locations till last iteration, compute
##the _expected_ centroid assignments.
# Iterate over each vector
for vector_n in range(len(_UpperCAmelCase ) ):
_UpperCAmelCase = vectors[vector_n]
# Compute Euclidean distance between this vector and each
# centroid. Remember that this list cannot be named
#'centroid_distances', since that is the input to the
# cluster assignment node.
_UpperCAmelCase = [
sess.run(_UpperCAmelCase , feed_dict={va: vect, va: sess.run(_UpperCAmelCase )} )
for centroid in centroids
]
# Now use the cluster assignment node, with the distances
# as the input
_UpperCAmelCase = sess.run(
_UpperCAmelCase , feed_dict={centroid_distances: distances} )
# Now assign the value to the appropriate state variable
sess.run(
cluster_assigns[vector_n] , feed_dict={assignment_value: assignment} )
##MAXIMIZATION STEP
# Based on the expected state computed from the Expectation Step,
# compute the locations of the centroids so as to maximize the
# overall objective of minimizing within-cluster Sum-of-Squares
for cluster_n in range(_UpperCAmelCase ):
# Collect all the vectors assigned to this cluster
_UpperCAmelCase = [
vectors[i]
for i in range(len(_UpperCAmelCase ) )
if sess.run(assignments[i] ) == cluster_n
]
# Compute new centroid location
_UpperCAmelCase = sess.run(
_UpperCAmelCase , feed_dict={mean_input: array(_UpperCAmelCase )} )
# Assign value to appropriate variable
sess.run(
cent_assigns[cluster_n] , feed_dict={centroid_value: new_location} )
# Return centroids and assignments
_UpperCAmelCase = sess.run(_UpperCAmelCase )
_UpperCAmelCase = sess.run(_UpperCAmelCase )
return centroids, assignments
| 339 |
import os
import sys
import unittest
UpperCAmelCase__ = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init # noqa: E402
# Align TRANSFORMERS_PATH in check_dummies with the current path
UpperCAmelCase__ = os.path.join(git_repo_path, "src", "diffusers")
class __lowerCAmelCase ( unittest.TestCase ):
def _lowerCamelCase ( self : Tuple) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase = find_backend(' if not is_torch_available():')
self.assertEqual(A , 'torch')
# backend_with_underscore = find_backend(" if not is_tensorflow_text_available():")
# self.assertEqual(backend_with_underscore, "tensorflow_text")
_UpperCAmelCase = find_backend(' if not (is_torch_available() and is_transformers_available()):')
self.assertEqual(A , 'torch_and_transformers')
# double_backend_with_underscore = find_backend(
# " if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
# )
# self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text")
_UpperCAmelCase = find_backend(
' if not (is_torch_available() and is_transformers_available() and is_onnx_available()):')
self.assertEqual(A , 'torch_and_transformers_and_onnx')
def _lowerCamelCase ( self : int) -> Dict:
"""simple docstring"""
_UpperCAmelCase = read_init()
# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
self.assertIn('torch' , A)
self.assertIn('torch_and_transformers' , A)
self.assertIn('flax_and_transformers' , A)
self.assertIn('torch_and_transformers_and_onnx' , A)
# Likewise, we can't assert on the exact content of a key
self.assertIn('UNet2DModel' , objects['torch'])
self.assertIn('FlaxUNet2DConditionModel' , objects['flax'])
self.assertIn('StableDiffusionPipeline' , objects['torch_and_transformers'])
self.assertIn('FlaxStableDiffusionPipeline' , objects['flax_and_transformers'])
self.assertIn('LMSDiscreteScheduler' , objects['torch_and_scipy'])
self.assertIn('OnnxStableDiffusionPipeline' , objects['torch_and_transformers_and_onnx'])
def _lowerCamelCase ( self : Union[str, Any]) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase = create_dummy_object('CONSTANT' , '\'torch\'')
self.assertEqual(A , '\nCONSTANT = None\n')
_UpperCAmelCase = create_dummy_object('function' , '\'torch\'')
self.assertEqual(
A , '\ndef function(*args, **kwargs):\n requires_backends(function, \'torch\')\n')
_UpperCAmelCase = '\nclass FakeClass(metaclass=DummyObject):\n _backends = \'torch\'\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, \'torch\')\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, \'torch\')\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, \'torch\')\n'
_UpperCAmelCase = create_dummy_object('FakeClass' , '\'torch\'')
self.assertEqual(A , A)
def _lowerCamelCase ( self : Dict) -> int:
"""simple docstring"""
_UpperCAmelCase = '# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n requires_backends(function, ["torch"])\n\n\nclass FakeClass(metaclass=DummyObject):\n _backends = ["torch"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, ["torch"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, ["torch"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, ["torch"])\n'
_UpperCAmelCase = create_dummy_files({'torch': ['CONSTANT', 'function', 'FakeClass']})
self.assertEqual(dummy_files['torch'] , A)
| 339 | 1 |
from __future__ import annotations
from collections import Counter
from random import random
class __lowerCAmelCase :
def __init__( self : List[Any]) -> Dict:
"""simple docstring"""
_UpperCAmelCase = {}
def _lowerCamelCase ( self : Tuple , A : str) -> None:
"""simple docstring"""
_UpperCAmelCase = {}
def _lowerCamelCase ( self : Dict , A : str , A : str , A : float) -> None:
"""simple docstring"""
if nodea not in self.connections:
self.add_node(A)
if nodea not in self.connections:
self.add_node(A)
_UpperCAmelCase = probability
def _lowerCamelCase ( self : Union[str, Any]) -> list[str]:
"""simple docstring"""
return list(self.connections)
def _lowerCamelCase ( self : List[Any] , A : str) -> str:
"""simple docstring"""
_UpperCAmelCase = 0
_UpperCAmelCase = random()
for dest in self.connections[node]:
current_probability += self.connections[node][dest]
if current_probability > random_value:
return dest
return ""
def A ( _UpperCAmelCase : str , _UpperCAmelCase : list[tuple[str, str, float]] , _UpperCAmelCase : int ) -> dict[str, int]:
'''simple docstring'''
_UpperCAmelCase = MarkovChainGraphUndirectedUnweighted()
for nodea, nodea, probability in transitions:
graph.add_transition_probability(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
_UpperCAmelCase = Counter(graph.get_nodes() )
_UpperCAmelCase = start
for _ in range(_UpperCAmelCase ):
_UpperCAmelCase = graph.transition(_UpperCAmelCase )
visited[node] += 1
return visited
if __name__ == "__main__":
import doctest
doctest.testmod()
| 339 |
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartForSequenceClassification,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
TapexTokenizer,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version
from transformers.utils.versions import require_version
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.17.0.dev0")
require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")
UpperCAmelCase__ = logging.getLogger(__name__)
@dataclass
class __lowerCAmelCase :
UpperCamelCase = field(
default='''tab_fact''' , metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''} )
UpperCamelCase = field(
default='''tab_fact''' , metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''} , )
UpperCamelCase = field(
default=1_0_2_4 , metadata={
'''help''': (
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
} , )
UpperCamelCase = field(
default=A , metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''} )
UpperCamelCase = field(
default=A , metadata={
'''help''': (
'''Whether to pad all samples to `max_seq_length`. '''
'''If False, will pad the samples dynamically when batching to the maximum length in the batch.'''
)
} , )
UpperCamelCase = field(
default=A , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of training examples to this '''
'''value if set.'''
)
} , )
UpperCamelCase = field(
default=A , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
'''value if set.'''
)
} , )
UpperCamelCase = field(
default=A , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of prediction examples to this '''
'''value if set.'''
)
} , )
UpperCamelCase = field(
default=A , metadata={'''help''': '''A csv or a json file containing the training data.'''} )
UpperCamelCase = field(
default=A , metadata={'''help''': '''A csv or a json file containing the validation data.'''} )
UpperCamelCase = field(default=A , metadata={'''help''': '''A csv or a json file containing the test data.'''} )
def _lowerCamelCase ( self : str) -> List[Any]:
"""simple docstring"""
if self.dataset_name is not None:
pass
elif self.train_file is None or self.validation_file is None:
raise ValueError('Need either a GLUE task, a training/validation file or a dataset name.')
else:
_UpperCAmelCase = self.train_file.split('.')[-1]
assert train_extension in ["csv", "json"], "`train_file` should be a csv or a json file."
_UpperCAmelCase = self.validation_file.split('.')[-1]
assert (
validation_extension == train_extension
), "`validation_file` should have the same extension (csv or json) as `train_file`."
@dataclass
class __lowerCAmelCase :
UpperCamelCase = field(
default=A , metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} )
UpperCamelCase = field(
default=A , metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
UpperCamelCase = field(
default=A , metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} )
UpperCamelCase = field(
default=A , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''} , )
UpperCamelCase = field(
default=A , metadata={'''help''': '''Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'''} , )
UpperCamelCase = field(
default='''main''' , metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''} , )
UpperCamelCase = field(
default=A , metadata={
'''help''': (
'''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
'''with private models).'''
)
} , )
def A ( ) -> Optional[int]:
'''simple docstring'''
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
_UpperCAmelCase = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith('.json' ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = parser.parse_args_into_dataclasses()
# Setup logging
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s' , datefmt='%m/%d/%Y %H:%M:%S' , handlers=[logging.StreamHandler(sys.stdout )] , )
_UpperCAmelCase = training_args.get_process_log_level()
logger.setLevel(_UpperCAmelCase )
datasets.utils.logging.set_verbosity(_UpperCAmelCase )
transformers.utils.logging.set_verbosity(_UpperCAmelCase )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
F"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
+ F"distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}" )
logger.info(F"Training/evaluation parameters {training_args}" )
# Detecting last checkpoint.
_UpperCAmelCase = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
_UpperCAmelCase = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
F"Output directory ({training_args.output_dir}) already exists and is not empty. "
'Use --overwrite_output_dir to overcome.' )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
F"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.' )
# Set seed before initializing model.
set_seed(training_args.seed )
# Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
# or specify a GLUE benchmark task (the dataset will be downloaded automatically from the datasets Hub).
#
# For JSON files, this script will use the `question` column for the input question and `table` column for the corresponding table.
#
# If the CSVs/JSONs contain only one non-label column, the script does single sentence classification on this
# single column. You can easily tweak this behavior (see below)
#
# In distributed training, the load_dataset function guarantee that only one local process can concurrently
# download the dataset.
if data_args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
_UpperCAmelCase = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , cache_dir=model_args.cache_dir )
else:
# Loading a dataset from your local files.
# CSV/JSON training and evaluation files are needed.
_UpperCAmelCase = {'train': data_args.train_file, 'validation': data_args.validation_file}
# Get the test dataset: you can provide your own CSV/JSON test file (see below)
# when you use `do_predict` without specifying a GLUE benchmark task.
if training_args.do_predict:
if data_args.test_file is not None:
_UpperCAmelCase = data_args.train_file.split('.' )[-1]
_UpperCAmelCase = data_args.test_file.split('.' )[-1]
assert (
test_extension == train_extension
), "`test_file` should have the same extension (csv or json) as `train_file`."
_UpperCAmelCase = data_args.test_file
else:
raise ValueError('Need either a GLUE task or a test file for `do_predict`.' )
for key in data_files.keys():
logger.info(F"load a local file for {key}: {data_files[key]}" )
if data_args.train_file.endswith('.csv' ):
# Loading a dataset from local csv files
_UpperCAmelCase = load_dataset('csv' , data_files=_UpperCAmelCase , cache_dir=model_args.cache_dir )
else:
# Loading a dataset from local json files
_UpperCAmelCase = load_dataset('json' , data_files=_UpperCAmelCase , cache_dir=model_args.cache_dir )
# See more about loading any type of standard or custom dataset at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Labels
_UpperCAmelCase = raw_datasets['train'].features['label'].names
_UpperCAmelCase = len(_UpperCAmelCase )
# Load pretrained model and tokenizer
#
# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
_UpperCAmelCase = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=_UpperCAmelCase , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# load tapex tokenizer
_UpperCAmelCase = TapexTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast_tokenizer , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , add_prefix_space=_UpperCAmelCase , )
_UpperCAmelCase = BartForSequenceClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool('.ckpt' in model_args.model_name_or_path ) , config=_UpperCAmelCase , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# Padding strategy
if data_args.pad_to_max_length:
_UpperCAmelCase = 'max_length'
else:
# We will pad later, dynamically at batch creation, to the max sequence length in each batch
_UpperCAmelCase = False
# Some models have set the order of the labels to use, so let's make sure we do use it.
_UpperCAmelCase = {'Refused': 0, 'Entailed': 1}
_UpperCAmelCase = {0: 'Refused', 1: 'Entailed'}
if data_args.max_seq_length > tokenizer.model_max_length:
logger.warning(
F"The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the"
F"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}." )
_UpperCAmelCase = min(data_args.max_seq_length , tokenizer.model_max_length )
def preprocess_tabfact_function(_UpperCAmelCase : Union[str, Any] ):
# Tokenize the texts
def _convert_table_text_to_pandas(_UpperCAmelCase : Dict ):
_UpperCAmelCase = [_table_row.split('#' ) for _table_row in _table_text.strip('\n' ).split('\n' )]
_UpperCAmelCase = pd.DataFrame.from_records(_table_content[1:] , columns=_table_content[0] )
return _table_pd
_UpperCAmelCase = examples['statement']
_UpperCAmelCase = list(map(_convert_table_text_to_pandas , examples['table_text'] ) )
_UpperCAmelCase = tokenizer(_UpperCAmelCase , _UpperCAmelCase , padding=_UpperCAmelCase , max_length=_UpperCAmelCase , truncation=_UpperCAmelCase )
_UpperCAmelCase = examples['label']
return result
with training_args.main_process_first(desc='dataset map pre-processing' ):
_UpperCAmelCase = raw_datasets.map(
_UpperCAmelCase , batched=_UpperCAmelCase , load_from_cache_file=not data_args.overwrite_cache , desc='Running tokenizer on dataset' , )
if training_args.do_train:
if "train" not in raw_datasets:
raise ValueError('--do_train requires a train dataset' )
_UpperCAmelCase = raw_datasets['train']
if data_args.max_train_samples is not None:
_UpperCAmelCase = train_dataset.select(range(data_args.max_train_samples ) )
if training_args.do_eval:
if "validation" not in raw_datasets and "validation_matched" not in raw_datasets:
raise ValueError('--do_eval requires a validation dataset' )
_UpperCAmelCase = raw_datasets['validation']
if data_args.max_eval_samples is not None:
_UpperCAmelCase = eval_dataset.select(range(data_args.max_eval_samples ) )
if training_args.do_predict or data_args.test_file is not None:
if "test" not in raw_datasets and "test_matched" not in raw_datasets:
raise ValueError('--do_predict requires a test dataset' )
_UpperCAmelCase = raw_datasets['test']
if data_args.max_predict_samples is not None:
_UpperCAmelCase = predict_dataset.select(range(data_args.max_predict_samples ) )
# Log a few random samples from the training set:
if training_args.do_train:
for index in random.sample(range(len(_UpperCAmelCase ) ) , 3 ):
logger.info(F"Sample {index} of the training set: {train_dataset[index]}." )
# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
# predictions and label_ids field) and has to return a dictionary string to float.
def compute_metrics(_UpperCAmelCase : EvalPrediction ):
_UpperCAmelCase = p.predictions[0] if isinstance(p.predictions , _UpperCAmelCase ) else p.predictions
_UpperCAmelCase = np.argmax(_UpperCAmelCase , axis=1 )
return {"accuracy": (preds == p.label_ids).astype(np.floataa ).mean().item()}
# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
if data_args.pad_to_max_length:
_UpperCAmelCase = default_data_collator
elif training_args.fpaa:
_UpperCAmelCase = DataCollatorWithPadding(_UpperCAmelCase , pad_to_multiple_of=8 )
else:
_UpperCAmelCase = None
# Initialize our Trainer
_UpperCAmelCase = Trainer(
model=_UpperCAmelCase , args=_UpperCAmelCase , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , compute_metrics=_UpperCAmelCase , tokenizer=_UpperCAmelCase , data_collator=_UpperCAmelCase , )
# Training
if training_args.do_train:
_UpperCAmelCase = None
if training_args.resume_from_checkpoint is not None:
_UpperCAmelCase = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
_UpperCAmelCase = last_checkpoint
_UpperCAmelCase = trainer.train(resume_from_checkpoint=_UpperCAmelCase )
_UpperCAmelCase = train_result.metrics
_UpperCAmelCase = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(_UpperCAmelCase )
)
_UpperCAmelCase = min(_UpperCAmelCase , len(_UpperCAmelCase ) )
trainer.save_model() # Saves the tokenizer too for easy upload
trainer.log_metrics('train' , _UpperCAmelCase )
trainer.save_metrics('train' , _UpperCAmelCase )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info('*** Evaluate ***' )
_UpperCAmelCase = trainer.evaluate(eval_dataset=_UpperCAmelCase )
_UpperCAmelCase = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(_UpperCAmelCase )
_UpperCAmelCase = min(_UpperCAmelCase , len(_UpperCAmelCase ) )
trainer.log_metrics('eval' , _UpperCAmelCase )
trainer.save_metrics('eval' , _UpperCAmelCase )
if training_args.do_predict:
logger.info('*** Predict ***' )
# Removing the `label` columns because it contains -1 and Trainer won't like that.
_UpperCAmelCase = predict_dataset.remove_columns('label' )
_UpperCAmelCase = trainer.predict(_UpperCAmelCase , metric_key_prefix='predict' ).predictions
_UpperCAmelCase = np.argmax(_UpperCAmelCase , axis=1 )
_UpperCAmelCase = os.path.join(training_args.output_dir , 'predict_results_tabfact.txt' )
if trainer.is_world_process_zero():
with open(_UpperCAmelCase , 'w' ) as writer:
logger.info('***** Predict Results *****' )
writer.write('index\tprediction\n' )
for index, item in enumerate(_UpperCAmelCase ):
_UpperCAmelCase = label_list[item]
writer.write(F"{index}\t{item}\n" )
_UpperCAmelCase = {'finetuned_from': model_args.model_name_or_path, 'tasks': 'text-classification'}
if training_args.push_to_hub:
trainer.push_to_hub(**_UpperCAmelCase )
else:
trainer.create_model_card(**_UpperCAmelCase )
def A ( _UpperCAmelCase : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 339 | 1 |
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 __lowerCAmelCase ( A , unittest.TestCase ):
UpperCamelCase = UnCLIPImageVariationPipeline
UpperCamelCase = IMAGE_VARIATION_PARAMS - {'''height''', '''width''', '''guidance_scale'''}
UpperCamelCase = IMAGE_VARIATION_BATCH_PARAMS
UpperCamelCase = [
'''generator''',
'''return_dict''',
'''decoder_num_inference_steps''',
'''super_res_num_inference_steps''',
]
UpperCamelCase = False
@property
def _lowerCamelCase ( self : int) -> Optional[int]:
"""simple docstring"""
return 32
@property
def _lowerCamelCase ( self : str) -> Optional[Any]:
"""simple docstring"""
return 32
@property
def _lowerCamelCase ( self : str) -> int:
"""simple docstring"""
return self.time_input_dim
@property
def _lowerCamelCase ( self : List[Any]) -> Optional[Any]:
"""simple docstring"""
return self.time_input_dim * 4
@property
def _lowerCamelCase ( self : Optional[int]) -> int:
"""simple docstring"""
return 1_00
@property
def _lowerCamelCase ( self : Tuple) -> str:
"""simple docstring"""
_UpperCAmelCase = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')
return tokenizer
@property
def _lowerCamelCase ( self : Optional[Any]) -> Tuple:
"""simple docstring"""
torch.manual_seed(0)
_UpperCAmelCase = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=self.text_embedder_hidden_size , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=10_00 , )
return CLIPTextModelWithProjection(A)
@property
def _lowerCamelCase ( self : Optional[int]) -> List[str]:
"""simple docstring"""
torch.manual_seed(0)
_UpperCAmelCase = 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(A)
@property
def _lowerCamelCase ( self : List[str]) -> Optional[int]:
"""simple docstring"""
torch.manual_seed(0)
_UpperCAmelCase = {
'clip_embeddings_dim': self.text_embedder_hidden_size,
'time_embed_dim': self.time_embed_dim,
'cross_attention_dim': self.cross_attention_dim,
}
_UpperCAmelCase = UnCLIPTextProjModel(**A)
return model
@property
def _lowerCamelCase ( self : List[str]) -> Optional[Any]:
"""simple docstring"""
torch.manual_seed(0)
_UpperCAmelCase = {
'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 = UNetaDConditionModel(**A)
return model
@property
def _lowerCamelCase ( self : str) -> int:
"""simple docstring"""
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 _lowerCamelCase ( self : Tuple) -> Optional[int]:
"""simple docstring"""
torch.manual_seed(0)
_UpperCAmelCase = UNetaDModel(**self.dummy_super_res_kwargs)
return model
@property
def _lowerCamelCase ( self : str) -> int:
"""simple docstring"""
torch.manual_seed(1)
_UpperCAmelCase = UNetaDModel(**self.dummy_super_res_kwargs)
return model
def _lowerCamelCase ( self : str) -> str:
"""simple docstring"""
_UpperCAmelCase = self.dummy_decoder
_UpperCAmelCase = self.dummy_text_proj
_UpperCAmelCase = self.dummy_text_encoder
_UpperCAmelCase = self.dummy_tokenizer
_UpperCAmelCase = self.dummy_super_res_first
_UpperCAmelCase = self.dummy_super_res_last
_UpperCAmelCase = UnCLIPScheduler(
variance_type='learned_range' , prediction_type='epsilon' , num_train_timesteps=10_00 , )
_UpperCAmelCase = UnCLIPScheduler(
variance_type='fixed_small_log' , prediction_type='epsilon' , num_train_timesteps=10_00 , )
_UpperCAmelCase = CLIPImageProcessor(crop_size=32 , size=32)
_UpperCAmelCase = 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 _lowerCamelCase ( self : str , A : Dict , A : int=0 , A : Tuple=True) -> Union[str, Any]:
"""simple docstring"""
_UpperCAmelCase = floats_tensor((1, 3, 32, 32) , rng=random.Random(A)).to(A)
if str(A).startswith('mps'):
_UpperCAmelCase = torch.manual_seed(A)
else:
_UpperCAmelCase = torch.Generator(device=A).manual_seed(A)
if pil_image:
_UpperCAmelCase = input_image * 0.5 + 0.5
_UpperCAmelCase = input_image.clamp(0 , 1)
_UpperCAmelCase = input_image.cpu().permute(0 , 2 , 3 , 1).float().numpy()
_UpperCAmelCase = DiffusionPipeline.numpy_to_pil(A)[0]
return {
"image": input_image,
"generator": generator,
"decoder_num_inference_steps": 2,
"super_res_num_inference_steps": 2,
"output_type": "np",
}
def _lowerCamelCase ( self : str) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase = 'cpu'
_UpperCAmelCase = self.get_dummy_components()
_UpperCAmelCase = self.pipeline_class(**A)
_UpperCAmelCase = pipe.to(A)
pipe.set_progress_bar_config(disable=A)
_UpperCAmelCase = self.get_dummy_inputs(A , pil_image=A)
_UpperCAmelCase = pipe(**A)
_UpperCAmelCase = output.images
_UpperCAmelCase = self.get_dummy_inputs(A , pil_image=A)
_UpperCAmelCase = pipe(
**A , return_dict=A , )[0]
_UpperCAmelCase = image[0, -3:, -3:, -1]
_UpperCAmelCase = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
_UpperCAmelCase = np.array(
[
0.9_9_9_7,
0.0_0_0_2,
0.9_9_9_7,
0.9_9_9_7,
0.9_9_6_9,
0.0_0_2_3,
0.9_9_9_7,
0.9_9_6_9,
0.9_9_7_0,
])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1E-2
def _lowerCamelCase ( self : Optional[int]) -> int:
"""simple docstring"""
_UpperCAmelCase = 'cpu'
_UpperCAmelCase = self.get_dummy_components()
_UpperCAmelCase = self.pipeline_class(**A)
_UpperCAmelCase = pipe.to(A)
pipe.set_progress_bar_config(disable=A)
_UpperCAmelCase = self.get_dummy_inputs(A , pil_image=A)
_UpperCAmelCase = pipe(**A)
_UpperCAmelCase = output.images
_UpperCAmelCase = self.get_dummy_inputs(A , pil_image=A)
_UpperCAmelCase = pipe(
**A , return_dict=A , )[0]
_UpperCAmelCase = image[0, -3:, -3:, -1]
_UpperCAmelCase = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
_UpperCAmelCase = np.array([0.9_9_9_7, 0.0_0_0_3, 0.9_9_9_7, 0.9_9_9_7, 0.9_9_7_0, 0.0_0_2_4, 0.9_9_9_7, 0.9_9_7_1, 0.9_9_7_1])
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 _lowerCamelCase ( self : Union[str, Any]) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase = 'cpu'
_UpperCAmelCase = self.get_dummy_components()
_UpperCAmelCase = self.pipeline_class(**A)
_UpperCAmelCase = pipe.to(A)
pipe.set_progress_bar_config(disable=A)
_UpperCAmelCase = self.get_dummy_inputs(A , pil_image=A)
_UpperCAmelCase = [
pipeline_inputs['image'],
pipeline_inputs['image'],
]
_UpperCAmelCase = pipe(**A)
_UpperCAmelCase = output.images
_UpperCAmelCase = self.get_dummy_inputs(A , pil_image=A)
_UpperCAmelCase = [
tuple_pipeline_inputs['image'],
tuple_pipeline_inputs['image'],
]
_UpperCAmelCase = pipe(
**A , return_dict=A , )[0]
_UpperCAmelCase = image[0, -3:, -3:, -1]
_UpperCAmelCase = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (2, 64, 64, 3)
_UpperCAmelCase = np.array(
[
0.9_9_9_7,
0.9_9_8_9,
0.0_0_0_8,
0.0_0_2_1,
0.9_9_6_0,
0.0_0_1_8,
0.0_0_1_4,
0.0_0_0_2,
0.9_9_3_3,
])
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 _lowerCamelCase ( self : List[Any]) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase = torch.device('cpu')
class __lowerCAmelCase :
UpperCamelCase = 1
_UpperCAmelCase = self.get_dummy_components()
_UpperCAmelCase = self.pipeline_class(**A)
_UpperCAmelCase = pipe.to(A)
pipe.set_progress_bar_config(disable=A)
_UpperCAmelCase = torch.Generator(device=A).manual_seed(0)
_UpperCAmelCase = pipe.decoder.dtype
_UpperCAmelCase = 1
_UpperCAmelCase = (
batch_size,
pipe.decoder.config.in_channels,
pipe.decoder.config.sample_size,
pipe.decoder.config.sample_size,
)
_UpperCAmelCase = pipe.prepare_latents(
A , dtype=A , device=A , generator=A , latents=A , scheduler=DummyScheduler())
_UpperCAmelCase = (
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 = pipe.prepare_latents(
A , dtype=A , device=A , generator=A , latents=A , scheduler=DummyScheduler())
_UpperCAmelCase = self.get_dummy_inputs(A , pil_image=A)
_UpperCAmelCase = pipe(
**A , decoder_latents=A , super_res_latents=A).images
_UpperCAmelCase = self.get_dummy_inputs(A , pil_image=A)
# Don't pass image, instead pass embedding
_UpperCAmelCase = pipeline_inputs.pop('image')
_UpperCAmelCase = pipe.image_encoder(A).image_embeds
_UpperCAmelCase = pipe(
**A , decoder_latents=A , super_res_latents=A , image_embeddings=A , ).images
# make sure passing text embeddings manually is identical
assert np.abs(img_out_a - img_out_a).max() < 1E-4
@skip_mps
def _lowerCamelCase ( self : Optional[Any]) -> Any:
"""simple docstring"""
_UpperCAmelCase = torch_device == 'cpu'
# Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
_UpperCAmelCase = 1E-2
self._test_attention_slicing_forward_pass(
test_max_difference=A , expected_max_diff=A)
@skip_mps
def _lowerCamelCase ( self : Any) -> Union[str, Any]:
"""simple docstring"""
_UpperCAmelCase = torch_device == 'cpu'
_UpperCAmelCase = True
_UpperCAmelCase = [
'decoder_num_inference_steps',
'super_res_num_inference_steps',
]
self._test_inference_batch_single_identical(
test_max_difference=A , relax_max_difference=A , additional_params_copy_to_batched_inputs=A , )
def _lowerCamelCase ( self : str) -> Tuple:
"""simple docstring"""
_UpperCAmelCase = [
'decoder_num_inference_steps',
'super_res_num_inference_steps',
]
if torch_device == "mps":
# TODO: MPS errors with larger batch sizes
_UpperCAmelCase = [2, 3]
self._test_inference_batch_consistent(
batch_sizes=A , additional_params_copy_to_batched_inputs=A , )
else:
self._test_inference_batch_consistent(
additional_params_copy_to_batched_inputs=A)
@skip_mps
def _lowerCamelCase ( self : Tuple) -> Optional[Any]:
"""simple docstring"""
return super().test_dict_tuple_outputs_equivalent()
@skip_mps
def _lowerCamelCase ( self : Dict) -> List[str]:
"""simple docstring"""
return super().test_save_load_local()
@skip_mps
def _lowerCamelCase ( self : Tuple) -> Optional[Any]:
"""simple docstring"""
return super().test_save_load_optional_components()
@slow
@require_torch_gpu
class __lowerCAmelCase ( unittest.TestCase ):
def _lowerCamelCase ( self : Any) -> Tuple:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _lowerCamelCase ( self : Tuple) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png')
_UpperCAmelCase = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/unclip/karlo_v1_alpha_cat_variation_fp16.npy')
_UpperCAmelCase = UnCLIPImageVariationPipeline.from_pretrained(
'kakaobrain/karlo-v1-alpha-image-variations' , torch_dtype=torch.floataa)
_UpperCAmelCase = pipeline.to(A)
pipeline.set_progress_bar_config(disable=A)
_UpperCAmelCase = torch.Generator(device='cpu').manual_seed(0)
_UpperCAmelCase = pipeline(
A , generator=A , output_type='np' , )
_UpperCAmelCase = output.images[0]
assert image.shape == (2_56, 2_56, 3)
assert_mean_pixel_difference(A , A , 15)
| 339 |
# This code is adapted from OpenAI's release
# https://github.com/openai/human-eval/blob/master/human_eval/execution.py
import contextlib
import faulthandler
import io
import multiprocessing
import os
import platform
import signal
import tempfile
def A ( _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Dict , _UpperCAmelCase : Dict , _UpperCAmelCase : Dict ) -> Any:
'''simple docstring'''
_UpperCAmelCase = multiprocessing.Manager()
_UpperCAmelCase = manager.list()
_UpperCAmelCase = multiprocessing.Process(target=_UpperCAmelCase , args=(check_program, result, timeout) )
p.start()
p.join(timeout=timeout + 1 )
if p.is_alive():
p.kill()
if not result:
result.append('timed out' )
return {
"task_id": task_id,
"passed": result[0] == "passed",
"result": result[0],
"completion_id": completion_id,
}
def A ( _UpperCAmelCase : str , _UpperCAmelCase : List[str] , _UpperCAmelCase : Dict ) -> Optional[int]:
'''simple docstring'''
with create_tempdir():
# These system calls are needed when cleaning up tempdir.
import os
import shutil
_UpperCAmelCase = shutil.rmtree
_UpperCAmelCase = os.rmdir
_UpperCAmelCase = os.chdir
# Disable functionalities that can make destructive changes to the test.
reliability_guard()
# Run program.
try:
_UpperCAmelCase = {}
with swallow_io():
with time_limit(_UpperCAmelCase ):
exec(_UpperCAmelCase , _UpperCAmelCase )
result.append('passed' )
except TimeoutException:
result.append('timed out' )
except BaseException as e:
result.append(F"failed: {e}" )
# Needed for cleaning up.
_UpperCAmelCase = rmtree
_UpperCAmelCase = rmdir
_UpperCAmelCase = chdir
@contextlib.contextmanager
def A ( _UpperCAmelCase : Union[str, Any] ) -> Any:
'''simple docstring'''
def signal_handler(_UpperCAmelCase : List[Any] , _UpperCAmelCase : Dict ):
raise TimeoutException('Timed out!' )
signal.setitimer(signal.ITIMER_REAL , _UpperCAmelCase )
signal.signal(signal.SIGALRM , _UpperCAmelCase )
try:
yield
finally:
signal.setitimer(signal.ITIMER_REAL , 0 )
@contextlib.contextmanager
def A ( ) -> Optional[int]:
'''simple docstring'''
_UpperCAmelCase = WriteOnlyStringIO()
with contextlib.redirect_stdout(_UpperCAmelCase ):
with contextlib.redirect_stderr(_UpperCAmelCase ):
with redirect_stdin(_UpperCAmelCase ):
yield
@contextlib.contextmanager
def A ( ) -> Any:
'''simple docstring'''
with tempfile.TemporaryDirectory() as dirname:
with chdir(_UpperCAmelCase ):
yield dirname
class __lowerCAmelCase ( A ):
pass
class __lowerCAmelCase ( io.StringIO ):
def _lowerCamelCase ( self : Tuple , *A : str , **A : Any) -> Any:
"""simple docstring"""
raise OSError
def _lowerCamelCase ( self : List[str] , *A : Optional[Any] , **A : Optional[Any]) -> Optional[int]:
"""simple docstring"""
raise OSError
def _lowerCamelCase ( self : str , *A : List[str] , **A : List[Any]) -> Union[str, Any]:
"""simple docstring"""
raise OSError
def _lowerCamelCase ( self : Union[str, Any] , *A : Optional[Any] , **A : List[str]) -> Optional[int]:
"""simple docstring"""
return False
class __lowerCAmelCase ( contextlib._RedirectStream ): # type: ignore
UpperCamelCase = '''stdin'''
@contextlib.contextmanager
def A ( _UpperCAmelCase : List[Any] ) -> Dict:
'''simple docstring'''
if root == ".":
yield
return
_UpperCAmelCase = os.getcwd()
os.chdir(_UpperCAmelCase )
try:
yield
except BaseException as exc:
raise exc
finally:
os.chdir(_UpperCAmelCase )
def A ( _UpperCAmelCase : List[str]=None ) -> Any:
'''simple docstring'''
if maximum_memory_bytes is not None:
import resource
resource.setrlimit(resource.RLIMIT_AS , (maximum_memory_bytes, maximum_memory_bytes) )
resource.setrlimit(resource.RLIMIT_DATA , (maximum_memory_bytes, maximum_memory_bytes) )
if not platform.uname().system == "Darwin":
resource.setrlimit(resource.RLIMIT_STACK , (maximum_memory_bytes, maximum_memory_bytes) )
faulthandler.disable()
import builtins
_UpperCAmelCase = None
_UpperCAmelCase = None
import os
_UpperCAmelCase = '1'
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
import shutil
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
import subprocess
_UpperCAmelCase = None # type: ignore
_UpperCAmelCase = None
import sys
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
| 339 | 1 |
def A ( _UpperCAmelCase : float , _UpperCAmelCase : float ) -> float:
'''simple docstring'''
return price * (1 + tax_rate)
if __name__ == "__main__":
print(f"""{price_plus_tax(100, 0.25) = }""")
print(f"""{price_plus_tax(125.50, 0.05) = }""")
| 339 |
import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock
import torch
from ..state import AcceleratorState, PartialState
from ..utils import (
gather,
is_bnb_available,
is_comet_ml_available,
is_datasets_available,
is_deepspeed_available,
is_mps_available,
is_safetensors_available,
is_tensorboard_available,
is_torch_version,
is_tpu_available,
is_transformers_available,
is_wandb_available,
is_xpu_available,
)
def A ( _UpperCAmelCase : Tuple , _UpperCAmelCase : Union[str, Any]=False ) -> str:
'''simple docstring'''
try:
_UpperCAmelCase = os.environ[key]
except KeyError:
# KEY isn't set, default to `default`.
_UpperCAmelCase = default
else:
# KEY is set, convert it to True or False.
try:
_UpperCAmelCase = strtobool(_UpperCAmelCase )
except ValueError:
# More values are supported, but let's keep the message simple.
raise ValueError(F"If set, {key} must be yes or no." )
return _value
UpperCAmelCase__ = parse_flag_from_env("RUN_SLOW", default=False)
def A ( _UpperCAmelCase : List[str] ) -> List[str]:
'''simple docstring'''
return unittest.skip('Test was skipped' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Dict ) -> str:
'''simple docstring'''
return unittest.skipUnless(_run_slow_tests , 'test is slow' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Any ) -> str:
'''simple docstring'''
return unittest.skipUnless(not torch.cuda.is_available() , 'test requires only a CPU' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Dict ) -> Dict:
'''simple docstring'''
return unittest.skipUnless(torch.cuda.is_available() , 'test requires a GPU' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Optional[Any] ) -> List[Any]:
'''simple docstring'''
return unittest.skipUnless(is_xpu_available() , 'test requires a XPU' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Optional[int] ) -> List[str]:
'''simple docstring'''
return unittest.skipUnless(is_mps_available() , 'test requires a `mps` backend support in `torch`' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
return unittest.skipUnless(
is_transformers_available() and is_datasets_available() , 'test requires the Hugging Face suite' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : str ) -> str:
'''simple docstring'''
return unittest.skipUnless(is_bnb_available() , 'test requires the bitsandbytes library' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
return unittest.skipUnless(is_tpu_available() , 'test requires TPU' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Optional[Any] ) -> str:
'''simple docstring'''
return unittest.skipUnless(torch.cuda.device_count() == 1 , 'test requires a GPU' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Tuple ) -> int:
'''simple docstring'''
return unittest.skipUnless(torch.xpu.device_count() == 1 , 'test requires a XPU' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Any ) -> Optional[int]:
'''simple docstring'''
return unittest.skipUnless(torch.cuda.device_count() > 1 , 'test requires multiple GPUs' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Tuple ) -> Any:
'''simple docstring'''
return unittest.skipUnless(torch.xpu.device_count() > 1 , 'test requires multiple XPUs' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Any ) -> Optional[int]:
'''simple docstring'''
return unittest.skipUnless(is_safetensors_available() , 'test requires safetensors' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : List[Any] ) -> Dict:
'''simple docstring'''
return unittest.skipUnless(is_deepspeed_available() , 'test requires DeepSpeed' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Optional[int] ) -> str:
'''simple docstring'''
return unittest.skipUnless(is_torch_version('>=' , '1.12.0' ) , 'test requires torch version >= 1.12.0' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Any=None , _UpperCAmelCase : List[Any]=None ) -> Dict:
'''simple docstring'''
if test_case is None:
return partial(_UpperCAmelCase , version=_UpperCAmelCase )
return unittest.skipUnless(is_torch_version('>=' , _UpperCAmelCase ) , F"test requires torch version >= {version}" )(_UpperCAmelCase )
def A ( _UpperCAmelCase : List[str] ) -> int:
'''simple docstring'''
return unittest.skipUnless(is_tensorboard_available() , 'test requires Tensorboard' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Union[str, Any] ) -> Union[str, Any]:
'''simple docstring'''
return unittest.skipUnless(is_wandb_available() , 'test requires wandb' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : List[str] ) -> Optional[int]:
'''simple docstring'''
return unittest.skipUnless(is_comet_ml_available() , 'test requires comet_ml' )(_UpperCAmelCase )
UpperCAmelCase__ = (
any([is_wandb_available(), is_tensorboard_available()]) and not is_comet_ml_available()
)
def A ( _UpperCAmelCase : List[str] ) -> Any:
'''simple docstring'''
return unittest.skipUnless(
_atleast_one_tracker_available , 'test requires at least one tracker to be available and for `comet_ml` to not be installed' , )(_UpperCAmelCase )
class __lowerCAmelCase ( unittest.TestCase ):
UpperCamelCase = True
@classmethod
def _lowerCamelCase ( cls : List[Any]) -> Tuple:
"""simple docstring"""
_UpperCAmelCase = tempfile.mkdtemp()
@classmethod
def _lowerCamelCase ( cls : Union[str, Any]) -> str:
"""simple docstring"""
if os.path.exists(cls.tmpdir):
shutil.rmtree(cls.tmpdir)
def _lowerCamelCase ( self : List[str]) -> List[Any]:
"""simple docstring"""
if self.clear_on_setup:
for path in Path(self.tmpdir).glob('**/*'):
if path.is_file():
path.unlink()
elif path.is_dir():
shutil.rmtree(A)
class __lowerCAmelCase ( unittest.TestCase ):
def _lowerCamelCase ( self : Dict) -> Tuple:
"""simple docstring"""
super().tearDown()
# Reset the state of the AcceleratorState singleton.
AcceleratorState._reset_state()
PartialState._reset_state()
class __lowerCAmelCase ( unittest.TestCase ):
def _lowerCamelCase ( self : Optional[int] , A : Union[mock.Mock, List[mock.Mock]]) -> Tuple:
"""simple docstring"""
_UpperCAmelCase = mocks if isinstance(A , (tuple, list)) else [mocks]
for m in self.mocks:
m.start()
self.addCleanup(m.stop)
def A ( _UpperCAmelCase : List[Any] ) -> int:
'''simple docstring'''
_UpperCAmelCase = AcceleratorState()
_UpperCAmelCase = tensor[None].clone().to(state.device )
_UpperCAmelCase = gather(_UpperCAmelCase ).cpu()
_UpperCAmelCase = tensor[0].cpu()
for i in range(tensors.shape[0] ):
if not torch.equal(tensors[i] , _UpperCAmelCase ):
return False
return True
class __lowerCAmelCase :
def __init__( self : Optional[Any] , A : Union[str, Any] , A : Optional[int] , A : str) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase = returncode
_UpperCAmelCase = stdout
_UpperCAmelCase = stderr
async def A ( _UpperCAmelCase : str , _UpperCAmelCase : Optional[int] ) -> Optional[Any]:
'''simple docstring'''
while True:
_UpperCAmelCase = await stream.readline()
if line:
callback(_UpperCAmelCase )
else:
break
async def A ( _UpperCAmelCase : Optional[int] , _UpperCAmelCase : List[str]=None , _UpperCAmelCase : str=None , _UpperCAmelCase : str=None , _UpperCAmelCase : Dict=False , _UpperCAmelCase : Union[str, Any]=False ) -> _RunOutput:
'''simple docstring'''
if echo:
print('\nRunning: ' , ' '.join(_UpperCAmelCase ) )
_UpperCAmelCase = await asyncio.create_subprocess_exec(
cmd[0] , *cmd[1:] , stdin=_UpperCAmelCase , stdout=asyncio.subprocess.PIPE , stderr=asyncio.subprocess.PIPE , env=_UpperCAmelCase , )
# note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe
# https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait
#
# If it starts hanging, will need to switch to the following code. The problem is that no data
# will be seen until it's done and if it hangs for example there will be no debug info.
# out, err = await p.communicate()
# return _RunOutput(p.returncode, out, err)
_UpperCAmelCase = []
_UpperCAmelCase = []
def tee(_UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : List[Any] , _UpperCAmelCase : str , _UpperCAmelCase : str="" ):
_UpperCAmelCase = line.decode('utf-8' ).rstrip()
sink.append(_UpperCAmelCase )
if not quiet:
print(_UpperCAmelCase , _UpperCAmelCase , file=_UpperCAmelCase )
# XXX: the timeout doesn't seem to make any difference here
await asyncio.wait(
[
asyncio.create_task(_read_stream(p.stdout , lambda _UpperCAmelCase : tee(_UpperCAmelCase , _UpperCAmelCase , sys.stdout , label='stdout:' ) ) ),
asyncio.create_task(_read_stream(p.stderr , lambda _UpperCAmelCase : tee(_UpperCAmelCase , _UpperCAmelCase , sys.stderr , label='stderr:' ) ) ),
] , timeout=_UpperCAmelCase , )
return _RunOutput(await p.wait() , _UpperCAmelCase , _UpperCAmelCase )
def A ( _UpperCAmelCase : str , _UpperCAmelCase : Dict=None , _UpperCAmelCase : str=None , _UpperCAmelCase : str=180 , _UpperCAmelCase : List[Any]=False , _UpperCAmelCase : List[Any]=True ) -> _RunOutput:
'''simple docstring'''
_UpperCAmelCase = asyncio.get_event_loop()
_UpperCAmelCase = loop.run_until_complete(
_stream_subprocess(_UpperCAmelCase , env=_UpperCAmelCase , stdin=_UpperCAmelCase , timeout=_UpperCAmelCase , quiet=_UpperCAmelCase , echo=_UpperCAmelCase ) )
_UpperCAmelCase = ' '.join(_UpperCAmelCase )
if result.returncode > 0:
_UpperCAmelCase = '\n'.join(result.stderr )
raise RuntimeError(
F"'{cmd_str}' failed with returncode {result.returncode}\n\n"
F"The combined stderr from workers follows:\n{stderr}" )
return result
class __lowerCAmelCase ( A ):
pass
def A ( _UpperCAmelCase : List[str] , _UpperCAmelCase : str=False ) -> Tuple:
'''simple docstring'''
try:
_UpperCAmelCase = subprocess.check_output(_UpperCAmelCase , stderr=subprocess.STDOUT )
if return_stdout:
if hasattr(_UpperCAmelCase , 'decode' ):
_UpperCAmelCase = output.decode('utf-8' )
return output
except subprocess.CalledProcessError as e:
raise SubprocessCallException(
F"Command `{' '.join(_UpperCAmelCase )}` failed with the following error:\n\n{e.output.decode()}" ) from e
| 339 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCAmelCase__ = {
"configuration_chinese_clip": [
"CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"ChineseCLIPConfig",
"ChineseCLIPOnnxConfig",
"ChineseCLIPTextConfig",
"ChineseCLIPVisionConfig",
],
"processing_chinese_clip": ["ChineseCLIPProcessor"],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ = ["ChineseCLIPFeatureExtractor"]
UpperCAmelCase__ = ["ChineseCLIPImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ = [
"CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
"ChineseCLIPModel",
"ChineseCLIPPreTrainedModel",
"ChineseCLIPTextModel",
"ChineseCLIPVisionModel",
]
if TYPE_CHECKING:
from .configuration_chinese_clip import (
CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
ChineseCLIPConfig,
ChineseCLIPOnnxConfig,
ChineseCLIPTextConfig,
ChineseCLIPVisionConfig,
)
from .processing_chinese_clip import ChineseCLIPProcessor
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_chinese_clip import ChineseCLIPFeatureExtractor, ChineseCLIPImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_chinese_clip import (
CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
ChineseCLIPModel,
ChineseCLIPPreTrainedModel,
ChineseCLIPTextModel,
ChineseCLIPVisionModel,
)
else:
import sys
UpperCAmelCase__ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 339 |
from __future__ import annotations
UpperCAmelCase__ = list[list[int]]
# assigning initial values to the grid
UpperCAmelCase__ = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5],
[0, 5, 0, 0, 9, 0, 6, 0, 0],
[1, 3, 0, 0, 0, 0, 2, 5, 0],
[0, 0, 0, 0, 0, 0, 0, 7, 4],
[0, 0, 5, 2, 0, 6, 3, 0, 0],
]
# a grid with no solution
UpperCAmelCase__ = [
[5, 0, 6, 5, 0, 8, 4, 0, 3],
[5, 2, 0, 0, 0, 0, 0, 0, 2],
[1, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5],
[0, 5, 0, 0, 9, 0, 6, 0, 0],
[1, 3, 0, 0, 0, 0, 2, 5, 0],
[0, 0, 0, 0, 0, 0, 0, 7, 4],
[0, 0, 5, 2, 0, 6, 3, 0, 0],
]
def A ( _UpperCAmelCase : Matrix , _UpperCAmelCase : int , _UpperCAmelCase : int , _UpperCAmelCase : int ) -> bool:
'''simple docstring'''
for i in range(9 ):
if grid[row][i] == n or grid[i][column] == n:
return False
for i in range(3 ):
for j in range(3 ):
if grid[(row - row % 3) + i][(column - column % 3) + j] == n:
return False
return True
def A ( _UpperCAmelCase : Matrix ) -> tuple[int, int] | None:
'''simple docstring'''
for i in range(9 ):
for j in range(9 ):
if grid[i][j] == 0:
return i, j
return None
def A ( _UpperCAmelCase : Matrix ) -> Matrix | None:
'''simple docstring'''
if location := find_empty_location(_UpperCAmelCase ):
_UpperCAmelCase , _UpperCAmelCase = location
else:
# If the location is ``None``, then the grid is solved.
return grid
for digit in range(1 , 10 ):
if is_safe(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
_UpperCAmelCase = digit
if sudoku(_UpperCAmelCase ) is not None:
return grid
_UpperCAmelCase = 0
return None
def A ( _UpperCAmelCase : Matrix ) -> None:
'''simple docstring'''
for row in grid:
for cell in row:
print(_UpperCAmelCase , end=' ' )
print()
if __name__ == "__main__":
# make a copy of grid so that you can compare with the unmodified grid
for example_grid in (initial_grid, no_solution):
print("\nExample grid:\n" + "=" * 20)
print_solution(example_grid)
print("\nExample grid solution:")
UpperCAmelCase__ = sudoku(example_grid)
if solution is not None:
print_solution(solution)
else:
print("Cannot find a solution.")
