code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
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"""simple docstring"""
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel
class UpperCAmelCase_ ( A__ ):
lowercase__ = ['''vqvae''']
def __init__( self : Dict , snake_case_ : AutoencoderKL , snake_case_ : UNetaDConditionModel , snake_case_ : Mel , snake_case_ : Union[DDIMScheduler, DDPMScheduler] , ) -> Union[str, Any]:
'''simple docstring'''
super().__init__()
self.register_modules(unet=snake_case_ , scheduler=snake_case_ , mel=snake_case_ , vqvae=snake_case_ )
def __magic_name__ ( self : int ) -> Tuple:
'''simple docstring'''
return 50 if isinstance(self.scheduler , snake_case_ ) else 1_000
@torch.no_grad()
def __call__( self : List[str] , snake_case_ : int = 1 , snake_case_ : str = None , snake_case_ : np.ndarray = None , snake_case_ : int = 0 , snake_case_ : int = 0 , snake_case_ : int = None , snake_case_ : torch.Generator = None , snake_case_ : float = 0 , snake_case_ : float = 0 , snake_case_ : torch.Generator = None , snake_case_ : float = 0 , snake_case_ : torch.Tensor = None , snake_case_ : torch.Tensor = None , snake_case_ : Union[str, Any]=True , ) -> Union[str, Any]:
'''simple docstring'''
A__ = steps or self.get_default_steps()
self.scheduler.set_timesteps(snake_case_ )
A__ = step_generator or generator
# For backwards compatibility
if type(self.unet.config.sample_size ) == int:
A__ = (self.unet.config.sample_size, self.unet.config.sample_size)
if noise is None:
A__ = randn_tensor(
(
batch_size,
self.unet.config.in_channels,
self.unet.config.sample_size[0],
self.unet.config.sample_size[1],
) , generator=snake_case_ , device=self.device , )
A__ = noise
A__ = None
if audio_file is not None or raw_audio is not None:
self.mel.load_audio(snake_case_ , snake_case_ )
A__ = self.mel.audio_slice_to_image(snake_case_ )
A__ = np.frombuffer(input_image.tobytes() , dtype="uint8" ).reshape(
(input_image.height, input_image.width) )
A__ = (input_image / 255) * 2 - 1
A__ = torch.tensor(input_image[np.newaxis, :, :] , dtype=torch.float ).to(self.device )
if self.vqvae is not None:
A__ = self.vqvae.encode(torch.unsqueeze(snake_case_ , 0 ) ).latent_dist.sample(
generator=snake_case_ )[0]
A__ = self.vqvae.config.scaling_factor * input_images
if start_step > 0:
A__ = self.scheduler.add_noise(snake_case_ , snake_case_ , self.scheduler.timesteps[start_step - 1] )
A__ = (
self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
)
A__ = int(mask_start_secs * pixels_per_second )
A__ = int(mask_end_secs * pixels_per_second )
A__ = self.scheduler.add_noise(snake_case_ , snake_case_ , torch.tensor(self.scheduler.timesteps[start_step:] ) )
for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:] ) ):
if isinstance(self.unet , snake_case_ ):
A__ = self.unet(snake_case_ , snake_case_ , snake_case_ )["sample"]
else:
A__ = self.unet(snake_case_ , snake_case_ )["sample"]
if isinstance(self.scheduler , snake_case_ ):
A__ = self.scheduler.step(
model_output=snake_case_ , timestep=snake_case_ , sample=snake_case_ , eta=snake_case_ , generator=snake_case_ , )["prev_sample"]
else:
A__ = self.scheduler.step(
model_output=snake_case_ , timestep=snake_case_ , sample=snake_case_ , generator=snake_case_ , )["prev_sample"]
if mask is not None:
if mask_start > 0:
A__ = mask[:, step, :, :mask_start]
if mask_end > 0:
A__ = mask[:, step, :, -mask_end:]
if self.vqvae is not None:
# 0.18215 was scaling factor used in training to ensure unit variance
A__ = 1 / self.vqvae.config.scaling_factor * images
A__ = self.vqvae.decode(snake_case_ )["sample"]
A__ = (images / 2 + 0.5).clamp(0 , 1 )
A__ = images.cpu().permute(0 , 2 , 3 , 1 ).numpy()
A__ = (images * 255).round().astype("uint8" )
A__ = list(
(Image.fromarray(_[:, :, 0] ) for _ in images)
if images.shape[3] == 1
else (Image.fromarray(snake_case_ , mode="RGB" ).convert("L" ) for _ in images) )
A__ = [self.mel.image_to_audio(snake_case_ ) for _ in images]