| 339 | 1 |
import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
UpperCAmelCase__ = {
"tiny.en": "https://openaipublic.azureedge.net/main/whisper/models/d3dd57d32accea0b295c96e26691aa14d8822fac7d9d27d5dc00b4ca2826dd03/tiny.en.pt",
"tiny": "https://openaipublic.azureedge.net/main/whisper/models/65147644a518d12f04e32d6f3b26facc3f8dd46e5390956a9424a650c0ce22b9/tiny.pt",
"base.en": "https://openaipublic.azureedge.net/main/whisper/models/25a8566e1d0c1e2231d1c762132cd20e0f96a85d16145c3a00adf5d1ac670ead/base.en.pt",
"base": "https://openaipublic.azureedge.net/main/whisper/models/ed3a0b6b1c0edf879ad9b11b1af5a0e6ab5db9205f891f668f8b0e6c6326e34e/base.pt",
"small.en": "https://openaipublic.azureedge.net/main/whisper/models/f953ad0fd29cacd07d5a9eda5624af0f6bcf2258be67c92b79389873d91e0872/small.en.pt",
"small": "https://openaipublic.azureedge.net/main/whisper/models/9ecf779972d90ba49c06d968637d720dd632c55bbf19d441fb42bf17a411e794/small.pt",
"medium.en": "https://openaipublic.azureedge.net/main/whisper/models/d7440d1dc186f76616474e0ff0b3b6b879abc9d1a4926b7adfa41db2d497ab4f/medium.en.pt",
"medium": "https://openaipublic.azureedge.net/main/whisper/models/345ae4da62f9b3d59415adc60127b97c714f32e89e936602e85993674d08dcb1/medium.pt",
"large": "https://openaipublic.azureedge.net/main/whisper/models/e4b87e7e0bf463eb8e6956e646f1e277e901512310def2c24bf0e11bd3c28e9a/large.pt",
"large-v2": "https://openaipublic.azureedge.net/main/whisper/models/81f7c96c852ee8fc832187b0132e569d6c3065a3252ed18e56effd0b6a73e524/large-v2.pt",
}
def A ( _UpperCAmelCase : Optional[int] ) -> str:
'''simple docstring'''
_UpperCAmelCase = ['layers', 'blocks']
for k in ignore_keys:
state_dict.pop(_UpperCAmelCase , _UpperCAmelCase )
UpperCAmelCase__ = {
"blocks": "layers",
"mlp.0": "fc1",
"mlp.2": "fc2",
"mlp_ln": "final_layer_norm",
".attn.query": ".self_attn.q_proj",
".attn.key": ".self_attn.k_proj",
".attn.value": ".self_attn.v_proj",
".attn_ln": ".self_attn_layer_norm",
".attn.out": ".self_attn.out_proj",
".cross_attn.query": ".encoder_attn.q_proj",
".cross_attn.key": ".encoder_attn.k_proj",
".cross_attn.value": ".encoder_attn.v_proj",
".cross_attn_ln": ".encoder_attn_layer_norm",
".cross_attn.out": ".encoder_attn.out_proj",
"decoder.ln.": "decoder.layer_norm.",
"encoder.ln.": "encoder.layer_norm.",
"token_embedding": "embed_tokens",
"encoder.positional_embedding": "encoder.embed_positions.weight",
"decoder.positional_embedding": "decoder.embed_positions.weight",
"ln_post": "layer_norm",
}
def A ( _UpperCAmelCase : Dict ) -> Optional[int]:
'''simple docstring'''
_UpperCAmelCase = list(s_dict.keys() )
for key in keys:
_UpperCAmelCase = key
for k, v in WHISPER_MAPPING.items():
if k in key:
_UpperCAmelCase = new_key.replace(_UpperCAmelCase , _UpperCAmelCase )
print(F"{key} -> {new_key}" )
_UpperCAmelCase = s_dict.pop(_UpperCAmelCase )
return s_dict
def A ( _UpperCAmelCase : List[Any] ) -> Union[str, Any]:
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase = emb.weight.shape
_UpperCAmelCase = nn.Linear(_UpperCAmelCase , _UpperCAmelCase , bias=_UpperCAmelCase )
_UpperCAmelCase = emb.weight.data
return lin_layer
def A ( _UpperCAmelCase : str , _UpperCAmelCase : str ) -> bytes:
'''simple docstring'''
os.makedirs(_UpperCAmelCase , exist_ok=_UpperCAmelCase )
_UpperCAmelCase = os.path.basename(_UpperCAmelCase )
_UpperCAmelCase = url.split('/' )[-2]
_UpperCAmelCase = os.path.join(_UpperCAmelCase , _UpperCAmelCase )
if os.path.exists(_UpperCAmelCase ) and not os.path.isfile(_UpperCAmelCase ):
raise RuntimeError(F"{download_target} exists and is not a regular file" )
if os.path.isfile(_UpperCAmelCase ):
_UpperCAmelCase = open(_UpperCAmelCase , 'rb' ).read()
if hashlib.shaaaa(_UpperCAmelCase ).hexdigest() == expected_shaaaa:
return model_bytes
else:
warnings.warn(F"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file" )
with urllib.request.urlopen(_UpperCAmelCase ) as source, open(_UpperCAmelCase , 'wb' ) as output:
with tqdm(
total=int(source.info().get('Content-Length' ) ) , ncols=80 , unit='iB' , unit_scale=_UpperCAmelCase , unit_divisor=1_024 ) as loop:
while True:
_UpperCAmelCase = source.read(8_192 )
if not buffer:
break
output.write(_UpperCAmelCase )
loop.update(len(_UpperCAmelCase ) )
_UpperCAmelCase = open(_UpperCAmelCase , 'rb' ).read()
if hashlib.shaaaa(_UpperCAmelCase ).hexdigest() != expected_shaaaa:
raise RuntimeError(
'Model has been downloaded but the SHA256 checksum does not not match. Please retry loading the model.' )
return model_bytes
def A ( _UpperCAmelCase : List[Any] , _UpperCAmelCase : Any ) -> Optional[int]:
'''simple docstring'''
if ".pt" not in checkpoint_path:
_UpperCAmelCase = _download(_MODELS[checkpoint_path] )
else:
_UpperCAmelCase = torch.load(_UpperCAmelCase , map_location='cpu' )
_UpperCAmelCase = original_checkpoint['dims']
_UpperCAmelCase = original_checkpoint['model_state_dict']
_UpperCAmelCase = state_dict['decoder.token_embedding.weight']
remove_ignore_keys_(_UpperCAmelCase )
rename_keys(_UpperCAmelCase )
_UpperCAmelCase = True
_UpperCAmelCase = state_dict['decoder.layers.0.fc1.weight'].shape[0]
_UpperCAmelCase = WhisperConfig(
vocab_size=dimensions['n_vocab'] , encoder_ffn_dim=_UpperCAmelCase , decoder_ffn_dim=_UpperCAmelCase , num_mel_bins=dimensions['n_mels'] , d_model=dimensions['n_audio_state'] , max_target_positions=dimensions['n_text_ctx'] , encoder_layers=dimensions['n_audio_layer'] , encoder_attention_heads=dimensions['n_audio_head'] , decoder_layers=dimensions['n_text_layer'] , decoder_attention_heads=dimensions['n_text_state'] , max_source_positions=dimensions['n_audio_ctx'] , )
_UpperCAmelCase = WhisperForConditionalGeneration(_UpperCAmelCase )
_UpperCAmelCase , _UpperCAmelCase = model.model.load_state_dict(_UpperCAmelCase , strict=_UpperCAmelCase )
if len(_UpperCAmelCase ) > 0 and not set(_UpperCAmelCase ) <= {
"encoder.embed_positions.weights",
"decoder.embed_positions.weights",
}:
raise ValueError(
'Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights` are allowed to be missing,'
F" but all the following weights are missing {missing}" )
if tie_embeds:
_UpperCAmelCase = make_linear_from_emb(model.model.decoder.embed_tokens )
else:
_UpperCAmelCase = proj_out_weights
model.save_pretrained(_UpperCAmelCase )
if __name__ == "__main__":
UpperCAmelCase__ = argparse.ArgumentParser()
# # Required parameters
parser.add_argument("--checkpoint_path", type=str, help="Patht to the downloaded checkpoints")
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
UpperCAmelCase__ = parser.parse_args()
convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)
| 339 |
import numpy as np
from nltk.translate import meteor_score
import datasets
from datasets.config import importlib_metadata, version
UpperCAmelCase__ = version.parse(importlib_metadata.version("nltk"))
if NLTK_VERSION >= version.Version("3.6.4"):
from nltk import word_tokenize
UpperCAmelCase__ = "\\n@inproceedings{banarjee2005,\n title = {{METEOR}: An Automatic Metric for {MT} Evaluation with Improved Correlation with Human Judgments},\n author = {Banerjee, Satanjeev and Lavie, Alon},\n booktitle = {Proceedings of the {ACL} Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization},\n month = jun,\n year = {2005},\n address = {Ann Arbor, Michigan},\n publisher = {Association for Computational Linguistics},\n url = {https://www.aclweb.org/anthology/W05-0909},\n pages = {65--72},\n}\n"
UpperCAmelCase__ = "\\nMETEOR, an automatic metric for machine translation evaluation\nthat is based on a generalized concept of unigram matching between the\nmachine-produced translation and human-produced reference translations.\nUnigrams can be matched based on their surface forms, stemmed forms,\nand meanings; furthermore, METEOR can be easily extended to include more\nadvanced matching strategies. Once all generalized unigram matches\nbetween the two strings have been found, METEOR computes a score for\nthis matching using a combination of unigram-precision, unigram-recall, and\na measure of fragmentation that is designed to directly capture how\nwell-ordered the matched words in the machine translation are in relation\nto the reference.\n\nMETEOR gets an R correlation value of 0.347 with human evaluation on the Arabic\ndata and 0.331 on the Chinese data. This is shown to be an improvement on\nusing simply unigram-precision, unigram-recall and their harmonic F1\ncombination.\n"
UpperCAmelCase__ = "\nComputes METEOR score of translated segments against one or more references.\nArgs:\n predictions: list of predictions to score. Each prediction\n should be a string with tokens separated by spaces.\n references: list of reference for each prediction. Each\n reference should be a string with tokens separated by spaces.\n alpha: Parameter for controlling relative weights of precision and recall. default: 0.9\n beta: Parameter for controlling shape of penalty as a function of fragmentation. default: 3\n gamma: Relative weight assigned to fragmentation penalty. default: 0.5\nReturns:\n 'meteor': meteor score.\nExamples:\n\n >>> meteor = datasets.load_metric('meteor')\n >>> predictions = [\"It is a guide to action which ensures that the military always obeys the commands of the party\"]\n >>> references = [\"It is a guide to action that ensures that the military will forever heed Party commands\"]\n >>> results = meteor.compute(predictions=predictions, references=references)\n >>> print(round(results[\"meteor\"], 4))\n 0.6944\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __lowerCAmelCase ( datasets.Metric ):
def _lowerCamelCase ( self : List[Any]) -> List[Any]:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'predictions': datasets.Value('string' , id='sequence'),
'references': datasets.Value('string' , id='sequence'),
}) , codebase_urls=['https://github.com/nltk/nltk/blob/develop/nltk/translate/meteor_score.py'] , reference_urls=[
'https://www.nltk.org/api/nltk.translate.html#module-nltk.translate.meteor_score',
'https://en.wikipedia.org/wiki/METEOR',
] , )
def _lowerCamelCase ( self : Optional[Any] , A : List[str]) -> List[Any]:
"""simple docstring"""
import nltk
nltk.download('wordnet')
if NLTK_VERSION >= version.Version('3.6.5'):
nltk.download('punkt')
if NLTK_VERSION >= version.Version('3.6.6'):
nltk.download('omw-1.4')
def _lowerCamelCase ( self : Optional[Any] , A : Tuple , A : Optional[int] , A : List[Any]=0.9 , A : Optional[Any]=3 , A : Optional[int]=0.5) -> Any:
"""simple docstring"""
if NLTK_VERSION >= version.Version('3.6.5'):
_UpperCAmelCase = [
meteor_score.single_meteor_score(
word_tokenize(A) , word_tokenize(A) , alpha=A , beta=A , gamma=A)
for ref, pred in zip(A , A)
]
else:
_UpperCAmelCase = [
meteor_score.single_meteor_score(A , A , alpha=A , beta=A , gamma=A)
for ref, pred in zip(A , A)
]
return {"meteor": np.mean(A)}
| 339 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
UpperCAmelCase__ = {"tokenization_herbert": ["HerbertTokenizer"]}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ = ["HerbertTokenizerFast"]
if TYPE_CHECKING:
from .tokenization_herbert import HerbertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_herbert_fast import HerbertTokenizerFast
else:
import sys
UpperCAmelCase__ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 339 |
import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
UpperCAmelCase__ = {
"tiny.en": "https://openaipublic.azureedge.net/main/whisper/models/d3dd57d32accea0b295c96e26691aa14d8822fac7d9d27d5dc00b4ca2826dd03/tiny.en.pt",
"tiny": "https://openaipublic.azureedge.net/main/whisper/models/65147644a518d12f04e32d6f3b26facc3f8dd46e5390956a9424a650c0ce22b9/tiny.pt",
"base.en": "https://openaipublic.azureedge.net/main/whisper/models/25a8566e1d0c1e2231d1c762132cd20e0f96a85d16145c3a00adf5d1ac670ead/base.en.pt",
"base": "https://openaipublic.azureedge.net/main/whisper/models/ed3a0b6b1c0edf879ad9b11b1af5a0e6ab5db9205f891f668f8b0e6c6326e34e/base.pt",
"small.en": "https://openaipublic.azureedge.net/main/whisper/models/f953ad0fd29cacd07d5a9eda5624af0f6bcf2258be67c92b79389873d91e0872/small.en.pt",
"small": "https://openaipublic.azureedge.net/main/whisper/models/9ecf779972d90ba49c06d968637d720dd632c55bbf19d441fb42bf17a411e794/small.pt",
"medium.en": "https://openaipublic.azureedge.net/main/whisper/models/d7440d1dc186f76616474e0ff0b3b6b879abc9d1a4926b7adfa41db2d497ab4f/medium.en.pt",
"medium": "https://openaipublic.azureedge.net/main/whisper/models/345ae4da62f9b3d59415adc60127b97c714f32e89e936602e85993674d08dcb1/medium.pt",
"large": "https://openaipublic.azureedge.net/main/whisper/models/e4b87e7e0bf463eb8e6956e646f1e277e901512310def2c24bf0e11bd3c28e9a/large.pt",
"large-v2": "https://openaipublic.azureedge.net/main/whisper/models/81f7c96c852ee8fc832187b0132e569d6c3065a3252ed18e56effd0b6a73e524/large-v2.pt",
}
def A ( _UpperCAmelCase : Optional[int] ) -> str:
'''simple docstring'''
_UpperCAmelCase = ['layers', 'blocks']
for k in ignore_keys:
state_dict.pop(_UpperCAmelCase , _UpperCAmelCase )
UpperCAmelCase__ = {
"blocks": "layers",
"mlp.0": "fc1",
"mlp.2": "fc2",
"mlp_ln": "final_layer_norm",
".attn.query": ".self_attn.q_proj",
".attn.key": ".self_attn.k_proj",
".attn.value": ".self_attn.v_proj",
".attn_ln": ".self_attn_layer_norm",
".attn.out": ".self_attn.out_proj",
".cross_attn.query": ".encoder_attn.q_proj",
".cross_attn.key": ".encoder_attn.k_proj",
".cross_attn.value": ".encoder_attn.v_proj",
".cross_attn_ln": ".encoder_attn_layer_norm",
".cross_attn.out": ".encoder_attn.out_proj",
"decoder.ln.": "decoder.layer_norm.",
"encoder.ln.": "encoder.layer_norm.",
"token_embedding": "embed_tokens",
"encoder.positional_embedding": "encoder.embed_positions.weight",
"decoder.positional_embedding": "decoder.embed_positions.weight",
"ln_post": "layer_norm",
}
def A ( _UpperCAmelCase : Dict ) -> Optional[int]:
'''simple docstring'''
_UpperCAmelCase = list(s_dict.keys() )
for key in keys:
_UpperCAmelCase = key
for k, v in WHISPER_MAPPING.items():
if k in key:
_UpperCAmelCase = new_key.replace(_UpperCAmelCase , _UpperCAmelCase )
print(F"{key} -> {new_key}" )
_UpperCAmelCase = s_dict.pop(_UpperCAmelCase )
return s_dict
def A ( _UpperCAmelCase : List[Any] ) -> Union[str, Any]:
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase = emb.weight.shape
_UpperCAmelCase = nn.Linear(_UpperCAmelCase , _UpperCAmelCase , bias=_UpperCAmelCase )
_UpperCAmelCase = emb.weight.data
return lin_layer
def A ( _UpperCAmelCase : str , _UpperCAmelCase : str ) -> bytes:
'''simple docstring'''
os.makedirs(_UpperCAmelCase , exist_ok=_UpperCAmelCase )
_UpperCAmelCase = os.path.basename(_UpperCAmelCase )
_UpperCAmelCase = url.split('/' )[-2]
_UpperCAmelCase = os.path.join(_UpperCAmelCase , _UpperCAmelCase )
if os.path.exists(_UpperCAmelCase ) and not os.path.isfile(_UpperCAmelCase ):
raise RuntimeError(F"{download_target} exists and is not a regular file" )
if os.path.isfile(_UpperCAmelCase ):
_UpperCAmelCase = open(_UpperCAmelCase , 'rb' ).read()
if hashlib.shaaaa(_UpperCAmelCase ).hexdigest() == expected_shaaaa:
return model_bytes
else:
warnings.warn(F"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file" )
with urllib.request.urlopen(_UpperCAmelCase ) as source, open(_UpperCAmelCase , 'wb' ) as output:
with tqdm(
total=int(source.info().get('Content-Length' ) ) , ncols=80 , unit='iB' , unit_scale=_UpperCAmelCase , unit_divisor=1_024 ) as loop:
while True:
_UpperCAmelCase = source.read(8_192 )
if not buffer:
break
output.write(_UpperCAmelCase )
loop.update(len(_UpperCAmelCase ) )
_UpperCAmelCase = open(_UpperCAmelCase , 'rb' ).read()
if hashlib.shaaaa(_UpperCAmelCase ).hexdigest() != expected_shaaaa:
raise RuntimeError(
'Model has been downloaded but the SHA256 checksum does not not match. Please retry loading the model.' )
return model_bytes
def A ( _UpperCAmelCase : List[Any] , _UpperCAmelCase : Any ) -> Optional[int]:
'''simple docstring'''
if ".pt" not in checkpoint_path:
_UpperCAmelCase = _download(_MODELS[checkpoint_path] )
else:
_UpperCAmelCase = torch.load(_UpperCAmelCase , map_location='cpu' )
_UpperCAmelCase = original_checkpoint['dims']
_UpperCAmelCase = original_checkpoint['model_state_dict']
_UpperCAmelCase = state_dict['decoder.token_embedding.weight']
remove_ignore_keys_(_UpperCAmelCase )
rename_keys(_UpperCAmelCase )
_UpperCAmelCase = True
_UpperCAmelCase = state_dict['decoder.layers.0.fc1.weight'].shape[0]
_UpperCAmelCase = WhisperConfig(
vocab_size=dimensions['n_vocab'] , encoder_ffn_dim=_UpperCAmelCase , decoder_ffn_dim=_UpperCAmelCase , num_mel_bins=dimensions['n_mels'] , d_model=dimensions['n_audio_state'] , max_target_positions=dimensions['n_text_ctx'] , encoder_layers=dimensions['n_audio_layer'] , encoder_attention_heads=dimensions['n_audio_head'] , decoder_layers=dimensions['n_text_layer'] , decoder_attention_heads=dimensions['n_text_state'] , max_source_positions=dimensions['n_audio_ctx'] , )
_UpperCAmelCase = WhisperForConditionalGeneration(_UpperCAmelCase )
_UpperCAmelCase , _UpperCAmelCase = model.model.load_state_dict(_UpperCAmelCase , strict=_UpperCAmelCase )
if len(_UpperCAmelCase ) > 0 and not set(_UpperCAmelCase ) <= {
"encoder.embed_positions.weights",
"decoder.embed_positions.weights",
}:
raise ValueError(
'Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights` are allowed to be missing,'
F" but all the following weights are missing {missing}" )
if tie_embeds:
_UpperCAmelCase = make_linear_from_emb(model.model.decoder.embed_tokens )
else:
_UpperCAmelCase = proj_out_weights
model.save_pretrained(_UpperCAmelCase )
if __name__ == "__main__":
UpperCAmelCase__ = argparse.ArgumentParser()
# # Required parameters
parser.add_argument("--checkpoint_path", type=str, help="Patht to the downloaded checkpoints")
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
UpperCAmelCase__ = parser.parse_args()
convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)
| 339 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
"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 __lowerCAmelCase ( A ):
UpperCamelCase = '''xmod'''
def __init__( self : Optional[Any] , A : str=3_05_22 , A : List[Any]=7_68 , A : List[str]=12 , A : Union[str, Any]=12 , A : List[Any]=30_72 , A : Tuple="gelu" , A : List[Any]=0.1 , A : Union[str, Any]=0.1 , A : Any=5_12 , A : Tuple=2 , A : Any=0.0_2 , A : Tuple=1E-12 , A : List[str]=1 , A : Any=0 , A : Optional[Any]=2 , A : List[str]="absolute" , A : Union[str, Any]=True , A : int=None , A : Optional[int]=False , A : Optional[int]=2 , A : Tuple=False , A : int=True , A : str=True , A : Tuple=("en_XX",) , A : Optional[Any]=None , **A : List[str] , ) -> Optional[int]:
"""simple docstring"""
super().__init__(pad_token_id=A , bos_token_id=A , eos_token_id=A , **A)
_UpperCAmelCase = vocab_size
_UpperCAmelCase = hidden_size
_UpperCAmelCase = num_hidden_layers
_UpperCAmelCase = num_attention_heads
_UpperCAmelCase = hidden_act
_UpperCAmelCase = intermediate_size
_UpperCAmelCase = hidden_dropout_prob
_UpperCAmelCase = attention_probs_dropout_prob
_UpperCAmelCase = max_position_embeddings
_UpperCAmelCase = type_vocab_size
_UpperCAmelCase = initializer_range
_UpperCAmelCase = layer_norm_eps
_UpperCAmelCase = position_embedding_type
_UpperCAmelCase = use_cache
_UpperCAmelCase = classifier_dropout
_UpperCAmelCase = pre_norm
_UpperCAmelCase = adapter_reduction_factor
_UpperCAmelCase = adapter_layer_norm
_UpperCAmelCase = adapter_reuse_layer_norm
_UpperCAmelCase = ln_before_adapter
_UpperCAmelCase = list(A)
_UpperCAmelCase = default_language
class __lowerCAmelCase ( A ):
@property
def _lowerCamelCase ( self : Any) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
if self.task == "multiple-choice":
_UpperCAmelCase = {0: 'batch', 1: 'choice', 2: 'sequence'}
else:
_UpperCAmelCase = {0: 'batch', 1: 'sequence'}
return OrderedDict(
[
('input_ids', dynamic_axis),
('attention_mask', dynamic_axis),
])
| 339 |
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
UpperCAmelCase__ = datasets.utils.logging.get_logger(__name__)
class __lowerCAmelCase ( folder_based_builder.FolderBasedBuilderConfig ):
UpperCamelCase = None
UpperCamelCase = None
class __lowerCAmelCase ( folder_based_builder.FolderBasedBuilder ):
UpperCamelCase = datasets.Audio()
UpperCamelCase = '''audio'''
UpperCamelCase = AudioFolderConfig
UpperCamelCase = 42 # definition at the bottom of the script
UpperCamelCase = AudioClassification(audio_column='''audio''' , label_column='''label''' )
UpperCAmelCase__ = [
".aiff",
".au",
".avr",
".caf",
".flac",
".htk",
".svx",
".mat4",
".mat5",
".mpc2k",
".ogg",
".paf",
".pvf",
".raw",
".rf64",
".sd2",
".sds",
".ircam",
".voc",
".w64",
".wav",
".nist",
".wavex",
".wve",
".xi",
".mp3",
".opus",
]
UpperCAmelCase__ = AUDIO_EXTENSIONS
| 339 | 1 |
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def A ( _UpperCAmelCase : NDArray[floataa] , _UpperCAmelCase : NDArray[floataa] , _UpperCAmelCase : list[int] , _UpperCAmelCase : int , ) -> list[float]:
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase = coefficient_matrix.shape
_UpperCAmelCase , _UpperCAmelCase = constant_matrix.shape
if rowsa != colsa:
_UpperCAmelCase = F"Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}"
raise ValueError(_UpperCAmelCase )
if colsa != 1:
_UpperCAmelCase = F"Constant matrix must be nx1 but received {rowsa}x{colsa}"
raise ValueError(_UpperCAmelCase )
if rowsa != rowsa:
_UpperCAmelCase = (
'Coefficient and constant matrices dimensions must be nxn and nx1 but '
F"received {rowsa}x{colsa} and {rowsa}x{colsa}"
)
raise ValueError(_UpperCAmelCase )
if len(_UpperCAmelCase ) != rowsa:
_UpperCAmelCase = (
'Number of initial values must be equal to number of rows in coefficient '
F"matrix but received {len(_UpperCAmelCase )} and {rowsa}"
)
raise ValueError(_UpperCAmelCase )
if iterations <= 0:
raise ValueError('Iterations must be at least 1' )
_UpperCAmelCase = np.concatenate(
(coefficient_matrix, constant_matrix) , axis=1 )
_UpperCAmelCase , _UpperCAmelCase = table.shape
strictly_diagonally_dominant(_UpperCAmelCase )
# Iterates the whole matrix for given number of times
for _ in range(_UpperCAmelCase ):
_UpperCAmelCase = []
for row in range(_UpperCAmelCase ):
_UpperCAmelCase = 0
for col in range(_UpperCAmelCase ):
if col == row:
_UpperCAmelCase = table[row][col]
elif col == cols - 1:
_UpperCAmelCase = table[row][col]
else:
temp += (-1) * table[row][col] * init_val[col]
_UpperCAmelCase = (temp + val) / denom
new_val.append(_UpperCAmelCase )
_UpperCAmelCase = new_val
return [float(_UpperCAmelCase ) for i in new_val]
def A ( _UpperCAmelCase : NDArray[floataa] ) -> bool:
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase = table.shape
_UpperCAmelCase = True
for i in range(0 , _UpperCAmelCase ):
_UpperCAmelCase = 0
for j in range(0 , cols - 1 ):
if i == j:
continue
else:
total += table[i][j]
if table[i][i] <= total:
raise ValueError('Coefficient matrix is not strictly diagonally dominant' )
return is_diagonally_dominant
# Test Cases
if __name__ == "__main__":
import doctest
doctest.testmod()
| 339 |
import sys
from collections import defaultdict
class __lowerCAmelCase :
def __init__( self : int) -> str:
"""simple docstring"""
_UpperCAmelCase = []
def _lowerCamelCase ( self : Any , A : List[str]) -> int:
"""simple docstring"""
return self.node_position[vertex]
def _lowerCamelCase ( self : Optional[Any] , A : Optional[int] , A : str) -> List[str]:
"""simple docstring"""
_UpperCAmelCase = pos
def _lowerCamelCase ( self : Tuple , A : Tuple , A : Dict , A : List[str] , A : Optional[Any]) -> Dict:
"""simple docstring"""
if start > size // 2 - 1:
return
else:
if 2 * start + 2 >= size:
_UpperCAmelCase = 2 * start + 1
else:
if heap[2 * start + 1] < heap[2 * start + 2]:
_UpperCAmelCase = 2 * start + 1
else:
_UpperCAmelCase = 2 * start + 2
if heap[smallest_child] < heap[start]:
_UpperCAmelCase , _UpperCAmelCase = heap[smallest_child], positions[smallest_child]
_UpperCAmelCase , _UpperCAmelCase = (
heap[start],
positions[start],
)
_UpperCAmelCase , _UpperCAmelCase = temp, tempa
_UpperCAmelCase = self.get_position(positions[smallest_child])
self.set_position(
positions[smallest_child] , self.get_position(positions[start]))
self.set_position(positions[start] , A)
self.top_to_bottom(A , A , A , A)
def _lowerCamelCase ( self : Optional[int] , A : str , A : Optional[Any] , A : Optional[int] , A : str) -> Any:
"""simple docstring"""
_UpperCAmelCase = position[index]
while index != 0:
_UpperCAmelCase = int((index - 2) / 2) if index % 2 == 0 else int((index - 1) / 2)
if val < heap[parent]:
_UpperCAmelCase = heap[parent]
_UpperCAmelCase = position[parent]
self.set_position(position[parent] , A)
else:
_UpperCAmelCase = val
_UpperCAmelCase = temp
self.set_position(A , A)
break
_UpperCAmelCase = parent
else:
_UpperCAmelCase = val
_UpperCAmelCase = temp
self.set_position(A , 0)
def _lowerCamelCase ( self : Union[str, Any] , A : Optional[int] , A : Tuple) -> str:
"""simple docstring"""
_UpperCAmelCase = len(A) // 2 - 1
for i in range(A , -1 , -1):
self.top_to_bottom(A , A , len(A) , A)
def _lowerCamelCase ( self : Optional[int] , A : int , A : str) -> List[str]:
"""simple docstring"""
_UpperCAmelCase = positions[0]
_UpperCAmelCase = sys.maxsize
self.top_to_bottom(A , 0 , len(A) , A)
return temp
def A ( _UpperCAmelCase : int ) -> Any:
'''simple docstring'''
_UpperCAmelCase = Heap()
_UpperCAmelCase = [0] * len(_UpperCAmelCase )
_UpperCAmelCase = [-1] * len(_UpperCAmelCase ) # Neighboring Tree Vertex of selected vertex
# Minimum Distance of explored vertex with neighboring vertex of partial tree
# formed in graph
_UpperCAmelCase = [] # Heap of Distance of vertices from their neighboring vertex
_UpperCAmelCase = []
for vertex in range(len(_UpperCAmelCase ) ):
distance_tv.append(sys.maxsize )
positions.append(_UpperCAmelCase )
heap.node_position.append(_UpperCAmelCase )
_UpperCAmelCase = []
_UpperCAmelCase = 1
_UpperCAmelCase = sys.maxsize
for neighbor, distance in adjacency_list[0]:
_UpperCAmelCase = 0
_UpperCAmelCase = distance
heap.heapify(_UpperCAmelCase , _UpperCAmelCase )
for _ in range(1 , len(_UpperCAmelCase ) ):
_UpperCAmelCase = heap.delete_minimum(_UpperCAmelCase , _UpperCAmelCase )
if visited[vertex] == 0:
tree_edges.append((nbr_tv[vertex], vertex) )
_UpperCAmelCase = 1
for neighbor, distance in adjacency_list[vertex]:
if (
visited[neighbor] == 0
and distance < distance_tv[heap.get_position(_UpperCAmelCase )]
):
_UpperCAmelCase = distance
heap.bottom_to_top(
_UpperCAmelCase , heap.get_position(_UpperCAmelCase ) , _UpperCAmelCase , _UpperCAmelCase )
_UpperCAmelCase = vertex
return tree_edges
if __name__ == "__main__": # pragma: no cover
# < --------- Prims Algorithm --------- >
UpperCAmelCase__ = int(input("Enter number of edges: ").strip())
UpperCAmelCase__ = defaultdict(list)
for _ in range(edges_number):
UpperCAmelCase__ = [int(x) for x in input().strip().split()]
adjacency_list[edge[0]].append([edge[1], edge[2]])
adjacency_list[edge[1]].append([edge[0], edge[2]])
print(prisms_algorithm(adjacency_list))
| 339 | 1 |
import os
import random
import sys
from . import cryptomath_module as cryptoMath # noqa: N812
from . import rabin_miller as rabinMiller # noqa: N812
def A ( ) -> None:
'''simple docstring'''
print('Making key files...' )
make_key_files('rsa' , 1_024 )
print('Key files generation successful.' )
def A ( _UpperCAmelCase : int ) -> tuple[tuple[int, int], tuple[int, int]]:
'''simple docstring'''
print('Generating prime p...' )
_UpperCAmelCase = rabinMiller.generate_large_prime(_UpperCAmelCase )
print('Generating prime q...' )
_UpperCAmelCase = rabinMiller.generate_large_prime(_UpperCAmelCase )
_UpperCAmelCase = p * q
print('Generating e that is relatively prime to (p - 1) * (q - 1)...' )
while True:
_UpperCAmelCase = random.randrange(2 ** (key_size - 1) , 2 ** (key_size) )
if cryptoMath.gcd(_UpperCAmelCase , (p - 1) * (q - 1) ) == 1:
break
print('Calculating d that is mod inverse of e...' )
_UpperCAmelCase = cryptoMath.find_mod_inverse(_UpperCAmelCase , (p - 1) * (q - 1) )
_UpperCAmelCase = (n, e)
_UpperCAmelCase = (n, d)
return (public_key, private_key)
def A ( _UpperCAmelCase : str , _UpperCAmelCase : int ) -> None:
'''simple docstring'''
if os.path.exists(F"{name}_pubkey.txt" ) or os.path.exists(F"{name}_privkey.txt" ):
print('\nWARNING:' )
print(
F"\"{name}_pubkey.txt\" or \"{name}_privkey.txt\" already exists. \n"
'Use a different name or delete these files and re-run this program.' )
sys.exit()
_UpperCAmelCase , _UpperCAmelCase = generate_key(_UpperCAmelCase )
print(F"\nWriting public key to file {name}_pubkey.txt..." )
with open(F"{name}_pubkey.txt" , 'w' ) as out_file:
out_file.write(F"{key_size},{public_key[0]},{public_key[1]}" )
print(F"Writing private key to file {name}_privkey.txt..." )
with open(F"{name}_privkey.txt" , 'w' ) as out_file:
out_file.write(F"{key_size},{private_key[0]},{private_key[1]}" )
if __name__ == "__main__":
main()
| 339 |
import torch
from transformers import CamembertForMaskedLM, CamembertTokenizer
def A ( _UpperCAmelCase : str , _UpperCAmelCase : Any , _UpperCAmelCase : List[str] , _UpperCAmelCase : Optional[int]=5 ) -> List[Any]:
'''simple docstring'''
# Adapted from https://github.com/pytorch/fairseq/blob/master/fairseq/models/roberta/hub_interface.py
assert masked_input.count('<mask>' ) == 1
_UpperCAmelCase = torch.tensor(tokenizer.encode(_UpperCAmelCase , add_special_tokens=_UpperCAmelCase ) ).unsqueeze(0 ) # Batch size 1
_UpperCAmelCase = model(_UpperCAmelCase )[0] # The last hidden-state is the first element of the output tuple
_UpperCAmelCase = (input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
_UpperCAmelCase = logits[0, masked_index, :]
_UpperCAmelCase = logits.softmax(dim=0 )
_UpperCAmelCase , _UpperCAmelCase = prob.topk(k=_UpperCAmelCase , dim=0 )
_UpperCAmelCase = ' '.join(
[tokenizer.convert_ids_to_tokens(indices[i].item() ) for i in range(len(_UpperCAmelCase ) )] )
_UpperCAmelCase = tokenizer.mask_token
_UpperCAmelCase = []
for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(' ' ) ):
_UpperCAmelCase = predicted_token_bpe.replace('\u2581' , ' ' )
if " {0}".format(_UpperCAmelCase ) in masked_input:
topk_filled_outputs.append(
(
masked_input.replace(' {0}'.format(_UpperCAmelCase ) , _UpperCAmelCase ),
values[index].item(),
predicted_token,
) )
else:
topk_filled_outputs.append(
(
masked_input.replace(_UpperCAmelCase , _UpperCAmelCase ),
values[index].item(),
predicted_token,
) )
return topk_filled_outputs
UpperCAmelCase__ = CamembertTokenizer.from_pretrained("camembert-base")
UpperCAmelCase__ = CamembertForMaskedLM.from_pretrained("camembert-base")
model.eval()
UpperCAmelCase__ = "Le camembert est <mask> :)"
print(fill_mask(masked_input, model, tokenizer, topk=3))
| 339 | 1 |
def A ( ) -> Dict:
'''simple docstring'''
_UpperCAmelCase = 0
for i in range(1 , 1_001 ):
total += i**i
return str(_UpperCAmelCase )[-10:]
if __name__ == "__main__":
print(solution())
| 339 |
import math
import unittest
def A ( _UpperCAmelCase : int ) -> bool:
'''simple docstring'''
assert isinstance(_UpperCAmelCase , _UpperCAmelCase ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(_UpperCAmelCase ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
class __lowerCAmelCase ( unittest.TestCase ):
def _lowerCamelCase ( self : Tuple) -> Union[str, Any]:
"""simple docstring"""
self.assertTrue(is_prime(2))
self.assertTrue(is_prime(3))
self.assertTrue(is_prime(5))
self.assertTrue(is_prime(7))
self.assertTrue(is_prime(11))
self.assertTrue(is_prime(13))
self.assertTrue(is_prime(17))
self.assertTrue(is_prime(19))
self.assertTrue(is_prime(23))
self.assertTrue(is_prime(29))
def _lowerCamelCase ( self : Optional[int]) -> Any:
"""simple docstring"""
with self.assertRaises(A):
is_prime(-19)
self.assertFalse(
is_prime(0) , 'Zero doesn\'t have any positive factors, primes must have exactly two.' , )
self.assertFalse(
is_prime(1) , 'One only has 1 positive factor, primes must have exactly two.' , )
self.assertFalse(is_prime(2 * 2))
self.assertFalse(is_prime(2 * 3))
self.assertFalse(is_prime(3 * 3))
self.assertFalse(is_prime(3 * 5))
self.assertFalse(is_prime(3 * 5 * 7))
if __name__ == "__main__":
unittest.main()
| 339 | 1 |
import warnings
from ...utils import logging
from .image_processing_yolos import YolosImageProcessor
UpperCAmelCase__ = logging.get_logger(__name__)
class __lowerCAmelCase ( A ):
def __init__( self : Tuple , *A : List[Any] , **A : Tuple) -> None:
"""simple docstring"""
warnings.warn(
'The class YolosFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
' use YolosImageProcessor instead.' , A , )
super().__init__(*A , **A)
| 339 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
UpperCAmelCase__ = "\\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"
UpperCAmelCase__ = "\\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"
UpperCAmelCase__ = "\\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 __lowerCAmelCase ( datasets.Metric ):
def _lowerCamelCase ( self : str) -> MetricInfo:
"""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 _lowerCamelCase ( self : Union[str, Any] , A : List[List[List[str]]] , A : List[List[str]] , A : int = 1 , A : int = 4 , ) -> Dict[str, float]:
"""simple docstring"""
return {
"google_bleu": gleu_score.corpus_gleu(
list_of_references=A , hypotheses=A , min_len=A , max_len=A)
}
| 339 | 1 |
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 __lowerCAmelCase ( A , unittest.TestCase ):
UpperCamelCase = CodeGenTokenizer
UpperCamelCase = CodeGenTokenizerFast
UpperCamelCase = True
UpperCamelCase = {'''add_prefix_space''': True}
UpperCamelCase = False
def _lowerCamelCase ( self : str) -> Union[str, Any]:
"""simple docstring"""
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
_UpperCAmelCase = [
'l',
'o',
'w',
'e',
'r',
's',
't',
'i',
'd',
'n',
'\u0120',
'\u0120l',
'\u0120n',
'\u0120lo',
'\u0120low',
'er',
'\u0120lowest',
'\u0120newer',
'\u0120wider',
'<unk>',
'<|endoftext|>',
]
_UpperCAmelCase = dict(zip(A , range(len(A))))
_UpperCAmelCase = ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
_UpperCAmelCase = {'unk_token': '<unk>'}
_UpperCAmelCase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'])
_UpperCAmelCase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['merges_file'])
with open(self.vocab_file , 'w' , encoding='utf-8') as fp:
fp.write(json.dumps(A) + '\n')
with open(self.merges_file , 'w' , encoding='utf-8') as fp:
fp.write('\n'.join(A))
def _lowerCamelCase ( self : Optional[Any] , **A : Union[str, Any]) -> Union[str, Any]:
"""simple docstring"""
kwargs.update(self.special_tokens_map)
return CodeGenTokenizer.from_pretrained(self.tmpdirname , **A)
def _lowerCamelCase ( self : int , **A : str) -> Any:
"""simple docstring"""
kwargs.update(self.special_tokens_map)
return CodeGenTokenizerFast.from_pretrained(self.tmpdirname , **A)
def _lowerCamelCase ( self : List[str] , A : Tuple) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase = 'lower newer'
_UpperCAmelCase = 'lower newer'
return input_text, output_text
def _lowerCamelCase ( self : Dict) -> Tuple:
"""simple docstring"""
_UpperCAmelCase = CodeGenTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map)
_UpperCAmelCase = 'lower newer'
_UpperCAmelCase = ['\u0120low', 'er', '\u0120', 'n', 'e', 'w', 'er']
_UpperCAmelCase = tokenizer.tokenize(A , add_prefix_space=A)
self.assertListEqual(A , A)
_UpperCAmelCase = tokens + [tokenizer.unk_token]
_UpperCAmelCase = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(tokenizer.convert_tokens_to_ids(A) , A)
def _lowerCamelCase ( self : Optional[int]) -> Dict:
"""simple docstring"""
if not self.test_rust_tokenizer:
return
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = self.get_rust_tokenizer(add_prefix_space=A)
_UpperCAmelCase = 'lower newer'
# Testing tokenization
_UpperCAmelCase = tokenizer.tokenize(A , add_prefix_space=A)
_UpperCAmelCase = rust_tokenizer.tokenize(A)
self.assertListEqual(A , A)
# Testing conversion to ids without special tokens
_UpperCAmelCase = tokenizer.encode(A , add_special_tokens=A , add_prefix_space=A)
_UpperCAmelCase = rust_tokenizer.encode(A , add_special_tokens=A)
self.assertListEqual(A , A)
# Testing conversion to ids with special tokens
_UpperCAmelCase = self.get_rust_tokenizer(add_prefix_space=A)
_UpperCAmelCase = tokenizer.encode(A , add_prefix_space=A)
_UpperCAmelCase = rust_tokenizer.encode(A)
self.assertListEqual(A , A)
# Testing the unknown token
_UpperCAmelCase = tokens + [rust_tokenizer.unk_token]
_UpperCAmelCase = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(A) , A)
def _lowerCamelCase ( self : int , *A : int , **A : List[Any]) -> Any:
"""simple docstring"""
pass
def _lowerCamelCase ( self : Any , A : Optional[Any]=15) -> Any:
"""simple docstring"""
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"):
_UpperCAmelCase = self.rust_tokenizer_class.from_pretrained(A , **A)
# Simple input
_UpperCAmelCase = 'This is a simple input'
_UpperCAmelCase = ['This is a simple input 1', 'This is a simple input 2']
_UpperCAmelCase = ('This is a simple input', 'This is a pair')
_UpperCAmelCase = [
('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(A , tokenizer_r.encode , A , max_length=A , padding='max_length')
# Simple input
self.assertRaises(A , tokenizer_r.encode_plus , A , max_length=A , padding='max_length')
# Simple input
self.assertRaises(
A , tokenizer_r.batch_encode_plus , A , max_length=A , padding='max_length' , )
# Pair input
self.assertRaises(A , tokenizer_r.encode , A , max_length=A , padding='max_length')
# Pair input
self.assertRaises(A , tokenizer_r.encode_plus , A , max_length=A , padding='max_length')
# Pair input
self.assertRaises(
A , tokenizer_r.batch_encode_plus , A , max_length=A , padding='max_length' , )
def _lowerCamelCase ( self : Tuple) -> List[str]:
"""simple docstring"""
_UpperCAmelCase = CodeGenTokenizer.from_pretrained(self.tmpdirname , pad_token='<pad>')
# Simple input
_UpperCAmelCase = 'This is a simple input'
_UpperCAmelCase = ['This is a simple input looooooooong', 'This is a simple input']
_UpperCAmelCase = ('This is a simple input', 'This is a pair')
_UpperCAmelCase = [
('This is a simple input loooooong', 'This is a simple input'),
('This is a simple pair loooooong', 'This is a simple pair'),
]
_UpperCAmelCase = tokenizer.pad_token_id
_UpperCAmelCase = tokenizer(A , padding='max_length' , max_length=30 , return_tensors='np')
_UpperCAmelCase = tokenizer(A , padding=A , truncate=A , return_tensors='np')
_UpperCAmelCase = tokenizer(*A , padding='max_length' , max_length=60 , return_tensors='np')
_UpperCAmelCase = tokenizer(A , padding=A , truncate=A , 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 _lowerCamelCase ( self : Dict) -> str:
"""simple docstring"""
_UpperCAmelCase = '$$$'
_UpperCAmelCase = CodeGenTokenizer.from_pretrained(self.tmpdirname , bos_token=A , add_bos_token=A)
_UpperCAmelCase = 'This is a simple input'
_UpperCAmelCase = ['This is a simple input 1', 'This is a simple input 2']
_UpperCAmelCase = tokenizer.bos_token_id
_UpperCAmelCase = tokenizer(A)
_UpperCAmelCase = tokenizer(A)
self.assertEqual(out_s.input_ids[0] , A)
self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids))
_UpperCAmelCase = tokenizer.decode(out_s.input_ids)
_UpperCAmelCase = tokenizer.batch_decode(out_sa.input_ids)
self.assertEqual(decode_s.split()[0] , A)
self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa))
@slow
def _lowerCamelCase ( self : List[Any]) -> List[str]:
"""simple docstring"""
_UpperCAmelCase = CodeGenTokenizer.from_pretrained('Salesforce/codegen-350M-mono')
_UpperCAmelCase = '\nif len_a > len_b:\n result = a\nelse:\n result = b\n\n\n\n#'
_UpperCAmelCase = '\nif len_a > len_b: result = a\nelse: result = b'
_UpperCAmelCase = tokenizer.encode(A)
_UpperCAmelCase = ['^#', re.escape('<|endoftext|>'), '^\'\'\'', '^"""', '\n\n\n']
_UpperCAmelCase = tokenizer.decode(A , truncate_before_pattern=A)
self.assertEqual(A , A)
def _lowerCamelCase ( self : Tuple) -> List[str]:
"""simple docstring"""
pass
| 339 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
if is_sentencepiece_available():
from ..ta.tokenization_ta import TaTokenizer
else:
from ...utils.dummy_sentencepiece_objects import TaTokenizer
UpperCAmelCase__ = TaTokenizer
if is_tokenizers_available():
from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
from ...utils.dummy_tokenizers_objects import TaTokenizerFast
UpperCAmelCase__ = TaTokenizerFast
UpperCAmelCase__ = {"configuration_mt5": ["MT5Config", "MT5OnnxConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ = [
"MT5EncoderModel",
"MT5ForConditionalGeneration",
"MT5ForQuestionAnswering",
"MT5Model",
"MT5PreTrainedModel",
"MT5Stack",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ = ["TFMT5EncoderModel", "TFMT5ForConditionalGeneration", "TFMT5Model"]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ = ["FlaxMT5EncoderModel", "FlaxMT5ForConditionalGeneration", "FlaxMT5Model"]
if TYPE_CHECKING:
from .configuration_mta import MTaConfig, MTaOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mta import (
MTaEncoderModel,
MTaForConditionalGeneration,
MTaForQuestionAnswering,
MTaModel,
MTaPreTrainedModel,
MTaStack,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel
else:
import sys
UpperCAmelCase__ = _LazyModule(
__name__,
globals()["__file__"],
_import_structure,
extra_objects={"MT5Tokenizer": MTaTokenizer, "MT5TokenizerFast": MTaTokenizerFast},
module_spec=__spec__,
)
| 339 | 1 |
def A ( _UpperCAmelCase : int , _UpperCAmelCase : int ) -> int:
'''simple docstring'''
return abs(_UpperCAmelCase ) if a == 0 else greatest_common_divisor(b % a , _UpperCAmelCase )
def A ( _UpperCAmelCase : int , _UpperCAmelCase : int ) -> int:
'''simple docstring'''
while y: # --> when y=0 then loop will terminate and return x as final GCD.
_UpperCAmelCase , _UpperCAmelCase = y, x % y
return abs(_UpperCAmelCase )
def A ( ) -> Dict:
'''simple docstring'''
try:
_UpperCAmelCase = input('Enter two integers separated by comma (,): ' ).split(',' )
_UpperCAmelCase = int(nums[0] )
_UpperCAmelCase = int(nums[1] )
print(
F"greatest_common_divisor({num_a}, {num_a}) = "
F"{greatest_common_divisor(_UpperCAmelCase , _UpperCAmelCase )}" )
print(F"By iterative gcd({num_a}, {num_a}) = {gcd_by_iterative(_UpperCAmelCase , _UpperCAmelCase )}" )
except (IndexError, UnboundLocalError, ValueError):
print('Wrong input' )
if __name__ == "__main__":
main()
| 339 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
"s-JoL/Open-Llama-V1": "https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json",
}
class __lowerCAmelCase ( A ):
UpperCamelCase = '''open-llama'''
def __init__( self : str , A : List[Any]=10_00_00 , A : Tuple=40_96 , A : Tuple=1_10_08 , A : List[str]=32 , A : Tuple=32 , A : Optional[Any]="silu" , A : int=20_48 , A : Optional[Any]=0.0_2 , A : Dict=1E-6 , A : Optional[Any]=True , A : List[Any]=0 , A : Dict=1 , A : int=2 , A : Dict=False , A : Optional[int]=True , A : List[Any]=0.1 , A : str=0.1 , A : Dict=True , A : Optional[Any]=True , A : Dict=None , **A : Union[str, Any] , ) -> Dict:
"""simple docstring"""
_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' , A)
_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=A , bos_token_id=A , eos_token_id=A , tie_word_embeddings=A , **A , )
def _lowerCamelCase ( self : List[str]) -> Union[str, Any]:
"""simple docstring"""
if self.rope_scaling is None:
return
if not isinstance(self.rope_scaling , A) or len(self.rope_scaling) != 2:
raise ValueError(
'`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '
F"got {self.rope_scaling}")
_UpperCAmelCase = self.rope_scaling.get('type' , A)
_UpperCAmelCase = self.rope_scaling.get('factor' , A)
if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
raise ValueError(
F"`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}")
if rope_scaling_factor is None or not isinstance(A , A) or rope_scaling_factor <= 1.0:
raise ValueError(F"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}")
| 339 | 1 |
from math import sqrt
def A ( _UpperCAmelCase : int ) -> bool:
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(sqrt(_UpperCAmelCase ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def A ( _UpperCAmelCase : int = 10_001 ) -> int:
'''simple docstring'''
_UpperCAmelCase = 0
_UpperCAmelCase = 1
while count != nth and number < 3:
number += 1
if is_prime(_UpperCAmelCase ):
count += 1
while count != nth:
number += 2
if is_prime(_UpperCAmelCase ):
count += 1
return number
if __name__ == "__main__":
print(f"""{solution() = }""")
| 339 |
def A ( _UpperCAmelCase : str ) -> bool:
'''simple docstring'''
return credit_card_number.startswith(('34', '35', '37', '4', '5', '6') )
def A ( _UpperCAmelCase : str ) -> bool:
'''simple docstring'''
_UpperCAmelCase = credit_card_number
_UpperCAmelCase = 0
_UpperCAmelCase = len(_UpperCAmelCase ) - 2
for i in range(_UpperCAmelCase , -1 , -2 ):
# double the value of every second digit
_UpperCAmelCase = int(cc_number[i] )
digit *= 2
# If doubling of a number results in a two digit number
# i.e greater than 9(e.g., 6 × 2 = 12),
# then add the digits of the product (e.g., 12: 1 + 2 = 3, 15: 1 + 5 = 6),
# to get a single digit number.
if digit > 9:
digit %= 10
digit += 1
_UpperCAmelCase = cc_number[:i] + str(_UpperCAmelCase ) + cc_number[i + 1 :]
total += digit
# Sum up the remaining digits
for i in range(len(_UpperCAmelCase ) - 1 , -1 , -2 ):
total += int(cc_number[i] )
return total % 10 == 0
def A ( _UpperCAmelCase : str ) -> bool:
'''simple docstring'''
_UpperCAmelCase = F"{credit_card_number} is an invalid credit card number because"
if not credit_card_number.isdigit():
print(F"{error_message} it has nonnumerical characters." )
return False
if not 13 <= len(_UpperCAmelCase ) <= 16:
print(F"{error_message} of its length." )
return False
if not validate_initial_digits(_UpperCAmelCase ):
print(F"{error_message} of its first two digits." )
return False
if not luhn_validation(_UpperCAmelCase ):
print(F"{error_message} it fails the Luhn check." )
return False
print(F"{credit_card_number} is a valid credit card number." )
return True
if __name__ == "__main__":
import doctest
doctest.testmod()
validate_credit_card_number("4111111111111111")
validate_credit_card_number("32323")
| 339 | 1 |
import argparse
import os
import torch
from transformers import FlavaImageCodebook, FlavaImageCodebookConfig
def A ( _UpperCAmelCase : List[str] , _UpperCAmelCase : str , _UpperCAmelCase : str , _UpperCAmelCase : Any ) -> int:
'''simple docstring'''
_UpperCAmelCase = s.rsplit(_UpperCAmelCase , _UpperCAmelCase )
return new.join(_UpperCAmelCase )
def A ( _UpperCAmelCase : str ) -> Union[str, Any]:
'''simple docstring'''
# encoder.embeddings are double copied in original FLAVA
return sum(param.float().sum() if 'encoder.embeddings' not in key else 0 for key, param in state_dict.items() )
def A ( _UpperCAmelCase : Union[str, Any] ) -> Tuple:
'''simple docstring'''
_UpperCAmelCase = {}
_UpperCAmelCase = ['group_1', 'group_2', 'group_3', 'group_4']
for key, value in state_dict.items():
for group_key in group_keys:
if group_key in key:
_UpperCAmelCase = key.replace(F"{group_key}." , F"{group_key}.group." )
if "res_path" in key:
_UpperCAmelCase = key.replace('res_path.' , 'res_path.path.' )
if key.endswith('.w' ):
_UpperCAmelCase = rreplace(_UpperCAmelCase , '.w' , '.weight' , 1 )
if key.endswith('.b' ):
_UpperCAmelCase = rreplace(_UpperCAmelCase , '.b' , '.bias' , 1 )
_UpperCAmelCase = value.float()
return upgrade
@torch.no_grad()
def A ( _UpperCAmelCase : Dict , _UpperCAmelCase : List[Any] , _UpperCAmelCase : Optional[int]=None , _UpperCAmelCase : Optional[int]=True ) -> Any:
'''simple docstring'''
from dall_e import Encoder
_UpperCAmelCase = Encoder()
if os.path.exists(_UpperCAmelCase ):
_UpperCAmelCase = torch.load(_UpperCAmelCase )
else:
_UpperCAmelCase = torch.hub.load_state_dict_from_url(_UpperCAmelCase )
if isinstance(_UpperCAmelCase , _UpperCAmelCase ):
_UpperCAmelCase = ckpt.state_dict()
encoder.load_state_dict(_UpperCAmelCase )
if config_path is not None:
_UpperCAmelCase = FlavaImageCodebookConfig.from_pretrained(_UpperCAmelCase )
else:
_UpperCAmelCase = FlavaImageCodebookConfig()
_UpperCAmelCase = FlavaImageCodebook(_UpperCAmelCase ).eval()
_UpperCAmelCase = encoder.state_dict()
_UpperCAmelCase = upgrade_state_dict(_UpperCAmelCase )
hf_model.load_state_dict(_UpperCAmelCase )
_UpperCAmelCase = hf_model.state_dict()
_UpperCAmelCase = count_parameters(_UpperCAmelCase )
_UpperCAmelCase = count_parameters(_UpperCAmelCase )
assert torch.allclose(_UpperCAmelCase , _UpperCAmelCase , atol=1E-3 )
if save_checkpoint:
hf_model.save_pretrained(_UpperCAmelCase )
else:
return hf_state_dict
if __name__ == "__main__":
UpperCAmelCase__ = 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 flava checkpoint")
parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
UpperCAmelCase__ = parser.parse_args()
convert_dalle_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
| 339 |
from functools import reduce
UpperCAmelCase__ = (
"73167176531330624919225119674426574742355349194934"
"96983520312774506326239578318016984801869478851843"
"85861560789112949495459501737958331952853208805511"
"12540698747158523863050715693290963295227443043557"
"66896648950445244523161731856403098711121722383113"
"62229893423380308135336276614282806444486645238749"
"30358907296290491560440772390713810515859307960866"
"70172427121883998797908792274921901699720888093776"
"65727333001053367881220235421809751254540594752243"
"52584907711670556013604839586446706324415722155397"
"53697817977846174064955149290862569321978468622482"
"83972241375657056057490261407972968652414535100474"
"82166370484403199890008895243450658541227588666881"
"16427171479924442928230863465674813919123162824586"
"17866458359124566529476545682848912883142607690042"
"24219022671055626321111109370544217506941658960408"
"07198403850962455444362981230987879927244284909188"
"84580156166097919133875499200524063689912560717606"
"05886116467109405077541002256983155200055935729725"
"71636269561882670428252483600823257530420752963450"
)
def A ( _UpperCAmelCase : str = N ) -> int:
'''simple docstring'''
return max(
# mypy cannot properly interpret reduce
int(reduce(lambda _UpperCAmelCase , _UpperCAmelCase : str(int(_UpperCAmelCase ) * int(_UpperCAmelCase ) ) , n[i : i + 13] ) )
for i in range(len(_UpperCAmelCase ) - 12 ) )
if __name__ == "__main__":
print(f"""{solution() = }""")
| 339 | 1 |
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results
import re
import subprocess
import sys
UpperCAmelCase__ = subprocess.check_output("git merge-base main HEAD".split()).decode("utf-8")
UpperCAmelCase__ = subprocess.check_output(f"""git diff --name-only {fork_point_sha}""".split()).decode("utf-8").split()
UpperCAmelCase__ = "|".join(sys.argv[1:])
UpperCAmelCase__ = re.compile(rf"""^({joined_dirs}).*?\.py$""")
UpperCAmelCase__ = [x for x in modified_files if regex.match(x)]
print(" ".join(relevant_modified_files), end="")
| 339 |
from __future__ import annotations
from collections.abc import Callable
UpperCAmelCase__ = list[list[float | int]]
def A ( _UpperCAmelCase : Matrix , _UpperCAmelCase : Matrix ) -> Matrix:
'''simple docstring'''
_UpperCAmelCase = len(_UpperCAmelCase )
_UpperCAmelCase = [[0 for _ in range(size + 1 )] for _ in range(_UpperCAmelCase )]
_UpperCAmelCase = 42
_UpperCAmelCase = 42
_UpperCAmelCase = 42
_UpperCAmelCase = 42
_UpperCAmelCase = 42
_UpperCAmelCase = 42
for row in range(_UpperCAmelCase ):
for col in range(_UpperCAmelCase ):
_UpperCAmelCase = matrix[row][col]
_UpperCAmelCase = vector[row][0]
_UpperCAmelCase = 0
_UpperCAmelCase = 0
while row < size and col < size:
# pivoting
_UpperCAmelCase = max((abs(augmented[rowa][col] ), rowa) for rowa in range(_UpperCAmelCase , _UpperCAmelCase ) )[
1
]
if augmented[pivot_row][col] == 0:
col += 1
continue
else:
_UpperCAmelCase , _UpperCAmelCase = augmented[pivot_row], augmented[row]
for rowa in range(row + 1 , _UpperCAmelCase ):
_UpperCAmelCase = augmented[rowa][col] / augmented[row][col]
_UpperCAmelCase = 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 ):
_UpperCAmelCase = 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 A ( _UpperCAmelCase : list[int] ) -> Callable[[int], int]:
'''simple docstring'''
_UpperCAmelCase = len(_UpperCAmelCase )
_UpperCAmelCase = [[0 for _ in range(_UpperCAmelCase )] for _ in range(_UpperCAmelCase )]
_UpperCAmelCase = [[0] for _ in range(_UpperCAmelCase )]
_UpperCAmelCase = 42
_UpperCAmelCase = 42
_UpperCAmelCase = 42
_UpperCAmelCase = 42
for x_val, y_val in enumerate(_UpperCAmelCase ):
for col in range(_UpperCAmelCase ):
_UpperCAmelCase = (x_val + 1) ** (size - col - 1)
_UpperCAmelCase = y_val
_UpperCAmelCase = 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 A ( _UpperCAmelCase : int ) -> 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 A ( _UpperCAmelCase : Callable[[int], int] = question_function , _UpperCAmelCase : int = 10 ) -> int:
'''simple docstring'''
_UpperCAmelCase = [func(_UpperCAmelCase ) for x_val in range(1 , order + 1 )]
_UpperCAmelCase = [
interpolate(data_points[:max_coeff] ) for max_coeff in range(1 , order + 1 )
]
_UpperCAmelCase = 0
_UpperCAmelCase = 42
_UpperCAmelCase = 42
for poly in polynomials:
_UpperCAmelCase = 1
while func(_UpperCAmelCase ) == poly(_UpperCAmelCase ):
x_val += 1
ret += poly(_UpperCAmelCase )
return ret
if __name__ == "__main__":
print(f"""{solution() = }""")
| 339 | 1 |
import contextlib
import os
import sqlitea
import pytest
from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy
def A ( _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : Union[str, Any] ) -> str:
'''simple docstring'''
assert isinstance(_UpperCAmelCase , _UpperCAmelCase )
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@require_sqlalchemy
@pytest.mark.parametrize('keep_in_memory' , [False, True] )
def A ( _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : str , _UpperCAmelCase : Optional[int] ) -> Any:
'''simple docstring'''
_UpperCAmelCase = tmp_path / 'cache'
_UpperCAmelCase = {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
_UpperCAmelCase = SqlDatasetReader(
'dataset' , 'sqlite:///' + sqlite_path , cache_dir=_UpperCAmelCase , keep_in_memory=_UpperCAmelCase ).read()
_check_sql_dataset(_UpperCAmelCase , _UpperCAmelCase )
@require_sqlalchemy
@pytest.mark.parametrize(
'features' , [
None,
{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'},
{'col_1': 'string', 'col_2': 'string', 'col_3': 'string'},
{'col_1': 'int32', 'col_2': 'int32', 'col_3': 'int32'},
{'col_1': 'float32', 'col_2': 'float32', 'col_3': 'float32'},
] , )
def A ( _UpperCAmelCase : Tuple , _UpperCAmelCase : int , _UpperCAmelCase : Tuple , _UpperCAmelCase : Tuple ) -> List[Any]:
'''simple docstring'''
_UpperCAmelCase = tmp_path / 'cache'
_UpperCAmelCase = {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
_UpperCAmelCase = features.copy() if features else default_expected_features
_UpperCAmelCase = (
Features({feature: Value(_UpperCAmelCase ) for feature, dtype in features.items()} ) if features is not None else None
)
_UpperCAmelCase = SqlDatasetReader('dataset' , 'sqlite:///' + sqlite_path , features=_UpperCAmelCase , cache_dir=_UpperCAmelCase ).read()
_check_sql_dataset(_UpperCAmelCase , _UpperCAmelCase )
def A ( _UpperCAmelCase : Any ) -> int:
'''simple docstring'''
with contextlib.closing(sqlitea.connect(_UpperCAmelCase ) ) as con:
_UpperCAmelCase = con.cursor()
cur.execute('SELECT * FROM dataset' )
for row in cur:
yield row
@require_sqlalchemy
def A ( _UpperCAmelCase : Any , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Dict ) -> Dict:
'''simple docstring'''
_UpperCAmelCase = tmp_path / 'cache'
_UpperCAmelCase = os.path.join(_UpperCAmelCase , 'tmp.sql' )
_UpperCAmelCase = SqlDatasetReader('dataset' , 'sqlite:///' + sqlite_path , cache_dir=_UpperCAmelCase ).read()
SqlDatasetWriter(_UpperCAmelCase , 'dataset' , 'sqlite:///' + output_sqlite_path , num_proc=1 ).write()
_UpperCAmelCase = iter_sql_file(_UpperCAmelCase )
_UpperCAmelCase = iter_sql_file(_UpperCAmelCase )
for rowa, rowa in zip(_UpperCAmelCase , _UpperCAmelCase ):
assert rowa == rowa
@require_sqlalchemy
def A ( _UpperCAmelCase : Optional[int] , _UpperCAmelCase : List[str] , _UpperCAmelCase : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
_UpperCAmelCase = tmp_path / 'cache'
_UpperCAmelCase = os.path.join(_UpperCAmelCase , 'tmp.sql' )
_UpperCAmelCase = SqlDatasetReader('dataset' , 'sqlite:///' + sqlite_path , cache_dir=_UpperCAmelCase ).read()
SqlDatasetWriter(_UpperCAmelCase , 'dataset' , 'sqlite:///' + output_sqlite_path , num_proc=2 ).write()
_UpperCAmelCase = iter_sql_file(_UpperCAmelCase )
_UpperCAmelCase = iter_sql_file(_UpperCAmelCase )
for rowa, rowa in zip(_UpperCAmelCase , _UpperCAmelCase ):
assert rowa == rowa
@require_sqlalchemy
def A ( _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Optional[int] ) -> Union[str, Any]:
'''simple docstring'''
_UpperCAmelCase = tmp_path / 'cache'
_UpperCAmelCase = os.path.join(_UpperCAmelCase , 'tmp.sql' )
_UpperCAmelCase = SqlDatasetReader('dataset' , 'sqlite:///' + sqlite_path , cache_dir=_UpperCAmelCase ).read()
with pytest.raises(_UpperCAmelCase ):
SqlDatasetWriter(_UpperCAmelCase , 'dataset' , 'sqlite:///' + output_sqlite_path , num_proc=0 ).write()
| 339 |
from __future__ import annotations
def A ( _UpperCAmelCase : list[int] ) -> bool:
'''simple docstring'''
return len(set(_UpperCAmelCase ) ) == len(_UpperCAmelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 339 | 1 |
from __future__ import annotations
def A ( _UpperCAmelCase : list[float] , _UpperCAmelCase : int ) -> Union[str, Any]:
'''simple docstring'''
print(F"Vertex\tShortest Distance from vertex {src}" )
for i, d in enumerate(_UpperCAmelCase ):
print(F"{i}\t\t{d}" )
def A ( _UpperCAmelCase : list[dict[str, int]] , _UpperCAmelCase : list[float] , _UpperCAmelCase : int ) -> str:
'''simple docstring'''
for j in range(_UpperCAmelCase ):
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = (graph[j][k] for k in ['src', 'dst', 'weight'])
if distance[u] != float('inf' ) and distance[u] + w < distance[v]:
return True
return False
def A ( _UpperCAmelCase : list[dict[str, int]] , _UpperCAmelCase : int , _UpperCAmelCase : int , _UpperCAmelCase : int ) -> list[float]:
'''simple docstring'''
_UpperCAmelCase = [float('inf' )] * vertex_count
_UpperCAmelCase = 0.0
for _ in range(vertex_count - 1 ):
for j in range(_UpperCAmelCase ):
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = (graph[j][k] for k in ['src', 'dst', 'weight'])
if distance[u] != float('inf' ) and distance[u] + w < distance[v]:
_UpperCAmelCase = distance[u] + w
_UpperCAmelCase = check_negative_cycle(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
if negative_cycle_exists:
raise Exception('Negative cycle found' )
return distance
if __name__ == "__main__":
import doctest
doctest.testmod()
UpperCAmelCase__ = int(input("Enter number of vertices: ").strip())
UpperCAmelCase__ = int(input("Enter number of edges: ").strip())
UpperCAmelCase__ = [{} for _ in range(E)]
for i in range(E):
print("Edge ", i + 1)
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ = (
int(x)
for x in input("Enter source, destination, weight: ").strip().split(" ")
)
UpperCAmelCase__ = {"src": src, "dst": dest, "weight": weight}
UpperCAmelCase__ = int(input("\nEnter shortest path source:").strip())
UpperCAmelCase__ = bellman_ford(graph, V, E, source)
print_distance(shortest_distance, 0)
| 339 |
import os
UpperCAmelCase__ = {"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1000}
def A ( _UpperCAmelCase : str ) -> int:
'''simple docstring'''
_UpperCAmelCase = 0
_UpperCAmelCase = 0
while index < len(_UpperCAmelCase ) - 1:
_UpperCAmelCase = SYMBOLS[numerals[index]]
_UpperCAmelCase = SYMBOLS[numerals[index + 1]]
if current_value < next_value:
total_value -= current_value
else:
total_value += current_value
index += 1
total_value += SYMBOLS[numerals[index]]
return total_value
def A ( _UpperCAmelCase : int ) -> str:
'''simple docstring'''
_UpperCAmelCase = ''
_UpperCAmelCase = num // 1_000
numerals += m_count * "M"
num %= 1_000
_UpperCAmelCase = num // 100
if c_count == 9:
numerals += "CM"
c_count -= 9
elif c_count == 4:
numerals += "CD"
c_count -= 4
if c_count >= 5:
numerals += "D"
c_count -= 5
numerals += c_count * "C"
num %= 100
_UpperCAmelCase = num // 10
if x_count == 9:
numerals += "XC"
x_count -= 9
elif x_count == 4:
numerals += "XL"
x_count -= 4
if x_count >= 5:
numerals += "L"
x_count -= 5
numerals += x_count * "X"
num %= 10
if num == 9:
numerals += "IX"
num -= 9
elif num == 4:
numerals += "IV"
num -= 4
if num >= 5:
numerals += "V"
num -= 5
numerals += num * "I"
return numerals
def A ( _UpperCAmelCase : str = "/p089_roman.txt" ) -> int:
'''simple docstring'''
_UpperCAmelCase = 0
with open(os.path.dirname(_UpperCAmelCase ) + roman_numerals_filename ) as filea:
_UpperCAmelCase = filea.readlines()
for line in lines:
_UpperCAmelCase = line.strip()
_UpperCAmelCase = parse_roman_numerals(_UpperCAmelCase )
_UpperCAmelCase = generate_roman_numerals(_UpperCAmelCase )
savings += len(_UpperCAmelCase ) - len(_UpperCAmelCase )
return savings
if __name__ == "__main__":
print(f"""{solution() = }""")
| 339 | 1 |
UpperCAmelCase__ = [
(1000, "M"),
(900, "CM"),
(500, "D"),
(400, "CD"),
(100, "C"),
(90, "XC"),
(50, "L"),
(40, "XL"),
(10, "X"),
(9, "IX"),
(5, "V"),
(4, "IV"),
(1, "I"),
]
def A ( _UpperCAmelCase : str ) -> int:
'''simple docstring'''
_UpperCAmelCase = {'I': 1, 'V': 5, 'X': 10, 'L': 50, 'C': 100, 'D': 500, 'M': 1_000}
_UpperCAmelCase = 0
_UpperCAmelCase = 0
while place < len(_UpperCAmelCase ):
if (place + 1 < len(_UpperCAmelCase )) and (vals[roman[place]] < vals[roman[place + 1]]):
total += vals[roman[place + 1]] - vals[roman[place]]
place += 2
else:
total += vals[roman[place]]
place += 1
return total
def A ( _UpperCAmelCase : int ) -> str:
'''simple docstring'''
_UpperCAmelCase = []
for arabic, roman in ROMAN:
((_UpperCAmelCase) , (_UpperCAmelCase)) = divmod(_UpperCAmelCase , _UpperCAmelCase )
result.append(roman * factor )
if number == 0:
break
return "".join(_UpperCAmelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 339 |
import requests
from bsa import BeautifulSoup
def A ( _UpperCAmelCase : str , _UpperCAmelCase : dict ) -> str:
'''simple docstring'''
_UpperCAmelCase = BeautifulSoup(requests.get(_UpperCAmelCase , params=_UpperCAmelCase ).content , 'html.parser' )
_UpperCAmelCase = soup.find('div' , attrs={'class': 'gs_ri'} )
_UpperCAmelCase = div.find('div' , attrs={'class': 'gs_fl'} ).find_all('a' )
return anchors[2].get_text()
if __name__ == "__main__":
UpperCAmelCase__ = {
"title": (
"Precisely geometry controlled microsupercapacitors for ultrahigh areal "
"capacitance, volumetric capacitance, and energy density"
),
"journal": "Chem. Mater.",
"volume": 30,
"pages": "3979-3990",
"year": 2018,
"hl": "en",
}
print(get_citation("https://scholar.google.com/scholar_lookup", params=params))
| 339 | 1 |
from math import factorial
UpperCAmelCase__ = {str(digit): factorial(digit) for digit in range(10)}
def A ( _UpperCAmelCase : int ) -> int:
'''simple docstring'''
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
raise TypeError('Parameter number must be int' )
if number < 0:
raise ValueError('Parameter number must be greater than or equal to 0' )
# Converts number in string to iterate on its digits and adds its factorial.
return sum(DIGIT_FACTORIAL[digit] for digit in str(_UpperCAmelCase ) )
def A ( _UpperCAmelCase : int = 60 , _UpperCAmelCase : int = 1_000_000 ) -> int:
'''simple docstring'''
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ) or not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
raise TypeError('Parameters chain_length and number_limit must be int' )
if chain_length <= 0 or number_limit <= 0:
raise ValueError(
'Parameters chain_length and number_limit must be greater than 0' )
# the counter for the chains with the exact desired length
_UpperCAmelCase = 0
# the cached sizes of the previous chains
_UpperCAmelCase = {}
for start_chain_element in range(1 , _UpperCAmelCase ):
# The temporary set will contain the elements of the chain
_UpperCAmelCase = set()
_UpperCAmelCase = 0
# Stop computing the chain when you find a cached size, a repeating item or the
# length is greater then the desired one.
_UpperCAmelCase = start_chain_element
while (
chain_element not in chain_sets_lengths
and chain_element not in chain_set
and chain_set_length <= chain_length
):
chain_set.add(_UpperCAmelCase )
chain_set_length += 1
_UpperCAmelCase = digit_factorial_sum(_UpperCAmelCase )
if chain_element in chain_sets_lengths:
chain_set_length += chain_sets_lengths[chain_element]
_UpperCAmelCase = chain_set_length
# If chain contains the exact amount of elements increase the counter
if chain_set_length == chain_length:
chains_counter += 1
return chains_counter
if __name__ == "__main__":
import doctest
doctest.testmod()
print(f"""{solution()}""")
| 339 |
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.roformer.modeling_flax_roformer import (
FlaxRoFormerForMaskedLM,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerModel,
)
class __lowerCAmelCase ( unittest.TestCase ):
def __init__( self : Optional[Any] , A : Dict , A : Union[str, Any]=13 , A : Dict=7 , A : Dict=True , A : Tuple=True , A : Union[str, Any]=True , A : int=True , A : Optional[int]=99 , A : List[str]=32 , A : List[Any]=5 , A : int=4 , A : Any=37 , A : Optional[int]="gelu" , A : Optional[Any]=0.1 , A : Any=0.1 , A : Union[str, Any]=5_12 , A : int=16 , A : List[str]=2 , A : Union[str, Any]=0.0_2 , A : Union[str, Any]=4 , ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase = parent
_UpperCAmelCase = batch_size
_UpperCAmelCase = seq_length
_UpperCAmelCase = is_training
_UpperCAmelCase = use_attention_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_choices
def _lowerCamelCase ( self : Optional[Any]) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
_UpperCAmelCase = None
if self.use_attention_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 = RoFormerConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=A , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def _lowerCamelCase ( self : List[Any]) -> List[str]:
"""simple docstring"""
_UpperCAmelCase = self.prepare_config_and_inputs()
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = config_and_inputs
_UpperCAmelCase = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
return config, inputs_dict
@require_flax
class __lowerCAmelCase ( A , unittest.TestCase ):
UpperCamelCase = True
UpperCamelCase = (
(
FlaxRoFormerModel,
FlaxRoFormerForMaskedLM,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
)
if is_flax_available()
else ()
)
def _lowerCamelCase ( self : Optional[int]) -> Any:
"""simple docstring"""
_UpperCAmelCase = FlaxRoFormerModelTester(self)
@slow
def _lowerCamelCase ( self : List[Any]) -> Dict:
"""simple docstring"""
for model_class_name in self.all_model_classes:
_UpperCAmelCase = model_class_name.from_pretrained('junnyu/roformer_chinese_small' , from_pt=A)
_UpperCAmelCase = model(np.ones((1, 1)))
self.assertIsNotNone(A)
@require_flax
class __lowerCAmelCase ( unittest.TestCase ):
@slow
def _lowerCamelCase ( self : List[Any]) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase = FlaxRoFormerForMaskedLM.from_pretrained('junnyu/roformer_chinese_base')
_UpperCAmelCase = jnp.array([[0, 1, 2, 3, 4, 5]])
_UpperCAmelCase = model(A)[0]
_UpperCAmelCase = 5_00_00
_UpperCAmelCase = (1, 6, vocab_size)
self.assertEqual(output.shape , A)
_UpperCAmelCase = jnp.array(
[[[-0.1_2_0_5, -1.0_2_6_5, 0.2_9_2_2], [-1.5_1_3_4, 0.1_9_7_4, 0.1_5_1_9], [-5.0_1_3_5, -3.9_0_0_3, -0.8_4_0_4]]])
self.assertTrue(jnp.allclose(output[:, :3, :3] , A , atol=1E-4))
| 339 | 1 |
UpperCAmelCase__ = {
"Pillow": "Pillow",
"accelerate": "accelerate>=0.11.0",
"compel": "compel==0.1.8",
"black": "black~=23.1",
"datasets": "datasets",
"filelock": "filelock",
"flax": "flax>=0.4.1",
"hf-doc-builder": "hf-doc-builder>=0.3.0",
"huggingface-hub": "huggingface-hub>=0.13.2",
"requests-mock": "requests-mock==1.10.0",
"importlib_metadata": "importlib_metadata",
"invisible-watermark": "invisible-watermark",
"isort": "isort>=5.5.4",
"jax": "jax>=0.2.8,!=0.3.2",
"jaxlib": "jaxlib>=0.1.65",
"Jinja2": "Jinja2",
"k-diffusion": "k-diffusion>=0.0.12",
"torchsde": "torchsde",
"note_seq": "note_seq",
"librosa": "librosa",
"numpy": "numpy",
"omegaconf": "omegaconf",
"parameterized": "parameterized",
"protobuf": "protobuf>=3.20.3,<4",
"pytest": "pytest",
"pytest-timeout": "pytest-timeout",
"pytest-xdist": "pytest-xdist",
"ruff": "ruff>=0.0.241",
"safetensors": "safetensors",
"sentencepiece": "sentencepiece>=0.1.91,!=0.1.92",
"scipy": "scipy",
"onnx": "onnx",
"regex": "regex!=2019.12.17",
"requests": "requests",
"tensorboard": "tensorboard",
"torch": "torch>=1.4",
"torchvision": "torchvision",
"transformers": "transformers>=4.25.1",
"urllib3": "urllib3<=2.0.0",
}
| 339 |
UpperCAmelCase__ = {}
def A ( _UpperCAmelCase : int , _UpperCAmelCase : int , _UpperCAmelCase : int ) -> int:
'''simple docstring'''
# if we are absent twice, or late 3 consecutive days,
# no further prize strings are possible
if late == 3 or absent == 2:
return 0
# if we have no days left, and have not failed any other rules,
# we have a prize string
if days == 0:
return 1
# No easy solution, so now we need to do the recursive calculation
# First, check if the combination is already in the cache, and
# if yes, return the stored value from there since we already
# know the number of possible prize strings from this point on
_UpperCAmelCase = (days, absent, late)
if key in cache:
return cache[key]
# now we calculate the three possible ways that can unfold from
# this point on, depending on our attendance today
# 1) if we are late (but not absent), the "absent" counter stays as
# it is, but the "late" counter increases by one
_UpperCAmelCase = _calculate(days - 1 , _UpperCAmelCase , late + 1 )
# 2) if we are absent, the "absent" counter increases by 1, and the
# "late" counter resets to 0
_UpperCAmelCase = _calculate(days - 1 , absent + 1 , 0 )
# 3) if we are on time, this resets the "late" counter and keeps the
# absent counter
_UpperCAmelCase = _calculate(days - 1 , _UpperCAmelCase , 0 )
_UpperCAmelCase = state_late + state_absent + state_ontime
_UpperCAmelCase = prizestrings
return prizestrings
def A ( _UpperCAmelCase : int = 30 ) -> int:
'''simple docstring'''
return _calculate(_UpperCAmelCase , absent=0 , late=0 )
if __name__ == "__main__":
print(solution())
| 339 | 1 |
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# 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.
# this script dumps information about the environment
import os
import sys
import transformers
UpperCAmelCase__ = "3"
print("Python version:", sys.version)
print("transformers version:", transformers.__version__)
try:
import torch
print("Torch version:", torch.__version__)
print("Cuda available:", torch.cuda.is_available())
print("Cuda version:", torch.version.cuda)
print("CuDNN version:", torch.backends.cudnn.version())
print("Number of GPUs available:", torch.cuda.device_count())
print("NCCL version:", torch.cuda.nccl.version())
except ImportError:
print("Torch version:", None)
try:
import deepspeed
print("DeepSpeed version:", deepspeed.__version__)
except ImportError:
print("DeepSpeed version:", None)
try:
import tensorflow as tf
print("TensorFlow version:", tf.__version__)
print("TF GPUs available:", bool(tf.config.list_physical_devices("GPU")))
print("Number of TF GPUs available:", len(tf.config.list_physical_devices("GPU")))
except ImportError:
print("TensorFlow version:", None)
| 339 |
import os
import sys
import unittest
UpperCAmelCase__ = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init # noqa: E402
# Align TRANSFORMERS_PATH in check_dummies with the current path
UpperCAmelCase__ = os.path.join(git_repo_path, "src", "diffusers")
class __lowerCAmelCase ( unittest.TestCase ):
def _lowerCamelCase ( self : Tuple) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase = find_backend(' if not is_torch_available():')
self.assertEqual(A , 'torch')
# backend_with_underscore = find_backend(" if not is_tensorflow_text_available():")
# self.assertEqual(backend_with_underscore, "tensorflow_text")
_UpperCAmelCase = find_backend(' if not (is_torch_available() and is_transformers_available()):')
self.assertEqual(A , 'torch_and_transformers')
# double_backend_with_underscore = find_backend(
# " if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
# )
# self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text")
_UpperCAmelCase = find_backend(
' if not (is_torch_available() and is_transformers_available() and is_onnx_available()):')
self.assertEqual(A , 'torch_and_transformers_and_onnx')
def _lowerCamelCase ( self : int) -> Dict:
"""simple docstring"""
_UpperCAmelCase = read_init()
# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
self.assertIn('torch' , A)
self.assertIn('torch_and_transformers' , A)
self.assertIn('flax_and_transformers' , A)
self.assertIn('torch_and_transformers_and_onnx' , A)
# Likewise, we can't assert on the exact content of a key
self.assertIn('UNet2DModel' , objects['torch'])
self.assertIn('FlaxUNet2DConditionModel' , objects['flax'])
self.assertIn('StableDiffusionPipeline' , objects['torch_and_transformers'])
self.assertIn('FlaxStableDiffusionPipeline' , objects['flax_and_transformers'])
self.assertIn('LMSDiscreteScheduler' , objects['torch_and_scipy'])
self.assertIn('OnnxStableDiffusionPipeline' , objects['torch_and_transformers_and_onnx'])
def _lowerCamelCase ( self : Union[str, Any]) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase = create_dummy_object('CONSTANT' , '\'torch\'')
self.assertEqual(A , '\nCONSTANT = None\n')
_UpperCAmelCase = create_dummy_object('function' , '\'torch\'')
self.assertEqual(
A , '\ndef function(*args, **kwargs):\n requires_backends(function, \'torch\')\n')
_UpperCAmelCase = '\nclass FakeClass(metaclass=DummyObject):\n _backends = \'torch\'\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, \'torch\')\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, \'torch\')\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, \'torch\')\n'
_UpperCAmelCase = create_dummy_object('FakeClass' , '\'torch\'')
self.assertEqual(A , A)
def _lowerCamelCase ( self : Dict) -> int:
"""simple docstring"""
_UpperCAmelCase = '# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n requires_backends(function, ["torch"])\n\n\nclass FakeClass(metaclass=DummyObject):\n _backends = ["torch"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, ["torch"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, ["torch"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, ["torch"])\n'
_UpperCAmelCase = create_dummy_files({'torch': ['CONSTANT', 'function', 'FakeClass']})
self.assertEqual(dummy_files['torch'] , A)
| 339 | 1 |
def A ( _UpperCAmelCase : int , _UpperCAmelCase : int ) -> bool:
'''simple docstring'''
return numa ^ numa < 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 339 |
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartForSequenceClassification,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
TapexTokenizer,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version
from transformers.utils.versions import require_version
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.17.0.dev0")
require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")
UpperCAmelCase__ = logging.getLogger(__name__)
@dataclass
class __lowerCAmelCase :
UpperCamelCase = field(
default='''tab_fact''' , metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''} )
UpperCamelCase = field(
default='''tab_fact''' , metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''} , )
UpperCamelCase = field(
default=1_0_2_4 , metadata={
'''help''': (
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
} , )
UpperCamelCase = field(
default=A , metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''} )
UpperCamelCase = field(
default=A , metadata={
'''help''': (
'''Whether to pad all samples to `max_seq_length`. '''
'''If False, will pad the samples dynamically when batching to the maximum length in the batch.'''
)
} , )
UpperCamelCase = field(
default=A , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of training examples to this '''
'''value if set.'''
)
} , )
UpperCamelCase = field(
default=A , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
'''value if set.'''
)
} , )
UpperCamelCase = field(
default=A , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of prediction examples to this '''
'''value if set.'''
)
} , )
UpperCamelCase = field(
default=A , metadata={'''help''': '''A csv or a json file containing the training data.'''} )
UpperCamelCase = field(
default=A , metadata={'''help''': '''A csv or a json file containing the validation data.'''} )
UpperCamelCase = field(default=A , metadata={'''help''': '''A csv or a json file containing the test data.'''} )
def _lowerCamelCase ( self : str) -> List[Any]:
"""simple docstring"""
if self.dataset_name is not None:
pass
elif self.train_file is None or self.validation_file is None:
raise ValueError('Need either a GLUE task, a training/validation file or a dataset name.')
else:
_UpperCAmelCase = self.train_file.split('.')[-1]
assert train_extension in ["csv", "json"], "`train_file` should be a csv or a json file."
_UpperCAmelCase = self.validation_file.split('.')[-1]
assert (
validation_extension == train_extension
), "`validation_file` should have the same extension (csv or json) as `train_file`."
@dataclass
class __lowerCAmelCase :
UpperCamelCase = field(
default=A , metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} )
UpperCamelCase = field(
default=A , metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
UpperCamelCase = field(
default=A , metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} )
UpperCamelCase = field(
default=A , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''} , )
UpperCamelCase = field(
default=A , metadata={'''help''': '''Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'''} , )
UpperCamelCase = field(
default='''main''' , metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''} , )
UpperCamelCase = field(
default=A , metadata={
'''help''': (
'''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
'''with private models).'''
)
} , )
def A ( ) -> Optional[int]:
'''simple docstring'''
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
_UpperCAmelCase = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith('.json' ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = parser.parse_args_into_dataclasses()
# Setup logging
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s' , datefmt='%m/%d/%Y %H:%M:%S' , handlers=[logging.StreamHandler(sys.stdout )] , )
_UpperCAmelCase = training_args.get_process_log_level()
logger.setLevel(_UpperCAmelCase )
datasets.utils.logging.set_verbosity(_UpperCAmelCase )
transformers.utils.logging.set_verbosity(_UpperCAmelCase )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
F"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
+ F"distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}" )
logger.info(F"Training/evaluation parameters {training_args}" )
# Detecting last checkpoint.
_UpperCAmelCase = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
_UpperCAmelCase = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
F"Output directory ({training_args.output_dir}) already exists and is not empty. "
'Use --overwrite_output_dir to overcome.' )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
F"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.' )
# Set seed before initializing model.
set_seed(training_args.seed )
# Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
# or specify a GLUE benchmark task (the dataset will be downloaded automatically from the datasets Hub).
#
# For JSON files, this script will use the `question` column for the input question and `table` column for the corresponding table.
#
# If the CSVs/JSONs contain only one non-label column, the script does single sentence classification on this
# single column. You can easily tweak this behavior (see below)
#
# In distributed training, the load_dataset function guarantee that only one local process can concurrently
# download the dataset.
if data_args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
_UpperCAmelCase = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , cache_dir=model_args.cache_dir )
else:
# Loading a dataset from your local files.
# CSV/JSON training and evaluation files are needed.
_UpperCAmelCase = {'train': data_args.train_file, 'validation': data_args.validation_file}
# Get the test dataset: you can provide your own CSV/JSON test file (see below)
# when you use `do_predict` without specifying a GLUE benchmark task.
if training_args.do_predict:
if data_args.test_file is not None:
_UpperCAmelCase = data_args.train_file.split('.' )[-1]
_UpperCAmelCase = data_args.test_file.split('.' )[-1]
assert (
test_extension == train_extension
), "`test_file` should have the same extension (csv or json) as `train_file`."
_UpperCAmelCase = data_args.test_file
else:
raise ValueError('Need either a GLUE task or a test file for `do_predict`.' )
for key in data_files.keys():
logger.info(F"load a local file for {key}: {data_files[key]}" )
if data_args.train_file.endswith('.csv' ):
# Loading a dataset from local csv files
_UpperCAmelCase = load_dataset('csv' , data_files=_UpperCAmelCase , cache_dir=model_args.cache_dir )
else:
# Loading a dataset from local json files
_UpperCAmelCase = load_dataset('json' , data_files=_UpperCAmelCase , cache_dir=model_args.cache_dir )
# See more about loading any type of standard or custom dataset at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Labels
_UpperCAmelCase = raw_datasets['train'].features['label'].names
_UpperCAmelCase = len(_UpperCAmelCase )
# Load pretrained model and tokenizer
#
# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
_UpperCAmelCase = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=_UpperCAmelCase , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# load tapex tokenizer
_UpperCAmelCase = TapexTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast_tokenizer , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , add_prefix_space=_UpperCAmelCase , )
_UpperCAmelCase = BartForSequenceClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool('.ckpt' in model_args.model_name_or_path ) , config=_UpperCAmelCase , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# Padding strategy
if data_args.pad_to_max_length:
_UpperCAmelCase = 'max_length'
else:
# We will pad later, dynamically at batch creation, to the max sequence length in each batch
_UpperCAmelCase = False
# Some models have set the order of the labels to use, so let's make sure we do use it.
_UpperCAmelCase = {'Refused': 0, 'Entailed': 1}
_UpperCAmelCase = {0: 'Refused', 1: 'Entailed'}
if data_args.max_seq_length > tokenizer.model_max_length:
logger.warning(
F"The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the"
F"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}." )
_UpperCAmelCase = min(data_args.max_seq_length , tokenizer.model_max_length )
def preprocess_tabfact_function(_UpperCAmelCase : Union[str, Any] ):
# Tokenize the texts
def _convert_table_text_to_pandas(_UpperCAmelCase : Dict ):
_UpperCAmelCase = [_table_row.split('#' ) for _table_row in _table_text.strip('\n' ).split('\n' )]
_UpperCAmelCase = pd.DataFrame.from_records(_table_content[1:] , columns=_table_content[0] )
return _table_pd
_UpperCAmelCase = examples['statement']
_UpperCAmelCase = list(map(_convert_table_text_to_pandas , examples['table_text'] ) )
_UpperCAmelCase = tokenizer(_UpperCAmelCase , _UpperCAmelCase , padding=_UpperCAmelCase , max_length=_UpperCAmelCase , truncation=_UpperCAmelCase )
_UpperCAmelCase = examples['label']
return result
with training_args.main_process_first(desc='dataset map pre-processing' ):
_UpperCAmelCase = raw_datasets.map(
_UpperCAmelCase , batched=_UpperCAmelCase , load_from_cache_file=not data_args.overwrite_cache , desc='Running tokenizer on dataset' , )
if training_args.do_train:
if "train" not in raw_datasets:
raise ValueError('--do_train requires a train dataset' )
_UpperCAmelCase = raw_datasets['train']
if data_args.max_train_samples is not None:
_UpperCAmelCase = train_dataset.select(range(data_args.max_train_samples ) )
if training_args.do_eval:
if "validation" not in raw_datasets and "validation_matched" not in raw_datasets:
raise ValueError('--do_eval requires a validation dataset' )
_UpperCAmelCase = raw_datasets['validation']
if data_args.max_eval_samples is not None:
_UpperCAmelCase = eval_dataset.select(range(data_args.max_eval_samples ) )
if training_args.do_predict or data_args.test_file is not None:
if "test" not in raw_datasets and "test_matched" not in raw_datasets:
raise ValueError('--do_predict requires a test dataset' )
_UpperCAmelCase = raw_datasets['test']
if data_args.max_predict_samples is not None:
_UpperCAmelCase = predict_dataset.select(range(data_args.max_predict_samples ) )
# Log a few random samples from the training set:
if training_args.do_train:
for index in random.sample(range(len(_UpperCAmelCase ) ) , 3 ):
logger.info(F"Sample {index} of the training set: {train_dataset[index]}." )
# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
# predictions and label_ids field) and has to return a dictionary string to float.
def compute_metrics(_UpperCAmelCase : EvalPrediction ):
_UpperCAmelCase = p.predictions[0] if isinstance(p.predictions , _UpperCAmelCase ) else p.predictions
_UpperCAmelCase = np.argmax(_UpperCAmelCase , axis=1 )
return {"accuracy": (preds == p.label_ids).astype(np.floataa ).mean().item()}
# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
if data_args.pad_to_max_length:
_UpperCAmelCase = default_data_collator
elif training_args.fpaa:
_UpperCAmelCase = DataCollatorWithPadding(_UpperCAmelCase , pad_to_multiple_of=8 )
else:
_UpperCAmelCase = None
# Initialize our Trainer
_UpperCAmelCase = Trainer(
model=_UpperCAmelCase , args=_UpperCAmelCase , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , compute_metrics=_UpperCAmelCase , tokenizer=_UpperCAmelCase , data_collator=_UpperCAmelCase , )
# Training
if training_args.do_train:
_UpperCAmelCase = None
if training_args.resume_from_checkpoint is not None:
_UpperCAmelCase = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
_UpperCAmelCase = last_checkpoint
_UpperCAmelCase = trainer.train(resume_from_checkpoint=_UpperCAmelCase )
_UpperCAmelCase = train_result.metrics
_UpperCAmelCase = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(_UpperCAmelCase )
)
_UpperCAmelCase = min(_UpperCAmelCase , len(_UpperCAmelCase ) )
trainer.save_model() # Saves the tokenizer too for easy upload
trainer.log_metrics('train' , _UpperCAmelCase )
trainer.save_metrics('train' , _UpperCAmelCase )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info('*** Evaluate ***' )
_UpperCAmelCase = trainer.evaluate(eval_dataset=_UpperCAmelCase )
_UpperCAmelCase = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(_UpperCAmelCase )
_UpperCAmelCase = min(_UpperCAmelCase , len(_UpperCAmelCase ) )
trainer.log_metrics('eval' , _UpperCAmelCase )
trainer.save_metrics('eval' , _UpperCAmelCase )
if training_args.do_predict:
logger.info('*** Predict ***' )
# Removing the `label` columns because it contains -1 and Trainer won't like that.
_UpperCAmelCase = predict_dataset.remove_columns('label' )
_UpperCAmelCase = trainer.predict(_UpperCAmelCase , metric_key_prefix='predict' ).predictions
_UpperCAmelCase = np.argmax(_UpperCAmelCase , axis=1 )
_UpperCAmelCase = os.path.join(training_args.output_dir , 'predict_results_tabfact.txt' )
if trainer.is_world_process_zero():
with open(_UpperCAmelCase , 'w' ) as writer:
logger.info('***** Predict Results *****' )
writer.write('index\tprediction\n' )
for index, item in enumerate(_UpperCAmelCase ):
_UpperCAmelCase = label_list[item]
writer.write(F"{index}\t{item}\n" )
_UpperCAmelCase = {'finetuned_from': model_args.model_name_or_path, 'tasks': 'text-classification'}
if training_args.push_to_hub:
trainer.push_to_hub(**_UpperCAmelCase )
else:
trainer.create_model_card(**_UpperCAmelCase )
def A ( _UpperCAmelCase : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 339 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
"s-JoL/Open-Llama-V1": "https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json",
}
class __lowerCAmelCase ( A ):
UpperCamelCase = '''open-llama'''
def __init__( self : str , A : List[Any]=10_00_00 , A : Tuple=40_96 , A : Tuple=1_10_08 , A : List[str]=32 , A : Tuple=32 , A : Optional[Any]="silu" , A : int=20_48 , A : Optional[Any]=0.0_2 , A : Dict=1E-6 , A : Optional[Any]=True , A : List[Any]=0 , A : Dict=1 , A : int=2 , A : Dict=False , A : Optional[int]=True , A : List[Any]=0.1 , A : str=0.1 , A : Dict=True , A : Optional[Any]=True , A : Dict=None , **A : Union[str, Any] , ) -> Dict:
"""simple docstring"""
_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' , A)
_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=A , bos_token_id=A , eos_token_id=A , tie_word_embeddings=A , **A , )
def _lowerCamelCase ( self : List[str]) -> Union[str, Any]:
"""simple docstring"""
if self.rope_scaling is None:
return
if not isinstance(self.rope_scaling , A) or len(self.rope_scaling) != 2:
raise ValueError(
'`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '
F"got {self.rope_scaling}")
_UpperCAmelCase = self.rope_scaling.get('type' , A)
_UpperCAmelCase = self.rope_scaling.get('factor' , A)
if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
raise ValueError(
F"`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}")
if rope_scaling_factor is None or not isinstance(A , A) or rope_scaling_factor <= 1.0:
raise ValueError(F"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}")
| 339 |
# This code is adapted from OpenAI's release
# https://github.com/openai/human-eval/blob/master/human_eval/execution.py
import contextlib
import faulthandler
import io
import multiprocessing
import os
import platform
import signal
import tempfile
def A ( _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Dict , _UpperCAmelCase : Dict , _UpperCAmelCase : Dict ) -> Any:
'''simple docstring'''
_UpperCAmelCase = multiprocessing.Manager()
_UpperCAmelCase = manager.list()
_UpperCAmelCase = multiprocessing.Process(target=_UpperCAmelCase , args=(check_program, result, timeout) )
p.start()
p.join(timeout=timeout + 1 )
if p.is_alive():
p.kill()
if not result:
result.append('timed out' )
return {
"task_id": task_id,
"passed": result[0] == "passed",
"result": result[0],
"completion_id": completion_id,
}
def A ( _UpperCAmelCase : str , _UpperCAmelCase : List[str] , _UpperCAmelCase : Dict ) -> Optional[int]:
'''simple docstring'''
with create_tempdir():
# These system calls are needed when cleaning up tempdir.
import os
import shutil
_UpperCAmelCase = shutil.rmtree
_UpperCAmelCase = os.rmdir
_UpperCAmelCase = os.chdir
# Disable functionalities that can make destructive changes to the test.
reliability_guard()
# Run program.
try:
_UpperCAmelCase = {}
with swallow_io():
with time_limit(_UpperCAmelCase ):
exec(_UpperCAmelCase , _UpperCAmelCase )
result.append('passed' )
except TimeoutException:
result.append('timed out' )
except BaseException as e:
result.append(F"failed: {e}" )
# Needed for cleaning up.
_UpperCAmelCase = rmtree
_UpperCAmelCase = rmdir
_UpperCAmelCase = chdir
@contextlib.contextmanager
def A ( _UpperCAmelCase : Union[str, Any] ) -> Any:
'''simple docstring'''
def signal_handler(_UpperCAmelCase : List[Any] , _UpperCAmelCase : Dict ):
raise TimeoutException('Timed out!' )
signal.setitimer(signal.ITIMER_REAL , _UpperCAmelCase )
signal.signal(signal.SIGALRM , _UpperCAmelCase )
try:
yield
finally:
signal.setitimer(signal.ITIMER_REAL , 0 )
@contextlib.contextmanager
def A ( ) -> Optional[int]:
'''simple docstring'''
_UpperCAmelCase = WriteOnlyStringIO()
with contextlib.redirect_stdout(_UpperCAmelCase ):
with contextlib.redirect_stderr(_UpperCAmelCase ):
with redirect_stdin(_UpperCAmelCase ):
yield
@contextlib.contextmanager
def A ( ) -> Any:
'''simple docstring'''
with tempfile.TemporaryDirectory() as dirname:
with chdir(_UpperCAmelCase ):
yield dirname
class __lowerCAmelCase ( A ):
pass
class __lowerCAmelCase ( io.StringIO ):
def _lowerCamelCase ( self : Tuple , *A : str , **A : Any) -> Any:
"""simple docstring"""
raise OSError
def _lowerCamelCase ( self : List[str] , *A : Optional[Any] , **A : Optional[Any]) -> Optional[int]:
"""simple docstring"""
raise OSError
def _lowerCamelCase ( self : str , *A : List[str] , **A : List[Any]) -> Union[str, Any]:
"""simple docstring"""
raise OSError
def _lowerCamelCase ( self : Union[str, Any] , *A : Optional[Any] , **A : List[str]) -> Optional[int]:
"""simple docstring"""
return False
class __lowerCAmelCase ( contextlib._RedirectStream ): # type: ignore
UpperCamelCase = '''stdin'''
@contextlib.contextmanager
def A ( _UpperCAmelCase : List[Any] ) -> Dict:
'''simple docstring'''
if root == ".":
yield
return
_UpperCAmelCase = os.getcwd()
os.chdir(_UpperCAmelCase )
try:
yield
except BaseException as exc:
raise exc
finally:
os.chdir(_UpperCAmelCase )
def A ( _UpperCAmelCase : List[str]=None ) -> Any:
'''simple docstring'''
if maximum_memory_bytes is not None:
import resource
resource.setrlimit(resource.RLIMIT_AS , (maximum_memory_bytes, maximum_memory_bytes) )
resource.setrlimit(resource.RLIMIT_DATA , (maximum_memory_bytes, maximum_memory_bytes) )
if not platform.uname().system == "Darwin":
resource.setrlimit(resource.RLIMIT_STACK , (maximum_memory_bytes, maximum_memory_bytes) )
faulthandler.disable()
import builtins
_UpperCAmelCase = None
_UpperCAmelCase = None
import os
_UpperCAmelCase = '1'
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
import shutil
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
import subprocess
_UpperCAmelCase = None # type: ignore
_UpperCAmelCase = None
import sys
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
| 339 | 1 |
UpperCAmelCase__ = {}
def A ( _UpperCAmelCase : int , _UpperCAmelCase : int , _UpperCAmelCase : int ) -> int:
'''simple docstring'''
# if we are absent twice, or late 3 consecutive days,
# no further prize strings are possible
if late == 3 or absent == 2:
return 0
# if we have no days left, and have not failed any other rules,
# we have a prize string
if days == 0:
return 1
# No easy solution, so now we need to do the recursive calculation
# First, check if the combination is already in the cache, and
# if yes, return the stored value from there since we already
# know the number of possible prize strings from this point on
_UpperCAmelCase = (days, absent, late)
if key in cache:
return cache[key]
# now we calculate the three possible ways that can unfold from
# this point on, depending on our attendance today
# 1) if we are late (but not absent), the "absent" counter stays as
# it is, but the "late" counter increases by one
_UpperCAmelCase = _calculate(days - 1 , _UpperCAmelCase , late + 1 )
# 2) if we are absent, the "absent" counter increases by 1, and the
# "late" counter resets to 0
_UpperCAmelCase = _calculate(days - 1 , absent + 1 , 0 )
# 3) if we are on time, this resets the "late" counter and keeps the
# absent counter
_UpperCAmelCase = _calculate(days - 1 , _UpperCAmelCase , 0 )
_UpperCAmelCase = state_late + state_absent + state_ontime
_UpperCAmelCase = prizestrings
return prizestrings
def A ( _UpperCAmelCase : int = 30 ) -> int:
'''simple docstring'''
return _calculate(_UpperCAmelCase , absent=0 , late=0 )
if __name__ == "__main__":
print(solution())
| 339 |
import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock
import torch
from ..state import AcceleratorState, PartialState
from ..utils import (
gather,
is_bnb_available,
is_comet_ml_available,
is_datasets_available,
is_deepspeed_available,
is_mps_available,
is_safetensors_available,
is_tensorboard_available,
is_torch_version,
is_tpu_available,
is_transformers_available,
is_wandb_available,
is_xpu_available,
)
def A ( _UpperCAmelCase : Tuple , _UpperCAmelCase : Union[str, Any]=False ) -> str:
'''simple docstring'''
try:
_UpperCAmelCase = os.environ[key]
except KeyError:
# KEY isn't set, default to `default`.
_UpperCAmelCase = default
else:
# KEY is set, convert it to True or False.
try:
_UpperCAmelCase = strtobool(_UpperCAmelCase )
except ValueError:
# More values are supported, but let's keep the message simple.
raise ValueError(F"If set, {key} must be yes or no." )
return _value
UpperCAmelCase__ = parse_flag_from_env("RUN_SLOW", default=False)
def A ( _UpperCAmelCase : List[str] ) -> List[str]:
'''simple docstring'''
return unittest.skip('Test was skipped' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Dict ) -> str:
'''simple docstring'''
return unittest.skipUnless(_run_slow_tests , 'test is slow' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Any ) -> str:
'''simple docstring'''
return unittest.skipUnless(not torch.cuda.is_available() , 'test requires only a CPU' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Dict ) -> Dict:
'''simple docstring'''
return unittest.skipUnless(torch.cuda.is_available() , 'test requires a GPU' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Optional[Any] ) -> List[Any]:
'''simple docstring'''
return unittest.skipUnless(is_xpu_available() , 'test requires a XPU' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Optional[int] ) -> List[str]:
'''simple docstring'''
return unittest.skipUnless(is_mps_available() , 'test requires a `mps` backend support in `torch`' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
return unittest.skipUnless(
is_transformers_available() and is_datasets_available() , 'test requires the Hugging Face suite' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : str ) -> str:
'''simple docstring'''
return unittest.skipUnless(is_bnb_available() , 'test requires the bitsandbytes library' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
return unittest.skipUnless(is_tpu_available() , 'test requires TPU' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Optional[Any] ) -> str:
'''simple docstring'''
return unittest.skipUnless(torch.cuda.device_count() == 1 , 'test requires a GPU' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Tuple ) -> int:
'''simple docstring'''
return unittest.skipUnless(torch.xpu.device_count() == 1 , 'test requires a XPU' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Any ) -> Optional[int]:
'''simple docstring'''
return unittest.skipUnless(torch.cuda.device_count() > 1 , 'test requires multiple GPUs' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Tuple ) -> Any:
'''simple docstring'''
return unittest.skipUnless(torch.xpu.device_count() > 1 , 'test requires multiple XPUs' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Any ) -> Optional[int]:
'''simple docstring'''
return unittest.skipUnless(is_safetensors_available() , 'test requires safetensors' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : List[Any] ) -> Dict:
'''simple docstring'''
return unittest.skipUnless(is_deepspeed_available() , 'test requires DeepSpeed' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Optional[int] ) -> str:
'''simple docstring'''
return unittest.skipUnless(is_torch_version('>=' , '1.12.0' ) , 'test requires torch version >= 1.12.0' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Any=None , _UpperCAmelCase : List[Any]=None ) -> Dict:
'''simple docstring'''
if test_case is None:
return partial(_UpperCAmelCase , version=_UpperCAmelCase )
return unittest.skipUnless(is_torch_version('>=' , _UpperCAmelCase ) , F"test requires torch version >= {version}" )(_UpperCAmelCase )
def A ( _UpperCAmelCase : List[str] ) -> int:
'''simple docstring'''
return unittest.skipUnless(is_tensorboard_available() , 'test requires Tensorboard' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Union[str, Any] ) -> Union[str, Any]:
'''simple docstring'''
return unittest.skipUnless(is_wandb_available() , 'test requires wandb' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : List[str] ) -> Optional[int]:
'''simple docstring'''
return unittest.skipUnless(is_comet_ml_available() , 'test requires comet_ml' )(_UpperCAmelCase )
UpperCAmelCase__ = (
any([is_wandb_available(), is_tensorboard_available()]) and not is_comet_ml_available()
)
def A ( _UpperCAmelCase : List[str] ) -> Any:
'''simple docstring'''
return unittest.skipUnless(
_atleast_one_tracker_available , 'test requires at least one tracker to be available and for `comet_ml` to not be installed' , )(_UpperCAmelCase )
class __lowerCAmelCase ( unittest.TestCase ):
UpperCamelCase = True
@classmethod
def _lowerCamelCase ( cls : List[Any]) -> Tuple:
"""simple docstring"""
_UpperCAmelCase = tempfile.mkdtemp()
@classmethod
def _lowerCamelCase ( cls : Union[str, Any]) -> str:
"""simple docstring"""
if os.path.exists(cls.tmpdir):
shutil.rmtree(cls.tmpdir)
def _lowerCamelCase ( self : List[str]) -> List[Any]:
"""simple docstring"""
if self.clear_on_setup:
for path in Path(self.tmpdir).glob('**/*'):
if path.is_file():
path.unlink()
elif path.is_dir():
shutil.rmtree(A)
class __lowerCAmelCase ( unittest.TestCase ):
def _lowerCamelCase ( self : Dict) -> Tuple:
"""simple docstring"""
super().tearDown()
# Reset the state of the AcceleratorState singleton.
AcceleratorState._reset_state()
PartialState._reset_state()
class __lowerCAmelCase ( unittest.TestCase ):
def _lowerCamelCase ( self : Optional[int] , A : Union[mock.Mock, List[mock.Mock]]) -> Tuple:
"""simple docstring"""
_UpperCAmelCase = mocks if isinstance(A , (tuple, list)) else [mocks]
for m in self.mocks:
m.start()
self.addCleanup(m.stop)
def A ( _UpperCAmelCase : List[Any] ) -> int:
'''simple docstring'''
_UpperCAmelCase = AcceleratorState()
_UpperCAmelCase = tensor[None].clone().to(state.device )
_UpperCAmelCase = gather(_UpperCAmelCase ).cpu()
_UpperCAmelCase = tensor[0].cpu()
for i in range(tensors.shape[0] ):
if not torch.equal(tensors[i] , _UpperCAmelCase ):
return False
return True
class __lowerCAmelCase :
def __init__( self : Optional[Any] , A : Union[str, Any] , A : Optional[int] , A : str) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase = returncode
_UpperCAmelCase = stdout
_UpperCAmelCase = stderr
async def A ( _UpperCAmelCase : str , _UpperCAmelCase : Optional[int] ) -> Optional[Any]:
'''simple docstring'''
while True:
_UpperCAmelCase = await stream.readline()
if line:
callback(_UpperCAmelCase )
else:
break
async def A ( _UpperCAmelCase : Optional[int] , _UpperCAmelCase : List[str]=None , _UpperCAmelCase : str=None , _UpperCAmelCase : str=None , _UpperCAmelCase : Dict=False , _UpperCAmelCase : Union[str, Any]=False ) -> _RunOutput:
'''simple docstring'''
if echo:
print('\nRunning: ' , ' '.join(_UpperCAmelCase ) )
_UpperCAmelCase = await asyncio.create_subprocess_exec(
cmd[0] , *cmd[1:] , stdin=_UpperCAmelCase , stdout=asyncio.subprocess.PIPE , stderr=asyncio.subprocess.PIPE , env=_UpperCAmelCase , )