if not return_dict:
return images, (self.mel.get_sample_rate(), audios)
return BaseOutput(**AudioPipelineOutput(np.array(snake_case_ )[:, np.newaxis, :] ) , **ImagePipelineOutput(snake_case_ ) )
@torch.no_grad()
def __magic_name__ ( self : Dict , snake_case_ : List[Image.Image] , snake_case_ : int = 50 ) -> Dict:
'''simple docstring'''
assert isinstance(self.scheduler , snake_case_ )
self.scheduler.set_timesteps(snake_case_ )
A__ = np.array(
[np.frombuffer(image.tobytes() , dtype="uint8" ).reshape((1, image.height, image.width) ) for image in images] )
A__ = (sample / 255) * 2 - 1
A__ = torch.Tensor(snake_case_ ).to(self.device )
for t in self.progress_bar(torch.flip(self.scheduler.timesteps , (0,) ) ):
A__ = t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
A__ = self.scheduler.alphas_cumprod[t]
A__ = (
self.scheduler.alphas_cumprod[prev_timestep]
if prev_timestep >= 0
else self.scheduler.final_alpha_cumprod
)
A__ = 1 - alpha_prod_t
A__ = self.unet(snake_case_ , snake_case_ )["sample"]
A__ = (1 - alpha_prod_t_prev) ** 0.5 * model_output
A__ = (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
A__ = sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output
return sample
@staticmethod
def __magic_name__ ( snake_case_ : torch.Tensor , snake_case_ : torch.Tensor , snake_case_ : float ) -> Dict:
'''simple docstring'''
A__ = acos(torch.dot(torch.flatten(snake_case_ ) , torch.flatten(snake_case_ ) ) / torch.norm(snake_case_ ) / torch.norm(snake_case_ ) )
return sin((1 - alpha) * theta ) * xa / sin(snake_case_ ) + sin(alpha * theta ) * xa / sin(snake_case_ )
| 247 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
A__ : List[Any] = logging.get_logger(__name__)
A__ : str = {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}
A__ : int = {
'vocab_file': {
'distilbert-base-uncased': 'https://huggingface.co/distilbert-base-uncased/resolve/main/vocab.txt',
'distilbert-base-uncased-distilled-squad': (
'https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/vocab.txt'
),
'distilbert-base-cased': 'https://huggingface.co/distilbert-base-cased/resolve/main/vocab.txt',
'distilbert-base-cased-distilled-squad': (
'https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/vocab.txt'
),
'distilbert-base-german-cased': 'https://huggingface.co/distilbert-base-german-cased/resolve/main/vocab.txt',
'distilbert-base-multilingual-cased': (
'https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/vocab.txt'
),
},
'tokenizer_file': {
'distilbert-base-uncased': 'https://huggingface.co/distilbert-base-uncased/resolve/main/tokenizer.json',
'distilbert-base-uncased-distilled-squad': (
'https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/tokenizer.json'
),
'distilbert-base-cased': 'https://huggingface.co/distilbert-base-cased/resolve/main/tokenizer.json',
'distilbert-base-cased-distilled-squad': (
'https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/tokenizer.json'
),
'distilbert-base-german-cased': (
'https://huggingface.co/distilbert-base-german-cased/resolve/main/tokenizer.json'
),
'distilbert-base-multilingual-cased': (
'https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/tokenizer.json'
),
},
}
A__ : Optional[Any] = {
'distilbert-base-uncased': 5_12,
'distilbert-base-uncased-distilled-squad': 5_12,
'distilbert-base-cased': 5_12,
'distilbert-base-cased-distilled-squad': 5_12,
'distilbert-base-german-cased': 5_12,
'distilbert-base-multilingual-cased': 5_12,
}
A__ : List[str] = {
'distilbert-base-uncased': {'do_lower_case': True},
'distilbert-base-uncased-distilled-squad': {'do_lower_case': True},
'distilbert-base-cased': {'do_lower_case': False},
'distilbert-base-cased-distilled-squad': {'do_lower_case': False},
'distilbert-base-german-cased': {'do_lower_case': False},
'distilbert-base-multilingual-cased': {'do_lower_case': False},
}
class _UpperCAmelCase ( A__ ):
"""simple docstring"""
lowercase__ = VOCAB_FILES_NAMES
lowercase__ = PRETRAINED_VOCAB_FILES_MAP