# note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe
# https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait
#
# If it starts hanging, will need to switch to the following code. The problem is that no data
# will be seen until it's done and if it hangs for example there will be no debug info.
# out, err = await p.communicate()
# return _RunOutput(p.returncode, out, err)
_UpperCAmelCase = []
_UpperCAmelCase = []
def tee(_UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : List[Any] , _UpperCAmelCase : str , _UpperCAmelCase : str="" ):
_UpperCAmelCase = line.decode('utf-8' ).rstrip()
sink.append(_UpperCAmelCase )
if not quiet:
print(_UpperCAmelCase , _UpperCAmelCase , file=_UpperCAmelCase )
# XXX: the timeout doesn't seem to make any difference here
await asyncio.wait(
[
asyncio.create_task(_read_stream(p.stdout , lambda _UpperCAmelCase : tee(_UpperCAmelCase , _UpperCAmelCase , sys.stdout , label='stdout:' ) ) ),
asyncio.create_task(_read_stream(p.stderr , lambda _UpperCAmelCase : tee(_UpperCAmelCase , _UpperCAmelCase , sys.stderr , label='stderr:' ) ) ),
] , timeout=_UpperCAmelCase , )
return _RunOutput(await p.wait() , _UpperCAmelCase , _UpperCAmelCase )
def A ( _UpperCAmelCase : str , _UpperCAmelCase : Dict=None , _UpperCAmelCase : str=None , _UpperCAmelCase : str=180 , _UpperCAmelCase : List[Any]=False , _UpperCAmelCase : List[Any]=True ) -> _RunOutput:
'''simple docstring'''
_UpperCAmelCase = asyncio.get_event_loop()
_UpperCAmelCase = loop.run_until_complete(
_stream_subprocess(_UpperCAmelCase , env=_UpperCAmelCase , stdin=_UpperCAmelCase , timeout=_UpperCAmelCase , quiet=_UpperCAmelCase , echo=_UpperCAmelCase ) )
_UpperCAmelCase = ' '.join(_UpperCAmelCase )
if result.returncode > 0:
_UpperCAmelCase = '\n'.join(result.stderr )
raise RuntimeError(
F"'{cmd_str}' failed with returncode {result.returncode}\n\n"
F"The combined stderr from workers follows:\n{stderr}" )
return result
class __lowerCAmelCase ( A ):
pass
def A ( _UpperCAmelCase : List[str] , _UpperCAmelCase : str=False ) -> Tuple:
'''simple docstring'''
try:
_UpperCAmelCase = subprocess.check_output(_UpperCAmelCase , stderr=subprocess.STDOUT )
if return_stdout:
if hasattr(_UpperCAmelCase , 'decode' ):
_UpperCAmelCase = output.decode('utf-8' )
return output
except subprocess.CalledProcessError as e:
raise SubprocessCallException(
F"Command `{' '.join(_UpperCAmelCase )}` failed with the following error:\n\n{e.output.decode()}" ) from e
| 339 | 1 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import CLIPImageProcessor, CLIPProcessor
@require_vision
class __lowerCAmelCase ( unittest.TestCase ):
def _lowerCamelCase ( self : List[str]) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase = tempfile.mkdtemp()
# fmt: off
_UpperCAmelCase = ['l', 'o', 'w', 'e', 'r', 's', 't', 'i', 'd', 'n', 'lo', 'l</w>', 'w</w>', 'r</w>', 't</w>', 'low</w>', 'er</w>', 'lowest</w>', 'newer</w>', 'wider', '<unk>', '<|startoftext|>', '<|endoftext|>']
# fmt: on
_UpperCAmelCase = dict(zip(A , range(len(A))))
_UpperCAmelCase = ['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>', '']
_UpperCAmelCase = {'unk_token': '<unk>'}
_UpperCAmelCase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'])
_UpperCAmelCase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['merges_file'])
with open(self.vocab_file , 'w' , encoding='utf-8') as fp:
fp.write(json.dumps(A) + '\n')
with open(self.merges_file , 'w' , encoding='utf-8') as fp:
fp.write('\n'.join(A))
_UpperCAmelCase = {
'do_resize': True,
'size': 20,
'do_center_crop': True,
'crop_size': 18,
'do_normalize': True,
'image_mean': [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
'image_std': [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
}
_UpperCAmelCase = os.path.join(self.tmpdirname , A)
with open(self.image_processor_file , 'w' , encoding='utf-8') as fp:
json.dump(A , A)
def _lowerCamelCase ( self : Union[str, Any] , **A : Optional[Any]) -> Union[str, Any]:
"""simple docstring"""
return CLIPTokenizer.from_pretrained(self.tmpdirname , **A)
def _lowerCamelCase ( self : Optional[int] , **A : Tuple) -> str:
"""simple docstring"""
return CLIPTokenizerFast.from_pretrained(self.tmpdirname , **A)
def _lowerCamelCase ( self : Union[str, Any] , **A : Any) -> Optional[Any]:
"""simple docstring"""
return CLIPImageProcessor.from_pretrained(self.tmpdirname , **A)
def _lowerCamelCase ( self : Dict) -> List[Any]:
"""simple docstring"""
shutil.rmtree(self.tmpdirname)
def _lowerCamelCase ( self : Any) -> Tuple:
"""simple docstring"""
_UpperCAmelCase = [np.random.randint(2_55 , size=(3, 30, 4_00) , dtype=np.uinta)]
_UpperCAmelCase = [Image.fromarray(np.moveaxis(A , 0 , -1)) for x in image_inputs]
return image_inputs
def _lowerCamelCase ( self : int) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = self.get_rust_tokenizer()
_UpperCAmelCase = self.get_image_processor()
_UpperCAmelCase = CLIPProcessor(tokenizer=A , image_processor=A)
processor_slow.save_pretrained(self.tmpdirname)
_UpperCAmelCase = CLIPProcessor.from_pretrained(self.tmpdirname , use_fast=A)
_UpperCAmelCase = CLIPProcessor(tokenizer=A , image_processor=A)
processor_fast.save_pretrained(self.tmpdirname)
_UpperCAmelCase = CLIPProcessor.from_pretrained(self.tmpdirname)
self.assertEqual(processor_slow.tokenizer.get_vocab() , tokenizer_slow.get_vocab())
self.assertEqual(processor_fast.tokenizer.get_vocab() , tokenizer_fast.get_vocab())
self.assertEqual(tokenizer_slow.get_vocab() , tokenizer_fast.get_vocab())
self.assertIsInstance(processor_slow.tokenizer , A)
self.assertIsInstance(processor_fast.tokenizer , A)
self.assertEqual(processor_slow.image_processor.to_json_string() , image_processor.to_json_string())
self.assertEqual(processor_fast.image_processor.to_json_string() , image_processor.to_json_string())
self.assertIsInstance(processor_slow.image_processor , A)
self.assertIsInstance(processor_fast.image_processor , A)
def _lowerCamelCase ( self : List[str]) -> int:
"""simple docstring"""
_UpperCAmelCase = CLIPProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor())
processor.save_pretrained(self.tmpdirname)
_UpperCAmelCase = self.get_tokenizer(bos_token='(BOS)' , eos_token='(EOS)')
_UpperCAmelCase = self.get_image_processor(do_normalize=A , padding_value=1.0)
_UpperCAmelCase = CLIPProcessor.from_pretrained(
self.tmpdirname , bos_token='(BOS)' , eos_token='(EOS)' , do_normalize=A , padding_value=1.0)
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab())
self.assertIsInstance(processor.tokenizer , A)
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string())
self.assertIsInstance(processor.image_processor , A)
def _lowerCamelCase ( self : Optional[int]) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase = self.get_image_processor()
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = CLIPProcessor(tokenizer=A , image_processor=A)
_UpperCAmelCase = self.prepare_image_inputs()
_UpperCAmelCase = image_processor(A , return_tensors='np')
_UpperCAmelCase = processor(images=A , return_tensors='np')
for key in input_image_proc.keys():
self.assertAlmostEqual(input_image_proc[key].sum() , input_processor[key].sum() , delta=1E-2)
def _lowerCamelCase ( self : str) -> Any:
"""simple docstring"""
_UpperCAmelCase = self.get_image_processor()
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = CLIPProcessor(tokenizer=A , image_processor=A)
_UpperCAmelCase = 'lower newer'
_UpperCAmelCase = processor(text=A)
_UpperCAmelCase = tokenizer(A)
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key])
def _lowerCamelCase ( self : List[Any]) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase = self.get_image_processor()
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = CLIPProcessor(tokenizer=A , image_processor=A)
_UpperCAmelCase = 'lower newer'
_UpperCAmelCase = self.prepare_image_inputs()
_UpperCAmelCase = processor(text=A , images=A)
self.assertListEqual(list(inputs.keys()) , ['input_ids', 'attention_mask', 'pixel_values'])
# test if it raises when no input is passed
with pytest.raises(A):
processor()
def _lowerCamelCase ( self : Tuple) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase = self.get_image_processor()
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = CLIPProcessor(tokenizer=A , image_processor=A)
_UpperCAmelCase = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
_UpperCAmelCase = processor.batch_decode(A)
_UpperCAmelCase = tokenizer.batch_decode(A)
self.assertListEqual(A , A)
def _lowerCamelCase ( self : List[Any]) -> Tuple:
"""simple docstring"""
_UpperCAmelCase = self.get_image_processor()
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = CLIPProcessor(tokenizer=A , image_processor=A)
_UpperCAmelCase = 'lower newer'
_UpperCAmelCase = self.prepare_image_inputs()
_UpperCAmelCase = processor(text=A , images=A)
self.assertListEqual(list(inputs.keys()) , processor.model_input_names)
| 339 |
from __future__ import annotations
UpperCAmelCase__ = list[list[int]]
# assigning initial values to the grid
UpperCAmelCase__ = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5],
[0, 5, 0, 0, 9, 0, 6, 0, 0],
[1, 3, 0, 0, 0, 0, 2, 5, 0],
[0, 0, 0, 0, 0, 0, 0, 7, 4],
[0, 0, 5, 2, 0, 6, 3, 0, 0],
]
# a grid with no solution
UpperCAmelCase__ = [
[5, 0, 6, 5, 0, 8, 4, 0, 3],
[5, 2, 0, 0, 0, 0, 0, 0, 2],
[1, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5],
[0, 5, 0, 0, 9, 0, 6, 0, 0],
[1, 3, 0, 0, 0, 0, 2, 5, 0],
[0, 0, 0, 0, 0, 0, 0, 7, 4],
[0, 0, 5, 2, 0, 6, 3, 0, 0],
]
def A ( _UpperCAmelCase : Matrix , _UpperCAmelCase : int , _UpperCAmelCase : int , _UpperCAmelCase : int ) -> bool:
'''simple docstring'''
for i in range(9 ):
if grid[row][i] == n or grid[i][column] == n:
return False
for i in range(3 ):
for j in range(3 ):
if grid[(row - row % 3) + i][(column - column % 3) + j] == n:
return False
return True
def A ( _UpperCAmelCase : Matrix ) -> tuple[int, int] | None:
'''simple docstring'''
for i in range(9 ):
for j in range(9 ):
if grid[i][j] == 0:
return i, j
return None
def A ( _UpperCAmelCase : Matrix ) -> Matrix | None:
'''simple docstring'''
if location := find_empty_location(_UpperCAmelCase ):
_UpperCAmelCase , _UpperCAmelCase = location
else:
# If the location is ``None``, then the grid is solved.
return grid
for digit in range(1 , 10 ):
if is_safe(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
_UpperCAmelCase = digit
if sudoku(_UpperCAmelCase ) is not None:
return grid
_UpperCAmelCase = 0
return None
def A ( _UpperCAmelCase : Matrix ) -> None:
'''simple docstring'''
for row in grid:
for cell in row:
print(_UpperCAmelCase , end=' ' )
print()
if __name__ == "__main__":
# make a copy of grid so that you can compare with the unmodified grid
for example_grid in (initial_grid, no_solution):
print("\nExample grid:\n" + "=" * 20)
print_solution(example_grid)
print("\nExample grid solution:")
UpperCAmelCase__ = sudoku(example_grid)
if solution is not None:
print_solution(solution)
else:
print("Cannot find a solution.")
| 339 | 1 |
UpperCAmelCase__ = "0.18.2"
from .configuration_utils import ConfigMixin
from .utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_inflect_available,
is_invisible_watermark_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_librosa_available,
is_note_seq_available,
is_onnx_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
is_transformers_available,
is_transformers_version,
is_unidecode_available,
logging,
)
try:
if not is_onnx_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_onnx_objects import * # noqa F403
else:
from .pipelines import OnnxRuntimeModel
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_pt_objects import * # noqa F403
else:
from .models import (
AutoencoderKL,
ControlNetModel,
ModelMixin,
PriorTransformer,
TaFilmDecoder,
TransformeraDModel,
UNetaDModel,
UNetaDConditionModel,
UNetaDModel,
UNetaDConditionModel,
VQModel,
)
from .optimization import (
get_constant_schedule,
get_constant_schedule_with_warmup,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
get_scheduler,
)
from .pipelines import (
AudioPipelineOutput,
ConsistencyModelPipeline,
DanceDiffusionPipeline,
DDIMPipeline,
DDPMPipeline,
DiffusionPipeline,
DiTPipeline,
ImagePipelineOutput,
KarrasVePipeline,
LDMPipeline,
LDMSuperResolutionPipeline,
PNDMPipeline,
RePaintPipeline,
ScoreSdeVePipeline,
)
from .schedulers import (
CMStochasticIterativeScheduler,
DDIMInverseScheduler,
DDIMParallelScheduler,
DDIMScheduler,
DDPMParallelScheduler,
DDPMScheduler,
DEISMultistepScheduler,
DPMSolverMultistepInverseScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
HeunDiscreteScheduler,
IPNDMScheduler,
KarrasVeScheduler,
KDPMaAncestralDiscreteScheduler,
KDPMaDiscreteScheduler,
PNDMScheduler,
RePaintScheduler,
SchedulerMixin,
ScoreSdeVeScheduler,
UnCLIPScheduler,
UniPCMultistepScheduler,
VQDiffusionScheduler,
)
from .training_utils import EMAModel
try:
if not (is_torch_available() and is_scipy_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_scipy_objects import * # noqa F403
else:
from .schedulers import LMSDiscreteScheduler
try:
if not (is_torch_available() and is_torchsde_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_torchsde_objects import * # noqa F403
else:
from .schedulers import DPMSolverSDEScheduler
try:
if not (is_torch_available() and is_transformers_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .pipelines import (
AltDiffusionImgaImgPipeline,
AltDiffusionPipeline,
AudioLDMPipeline,
CycleDiffusionPipeline,
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
ImageTextPipelineOutput,
KandinskyImgaImgPipeline,
KandinskyInpaintPipeline,
KandinskyPipeline,
KandinskyPriorPipeline,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaControlnetPipeline,
KandinskyVaaImgaImgPipeline,
KandinskyVaaInpaintPipeline,
KandinskyVaaPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
KandinskyVaaPriorPipeline,
LDMTextToImagePipeline,
PaintByExamplePipeline,
SemanticStableDiffusionPipeline,
ShapEImgaImgPipeline,
ShapEPipeline,
StableDiffusionAttendAndExcitePipeline,
StableDiffusionControlNetImgaImgPipeline,
StableDiffusionControlNetInpaintPipeline,
StableDiffusionControlNetPipeline,
StableDiffusionDepthaImgPipeline,
StableDiffusionDiffEditPipeline,
StableDiffusionImageVariationPipeline,
StableDiffusionImgaImgPipeline,
StableDiffusionInpaintPipeline,
StableDiffusionInpaintPipelineLegacy,
StableDiffusionInstructPixaPixPipeline,
StableDiffusionLatentUpscalePipeline,
StableDiffusionLDMaDPipeline,
StableDiffusionModelEditingPipeline,
StableDiffusionPanoramaPipeline,
StableDiffusionParadigmsPipeline,
StableDiffusionPipeline,
StableDiffusionPipelineSafe,
StableDiffusionPixaPixZeroPipeline,
StableDiffusionSAGPipeline,
StableDiffusionUpscalePipeline,
StableUnCLIPImgaImgPipeline,
StableUnCLIPPipeline,
TextToVideoSDPipeline,
TextToVideoZeroPipeline,
UnCLIPImageVariationPipeline,
UnCLIPPipeline,
UniDiffuserModel,
UniDiffuserPipeline,
UniDiffuserTextDecoder,
VersatileDiffusionDualGuidedPipeline,
VersatileDiffusionImageVariationPipeline,
VersatileDiffusionPipeline,
VersatileDiffusionTextToImagePipeline,
VideoToVideoSDPipeline,
VQDiffusionPipeline,
)
try:
if not (is_torch_available() and is_transformers_available() and is_invisible_watermark_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_transformers_and_invisible_watermark_objects import * # noqa F403
else:
from .pipelines import StableDiffusionXLImgaImgPipeline, StableDiffusionXLPipeline
try:
if not (is_torch_available() and is_transformers_available() and is_k_diffusion_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_transformers_and_k_diffusion_objects import * # noqa F403
else:
from .pipelines import StableDiffusionKDiffusionPipeline
try:
if not (is_torch_available() and is_transformers_available() and is_onnx_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_transformers_and_onnx_objects import * # noqa F403
else:
from .pipelines import (
OnnxStableDiffusionImgaImgPipeline,
OnnxStableDiffusionInpaintPipeline,
OnnxStableDiffusionInpaintPipelineLegacy,
OnnxStableDiffusionPipeline,
OnnxStableDiffusionUpscalePipeline,
StableDiffusionOnnxPipeline,
)
try:
if not (is_torch_available() and is_librosa_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_librosa_objects import * # noqa F403
else:
from .pipelines import AudioDiffusionPipeline, Mel
try:
if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_transformers_and_torch_and_note_seq_objects import * # noqa F403
else:
from .pipelines import SpectrogramDiffusionPipeline
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_flax_objects import * # noqa F403
else:
from .models.controlnet_flax import FlaxControlNetModel
from .models.modeling_flax_utils import FlaxModelMixin
from .models.unet_ad_condition_flax import FlaxUNetaDConditionModel
from .models.vae_flax import FlaxAutoencoderKL
from .pipelines import FlaxDiffusionPipeline
from .schedulers import (
FlaxDDIMScheduler,
FlaxDDPMScheduler,
FlaxDPMSolverMultistepScheduler,
FlaxKarrasVeScheduler,
FlaxLMSDiscreteScheduler,
FlaxPNDMScheduler,
FlaxSchedulerMixin,
FlaxScoreSdeVeScheduler,
)
try:
if not (is_flax_available() and is_transformers_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_flax_and_transformers_objects import * # noqa F403
else:
from .pipelines import (
FlaxStableDiffusionControlNetPipeline,
FlaxStableDiffusionImgaImgPipeline,
FlaxStableDiffusionInpaintPipeline,
FlaxStableDiffusionPipeline,
)
try:
if not (is_note_seq_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_note_seq_objects import * # noqa F403
else:
from .pipelines import MidiProcessor
| 339 |
import numpy as np
from nltk.translate import meteor_score
import datasets
from datasets.config import importlib_metadata, version
UpperCAmelCase__ = version.parse(importlib_metadata.version("nltk"))
if NLTK_VERSION >= version.Version("3.6.4"):
from nltk import word_tokenize
UpperCAmelCase__ = "\\n@inproceedings{banarjee2005,\n title = {{METEOR}: An Automatic Metric for {MT} Evaluation with Improved Correlation with Human Judgments},\n author = {Banerjee, Satanjeev and Lavie, Alon},\n booktitle = {Proceedings of the {ACL} Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization},\n month = jun,\n year = {2005},\n address = {Ann Arbor, Michigan},\n publisher = {Association for Computational Linguistics},\n url = {https://www.aclweb.org/anthology/W05-0909},\n pages = {65--72},\n}\n"
UpperCAmelCase__ = "\\nMETEOR, an automatic metric for machine translation evaluation\nthat is based on a generalized concept of unigram matching between the\nmachine-produced translation and human-produced reference translations.\nUnigrams can be matched based on their surface forms, stemmed forms,\nand meanings; furthermore, METEOR can be easily extended to include more\nadvanced matching strategies. Once all generalized unigram matches\nbetween the two strings have been found, METEOR computes a score for\nthis matching using a combination of unigram-precision, unigram-recall, and\na measure of fragmentation that is designed to directly capture how\nwell-ordered the matched words in the machine translation are in relation\nto the reference.\n\nMETEOR gets an R correlation value of 0.347 with human evaluation on the Arabic\ndata and 0.331 on the Chinese data. This is shown to be an improvement on\nusing simply unigram-precision, unigram-recall and their harmonic F1\ncombination.\n"
UpperCAmelCase__ = "\nComputes METEOR score of translated segments against one or more references.\nArgs:\n predictions: list of predictions to score. Each prediction\n should be a string with tokens separated by spaces.\n references: list of reference for each prediction. Each\n reference should be a string with tokens separated by spaces.\n alpha: Parameter for controlling relative weights of precision and recall. default: 0.9\n beta: Parameter for controlling shape of penalty as a function of fragmentation. default: 3\n gamma: Relative weight assigned to fragmentation penalty. default: 0.5\nReturns:\n 'meteor': meteor score.\nExamples:\n\n >>> meteor = datasets.load_metric('meteor')\n >>> predictions = [\"It is a guide to action which ensures that the military always obeys the commands of the party\"]\n >>> references = [\"It is a guide to action that ensures that the military will forever heed Party commands\"]\n >>> results = meteor.compute(predictions=predictions, references=references)\n >>> print(round(results[\"meteor\"], 4))\n 0.6944\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __lowerCAmelCase ( datasets.Metric ):
def _lowerCamelCase ( self : List[Any]) -> List[Any]:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'predictions': datasets.Value('string' , id='sequence'),
'references': datasets.Value('string' , id='sequence'),
}) , codebase_urls=['https://github.com/nltk/nltk/blob/develop/nltk/translate/meteor_score.py'] , reference_urls=[
'https://www.nltk.org/api/nltk.translate.html#module-nltk.translate.meteor_score',
'https://en.wikipedia.org/wiki/METEOR',
] , )
def _lowerCamelCase ( self : Optional[Any] , A : List[str]) -> List[Any]:
"""simple docstring"""
import nltk
nltk.download('wordnet')
if NLTK_VERSION >= version.Version('3.6.5'):
nltk.download('punkt')
if NLTK_VERSION >= version.Version('3.6.6'):
nltk.download('omw-1.4')
def _lowerCamelCase ( self : Optional[Any] , A : Tuple , A : Optional[int] , A : List[Any]=0.9 , A : Optional[Any]=3 , A : Optional[int]=0.5) -> Any:
"""simple docstring"""
if NLTK_VERSION >= version.Version('3.6.5'):
_UpperCAmelCase = [
meteor_score.single_meteor_score(
word_tokenize(A) , word_tokenize(A) , alpha=A , beta=A , gamma=A)
for ref, pred in zip(A , A)
]
else:
_UpperCAmelCase = [
meteor_score.single_meteor_score(A , A , alpha=A , beta=A , gamma=A)
for ref, pred in zip(A , A)
]
return {"meteor": np.mean(A)}
| 339 | 1 |
import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch
import numpy as np
import pytest
from absl.testing import parameterized
import datasets
from datasets import load_metric
from .utils import for_all_test_methods, local, slow
# mark all tests as integration
UpperCAmelCase__ = pytest.mark.integration
UpperCAmelCase__ = {"comet"}
UpperCAmelCase__ = importlib.util.find_spec("fairseq") is not None
UpperCAmelCase__ = {"code_eval"}
UpperCAmelCase__ = os.name == "nt"
UpperCAmelCase__ = {"bertscore", "frugalscore", "perplexity"}
UpperCAmelCase__ = importlib.util.find_spec("transformers") is not None
def A ( _UpperCAmelCase : Any ) -> Dict:
'''simple docstring'''
@wraps(_UpperCAmelCase )
def wrapper(self : Tuple , _UpperCAmelCase : Any ):
if not _has_fairseq and metric_name in REQUIRE_FAIRSEQ:
self.skipTest('"test requires Fairseq"' )
else:
test_case(self , _UpperCAmelCase )
return wrapper
def A ( _UpperCAmelCase : str ) -> Any:
'''simple docstring'''
@wraps(_UpperCAmelCase )
def wrapper(self : List[str] , _UpperCAmelCase : Tuple ):
if not _has_transformers and metric_name in REQUIRE_TRANSFORMERS:
self.skipTest('"test requires transformers"' )
else:
test_case(self , _UpperCAmelCase )
return wrapper
def A ( _UpperCAmelCase : Dict ) -> List[str]:
'''simple docstring'''
@wraps(_UpperCAmelCase )
def wrapper(self : List[Any] , _UpperCAmelCase : Any ):
if _on_windows and metric_name in UNSUPPORTED_ON_WINDOWS:
self.skipTest('"test not supported on Windows"' )
else:
test_case(self , _UpperCAmelCase )
return wrapper
def A ( ) -> List[Any]:
'''simple docstring'''
_UpperCAmelCase = [metric_dir.split(os.sep )[-2] for metric_dir in glob.glob('./metrics/*/' )]
return [{"testcase_name": x, "metric_name": x} for x in metrics if x != "gleu"] # gleu is unfinished
@parameterized.named_parameters(get_local_metric_names() )
@for_all_test_methods(
A , A , A )
@local
class __lowerCAmelCase ( parameterized.TestCase ):
UpperCamelCase = {}
UpperCamelCase = None
@pytest.mark.filterwarnings('ignore:metric_module_factory is deprecated:FutureWarning')
@pytest.mark.filterwarnings('ignore:load_metric is deprecated:FutureWarning')
def _lowerCamelCase ( self : Optional[Any] , A : List[str]) -> str:
"""simple docstring"""
_UpperCAmelCase = '[...]'
_UpperCAmelCase = importlib.import_module(
datasets.load.metric_module_factory(os.path.join('metrics' , A)).module_path)
_UpperCAmelCase = datasets.load.import_main_class(metric_module.__name__ , dataset=A)
# check parameters
_UpperCAmelCase = inspect.signature(metric._compute).parameters
self.assertTrue(all(p.kind != p.VAR_KEYWORD for p in parameters.values())) # no **kwargs
# run doctest
with self.patch_intensive_calls(A , metric_module.__name__):
with self.use_local_metrics():
try:
_UpperCAmelCase = doctest.testmod(A , verbose=A , raise_on_error=A)
except doctest.UnexpectedException as e:
raise e.exc_info[1] # raise the exception that doctest caught
self.assertEqual(results.failed , 0)
self.assertGreater(results.attempted , 1)
@slow
def _lowerCamelCase ( self : Optional[Any] , A : Union[str, Any]) -> Union[str, Any]:
"""simple docstring"""
_UpperCAmelCase = '[...]'
_UpperCAmelCase = importlib.import_module(
datasets.load.metric_module_factory(os.path.join('metrics' , A)).module_path)
# run doctest
with self.use_local_metrics():
_UpperCAmelCase = doctest.testmod(A , verbose=A , raise_on_error=A)
self.assertEqual(results.failed , 0)
self.assertGreater(results.attempted , 1)
@contextmanager
def _lowerCamelCase ( self : List[str] , A : Optional[Any] , A : Any) -> Dict:
"""simple docstring"""
if metric_name in self.INTENSIVE_CALLS_PATCHER:
with self.INTENSIVE_CALLS_PATCHER[metric_name](A):
yield
else:
yield
@contextmanager
def _lowerCamelCase ( self : Dict) -> Any:
"""simple docstring"""
def load_local_metric(A : Tuple , *A : int , **A : List[Any]):
return load_metric(os.path.join('metrics' , A) , *A , **A)
with patch('datasets.load_metric') as mock_load_metric:
_UpperCAmelCase = load_local_metric
yield
@classmethod
def _lowerCamelCase ( cls : Optional[int] , A : int) -> Tuple:
"""simple docstring"""
def wrapper(A : Union[str, Any]):
_UpperCAmelCase = contextmanager(A)
_UpperCAmelCase = patcher
return patcher
return wrapper
@LocalMetricTest.register_intensive_calls_patcher('bleurt' )
def A ( _UpperCAmelCase : Union[str, Any] ) -> List[str]:
'''simple docstring'''
import tensorflow.compat.va as tf
from bleurt.score import Predictor
tf.flags.DEFINE_string('sv' , '' , '' ) # handle pytest cli flags
class __lowerCAmelCase ( A ):
def _lowerCamelCase ( self : Any , A : List[str]) -> Dict:
"""simple docstring"""
assert len(input_dict['input_ids']) == 2
return np.array([1.0_3, 1.0_4])
# mock predict_fn which is supposed to do a forward pass with a bleurt model
with patch('bleurt.score._create_predictor' ) as mock_create_predictor:
_UpperCAmelCase = MockedPredictor()
yield
@LocalMetricTest.register_intensive_calls_patcher('bertscore' )
def A ( _UpperCAmelCase : Any ) -> Tuple:
'''simple docstring'''
import torch
def bert_cos_score_idf(_UpperCAmelCase : str , _UpperCAmelCase : int , *_UpperCAmelCase : int , **_UpperCAmelCase : int ):
return torch.tensor([[1.0, 1.0, 1.0]] * len(_UpperCAmelCase ) )
# mock get_model which is supposed to do download a bert model
# mock bert_cos_score_idf which is supposed to do a forward pass with a bert model
with patch('bert_score.scorer.get_model' ), patch(
'bert_score.scorer.bert_cos_score_idf' ) as mock_bert_cos_score_idf:
_UpperCAmelCase = bert_cos_score_idf
yield
@LocalMetricTest.register_intensive_calls_patcher('comet' )
def A ( _UpperCAmelCase : Tuple ) -> Optional[Any]:
'''simple docstring'''
def load_from_checkpoint(_UpperCAmelCase : Optional[Any] ):
class __lowerCAmelCase :
def _lowerCamelCase ( self : Union[str, Any] , A : Union[str, Any] , *A : Union[str, Any] , **A : Union[str, Any]) -> List[Any]:
"""simple docstring"""
assert len(A) == 2
_UpperCAmelCase = [0.1_9, 0.9_2]
return scores, sum(A) / len(A)
return Model()
# mock load_from_checkpoint which is supposed to do download a bert model
# mock load_from_checkpoint which is supposed to do download a bert model
with patch('comet.download_model' ) as mock_download_model:
_UpperCAmelCase = None
with patch('comet.load_from_checkpoint' ) as mock_load_from_checkpoint:
_UpperCAmelCase = load_from_checkpoint
yield
def A ( ) -> Optional[int]:
'''simple docstring'''
_UpperCAmelCase = load_metric(os.path.join('metrics' , 'seqeval' ) )
_UpperCAmelCase = 'ERROR'
_UpperCAmelCase = F"Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {wrong_scheme}"
with pytest.raises(_UpperCAmelCase , match=re.escape(_UpperCAmelCase ) ):
metric.compute(predictions=[] , references=[] , scheme=_UpperCAmelCase )
| 339 |
import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
UpperCAmelCase__ = {
"tiny.en": "https://openaipublic.azureedge.net/main/whisper/models/d3dd57d32accea0b295c96e26691aa14d8822fac7d9d27d5dc00b4ca2826dd03/tiny.en.pt",
"tiny": "https://openaipublic.azureedge.net/main/whisper/models/65147644a518d12f04e32d6f3b26facc3f8dd46e5390956a9424a650c0ce22b9/tiny.pt",
"base.en": "https://openaipublic.azureedge.net/main/whisper/models/25a8566e1d0c1e2231d1c762132cd20e0f96a85d16145c3a00adf5d1ac670ead/base.en.pt",
"base": "https://openaipublic.azureedge.net/main/whisper/models/ed3a0b6b1c0edf879ad9b11b1af5a0e6ab5db9205f891f668f8b0e6c6326e34e/base.pt",
"small.en": "https://openaipublic.azureedge.net/main/whisper/models/f953ad0fd29cacd07d5a9eda5624af0f6bcf2258be67c92b79389873d91e0872/small.en.pt",
"small": "https://openaipublic.azureedge.net/main/whisper/models/9ecf779972d90ba49c06d968637d720dd632c55bbf19d441fb42bf17a411e794/small.pt",
"medium.en": "https://openaipublic.azureedge.net/main/whisper/models/d7440d1dc186f76616474e0ff0b3b6b879abc9d1a4926b7adfa41db2d497ab4f/medium.en.pt",
"medium": "https://openaipublic.azureedge.net/main/whisper/models/345ae4da62f9b3d59415adc60127b97c714f32e89e936602e85993674d08dcb1/medium.pt",
"large": "https://openaipublic.azureedge.net/main/whisper/models/e4b87e7e0bf463eb8e6956e646f1e277e901512310def2c24bf0e11bd3c28e9a/large.pt",
"large-v2": "https://openaipublic.azureedge.net/main/whisper/models/81f7c96c852ee8fc832187b0132e569d6c3065a3252ed18e56effd0b6a73e524/large-v2.pt",
}
def A ( _UpperCAmelCase : Optional[int] ) -> str:
'''simple docstring'''
_UpperCAmelCase = ['layers', 'blocks']
for k in ignore_keys:
state_dict.pop(_UpperCAmelCase , _UpperCAmelCase )
UpperCAmelCase__ = {
"blocks": "layers",
"mlp.0": "fc1",
"mlp.2": "fc2",
"mlp_ln": "final_layer_norm",
".attn.query": ".self_attn.q_proj",
".attn.key": ".self_attn.k_proj",
".attn.value": ".self_attn.v_proj",
".attn_ln": ".self_attn_layer_norm",
".attn.out": ".self_attn.out_proj",
".cross_attn.query": ".encoder_attn.q_proj",
".cross_attn.key": ".encoder_attn.k_proj",
".cross_attn.value": ".encoder_attn.v_proj",
".cross_attn_ln": ".encoder_attn_layer_norm",
".cross_attn.out": ".encoder_attn.out_proj",
"decoder.ln.": "decoder.layer_norm.",
"encoder.ln.": "encoder.layer_norm.",
"token_embedding": "embed_tokens",
"encoder.positional_embedding": "encoder.embed_positions.weight",
"decoder.positional_embedding": "decoder.embed_positions.weight",
"ln_post": "layer_norm",
}
def A ( _UpperCAmelCase : Dict ) -> Optional[int]:
'''simple docstring'''
_UpperCAmelCase = list(s_dict.keys() )
for key in keys:
_UpperCAmelCase = key
for k, v in WHISPER_MAPPING.items():
if k in key:
_UpperCAmelCase = new_key.replace(_UpperCAmelCase , _UpperCAmelCase )
print(F"{key} -> {new_key}" )
_UpperCAmelCase = s_dict.pop(_UpperCAmelCase )
return s_dict
def A ( _UpperCAmelCase : List[Any] ) -> Union[str, Any]:
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase = emb.weight.shape
_UpperCAmelCase = nn.Linear(_UpperCAmelCase , _UpperCAmelCase , bias=_UpperCAmelCase )
_UpperCAmelCase = emb.weight.data
return lin_layer
def A ( _UpperCAmelCase : str , _UpperCAmelCase : str ) -> bytes:
'''simple docstring'''
os.makedirs(_UpperCAmelCase , exist_ok=_UpperCAmelCase )
_UpperCAmelCase = os.path.basename(_UpperCAmelCase )
_UpperCAmelCase = url.split('/' )[-2]
_UpperCAmelCase = os.path.join(_UpperCAmelCase , _UpperCAmelCase )
if os.path.exists(_UpperCAmelCase ) and not os.path.isfile(_UpperCAmelCase ):
raise RuntimeError(F"{download_target} exists and is not a regular file" )
if os.path.isfile(_UpperCAmelCase ):
_UpperCAmelCase = open(_UpperCAmelCase , 'rb' ).read()
if hashlib.shaaaa(_UpperCAmelCase ).hexdigest() == expected_shaaaa:
return model_bytes
else:
warnings.warn(F"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file" )
with urllib.request.urlopen(_UpperCAmelCase ) as source, open(_UpperCAmelCase , 'wb' ) as output:
with tqdm(
total=int(source.info().get('Content-Length' ) ) , ncols=80 , unit='iB' , unit_scale=_UpperCAmelCase , unit_divisor=1_024 ) as loop:
while True:
_UpperCAmelCase = source.read(8_192 )
if not buffer:
break
output.write(_UpperCAmelCase )
loop.update(len(_UpperCAmelCase ) )
_UpperCAmelCase = open(_UpperCAmelCase , 'rb' ).read()
if hashlib.shaaaa(_UpperCAmelCase ).hexdigest() != expected_shaaaa:
raise RuntimeError(
'Model has been downloaded but the SHA256 checksum does not not match. Please retry loading the model.' )
return model_bytes
def A ( _UpperCAmelCase : List[Any] , _UpperCAmelCase : Any ) -> Optional[int]:
'''simple docstring'''
if ".pt" not in checkpoint_path:
_UpperCAmelCase = _download(_MODELS[checkpoint_path] )
else:
_UpperCAmelCase = torch.load(_UpperCAmelCase , map_location='cpu' )
_UpperCAmelCase = original_checkpoint['dims']
_UpperCAmelCase = original_checkpoint['model_state_dict']
_UpperCAmelCase = state_dict['decoder.token_embedding.weight']
remove_ignore_keys_(_UpperCAmelCase )
rename_keys(_UpperCAmelCase )
_UpperCAmelCase = True
_UpperCAmelCase = state_dict['decoder.layers.0.fc1.weight'].shape[0]
_UpperCAmelCase = WhisperConfig(
vocab_size=dimensions['n_vocab'] , encoder_ffn_dim=_UpperCAmelCase , decoder_ffn_dim=_UpperCAmelCase , num_mel_bins=dimensions['n_mels'] , d_model=dimensions['n_audio_state'] , max_target_positions=dimensions['n_text_ctx'] , encoder_layers=dimensions['n_audio_layer'] , encoder_attention_heads=dimensions['n_audio_head'] , decoder_layers=dimensions['n_text_layer'] , decoder_attention_heads=dimensions['n_text_state'] , max_source_positions=dimensions['n_audio_ctx'] , )
_UpperCAmelCase = WhisperForConditionalGeneration(_UpperCAmelCase )
_UpperCAmelCase , _UpperCAmelCase = model.model.load_state_dict(_UpperCAmelCase , strict=_UpperCAmelCase )
if len(_UpperCAmelCase ) > 0 and not set(_UpperCAmelCase ) <= {
"encoder.embed_positions.weights",
"decoder.embed_positions.weights",
}:
raise ValueError(
'Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights` are allowed to be missing,'
F" but all the following weights are missing {missing}" )
if tie_embeds:
_UpperCAmelCase = make_linear_from_emb(model.model.decoder.embed_tokens )
else:
_UpperCAmelCase = proj_out_weights
model.save_pretrained(_UpperCAmelCase )
if __name__ == "__main__":
UpperCAmelCase__ = argparse.ArgumentParser()
# # Required parameters
parser.add_argument("--checkpoint_path", type=str, help="Patht to the downloaded checkpoints")
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
UpperCAmelCase__ = parser.parse_args()
convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)
| 339 | 1 |
UpperCAmelCase__ = {
"a": "AAAAA",
"b": "AAAAB",
"c": "AAABA",
"d": "AAABB",
"e": "AABAA",
"f": "AABAB",
"g": "AABBA",
"h": "AABBB",
"i": "ABAAA",
"j": "BBBAA",
"k": "ABAAB",
"l": "ABABA",
"m": "ABABB",
"n": "ABBAA",
"o": "ABBAB",
"p": "ABBBA",
"q": "ABBBB",
"r": "BAAAA",
"s": "BAAAB",
"t": "BAABA",
"u": "BAABB",
"v": "BBBAB",
"w": "BABAA",
"x": "BABAB",
"y": "BABBA",
"z": "BABBB",
" ": " ",
}
UpperCAmelCase__ = {value: key for key, value in encode_dict.items()}
def A ( _UpperCAmelCase : str ) -> str:
'''simple docstring'''
_UpperCAmelCase = ''
for letter in word.lower():
if letter.isalpha() or letter == " ":
encoded += encode_dict[letter]
else:
raise Exception('encode() accepts only letters of the alphabet and spaces' )
return encoded
def A ( _UpperCAmelCase : str ) -> str:
'''simple docstring'''
if set(_UpperCAmelCase ) - {"A", "B", " "} != set():
raise Exception('decode() accepts only \'A\', \'B\' and spaces' )
_UpperCAmelCase = ''
for word in coded.split():
while len(_UpperCAmelCase ) != 0:
decoded += decode_dict[word[:5]]
_UpperCAmelCase = word[5:]
decoded += " "
return decoded.strip()
if __name__ == "__main__":
from doctest import testmod
testmod()
| 339 |
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
UpperCAmelCase__ = datasets.utils.logging.get_logger(__name__)
class __lowerCAmelCase ( folder_based_builder.FolderBasedBuilderConfig ):
UpperCamelCase = None
UpperCamelCase = None
class __lowerCAmelCase ( folder_based_builder.FolderBasedBuilder ):
UpperCamelCase = datasets.Audio()
UpperCamelCase = '''audio'''
UpperCamelCase = AudioFolderConfig
UpperCamelCase = 42 # definition at the bottom of the script
UpperCamelCase = AudioClassification(audio_column='''audio''' , label_column='''label''' )
UpperCAmelCase__ = [
".aiff",
".au",
".avr",
".caf",
".flac",
".htk",
".svx",
".mat4",
".mat5",
".mpc2k",
".ogg",
".paf",
".pvf",
".raw",
".rf64",
".sd2",
".sds",
".ircam",
".voc",
".w64",
".wav",
".nist",
".wavex",
".wve",
".xi",
".mp3",
".opus",
]
UpperCAmelCase__ = AUDIO_EXTENSIONS
| 339 | 1 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
UpperCAmelCase__ = ""
UpperCAmelCase__ = ""
UpperCAmelCase__ = ""
UpperCAmelCase__ = 1 # (0 is vertical, 1 is horizontal)
def A ( ) -> None:
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase = get_dataset(_UpperCAmelCase , _UpperCAmelCase )
print('Processing...' )
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = update_image_and_anno(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
for index, image in enumerate(_UpperCAmelCase ):
# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
_UpperCAmelCase = random_chars(32 )
_UpperCAmelCase = paths[index].split(os.sep )[-1].rsplit('.' , 1 )[0]
_UpperCAmelCase = F"{OUTPUT_DIR}/{file_name}_FLIP_{letter_code}"
cva.imwrite(F"/{file_root}.jpg" , _UpperCAmelCase , [cva.IMWRITE_JPEG_QUALITY, 85] )
print(F"Success {index+1}/{len(_UpperCAmelCase )} with {file_name}" )
_UpperCAmelCase = []
for anno in new_annos[index]:
_UpperCAmelCase = F"{anno[0]} {anno[1]} {anno[2]} {anno[3]} {anno[4]}"
annos_list.append(_UpperCAmelCase )
with open(F"/{file_root}.txt" , 'w' ) as outfile:
outfile.write('\n'.join(line for line in annos_list ) )
def A ( _UpperCAmelCase : str , _UpperCAmelCase : str ) -> tuple[list, list]:
'''simple docstring'''
_UpperCAmelCase = []
_UpperCAmelCase = []
for label_file in glob.glob(os.path.join(_UpperCAmelCase , '*.txt' ) ):
_UpperCAmelCase = label_file.split(os.sep )[-1].rsplit('.' , 1 )[0]
with open(_UpperCAmelCase ) as in_file:
_UpperCAmelCase = in_file.readlines()
_UpperCAmelCase = os.path.join(_UpperCAmelCase , F"{label_name}.jpg" )
_UpperCAmelCase = []
for obj_list in obj_lists:
_UpperCAmelCase = obj_list.rstrip('\n' ).split(' ' )
boxes.append(
[
int(obj[0] ),
float(obj[1] ),
float(obj[2] ),
float(obj[3] ),
float(obj[4] ),
] )
if not boxes:
continue
img_paths.append(_UpperCAmelCase )
labels.append(_UpperCAmelCase )
return img_paths, labels
def A ( _UpperCAmelCase : list , _UpperCAmelCase : list , _UpperCAmelCase : int = 1 ) -> tuple[list, list, list]:
'''simple docstring'''
_UpperCAmelCase = []
_UpperCAmelCase = []
_UpperCAmelCase = []
for idx in range(len(_UpperCAmelCase ) ):
_UpperCAmelCase = []
_UpperCAmelCase = img_list[idx]
path_list.append(_UpperCAmelCase )
_UpperCAmelCase = anno_list[idx]
_UpperCAmelCase = cva.imread(_UpperCAmelCase )
if flip_type == 1:
_UpperCAmelCase = cva.flip(_UpperCAmelCase , _UpperCAmelCase )
for bbox in img_annos:
_UpperCAmelCase = 1 - bbox[1]
new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]] )
elif flip_type == 0:
_UpperCAmelCase = cva.flip(_UpperCAmelCase , _UpperCAmelCase )
for bbox in img_annos:
_UpperCAmelCase = 1 - bbox[2]
new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]] )
new_annos_lists.append(_UpperCAmelCase )
new_imgs_list.append(_UpperCAmelCase )
return new_imgs_list, new_annos_lists, path_list
def A ( _UpperCAmelCase : int = 32 ) -> str:
'''simple docstring'''
assert number_char > 1, "The number of character should greater than 1"
_UpperCAmelCase = ascii_lowercase + digits
return "".join(random.choice(_UpperCAmelCase ) for _ in range(_UpperCAmelCase ) )
if __name__ == "__main__":
main()
print("DONE ✅")
| 339 |
import sys
from collections import defaultdict
class __lowerCAmelCase :
def __init__( self : int) -> str:
"""simple docstring"""
_UpperCAmelCase = []
def _lowerCamelCase ( self : Any , A : List[str]) -> int:
"""simple docstring"""
return self.node_position[vertex]
def _lowerCamelCase ( self : Optional[Any] , A : Optional[int] , A : str) -> List[str]:
"""simple docstring"""
_UpperCAmelCase = pos
def _lowerCamelCase ( self : Tuple , A : Tuple , A : Dict , A : List[str] , A : Optional[Any]) -> Dict:
"""simple docstring"""
if start > size // 2 - 1:
return
else:
if 2 * start + 2 >= size:
_UpperCAmelCase = 2 * start + 1
else:
if heap[2 * start + 1] < heap[2 * start + 2]:
_UpperCAmelCase = 2 * start + 1
else:
_UpperCAmelCase = 2 * start + 2
if heap[smallest_child] < heap[start]:
_UpperCAmelCase , _UpperCAmelCase = heap[smallest_child], positions[smallest_child]
_UpperCAmelCase , _UpperCAmelCase = (
heap[start],
positions[start],
)
_UpperCAmelCase , _UpperCAmelCase = temp, tempa
_UpperCAmelCase = self.get_position(positions[smallest_child])
self.set_position(
positions[smallest_child] , self.get_position(positions[start]))
self.set_position(positions[start] , A)
self.top_to_bottom(A , A , A , A)
def _lowerCamelCase ( self : Optional[int] , A : str , A : Optional[Any] , A : Optional[int] , A : str) -> Any:
"""simple docstring"""
_UpperCAmelCase = position[index]
while index != 0:
_UpperCAmelCase = int((index - 2) / 2) if index % 2 == 0 else int((index - 1) / 2)
if val < heap[parent]:
_UpperCAmelCase = heap[parent]
_UpperCAmelCase = position[parent]
self.set_position(position[parent] , A)
else:
_UpperCAmelCase = val
_UpperCAmelCase = temp
self.set_position(A , A)
break
_UpperCAmelCase = parent
else:
_UpperCAmelCase = val
_UpperCAmelCase = temp
self.set_position(A , 0)
def _lowerCamelCase ( self : Union[str, Any] , A : Optional[int] , A : Tuple) -> str:
"""simple docstring"""
_UpperCAmelCase = len(A) // 2 - 1
for i in range(A , -1 , -1):
self.top_to_bottom(A , A , len(A) , A)
def _lowerCamelCase ( self : Optional[int] , A : int , A : str) -> List[str]:
"""simple docstring"""
_UpperCAmelCase = positions[0]
_UpperCAmelCase = sys.maxsize
self.top_to_bottom(A , 0 , len(A) , A)
return temp
def A ( _UpperCAmelCase : int ) -> Any:
'''simple docstring'''
_UpperCAmelCase = Heap()
_UpperCAmelCase = [0] * len(_UpperCAmelCase )
_UpperCAmelCase = [-1] * len(_UpperCAmelCase ) # Neighboring Tree Vertex of selected vertex
# Minimum Distance of explored vertex with neighboring vertex of partial tree
# formed in graph
_UpperCAmelCase = [] # Heap of Distance of vertices from their neighboring vertex
_UpperCAmelCase = []
for vertex in range(len(_UpperCAmelCase ) ):
distance_tv.append(sys.maxsize )
positions.append(_UpperCAmelCase )
heap.node_position.append(_UpperCAmelCase )
_UpperCAmelCase = []
_UpperCAmelCase = 1
_UpperCAmelCase = sys.maxsize
for neighbor, distance in adjacency_list[0]:
_UpperCAmelCase = 0
_UpperCAmelCase = distance
heap.heapify(_UpperCAmelCase , _UpperCAmelCase )
for _ in range(1 , len(_UpperCAmelCase ) ):
_UpperCAmelCase = heap.delete_minimum(_UpperCAmelCase , _UpperCAmelCase )
if visited[vertex] == 0:
tree_edges.append((nbr_tv[vertex], vertex) )
_UpperCAmelCase = 1
for neighbor, distance in adjacency_list[vertex]:
if (
visited[neighbor] == 0
and distance < distance_tv[heap.get_position(_UpperCAmelCase )]
):
_UpperCAmelCase = distance
heap.bottom_to_top(
_UpperCAmelCase , heap.get_position(_UpperCAmelCase ) , _UpperCAmelCase , _UpperCAmelCase )
_UpperCAmelCase = vertex
return tree_edges
if __name__ == "__main__": # pragma: no cover
# < --------- Prims Algorithm --------- >
UpperCAmelCase__ = int(input("Enter number of edges: ").strip())
UpperCAmelCase__ = defaultdict(list)
for _ in range(edges_number):
UpperCAmelCase__ = [int(x) for x in input().strip().split()]
adjacency_list[edge[0]].append([edge[1], edge[2]])
adjacency_list[edge[1]].append([edge[0], edge[2]])
print(prisms_algorithm(adjacency_list))
| 339 | 1 |
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.31.0")
require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")
UpperCAmelCase__ = logging.getLogger(__name__)
@dataclass
class __lowerCAmelCase :
UpperCamelCase = field(
default=1_2_8 , metadata={
'''help''': (
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
} , )
UpperCamelCase = field(
default=A , metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''} )
UpperCamelCase = field(
default=A , metadata={
'''help''': (
'''Whether to pad all samples to `max_seq_length`. '''
'''If False, will pad the samples dynamically when batching to the maximum length in the batch.'''
)
} , )
UpperCamelCase = field(
default=A , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of training examples to this '''
'''value if set.'''
)
} , )
UpperCamelCase = field(
default=A , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
'''value if set.'''
)
} , )
UpperCamelCase = field(
default=A , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of prediction examples to this '''
'''value if set.'''
)
} , )
@dataclass
class __lowerCAmelCase :
UpperCamelCase = field(
default=A , metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} )
UpperCamelCase = field(
default=A , metadata={'''help''': '''Evaluation language. Also train language if `train_language` is set to None.'''} )
UpperCamelCase = field(
default=A , metadata={'''help''': '''Train language if it is different from the evaluation language.'''} )
UpperCamelCase = field(
default=A , metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
UpperCamelCase = field(
default=A , metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} )
UpperCamelCase = field(
default=A , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''} , )
UpperCamelCase = field(
default=A , metadata={'''help''': '''arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()'''} , )
UpperCamelCase = field(
default=A , metadata={'''help''': '''Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'''} , )
UpperCamelCase = field(
default='''main''' , metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''} , )
UpperCamelCase = field(
default=A , metadata={
'''help''': (
'''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
'''with private models).'''
)
} , )
UpperCamelCase = field(
default=A , metadata={'''help''': '''Will enable to load a pretrained model whose head dimensions are different.'''} , )
def A ( ) -> Tuple:
'''simple docstring'''
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
_UpperCAmelCase = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry('run_xnli' , _UpperCAmelCase )
# Setup logging
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s' , datefmt='%m/%d/%Y %H:%M:%S' , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
_UpperCAmelCase = training_args.get_process_log_level()
logger.setLevel(_UpperCAmelCase )
datasets.utils.logging.set_verbosity(_UpperCAmelCase )
transformers.utils.logging.set_verbosity(_UpperCAmelCase )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
F"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
+ F"distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}" )
logger.info(F"Training/evaluation parameters {training_args}" )
# Detecting last checkpoint.
_UpperCAmelCase = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
_UpperCAmelCase = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
F"Output directory ({training_args.output_dir}) already exists and is not empty. "
'Use --overwrite_output_dir to overcome.' )
elif last_checkpoint is not None:
logger.info(
F"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.' )
# Set seed before initializing model.
set_seed(training_args.seed )
# In distributed training, the load_dataset function guarantees that only one local process can concurrently
# download the dataset.
# Downloading and loading xnli dataset from the hub.
if training_args.do_train:
if model_args.train_language is None:
_UpperCAmelCase = load_dataset(
'xnli' , model_args.language , split='train' , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
else:
_UpperCAmelCase = load_dataset(
'xnli' , model_args.train_language , split='train' , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
_UpperCAmelCase = train_dataset.features['label'].names
if training_args.do_eval:
_UpperCAmelCase = load_dataset(
'xnli' , model_args.language , split='validation' , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
_UpperCAmelCase = eval_dataset.features['label'].names
if training_args.do_predict:
_UpperCAmelCase = load_dataset(
'xnli' , model_args.language , split='test' , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
_UpperCAmelCase = predict_dataset.features['label'].names
# Labels
_UpperCAmelCase = len(_UpperCAmelCase )
# Load pretrained model and tokenizer
# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
_UpperCAmelCase = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=_UpperCAmelCase , idalabel={str(_UpperCAmelCase ): label for i, label in enumerate(_UpperCAmelCase )} , labelaid={label: i for i, label in enumerate(_UpperCAmelCase )} , finetuning_task='xnli' , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
_UpperCAmelCase = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , do_lower_case=model_args.do_lower_case , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast_tokenizer , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
_UpperCAmelCase = AutoModelForSequenceClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool('.ckpt' in model_args.model_name_or_path ) , config=_UpperCAmelCase , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , ignore_mismatched_sizes=model_args.ignore_mismatched_sizes , )
# Preprocessing the datasets
# Padding strategy
if data_args.pad_to_max_length:
_UpperCAmelCase = 'max_length'
else:
# We will pad later, dynamically at batch creation, to the max sequence length in each batch
_UpperCAmelCase = False
def preprocess_function(_UpperCAmelCase : List[str] ):
# Tokenize the texts
return tokenizer(
examples['premise'] , examples['hypothesis'] , padding=_UpperCAmelCase , max_length=data_args.max_seq_length , truncation=_UpperCAmelCase , )
if training_args.do_train:
if data_args.max_train_samples is not None:
_UpperCAmelCase = min(len(_UpperCAmelCase ) , data_args.max_train_samples )
_UpperCAmelCase = train_dataset.select(range(_UpperCAmelCase ) )
with training_args.main_process_first(desc='train dataset map pre-processing' ):
_UpperCAmelCase = train_dataset.map(
_UpperCAmelCase , batched=_UpperCAmelCase , load_from_cache_file=not data_args.overwrite_cache , desc='Running tokenizer on train dataset' , )
# Log a few random samples from the training set:
for index in random.sample(range(len(_UpperCAmelCase ) ) , 3 ):
logger.info(F"Sample {index} of the training set: {train_dataset[index]}." )
if training_args.do_eval:
if data_args.max_eval_samples is not None:
_UpperCAmelCase = min(len(_UpperCAmelCase ) , data_args.max_eval_samples )
_UpperCAmelCase = eval_dataset.select(range(_UpperCAmelCase ) )
with training_args.main_process_first(desc='validation dataset map pre-processing' ):
_UpperCAmelCase = eval_dataset.map(
_UpperCAmelCase , batched=_UpperCAmelCase , load_from_cache_file=not data_args.overwrite_cache , desc='Running tokenizer on validation dataset' , )
if training_args.do_predict:
if data_args.max_predict_samples is not None:
_UpperCAmelCase = min(len(_UpperCAmelCase ) , data_args.max_predict_samples )
_UpperCAmelCase = predict_dataset.select(range(_UpperCAmelCase ) )
with training_args.main_process_first(desc='prediction dataset map pre-processing' ):
_UpperCAmelCase = predict_dataset.map(
_UpperCAmelCase , batched=_UpperCAmelCase , load_from_cache_file=not data_args.overwrite_cache , desc='Running tokenizer on prediction dataset' , )
# Get the metric function
_UpperCAmelCase = evaluate.load('xnli' )
# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
# predictions and label_ids field) and has to return a dictionary string to float.
def compute_metrics(_UpperCAmelCase : EvalPrediction ):
_UpperCAmelCase = p.predictions[0] if isinstance(p.predictions , _UpperCAmelCase ) else p.predictions
_UpperCAmelCase = np.argmax(_UpperCAmelCase , axis=1 )
return metric.compute(predictions=_UpperCAmelCase , references=p.label_ids )
# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
if data_args.pad_to_max_length:
_UpperCAmelCase = default_data_collator
elif training_args.fpaa:
_UpperCAmelCase = DataCollatorWithPadding(_UpperCAmelCase , pad_to_multiple_of=8 )
else:
_UpperCAmelCase = None
# Initialize our Trainer
_UpperCAmelCase = Trainer(
model=_UpperCAmelCase , args=_UpperCAmelCase , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , compute_metrics=_UpperCAmelCase , tokenizer=_UpperCAmelCase , data_collator=_UpperCAmelCase , )
# Training
if training_args.do_train:
_UpperCAmelCase = None
if training_args.resume_from_checkpoint is not None:
_UpperCAmelCase = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
_UpperCAmelCase = last_checkpoint
_UpperCAmelCase = trainer.train(resume_from_checkpoint=_UpperCAmelCase )
_UpperCAmelCase = train_result.metrics
_UpperCAmelCase = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(_UpperCAmelCase )
)
_UpperCAmelCase = min(_UpperCAmelCase , len(_UpperCAmelCase ) )
trainer.save_model() # Saves the tokenizer too for easy upload
trainer.log_metrics('train' , _UpperCAmelCase )
trainer.save_metrics('train' , _UpperCAmelCase )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info('*** Evaluate ***' )
_UpperCAmelCase = trainer.evaluate(eval_dataset=_UpperCAmelCase )
_UpperCAmelCase = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(_UpperCAmelCase )
_UpperCAmelCase = min(_UpperCAmelCase , len(_UpperCAmelCase ) )
trainer.log_metrics('eval' , _UpperCAmelCase )
trainer.save_metrics('eval' , _UpperCAmelCase )
# Prediction
if training_args.do_predict:
logger.info('*** Predict ***' )
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = trainer.predict(_UpperCAmelCase , metric_key_prefix='predict' )
_UpperCAmelCase = (
data_args.max_predict_samples if data_args.max_predict_samples is not None else len(_UpperCAmelCase )
)
_UpperCAmelCase = min(_UpperCAmelCase , len(_UpperCAmelCase ) )
trainer.log_metrics('predict' , _UpperCAmelCase )
trainer.save_metrics('predict' , _UpperCAmelCase )
_UpperCAmelCase = np.argmax(_UpperCAmelCase , axis=1 )
_UpperCAmelCase = os.path.join(training_args.output_dir , 'predictions.txt' )
if trainer.is_world_process_zero():
with open(_UpperCAmelCase , 'w' ) as writer:
writer.write('index\tprediction\n' )
for index, item in enumerate(_UpperCAmelCase ):
_UpperCAmelCase = label_list[item]
writer.write(F"{index}\t{item}\n" )
if __name__ == "__main__":
main()
| 339 |
import torch
from transformers import CamembertForMaskedLM, CamembertTokenizer
def A ( _UpperCAmelCase : str , _UpperCAmelCase : Any , _UpperCAmelCase : List[str] , _UpperCAmelCase : Optional[int]=5 ) -> List[Any]:
'''simple docstring'''
# Adapted from https://github.com/pytorch/fairseq/blob/master/fairseq/models/roberta/hub_interface.py
assert masked_input.count('<mask>' ) == 1
_UpperCAmelCase = torch.tensor(tokenizer.encode(_UpperCAmelCase , add_special_tokens=_UpperCAmelCase ) ).unsqueeze(0 ) # Batch size 1
_UpperCAmelCase = model(_UpperCAmelCase )[0] # The last hidden-state is the first element of the output tuple
_UpperCAmelCase = (input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
_UpperCAmelCase = logits[0, masked_index, :]
_UpperCAmelCase = logits.softmax(dim=0 )
_UpperCAmelCase , _UpperCAmelCase = prob.topk(k=_UpperCAmelCase , dim=0 )
_UpperCAmelCase = ' '.join(
[tokenizer.convert_ids_to_tokens(indices[i].item() ) for i in range(len(_UpperCAmelCase ) )] )
_UpperCAmelCase = tokenizer.mask_token
_UpperCAmelCase = []
for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(' ' ) ):
_UpperCAmelCase = predicted_token_bpe.replace('\u2581' , ' ' )
if " {0}".format(_UpperCAmelCase ) in masked_input:
topk_filled_outputs.append(
(
masked_input.replace(' {0}'.format(_UpperCAmelCase ) , _UpperCAmelCase ),
values[index].item(),
predicted_token,
) )
else:
topk_filled_outputs.append(
(
masked_input.replace(_UpperCAmelCase , _UpperCAmelCase ),
values[index].item(),
predicted_token,
) )
return topk_filled_outputs
UpperCAmelCase__ = CamembertTokenizer.from_pretrained("camembert-base")
UpperCAmelCase__ = CamembertForMaskedLM.from_pretrained("camembert-base")
model.eval()
UpperCAmelCase__ = "Le camembert est <mask> :)"
print(fill_mask(masked_input, model, tokenizer, topk=3))
| 339 | 1 |
import itertools
import string
from collections.abc import Generator, Iterable
def A ( _UpperCAmelCase : Iterable[str] , _UpperCAmelCase : int ) -> Generator[tuple[str, ...], None, None]:
'''simple docstring'''
_UpperCAmelCase = iter(_UpperCAmelCase )
while True:
_UpperCAmelCase = tuple(itertools.islice(_UpperCAmelCase , _UpperCAmelCase ) )
if not chunk:
return
yield chunk
def A ( _UpperCAmelCase : str ) -> str:
'''simple docstring'''
_UpperCAmelCase = ''.join([c.upper() for c in dirty if c in string.ascii_letters] )
_UpperCAmelCase = ''
if len(_UpperCAmelCase ) < 2:
return dirty
for i in range(len(_UpperCAmelCase ) - 1 ):
clean += dirty[i]
if dirty[i] == dirty[i + 1]:
clean += "X"
clean += dirty[-1]
if len(_UpperCAmelCase ) & 1:
clean += "X"
return clean
def A ( _UpperCAmelCase : str ) -> list[str]:
'''simple docstring'''
# I and J are used interchangeably to allow
# us to use a 5x5 table (25 letters)
_UpperCAmelCase = 'ABCDEFGHIKLMNOPQRSTUVWXYZ'
# we're using a list instead of a '2d' array because it makes the math
# for setting up the table and doing the actual encoding/decoding simpler
_UpperCAmelCase = []
# copy key chars into the table if they are in `alphabet` ignoring duplicates
for char in key.upper():
if char not in table and char in alphabet:
table.append(_UpperCAmelCase )
# fill the rest of the table in with the remaining alphabet chars
for char in alphabet:
if char not in table:
table.append(_UpperCAmelCase )
return table
def A ( _UpperCAmelCase : str , _UpperCAmelCase : str ) -> str:
'''simple docstring'''
_UpperCAmelCase = generate_table(_UpperCAmelCase )
_UpperCAmelCase = prepare_input(_UpperCAmelCase )
_UpperCAmelCase = ''
# https://en.wikipedia.org/wiki/Playfair_cipher#Description
for chara, chara in chunker(_UpperCAmelCase , 2 ):
_UpperCAmelCase , _UpperCAmelCase = divmod(table.index(_UpperCAmelCase ) , 5 )
_UpperCAmelCase , _UpperCAmelCase = divmod(table.index(_UpperCAmelCase ) , 5 )
if rowa == rowa:
ciphertext += table[rowa * 5 + (cola + 1) % 5]
ciphertext += table[rowa * 5 + (cola + 1) % 5]
elif cola == cola:
ciphertext += table[((rowa + 1) % 5) * 5 + cola]
ciphertext += table[((rowa + 1) % 5) * 5 + cola]
else: # rectangle
ciphertext += table[rowa * 5 + cola]
ciphertext += table[rowa * 5 + cola]
return ciphertext
def A ( _UpperCAmelCase : str , _UpperCAmelCase : str ) -> str:
'''simple docstring'''
_UpperCAmelCase = generate_table(_UpperCAmelCase )
_UpperCAmelCase = ''
# https://en.wikipedia.org/wiki/Playfair_cipher#Description
for chara, chara in chunker(_UpperCAmelCase , 2 ):
_UpperCAmelCase , _UpperCAmelCase = divmod(table.index(_UpperCAmelCase ) , 5 )
_UpperCAmelCase , _UpperCAmelCase = divmod(table.index(_UpperCAmelCase ) , 5 )
if rowa == rowa:
plaintext += table[rowa * 5 + (cola - 1) % 5]
plaintext += table[rowa * 5 + (cola - 1) % 5]
elif cola == cola:
plaintext += table[((rowa - 1) % 5) * 5 + cola]
plaintext += table[((rowa - 1) % 5) * 5 + cola]
else: # rectangle
plaintext += table[rowa * 5 + cola]
plaintext += table[rowa * 5 + cola]
return plaintext
| 339 |
import math
import unittest
def A ( _UpperCAmelCase : int ) -> bool:
'''simple docstring'''
assert isinstance(_UpperCAmelCase , _UpperCAmelCase ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(_UpperCAmelCase ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
class __lowerCAmelCase ( unittest.TestCase ):
def _lowerCamelCase ( self : Tuple) -> Union[str, Any]:
"""simple docstring"""
self.assertTrue(is_prime(2))
self.assertTrue(is_prime(3))
self.assertTrue(is_prime(5))
self.assertTrue(is_prime(7))
self.assertTrue(is_prime(11))
self.assertTrue(is_prime(13))
self.assertTrue(is_prime(17))
self.assertTrue(is_prime(19))
self.assertTrue(is_prime(23))
self.assertTrue(is_prime(29))
def _lowerCamelCase ( self : Optional[int]) -> Any:
"""simple docstring"""
with self.assertRaises(A):
is_prime(-19)
self.assertFalse(
is_prime(0) , 'Zero doesn\'t have any positive factors, primes must have exactly two.' , )
self.assertFalse(
is_prime(1) , 'One only has 1 positive factor, primes must have exactly two.' , )
self.assertFalse(is_prime(2 * 2))
self.assertFalse(is_prime(2 * 3))
self.assertFalse(is_prime(3 * 3))
self.assertFalse(is_prime(3 * 5))
self.assertFalse(is_prime(3 * 5 * 7))
if __name__ == "__main__":
unittest.main()
| 339 | 1 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
UpperCAmelCase__ = "\\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"
UpperCAmelCase__ = "\\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"
UpperCAmelCase__ = "\\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 __lowerCAmelCase ( datasets.Metric ):
def _lowerCamelCase ( self : str) -> MetricInfo:
"""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 _lowerCamelCase ( self : Union[str, Any] , A : List[List[List[str]]] , A : List[List[str]] , A : int = 1 , A : int = 4 , ) -> Dict[str, float]:
"""simple docstring"""
return {
"google_bleu": gleu_score.corpus_gleu(
list_of_references=A , hypotheses=A , min_len=A , max_len=A)
}
| 339 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
UpperCAmelCase__ = "\\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"
UpperCAmelCase__ = "\\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"
UpperCAmelCase__ = "\\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 __lowerCAmelCase ( datasets.Metric ):
def _lowerCamelCase ( self : str) -> MetricInfo:
"""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 _lowerCamelCase ( self : Union[str, Any] , A : List[List[List[str]]] , A : List[List[str]] , A : int = 1 , A : int = 4 , ) -> Dict[str, float]:
"""simple docstring"""
return {
"google_bleu": gleu_score.corpus_gleu(
list_of_references=A , hypotheses=A , min_len=A , max_len=A)
}
| 339 | 1 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from pathlib import Path
import torch
from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter
UpperCAmelCase__ = "Create a default config file for Accelerate with only a few flags set."
def A ( _UpperCAmelCase : List[str]="no" , _UpperCAmelCase : str = default_json_config_file , _UpperCAmelCase : bool = False ) -> Optional[int]:
'''simple docstring'''
_UpperCAmelCase = Path(_UpperCAmelCase )
path.parent.mkdir(parents=_UpperCAmelCase , exist_ok=_UpperCAmelCase )
if path.exists():
print(
F"Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`." )
return False
_UpperCAmelCase = mixed_precision.lower()
if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
raise ValueError(
F"`mixed_precision` should be one of 'no', 'fp16', 'bf16', or 'fp8'. Received {mixed_precision}" )
_UpperCAmelCase = {
'compute_environment': 'LOCAL_MACHINE',
'mixed_precision': mixed_precision,
}
if torch.cuda.is_available():
_UpperCAmelCase = torch.cuda.device_count()
_UpperCAmelCase = num_gpus
_UpperCAmelCase = False
if num_gpus > 1:
_UpperCAmelCase = 'MULTI_GPU'
else:
_UpperCAmelCase = 'NO'
elif is_xpu_available() and use_xpu:
_UpperCAmelCase = torch.xpu.device_count()
_UpperCAmelCase = num_xpus
_UpperCAmelCase = False
if num_xpus > 1:
_UpperCAmelCase = 'MULTI_XPU'
else:
_UpperCAmelCase = 'NO'
elif is_npu_available():
_UpperCAmelCase = torch.npu.device_count()
_UpperCAmelCase = num_npus
_UpperCAmelCase = False
if num_npus > 1:
_UpperCAmelCase = 'MULTI_NPU'
else:
_UpperCAmelCase = 'NO'
else:
_UpperCAmelCase = 0
_UpperCAmelCase = True
_UpperCAmelCase = 1
_UpperCAmelCase = 'NO'
_UpperCAmelCase = ClusterConfig(**_UpperCAmelCase )
config.to_json_file(_UpperCAmelCase )
return path
def A ( _UpperCAmelCase : List[str] , _UpperCAmelCase : Optional[int] ) -> Dict:
'''simple docstring'''
_UpperCAmelCase = parser.add_parser('default' , parents=_UpperCAmelCase , help=_UpperCAmelCase , formatter_class=_UpperCAmelCase )
parser.add_argument(
'--config_file' , default=_UpperCAmelCase , help=(
'The path to use to store the config file. Will default to a file named default_config.yaml in the cache '
'location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '
'such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '
'with \'huggingface\'.'
) , dest='save_location' , )
parser.add_argument(
'--mixed_precision' , choices=['no', 'fp16', 'bf16'] , type=_UpperCAmelCase , help='Whether or not to use mixed precision training. '
'Choose between FP16 and BF16 (bfloat16) training. '
'BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later.' , default='no' , )
parser.set_defaults(func=_UpperCAmelCase )
return parser
def A ( _UpperCAmelCase : List[str] ) -> Optional[Any]:
'''simple docstring'''
_UpperCAmelCase = write_basic_config(args.mixed_precision , args.save_location )
if config_file:
print(F"accelerate configuration saved at {config_file}" )
| 339 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
if is_sentencepiece_available():
from ..ta.tokenization_ta import TaTokenizer
else:
from ...utils.dummy_sentencepiece_objects import TaTokenizer
UpperCAmelCase__ = TaTokenizer
if is_tokenizers_available():
from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
from ...utils.dummy_tokenizers_objects import TaTokenizerFast
UpperCAmelCase__ = TaTokenizerFast
UpperCAmelCase__ = {"configuration_mt5": ["MT5Config", "MT5OnnxConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ = [
"MT5EncoderModel",
"MT5ForConditionalGeneration",
"MT5ForQuestionAnswering",
"MT5Model",
"MT5PreTrainedModel",
"MT5Stack",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ = ["TFMT5EncoderModel", "TFMT5ForConditionalGeneration", "TFMT5Model"]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ = ["FlaxMT5EncoderModel", "FlaxMT5ForConditionalGeneration", "FlaxMT5Model"]
if TYPE_CHECKING:
from .configuration_mta import MTaConfig, MTaOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mta import (
MTaEncoderModel,
MTaForConditionalGeneration,
MTaForQuestionAnswering,
MTaModel,
MTaPreTrainedModel,
MTaStack,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel
else:
import sys
UpperCAmelCase__ = _LazyModule(
__name__,
globals()["__file__"],
_import_structure,
extra_objects={"MT5Tokenizer": MTaTokenizer, "MT5TokenizerFast": MTaTokenizerFast},
module_spec=__spec__,
)
| 339 | 1 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, TensorType
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
"openai/imagegpt-small": "",
"openai/imagegpt-medium": "",
"openai/imagegpt-large": "",
}
class __lowerCAmelCase ( A ):
UpperCamelCase = '''imagegpt'''
UpperCamelCase = ['''past_key_values''']
UpperCamelCase = {
'''hidden_size''': '''n_embd''',
'''max_position_embeddings''': '''n_positions''',
'''num_attention_heads''': '''n_head''',
'''num_hidden_layers''': '''n_layer''',
}
def __init__( self : Tuple , A : List[str]=5_12 + 1 , A : Any=32 * 32 , A : List[Any]=5_12 , A : Tuple=24 , A : Optional[int]=8 , A : Any=None , A : Optional[int]="quick_gelu" , A : List[Any]=0.1 , A : str=0.1 , A : Any=0.1 , A : int=1E-5 , A : Optional[Any]=0.0_2 , A : str=True , A : int=True , A : List[str]=False , A : Union[str, Any]=False , A : Union[str, Any]=False , **A : List[Any] , ) -> Tuple:
"""simple docstring"""
_UpperCAmelCase = vocab_size
_UpperCAmelCase = n_positions
_UpperCAmelCase = n_embd
_UpperCAmelCase = n_layer
_UpperCAmelCase = n_head
_UpperCAmelCase = n_inner
_UpperCAmelCase = activation_function
_UpperCAmelCase = resid_pdrop
_UpperCAmelCase = embd_pdrop
_UpperCAmelCase = attn_pdrop
_UpperCAmelCase = layer_norm_epsilon
_UpperCAmelCase = initializer_range
_UpperCAmelCase = scale_attn_weights
_UpperCAmelCase = use_cache
_UpperCAmelCase = scale_attn_by_inverse_layer_idx
_UpperCAmelCase = reorder_and_upcast_attn
_UpperCAmelCase = tie_word_embeddings
super().__init__(tie_word_embeddings=A , **A)
class __lowerCAmelCase ( A ):
@property
def _lowerCamelCase ( self : Dict) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
return OrderedDict(
[
('input_ids', {0: 'batch', 1: 'sequence'}),
])
def _lowerCamelCase ( self : str , A : "FeatureExtractionMixin" , A : int = 1 , A : int = -1 , A : bool = False , A : Optional["TensorType"] = None , A : int = 3 , A : int = 32 , A : int = 32 , ) -> Mapping[str, Any]:
"""simple docstring"""
_UpperCAmelCase = self._generate_dummy_images(A , A , A , A)
_UpperCAmelCase = dict(preprocessor(images=A , return_tensors=A))
return inputs
| 339 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
"s-JoL/Open-Llama-V1": "https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json",
}
class __lowerCAmelCase ( A ):
UpperCamelCase = '''open-llama'''
def __init__( self : str , A : List[Any]=10_00_00 , A : Tuple=40_96 , A : Tuple=1_10_08 , A : List[str]=32 , A : Tuple=32 , A : Optional[Any]="silu" , A : int=20_48 , A : Optional[Any]=0.0_2 , A : Dict=1E-6 , A : Optional[Any]=True , A : List[Any]=0 , A : Dict=1 , A : int=2 , A : Dict=False , A : Optional[int]=True , A : List[Any]=0.1 , A : str=0.1 , A : Dict=True , A : Optional[Any]=True , A : Dict=None , **A : Union[str, Any] , ) -> Dict:
"""simple docstring"""
_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' , A)
_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=A , bos_token_id=A , eos_token_id=A , tie_word_embeddings=A , **A , )
def _lowerCamelCase ( self : List[str]) -> Union[str, Any]:
"""simple docstring"""
if self.rope_scaling is None:
return
if not isinstance(self.rope_scaling , A) or len(self.rope_scaling) != 2:
raise ValueError(
'`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '
F"got {self.rope_scaling}")
_UpperCAmelCase = self.rope_scaling.get('type' , A)
_UpperCAmelCase = self.rope_scaling.get('factor' , A)
if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
raise ValueError(
F"`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}")
if rope_scaling_factor is None or not isinstance(A , A) or rope_scaling_factor <= 1.0:
raise ValueError(F"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}")
| 339 | 1 |
# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the config
# - generate model_cards - useful if you have multiple models from the same paper
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()
UpperCAmelCase__ = 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
UpperCAmelCase__ = {
# fairseq:
"wmt19-ru-en": {"length_penalty": 1.1},
"wmt19-en-ru": {"length_penalty": 1.15},
"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
UpperCAmelCase__ = {}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
UpperCAmelCase__ = "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",
]:
UpperCAmelCase__ = "allenai"
def A ( _UpperCAmelCase : str ) -> Union[str, Any]:
'''simple docstring'''
# (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}
_UpperCAmelCase = dict((re.sub(R'@@$' , '' , _UpperCAmelCase ), v) if k.endswith('@@' ) else (re.sub(R'$' , '</w>' , _UpperCAmelCase ), v) for k, v in d.items() )
_UpperCAmelCase = '<s> <pad> </s> <unk>'.split()
# restore the special tokens
for k in keep_keys:
del da[F"{k}</w>"]
_UpperCAmelCase = d[k] # restore
return da
def A ( _UpperCAmelCase : Optional[int] , _UpperCAmelCase : int ) -> str:
'''simple docstring'''
# prep
assert os.path.exists(_UpperCAmelCase )
os.makedirs(_UpperCAmelCase , exist_ok=_UpperCAmelCase )
print(F"Writing results to {pytorch_dump_folder_path}" )
# handle various types of models
_UpperCAmelCase = basename(_UpperCAmelCase )
_UpperCAmelCase = dirname(_UpperCAmelCase )
_UpperCAmelCase = fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
_UpperCAmelCase = cls.hub_models()
_UpperCAmelCase = {'bpe': 'fastbpe', 'tokenizer': 'moses'}
_UpperCAmelCase = '.'
# 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}" )
_UpperCAmelCase = hub_utils.from_pretrained(
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , archive_map=_UpperCAmelCase , **_UpperCAmelCase )
_UpperCAmelCase = vars(chkpt['args']['model'] )
_UpperCAmelCase = args['source_lang']
_UpperCAmelCase = args['target_lang']
_UpperCAmelCase = dirname(_UpperCAmelCase )
_UpperCAmelCase = basename(_UpperCAmelCase )
# dicts
_UpperCAmelCase = os.path.join(_UpperCAmelCase , F"dict.{src_lang}.txt" )
_UpperCAmelCase = os.path.join(_UpperCAmelCase , F"dict.{tgt_lang}.txt" )
_UpperCAmelCase = Dictionary.load(_UpperCAmelCase )
_UpperCAmelCase = rewrite_dict_keys(src_dict.indices )
_UpperCAmelCase = len(_UpperCAmelCase )
_UpperCAmelCase = os.path.join(_UpperCAmelCase , 'vocab-src.json' )
print(F"Generating {src_vocab_file} of {src_vocab_size} of {src_lang} records" )
with open(_UpperCAmelCase , 'w' , encoding='utf-8' ) as f:
f.write(json.dumps(_UpperCAmelCase , ensure_ascii=_UpperCAmelCase , indent=_UpperCAmelCase ) )
# 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
_UpperCAmelCase = True
for k in src_vocab.keys():
if not k.islower():
_UpperCAmelCase = False
break
_UpperCAmelCase = Dictionary.load(_UpperCAmelCase )
_UpperCAmelCase = rewrite_dict_keys(tgt_dict.indices )
_UpperCAmelCase = len(_UpperCAmelCase )
_UpperCAmelCase = os.path.join(_UpperCAmelCase , 'vocab-tgt.json' )
print(F"Generating {tgt_vocab_file} of {tgt_vocab_size} of {tgt_lang} records" )
with open(_UpperCAmelCase , 'w' , encoding='utf-8' ) as f:
f.write(json.dumps(_UpperCAmelCase , ensure_ascii=_UpperCAmelCase , indent=_UpperCAmelCase ) )
# merges_file (bpecodes)
_UpperCAmelCase = os.path.join(_UpperCAmelCase , VOCAB_FILES_NAMES['merges_file'] )
for fn in ["bpecodes", "code"]: # older fairseq called the merges file "code"
_UpperCAmelCase = os.path.join(_UpperCAmelCase , _UpperCAmelCase )
if os.path.exists(_UpperCAmelCase ):
break
with open(_UpperCAmelCase , encoding='utf-8' ) as fin:
_UpperCAmelCase = fin.read()
_UpperCAmelCase = re.sub(R' \d+$' , '' , _UpperCAmelCase , 0 , re.M ) # remove frequency number
print(F"Generating {merges_file}" )
with open(_UpperCAmelCase , 'w' , encoding='utf-8' ) as fout:
fout.write(_UpperCAmelCase )
# model config
_UpperCAmelCase = os.path.join(_UpperCAmelCase , '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']}"
_UpperCAmelCase = {
'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
_UpperCAmelCase = 5
_UpperCAmelCase = False
if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
_UpperCAmelCase = best_score_hparams[model_dir]['length_penalty']
else:
_UpperCAmelCase = 1.0
print(F"Generating {fsmt_model_config_file}" )
with open(_UpperCAmelCase , 'w' , encoding='utf-8' ) as f:
f.write(json.dumps(_UpperCAmelCase , ensure_ascii=_UpperCAmelCase , indent=_UpperCAmelCase ) )
# tokenizer config
_UpperCAmelCase = os.path.join(_UpperCAmelCase , _UpperCAmelCase )
_UpperCAmelCase = {
'langs': [src_lang, tgt_lang],
'model_max_length': 1_024,
'do_lower_case': do_lower_case,
}
print(F"Generating {fsmt_tokenizer_config_file}" )
with open(_UpperCAmelCase , 'w' , encoding='utf-8' ) as f:
f.write(json.dumps(_UpperCAmelCase , ensure_ascii=_UpperCAmelCase , indent=_UpperCAmelCase ) )
# model
_UpperCAmelCase = chkpt['models'][0]
_UpperCAmelCase = model.state_dict()
# rename keys to start with 'model.'
_UpperCAmelCase = OrderedDict(('model.' + k, v) for k, v in model_state_dict.items() )
# remove unneeded keys
_UpperCAmelCase = [
'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(_UpperCAmelCase , _UpperCAmelCase )
_UpperCAmelCase = FSMTConfig.from_pretrained(_UpperCAmelCase )
_UpperCAmelCase = FSMTForConditionalGeneration(_UpperCAmelCase )
# check that it loads ok
model_new.load_state_dict(_UpperCAmelCase , strict=_UpperCAmelCase )
# save
_UpperCAmelCase = os.path.join(_UpperCAmelCase , _UpperCAmelCase )
print(F"Generating {pytorch_weights_dump_path}" )
torch.save(_UpperCAmelCase , _UpperCAmelCase )
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__":
UpperCAmelCase__ = 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."
)
UpperCAmelCase__ = parser.parse_args()
convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)
| 339 |
def A ( _UpperCAmelCase : str ) -> bool:
'''simple docstring'''
return credit_card_number.startswith(('34', '35', '37', '4', '5', '6') )
def A ( _UpperCAmelCase : str ) -> bool:
'''simple docstring'''
_UpperCAmelCase = credit_card_number
_UpperCAmelCase = 0
_UpperCAmelCase = len(_UpperCAmelCase ) - 2
for i in range(_UpperCAmelCase , -1 , -2 ):
# double the value of every second digit
_UpperCAmelCase = int(cc_number[i] )
digit *= 2
# If doubling of a number results in a two digit number
# i.e greater than 9(e.g., 6 × 2 = 12),
# then add the digits of the product (e.g., 12: 1 + 2 = 3, 15: 1 + 5 = 6),
# to get a single digit number.
if digit > 9:
digit %= 10
digit += 1
_UpperCAmelCase = cc_number[:i] + str(_UpperCAmelCase ) + cc_number[i + 1 :]
total += digit
# Sum up the remaining digits
for i in range(len(_UpperCAmelCase ) - 1 , -1 , -2 ):
total += int(cc_number[i] )
return total % 10 == 0
def A ( _UpperCAmelCase : str ) -> bool:
'''simple docstring'''
_UpperCAmelCase = F"{credit_card_number} is an invalid credit card number because"
if not credit_card_number.isdigit():
print(F"{error_message} it has nonnumerical characters." )
return False
if not 13 <= len(_UpperCAmelCase ) <= 16:
print(F"{error_message} of its length." )
return False
if not validate_initial_digits(_UpperCAmelCase ):
print(F"{error_message} of its first two digits." )
return False
if not luhn_validation(_UpperCAmelCase ):
print(F"{error_message} it fails the Luhn check." )
return False
print(F"{credit_card_number} is a valid credit card number." )
return True
if __name__ == "__main__":
import doctest
doctest.testmod()
validate_credit_card_number("4111111111111111")
validate_credit_card_number("32323")
| 339 | 1 |
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel
class __lowerCAmelCase :
def __init__( self : Dict , A : Optional[Any] , A : List[str]=99 , A : int=13 , A : str=7 , A : Optional[Any]=9 , A : List[str]=True , A : List[str]=True , A : List[str]=False , A : Tuple=32 , A : Optional[int]=5 , A : Any=4 , A : Any=37 , A : Tuple=8 , A : Optional[int]=0.1 , A : Union[str, Any]=0.0_0_2 , A : Dict=1 , A : int=0 , A : Optional[int]=0 , A : Any=None , A : Tuple=None , ) -> List[str]:
"""simple docstring"""
_UpperCAmelCase = parent
_UpperCAmelCase = batch_size
_UpperCAmelCase = encoder_seq_length
_UpperCAmelCase = decoder_seq_length
# For common tests
_UpperCAmelCase = self.decoder_seq_length
_UpperCAmelCase = is_training
_UpperCAmelCase = use_attention_mask
_UpperCAmelCase = use_labels
_UpperCAmelCase = vocab_size
_UpperCAmelCase = hidden_size
_UpperCAmelCase = num_hidden_layers
_UpperCAmelCase = num_attention_heads
_UpperCAmelCase = d_ff
_UpperCAmelCase = relative_attention_num_buckets
_UpperCAmelCase = dropout_rate
_UpperCAmelCase = initializer_factor
_UpperCAmelCase = eos_token_id
_UpperCAmelCase = pad_token_id
_UpperCAmelCase = decoder_start_token_id
_UpperCAmelCase = None
_UpperCAmelCase = decoder_layers
def _lowerCamelCase ( self : int) -> Optional[int]:
"""simple docstring"""
return TaConfig.from_pretrained('google/umt5-base')
def _lowerCamelCase ( self : Tuple , A : Optional[Any] , A : Union[str, Any] , A : int , A : int=None , A : int=None , A : Any=None , A : Any=None , A : str=None , ) -> str:
"""simple docstring"""
if attention_mask is None:
_UpperCAmelCase = input_ids.ne(config.pad_token_id)
if decoder_attention_mask is None:
_UpperCAmelCase = decoder_input_ids.ne(config.pad_token_id)
if head_mask is None:
_UpperCAmelCase = torch.ones(config.num_hidden_layers , config.num_attention_heads , device=A)
if decoder_head_mask is None:
_UpperCAmelCase = torch.ones(config.num_decoder_layers , config.num_attention_heads , device=A)
if cross_attn_head_mask is None:
_UpperCAmelCase = torch.ones(
config.num_decoder_layers , config.num_attention_heads , device=A)
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
def _lowerCamelCase ( self : int) -> Any:
"""simple docstring"""
_UpperCAmelCase = ids_tensor([self.batch_size, self.encoder_seq_length] , self.vocab_size)
_UpperCAmelCase = ids_tensor([self.batch_size, self.decoder_seq_length] , self.vocab_size)
# we need to clamp the input ids here to avoid having pad token in between
# this is because for NllbMoe the position_ids are prepared such that
# all pad tokens have pos id = 2 and rest are between 2..seq_length
# and the seq_length here is seq_length - num_pad_tokens
# but when using past, there is no way of knowing if the past input ids had
# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
# position_ids being off by num_pad_tokens in past input
_UpperCAmelCase = input_ids.clamp(self.pad_token_id + 1)
_UpperCAmelCase = decoder_input_ids.clamp(self.pad_token_id + 1)
_UpperCAmelCase = self.get_config()
_UpperCAmelCase = config.num_attention_heads
_UpperCAmelCase = self.prepare_inputs_dict(A , A , A)
return config, input_dict
def _lowerCamelCase ( self : Tuple) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase , _UpperCAmelCase = self.prepare_config_and_inputs()
return config, inputs_dict
def _lowerCamelCase ( self : Optional[int]) -> int:
"""simple docstring"""
return TaConfig(
vocab_size=1_66 , d_model=self.hidden_size , d_ff=self.d_ff , d_kv=self.hidden_size // self.num_attention_heads , num_layers=self.num_hidden_layers , num_decoder_layers=self.decoder_layers , num_heads=self.num_attention_heads , relative_attention_num_buckets=self.relative_attention_num_buckets , dropout_rate=self.dropout_rate , initializer_factor=self.initializer_factor , eos_token_id=self.eos_token_id , bos_token_id=self.pad_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , )
def _lowerCamelCase ( self : Dict) -> Any:
"""simple docstring"""
return TaConfig(
vocab_size=self.vocab_size , d_model=self.hidden_size , d_ff=self.d_ff , d_kv=self.hidden_size // self.num_attention_heads , num_layers=self.num_hidden_layers , num_decoder_layers=self.decoder_layers , num_heads=self.num_attention_heads , relative_attention_num_buckets=self.relative_attention_num_buckets , dropout_rate=self.dropout_rate , initializer_factor=self.initializer_factor , eos_token_id=self.eos_token_id , bos_token_id=self.pad_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , )
def _lowerCamelCase ( self : str , A : Dict , A : str , A : Dict , A : int , A : Any , A : List[str] , ) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase = UMTaModel(config=A)
model.to(A)
model.eval()
_UpperCAmelCase = model(
input_ids=A , decoder_input_ids=A , attention_mask=A , decoder_attention_mask=A , )
_UpperCAmelCase = model(input_ids=A , decoder_input_ids=A)
_UpperCAmelCase = result.last_hidden_state
_UpperCAmelCase = result.past_key_values
_UpperCAmelCase = result.encoder_last_hidden_state
self.parent.assertEqual(encoder_output.size() , (self.batch_size, self.encoder_seq_length, self.hidden_size))
self.parent.assertEqual(decoder_output.size() , (self.batch_size, self.decoder_seq_length, self.hidden_size))
# There should be `num_layers` key value embeddings stored in decoder_past
self.parent.assertEqual(len(A) , config.num_layers)
# There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
self.parent.assertEqual(len(decoder_past[0]) , 4)
def _lowerCamelCase ( self : Any , A : Optional[Any] , A : int , A : Dict , A : List[Any] , A : Any , A : Optional[Any] , ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase = UMTaModel(config=A).get_decoder().to(A).eval()
# first forward pass
_UpperCAmelCase = model(A , use_cache=A)
_UpperCAmelCase = model(A)
_UpperCAmelCase = model(A , use_cache=A)
self.parent.assertTrue(len(A) == len(A))
self.parent.assertTrue(len(A) == len(A) + 1)
_UpperCAmelCase , _UpperCAmelCase = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
_UpperCAmelCase = ids_tensor((self.batch_size, 1) , config.vocab_size)
# append to next input_ids and
_UpperCAmelCase = torch.cat([input_ids, next_tokens] , dim=-1)
_UpperCAmelCase = model(A)['last_hidden_state']
_UpperCAmelCase = model(A , past_key_values=A)['last_hidden_state']
# select random slice
_UpperCAmelCase = ids_tensor((1,) , output_from_past.shape[-1]).item()
_UpperCAmelCase = output_from_no_past[:, -1, random_slice_idx].detach()
_UpperCAmelCase = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(A , A , atol=1E-3))
def _lowerCamelCase ( self : str , A : List[Any] , A : List[Any] , ) -> str:
"""simple docstring"""
_UpperCAmelCase = UMTaModel(config=A).to(A).half().eval()
_UpperCAmelCase = model(**A)['last_hidden_state']
self.parent.assertFalse(torch.isnan(A).any().item())
@require_torch
class __lowerCAmelCase ( A , A , A , unittest.TestCase ):
UpperCamelCase = (
(UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
)
UpperCamelCase = (UMTaForConditionalGeneration,) if is_torch_available() else ()
UpperCamelCase = (
{
'''conversational''': UMTaForConditionalGeneration,
'''feature-extraction''': UMTaModel,
'''summarization''': UMTaForConditionalGeneration,
'''text2text-generation''': UMTaForConditionalGeneration,
'''translation''': UMTaForConditionalGeneration,
'''question-answering''': UMTaForQuestionAnswering,
}
if is_torch_available()
else {}
)
UpperCamelCase = True
UpperCamelCase = False
UpperCamelCase = False
UpperCamelCase = True
UpperCamelCase = True
# The small UMT5 model needs higher percentages for CPU/MP tests
UpperCamelCase = [0.8, 0.9]
def _lowerCamelCase ( self : List[str]) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase = UMTaModelTester(self)
@unittest.skip('Test has a segmentation fault on torch 1.8.0')
def _lowerCamelCase ( self : Tuple) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
_UpperCAmelCase = UMTaModel(config_and_inputs[0]).to(A)
with tempfile.TemporaryDirectory() as tmpdirname:
torch.onnx.export(
A , (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2]) , F"{tmpdirname}/t5_test.onnx" , export_params=A , opset_version=9 , input_names=['input_ids', 'decoder_input_ids'] , )
@unittest.skipIf(torch_device == 'cpu' , 'Cant do half precision')
def _lowerCamelCase ( self : Dict) -> List[str]:
"""simple docstring"""
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model_fpaa_forward(*A)
def _lowerCamelCase ( self : Tuple) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase = ['encoder_attentions', 'decoder_attentions', 'cross_attentions']
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
_UpperCAmelCase = config_and_inputs[0]
_UpperCAmelCase = UMTaForConditionalGeneration(A).eval()
model.to(A)
_UpperCAmelCase = {
'head_mask': torch.zeros(config.num_layers , config.num_heads , device=A),
'decoder_head_mask': torch.zeros(config.num_decoder_layers , config.num_heads , device=A),
'cross_attn_head_mask': torch.zeros(config.num_decoder_layers , config.num_heads , device=A),
}
for attn_name, (name, mask) in zip(A , head_masking.items()):
_UpperCAmelCase = {name: mask}
# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
if name == "head_mask":
_UpperCAmelCase = torch.ones(
config.num_decoder_layers , config.num_heads , device=A)
_UpperCAmelCase = model.generate(
config_and_inputs[1]['input_ids'] , num_beams=1 , max_length=3 , output_attentions=A , return_dict_in_generate=A , **A , )
# We check the state of decoder_attentions and cross_attentions just from the last step
_UpperCAmelCase = out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
self.assertEqual(sum([w.sum().item() for w in attn_weights]) , 0.0)
@unittest.skip('Does not work on the tiny model as we keep hitting edge cases.')
def _lowerCamelCase ( self : List[Any]) -> Tuple:
"""simple docstring"""
pass
@require_torch
@require_sentencepiece
@require_tokenizers
class __lowerCAmelCase ( unittest.TestCase ):
@slow
@unittest.skip(
'Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged')
def _lowerCamelCase ( self : Any) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase = UMTaForConditionalGeneration.from_pretrained('google/umt5-small' , return_dict=A).to(A)
_UpperCAmelCase = AutoTokenizer.from_pretrained('google/umt5-small' , use_fast=A , legacy=A)
_UpperCAmelCase = [
'Bonjour monsieur <extra_id_0> bien <extra_id_1>.',
'No se como puedo <extra_id_0>.',
'This is the reason why we <extra_id_0> them.',
'The <extra_id_0> walks in <extra_id_1>, seats',
'A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.',
]
_UpperCAmelCase = tokenizer(A , return_tensors='pt' , padding=A).input_ids
# fmt: off
_UpperCAmelCase = torch.tensor(
[
[ 3_85_30, 21_07_03, 25_62_99, 14_10, 25_62_98, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 8_26, 3_21, 6_71, 2_59_22, 25_62_99, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 14_60, 3_39, 3_12, 1_90_14, 1_06_20, 7_58, 25_62_99, 23_55,2_74, 1, 0, 0, 0, 0, 0, 0,0, 0],
[ 5_17, 25_62_99, 1_48_69, 2_81, 3_01, 25_62_98, 2_75, 11_99_83,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 3_20, 25_62_99, 1_48_69, 2_81, 22_34, 2_89, 22_75, 3_33,6_13_91, 2_89, 25_62_98, 5_43, 25_62_97, 16_87_14, 3_29, 25_62_96,2_74, 1],
])
# fmt: on
torch.testing.assert_allclose(A , A)
_UpperCAmelCase = model.generate(input_ids.to(A))
_UpperCAmelCase = [
'<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>',
'<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
'<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
'<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
'<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
]
_UpperCAmelCase = tokenizer.batch_decode(A)
self.assertEqual(A , A)
| 339 |
from functools import reduce
UpperCAmelCase__ = (
"73167176531330624919225119674426574742355349194934"
"96983520312774506326239578318016984801869478851843"
"85861560789112949495459501737958331952853208805511"
"12540698747158523863050715693290963295227443043557"
"66896648950445244523161731856403098711121722383113"
"62229893423380308135336276614282806444486645238749"
"30358907296290491560440772390713810515859307960866"
"70172427121883998797908792274921901699720888093776"
"65727333001053367881220235421809751254540594752243"
"52584907711670556013604839586446706324415722155397"
"53697817977846174064955149290862569321978468622482"
"83972241375657056057490261407972968652414535100474"
"82166370484403199890008895243450658541227588666881"
"16427171479924442928230863465674813919123162824586"
"17866458359124566529476545682848912883142607690042"
"24219022671055626321111109370544217506941658960408"
"07198403850962455444362981230987879927244284909188"
"84580156166097919133875499200524063689912560717606"
"05886116467109405077541002256983155200055935729725"
"71636269561882670428252483600823257530420752963450"
)
def A ( _UpperCAmelCase : str = N ) -> int:
'''simple docstring'''
return max(
# mypy cannot properly interpret reduce
int(reduce(lambda _UpperCAmelCase , _UpperCAmelCase : str(int(_UpperCAmelCase ) * int(_UpperCAmelCase ) ) , n[i : i + 13] ) )
for i in range(len(_UpperCAmelCase ) - 12 ) )
if __name__ == "__main__":
print(f"""{solution() = }""")
| 339 | 1 |
import os
import sys
import unittest
UpperCAmelCase__ = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init # noqa: E402
# Align TRANSFORMERS_PATH in check_dummies with the current path
UpperCAmelCase__ = os.path.join(git_repo_path, "src", "diffusers")
class __lowerCAmelCase ( unittest.TestCase ):
def _lowerCamelCase ( self : Tuple) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase = find_backend(' if not is_torch_available():')
self.assertEqual(A , 'torch')
# backend_with_underscore = find_backend(" if not is_tensorflow_text_available():")
# self.assertEqual(backend_with_underscore, "tensorflow_text")
_UpperCAmelCase = find_backend(' if not (is_torch_available() and is_transformers_available()):')
self.assertEqual(A , 'torch_and_transformers')
# double_backend_with_underscore = find_backend(
# " if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
# )
# self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text")
_UpperCAmelCase = find_backend(
' if not (is_torch_available() and is_transformers_available() and is_onnx_available()):')
self.assertEqual(A , 'torch_and_transformers_and_onnx')
def _lowerCamelCase ( self : int) -> Dict:
"""simple docstring"""
_UpperCAmelCase = read_init()
# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
self.assertIn('torch' , A)
self.assertIn('torch_and_transformers' , A)
self.assertIn('flax_and_transformers' , A)
self.assertIn('torch_and_transformers_and_onnx' , A)
# Likewise, we can't assert on the exact content of a key
self.assertIn('UNet2DModel' , objects['torch'])
self.assertIn('FlaxUNet2DConditionModel' , objects['flax'])
self.assertIn('StableDiffusionPipeline' , objects['torch_and_transformers'])
self.assertIn('FlaxStableDiffusionPipeline' , objects['flax_and_transformers'])
self.assertIn('LMSDiscreteScheduler' , objects['torch_and_scipy'])
self.assertIn('OnnxStableDiffusionPipeline' , objects['torch_and_transformers_and_onnx'])
def _lowerCamelCase ( self : Union[str, Any]) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase = create_dummy_object('CONSTANT' , '\'torch\'')
self.assertEqual(A , '\nCONSTANT = None\n')
_UpperCAmelCase = create_dummy_object('function' , '\'torch\'')
self.assertEqual(
A , '\ndef function(*args, **kwargs):\n requires_backends(function, \'torch\')\n')
_UpperCAmelCase = '\nclass FakeClass(metaclass=DummyObject):\n _backends = \'torch\'\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, \'torch\')\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, \'torch\')\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, \'torch\')\n'
_UpperCAmelCase = create_dummy_object('FakeClass' , '\'torch\'')
self.assertEqual(A , A)
def _lowerCamelCase ( self : Dict) -> int:
"""simple docstring"""
_UpperCAmelCase = '# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n requires_backends(function, ["torch"])\n\n\nclass FakeClass(metaclass=DummyObject):\n _backends = ["torch"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, ["torch"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, ["torch"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, ["torch"])\n'
_UpperCAmelCase = create_dummy_files({'torch': ['CONSTANT', 'function', 'FakeClass']})
self.assertEqual(dummy_files['torch'] , A)
| 339 |
from __future__ import annotations
from collections.abc import Callable
UpperCAmelCase__ = list[list[float | int]]
def A ( _UpperCAmelCase : Matrix , _UpperCAmelCase : Matrix ) -> Matrix:
'''simple docstring'''
_UpperCAmelCase = len(_UpperCAmelCase )
_UpperCAmelCase = [[0 for _ in range(size + 1 )] for _ in range(_UpperCAmelCase )]
_UpperCAmelCase = 42
_UpperCAmelCase = 42
_UpperCAmelCase = 42
_UpperCAmelCase = 42
_UpperCAmelCase = 42
_UpperCAmelCase = 42
for row in range(_UpperCAmelCase ):
for col in range(_UpperCAmelCase ):
_UpperCAmelCase = matrix[row][col]
_UpperCAmelCase = vector[row][0]
_UpperCAmelCase = 0
_UpperCAmelCase = 0
while row < size and col < size:
# pivoting
_UpperCAmelCase = max((abs(augmented[rowa][col] ), rowa) for rowa in range(_UpperCAmelCase , _UpperCAmelCase ) )[
1
]
if augmented[pivot_row][col] == 0:
col += 1
continue
else:
_UpperCAmelCase , _UpperCAmelCase = augmented[pivot_row], augmented[row]
for rowa in range(row + 1 , _UpperCAmelCase ):
_UpperCAmelCase = augmented[rowa][col] / augmented[row][col]
_UpperCAmelCase = 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 ):
_UpperCAmelCase = 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 A ( _UpperCAmelCase : list[int] ) -> Callable[[int], int]:
'''simple docstring'''
_UpperCAmelCase = len(_UpperCAmelCase )
_UpperCAmelCase = [[0 for _ in range(_UpperCAmelCase )] for _ in range(_UpperCAmelCase )]
_UpperCAmelCase = [[0] for _ in range(_UpperCAmelCase )]
_UpperCAmelCase = 42
_UpperCAmelCase = 42
_UpperCAmelCase = 42
_UpperCAmelCase = 42
for x_val, y_val in enumerate(_UpperCAmelCase ):
for col in range(_UpperCAmelCase ):
_UpperCAmelCase = (x_val + 1) ** (size - col - 1)
_UpperCAmelCase = y_val
_UpperCAmelCase = 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 A ( _UpperCAmelCase : int ) -> 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 A ( _UpperCAmelCase : Callable[[int], int] = question_function , _UpperCAmelCase : int = 10 ) -> int:
'''simple docstring'''
_UpperCAmelCase = [func(_UpperCAmelCase ) for x_val in range(1 , order + 1 )]
_UpperCAmelCase = [
interpolate(data_points[:max_coeff] ) for max_coeff in range(1 , order + 1 )
]
_UpperCAmelCase = 0
_UpperCAmelCase = 42
_UpperCAmelCase = 42
for poly in polynomials:
_UpperCAmelCase = 1
while func(_UpperCAmelCase ) == poly(_UpperCAmelCase ):
x_val += 1
ret += poly(_UpperCAmelCase )
return ret
if __name__ == "__main__":
print(f"""{solution() = }""")
| 339 | 1 |
import darl # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline
UpperCAmelCase__ = {
"n_samples": 64,
"horizon": 32,
"num_inference_steps": 20,
"n_guide_steps": 2, # can set to 0 for faster sampling, does not use value network
"scale_grad_by_std": True,
"scale": 0.1,
"eta": 0.0,
"t_grad_cutoff": 2,
"device": "cpu",
}
if __name__ == "__main__":
UpperCAmelCase__ = "hopper-medium-v2"
UpperCAmelCase__ = gym.make(env_name)
UpperCAmelCase__ = ValueGuidedRLPipeline.from_pretrained(
"bglick13/hopper-medium-v2-value-function-hor32",
env=env,
)
env.seed(0)
UpperCAmelCase__ = env.reset()
UpperCAmelCase__ = 0
UpperCAmelCase__ = 0
UpperCAmelCase__ = 1000
UpperCAmelCase__ = [obs.copy()]
try:
for t in tqdm.tqdm(range(T)):
# call the policy
UpperCAmelCase__ = pipeline(obs, planning_horizon=32)
# execute action in environment
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ = env.step(denorm_actions)
UpperCAmelCase__ = env.get_normalized_score(total_reward)
# update return
total_reward += reward
total_score += score
print(
f"""Step: {t}, Reward: {reward}, Total Reward: {total_reward}, Score: {score}, Total Score:"""
f""" {total_score}"""
)
# save observations for rendering
rollout.append(next_observation.copy())
UpperCAmelCase__ = next_observation
except KeyboardInterrupt:
pass
print(f"""Total reward: {total_reward}""")
| 339 |
from __future__ import annotations
def A ( _UpperCAmelCase : list[int] ) -> bool:
'''simple docstring'''
return len(set(_UpperCAmelCase ) ) == len(_UpperCAmelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 339 | 1 |
import argparse
import torch
from safetensors.torch import load_file
from diffusers import StableDiffusionPipeline
def A ( _UpperCAmelCase : str , _UpperCAmelCase : Any , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : List[Any] , _UpperCAmelCase : int ) -> Any:
'''simple docstring'''
# load base model
_UpperCAmelCase = StableDiffusionPipeline.from_pretrained(_UpperCAmelCase , torch_dtype=torch.floataa )
# load LoRA weight from .safetensors
_UpperCAmelCase = load_file(_UpperCAmelCase )
_UpperCAmelCase = []
# directly update weight in diffusers model
for key in state_dict:
# it is suggested to print out the key, it usually will be something like below
# "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight"
# as we have set the alpha beforehand, so just skip
if ".alpha" in key or key in visited:
continue
if "text" in key:
_UpperCAmelCase = key.split('.' )[0].split(LORA_PREFIX_TEXT_ENCODER + '_' )[-1].split('_' )
_UpperCAmelCase = pipeline.text_encoder
else:
_UpperCAmelCase = key.split('.' )[0].split(LORA_PREFIX_UNET + '_' )[-1].split('_' )
_UpperCAmelCase = pipeline.unet
# find the target layer
_UpperCAmelCase = layer_infos.pop(0 )
while len(_UpperCAmelCase ) > -1:
try:
_UpperCAmelCase = curr_layer.__getattr__(_UpperCAmelCase )
if len(_UpperCAmelCase ) > 0:
_UpperCAmelCase = layer_infos.pop(0 )
elif len(_UpperCAmelCase ) == 0:
break
except Exception:
if len(_UpperCAmelCase ) > 0:
temp_name += "_" + layer_infos.pop(0 )
else:
_UpperCAmelCase = layer_infos.pop(0 )
_UpperCAmelCase = []
if "lora_down" in key:
pair_keys.append(key.replace('lora_down' , 'lora_up' ) )
pair_keys.append(_UpperCAmelCase )
else:
pair_keys.append(_UpperCAmelCase )
pair_keys.append(key.replace('lora_up' , 'lora_down' ) )
# update weight
if len(state_dict[pair_keys[0]].shape ) == 4:
_UpperCAmelCase = state_dict[pair_keys[0]].squeeze(3 ).squeeze(2 ).to(torch.floataa )
_UpperCAmelCase = state_dict[pair_keys[1]].squeeze(3 ).squeeze(2 ).to(torch.floataa )
curr_layer.weight.data += alpha * torch.mm(_UpperCAmelCase , _UpperCAmelCase ).unsqueeze(2 ).unsqueeze(3 )
else:
_UpperCAmelCase = state_dict[pair_keys[0]].to(torch.floataa )
_UpperCAmelCase = state_dict[pair_keys[1]].to(torch.floataa )
curr_layer.weight.data += alpha * torch.mm(_UpperCAmelCase , _UpperCAmelCase )
# update visited list
for item in pair_keys:
visited.append(_UpperCAmelCase )
return pipeline
if __name__ == "__main__":
UpperCAmelCase__ = argparse.ArgumentParser()
parser.add_argument(
"--base_model_path", default=None, type=str, required=True, help="Path to the base model in diffusers format."