lowercase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase__ = PRETRAINED_INIT_CONFIGURATION
lowercase__ = ["""input_ids""", """attention_mask"""]
lowercase__ = DistilBertTokenizer
def __init__( self : List[Any], lowerCamelCase : List[Any]=None, lowerCamelCase : Dict=None, lowerCamelCase : str=True, lowerCamelCase : Optional[int]="[UNK]", lowerCamelCase : Optional[Any]="[SEP]", lowerCamelCase : List[Any]="[PAD]", lowerCamelCase : Any="[CLS]", lowerCamelCase : Union[str, Any]="[MASK]", lowerCamelCase : str=True, lowerCamelCase : int=None, **lowerCamelCase : Union[str, Any], ):
'''simple docstring'''
super().__init__(
lowerCamelCase, tokenizer_file=lowerCamelCase, do_lower_case=lowerCamelCase, unk_token=lowerCamelCase, sep_token=lowerCamelCase, pad_token=lowerCamelCase, cls_token=lowerCamelCase, mask_token=lowerCamelCase, tokenize_chinese_chars=lowerCamelCase, strip_accents=lowerCamelCase, **lowerCamelCase, )
lowercase__ = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('''lowercase''', lowerCamelCase ) != do_lower_case
or normalizer_state.get('''strip_accents''', lowerCamelCase ) != strip_accents
or normalizer_state.get('''handle_chinese_chars''', lowerCamelCase ) != tokenize_chinese_chars
):
lowercase__ = getattr(lowerCamelCase, normalizer_state.pop('''type''' ) )
lowercase__ = do_lower_case
lowercase__ = strip_accents
lowercase__ = tokenize_chinese_chars
lowercase__ = normalizer_class(**lowerCamelCase )
lowercase__ = do_lower_case
def lowercase__ ( self : str, lowerCamelCase : Optional[Any], lowerCamelCase : List[Any]=None ):
'''simple docstring'''
lowercase__ = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def lowercase__ ( self : Union[str, Any], lowerCamelCase : List[int], lowerCamelCase : Optional[List[int]] = None ):
'''simple docstring'''
lowercase__ = [self.sep_token_id]
lowercase__ = [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 lowercase__ ( self : str, lowerCamelCase : str, lowerCamelCase : Optional[str] = None ):
'''simple docstring'''
lowercase__ = self._tokenizer.model.save(lowerCamelCase, name=lowerCamelCase )
return tuple(lowerCamelCase )
| 207 | 0 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_big_bird import BigBirdTokenizer
else:
UpperCamelCase_ : List[Any] = None
UpperCamelCase_ : Tuple = logging.get_logger(__name__)
UpperCamelCase_ : Any = {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}
UpperCamelCase_ : Any = {
'''vocab_file''': {
'''google/bigbird-roberta-base''': '''https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model''',
'''google/bigbird-roberta-large''': (
'''https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'''
),
'''google/bigbird-base-trivia-itc''': (
'''https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'''
),
},
'''tokenizer_file''': {
'''google/bigbird-roberta-base''': (
'''https://huggingface.co/google/bigbird-roberta-base/resolve/main/tokenizer.json'''
),
'''google/bigbird-roberta-large''': (
'''https://huggingface.co/google/bigbird-roberta-large/resolve/main/tokenizer.json'''
),
'''google/bigbird-base-trivia-itc''': (
'''https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/tokenizer.json'''
),
},
}
UpperCamelCase_ : Optional[int] = {
'''google/bigbird-roberta-base''': 4096,
'''google/bigbird-roberta-large''': 4096,
'''google/bigbird-base-trivia-itc''': 4096,
}
UpperCamelCase_ : List[str] = '''▁'''
class _a ( __lowerCAmelCase ):
SCREAMING_SNAKE_CASE_ : List[Any] = VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE_ : List[Any] = PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE_ : Any = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
SCREAMING_SNAKE_CASE_ : str = BigBirdTokenizer
SCREAMING_SNAKE_CASE_ : Tuple = ["""input_ids""", """attention_mask"""]
SCREAMING_SNAKE_CASE_ : List[int] = []