)
parser.add_argument(
"--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
)
parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
parser.add_argument(
"--lora_prefix_unet", default="lora_unet", type=str, help="The prefix of UNet weight in safetensors"
)
parser.add_argument(
"--lora_prefix_text_encoder",
default="lora_te",
type=str,
help="The prefix of text encoder weight in safetensors",
)
parser.add_argument("--alpha", default=0.75, type=float, help="The merging ratio in W = W0 + alpha * deltaW")
parser.add_argument(
"--to_safetensors", action="store_true", help="Whether to store pipeline in safetensors format or not."
)
parser.add_argument("--device", type=str, help="Device to use (e.g. cpu, cuda:0, cuda:1, etc.)")
UpperCAmelCase__ = parser.parse_args()
UpperCAmelCase__ = args.base_model_path
UpperCAmelCase__ = args.checkpoint_path
UpperCAmelCase__ = args.dump_path
UpperCAmelCase__ = args.lora_prefix_unet
UpperCAmelCase__ = args.lora_prefix_text_encoder
UpperCAmelCase__ = args.alpha
UpperCAmelCase__ = convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)
UpperCAmelCase__ = pipe.to(args.device)
pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
| 339 |
import os
UpperCAmelCase__ = {"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1000}
def A ( _UpperCAmelCase : str ) -> int:
'''simple docstring'''
_UpperCAmelCase = 0
_UpperCAmelCase = 0
while index < len(_UpperCAmelCase ) - 1:
_UpperCAmelCase = SYMBOLS[numerals[index]]
_UpperCAmelCase = SYMBOLS[numerals[index + 1]]
if current_value < next_value:
total_value -= current_value
else:
total_value += current_value
index += 1
total_value += SYMBOLS[numerals[index]]
return total_value
def A ( _UpperCAmelCase : int ) -> str:
'''simple docstring'''
_UpperCAmelCase = ''
_UpperCAmelCase = num // 1_000
numerals += m_count * "M"
num %= 1_000
_UpperCAmelCase = num // 100
if c_count == 9:
numerals += "CM"
c_count -= 9
elif c_count == 4:
numerals += "CD"
c_count -= 4
if c_count >= 5:
numerals += "D"
c_count -= 5
numerals += c_count * "C"
num %= 100
_UpperCAmelCase = num // 10
if x_count == 9:
numerals += "XC"
x_count -= 9
elif x_count == 4:
numerals += "XL"
x_count -= 4
if x_count >= 5:
numerals += "L"
x_count -= 5
numerals += x_count * "X"
num %= 10
if num == 9:
numerals += "IX"
num -= 9
elif num == 4:
numerals += "IV"
num -= 4
if num >= 5:
numerals += "V"
num -= 5
numerals += num * "I"
return numerals
def A ( _UpperCAmelCase : str = "/p089_roman.txt" ) -> int:
'''simple docstring'''
_UpperCAmelCase = 0
with open(os.path.dirname(_UpperCAmelCase ) + roman_numerals_filename ) as filea:
_UpperCAmelCase = filea.readlines()
for line in lines:
_UpperCAmelCase = line.strip()
_UpperCAmelCase = parse_roman_numerals(_UpperCAmelCase )
_UpperCAmelCase = generate_roman_numerals(_UpperCAmelCase )
savings += len(_UpperCAmelCase ) - len(_UpperCAmelCase )
return savings
if __name__ == "__main__":
print(f"""{solution() = }""")
| 339 | 1 |
import sys
UpperCAmelCase__ = (
"73167176531330624919225119674426574742355349194934"
"96983520312774506326239578318016984801869478851843"
"85861560789112949495459501737958331952853208805511"
"12540698747158523863050715693290963295227443043557"
"66896648950445244523161731856403098711121722383113"
"62229893423380308135336276614282806444486645238749"
"30358907296290491560440772390713810515859307960866"
"70172427121883998797908792274921901699720888093776"
"65727333001053367881220235421809751254540594752243"
"52584907711670556013604839586446706324415722155397"
"53697817977846174064955149290862569321978468622482"
"83972241375657056057490261407972968652414535100474"
"82166370484403199890008895243450658541227588666881"
"16427171479924442928230863465674813919123162824586"
"17866458359124566529476545682848912883142607690042"
"24219022671055626321111109370544217506941658960408"
"07198403850962455444362981230987879927244284909188"
"84580156166097919133875499200524063689912560717606"
"05886116467109405077541002256983155200055935729725"
"71636269561882670428252483600823257530420752963450"
)
def A ( _UpperCAmelCase : str = N ) -> int:
'''simple docstring'''
_UpperCAmelCase = -sys.maxsize - 1
for i in range(len(_UpperCAmelCase ) - 12 ):
_UpperCAmelCase = 1
for j in range(13 ):
product *= int(n[i + j] )
if product > largest_product:
_UpperCAmelCase = product
return largest_product
if __name__ == "__main__":
print(f"""{solution() = }""")
| 339 |
import requests
from bsa import BeautifulSoup
def A ( _UpperCAmelCase : str , _UpperCAmelCase : dict ) -> str:
'''simple docstring'''
_UpperCAmelCase = BeautifulSoup(requests.get(_UpperCAmelCase , params=_UpperCAmelCase ).content , 'html.parser' )
_UpperCAmelCase = soup.find('div' , attrs={'class': 'gs_ri'} )
_UpperCAmelCase = div.find('div' , attrs={'class': 'gs_fl'} ).find_all('a' )
return anchors[2].get_text()
if __name__ == "__main__":
UpperCAmelCase__ = {
"title": (
"Precisely geometry controlled microsupercapacitors for ultrahigh areal "
"capacitance, volumetric capacitance, and energy density"
),
"journal": "Chem. Mater.",
"volume": 30,
"pages": "3979-3990",
"year": 2018,
"hl": "en",
}
print(get_citation("https://scholar.google.com/scholar_lookup", params=params))
| 339 | 1 |
from __future__ import annotations
from typing import TypedDict
class __lowerCAmelCase ( A ):
UpperCamelCase = 42
UpperCamelCase = 42
def A ( _UpperCAmelCase : str ) -> list[str]:
'''simple docstring'''
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
raise TypeError('The parameter s type must be str.' )
return [s[i:] + s[:i] for i in range(len(_UpperCAmelCase ) )]
def A ( _UpperCAmelCase : str ) -> BWTTransformDict:
'''simple docstring'''
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
raise TypeError('The parameter s type must be str.' )
if not s:
raise ValueError('The parameter s must not be empty.' )
_UpperCAmelCase = all_rotations(_UpperCAmelCase )
rotations.sort() # sort the list of rotations in alphabetically order
# make a string composed of the last char of each rotation
_UpperCAmelCase = {
"bwt_string": "".join([word[-1] for word in rotations] ),
"idx_original_string": rotations.index(_UpperCAmelCase ),
}
return response
def A ( _UpperCAmelCase : str , _UpperCAmelCase : int ) -> str:
'''simple docstring'''
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
raise TypeError('The parameter bwt_string type must be str.' )
if not bwt_string:
raise ValueError('The parameter bwt_string must not be empty.' )
try:
_UpperCAmelCase = int(_UpperCAmelCase )
except ValueError:
raise TypeError(
'The parameter idx_original_string type must be int or passive'
' of cast to int.' )
if idx_original_string < 0:
raise ValueError('The parameter idx_original_string must not be lower than 0.' )
if idx_original_string >= len(_UpperCAmelCase ):
raise ValueError(
'The parameter idx_original_string must be lower than' ' len(bwt_string).' )
_UpperCAmelCase = [''] * len(_UpperCAmelCase )
for _ in range(len(_UpperCAmelCase ) ):
for i in range(len(_UpperCAmelCase ) ):
_UpperCAmelCase = bwt_string[i] + ordered_rotations[i]
ordered_rotations.sort()
return ordered_rotations[idx_original_string]
if __name__ == "__main__":
UpperCAmelCase__ = "Provide a string that I will generate its BWT transform: "
UpperCAmelCase__ = input(entry_msg).strip()
UpperCAmelCase__ = bwt_transform(s)
print(
f"""Burrows Wheeler transform for string '{s}' results """
f"""in '{result["bwt_string"]}'"""
)
UpperCAmelCase__ = reverse_bwt(result["bwt_string"], result["idx_original_string"])
print(
f"""Reversing Burrows Wheeler transform for entry '{result["bwt_string"]}' """
f"""we get original string '{original_string}'"""
)
| 339 |
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.roformer.modeling_flax_roformer import (
FlaxRoFormerForMaskedLM,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerModel,
)
class __lowerCAmelCase ( unittest.TestCase ):
def __init__( self : Optional[Any] , A : Dict , A : Union[str, Any]=13 , A : Dict=7 , A : Dict=True , A : Tuple=True , A : Union[str, Any]=True , A : int=True , A : Optional[int]=99 , A : List[str]=32 , A : List[Any]=5 , A : int=4 , A : Any=37 , A : Optional[int]="gelu" , A : Optional[Any]=0.1 , A : Any=0.1 , A : Union[str, Any]=5_12 , A : int=16 , A : List[str]=2 , A : Union[str, Any]=0.0_2 , A : Union[str, Any]=4 , ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase = parent
_UpperCAmelCase = batch_size
_UpperCAmelCase = seq_length
_UpperCAmelCase = is_training
_UpperCAmelCase = use_attention_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_choices
def _lowerCamelCase ( self : Optional[Any]) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
_UpperCAmelCase = None
if self.use_attention_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 = RoFormerConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=A , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def _lowerCamelCase ( self : List[Any]) -> List[str]:
"""simple docstring"""
_UpperCAmelCase = self.prepare_config_and_inputs()
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = config_and_inputs
_UpperCAmelCase = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
return config, inputs_dict
@require_flax
class __lowerCAmelCase ( A , unittest.TestCase ):
UpperCamelCase = True
UpperCamelCase = (
(
FlaxRoFormerModel,
FlaxRoFormerForMaskedLM,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
)
if is_flax_available()
else ()
)
def _lowerCamelCase ( self : Optional[int]) -> Any:
"""simple docstring"""
_UpperCAmelCase = FlaxRoFormerModelTester(self)
@slow
def _lowerCamelCase ( self : List[Any]) -> Dict:
"""simple docstring"""
for model_class_name in self.all_model_classes:
_UpperCAmelCase = model_class_name.from_pretrained('junnyu/roformer_chinese_small' , from_pt=A)
_UpperCAmelCase = model(np.ones((1, 1)))
self.assertIsNotNone(A)
@require_flax
class __lowerCAmelCase ( unittest.TestCase ):
@slow
def _lowerCamelCase ( self : List[Any]) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase = FlaxRoFormerForMaskedLM.from_pretrained('junnyu/roformer_chinese_base')
_UpperCAmelCase = jnp.array([[0, 1, 2, 3, 4, 5]])
_UpperCAmelCase = model(A)[0]
_UpperCAmelCase = 5_00_00
_UpperCAmelCase = (1, 6, vocab_size)
self.assertEqual(output.shape , A)
_UpperCAmelCase = jnp.array(
[[[-0.1_2_0_5, -1.0_2_6_5, 0.2_9_2_2], [-1.5_1_3_4, 0.1_9_7_4, 0.1_5_1_9], [-5.0_1_3_5, -3.9_0_0_3, -0.8_4_0_4]]])
self.assertTrue(jnp.allclose(output[:, :3, :3] , A , atol=1E-4))
| 339 | 1 |
from sklearn.metrics import mean_squared_error
import datasets
UpperCAmelCase__ = "\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n journal={Journal of Machine Learning Research},\n volume={12},\n pages={2825--2830},\n year={2011}\n}\n"
UpperCAmelCase__ = "\\nMean Squared Error(MSE) is the average of the square of difference between the predicted\nand actual values.\n"
UpperCAmelCase__ = "\nArgs:\n predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)\n Estimated target values.\n references: array-like of shape (n_samples,) or (n_samples, n_outputs)\n Ground truth (correct) target values.\n sample_weight: array-like of shape (n_samples,), default=None\n Sample weights.\n multioutput: {\"raw_values\", \"uniform_average\"} or array-like of shape (n_outputs,), default=\"uniform_average\"\n Defines aggregating of multiple output values. Array-like value defines weights used to average errors.\n\n \"raw_values\" : Returns a full set of errors in case of multioutput input.\n\n \"uniform_average\" : Errors of all outputs are averaged with uniform weight.\n\n squared : bool, default=True\n If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.\n\nReturns:\n mse : mean squared error.\nExamples:\n\n >>> mse_metric = datasets.load_metric(\"mse\")\n >>> predictions = [2.5, 0.0, 2, 8]\n >>> references = [3, -0.5, 2, 7]\n >>> results = mse_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {'mse': 0.375}\n >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)\n >>> print(rmse_result)\n {'mse': 0.6123724356957945}\n\n If you're using multi-dimensional lists, then set the config as follows :\n\n >>> mse_metric = datasets.load_metric(\"mse\", \"multilist\")\n >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]\n >>> references = [[0, 2], [-1, 2], [8, -5]]\n >>> results = mse_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {'mse': 0.7083333333333334}\n >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput='raw_values')\n >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n {'mse': array([0.41666667, 1. ])}\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __lowerCAmelCase ( datasets.Metric ):
def _lowerCamelCase ( self : Any) -> List[str]:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(self._get_feature_types()) , reference_urls=[
'https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html'
] , )
def _lowerCamelCase ( self : List[Any]) -> Union[str, Any]:
"""simple docstring"""
if self.config_name == "multilist":
return {
"predictions": datasets.Sequence(datasets.Value('float')),
"references": datasets.Sequence(datasets.Value('float')),
}
else:
return {
"predictions": datasets.Value('float'),
"references": datasets.Value('float'),
}
def _lowerCamelCase ( self : str , A : Optional[int] , A : List[str] , A : str=None , A : List[str]="uniform_average" , A : Tuple=True) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase = mean_squared_error(
A , A , sample_weight=A , multioutput=A , squared=A)
return {"mse": mse}
| 339 |
UpperCAmelCase__ = {}
def A ( _UpperCAmelCase : int , _UpperCAmelCase : int , _UpperCAmelCase : int ) -> int:
'''simple docstring'''
# if we are absent twice, or late 3 consecutive days,
# no further prize strings are possible
if late == 3 or absent == 2:
return 0
# if we have no days left, and have not failed any other rules,
# we have a prize string
if days == 0:
return 1
# No easy solution, so now we need to do the recursive calculation
# First, check if the combination is already in the cache, and
# if yes, return the stored value from there since we already
# know the number of possible prize strings from this point on
_UpperCAmelCase = (days, absent, late)
if key in cache:
return cache[key]
# now we calculate the three possible ways that can unfold from
# this point on, depending on our attendance today
# 1) if we are late (but not absent), the "absent" counter stays as
# it is, but the "late" counter increases by one
_UpperCAmelCase = _calculate(days - 1 , _UpperCAmelCase , late + 1 )
# 2) if we are absent, the "absent" counter increases by 1, and the
# "late" counter resets to 0
_UpperCAmelCase = _calculate(days - 1 , absent + 1 , 0 )
# 3) if we are on time, this resets the "late" counter and keeps the
# absent counter
_UpperCAmelCase = _calculate(days - 1 , _UpperCAmelCase , 0 )
_UpperCAmelCase = state_late + state_absent + state_ontime
_UpperCAmelCase = prizestrings
return prizestrings
def A ( _UpperCAmelCase : int = 30 ) -> int:
'''simple docstring'''
return _calculate(_UpperCAmelCase , absent=0 , late=0 )
if __name__ == "__main__":
print(solution())
| 339 | 1 |
def A ( _UpperCAmelCase : list[int] , _UpperCAmelCase : str ) -> list[int]:
'''simple docstring'''
_UpperCAmelCase = int(_UpperCAmelCase )
# Initialize Result
_UpperCAmelCase = []
# Traverse through all denomination
for denomination in reversed(_UpperCAmelCase ):
# Find denominations
while int(_UpperCAmelCase ) >= int(_UpperCAmelCase ):
total_value -= int(_UpperCAmelCase )
answer.append(_UpperCAmelCase ) # Append the "answers" array
return answer
# Driver Code
if __name__ == "__main__":
UpperCAmelCase__ = []
UpperCAmelCase__ = "0"
if (
input("Do you want to enter your denominations ? (yY/n): ").strip().lower()
== "y"
):
UpperCAmelCase__ = int(input("Enter the number of denominations you want to add: ").strip())
for i in range(0, n):
denominations.append(int(input(f"""Denomination {i}: """).strip()))
UpperCAmelCase__ = input("Enter the change you want to make in Indian Currency: ").strip()
else:
# All denominations of Indian Currency if user does not enter
UpperCAmelCase__ = [1, 2, 5, 10, 20, 50, 100, 500, 2000]
UpperCAmelCase__ = input("Enter the change you want to make: ").strip()
if int(value) == 0 or int(value) < 0:
print("The total value cannot be zero or negative.")
else:
print(f"""Following is minimal change for {value}: """)
UpperCAmelCase__ = find_minimum_change(denominations, value)
# Print result
for i in range(len(answer)):
print(answer[i], end=" ")
| 339 |
import os
import sys
import unittest
UpperCAmelCase__ = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init # noqa: E402
# Align TRANSFORMERS_PATH in check_dummies with the current path
UpperCAmelCase__ = os.path.join(git_repo_path, "src", "diffusers")
class __lowerCAmelCase ( unittest.TestCase ):
def _lowerCamelCase ( self : Tuple) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase = find_backend(' if not is_torch_available():')
self.assertEqual(A , 'torch')
# backend_with_underscore = find_backend(" if not is_tensorflow_text_available():")
# self.assertEqual(backend_with_underscore, "tensorflow_text")
_UpperCAmelCase = find_backend(' if not (is_torch_available() and is_transformers_available()):')
self.assertEqual(A , 'torch_and_transformers')
# double_backend_with_underscore = find_backend(
# " if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
# )
# self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text")
_UpperCAmelCase = find_backend(
' if not (is_torch_available() and is_transformers_available() and is_onnx_available()):')
self.assertEqual(A , 'torch_and_transformers_and_onnx')
def _lowerCamelCase ( self : int) -> Dict:
"""simple docstring"""
_UpperCAmelCase = read_init()
# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
self.assertIn('torch' , A)
self.assertIn('torch_and_transformers' , A)
self.assertIn('flax_and_transformers' , A)
self.assertIn('torch_and_transformers_and_onnx' , A)
# Likewise, we can't assert on the exact content of a key
self.assertIn('UNet2DModel' , objects['torch'])
self.assertIn('FlaxUNet2DConditionModel' , objects['flax'])
self.assertIn('StableDiffusionPipeline' , objects['torch_and_transformers'])
self.assertIn('FlaxStableDiffusionPipeline' , objects['flax_and_transformers'])
self.assertIn('LMSDiscreteScheduler' , objects['torch_and_scipy'])
self.assertIn('OnnxStableDiffusionPipeline' , objects['torch_and_transformers_and_onnx'])
def _lowerCamelCase ( self : Union[str, Any]) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase = create_dummy_object('CONSTANT' , '\'torch\'')
self.assertEqual(A , '\nCONSTANT = None\n')
_UpperCAmelCase = create_dummy_object('function' , '\'torch\'')
self.assertEqual(
A , '\ndef function(*args, **kwargs):\n requires_backends(function, \'torch\')\n')
_UpperCAmelCase = '\nclass FakeClass(metaclass=DummyObject):\n _backends = \'torch\'\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, \'torch\')\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, \'torch\')\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, \'torch\')\n'
_UpperCAmelCase = create_dummy_object('FakeClass' , '\'torch\'')
self.assertEqual(A , A)
def _lowerCamelCase ( self : Dict) -> int:
"""simple docstring"""
_UpperCAmelCase = '# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n requires_backends(function, ["torch"])\n\n\nclass FakeClass(metaclass=DummyObject):\n _backends = ["torch"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, ["torch"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, ["torch"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, ["torch"])\n'
_UpperCAmelCase = create_dummy_files({'torch': ['CONSTANT', 'function', 'FakeClass']})
self.assertEqual(dummy_files['torch'] , A)
| 339 | 1 |
import unittest
from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
UpperCAmelCase__ = get_tests_dir("fixtures/test_sentencepiece.model")
@require_sentencepiece
@require_tokenizers
class __lowerCAmelCase ( A , unittest.TestCase ):
UpperCamelCase = ReformerTokenizer
UpperCamelCase = ReformerTokenizerFast
UpperCamelCase = True
UpperCamelCase = False
UpperCamelCase = True
def _lowerCamelCase ( self : Optional[Any]) -> Tuple:
"""simple docstring"""
super().setUp()
_UpperCAmelCase = ReformerTokenizer(A , keep_accents=A)
tokenizer.save_pretrained(self.tmpdirname)
def _lowerCamelCase ( self : str) -> List[str]:
"""simple docstring"""
_UpperCAmelCase = '<s>'
_UpperCAmelCase = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(A) , A)
self.assertEqual(self.get_tokenizer()._convert_id_to_token(A) , A)
def _lowerCamelCase ( self : Any) -> Union[str, Any]:
"""simple docstring"""
_UpperCAmelCase = list(self.get_tokenizer().get_vocab().keys())
self.assertEqual(vocab_keys[0] , '<unk>')
self.assertEqual(vocab_keys[1] , '<s>')
self.assertEqual(vocab_keys[-1] , 'j')
self.assertEqual(len(A) , 10_00)
def _lowerCamelCase ( self : int) -> List[Any]:
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size , 10_00)
def _lowerCamelCase ( self : Optional[Any]) -> Optional[int]:
"""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(A)
_UpperCAmelCase = rust_tokenizer.tokenize(A)
self.assertListEqual(A , A)
_UpperCAmelCase = tokenizer.encode(A , add_special_tokens=A)
_UpperCAmelCase = rust_tokenizer.encode(A , add_special_tokens=A)
self.assertListEqual(A , A)
_UpperCAmelCase = self.get_rust_tokenizer()
_UpperCAmelCase = tokenizer.encode(A)
_UpperCAmelCase = rust_tokenizer.encode(A)
self.assertListEqual(A , A)
def _lowerCamelCase ( self : Optional[Any] , A : Dict=15) -> Any:
"""simple docstring"""
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"):
_UpperCAmelCase = self.rust_tokenizer_class.from_pretrained(A , **A)
# Simple input
_UpperCAmelCase = 'This is a simple input'
_UpperCAmelCase = ['This is a simple input 1', 'This is a simple input 2']
_UpperCAmelCase = ('This is a simple input', 'This is a pair')
_UpperCAmelCase = [
('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(A , tokenizer_r.encode , A , max_length=A , padding='max_length')
# Simple input
self.assertRaises(A , tokenizer_r.encode_plus , A , max_length=A , padding='max_length')
# Simple input
self.assertRaises(
A , tokenizer_r.batch_encode_plus , A , max_length=A , padding='max_length' , )
# Pair input
self.assertRaises(A , tokenizer_r.encode , A , max_length=A , padding='max_length')
# Pair input
self.assertRaises(A , tokenizer_r.encode_plus , A , max_length=A , padding='max_length')
# Pair input
self.assertRaises(
A , tokenizer_r.batch_encode_plus , A , max_length=A , padding='max_length' , )
def _lowerCamelCase ( self : Union[str, Any]) -> str:
"""simple docstring"""
pass
def _lowerCamelCase ( self : Union[str, Any]) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase = ReformerTokenizer(A , keep_accents=A)
_UpperCAmelCase = tokenizer.tokenize('This is a test')
self.assertListEqual(A , ['▁This', '▁is', '▁a', '▁t', 'est'])
self.assertListEqual(
tokenizer.convert_tokens_to_ids(A) , [2_85, 46, 10, 1_70, 3_82] , )
_UpperCAmelCase = tokenizer.tokenize('I was born in 92000, and this is falsé.')
self.assertListEqual(
A , [
SPIECE_UNDERLINE + 'I',
SPIECE_UNDERLINE + 'was',
SPIECE_UNDERLINE + 'b',
'or',
'n',
SPIECE_UNDERLINE + 'in',
SPIECE_UNDERLINE + '',
'9',
'2',
'0',
'0',
'0',
',',
SPIECE_UNDERLINE + 'and',
SPIECE_UNDERLINE + 'this',
SPIECE_UNDERLINE + 'is',
SPIECE_UNDERLINE + 'f',
'al',
's',
'é',
'.',
] , )
_UpperCAmelCase = tokenizer.convert_tokens_to_ids(A)
self.assertListEqual(
A , [8, 21, 84, 55, 24, 19, 7, 0, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 0, 4] , )
_UpperCAmelCase = tokenizer.convert_ids_to_tokens(A)
self.assertListEqual(
A , [
SPIECE_UNDERLINE + 'I',
SPIECE_UNDERLINE + 'was',
SPIECE_UNDERLINE + 'b',
'or',
'n',
SPIECE_UNDERLINE + 'in',
SPIECE_UNDERLINE + '',
'<unk>',
'2',
'0',
'0',
'0',
',',
SPIECE_UNDERLINE + 'and',
SPIECE_UNDERLINE + 'this',
SPIECE_UNDERLINE + 'is',
SPIECE_UNDERLINE + 'f',
'al',
's',
'<unk>',
'.',
] , )
@cached_property
def _lowerCamelCase ( self : Optional[int]) -> int:
"""simple docstring"""
return ReformerTokenizer.from_pretrained('google/reformer-crime-and-punishment')
@slow
def _lowerCamelCase ( self : List[str]) -> str:
"""simple docstring"""
_UpperCAmelCase = 'Hello World!'
_UpperCAmelCase = [1_26, 32, 2_62, 1_52, 38, 72, 2_87]
self.assertListEqual(A , self.big_tokenizer.encode(A))
@slow
def _lowerCamelCase ( self : int) -> Any:
"""simple docstring"""
_UpperCAmelCase = (
'This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'
' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'
)
_UpperCAmelCase = [
1_08,
2_65,
24,
1_11,
4,
2_58,
1_56,
35,
28,
2_75,
3,
2_59,
2_97,
2_60,
84,
4,
35,
1_10,
44,
8,
2_59,
91,
2_68,
21,
11,
2_09,
2_74,
1_09,
2_66,
2_77,
1_17,
86,
93,
3_15,
2_58,
2_78,
2_58,
2_77,
2_58,
0,
2_58,
2_88,
2_58,
3_19,
2_58,
0,
2_58,
0,
2_58,
0,
2_58,
0,
2_58,
2_87,
2_58,
3_15,
2_58,
2_89,
2_58,
2_78,
99,
2_69,
2_66,
2_62,
8,
2_59,
2_41,
4,
2_17,
2_30,
2_68,
2_66,
55,
1_68,
1_06,
75,
1_93,
2_66,
2_23,
27,
49,
26,
2_82,
25,
2_64,
2_99,
19,
26,
0,
2_58,
2_77,
1_17,
86,
93,
1_76,
1_83,
2_70,
11,
2_62,
42,
61,
2_65,
]
self.assertListEqual(A , self.big_tokenizer.encode(A))
@require_torch
@slow
def _lowerCamelCase ( self : int) -> Optional[int]:
"""simple docstring"""
import torch
from transformers import ReformerConfig, ReformerModel
# Build sequence
_UpperCAmelCase = list(self.big_tokenizer.get_vocab().keys())[:10]
_UpperCAmelCase = ' '.join(A)
_UpperCAmelCase = self.big_tokenizer.encode_plus(A , return_tensors='pt')
_UpperCAmelCase = self.big_tokenizer.batch_encode_plus([sequence, sequence] , return_tensors='pt')
_UpperCAmelCase = ReformerConfig()
# The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
_UpperCAmelCase = encoded_sequence['input_ids'].shape
_UpperCAmelCase = ReformerModel(A)
# Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size
with torch.no_grad():
model(**A)
model(**A)
@slow
def _lowerCamelCase ( self : List[str]) -> List[str]:
"""simple docstring"""
_UpperCAmelCase = {'input_ids': [[1_08, 2_65, 24, 1_11, 4, 2_58, 1_56, 7, 51, 2_79, 58, 7, 76, 25, 69, 2_78], [1_40, 2_43, 2_64, 1_34, 17, 2_67, 77, 2_63, 22, 2_62, 2_97, 2_58, 3_04, 1_77, 2_79, 2_66, 14, 89, 13, 35, 2_61, 2_99, 2_72, 1_37, 2_75, 2_78]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501
# fmt: on
# This tokenizer does not know some characters like ")".
# That is the reason why we use very simple texts here.
# Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064
_UpperCAmelCase = [
'This is a very simple sentence.',
'The quick brown fox jumps over the lazy dog.',
]
self.tokenizer_integration_test_util(
expected_encoding=A , model_name='google/reformer-crime-and-punishment' , revision='0e6c3decb8211d49bf881013425dc8b0448b3f5a' , padding=A , sequences=A , )
| 339 |
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartForSequenceClassification,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
TapexTokenizer,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version
from transformers.utils.versions import require_version
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.17.0.dev0")
require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")
UpperCAmelCase__ = logging.getLogger(__name__)
@dataclass
class __lowerCAmelCase :
UpperCamelCase = field(
default='''tab_fact''' , metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''} )
UpperCamelCase = field(
default='''tab_fact''' , metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''} , )
UpperCamelCase = field(
default=1_0_2_4 , metadata={
'''help''': (
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
} , )
UpperCamelCase = field(
default=A , metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''} )
UpperCamelCase = field(
default=A , metadata={
'''help''': (
'''Whether to pad all samples to `max_seq_length`. '''
'''If False, will pad the samples dynamically when batching to the maximum length in the batch.'''
)
} , )
UpperCamelCase = field(
default=A , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of training examples to this '''
'''value if set.'''
)
} , )
UpperCamelCase = field(
default=A , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
'''value if set.'''
)
} , )
UpperCamelCase = field(
default=A , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of prediction examples to this '''
'''value if set.'''
)
} , )
UpperCamelCase = field(
default=A , metadata={'''help''': '''A csv or a json file containing the training data.'''} )
UpperCamelCase = field(
default=A , metadata={'''help''': '''A csv or a json file containing the validation data.'''} )
UpperCamelCase = field(default=A , metadata={'''help''': '''A csv or a json file containing the test data.'''} )
def _lowerCamelCase ( self : str) -> List[Any]:
"""simple docstring"""
if self.dataset_name is not None:
pass
elif self.train_file is None or self.validation_file is None:
raise ValueError('Need either a GLUE task, a training/validation file or a dataset name.')
else:
_UpperCAmelCase = self.train_file.split('.')[-1]
assert train_extension in ["csv", "json"], "`train_file` should be a csv or a json file."
_UpperCAmelCase = self.validation_file.split('.')[-1]
assert (
validation_extension == train_extension
), "`validation_file` should have the same extension (csv or json) as `train_file`."
@dataclass
class __lowerCAmelCase :
UpperCamelCase = field(
default=A , metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} )
UpperCamelCase = field(
default=A , metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
UpperCamelCase = field(
default=A , metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} )
UpperCamelCase = field(
default=A , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''} , )
UpperCamelCase = field(
default=A , metadata={'''help''': '''Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'''} , )
UpperCamelCase = field(
default='''main''' , metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''} , )
UpperCamelCase = field(
default=A , metadata={
'''help''': (
'''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
'''with private models).'''
)
} , )
def A ( ) -> Optional[int]:
'''simple docstring'''
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
_UpperCAmelCase = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith('.json' ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = parser.parse_args_into_dataclasses()
# Setup logging
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s' , datefmt='%m/%d/%Y %H:%M:%S' , handlers=[logging.StreamHandler(sys.stdout )] , )
_UpperCAmelCase = training_args.get_process_log_level()
logger.setLevel(_UpperCAmelCase )
datasets.utils.logging.set_verbosity(_UpperCAmelCase )
transformers.utils.logging.set_verbosity(_UpperCAmelCase )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
F"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
+ F"distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}" )
logger.info(F"Training/evaluation parameters {training_args}" )
# Detecting last checkpoint.
_UpperCAmelCase = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
_UpperCAmelCase = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
F"Output directory ({training_args.output_dir}) already exists and is not empty. "
'Use --overwrite_output_dir to overcome.' )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
F"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.' )
# Set seed before initializing model.
set_seed(training_args.seed )
# Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
# or specify a GLUE benchmark task (the dataset will be downloaded automatically from the datasets Hub).
#
# For JSON files, this script will use the `question` column for the input question and `table` column for the corresponding table.
#
# If the CSVs/JSONs contain only one non-label column, the script does single sentence classification on this
# single column. You can easily tweak this behavior (see below)
#
# In distributed training, the load_dataset function guarantee that only one local process can concurrently
# download the dataset.
if data_args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
_UpperCAmelCase = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , cache_dir=model_args.cache_dir )
else:
# Loading a dataset from your local files.
# CSV/JSON training and evaluation files are needed.
_UpperCAmelCase = {'train': data_args.train_file, 'validation': data_args.validation_file}
# Get the test dataset: you can provide your own CSV/JSON test file (see below)
# when you use `do_predict` without specifying a GLUE benchmark task.
if training_args.do_predict:
if data_args.test_file is not None:
_UpperCAmelCase = data_args.train_file.split('.' )[-1]
_UpperCAmelCase = data_args.test_file.split('.' )[-1]
assert (
test_extension == train_extension
), "`test_file` should have the same extension (csv or json) as `train_file`."
_UpperCAmelCase = data_args.test_file
else:
raise ValueError('Need either a GLUE task or a test file for `do_predict`.' )
for key in data_files.keys():
logger.info(F"load a local file for {key}: {data_files[key]}" )
if data_args.train_file.endswith('.csv' ):
# Loading a dataset from local csv files
_UpperCAmelCase = load_dataset('csv' , data_files=_UpperCAmelCase , cache_dir=model_args.cache_dir )
else:
# Loading a dataset from local json files
_UpperCAmelCase = load_dataset('json' , data_files=_UpperCAmelCase , cache_dir=model_args.cache_dir )
# See more about loading any type of standard or custom dataset at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Labels
_UpperCAmelCase = raw_datasets['train'].features['label'].names
_UpperCAmelCase = len(_UpperCAmelCase )
# Load pretrained model and tokenizer
#
# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
_UpperCAmelCase = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=_UpperCAmelCase , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# load tapex tokenizer
_UpperCAmelCase = TapexTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast_tokenizer , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , add_prefix_space=_UpperCAmelCase , )
_UpperCAmelCase = BartForSequenceClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool('.ckpt' in model_args.model_name_or_path ) , config=_UpperCAmelCase , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# Padding strategy
if data_args.pad_to_max_length:
_UpperCAmelCase = 'max_length'
else:
# We will pad later, dynamically at batch creation, to the max sequence length in each batch
_UpperCAmelCase = False
# Some models have set the order of the labels to use, so let's make sure we do use it.
_UpperCAmelCase = {'Refused': 0, 'Entailed': 1}
_UpperCAmelCase = {0: 'Refused', 1: 'Entailed'}
if data_args.max_seq_length > tokenizer.model_max_length:
logger.warning(
F"The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the"
F"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}." )
_UpperCAmelCase = min(data_args.max_seq_length , tokenizer.model_max_length )
def preprocess_tabfact_function(_UpperCAmelCase : Union[str, Any] ):
# Tokenize the texts
def _convert_table_text_to_pandas(_UpperCAmelCase : Dict ):
_UpperCAmelCase = [_table_row.split('#' ) for _table_row in _table_text.strip('\n' ).split('\n' )]
_UpperCAmelCase = pd.DataFrame.from_records(_table_content[1:] , columns=_table_content[0] )
return _table_pd
_UpperCAmelCase = examples['statement']
_UpperCAmelCase = list(map(_convert_table_text_to_pandas , examples['table_text'] ) )
_UpperCAmelCase = tokenizer(_UpperCAmelCase , _UpperCAmelCase , padding=_UpperCAmelCase , max_length=_UpperCAmelCase , truncation=_UpperCAmelCase )
_UpperCAmelCase = examples['label']
return result
with training_args.main_process_first(desc='dataset map pre-processing' ):
_UpperCAmelCase = raw_datasets.map(
_UpperCAmelCase , batched=_UpperCAmelCase , load_from_cache_file=not data_args.overwrite_cache , desc='Running tokenizer on dataset' , )
if training_args.do_train:
if "train" not in raw_datasets:
raise ValueError('--do_train requires a train dataset' )
_UpperCAmelCase = raw_datasets['train']
if data_args.max_train_samples is not None:
_UpperCAmelCase = train_dataset.select(range(data_args.max_train_samples ) )
if training_args.do_eval:
if "validation" not in raw_datasets and "validation_matched" not in raw_datasets:
raise ValueError('--do_eval requires a validation dataset' )
_UpperCAmelCase = raw_datasets['validation']
if data_args.max_eval_samples is not None:
_UpperCAmelCase = eval_dataset.select(range(data_args.max_eval_samples ) )
if training_args.do_predict or data_args.test_file is not None:
if "test" not in raw_datasets and "test_matched" not in raw_datasets:
raise ValueError('--do_predict requires a test dataset' )
_UpperCAmelCase = raw_datasets['test']
if data_args.max_predict_samples is not None:
_UpperCAmelCase = predict_dataset.select(range(data_args.max_predict_samples ) )
# Log a few random samples from the training set:
if training_args.do_train:
for index in random.sample(range(len(_UpperCAmelCase ) ) , 3 ):
logger.info(F"Sample {index} of the training set: {train_dataset[index]}." )
# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
# predictions and label_ids field) and has to return a dictionary string to float.
def compute_metrics(_UpperCAmelCase : EvalPrediction ):
_UpperCAmelCase = p.predictions[0] if isinstance(p.predictions , _UpperCAmelCase ) else p.predictions
_UpperCAmelCase = np.argmax(_UpperCAmelCase , axis=1 )
return {"accuracy": (preds == p.label_ids).astype(np.floataa ).mean().item()}
# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
if data_args.pad_to_max_length:
_UpperCAmelCase = default_data_collator
elif training_args.fpaa:
_UpperCAmelCase = DataCollatorWithPadding(_UpperCAmelCase , pad_to_multiple_of=8 )
else:
_UpperCAmelCase = None
# Initialize our Trainer
_UpperCAmelCase = Trainer(
model=_UpperCAmelCase , args=_UpperCAmelCase , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , compute_metrics=_UpperCAmelCase , tokenizer=_UpperCAmelCase , data_collator=_UpperCAmelCase , )
# Training
if training_args.do_train:
_UpperCAmelCase = None
if training_args.resume_from_checkpoint is not None:
_UpperCAmelCase = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
_UpperCAmelCase = last_checkpoint
_UpperCAmelCase = trainer.train(resume_from_checkpoint=_UpperCAmelCase )
_UpperCAmelCase = train_result.metrics
_UpperCAmelCase = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(_UpperCAmelCase )
)
_UpperCAmelCase = min(_UpperCAmelCase , len(_UpperCAmelCase ) )
trainer.save_model() # Saves the tokenizer too for easy upload
trainer.log_metrics('train' , _UpperCAmelCase )
trainer.save_metrics('train' , _UpperCAmelCase )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info('*** Evaluate ***' )
_UpperCAmelCase = trainer.evaluate(eval_dataset=_UpperCAmelCase )
_UpperCAmelCase = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(_UpperCAmelCase )
_UpperCAmelCase = min(_UpperCAmelCase , len(_UpperCAmelCase ) )
trainer.log_metrics('eval' , _UpperCAmelCase )
trainer.save_metrics('eval' , _UpperCAmelCase )
if training_args.do_predict:
logger.info('*** Predict ***' )
# Removing the `label` columns because it contains -1 and Trainer won't like that.
_UpperCAmelCase = predict_dataset.remove_columns('label' )
_UpperCAmelCase = trainer.predict(_UpperCAmelCase , metric_key_prefix='predict' ).predictions
_UpperCAmelCase = np.argmax(_UpperCAmelCase , axis=1 )
_UpperCAmelCase = os.path.join(training_args.output_dir , 'predict_results_tabfact.txt' )
if trainer.is_world_process_zero():
with open(_UpperCAmelCase , 'w' ) as writer:
logger.info('***** Predict Results *****' )
writer.write('index\tprediction\n' )
for index, item in enumerate(_UpperCAmelCase ):
_UpperCAmelCase = label_list[item]
writer.write(F"{index}\t{item}\n" )
_UpperCAmelCase = {'finetuned_from': model_args.model_name_or_path, 'tasks': 'text-classification'}
if training_args.push_to_hub:
trainer.push_to_hub(**_UpperCAmelCase )
else:
trainer.create_model_card(**_UpperCAmelCase )
def A ( _UpperCAmelCase : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 339 | 1 |
import math
def A ( _UpperCAmelCase : int ) -> list[int]:
'''simple docstring'''
_UpperCAmelCase = []
_UpperCAmelCase = 2
_UpperCAmelCase = int(math.sqrt(_UpperCAmelCase ) ) # Size of every segment
_UpperCAmelCase = [True] * (end + 1)
_UpperCAmelCase = []
while start <= end:
if temp[start] is True:
in_prime.append(_UpperCAmelCase )
for i in range(start * start , end + 1 , _UpperCAmelCase ):
_UpperCAmelCase = False
start += 1
prime += in_prime
_UpperCAmelCase = end + 1
_UpperCAmelCase = min(2 * end , _UpperCAmelCase )
while low <= n:
_UpperCAmelCase = [True] * (high - low + 1)
for each in in_prime:
_UpperCAmelCase = math.floor(low / each ) * each
if t < low:
t += each
for j in range(_UpperCAmelCase , high + 1 , _UpperCAmelCase ):
_UpperCAmelCase = False
for j in range(len(_UpperCAmelCase ) ):
if temp[j] is True:
prime.append(j + low )
_UpperCAmelCase = high + 1
_UpperCAmelCase = min(high + end , _UpperCAmelCase )
return prime
print(sieve(10**6))
| 339 |
# This code is adapted from OpenAI's release
# https://github.com/openai/human-eval/blob/master/human_eval/execution.py
import contextlib
import faulthandler
import io
import multiprocessing
import os
import platform
import signal
import tempfile
def A ( _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Dict , _UpperCAmelCase : Dict , _UpperCAmelCase : Dict ) -> Any:
'''simple docstring'''
_UpperCAmelCase = multiprocessing.Manager()
_UpperCAmelCase = manager.list()
_UpperCAmelCase = multiprocessing.Process(target=_UpperCAmelCase , args=(check_program, result, timeout) )
p.start()
p.join(timeout=timeout + 1 )
if p.is_alive():
p.kill()
if not result:
result.append('timed out' )
return {
"task_id": task_id,
"passed": result[0] == "passed",
"result": result[0],
"completion_id": completion_id,
}
def A ( _UpperCAmelCase : str , _UpperCAmelCase : List[str] , _UpperCAmelCase : Dict ) -> Optional[int]:
'''simple docstring'''
with create_tempdir():
# These system calls are needed when cleaning up tempdir.
import os
import shutil
_UpperCAmelCase = shutil.rmtree
_UpperCAmelCase = os.rmdir
_UpperCAmelCase = os.chdir
# Disable functionalities that can make destructive changes to the test.
reliability_guard()
# Run program.
try:
_UpperCAmelCase = {}
with swallow_io():
with time_limit(_UpperCAmelCase ):
exec(_UpperCAmelCase , _UpperCAmelCase )
result.append('passed' )
except TimeoutException:
result.append('timed out' )
except BaseException as e:
result.append(F"failed: {e}" )
# Needed for cleaning up.
_UpperCAmelCase = rmtree
_UpperCAmelCase = rmdir
_UpperCAmelCase = chdir
@contextlib.contextmanager
def A ( _UpperCAmelCase : Union[str, Any] ) -> Any:
'''simple docstring'''
def signal_handler(_UpperCAmelCase : List[Any] , _UpperCAmelCase : Dict ):
raise TimeoutException('Timed out!' )
signal.setitimer(signal.ITIMER_REAL , _UpperCAmelCase )
signal.signal(signal.SIGALRM , _UpperCAmelCase )
try:
yield
finally:
signal.setitimer(signal.ITIMER_REAL , 0 )
@contextlib.contextmanager
def A ( ) -> Optional[int]:
'''simple docstring'''
_UpperCAmelCase = WriteOnlyStringIO()
with contextlib.redirect_stdout(_UpperCAmelCase ):
with contextlib.redirect_stderr(_UpperCAmelCase ):
with redirect_stdin(_UpperCAmelCase ):
yield
@contextlib.contextmanager
def A ( ) -> Any:
'''simple docstring'''
with tempfile.TemporaryDirectory() as dirname:
with chdir(_UpperCAmelCase ):
yield dirname
class __lowerCAmelCase ( A ):
pass
class __lowerCAmelCase ( io.StringIO ):
def _lowerCamelCase ( self : Tuple , *A : str , **A : Any) -> Any:
"""simple docstring"""
raise OSError
def _lowerCamelCase ( self : List[str] , *A : Optional[Any] , **A : Optional[Any]) -> Optional[int]:
"""simple docstring"""
raise OSError
def _lowerCamelCase ( self : str , *A : List[str] , **A : List[Any]) -> Union[str, Any]:
"""simple docstring"""
raise OSError
def _lowerCamelCase ( self : Union[str, Any] , *A : Optional[Any] , **A : List[str]) -> Optional[int]:
"""simple docstring"""
return False
class __lowerCAmelCase ( contextlib._RedirectStream ): # type: ignore
UpperCamelCase = '''stdin'''
@contextlib.contextmanager
def A ( _UpperCAmelCase : List[Any] ) -> Dict:
'''simple docstring'''
if root == ".":
yield
return
_UpperCAmelCase = os.getcwd()
os.chdir(_UpperCAmelCase )
try:
yield
except BaseException as exc:
raise exc
finally:
os.chdir(_UpperCAmelCase )
def A ( _UpperCAmelCase : List[str]=None ) -> Any:
'''simple docstring'''
if maximum_memory_bytes is not None:
import resource
resource.setrlimit(resource.RLIMIT_AS , (maximum_memory_bytes, maximum_memory_bytes) )
resource.setrlimit(resource.RLIMIT_DATA , (maximum_memory_bytes, maximum_memory_bytes) )
if not platform.uname().system == "Darwin":
resource.setrlimit(resource.RLIMIT_STACK , (maximum_memory_bytes, maximum_memory_bytes) )
faulthandler.disable()
import builtins
_UpperCAmelCase = None
_UpperCAmelCase = None
import os
_UpperCAmelCase = '1'
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
import shutil
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
import subprocess
_UpperCAmelCase = None # type: ignore
_UpperCAmelCase = None
import sys
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
| 339 | 1 |
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
UpperCAmelCase__ = datasets.utils.logging.get_logger(__name__)
class __lowerCAmelCase ( folder_based_builder.FolderBasedBuilderConfig ):
UpperCamelCase = None
UpperCamelCase = None
class __lowerCAmelCase ( folder_based_builder.FolderBasedBuilder ):
UpperCamelCase = datasets.Audio()
UpperCamelCase = '''audio'''
UpperCamelCase = AudioFolderConfig
UpperCamelCase = 42 # definition at the bottom of the script
UpperCamelCase = AudioClassification(audio_column='''audio''' , label_column='''label''' )
UpperCAmelCase__ = [
".aiff",
".au",
".avr",
".caf",
".flac",
".htk",
".svx",
".mat4",
".mat5",
".mpc2k",
".ogg",
".paf",
".pvf",
".raw",
".rf64",
".sd2",
".sds",
".ircam",
".voc",
".w64",
".wav",
".nist",
".wavex",
".wve",
".xi",
".mp3",
".opus",
]
UpperCAmelCase__ = AUDIO_EXTENSIONS
| 339 |
import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock
import torch
from ..state import AcceleratorState, PartialState
from ..utils import (
gather,
is_bnb_available,
is_comet_ml_available,
is_datasets_available,
is_deepspeed_available,
is_mps_available,
is_safetensors_available,
is_tensorboard_available,
is_torch_version,
is_tpu_available,
is_transformers_available,
is_wandb_available,
is_xpu_available,
)
def A ( _UpperCAmelCase : Tuple , _UpperCAmelCase : Union[str, Any]=False ) -> str:
'''simple docstring'''
try:
_UpperCAmelCase = os.environ[key]
except KeyError:
# KEY isn't set, default to `default`.
_UpperCAmelCase = default
else:
# KEY is set, convert it to True or False.
try:
_UpperCAmelCase = strtobool(_UpperCAmelCase )
except ValueError:
# More values are supported, but let's keep the message simple.
raise ValueError(F"If set, {key} must be yes or no." )
return _value
UpperCAmelCase__ = parse_flag_from_env("RUN_SLOW", default=False)
def A ( _UpperCAmelCase : List[str] ) -> List[str]:
'''simple docstring'''
return unittest.skip('Test was skipped' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Dict ) -> str:
'''simple docstring'''
return unittest.skipUnless(_run_slow_tests , 'test is slow' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Any ) -> str:
'''simple docstring'''
return unittest.skipUnless(not torch.cuda.is_available() , 'test requires only a CPU' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Dict ) -> Dict:
'''simple docstring'''
return unittest.skipUnless(torch.cuda.is_available() , 'test requires a GPU' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Optional[Any] ) -> List[Any]:
'''simple docstring'''
return unittest.skipUnless(is_xpu_available() , 'test requires a XPU' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Optional[int] ) -> List[str]:
'''simple docstring'''
return unittest.skipUnless(is_mps_available() , 'test requires a `mps` backend support in `torch`' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
return unittest.skipUnless(
is_transformers_available() and is_datasets_available() , 'test requires the Hugging Face suite' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : str ) -> str:
'''simple docstring'''
return unittest.skipUnless(is_bnb_available() , 'test requires the bitsandbytes library' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
return unittest.skipUnless(is_tpu_available() , 'test requires TPU' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Optional[Any] ) -> str:
'''simple docstring'''
return unittest.skipUnless(torch.cuda.device_count() == 1 , 'test requires a GPU' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Tuple ) -> int:
'''simple docstring'''
return unittest.skipUnless(torch.xpu.device_count() == 1 , 'test requires a XPU' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Any ) -> Optional[int]:
'''simple docstring'''
return unittest.skipUnless(torch.cuda.device_count() > 1 , 'test requires multiple GPUs' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Tuple ) -> Any:
'''simple docstring'''
return unittest.skipUnless(torch.xpu.device_count() > 1 , 'test requires multiple XPUs' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Any ) -> Optional[int]:
'''simple docstring'''
return unittest.skipUnless(is_safetensors_available() , 'test requires safetensors' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : List[Any] ) -> Dict:
'''simple docstring'''
return unittest.skipUnless(is_deepspeed_available() , 'test requires DeepSpeed' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Optional[int] ) -> str:
'''simple docstring'''
return unittest.skipUnless(is_torch_version('>=' , '1.12.0' ) , 'test requires torch version >= 1.12.0' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Any=None , _UpperCAmelCase : List[Any]=None ) -> Dict:
'''simple docstring'''
if test_case is None:
return partial(_UpperCAmelCase , version=_UpperCAmelCase )
return unittest.skipUnless(is_torch_version('>=' , _UpperCAmelCase ) , F"test requires torch version >= {version}" )(_UpperCAmelCase )
def A ( _UpperCAmelCase : List[str] ) -> int:
'''simple docstring'''
return unittest.skipUnless(is_tensorboard_available() , 'test requires Tensorboard' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Union[str, Any] ) -> Union[str, Any]:
'''simple docstring'''
return unittest.skipUnless(is_wandb_available() , 'test requires wandb' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : List[str] ) -> Optional[int]:
'''simple docstring'''
return unittest.skipUnless(is_comet_ml_available() , 'test requires comet_ml' )(_UpperCAmelCase )
UpperCAmelCase__ = (
any([is_wandb_available(), is_tensorboard_available()]) and not is_comet_ml_available()
)
def A ( _UpperCAmelCase : List[str] ) -> Any:
'''simple docstring'''
return unittest.skipUnless(
_atleast_one_tracker_available , 'test requires at least one tracker to be available and for `comet_ml` to not be installed' , )(_UpperCAmelCase )
class __lowerCAmelCase ( unittest.TestCase ):
UpperCamelCase = True
@classmethod
def _lowerCamelCase ( cls : List[Any]) -> Tuple:
"""simple docstring"""
_UpperCAmelCase = tempfile.mkdtemp()
@classmethod
def _lowerCamelCase ( cls : Union[str, Any]) -> str:
"""simple docstring"""
if os.path.exists(cls.tmpdir):
shutil.rmtree(cls.tmpdir)
def _lowerCamelCase ( self : List[str]) -> List[Any]:
"""simple docstring"""
if self.clear_on_setup:
for path in Path(self.tmpdir).glob('**/*'):
if path.is_file():
path.unlink()
elif path.is_dir():
shutil.rmtree(A)
class __lowerCAmelCase ( unittest.TestCase ):
def _lowerCamelCase ( self : Dict) -> Tuple:
"""simple docstring"""
super().tearDown()
# Reset the state of the AcceleratorState singleton.
AcceleratorState._reset_state()
PartialState._reset_state()
class __lowerCAmelCase ( unittest.TestCase ):
def _lowerCamelCase ( self : Optional[int] , A : Union[mock.Mock, List[mock.Mock]]) -> Tuple:
"""simple docstring"""
_UpperCAmelCase = mocks if isinstance(A , (tuple, list)) else [mocks]
for m in self.mocks:
m.start()
self.addCleanup(m.stop)
def A ( _UpperCAmelCase : List[Any] ) -> int:
'''simple docstring'''
_UpperCAmelCase = AcceleratorState()
_UpperCAmelCase = tensor[None].clone().to(state.device )
_UpperCAmelCase = gather(_UpperCAmelCase ).cpu()
_UpperCAmelCase = tensor[0].cpu()
for i in range(tensors.shape[0] ):
if not torch.equal(tensors[i] , _UpperCAmelCase ):
return False
return True
class __lowerCAmelCase :
def __init__( self : Optional[Any] , A : Union[str, Any] , A : Optional[int] , A : str) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase = returncode
_UpperCAmelCase = stdout
_UpperCAmelCase = stderr
async def A ( _UpperCAmelCase : str , _UpperCAmelCase : Optional[int] ) -> Optional[Any]:
'''simple docstring'''
while True:
_UpperCAmelCase = await stream.readline()
if line:
callback(_UpperCAmelCase )
else:
break
async def A ( _UpperCAmelCase : Optional[int] , _UpperCAmelCase : List[str]=None , _UpperCAmelCase : str=None , _UpperCAmelCase : str=None , _UpperCAmelCase : Dict=False , _UpperCAmelCase : Union[str, Any]=False ) -> _RunOutput:
'''simple docstring'''
if echo:
print('\nRunning: ' , ' '.join(_UpperCAmelCase ) )
_UpperCAmelCase = await asyncio.create_subprocess_exec(
cmd[0] , *cmd[1:] , stdin=_UpperCAmelCase , stdout=asyncio.subprocess.PIPE , stderr=asyncio.subprocess.PIPE , env=_UpperCAmelCase , )
# note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe
# https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait
#
# If it starts hanging, will need to switch to the following code. The problem is that no data
# will be seen until it's done and if it hangs for example there will be no debug info.
# out, err = await p.communicate()
# return _RunOutput(p.returncode, out, err)
_UpperCAmelCase = []
_UpperCAmelCase = []
def tee(_UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : List[Any] , _UpperCAmelCase : str , _UpperCAmelCase : str="" ):
_UpperCAmelCase = line.decode('utf-8' ).rstrip()
sink.append(_UpperCAmelCase )
if not quiet:
print(_UpperCAmelCase , _UpperCAmelCase , file=_UpperCAmelCase )
# XXX: the timeout doesn't seem to make any difference here
await asyncio.wait(
[
asyncio.create_task(_read_stream(p.stdout , lambda _UpperCAmelCase : tee(_UpperCAmelCase , _UpperCAmelCase , sys.stdout , label='stdout:' ) ) ),
asyncio.create_task(_read_stream(p.stderr , lambda _UpperCAmelCase : tee(_UpperCAmelCase , _UpperCAmelCase , sys.stderr , label='stderr:' ) ) ),
] , timeout=_UpperCAmelCase , )
return _RunOutput(await p.wait() , _UpperCAmelCase , _UpperCAmelCase )
def A ( _UpperCAmelCase : str , _UpperCAmelCase : Dict=None , _UpperCAmelCase : str=None , _UpperCAmelCase : str=180 , _UpperCAmelCase : List[Any]=False , _UpperCAmelCase : List[Any]=True ) -> _RunOutput:
'''simple docstring'''
_UpperCAmelCase = asyncio.get_event_loop()
_UpperCAmelCase = loop.run_until_complete(
_stream_subprocess(_UpperCAmelCase , env=_UpperCAmelCase , stdin=_UpperCAmelCase , timeout=_UpperCAmelCase , quiet=_UpperCAmelCase , echo=_UpperCAmelCase ) )
_UpperCAmelCase = ' '.join(_UpperCAmelCase )
if result.returncode > 0:
_UpperCAmelCase = '\n'.join(result.stderr )
raise RuntimeError(
F"'{cmd_str}' failed with returncode {result.returncode}\n\n"
F"The combined stderr from workers follows:\n{stderr}" )
return result
class __lowerCAmelCase ( A ):
pass
def A ( _UpperCAmelCase : List[str] , _UpperCAmelCase : str=False ) -> Tuple:
'''simple docstring'''
try:
_UpperCAmelCase = subprocess.check_output(_UpperCAmelCase , stderr=subprocess.STDOUT )
if return_stdout:
if hasattr(_UpperCAmelCase , 'decode' ):
_UpperCAmelCase = output.decode('utf-8' )
return output
except subprocess.CalledProcessError as e:
raise SubprocessCallException(
F"Command `{' '.join(_UpperCAmelCase )}` failed with the following error:\n\n{e.output.decode()}" ) from e
| 339 | 1 |
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
@slow
@require_flax
class __lowerCAmelCase ( unittest.TestCase ):
def _lowerCamelCase ( self : Dict) -> int:
"""simple docstring"""
super().tearDown()
gc.collect()
def _lowerCamelCase ( self : Union[str, Any]) -> Any:
"""simple docstring"""
_UpperCAmelCase , _UpperCAmelCase = FlaxStableDiffusionPipeline.from_pretrained(
'stabilityai/stable-diffusion-2' , revision='bf16' , dtype=jnp.bfloataa , )
_UpperCAmelCase = 'A painting of a squirrel eating a burger'
_UpperCAmelCase = jax.device_count()
_UpperCAmelCase = num_samples * [prompt]
_UpperCAmelCase = sd_pipe.prepare_inputs(A)
_UpperCAmelCase = replicate(A)
_UpperCAmelCase = shard(A)
_UpperCAmelCase = jax.random.PRNGKey(0)
_UpperCAmelCase = jax.random.split(A , jax.device_count())
_UpperCAmelCase = sd_pipe(A , A , A , num_inference_steps=25 , jit=A)[0]
assert images.shape == (jax.device_count(), 1, 7_68, 7_68, 3)
_UpperCAmelCase = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:])
_UpperCAmelCase = images[0, 2_53:2_56, 2_53:2_56, -1]
_UpperCAmelCase = jnp.asarray(jax.device_get(image_slice.flatten()))
_UpperCAmelCase = jnp.array([0.4_2_3_8, 0.4_4_1_4, 0.4_3_9_5, 0.4_4_5_3, 0.4_6_2_9, 0.4_5_9_0, 0.4_5_3_1, 0.4_5_5_0_8, 0.4_5_1_2])
print(F"output_slice: {output_slice}")
assert jnp.abs(output_slice - expected_slice).max() < 1E-2
def _lowerCamelCase ( self : str) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase = 'stabilityai/stable-diffusion-2'
_UpperCAmelCase , _UpperCAmelCase = FlaxDPMSolverMultistepScheduler.from_pretrained(A , subfolder='scheduler')
_UpperCAmelCase , _UpperCAmelCase = FlaxStableDiffusionPipeline.from_pretrained(
A , scheduler=A , revision='bf16' , dtype=jnp.bfloataa , )
_UpperCAmelCase = scheduler_params
_UpperCAmelCase = 'A painting of a squirrel eating a burger'
_UpperCAmelCase = jax.device_count()
_UpperCAmelCase = num_samples * [prompt]
_UpperCAmelCase = sd_pipe.prepare_inputs(A)
_UpperCAmelCase = replicate(A)
_UpperCAmelCase = shard(A)
_UpperCAmelCase = jax.random.PRNGKey(0)
_UpperCAmelCase = jax.random.split(A , jax.device_count())
_UpperCAmelCase = sd_pipe(A , A , A , num_inference_steps=25 , jit=A)[0]
assert images.shape == (jax.device_count(), 1, 7_68, 7_68, 3)
_UpperCAmelCase = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:])
_UpperCAmelCase = images[0, 2_53:2_56, 2_53:2_56, -1]
_UpperCAmelCase = jnp.asarray(jax.device_get(image_slice.flatten()))
_UpperCAmelCase = jnp.array([0.4_3_3_6, 0.4_2_9_6_9, 0.4_4_5_3, 0.4_1_9_9, 0.4_2_9_7, 0.4_5_3_1, 0.4_4_3_4, 0.4_4_3_4, 0.4_2_9_7])
print(F"output_slice: {output_slice}")
assert jnp.abs(output_slice - expected_slice).max() < 1E-2
| 339 |
from __future__ import annotations
UpperCAmelCase__ = list[list[int]]
# assigning initial values to the grid
UpperCAmelCase__ = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5],
[0, 5, 0, 0, 9, 0, 6, 0, 0],
[1, 3, 0, 0, 0, 0, 2, 5, 0],
[0, 0, 0, 0, 0, 0, 0, 7, 4],
[0, 0, 5, 2, 0, 6, 3, 0, 0],
]
# a grid with no solution
UpperCAmelCase__ = [
[5, 0, 6, 5, 0, 8, 4, 0, 3],
[5, 2, 0, 0, 0, 0, 0, 0, 2],
[1, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5],
[0, 5, 0, 0, 9, 0, 6, 0, 0],
[1, 3, 0, 0, 0, 0, 2, 5, 0],
[0, 0, 0, 0, 0, 0, 0, 7, 4],
[0, 0, 5, 2, 0, 6, 3, 0, 0],
]
def A ( _UpperCAmelCase : Matrix , _UpperCAmelCase : int , _UpperCAmelCase : int , _UpperCAmelCase : int ) -> bool:
'''simple docstring'''
for i in range(9 ):
if grid[row][i] == n or grid[i][column] == n:
return False
for i in range(3 ):
for j in range(3 ):
if grid[(row - row % 3) + i][(column - column % 3) + j] == n:
return False
return True
def A ( _UpperCAmelCase : Matrix ) -> tuple[int, int] | None:
'''simple docstring'''
for i in range(9 ):
for j in range(9 ):
if grid[i][j] == 0:
return i, j
return None
def A ( _UpperCAmelCase : Matrix ) -> Matrix | None:
'''simple docstring'''
if location := find_empty_location(_UpperCAmelCase ):
_UpperCAmelCase , _UpperCAmelCase = location
else:
# If the location is ``None``, then the grid is solved.
return grid
for digit in range(1 , 10 ):
if is_safe(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
_UpperCAmelCase = digit
if sudoku(_UpperCAmelCase ) is not None:
return grid
_UpperCAmelCase = 0
return None
def A ( _UpperCAmelCase : Matrix ) -> None:
'''simple docstring'''
for row in grid:
for cell in row:
print(_UpperCAmelCase , end=' ' )
print()
if __name__ == "__main__":
# make a copy of grid so that you can compare with the unmodified grid
for example_grid in (initial_grid, no_solution):
print("\nExample grid:\n" + "=" * 20)
print_solution(example_grid)
print("\nExample grid solution:")
UpperCAmelCase__ = sudoku(example_grid)
if solution is not None:
print_solution(solution)
else:
print("Cannot find a solution.")
| 339 | 1 |
import os
import tempfile
import unittest
from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_PRETRAINING_MAPPING,
NezhaForMaskedLM,
NezhaForMultipleChoice,
NezhaForNextSentencePrediction,
NezhaForPreTraining,
NezhaForQuestionAnswering,
NezhaForSequenceClassification,
NezhaForTokenClassification,
NezhaModel,
)
from transformers.models.nezha.modeling_nezha import NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST
class __lowerCAmelCase :
def __init__( self : int , A : int , A : str=13 , A : int=7 , A : Union[str, Any]=True , A : Dict=True , A : Tuple=True , A : Tuple=True , A : Optional[int]=99 , A : List[Any]=32 , A : Any=5 , A : Tuple=4 , A : int=37 , A : int="gelu" , A : Union[str, Any]=0.1 , A : Optional[Any]=0.1 , A : Any=1_28 , A : Any=32 , A : List[str]=16 , A : str=2 , A : Tuple=0.0_2 , A : Optional[int]=3 , A : List[Any]=4 , A : List[str]=None , ) -> str:
"""simple docstring"""
_UpperCAmelCase = parent
_UpperCAmelCase = batch_size
_UpperCAmelCase = seq_length
_UpperCAmelCase = is_training
_UpperCAmelCase = use_input_mask
_UpperCAmelCase = use_token_type_ids
_UpperCAmelCase = use_labels
_UpperCAmelCase = vocab_size
_UpperCAmelCase = hidden_size
_UpperCAmelCase = num_hidden_layers
_UpperCAmelCase = num_attention_heads
_UpperCAmelCase = intermediate_size
_UpperCAmelCase = hidden_act
_UpperCAmelCase = hidden_dropout_prob
_UpperCAmelCase = attention_probs_dropout_prob
_UpperCAmelCase = max_position_embeddings
_UpperCAmelCase = type_vocab_size
_UpperCAmelCase = type_sequence_label_size
_UpperCAmelCase = initializer_range
_UpperCAmelCase = num_labels
_UpperCAmelCase = num_choices
_UpperCAmelCase = scope
def _lowerCamelCase ( self : Tuple) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
_UpperCAmelCase = None
if self.use_input_mask:
_UpperCAmelCase = random_attention_mask([self.batch_size, self.seq_length])
_UpperCAmelCase = None
if self.use_token_type_ids:
_UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size)
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
if self.use_labels:
_UpperCAmelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size)
_UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels)
_UpperCAmelCase = ids_tensor([self.batch_size] , self.num_choices)
_UpperCAmelCase = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def _lowerCamelCase ( self : str) -> Tuple:
"""simple docstring"""
return NezhaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=A , initializer_range=self.initializer_range , )
def _lowerCamelCase ( self : Any) -> List[Any]:
"""simple docstring"""
(
(
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) ,
) = self.prepare_config_and_inputs()
_UpperCAmelCase = True
_UpperCAmelCase = floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
_UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2)
return (
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def _lowerCamelCase ( self : List[Any] , A : Tuple , A : str , A : str , A : List[str] , A : str , A : Any , A : Optional[Any]) -> List[str]:
"""simple docstring"""
_UpperCAmelCase = NezhaModel(config=A)
model.to(A)
model.eval()
_UpperCAmelCase = model(A , attention_mask=A , token_type_ids=A)
_UpperCAmelCase = model(A , token_type_ids=A)
_UpperCAmelCase = model(A)
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 : int , A : List[Any] , A : Optional[int] , A : int , A : List[str] , A : Tuple , A : Union[str, Any] , A : List[str] , A : int , A : Optional[int] , ) -> Dict:
"""simple docstring"""
_UpperCAmelCase = True
_UpperCAmelCase = NezhaModel(A)
model.to(A)
model.eval()
_UpperCAmelCase = model(
A , attention_mask=A , token_type_ids=A , encoder_hidden_states=A , encoder_attention_mask=A , )
_UpperCAmelCase = model(
A , attention_mask=A , token_type_ids=A , encoder_hidden_states=A , )
_UpperCAmelCase = model(A , attention_mask=A , token_type_ids=A)
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 : Optional[Any] , A : Tuple , A : Tuple , A : Optional[Any] , A : Union[str, Any] , A : str , A : Optional[Any] , A : int) -> Tuple:
"""simple docstring"""
_UpperCAmelCase = NezhaForMaskedLM(config=A)
model.to(A)
model.eval()
_UpperCAmelCase = model(A , attention_mask=A , token_type_ids=A , labels=A)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size))
def _lowerCamelCase ( self : List[Any] , A : List[Any] , A : str , A : List[str] , A : str , A : int , A : Dict , A : int) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase = NezhaForNextSentencePrediction(config=A)
model.to(A)
model.eval()
_UpperCAmelCase = model(
A , attention_mask=A , token_type_ids=A , labels=A , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, 2))
def _lowerCamelCase ( self : List[Any] , A : List[str] , A : List[str] , A : Dict , A : List[str] , A : List[str] , A : Optional[int] , A : List[Any]) -> List[str]:
"""simple docstring"""
_UpperCAmelCase = NezhaForPreTraining(config=A)
model.to(A)
model.eval()
_UpperCAmelCase = model(
A , attention_mask=A , token_type_ids=A , labels=A , next_sentence_label=A , )
self.parent.assertEqual(result.prediction_logits.shape , (self.batch_size, self.seq_length, self.vocab_size))
self.parent.assertEqual(result.seq_relationship_logits.shape , (self.batch_size, 2))
def _lowerCamelCase ( self : List[str] , A : Any , A : Tuple , A : Union[str, Any] , A : Tuple , A : List[str] , A : Union[str, Any] , A : List[str]) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase = NezhaForQuestionAnswering(config=A)
model.to(A)
model.eval()
_UpperCAmelCase = model(
A , attention_mask=A , token_type_ids=A , start_positions=A , end_positions=A , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length))
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length))
def _lowerCamelCase ( self : Union[str, Any] , A : Union[str, Any] , A : Optional[int] , A : List[str] , A : Tuple , A : str , A : List[Any] , A : Tuple) -> int:
"""simple docstring"""
_UpperCAmelCase = self.num_labels
_UpperCAmelCase = NezhaForSequenceClassification(A)
model.to(A)
model.eval()
_UpperCAmelCase = model(A , attention_mask=A , token_type_ids=A , labels=A)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels))
def _lowerCamelCase ( self : Tuple , A : Any , A : List[Any] , A : Optional[int] , A : Optional[Any] , A : str , A : Union[str, Any] , A : Optional[int]) -> Tuple:
"""simple docstring"""
_UpperCAmelCase = self.num_labels
_UpperCAmelCase = NezhaForTokenClassification(config=A)
model.to(A)
model.eval()
_UpperCAmelCase = model(A , attention_mask=A , token_type_ids=A , labels=A)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels))
def _lowerCamelCase ( self : Union[str, Any] , A : List[str] , A : Union[str, Any] , A : List[str] , A : Tuple , A : Any , A : Tuple , A : List[Any]) -> Any:
"""simple docstring"""
_UpperCAmelCase = self.num_choices
_UpperCAmelCase = NezhaForMultipleChoice(config=A)
model.to(A)
model.eval()
_UpperCAmelCase = input_ids.unsqueeze(1).expand(-1 , self.num_choices , -1).contiguous()
_UpperCAmelCase = token_type_ids.unsqueeze(1).expand(-1 , self.num_choices , -1).contiguous()
_UpperCAmelCase = input_mask.unsqueeze(1).expand(-1 , self.num_choices , -1).contiguous()
_UpperCAmelCase = model(
A , attention_mask=A , token_type_ids=A , labels=A , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices))
def _lowerCamelCase ( self : Dict) -> Dict:
"""simple docstring"""
_UpperCAmelCase = self.prepare_config_and_inputs()
(
(
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) ,
) = config_and_inputs
_UpperCAmelCase = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class __lowerCAmelCase ( A , A , A , unittest.TestCase ):
UpperCamelCase = (
(
NezhaModel,
NezhaForMaskedLM,
NezhaForMultipleChoice,
NezhaForNextSentencePrediction,
NezhaForPreTraining,
NezhaForQuestionAnswering,
NezhaForSequenceClassification,
NezhaForTokenClassification,
)
if is_torch_available()
else ()
)
UpperCamelCase = (
{
'''feature-extraction''': NezhaModel,
'''fill-mask''': NezhaForMaskedLM,
'''question-answering''': NezhaForQuestionAnswering,
'''text-classification''': NezhaForSequenceClassification,
'''token-classification''': NezhaForTokenClassification,
'''zero-shot''': NezhaForSequenceClassification,
}
if is_torch_available()
else {}
)
UpperCamelCase = True
def _lowerCamelCase ( self : Tuple , A : Optional[int] , A : List[Any] , A : List[str]=False) -> List[str]:
"""simple docstring"""
_UpperCAmelCase = super()._prepare_for_class(A , A , return_labels=A)
if return_labels:
if model_class in get_values(A):
_UpperCAmelCase = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length) , dtype=torch.long , device=A)
_UpperCAmelCase = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=A)
return inputs_dict
def _lowerCamelCase ( self : List[Any]) -> Any:
"""simple docstring"""
_UpperCAmelCase = NezhaModelTester(self)
_UpperCAmelCase = ConfigTester(self , config_class=A , hidden_size=37)
def _lowerCamelCase ( self : List[Any]) -> Dict:
"""simple docstring"""
self.config_tester.run_common_tests()
def _lowerCamelCase ( self : Any) -> List[str]:
"""simple docstring"""
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A)
def _lowerCamelCase ( self : int) -> List[str]:
"""simple docstring"""
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(*A)
def _lowerCamelCase ( self : Dict) -> List[str]:
"""simple docstring"""
(
(
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) ,
) = self.model_tester.prepare_config_and_inputs_for_decoder()
_UpperCAmelCase = None
self.model_tester.create_and_check_model_as_decoder(
A , A , A , A , A , A , A , A , A , )
def _lowerCamelCase ( self : List[Any]) -> Dict:
"""simple docstring"""
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*A)
def _lowerCamelCase ( self : int) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*A)
def _lowerCamelCase ( self : int) -> int:
"""simple docstring"""
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_next_sequence_prediction(*A)
def _lowerCamelCase ( self : Dict) -> List[str]:
"""simple docstring"""
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*A)
def _lowerCamelCase ( self : List[str]) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*A)
def _lowerCamelCase ( self : str) -> str:
"""simple docstring"""
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*A)
def _lowerCamelCase ( self : str) -> Dict:
"""simple docstring"""
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*A)
@slow
def _lowerCamelCase ( self : List[Any]) -> Any:
"""simple docstring"""
for model_name in NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCAmelCase = NezhaModel.from_pretrained(A)
self.assertIsNotNone(A)
@slow
@require_torch_gpu
def _lowerCamelCase ( self : int) -> str:
"""simple docstring"""
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# NezhaForMultipleChoice behaves incorrectly in JIT environments.
if model_class == NezhaForMultipleChoice:
return
_UpperCAmelCase = True
_UpperCAmelCase = model_class(config=A)
_UpperCAmelCase = self._prepare_for_class(A , A)
_UpperCAmelCase = torch.jit.trace(
A , (inputs_dict['input_ids'].to('cpu'), inputs_dict['attention_mask'].to('cpu')))
with tempfile.TemporaryDirectory() as tmp:
torch.jit.save(A , os.path.join(A , 'bert.pt'))
_UpperCAmelCase = torch.jit.load(os.path.join(A , 'bert.pt') , map_location=A)
loaded(inputs_dict['input_ids'].to(A) , inputs_dict['attention_mask'].to(A))
@require_torch
class __lowerCAmelCase ( unittest.TestCase ):
@slow
def _lowerCamelCase ( self : Any) -> Tuple:
"""simple docstring"""
_UpperCAmelCase = NezhaModel.from_pretrained('sijunhe/nezha-cn-base')
_UpperCAmelCase = torch.tensor([[0, 1, 2, 3, 4, 5]])
_UpperCAmelCase = torch.tensor([[0, 1, 1, 1, 1, 1]])
with torch.no_grad():
_UpperCAmelCase = model(A , attention_mask=A)[0]
_UpperCAmelCase = torch.Size((1, 6, 7_68))
self.assertEqual(output.shape , A)
_UpperCAmelCase = torch.tensor([[[0.0_6_8_5, 0.2_4_4_1, 0.1_1_0_2], [0.0_6_0_0, 0.1_9_0_6, 0.1_3_4_9], [0.0_2_2_1, 0.0_8_1_9, 0.0_5_8_6]]])
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , A , atol=1E-4))
@slow
def _lowerCamelCase ( self : Dict) -> Tuple:
"""simple docstring"""
_UpperCAmelCase = NezhaForMaskedLM.from_pretrained('sijunhe/nezha-cn-base')
_UpperCAmelCase = torch.tensor([[0, 1, 2, 3, 4, 5]])
_UpperCAmelCase = torch.tensor([[1, 1, 1, 1, 1, 1]])
with torch.no_grad():
_UpperCAmelCase = model(A , attention_mask=A)[0]
_UpperCAmelCase = torch.Size((1, 6, 2_11_28))
self.assertEqual(output.shape , A)
_UpperCAmelCase = torch.tensor(
[[-2.7_9_3_9, -1.7_9_0_2, -2.2_1_8_9], [-2.8_5_8_5, -1.8_9_0_8, -2.3_7_2_3], [-2.6_4_9_9, -1.7_7_5_0, -2.2_5_5_8]])
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , A , atol=1E-4))
| 339 |
import numpy as np
from nltk.translate import meteor_score
import datasets
from datasets.config import importlib_metadata, version
UpperCAmelCase__ = version.parse(importlib_metadata.version("nltk"))
if NLTK_VERSION >= version.Version("3.6.4"):
from nltk import word_tokenize
UpperCAmelCase__ = "\\n@inproceedings{banarjee2005,\n title = {{METEOR}: An Automatic Metric for {MT} Evaluation with Improved Correlation with Human Judgments},\n author = {Banerjee, Satanjeev and Lavie, Alon},\n booktitle = {Proceedings of the {ACL} Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization},\n month = jun,\n year = {2005},\n address = {Ann Arbor, Michigan},\n publisher = {Association for Computational Linguistics},\n url = {https://www.aclweb.org/anthology/W05-0909},\n pages = {65--72},\n}\n"
UpperCAmelCase__ = "\\nMETEOR, an automatic metric for machine translation evaluation\nthat is based on a generalized concept of unigram matching between the\nmachine-produced translation and human-produced reference translations.\nUnigrams can be matched based on their surface forms, stemmed forms,\nand meanings; furthermore, METEOR can be easily extended to include more\nadvanced matching strategies. Once all generalized unigram matches\nbetween the two strings have been found, METEOR computes a score for\nthis matching using a combination of unigram-precision, unigram-recall, and\na measure of fragmentation that is designed to directly capture how\nwell-ordered the matched words in the machine translation are in relation\nto the reference.\n\nMETEOR gets an R correlation value of 0.347 with human evaluation on the Arabic\ndata and 0.331 on the Chinese data. This is shown to be an improvement on\nusing simply unigram-precision, unigram-recall and their harmonic F1\ncombination.\n"
UpperCAmelCase__ = "\nComputes METEOR score of translated segments against one or more references.\nArgs:\n predictions: list of predictions to score. Each prediction\n should be a string with tokens separated by spaces.\n references: list of reference for each prediction. Each\n reference should be a string with tokens separated by spaces.\n alpha: Parameter for controlling relative weights of precision and recall. default: 0.9\n beta: Parameter for controlling shape of penalty as a function of fragmentation. default: 3\n gamma: Relative weight assigned to fragmentation penalty. default: 0.5\nReturns:\n 'meteor': meteor score.\nExamples:\n\n >>> meteor = datasets.load_metric('meteor')\n >>> predictions = [\"It is a guide to action which ensures that the military always obeys the commands of the party\"]\n >>> references = [\"It is a guide to action that ensures that the military will forever heed Party commands\"]\n >>> results = meteor.compute(predictions=predictions, references=references)\n >>> print(round(results[\"meteor\"], 4))\n 0.6944\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __lowerCAmelCase ( datasets.Metric ):
def _lowerCamelCase ( self : List[Any]) -> List[Any]:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'predictions': datasets.Value('string' , id='sequence'),
'references': datasets.Value('string' , id='sequence'),
}) , codebase_urls=['https://github.com/nltk/nltk/blob/develop/nltk/translate/meteor_score.py'] , reference_urls=[
'https://www.nltk.org/api/nltk.translate.html#module-nltk.translate.meteor_score',
'https://en.wikipedia.org/wiki/METEOR',
] , )
def _lowerCamelCase ( self : Optional[Any] , A : List[str]) -> List[Any]:
"""simple docstring"""
import nltk
nltk.download('wordnet')
if NLTK_VERSION >= version.Version('3.6.5'):
nltk.download('punkt')
if NLTK_VERSION >= version.Version('3.6.6'):
nltk.download('omw-1.4')
def _lowerCamelCase ( self : Optional[Any] , A : Tuple , A : Optional[int] , A : List[Any]=0.9 , A : Optional[Any]=3 , A : Optional[int]=0.5) -> Any:
"""simple docstring"""
if NLTK_VERSION >= version.Version('3.6.5'):
_UpperCAmelCase = [
meteor_score.single_meteor_score(
word_tokenize(A) , word_tokenize(A) , alpha=A , beta=A , gamma=A)
for ref, pred in zip(A , A)
]
else:
_UpperCAmelCase = [
meteor_score.single_meteor_score(A , A , alpha=A , beta=A , gamma=A)
for ref, pred in zip(A , A)
]
return {"meteor": np.mean(A)}
| 339 | 1 |
import argparse
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
########################################################################
# This is a fully working simple example to use Accelerate
# and perform gradient accumulation
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
UpperCAmelCase__ = 16
UpperCAmelCase__ = 32
def A ( _UpperCAmelCase : Accelerator , _UpperCAmelCase : int = 16 ) -> List[str]:
'''simple docstring'''
_UpperCAmelCase = AutoTokenizer.from_pretrained('bert-base-cased' )
_UpperCAmelCase = load_dataset('glue' , 'mrpc' )
def tokenize_function(_UpperCAmelCase : Any ):
# max_length=None => use the model max length (it's actually the default)
_UpperCAmelCase = tokenizer(examples['sentence1'] , examples['sentence2'] , truncation=_UpperCAmelCase , max_length=_UpperCAmelCase )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
_UpperCAmelCase = datasets.map(
_UpperCAmelCase , batched=_UpperCAmelCase , remove_columns=['idx', 'sentence1', 'sentence2'] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
_UpperCAmelCase = tokenized_datasets.rename_column('label' , 'labels' )
def collate_fn(_UpperCAmelCase : Any ):
# On TPU it's best to pad everything to the same length or training will be very slow.
_UpperCAmelCase = 128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
_UpperCAmelCase = 16
elif accelerator.mixed_precision != "no":
_UpperCAmelCase = 8
else:
_UpperCAmelCase = None
return tokenizer.pad(
_UpperCAmelCase , padding='longest' , max_length=_UpperCAmelCase , pad_to_multiple_of=_UpperCAmelCase , return_tensors='pt' , )
# Instantiate dataloaders.
_UpperCAmelCase = DataLoader(
tokenized_datasets['train'] , shuffle=_UpperCAmelCase , collate_fn=_UpperCAmelCase , batch_size=_UpperCAmelCase )
_UpperCAmelCase = DataLoader(
tokenized_datasets['validation'] , shuffle=_UpperCAmelCase , collate_fn=_UpperCAmelCase , batch_size=_UpperCAmelCase )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
UpperCAmelCase__ = mocked_dataloaders # noqa: F811
def A ( _UpperCAmelCase : int , _UpperCAmelCase : str ) -> int:
'''simple docstring'''
# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS' , _UpperCAmelCase ) == "1":
_UpperCAmelCase = 2
# New Code #
_UpperCAmelCase = int(args.gradient_accumulation_steps )
# Initialize accelerator
_UpperCAmelCase = Accelerator(
cpu=args.cpu , mixed_precision=args.mixed_precision , gradient_accumulation_steps=_UpperCAmelCase )
if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
raise NotImplementedError(
'Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`' )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
_UpperCAmelCase = config['lr']
_UpperCAmelCase = int(config['num_epochs'] )
_UpperCAmelCase = int(config['seed'] )
_UpperCAmelCase = int(config['batch_size'] )
_UpperCAmelCase = evaluate.load('glue' , 'mrpc' )
set_seed(_UpperCAmelCase )
_UpperCAmelCase , _UpperCAmelCase = get_dataloaders(_UpperCAmelCase , _UpperCAmelCase )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
_UpperCAmelCase = AutoModelForSequenceClassification.from_pretrained('bert-base-cased' , return_dict=_UpperCAmelCase )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
_UpperCAmelCase = model.to(accelerator.device )
# Instantiate optimizer
_UpperCAmelCase = AdamW(params=model.parameters() , lr=_UpperCAmelCase )
# Instantiate scheduler
_UpperCAmelCase = get_linear_schedule_with_warmup(
optimizer=_UpperCAmelCase , num_warmup_steps=100 , num_training_steps=(len(_UpperCAmelCase ) * num_epochs) , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = accelerator.prepare(
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
# Now we train the model
for epoch in range(_UpperCAmelCase ):
model.train()
for step, batch in enumerate(_UpperCAmelCase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
# New code #
# We use the new `accumulate` context manager to perform gradient accumulation
# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
with accelerator.accumulate(_UpperCAmelCase ):
_UpperCAmelCase = model(**_UpperCAmelCase )
_UpperCAmelCase = output.loss
accelerator.backward(_UpperCAmelCase )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(_UpperCAmelCase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
_UpperCAmelCase = model(**_UpperCAmelCase )
_UpperCAmelCase = outputs.logits.argmax(dim=-1 )
_UpperCAmelCase , _UpperCAmelCase = accelerator.gather_for_metrics((predictions, batch['labels']) )
metric.add_batch(
predictions=_UpperCAmelCase , references=_UpperCAmelCase , )
_UpperCAmelCase = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F"epoch {epoch}:" , _UpperCAmelCase )
def A ( ) -> List[Any]:
'''simple docstring'''
_UpperCAmelCase = argparse.ArgumentParser(description='Simple example of training script.' )
parser.add_argument(
'--mixed_precision' , type=_UpperCAmelCase , default=_UpperCAmelCase , choices=['no', 'fp16', 'bf16', 'fp8'] , help='Whether to use mixed precision. Choose'
'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
'and an Nvidia Ampere GPU.' , )
# New Code #
parser.add_argument(
'--gradient_accumulation_steps' , type=_UpperCAmelCase , default=1 , help='The number of minibatches to be ran before gradients are accumulated.' , )
parser.add_argument('--cpu' , action='store_true' , help='If passed, will train on the CPU.' )
_UpperCAmelCase = parser.parse_args()
_UpperCAmelCase = {'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
training_function(_UpperCAmelCase , _UpperCAmelCase )
if __name__ == "__main__":
main()
| 339 |
import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
UpperCAmelCase__ = {
"tiny.en": "https://openaipublic.azureedge.net/main/whisper/models/d3dd57d32accea0b295c96e26691aa14d8822fac7d9d27d5dc00b4ca2826dd03/tiny.en.pt",
"tiny": "https://openaipublic.azureedge.net/main/whisper/models/65147644a518d12f04e32d6f3b26facc3f8dd46e5390956a9424a650c0ce22b9/tiny.pt",
"base.en": "https://openaipublic.azureedge.net/main/whisper/models/25a8566e1d0c1e2231d1c762132cd20e0f96a85d16145c3a00adf5d1ac670ead/base.en.pt",
"base": "https://openaipublic.azureedge.net/main/whisper/models/ed3a0b6b1c0edf879ad9b11b1af5a0e6ab5db9205f891f668f8b0e6c6326e34e/base.pt",
"small.en": "https://openaipublic.azureedge.net/main/whisper/models/f953ad0fd29cacd07d5a9eda5624af0f6bcf2258be67c92b79389873d91e0872/small.en.pt",
"small": "https://openaipublic.azureedge.net/main/whisper/models/9ecf779972d90ba49c06d968637d720dd632c55bbf19d441fb42bf17a411e794/small.pt",
"medium.en": "https://openaipublic.azureedge.net/main/whisper/models/d7440d1dc186f76616474e0ff0b3b6b879abc9d1a4926b7adfa41db2d497ab4f/medium.en.pt",
"medium": "https://openaipublic.azureedge.net/main/whisper/models/345ae4da62f9b3d59415adc60127b97c714f32e89e936602e85993674d08dcb1/medium.pt",
"large": "https://openaipublic.azureedge.net/main/whisper/models/e4b87e7e0bf463eb8e6956e646f1e277e901512310def2c24bf0e11bd3c28e9a/large.pt",
"large-v2": "https://openaipublic.azureedge.net/main/whisper/models/81f7c96c852ee8fc832187b0132e569d6c3065a3252ed18e56effd0b6a73e524/large-v2.pt",
}
def A ( _UpperCAmelCase : Optional[int] ) -> str:
'''simple docstring'''
_UpperCAmelCase = ['layers', 'blocks']
for k in ignore_keys:
state_dict.pop(_UpperCAmelCase , _UpperCAmelCase )
UpperCAmelCase__ = {
"blocks": "layers",
"mlp.0": "fc1",
"mlp.2": "fc2",
"mlp_ln": "final_layer_norm",
".attn.query": ".self_attn.q_proj",
".attn.key": ".self_attn.k_proj",
".attn.value": ".self_attn.v_proj",
".attn_ln": ".self_attn_layer_norm",
".attn.out": ".self_attn.out_proj",
".cross_attn.query": ".encoder_attn.q_proj",
".cross_attn.key": ".encoder_attn.k_proj",
".cross_attn.value": ".encoder_attn.v_proj",
".cross_attn_ln": ".encoder_attn_layer_norm",
".cross_attn.out": ".encoder_attn.out_proj",
"decoder.ln.": "decoder.layer_norm.",
"encoder.ln.": "encoder.layer_norm.",
"token_embedding": "embed_tokens",
"encoder.positional_embedding": "encoder.embed_positions.weight",
"decoder.positional_embedding": "decoder.embed_positions.weight",
"ln_post": "layer_norm",
}
def A ( _UpperCAmelCase : Dict ) -> Optional[int]:
'''simple docstring'''
_UpperCAmelCase = list(s_dict.keys() )
for key in keys:
_UpperCAmelCase = key
for k, v in WHISPER_MAPPING.items():
if k in key:
_UpperCAmelCase = new_key.replace(_UpperCAmelCase , _UpperCAmelCase )
print(F"{key} -> {new_key}" )
_UpperCAmelCase = s_dict.pop(_UpperCAmelCase )
return s_dict
def A ( _UpperCAmelCase : List[Any] ) -> Union[str, Any]:
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase = emb.weight.shape
_UpperCAmelCase = nn.Linear(_UpperCAmelCase , _UpperCAmelCase , bias=_UpperCAmelCase )
_UpperCAmelCase = emb.weight.data
return lin_layer
def A ( _UpperCAmelCase : str , _UpperCAmelCase : str ) -> bytes:
'''simple docstring'''
os.makedirs(_UpperCAmelCase , exist_ok=_UpperCAmelCase )
_UpperCAmelCase = os.path.basename(_UpperCAmelCase )
_UpperCAmelCase = url.split('/' )[-2]
_UpperCAmelCase = os.path.join(_UpperCAmelCase , _UpperCAmelCase )
if os.path.exists(_UpperCAmelCase ) and not os.path.isfile(_UpperCAmelCase ):
raise RuntimeError(F"{download_target} exists and is not a regular file" )
if os.path.isfile(_UpperCAmelCase ):
_UpperCAmelCase = open(_UpperCAmelCase , 'rb' ).read()
if hashlib.shaaaa(_UpperCAmelCase ).hexdigest() == expected_shaaaa:
return model_bytes
else:
warnings.warn(F"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file" )
with urllib.request.urlopen(_UpperCAmelCase ) as source, open(_UpperCAmelCase , 'wb' ) as output:
with tqdm(
total=int(source.info().get('Content-Length' ) ) , ncols=80 , unit='iB' , unit_scale=_UpperCAmelCase , unit_divisor=1_024 ) as loop:
while True:
_UpperCAmelCase = source.read(8_192 )
if not buffer:
break
output.write(_UpperCAmelCase )
loop.update(len(_UpperCAmelCase ) )
_UpperCAmelCase = open(_UpperCAmelCase , 'rb' ).read()
if hashlib.shaaaa(_UpperCAmelCase ).hexdigest() != expected_shaaaa:
raise RuntimeError(
'Model has been downloaded but the SHA256 checksum does not not match. Please retry loading the model.' )
return model_bytes
def A ( _UpperCAmelCase : List[Any] , _UpperCAmelCase : Any ) -> Optional[int]:
'''simple docstring'''
if ".pt" not in checkpoint_path:
_UpperCAmelCase = _download(_MODELS[checkpoint_path] )
else:
_UpperCAmelCase = torch.load(_UpperCAmelCase , map_location='cpu' )
_UpperCAmelCase = original_checkpoint['dims']
_UpperCAmelCase = original_checkpoint['model_state_dict']
_UpperCAmelCase = state_dict['decoder.token_embedding.weight']
remove_ignore_keys_(_UpperCAmelCase )
rename_keys(_UpperCAmelCase )
_UpperCAmelCase = True
_UpperCAmelCase = state_dict['decoder.layers.0.fc1.weight'].shape[0]
_UpperCAmelCase = WhisperConfig(
vocab_size=dimensions['n_vocab'] , encoder_ffn_dim=_UpperCAmelCase , decoder_ffn_dim=_UpperCAmelCase , num_mel_bins=dimensions['n_mels'] , d_model=dimensions['n_audio_state'] , max_target_positions=dimensions['n_text_ctx'] , encoder_layers=dimensions['n_audio_layer'] , encoder_attention_heads=dimensions['n_audio_head'] , decoder_layers=dimensions['n_text_layer'] , decoder_attention_heads=dimensions['n_text_state'] , max_source_positions=dimensions['n_audio_ctx'] , )
_UpperCAmelCase = WhisperForConditionalGeneration(_UpperCAmelCase )
_UpperCAmelCase , _UpperCAmelCase = model.model.load_state_dict(_UpperCAmelCase , strict=_UpperCAmelCase )
if len(_UpperCAmelCase ) > 0 and not set(_UpperCAmelCase ) <= {
"encoder.embed_positions.weights",
"decoder.embed_positions.weights",
}:
raise ValueError(
'Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights` are allowed to be missing,'
F" but all the following weights are missing {missing}" )
if tie_embeds:
_UpperCAmelCase = make_linear_from_emb(model.model.decoder.embed_tokens )
else:
_UpperCAmelCase = proj_out_weights
model.save_pretrained(_UpperCAmelCase )
if __name__ == "__main__":
UpperCAmelCase__ = argparse.ArgumentParser()
# # Required parameters
parser.add_argument("--checkpoint_path", type=str, help="Patht to the downloaded checkpoints")
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
UpperCAmelCase__ = parser.parse_args()
convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)
| 339 | 1 |
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def A ( _UpperCAmelCase : List[Any] , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Union[str, Any]=None ) -> Optional[Any]:
'''simple docstring'''
# set parameter of one layer
assert torch_layer.weight.shape == weight.shape, F"{torch_layer} layer.weight does not match"
_UpperCAmelCase = nn.Parameter(_UpperCAmelCase )
if bias is not None:
assert torch_layer.bias.shape == bias.shape, F"{torch_layer} layer.bias does not match"
_UpperCAmelCase = nn.Parameter(_UpperCAmelCase )
def A ( _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : Dict , _UpperCAmelCase : str ) -> List[Any]:
'''simple docstring'''
# set torch weights for 1-to-1 comparison
_UpperCAmelCase = np.asarray(weights[0] )
_UpperCAmelCase = np.asarray(weights[1] )
_UpperCAmelCase = np.asarray(weights[2] )
set_param(
torch_layer.self_attention.query_key , torch.tensor(_UpperCAmelCase ).transpose(1 , 2 ).contiguous().view(-1 , _UpperCAmelCase ) , )
set_param(
torch_layer.self_attention.value , torch.tensor(_UpperCAmelCase ).transpose(1 , 2 ).contiguous().view(-1 , _UpperCAmelCase ) , )
set_param(
torch_layer.output.dense , torch.tensor(_UpperCAmelCase ).view(-1 , _UpperCAmelCase ).contiguous().transpose(0 , 1 ) , )
def A ( _UpperCAmelCase : List[str] , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : List[Any] ) -> Union[str, Any]:
'''simple docstring'''
# set torch weights for 1-to-1 comparison
_UpperCAmelCase = np.asarray(weights[0] )
_UpperCAmelCase = np.asarray(weights[1] )
_UpperCAmelCase = np.asarray(weights[2] )
_UpperCAmelCase = np.asarray(weights[3] )
set_param(
torch_layer.self_attention.query , torch.tensor(_UpperCAmelCase ).transpose(1 , 2 ).contiguous().view(-1 , _UpperCAmelCase ) , )
set_param(
torch_layer.self_attention.key , torch.tensor(_UpperCAmelCase ).transpose(1 , 2 ).contiguous().view(-1 , _UpperCAmelCase ) , )
set_param(
torch_layer.self_attention.value , torch.tensor(_UpperCAmelCase ).transpose(1 , 2 ).contiguous().view(-1 , _UpperCAmelCase ) , )
set_param(
torch_layer.output.dense , torch.tensor(_UpperCAmelCase ).view(-1 , _UpperCAmelCase ).contiguous().transpose(0 , 1 ) , )
def A ( _UpperCAmelCase : Dict , _UpperCAmelCase : List[Any] , _UpperCAmelCase : Dict ) -> Any:
'''simple docstring'''
# layernorm 1
_UpperCAmelCase = weights[0][0][0]
_UpperCAmelCase = np.asarray(layer_norm_a[0] )
_UpperCAmelCase = np.asarray(layer_norm_a[1] )
set_param(
torch_block.attention.layer_norm , torch.tensor(_UpperCAmelCase ) , torch.tensor(_UpperCAmelCase ) , )
# lsh weights + output
_UpperCAmelCase = weights[0][1]
if len(_UpperCAmelCase ) < 4:
set_layer_weights_in_torch_lsh(_UpperCAmelCase , torch_block.attention , _UpperCAmelCase )
else:
set_layer_weights_in_torch_local(_UpperCAmelCase , torch_block.attention , _UpperCAmelCase )
# intermediate weighs
_UpperCAmelCase = weights[2][0][1][2]
# Chunked Feed Forward
if len(_UpperCAmelCase ) == 4:
_UpperCAmelCase = intermediate_weights[2]
# layernorm 2
_UpperCAmelCase = np.asarray(intermediate_weights[0][0] )
_UpperCAmelCase = np.asarray(intermediate_weights[0][1] )
set_param(
torch_block.feed_forward.layer_norm , torch.tensor(_UpperCAmelCase ) , torch.tensor(_UpperCAmelCase ) , )
# intermediate dense
_UpperCAmelCase = np.asarray(intermediate_weights[1][0] )
_UpperCAmelCase = np.asarray(intermediate_weights[1][1] )
set_param(
torch_block.feed_forward.dense.dense , torch.tensor(_UpperCAmelCase ).transpose(0 , 1 ).contiguous() , torch.tensor(_UpperCAmelCase ) , )
# intermediate out
_UpperCAmelCase = np.asarray(intermediate_weights[4][0] )
_UpperCAmelCase = np.asarray(intermediate_weights[4][1] )
set_param(
torch_block.feed_forward.output.dense , torch.tensor(_UpperCAmelCase ).transpose(0 , 1 ).contiguous() , torch.tensor(_UpperCAmelCase ) , )
def A ( _UpperCAmelCase : Any , _UpperCAmelCase : Dict , _UpperCAmelCase : List[str] ) -> Union[str, Any]:
'''simple docstring'''
# reformer model
_UpperCAmelCase = torch_model.reformer
# word embeds
_UpperCAmelCase = np.asarray(weights[1] )
set_param(
torch_model_reformer.embeddings.word_embeddings , torch.tensor(_UpperCAmelCase ) , )
if isinstance(weights[3] , _UpperCAmelCase ):
_UpperCAmelCase = torch_model_reformer.embeddings.position_embeddings
for emb_idx in range(len(position_embeddings.weights ) ):
_UpperCAmelCase = np.asarray(weights[3][emb_idx][0] )
assert (
position_embeddings.weights[emb_idx].shape == emb_weights.shape
), F"{position_embeddings[emb_idx]} emb does not match"
_UpperCAmelCase = nn.Parameter(torch.tensor(_UpperCAmelCase ) )
_UpperCAmelCase = weights[5]
assert len(torch_model_reformer.encoder.layers ) * 4 == len(
_UpperCAmelCase ), "HF and trax model do not have the same number of layers"
for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers ):
_UpperCAmelCase = trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
set_block_weights_in_torch(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
# output layer norm
_UpperCAmelCase = np.asarray(weights[7][0] )
_UpperCAmelCase = np.asarray(weights[7][1] )
set_param(
torch_model_reformer.encoder.layer_norm , torch.tensor(_UpperCAmelCase ) , torch.tensor(_UpperCAmelCase ) , )
# output embeddings
_UpperCAmelCase = np.asarray(weights[9][0] )
_UpperCAmelCase = np.asarray(weights[9][1] )
set_param(
torch_model.lm_head.decoder , torch.tensor(_UpperCAmelCase ).transpose(0 , 1 ).contiguous() , torch.tensor(_UpperCAmelCase ) , )
def A ( _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Dict , _UpperCAmelCase : Tuple ) -> Optional[int]:
'''simple docstring'''
# Initialise PyTorch model
_UpperCAmelCase = ReformerConfig.from_json_file(_UpperCAmelCase )
print(F"Building PyTorch model from configuration: {config}" )
_UpperCAmelCase = ReformerModelWithLMHead(_UpperCAmelCase )
with open(_UpperCAmelCase , 'rb' ) as f:
_UpperCAmelCase = pickle.load(_UpperCAmelCase )['weights']
set_model_weights_in_torch(_UpperCAmelCase , _UpperCAmelCase , config.hidden_size )
# Save pytorch-model
print(F"Save PyTorch model to {pytorch_dump_path}" )
torch.save(model.state_dict() , _UpperCAmelCase )
if __name__ == "__main__":
UpperCAmelCase__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--trax_model_pkl_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
)
parser.add_argument(
"--config_file",
default=None,
type=str,
required=True,
help=(
"The config json file corresponding to the pre-trained Reformer 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."
)
UpperCAmelCase__ = parser.parse_args()
convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)
| 339 |
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
UpperCAmelCase__ = datasets.utils.logging.get_logger(__name__)
class __lowerCAmelCase ( folder_based_builder.FolderBasedBuilderConfig ):
UpperCamelCase = None
UpperCamelCase = None
class __lowerCAmelCase ( folder_based_builder.FolderBasedBuilder ):
UpperCamelCase = datasets.Audio()
UpperCamelCase = '''audio'''
UpperCamelCase = AudioFolderConfig
UpperCamelCase = 42 # definition at the bottom of the script
UpperCamelCase = AudioClassification(audio_column='''audio''' , label_column='''label''' )
UpperCAmelCase__ = [
".aiff",
".au",
".avr",
".caf",
".flac",
".htk",
".svx",
".mat4",
".mat5",
".mpc2k",
".ogg",
".paf",
".pvf",
".raw",
".rf64",
".sd2",
".sds",
".ircam",
".voc",
".w64",
".wav",
".nist",
".wavex",
".wve",
".xi",
".mp3",
".opus",
]
UpperCAmelCase__ = AUDIO_EXTENSIONS
| 339 | 1 |
import argparse
import os
import re
UpperCAmelCase__ = "src/diffusers"
# Pattern that looks at the indentation in a line.
UpperCAmelCase__ = re.compile(r"^(\s*)\S")
# Pattern that matches `"key":" and puts `key` in group 0.
UpperCAmelCase__ = re.compile(r"^\s*\"([^\"]+)\":")
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
UpperCAmelCase__ = re.compile(r"^\s*_import_structure\[\"([^\"]+)\"\]")
# Pattern that matches `"key",` and puts `key` in group 0.
UpperCAmelCase__ = re.compile(r"^\s*\"([^\"]+)\",\s*$")
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
UpperCAmelCase__ = re.compile(r"\[([^\]]+)\]")
def A ( _UpperCAmelCase : Union[str, Any] ) -> Dict:
'''simple docstring'''
_UpperCAmelCase = _re_indent.search(_UpperCAmelCase )
return "" if search is None else search.groups()[0]
def A ( _UpperCAmelCase : Optional[int] , _UpperCAmelCase : str="" , _UpperCAmelCase : Dict=None , _UpperCAmelCase : Union[str, Any]=None ) -> str:
'''simple docstring'''
_UpperCAmelCase = 0
_UpperCAmelCase = code.split('\n' )
if start_prompt is not None:
while not lines[index].startswith(_UpperCAmelCase ):
index += 1
_UpperCAmelCase = ['\n'.join(lines[:index] )]
else:
_UpperCAmelCase = []
# We split into blocks until we get to the `end_prompt` (or the end of the block).
_UpperCAmelCase = [lines[index]]
index += 1
while index < len(_UpperCAmelCase ) and (end_prompt is None or not lines[index].startswith(_UpperCAmelCase )):
if len(lines[index] ) > 0 and get_indent(lines[index] ) == indent_level:
if len(_UpperCAmelCase ) > 0 and get_indent(current_block[-1] ).startswith(indent_level + ' ' ):
current_block.append(lines[index] )
blocks.append('\n'.join(_UpperCAmelCase ) )
if index < len(_UpperCAmelCase ) - 1:
_UpperCAmelCase = [lines[index + 1]]
index += 1
else:
_UpperCAmelCase = []
else:
blocks.append('\n'.join(_UpperCAmelCase ) )
_UpperCAmelCase = [lines[index]]
else:
current_block.append(lines[index] )
index += 1
# Adds current block if it's nonempty.
if len(_UpperCAmelCase ) > 0:
blocks.append('\n'.join(_UpperCAmelCase ) )
# Add final block after end_prompt if provided.
if end_prompt is not None and index < len(_UpperCAmelCase ):
blocks.append('\n'.join(lines[index:] ) )
return blocks
def A ( _UpperCAmelCase : Any ) -> Tuple:
'''simple docstring'''
def _inner(_UpperCAmelCase : Optional[int] ):
return key(_UpperCAmelCase ).lower().replace('_' , '' )
return _inner
def A ( _UpperCAmelCase : str , _UpperCAmelCase : int=None ) -> Optional[Any]:
'''simple docstring'''
# If no key is provided, we use a noop.
def noop(_UpperCAmelCase : Optional[int] ):
return x
if key is None:
_UpperCAmelCase = noop
# Constants are all uppercase, they go first.
_UpperCAmelCase = [obj for obj in objects if key(_UpperCAmelCase ).isupper()]
# Classes are not all uppercase but start with a capital, they go second.
_UpperCAmelCase = [obj for obj in objects if key(_UpperCAmelCase )[0].isupper() and not key(_UpperCAmelCase ).isupper()]
# Functions begin with a lowercase, they go last.
_UpperCAmelCase = [obj for obj in objects if not key(_UpperCAmelCase )[0].isupper()]
_UpperCAmelCase = ignore_underscore(_UpperCAmelCase )
return sorted(_UpperCAmelCase , key=_UpperCAmelCase ) + sorted(_UpperCAmelCase , key=_UpperCAmelCase ) + sorted(_UpperCAmelCase , key=_UpperCAmelCase )
def A ( _UpperCAmelCase : List[str] ) -> Optional[Any]:
'''simple docstring'''
# This inner function sort imports between [ ].
def _replace(_UpperCAmelCase : Union[str, Any] ):
_UpperCAmelCase = match.groups()[0]
if "," not in imports:
return F"[{imports}]"
_UpperCAmelCase = [part.strip().replace('"' , '' ) for part in imports.split(',' )]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1] ) == 0:
_UpperCAmelCase = keys[:-1]
return "[" + ", ".join([F"\"{k}\"" for k in sort_objects(_UpperCAmelCase )] ) + "]"
_UpperCAmelCase = import_statement.split('\n' )
if len(_UpperCAmelCase ) > 3:
# Here we have to sort internal imports that are on several lines (one per name):
# key: [
# "object1",
# "object2",
# ...
# ]
# We may have to ignore one or two lines on each side.
_UpperCAmelCase = 2 if lines[1].strip() == '[' else 1
_UpperCAmelCase = [(i, _re_strip_line.search(_UpperCAmelCase ).groups()[0]) for i, line in enumerate(lines[idx:-idx] )]
_UpperCAmelCase = sort_objects(_UpperCAmelCase , key=lambda _UpperCAmelCase : x[1] )
_UpperCAmelCase = [lines[x[0] + idx] for x in sorted_indices]
return "\n".join(lines[:idx] + sorted_lines + lines[-idx:] )
elif len(_UpperCAmelCase ) == 3:
# Here we have to sort internal imports that are on one separate line:
# key: [
# "object1", "object2", ...
# ]
if _re_bracket_content.search(lines[1] ) is not None:
_UpperCAmelCase = _re_bracket_content.sub(_replace , lines[1] )
else:
_UpperCAmelCase = [part.strip().replace('"' , '' ) for part in lines[1].split(',' )]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1] ) == 0:
_UpperCAmelCase = keys[:-1]
_UpperCAmelCase = get_indent(lines[1] ) + ', '.join([F"\"{k}\"" for k in sort_objects(_UpperCAmelCase )] )
return "\n".join(_UpperCAmelCase )
else:
# Finally we have to deal with imports fitting on one line
_UpperCAmelCase = _re_bracket_content.sub(_replace , _UpperCAmelCase )
return import_statement
def A ( _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : Tuple=True ) -> Optional[int]:
'''simple docstring'''
with open(_UpperCAmelCase , 'r' ) as f:
_UpperCAmelCase = f.read()
if "_import_structure" not in code:
return
# Blocks of indent level 0
_UpperCAmelCase = split_code_in_indented_blocks(
_UpperCAmelCase , start_prompt='_import_structure = {' , end_prompt='if TYPE_CHECKING:' )
# We ignore block 0 (everything until start_prompt) and the last block (everything after end_prompt).
for block_idx in range(1 , len(_UpperCAmelCase ) - 1 ):
# Check if the block contains some `_import_structure`s thingy to sort.
_UpperCAmelCase = main_blocks[block_idx]
_UpperCAmelCase = block.split('\n' )
# Get to the start of the imports.
_UpperCAmelCase = 0
while line_idx < len(_UpperCAmelCase ) and "_import_structure" not in block_lines[line_idx]:
# Skip dummy import blocks
if "import dummy" in block_lines[line_idx]:
_UpperCAmelCase = len(_UpperCAmelCase )
else:
line_idx += 1
if line_idx >= len(_UpperCAmelCase ):
continue
# Ignore beginning and last line: they don't contain anything.
_UpperCAmelCase = '\n'.join(block_lines[line_idx:-1] )
_UpperCAmelCase = get_indent(block_lines[1] )
# Slit the internal block into blocks of indent level 1.
_UpperCAmelCase = split_code_in_indented_blocks(_UpperCAmelCase , indent_level=_UpperCAmelCase )
# We have two categories of import key: list or _import_structure[key].append/extend
_UpperCAmelCase = _re_direct_key if '_import_structure' in block_lines[0] else _re_indirect_key
# Grab the keys, but there is a trap: some lines are empty or just comments.
_UpperCAmelCase = [(pattern.search(_UpperCAmelCase ).groups()[0] if pattern.search(_UpperCAmelCase ) is not None else None) for b in internal_blocks]
# We only sort the lines with a key.
_UpperCAmelCase = [(i, key) for i, key in enumerate(_UpperCAmelCase ) if key is not None]
_UpperCAmelCase = [x[0] for x in sorted(_UpperCAmelCase , key=lambda _UpperCAmelCase : x[1] )]
# We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
_UpperCAmelCase = 0
_UpperCAmelCase = []
for i in range(len(_UpperCAmelCase ) ):
if keys[i] is None:
reordered_blocks.append(internal_blocks[i] )
else:
_UpperCAmelCase = sort_objects_in_import(internal_blocks[sorted_indices[count]] )
reordered_blocks.append(_UpperCAmelCase )
count += 1
# And we put our main block back together with its first and last line.
_UpperCAmelCase = '\n'.join(block_lines[:line_idx] + reordered_blocks + [block_lines[-1]] )
if code != "\n".join(_UpperCAmelCase ):
if check_only:
return True
else:
print(F"Overwriting {file}." )
with open(_UpperCAmelCase , 'w' ) as f:
f.write('\n'.join(_UpperCAmelCase ) )
def A ( _UpperCAmelCase : List[Any]=True ) -> Tuple:
'''simple docstring'''
_UpperCAmelCase = []
for root, _, files in os.walk(_UpperCAmelCase ):
if "__init__.py" in files:
_UpperCAmelCase = sort_imports(os.path.join(_UpperCAmelCase , '__init__.py' ) , check_only=_UpperCAmelCase )
if result:
_UpperCAmelCase = [os.path.join(_UpperCAmelCase , '__init__.py' )]
if len(_UpperCAmelCase ) > 0:
raise ValueError(F"Would overwrite {len(_UpperCAmelCase )} files, run `make style`." )
if __name__ == "__main__":
UpperCAmelCase__ = argparse.ArgumentParser()
parser.add_argument("--check_only", action="store_true", help="Whether to only check or fix style.")