def __init__( self ,_SCREAMING_SNAKE_CASE=None ,_SCREAMING_SNAKE_CASE=None ,_SCREAMING_SNAKE_CASE="<unk>" ,_SCREAMING_SNAKE_CASE="<s>" ,_SCREAMING_SNAKE_CASE="</s>" ,_SCREAMING_SNAKE_CASE="<pad>" ,_SCREAMING_SNAKE_CASE="[SEP]" ,_SCREAMING_SNAKE_CASE="[MASK]" ,_SCREAMING_SNAKE_CASE="[CLS]" ,**_SCREAMING_SNAKE_CASE ,) -> Dict:
_snake_case = AddedToken(_SCREAMING_SNAKE_CASE ,lstrip=_SCREAMING_SNAKE_CASE ,rstrip=_SCREAMING_SNAKE_CASE ) if isinstance(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ) else bos_token
_snake_case = AddedToken(_SCREAMING_SNAKE_CASE ,lstrip=_SCREAMING_SNAKE_CASE ,rstrip=_SCREAMING_SNAKE_CASE ) if isinstance(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ) else eos_token
_snake_case = AddedToken(_SCREAMING_SNAKE_CASE ,lstrip=_SCREAMING_SNAKE_CASE ,rstrip=_SCREAMING_SNAKE_CASE ) if isinstance(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ) else unk_token
_snake_case = AddedToken(_SCREAMING_SNAKE_CASE ,lstrip=_SCREAMING_SNAKE_CASE ,rstrip=_SCREAMING_SNAKE_CASE ) if isinstance(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ) else pad_token
_snake_case = AddedToken(_SCREAMING_SNAKE_CASE ,lstrip=_SCREAMING_SNAKE_CASE ,rstrip=_SCREAMING_SNAKE_CASE ) if isinstance(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ) else cls_token
_snake_case = AddedToken(_SCREAMING_SNAKE_CASE ,lstrip=_SCREAMING_SNAKE_CASE ,rstrip=_SCREAMING_SNAKE_CASE ) if isinstance(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ) else sep_token
# Mask token behave like a normal word, i.e. include the space before it
_snake_case = AddedToken(_SCREAMING_SNAKE_CASE ,lstrip=_SCREAMING_SNAKE_CASE ,rstrip=_SCREAMING_SNAKE_CASE ) if isinstance(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ) else mask_token
super().__init__(
_SCREAMING_SNAKE_CASE ,tokenizer_file=_SCREAMING_SNAKE_CASE ,bos_token=_SCREAMING_SNAKE_CASE ,eos_token=_SCREAMING_SNAKE_CASE ,unk_token=_SCREAMING_SNAKE_CASE ,sep_token=_SCREAMING_SNAKE_CASE ,pad_token=_SCREAMING_SNAKE_CASE ,cls_token=_SCREAMING_SNAKE_CASE ,mask_token=_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE ,)
_snake_case = vocab_file
_snake_case = False if not self.vocab_file else True
def _lowercase ( self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = None ) -> List[int]:
_snake_case = [self.sep_token_id]
_snake_case = [self.cls_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def _lowercase ( self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = False ) -> List[int]:
if already_has_special_tokens:
if token_ids_a is not None:
raise ValueError(
"You should not supply a second sequence if the provided sequence of "
"ids is already formatted with special tokens for the model." )
return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]
if token_ids_a is None:
return [1] + ([0] * len(_SCREAMING_SNAKE_CASE )) + [1]
return [1] + ([0] * len(_SCREAMING_SNAKE_CASE )) + [1] + ([0] * len(_SCREAMING_SNAKE_CASE )) + [1]
def _lowercase ( self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = None ) -> List[int]:
_snake_case = [self.sep_token_id]
_snake_case = [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 _lowercase ( self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = None ) -> Tuple[str]:
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(_SCREAMING_SNAKE_CASE ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
_snake_case = os.path.join(
_SCREAMING_SNAKE_CASE ,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_SCREAMING_SNAKE_CASE ):
copyfile(self.vocab_file ,_SCREAMING_SNAKE_CASE )
return (out_vocab_file,)
| 142 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_big_bird import BigBirdTokenizer
else:
UpperCamelCase_ : List[Any] = None
UpperCamelCase_ : Tuple = logging.get_logger(__name__)
UpperCamelCase_ : Any = {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}
UpperCamelCase_ : Any = {
'''vocab_file''': {
'''google/bigbird-roberta-base''': '''https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model''',
'''google/bigbird-roberta-large''': (
'''https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'''