UpperCAmelCase__ = parser.parse_args()
sort_imports_in_all_inits(check_only=args.check_only)
| 339 |
import sys
from collections import defaultdict
class __lowerCAmelCase :
def __init__( self : int) -> str:
"""simple docstring"""
_UpperCAmelCase = []
def _lowerCamelCase ( self : Any , A : List[str]) -> int:
"""simple docstring"""
return self.node_position[vertex]
def _lowerCamelCase ( self : Optional[Any] , A : Optional[int] , A : str) -> List[str]:
"""simple docstring"""
_UpperCAmelCase = pos
def _lowerCamelCase ( self : Tuple , A : Tuple , A : Dict , A : List[str] , A : Optional[Any]) -> Dict:
"""simple docstring"""
if start > size // 2 - 1:
return
else:
if 2 * start + 2 >= size:
_UpperCAmelCase = 2 * start + 1
else:
if heap[2 * start + 1] < heap[2 * start + 2]:
_UpperCAmelCase = 2 * start + 1
else:
_UpperCAmelCase = 2 * start + 2
if heap[smallest_child] < heap[start]:
_UpperCAmelCase , _UpperCAmelCase = heap[smallest_child], positions[smallest_child]
_UpperCAmelCase , _UpperCAmelCase = (
heap[start],
positions[start],
)
_UpperCAmelCase , _UpperCAmelCase = temp, tempa
_UpperCAmelCase = self.get_position(positions[smallest_child])
self.set_position(
positions[smallest_child] , self.get_position(positions[start]))
self.set_position(positions[start] , A)
self.top_to_bottom(A , A , A , A)
def _lowerCamelCase ( self : Optional[int] , A : str , A : Optional[Any] , A : Optional[int] , A : str) -> Any:
"""simple docstring"""
_UpperCAmelCase = position[index]
while index != 0:
_UpperCAmelCase = int((index - 2) / 2) if index % 2 == 0 else int((index - 1) / 2)
if val < heap[parent]:
_UpperCAmelCase = heap[parent]
_UpperCAmelCase = position[parent]
self.set_position(position[parent] , A)
else:
_UpperCAmelCase = val
_UpperCAmelCase = temp
self.set_position(A , A)
break
_UpperCAmelCase = parent
else:
_UpperCAmelCase = val
_UpperCAmelCase = temp
self.set_position(A , 0)
def _lowerCamelCase ( self : Union[str, Any] , A : Optional[int] , A : Tuple) -> str:
"""simple docstring"""
_UpperCAmelCase = len(A) // 2 - 1
for i in range(A , -1 , -1):
self.top_to_bottom(A , A , len(A) , A)
def _lowerCamelCase ( self : Optional[int] , A : int , A : str) -> List[str]:
"""simple docstring"""
_UpperCAmelCase = positions[0]
_UpperCAmelCase = sys.maxsize
self.top_to_bottom(A , 0 , len(A) , A)
return temp
def A ( _UpperCAmelCase : int ) -> Any:
'''simple docstring'''
_UpperCAmelCase = Heap()
_UpperCAmelCase = [0] * len(_UpperCAmelCase )
_UpperCAmelCase = [-1] * len(_UpperCAmelCase ) # Neighboring Tree Vertex of selected vertex
# Minimum Distance of explored vertex with neighboring vertex of partial tree
# formed in graph
_UpperCAmelCase = [] # Heap of Distance of vertices from their neighboring vertex
_UpperCAmelCase = []
for vertex in range(len(_UpperCAmelCase ) ):
distance_tv.append(sys.maxsize )
positions.append(_UpperCAmelCase )
heap.node_position.append(_UpperCAmelCase )
_UpperCAmelCase = []
_UpperCAmelCase = 1
_UpperCAmelCase = sys.maxsize
for neighbor, distance in adjacency_list[0]:
_UpperCAmelCase = 0
_UpperCAmelCase = distance
heap.heapify(_UpperCAmelCase , _UpperCAmelCase )
for _ in range(1 , len(_UpperCAmelCase ) ):
_UpperCAmelCase = heap.delete_minimum(_UpperCAmelCase , _UpperCAmelCase )
if visited[vertex] == 0:
tree_edges.append((nbr_tv[vertex], vertex) )
_UpperCAmelCase = 1
for neighbor, distance in adjacency_list[vertex]:
if (
visited[neighbor] == 0
and distance < distance_tv[heap.get_position(_UpperCAmelCase )]
):
_UpperCAmelCase = distance
heap.bottom_to_top(
_UpperCAmelCase , heap.get_position(_UpperCAmelCase ) , _UpperCAmelCase , _UpperCAmelCase )
_UpperCAmelCase = vertex
return tree_edges
if __name__ == "__main__": # pragma: no cover
# < --------- Prims Algorithm --------- >
UpperCAmelCase__ = int(input("Enter number of edges: ").strip())
UpperCAmelCase__ = defaultdict(list)
for _ in range(edges_number):
UpperCAmelCase__ = [int(x) for x in input().strip().split()]
adjacency_list[edge[0]].append([edge[1], edge[2]])
adjacency_list[edge[1]].append([edge[0], edge[2]])
print(prisms_algorithm(adjacency_list))
| 339 | 1 |
import unittest
from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken
from ...test_tokenization_common import TokenizerTesterMixin
UpperCAmelCase__ = get_tests_dir("fixtures/test_sentencepiece_bpe_char.model")
@require_sentencepiece
@require_tokenizers
class __lowerCAmelCase ( A , unittest.TestCase ):
UpperCamelCase = SpeechTaTokenizer
UpperCamelCase = False
UpperCamelCase = True
def _lowerCamelCase ( self : Optional[int]) -> List[str]:
"""simple docstring"""
super().setUp()
# We have a SentencePiece fixture for testing
_UpperCAmelCase = SpeechTaTokenizer(A)
_UpperCAmelCase = AddedToken('<mask>' , lstrip=A , rstrip=A)
_UpperCAmelCase = mask_token
tokenizer.add_special_tokens({'mask_token': mask_token})
tokenizer.add_tokens(['<ctc_blank>'])
tokenizer.save_pretrained(self.tmpdirname)
def _lowerCamelCase ( self : Any , A : List[Any]) -> str:
"""simple docstring"""
_UpperCAmelCase = 'this is a test'
_UpperCAmelCase = 'this is a test'
return input_text, output_text
def _lowerCamelCase ( self : int , A : Dict , A : Any=False , A : Optional[Any]=20 , A : List[str]=5) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase , _UpperCAmelCase = self.get_input_output_texts(A)
_UpperCAmelCase = tokenizer.encode(A , add_special_tokens=A)
_UpperCAmelCase = tokenizer.decode(A , clean_up_tokenization_spaces=A)
return text, ids
def _lowerCamelCase ( self : str) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase = '<pad>'
_UpperCAmelCase = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(A) , A)
self.assertEqual(self.get_tokenizer()._convert_id_to_token(A) , A)
def _lowerCamelCase ( self : Optional[int]) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase = list(self.get_tokenizer().get_vocab().keys())
self.assertEqual(vocab_keys[0] , '<s>')
self.assertEqual(vocab_keys[1] , '<pad>')
self.assertEqual(vocab_keys[-4] , 'œ')
self.assertEqual(vocab_keys[-2] , '<mask>')
self.assertEqual(vocab_keys[-1] , '<ctc_blank>')
self.assertEqual(len(A) , 81)
def _lowerCamelCase ( self : List[str]) -> int:
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size , 79)
def _lowerCamelCase ( self : int) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase = self.get_tokenizers(do_lower_case=A)
for tokenizer in tokenizers:
with self.subTest(F"{tokenizer.__class__.__name__}"):
_UpperCAmelCase = tokenizer.vocab_size
_UpperCAmelCase = len(A)
self.assertNotEqual(A , 0)
# We usually have added tokens from the start in tests because our vocab fixtures are
# smaller than the original vocabs - let's not assert this
# self.assertEqual(vocab_size, all_size)
_UpperCAmelCase = ['aaaaa bbbbbb', 'cccccccccdddddddd']
_UpperCAmelCase = tokenizer.add_tokens(A)
_UpperCAmelCase = tokenizer.vocab_size
_UpperCAmelCase = len(A)
self.assertNotEqual(A , 0)
self.assertEqual(A , A)
self.assertEqual(A , len(A))
self.assertEqual(A , all_size + len(A))
_UpperCAmelCase = tokenizer.encode('aaaaa bbbbbb low cccccccccdddddddd l' , add_special_tokens=A)
self.assertGreaterEqual(len(A) , 4)
self.assertGreater(tokens[0] , tokenizer.vocab_size - 1)
self.assertGreater(tokens[-3] , tokenizer.vocab_size - 1)
_UpperCAmelCase = {'eos_token': '>>>>|||<||<<|<<', 'pad_token': '<<<<<|||>|>>>>|>'}
_UpperCAmelCase = tokenizer.add_special_tokens(A)
_UpperCAmelCase = tokenizer.vocab_size
_UpperCAmelCase = len(A)
self.assertNotEqual(A , 0)
self.assertEqual(A , A)
self.assertEqual(A , len(A))
self.assertEqual(A , all_size_a + len(A))
_UpperCAmelCase = tokenizer.encode(
'>>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l' , add_special_tokens=A)
self.assertGreaterEqual(len(A) , 6)
self.assertGreater(tokens[0] , tokenizer.vocab_size - 1)
self.assertGreater(tokens[0] , tokens[1])
self.assertGreater(tokens[-3] , tokenizer.vocab_size - 1)
self.assertGreater(tokens[-3] , tokens[-4])
self.assertEqual(tokens[0] , tokenizer.eos_token_id)
self.assertEqual(tokens[-3] , tokenizer.pad_token_id)
def _lowerCamelCase ( self : Tuple) -> Any:
"""simple docstring"""
pass
def _lowerCamelCase ( self : Tuple) -> List[Any]:
"""simple docstring"""
pass
def _lowerCamelCase ( self : List[str]) -> str:
"""simple docstring"""
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = tokenizer.tokenize('This is a test')
# fmt: off
self.assertListEqual(A , [SPIECE_UNDERLINE, 'T', 'h', 'i', 's', SPIECE_UNDERLINE, 'i', 's', SPIECE_UNDERLINE, 'a', SPIECE_UNDERLINE, 't', 'e', 's', 't'])
# fmt: on
self.assertListEqual(
tokenizer.convert_tokens_to_ids(A) , [4, 32, 11, 10, 12, 4, 10, 12, 4, 7, 4, 6, 5, 12, 6] , )
_UpperCAmelCase = tokenizer.tokenize('I was born in 92000, and this is falsé.')
self.assertListEqual(
A , [SPIECE_UNDERLINE, 'I', SPIECE_UNDERLINE, 'w', 'a', 's', SPIECE_UNDERLINE, 'b', 'o', 'r', 'n', SPIECE_UNDERLINE, 'i', 'n', SPIECE_UNDERLINE, '92000', ',', SPIECE_UNDERLINE, 'a', 'n', 'd', SPIECE_UNDERLINE, 't', 'h', 'i', 's', SPIECE_UNDERLINE, 'i', 's', SPIECE_UNDERLINE, 'f', 'a', 'l', 's', 'é', '.'])
_UpperCAmelCase = tokenizer.convert_tokens_to_ids(A)
# fmt: off
self.assertListEqual(A , [4, 30, 4, 20, 7, 12, 4, 25, 8, 13, 9, 4, 10, 9, 4, 3, 23, 4, 7, 9, 14, 4, 6, 11, 10, 12, 4, 10, 12, 4, 19, 7, 15, 12, 73, 26])
# fmt: on
_UpperCAmelCase = tokenizer.convert_ids_to_tokens(A)
self.assertListEqual(
A , [SPIECE_UNDERLINE, 'I', SPIECE_UNDERLINE, 'w', 'a', 's', SPIECE_UNDERLINE, 'b', 'o', 'r', 'n', SPIECE_UNDERLINE, 'i', 'n', SPIECE_UNDERLINE, '<unk>', ',', SPIECE_UNDERLINE, 'a', 'n', 'd', SPIECE_UNDERLINE, 't', 'h', 'i', 's', SPIECE_UNDERLINE, 'i', 's', SPIECE_UNDERLINE, 'f', 'a', 'l', 's', 'é', '.'])
@slow
def _lowerCamelCase ( self : Tuple) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase = [
'Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides '
'general-purpose architectures (BERT, GPT, RoBERTa, XLM, DistilBert, XLNet...) for Natural '
'Language Understanding (NLU) and Natural Language Generation (NLG) with over thirty-two pretrained '
'models in one hundred plus languages and deep interoperability between Jax, PyTorch and TensorFlow.',
'BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly '
'conditioning on both left and right context in all layers.',
'The quick brown fox jumps over the lazy dog.',
]
# fmt: off
_UpperCAmelCase = {
'input_ids': [
[4, 32, 13, 7, 9, 12, 19, 8, 13, 18, 5, 13, 12, 4, 64, 19, 8, 13, 18, 5, 13, 15, 22, 4, 28, 9, 8, 20, 9, 4, 7, 12, 4, 24, 22, 6, 8, 13, 17, 11, 39, 6, 13, 7, 9, 12, 19, 8, 13, 18, 5, 13, 12, 4, 7, 9, 14, 4, 24, 22, 6, 8, 13, 17, 11, 39, 24, 13, 5, 6, 13, 7, 10, 9, 5, 14, 39, 25, 5, 13, 6, 63, 4, 24, 13, 8, 27, 10, 14, 5, 12, 4, 21, 5, 9, 5, 13, 7, 15, 39, 24, 16, 13, 24, 8, 12, 5, 4, 7, 13, 17, 11, 10, 6, 5, 17, 6, 16, 13, 5, 12, 4, 64, 40, 47, 54, 32, 23, 4, 53, 49, 32, 23, 4, 54, 8, 40, 47, 54, 32, 7, 23, 4, 69, 52, 43, 23, 4, 51, 10, 12, 6, 10, 15, 40, 5, 13, 6, 23, 4, 69, 52, 48, 5, 6, 26, 26, 26, 63, 4, 19, 8, 13, 4, 48, 7, 6, 16, 13, 7, 15, 4, 52, 7, 9, 21, 16, 7, 21, 5, 4, 61, 9, 14, 5, 13, 12, 6, 7, 9, 14, 10, 9, 21, 4, 64, 48, 52, 61, 63, 4, 7, 9, 14, 4, 48, 7, 6, 16, 13, 7, 15, 4, 52, 7, 9, 21, 16, 7, 21, 5, 4, 53, 5, 9, 5, 13, 7, 6, 10, 8, 9, 4, 64, 48, 52, 53, 63, 4, 20, 10, 6, 11, 4, 8, 27, 5, 13, 4, 6, 11, 10, 13, 6, 22, 39, 6, 20, 8, 4, 24, 13, 5, 6, 13, 7, 10, 9, 5, 14, 4, 18, 8, 14, 5, 15, 12, 4, 10, 9, 4, 8, 9, 5, 4, 11, 16, 9, 14, 13, 5, 14, 4, 24, 15, 16, 12, 4, 15, 7, 9, 21, 16, 7, 21, 5, 12, 4, 7, 9, 14, 4, 14, 5, 5, 24, 4, 10, 9, 6, 5, 13, 8, 24, 5, 13, 7, 25, 10, 15, 10, 6, 22, 4, 25, 5, 6, 20, 5, 5, 9, 4, 58, 7, 37, 23, 4, 49, 22, 32, 8, 13, 17, 11, 4, 7, 9, 14, 4, 32, 5, 9, 12, 8, 13, 55, 15, 8, 20, 26, 2],
[4, 40, 47, 54, 32, 4, 10, 12, 4, 14, 5, 12, 10, 21, 9, 5, 14, 4, 6, 8, 4, 24, 13, 5, 39, 6, 13, 7, 10, 9, 4, 14, 5, 5, 24, 4, 25, 10, 14, 10, 13, 5, 17, 6, 10, 8, 9, 7, 15, 4, 13, 5, 24, 13, 5, 12, 5, 9, 6, 7, 6, 10, 8, 9, 12, 4, 19, 13, 8, 18, 4, 16, 9, 15, 7, 25, 5, 15, 5, 14, 4, 6, 5, 37, 6, 4, 25, 22, 4, 46, 8, 10, 9, 6, 15, 22, 4, 17, 8, 9, 14, 10, 6, 10, 8, 9, 10, 9, 21, 4, 8, 9, 4, 25, 8, 6, 11, 4, 15, 5, 19, 6, 4, 7, 9, 14, 4, 13, 10, 21, 11, 6, 4, 17, 8, 9, 6, 5, 37, 6, 4, 10, 9, 4, 7, 15, 15, 4, 15, 7, 22, 5, 13, 12, 26, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[4, 32, 11, 5, 4, 45, 16, 10, 17, 28, 4, 25, 13, 8, 20, 9, 4, 19, 8, 37, 4, 46, 16, 18, 24, 12, 4, 8, 27, 5, 13, 4, 6, 11, 5, 4, 15, 7, 57, 22, 4, 14, 8, 21, 26, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
],
'attention_mask': [
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
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]
}
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=A , model_name='microsoft/speecht5_asr' , revision='c5ef64c71905caeccde0e4462ef3f9077224c524' , sequences=A , )
| 339 |
import torch
from transformers import CamembertForMaskedLM, CamembertTokenizer
def A ( _UpperCAmelCase : str , _UpperCAmelCase : Any , _UpperCAmelCase : List[str] , _UpperCAmelCase : Optional[int]=5 ) -> List[Any]:
'''simple docstring'''
# Adapted from https://github.com/pytorch/fairseq/blob/master/fairseq/models/roberta/hub_interface.py
assert masked_input.count('<mask>' ) == 1
_UpperCAmelCase = torch.tensor(tokenizer.encode(_UpperCAmelCase , add_special_tokens=_UpperCAmelCase ) ).unsqueeze(0 ) # Batch size 1
_UpperCAmelCase = model(_UpperCAmelCase )[0] # The last hidden-state is the first element of the output tuple
_UpperCAmelCase = (input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
_UpperCAmelCase = logits[0, masked_index, :]
_UpperCAmelCase = logits.softmax(dim=0 )
_UpperCAmelCase , _UpperCAmelCase = prob.topk(k=_UpperCAmelCase , dim=0 )
_UpperCAmelCase = ' '.join(
[tokenizer.convert_ids_to_tokens(indices[i].item() ) for i in range(len(_UpperCAmelCase ) )] )
_UpperCAmelCase = tokenizer.mask_token
_UpperCAmelCase = []
for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(' ' ) ):
_UpperCAmelCase = predicted_token_bpe.replace('\u2581' , ' ' )
if " {0}".format(_UpperCAmelCase ) in masked_input:
topk_filled_outputs.append(
(
masked_input.replace(' {0}'.format(_UpperCAmelCase ) , _UpperCAmelCase ),
values[index].item(),
predicted_token,
) )
else:
topk_filled_outputs.append(
(
masked_input.replace(_UpperCAmelCase , _UpperCAmelCase ),
values[index].item(),
predicted_token,
) )
return topk_filled_outputs
UpperCAmelCase__ = CamembertTokenizer.from_pretrained("camembert-base")
UpperCAmelCase__ = CamembertForMaskedLM.from_pretrained("camembert-base")
model.eval()
UpperCAmelCase__ = "Le camembert est <mask> :)"
print(fill_mask(masked_input, model, tokenizer, topk=3))
| 339 | 1 |
# flake8: noqa
# Lint as: python3
UpperCAmelCase__ = [
"VerificationMode",
"Version",
"disable_progress_bar",
"enable_progress_bar",
"is_progress_bar_enabled",
"experimental",
]
from .info_utils import VerificationMode
from .logging import disable_progress_bar, enable_progress_bar, is_progress_bar_enabled
from .version import Version
from .experimental import experimental
| 339 |
import math
import unittest
def A ( _UpperCAmelCase : int ) -> bool:
'''simple docstring'''
assert isinstance(_UpperCAmelCase , _UpperCAmelCase ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(_UpperCAmelCase ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
class __lowerCAmelCase ( unittest.TestCase ):
def _lowerCamelCase ( self : Tuple) -> Union[str, Any]:
"""simple docstring"""
self.assertTrue(is_prime(2))
self.assertTrue(is_prime(3))
self.assertTrue(is_prime(5))
self.assertTrue(is_prime(7))
self.assertTrue(is_prime(11))
self.assertTrue(is_prime(13))
self.assertTrue(is_prime(17))
self.assertTrue(is_prime(19))
self.assertTrue(is_prime(23))
self.assertTrue(is_prime(29))
def _lowerCamelCase ( self : Optional[int]) -> Any:
"""simple docstring"""
with self.assertRaises(A):
is_prime(-19)
self.assertFalse(
is_prime(0) , 'Zero doesn\'t have any positive factors, primes must have exactly two.' , )
self.assertFalse(
is_prime(1) , 'One only has 1 positive factor, primes must have exactly two.' , )
self.assertFalse(is_prime(2 * 2))
self.assertFalse(is_prime(2 * 3))
self.assertFalse(is_prime(3 * 3))
self.assertFalse(is_prime(3 * 5))
self.assertFalse(is_prime(3 * 5 * 7))
if __name__ == "__main__":
unittest.main()
| 339 | 1 |
from __future__ import annotations
def A ( _UpperCAmelCase : list ) -> float:
'''simple docstring'''
if not nums:
raise ValueError('List is empty' )
return sum(_UpperCAmelCase ) / len(_UpperCAmelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 339 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
UpperCAmelCase__ = "\\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"
UpperCAmelCase__ = "\\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"
UpperCAmelCase__ = "\\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 __lowerCAmelCase ( datasets.Metric ):
def _lowerCamelCase ( self : str) -> MetricInfo:
"""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 _lowerCamelCase ( self : Union[str, Any] , A : List[List[List[str]]] , A : List[List[str]] , A : int = 1 , A : int = 4 , ) -> Dict[str, float]:
"""simple docstring"""
return {
"google_bleu": gleu_score.corpus_gleu(
list_of_references=A , hypotheses=A , min_len=A , max_len=A)
}
| 339 | 1 |
def A ( _UpperCAmelCase : list[int] ) -> int:
'''simple docstring'''
if not numbers:
return 0
if not isinstance(_UpperCAmelCase , (list, tuple) ) or not all(
isinstance(_UpperCAmelCase , _UpperCAmelCase ) for number in numbers ):
raise ValueError('numbers must be an iterable of integers' )
_UpperCAmelCase = _UpperCAmelCase = _UpperCAmelCase = numbers[0]
for i in range(1 , len(_UpperCAmelCase ) ):
# update the maximum and minimum subarray products
_UpperCAmelCase = numbers[i]
if number < 0:
_UpperCAmelCase , _UpperCAmelCase = min_till_now, max_till_now
_UpperCAmelCase = max(_UpperCAmelCase , max_till_now * number )
_UpperCAmelCase = min(_UpperCAmelCase , min_till_now * number )
# update the maximum product found till now
_UpperCAmelCase = max(_UpperCAmelCase , _UpperCAmelCase )
return max_prod
| 339 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
if is_sentencepiece_available():
from ..ta.tokenization_ta import TaTokenizer
else:
from ...utils.dummy_sentencepiece_objects import TaTokenizer
UpperCAmelCase__ = TaTokenizer
if is_tokenizers_available():
from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
from ...utils.dummy_tokenizers_objects import TaTokenizerFast
UpperCAmelCase__ = TaTokenizerFast
UpperCAmelCase__ = {"configuration_mt5": ["MT5Config", "MT5OnnxConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ = [
"MT5EncoderModel",
"MT5ForConditionalGeneration",
"MT5ForQuestionAnswering",
"MT5Model",
"MT5PreTrainedModel",
"MT5Stack",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ = ["TFMT5EncoderModel", "TFMT5ForConditionalGeneration", "TFMT5Model"]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ = ["FlaxMT5EncoderModel", "FlaxMT5ForConditionalGeneration", "FlaxMT5Model"]
if TYPE_CHECKING:
from .configuration_mta import MTaConfig, MTaOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mta import (
MTaEncoderModel,
MTaForConditionalGeneration,
MTaForQuestionAnswering,
MTaModel,
MTaPreTrainedModel,
MTaStack,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel
else:
import sys
UpperCAmelCase__ = _LazyModule(
__name__,
globals()["__file__"],
_import_structure,
extra_objects={"MT5Tokenizer": MTaTokenizer, "MT5TokenizerFast": MTaTokenizerFast},
module_spec=__spec__,
)
| 339 | 1 |
import cmath
import math
def A ( _UpperCAmelCase : float , _UpperCAmelCase : float , _UpperCAmelCase : float , _UpperCAmelCase : float ) -> complex:
'''simple docstring'''
_UpperCAmelCase = math.radians(_UpperCAmelCase )
_UpperCAmelCase = math.radians(_UpperCAmelCase )
# Convert voltage and current to rectangular form
_UpperCAmelCase = cmath.rect(_UpperCAmelCase , _UpperCAmelCase )
_UpperCAmelCase = cmath.rect(_UpperCAmelCase , _UpperCAmelCase )
# Calculate apparent power
return voltage_rect * current_rect
if __name__ == "__main__":
import doctest
doctest.testmod()
| 339 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
"s-JoL/Open-Llama-V1": "https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json",
}
class __lowerCAmelCase ( A ):
UpperCamelCase = '''open-llama'''
def __init__( self : str , A : List[Any]=10_00_00 , A : Tuple=40_96 , A : Tuple=1_10_08 , A : List[str]=32 , A : Tuple=32 , A : Optional[Any]="silu" , A : int=20_48 , A : Optional[Any]=0.0_2 , A : Dict=1E-6 , A : Optional[Any]=True , A : List[Any]=0 , A : Dict=1 , A : int=2 , A : Dict=False , A : Optional[int]=True , A : List[Any]=0.1 , A : str=0.1 , A : Dict=True , A : Optional[Any]=True , A : Dict=None , **A : Union[str, Any] , ) -> Dict:
"""simple docstring"""
_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' , A)
_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=A , bos_token_id=A , eos_token_id=A , tie_word_embeddings=A , **A , )
def _lowerCamelCase ( self : List[str]) -> Union[str, Any]:
"""simple docstring"""
if self.rope_scaling is None:
return
if not isinstance(self.rope_scaling , A) or len(self.rope_scaling) != 2:
raise ValueError(
'`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '
F"got {self.rope_scaling}")
_UpperCAmelCase = self.rope_scaling.get('type' , A)
_UpperCAmelCase = self.rope_scaling.get('factor' , A)
if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
raise ValueError(
F"`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}")
if rope_scaling_factor is None or not isinstance(A , A) or rope_scaling_factor <= 1.0:
raise ValueError(F"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}")
| 339 | 1 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import doctest
import sys
import warnings
from os.path import abspath, dirname, join
import _pytest
from transformers.testing_utils import HfDoctestModule, HfDocTestParser
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
UpperCAmelCase__ = abspath(join(dirname(__file__), "src"))
sys.path.insert(1, git_repo_path)
# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action="ignore", category=FutureWarning)
def A ( _UpperCAmelCase : Dict ) -> Dict:
'''simple docstring'''
config.addinivalue_line(
'markers' , 'is_pt_tf_cross_test: mark test to run only when PT and TF interactions are tested' )
config.addinivalue_line(
'markers' , 'is_pt_flax_cross_test: mark test to run only when PT and FLAX interactions are tested' )
config.addinivalue_line('markers' , 'is_pipeline_test: mark test to run only when pipelines are tested' )
config.addinivalue_line('markers' , 'is_staging_test: mark test to run only in the staging environment' )
config.addinivalue_line('markers' , 'accelerate_tests: mark test that require accelerate' )
config.addinivalue_line('markers' , 'tool_tests: mark the tool tests that are run on their specific schedule' )
def A ( _UpperCAmelCase : Tuple ) -> List[str]:
'''simple docstring'''
from transformers.testing_utils import pytest_addoption_shared
pytest_addoption_shared(_UpperCAmelCase )
def A ( _UpperCAmelCase : str ) -> Any:
'''simple docstring'''
from transformers.testing_utils import pytest_terminal_summary_main
_UpperCAmelCase = terminalreporter.config.getoption('--make-reports' )
if make_reports:
pytest_terminal_summary_main(_UpperCAmelCase , id=_UpperCAmelCase )
def A ( _UpperCAmelCase : int , _UpperCAmelCase : int ) -> Tuple:
'''simple docstring'''
# If no tests are collected, pytest exists with code 5, which makes the CI fail.
if exitstatus == 5:
_UpperCAmelCase = 0
# Doctest custom flag to ignore output.
UpperCAmelCase__ = doctest.register_optionflag("IGNORE_RESULT")
UpperCAmelCase__ = doctest.OutputChecker
class __lowerCAmelCase ( A ):
def _lowerCamelCase ( self : Dict , A : Dict , A : Any , A : Optional[Any]) -> List[Any]:
"""simple docstring"""
if IGNORE_RESULT & optionflags:
return True
return OutputChecker.check_output(self , A , A , A)
UpperCAmelCase__ = CustomOutputChecker
UpperCAmelCase__ = HfDoctestModule
UpperCAmelCase__ = HfDocTestParser
| 339 |
def A ( _UpperCAmelCase : str ) -> bool:
'''simple docstring'''
return credit_card_number.startswith(('34', '35', '37', '4', '5', '6') )
def A ( _UpperCAmelCase : str ) -> bool:
'''simple docstring'''
_UpperCAmelCase = credit_card_number
_UpperCAmelCase = 0
_UpperCAmelCase = len(_UpperCAmelCase ) - 2
for i in range(_UpperCAmelCase , -1 , -2 ):
# double the value of every second digit
_UpperCAmelCase = int(cc_number[i] )
digit *= 2
# If doubling of a number results in a two digit number
# i.e greater than 9(e.g., 6 × 2 = 12),
# then add the digits of the product (e.g., 12: 1 + 2 = 3, 15: 1 + 5 = 6),
# to get a single digit number.
if digit > 9:
digit %= 10
digit += 1
_UpperCAmelCase = cc_number[:i] + str(_UpperCAmelCase ) + cc_number[i + 1 :]
total += digit
# Sum up the remaining digits
for i in range(len(_UpperCAmelCase ) - 1 , -1 , -2 ):
total += int(cc_number[i] )
return total % 10 == 0
def A ( _UpperCAmelCase : str ) -> bool:
'''simple docstring'''
_UpperCAmelCase = F"{credit_card_number} is an invalid credit card number because"
if not credit_card_number.isdigit():
print(F"{error_message} it has nonnumerical characters." )
return False
if not 13 <= len(_UpperCAmelCase ) <= 16:
print(F"{error_message} of its length." )
return False
if not validate_initial_digits(_UpperCAmelCase ):
print(F"{error_message} of its first two digits." )
return False
if not luhn_validation(_UpperCAmelCase ):
print(F"{error_message} it fails the Luhn check." )
return False
print(F"{credit_card_number} is a valid credit card number." )
return True
if __name__ == "__main__":
import doctest
doctest.testmod()
validate_credit_card_number("4111111111111111")
validate_credit_card_number("32323")
| 339 | 1 |
def A ( _UpperCAmelCase : list[int] ) -> float:
'''simple docstring'''
if not nums: # Makes sure that the list is not empty
raise ValueError('List is empty' )
_UpperCAmelCase = sum(_UpperCAmelCase ) / len(_UpperCAmelCase ) # Calculate the average
return sum(abs(x - average ) for x in nums ) / len(_UpperCAmelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 339 |
from functools import reduce
UpperCAmelCase__ = (
"73167176531330624919225119674426574742355349194934"
"96983520312774506326239578318016984801869478851843"
"85861560789112949495459501737958331952853208805511"
"12540698747158523863050715693290963295227443043557"
"66896648950445244523161731856403098711121722383113"
"62229893423380308135336276614282806444486645238749"
"30358907296290491560440772390713810515859307960866"
"70172427121883998797908792274921901699720888093776"
"65727333001053367881220235421809751254540594752243"
"52584907711670556013604839586446706324415722155397"
"53697817977846174064955149290862569321978468622482"
"83972241375657056057490261407972968652414535100474"
"82166370484403199890008895243450658541227588666881"
"16427171479924442928230863465674813919123162824586"
"17866458359124566529476545682848912883142607690042"
"24219022671055626321111109370544217506941658960408"
"07198403850962455444362981230987879927244284909188"
"84580156166097919133875499200524063689912560717606"
"05886116467109405077541002256983155200055935729725"
"71636269561882670428252483600823257530420752963450"
)
def A ( _UpperCAmelCase : str = N ) -> int:
'''simple docstring'''
return max(
# mypy cannot properly interpret reduce
int(reduce(lambda _UpperCAmelCase , _UpperCAmelCase : str(int(_UpperCAmelCase ) * int(_UpperCAmelCase ) ) , n[i : i + 13] ) )
for i in range(len(_UpperCAmelCase ) - 12 ) )
if __name__ == "__main__":
print(f"""{solution() = }""")
| 339 | 1 |
from ...configuration_utils import PretrainedConfig
UpperCAmelCase__ = {
"google/tapas-base-finetuned-sqa": (
"https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json"
),
"google/tapas-base-finetuned-wtq": (
"https://huggingface.co/google/tapas-base-finetuned-wtq/resolve/main/config.json"
),
"google/tapas-base-finetuned-wikisql-supervised": (
"https://huggingface.co/google/tapas-base-finetuned-wikisql-supervised/resolve/main/config.json"
),
"google/tapas-base-finetuned-tabfact": (
"https://huggingface.co/google/tapas-base-finetuned-tabfact/resolve/main/config.json"
),
}
class __lowerCAmelCase ( A ):
UpperCamelCase = '''tapas'''
def __init__( self : Optional[int] , A : Any=3_05_22 , A : Dict=7_68 , A : Union[str, Any]=12 , A : Any=12 , A : Any=30_72 , A : Optional[Any]="gelu" , A : Any=0.1 , A : Tuple=0.1 , A : List[str]=10_24 , A : Union[str, Any]=[3, 2_56, 2_56, 2, 2_56, 2_56, 10] , A : Optional[int]=0.0_2 , A : int=1E-12 , A : int=0 , A : Optional[Any]=1_0.0 , A : Optional[Any]=0 , A : Dict=1.0 , A : Union[str, Any]=None , A : Optional[Any]=1.0 , A : int=False , A : List[str]=None , A : List[Any]=1.0 , A : List[str]=1.0 , A : List[Any]=False , A : Union[str, Any]=False , A : str="ratio" , A : Any=None , A : Optional[int]=None , A : Union[str, Any]=64 , A : int=32 , A : Dict=False , A : List[str]=True , A : Tuple=False , A : Optional[int]=False , A : Union[str, Any]=True , A : Tuple=False , A : Dict=None , A : List[str]=None , **A : Optional[Any] , ) -> Optional[Any]:
"""simple docstring"""
super().__init__(pad_token_id=A , **A)
# BERT hyperparameters (with updated max_position_embeddings and type_vocab_sizes)
_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_sizes
_UpperCAmelCase = initializer_range
_UpperCAmelCase = layer_norm_eps
# Fine-tuning task hyperparameters
_UpperCAmelCase = positive_label_weight
_UpperCAmelCase = num_aggregation_labels
_UpperCAmelCase = aggregation_loss_weight
_UpperCAmelCase = use_answer_as_supervision
_UpperCAmelCase = answer_loss_importance
_UpperCAmelCase = use_normalized_answer_loss
_UpperCAmelCase = huber_loss_delta
_UpperCAmelCase = temperature
_UpperCAmelCase = aggregation_temperature
_UpperCAmelCase = use_gumbel_for_cells
_UpperCAmelCase = use_gumbel_for_aggregation
_UpperCAmelCase = average_approximation_function
_UpperCAmelCase = cell_selection_preference
_UpperCAmelCase = answer_loss_cutoff
_UpperCAmelCase = max_num_rows
_UpperCAmelCase = max_num_columns
_UpperCAmelCase = average_logits_per_cell
_UpperCAmelCase = select_one_column
_UpperCAmelCase = allow_empty_column_selection
_UpperCAmelCase = init_cell_selection_weights_to_zero
_UpperCAmelCase = reset_position_index_per_cell
_UpperCAmelCase = disable_per_token_loss
# Aggregation hyperparameters
_UpperCAmelCase = aggregation_labels
_UpperCAmelCase = no_aggregation_label_index
if isinstance(self.aggregation_labels , A):
_UpperCAmelCase = {int(A): v for k, v in aggregation_labels.items()}
| 339 |
from __future__ import annotations
from collections.abc import Callable
UpperCAmelCase__ = list[list[float | int]]
def A ( _UpperCAmelCase : Matrix , _UpperCAmelCase : Matrix ) -> Matrix:
'''simple docstring'''
_UpperCAmelCase = len(_UpperCAmelCase )
_UpperCAmelCase = [[0 for _ in range(size + 1 )] for _ in range(_UpperCAmelCase )]
_UpperCAmelCase = 42
_UpperCAmelCase = 42
_UpperCAmelCase = 42
_UpperCAmelCase = 42
_UpperCAmelCase = 42
_UpperCAmelCase = 42
for row in range(_UpperCAmelCase ):
for col in range(_UpperCAmelCase ):
_UpperCAmelCase = matrix[row][col]
_UpperCAmelCase = vector[row][0]
_UpperCAmelCase = 0
_UpperCAmelCase = 0
while row < size and col < size:
# pivoting
_UpperCAmelCase = max((abs(augmented[rowa][col] ), rowa) for rowa in range(_UpperCAmelCase , _UpperCAmelCase ) )[
1
]
if augmented[pivot_row][col] == 0:
col += 1
continue
else:
_UpperCAmelCase , _UpperCAmelCase = augmented[pivot_row], augmented[row]
for rowa in range(row + 1 , _UpperCAmelCase ):
_UpperCAmelCase = augmented[rowa][col] / augmented[row][col]
_UpperCAmelCase = 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 ):
_UpperCAmelCase = 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 A ( _UpperCAmelCase : list[int] ) -> Callable[[int], int]:
'''simple docstring'''
_UpperCAmelCase = len(_UpperCAmelCase )
_UpperCAmelCase = [[0 for _ in range(_UpperCAmelCase )] for _ in range(_UpperCAmelCase )]
_UpperCAmelCase = [[0] for _ in range(_UpperCAmelCase )]
_UpperCAmelCase = 42
_UpperCAmelCase = 42
_UpperCAmelCase = 42
_UpperCAmelCase = 42
for x_val, y_val in enumerate(_UpperCAmelCase ):
for col in range(_UpperCAmelCase ):
_UpperCAmelCase = (x_val + 1) ** (size - col - 1)
_UpperCAmelCase = y_val
_UpperCAmelCase = 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 A ( _UpperCAmelCase : int ) -> 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 A ( _UpperCAmelCase : Callable[[int], int] = question_function , _UpperCAmelCase : int = 10 ) -> int:
'''simple docstring'''
_UpperCAmelCase = [func(_UpperCAmelCase ) for x_val in range(1 , order + 1 )]
_UpperCAmelCase = [
interpolate(data_points[:max_coeff] ) for max_coeff in range(1 , order + 1 )
]
_UpperCAmelCase = 0
_UpperCAmelCase = 42
_UpperCAmelCase = 42
for poly in polynomials:
_UpperCAmelCase = 1
while func(_UpperCAmelCase ) == poly(_UpperCAmelCase ):
x_val += 1
ret += poly(_UpperCAmelCase )
return ret
if __name__ == "__main__":
print(f"""{solution() = }""")
| 339 | 1 |
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __lowerCAmelCase ( A ):
UpperCamelCase = ['''image_processor''', '''tokenizer''']
UpperCamelCase = '''AutoImageProcessor'''
UpperCamelCase = '''AutoTokenizer'''
def __init__( self : str , A : Optional[Any] , A : Optional[int]) -> Tuple:
"""simple docstring"""
super().__init__(A , A)
_UpperCAmelCase = self.image_processor
def __call__( self : Tuple , A : List[Any]=None , A : Any=None , A : Dict=None , **A : List[Any]) -> Tuple:
"""simple docstring"""
if text is None and images is None:
raise ValueError('You have to specify either text or images. Both cannot be none.')
if text is not None:
_UpperCAmelCase = self.tokenizer(A , return_tensors=A , **A)
if images is not None:
_UpperCAmelCase = self.image_processor(A , return_tensors=A , **A)
if text is not None and images is not None:
_UpperCAmelCase = image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**A) , tensor_type=A)
def _lowerCamelCase ( self : Dict , *A : Union[str, Any] , **A : Tuple) -> Union[str, Any]:
"""simple docstring"""
return self.tokenizer.batch_decode(*A , **A)
def _lowerCamelCase ( self : int , *A : List[str] , **A : List[Any]) -> Union[str, Any]:
"""simple docstring"""
return self.tokenizer.decode(*A , **A)
@property
def _lowerCamelCase ( self : Union[str, Any]) -> int:
"""simple docstring"""
return ["input_ids", "attention_mask", "pixel_values"]
| 339 |
from __future__ import annotations
def A ( _UpperCAmelCase : list[int] ) -> bool:
'''simple docstring'''
return len(set(_UpperCAmelCase ) ) == len(_UpperCAmelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 339 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class __lowerCAmelCase ( unittest.TestCase ):
def __init__( self : Union[str, Any] , A : Optional[int] , A : Dict=13 , A : int=3 , A : Optional[int]=2_24 , A : Dict=30 , A : Any=4_00 , A : int=True , A : List[Any]=None , A : Optional[Any]=True , A : Tuple=[0.5, 0.5, 0.5] , A : Optional[Any]=[0.5, 0.5, 0.5] , ) -> str:
"""simple docstring"""
_UpperCAmelCase = size if size is not None else {'height': 18, 'width': 18}
_UpperCAmelCase = parent
_UpperCAmelCase = batch_size
_UpperCAmelCase = num_channels
_UpperCAmelCase = image_size
_UpperCAmelCase = min_resolution
_UpperCAmelCase = max_resolution
_UpperCAmelCase = do_resize
_UpperCAmelCase = size
_UpperCAmelCase = do_normalize
_UpperCAmelCase = image_mean
_UpperCAmelCase = image_std
def _lowerCamelCase ( self : Union[str, Any]) -> Optional[Any]:
"""simple docstring"""
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"size": self.size,
}
@require_torch
@require_vision
class __lowerCAmelCase ( A , unittest.TestCase ):
UpperCamelCase = ViTImageProcessor if is_vision_available() else None
def _lowerCamelCase ( self : Union[str, Any]) -> Union[str, Any]:
"""simple docstring"""
_UpperCAmelCase = EfficientFormerImageProcessorTester(self)
@property
def _lowerCamelCase ( self : Any) -> List[Any]:
"""simple docstring"""
return self.image_proc_tester.prepare_image_processor_dict()
def _lowerCamelCase ( self : Optional[Any]) -> Tuple:
"""simple docstring"""
_UpperCAmelCase = self.image_processing_class(**self.image_processor_dict)
self.assertTrue(hasattr(A , 'image_mean'))
self.assertTrue(hasattr(A , 'image_std'))
self.assertTrue(hasattr(A , 'do_normalize'))
self.assertTrue(hasattr(A , 'do_resize'))
self.assertTrue(hasattr(A , 'size'))
def _lowerCamelCase ( self : Any) -> str:
"""simple docstring"""
pass
def _lowerCamelCase ( self : List[str]) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase = self.image_processing_class(**self.image_processor_dict)
# create random PIL images
_UpperCAmelCase = prepare_image_inputs(self.image_proc_tester , equal_resolution=A)
for image in image_inputs:
self.assertIsInstance(A , Image.Image)
# Test not batched input
_UpperCAmelCase = image_processor(image_inputs[0] , return_tensors='pt').pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_proc_tester.num_channels,
self.image_proc_tester.size['height'],
self.image_proc_tester.size['width'],
) , )
# Test batched
_UpperCAmelCase = image_processor(A , return_tensors='pt').pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_proc_tester.batch_size,
self.image_proc_tester.num_channels,
self.image_proc_tester.size['height'],
self.image_proc_tester.size['width'],
) , )
def _lowerCamelCase ( self : Any) -> str:
"""simple docstring"""
_UpperCAmelCase = self.image_processing_class(**self.image_processor_dict)
# create random numpy tensors
_UpperCAmelCase = prepare_image_inputs(self.image_proc_tester , equal_resolution=A , numpify=A)
for image in image_inputs:
self.assertIsInstance(A , np.ndarray)
# Test not batched input
_UpperCAmelCase = image_processor(image_inputs[0] , return_tensors='pt').pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_proc_tester.num_channels,
self.image_proc_tester.size['height'],
self.image_proc_tester.size['width'],
) , )
# Test batched
_UpperCAmelCase = image_processor(A , return_tensors='pt').pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_proc_tester.batch_size,
self.image_proc_tester.num_channels,
self.image_proc_tester.size['height'],
self.image_proc_tester.size['width'],
) , )
def _lowerCamelCase ( self : Optional[Any]) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase = self.image_processing_class(**self.image_processor_dict)
# create random PyTorch tensors
_UpperCAmelCase = prepare_image_inputs(self.image_proc_tester , equal_resolution=A , torchify=A)
for image in image_inputs:
self.assertIsInstance(A , torch.Tensor)
# Test not batched input
_UpperCAmelCase = image_processor(image_inputs[0] , return_tensors='pt').pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_proc_tester.num_channels,
self.image_proc_tester.size['height'],
self.image_proc_tester.size['width'],
) , )
# Test batched
_UpperCAmelCase = image_processor(A , return_tensors='pt').pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_proc_tester.batch_size,
self.image_proc_tester.num_channels,
self.image_proc_tester.size['height'],
self.image_proc_tester.size['width'],
) , )
| 339 |
import os
UpperCAmelCase__ = {"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1000}
def A ( _UpperCAmelCase : str ) -> int:
'''simple docstring'''
_UpperCAmelCase = 0
_UpperCAmelCase = 0
while index < len(_UpperCAmelCase ) - 1:
_UpperCAmelCase = SYMBOLS[numerals[index]]
_UpperCAmelCase = SYMBOLS[numerals[index + 1]]
if current_value < next_value:
total_value -= current_value
else:
total_value += current_value
index += 1
total_value += SYMBOLS[numerals[index]]
return total_value
def A ( _UpperCAmelCase : int ) -> str:
'''simple docstring'''
_UpperCAmelCase = ''
_UpperCAmelCase = num // 1_000
numerals += m_count * "M"
num %= 1_000
_UpperCAmelCase = num // 100
if c_count == 9:
numerals += "CM"
c_count -= 9
elif c_count == 4:
numerals += "CD"
c_count -= 4
if c_count >= 5:
numerals += "D"
c_count -= 5
numerals += c_count * "C"
num %= 100
_UpperCAmelCase = num // 10
if x_count == 9:
numerals += "XC"
x_count -= 9
elif x_count == 4:
numerals += "XL"
x_count -= 4
if x_count >= 5:
numerals += "L"
x_count -= 5
numerals += x_count * "X"
num %= 10
if num == 9:
numerals += "IX"
num -= 9
elif num == 4:
numerals += "IV"
num -= 4
if num >= 5:
numerals += "V"
num -= 5
numerals += num * "I"
return numerals
def A ( _UpperCAmelCase : str = "/p089_roman.txt" ) -> int:
'''simple docstring'''
_UpperCAmelCase = 0
with open(os.path.dirname(_UpperCAmelCase ) + roman_numerals_filename ) as filea:
_UpperCAmelCase = filea.readlines()
for line in lines:
_UpperCAmelCase = line.strip()
_UpperCAmelCase = parse_roman_numerals(_UpperCAmelCase )
_UpperCAmelCase = generate_roman_numerals(_UpperCAmelCase )
savings += len(_UpperCAmelCase ) - len(_UpperCAmelCase )
return savings
if __name__ == "__main__":
print(f"""{solution() = }""")
| 339 | 1 |
# This is the module that test_patching.py uses to test patch_submodule()
import os # noqa: this is just for tests
import os as renamed_os # noqa: this is just for tests
from os import path # noqa: this is just for tests
from os import path as renamed_path # noqa: this is just for tests
from os.path import join # noqa: this is just for tests
from os.path import join as renamed_join # noqa: this is just for tests
UpperCAmelCase__ = open # noqa: we just need to have a builtin inside this module to test it properly
| 339 |
import requests
from bsa import BeautifulSoup
def A ( _UpperCAmelCase : str , _UpperCAmelCase : dict ) -> str:
'''simple docstring'''
_UpperCAmelCase = BeautifulSoup(requests.get(_UpperCAmelCase , params=_UpperCAmelCase ).content , 'html.parser' )
_UpperCAmelCase = soup.find('div' , attrs={'class': 'gs_ri'} )
_UpperCAmelCase = div.find('div' , attrs={'class': 'gs_fl'} ).find_all('a' )
return anchors[2].get_text()
if __name__ == "__main__":
UpperCAmelCase__ = {
"title": (
"Precisely geometry controlled microsupercapacitors for ultrahigh areal "
"capacitance, volumetric capacitance, and energy density"
),
"journal": "Chem. Mater.",
"volume": 30,
"pages": "3979-3990",
"year": 2018,
"hl": "en",
}
print(get_citation("https://scholar.google.com/scholar_lookup", params=params))
| 339 | 1 |
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