),
'''google/bigbird-base-trivia-itc''': (
'''https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'''
),
},
'''tokenizer_file''': {
'''google/bigbird-roberta-base''': (
'''https://huggingface.co/google/bigbird-roberta-base/resolve/main/tokenizer.json'''
),
'''google/bigbird-roberta-large''': (
'''https://huggingface.co/google/bigbird-roberta-large/resolve/main/tokenizer.json'''
),
'''google/bigbird-base-trivia-itc''': (
'''https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/tokenizer.json'''
),
},
}
UpperCamelCase_ : Optional[int] = {
'''google/bigbird-roberta-base''': 4096,
'''google/bigbird-roberta-large''': 4096,
'''google/bigbird-base-trivia-itc''': 4096,
}
UpperCamelCase_ : List[str] = '''▁'''
class _a ( __lowerCAmelCase ):
SCREAMING_SNAKE_CASE_ : List[Any] = VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE_ : List[Any] = PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE_ : Any = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
SCREAMING_SNAKE_CASE_ : str = BigBirdTokenizer
SCREAMING_SNAKE_CASE_ : Tuple = ["""input_ids""", """attention_mask"""]
SCREAMING_SNAKE_CASE_ : List[int] = []
def __init__( self ,_SCREAMING_SNAKE_CASE=None ,_SCREAMING_SNAKE_CASE=None ,_SCREAMING_SNAKE_CASE="<unk>" ,_SCREAMING_SNAKE_CASE="<s>" ,_SCREAMING_SNAKE_CASE="</s>" ,_SCREAMING_SNAKE_CASE="<pad>" ,_SCREAMING_SNAKE_CASE="[SEP]" ,_SCREAMING_SNAKE_CASE="[MASK]" ,_SCREAMING_SNAKE_CASE="[CLS]" ,**_SCREAMING_SNAKE_CASE ,) -> Dict:
_snake_case = AddedToken(_SCREAMING_SNAKE_CASE ,lstrip=_SCREAMING_SNAKE_CASE ,rstrip=_SCREAMING_SNAKE_CASE ) if isinstance(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ) else bos_token
_snake_case = AddedToken(_SCREAMING_SNAKE_CASE ,lstrip=_SCREAMING_SNAKE_CASE ,rstrip=_SCREAMING_SNAKE_CASE ) if isinstance(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ) else eos_token
_snake_case = AddedToken(_SCREAMING_SNAKE_CASE ,lstrip=_SCREAMING_SNAKE_CASE ,rstrip=_SCREAMING_SNAKE_CASE ) if isinstance(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ) else unk_token
_snake_case = AddedToken(_SCREAMING_SNAKE_CASE ,lstrip=_SCREAMING_SNAKE_CASE ,rstrip=_SCREAMING_SNAKE_CASE ) if isinstance(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ) else pad_token
_snake_case = AddedToken(_SCREAMING_SNAKE_CASE ,lstrip=_SCREAMING_SNAKE_CASE ,rstrip=_SCREAMING_SNAKE_CASE ) if isinstance(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ) else cls_token
_snake_case = AddedToken(_SCREAMING_SNAKE_CASE ,lstrip=_SCREAMING_SNAKE_CASE ,rstrip=_SCREAMING_SNAKE_CASE ) if isinstance(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ) else sep_token
# Mask token behave like a normal word, i.e. include the space before it
_snake_case = AddedToken(_SCREAMING_SNAKE_CASE ,lstrip=_SCREAMING_SNAKE_CASE ,rstrip=_SCREAMING_SNAKE_CASE ) if isinstance(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ) else mask_token
super().__init__(
_SCREAMING_SNAKE_CASE ,tokenizer_file=_SCREAMING_SNAKE_CASE ,bos_token=_SCREAMING_SNAKE_CASE ,eos_token=_SCREAMING_SNAKE_CASE ,unk_token=_SCREAMING_SNAKE_CASE ,sep_token=_SCREAMING_SNAKE_CASE ,pad_token=_SCREAMING_SNAKE_CASE ,cls_token=_SCREAMING_SNAKE_CASE ,mask_token=_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE ,)
_snake_case = vocab_file
_snake_case = False if not self.vocab_file else True
def _lowercase ( self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = None ) -> List[int]:
_snake_case = [self.sep_token_id]
_snake_case = [self.cls_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def _lowercase ( self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = False ) -> List[int]:
if already_has_special_tokens:
if token_ids_a is not None:
raise ValueError(
"You should not supply a second sequence if the provided sequence of "
"ids is already formatted with special tokens for the model." )
return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]
if token_ids_a is None:
return [1] + ([0] * len(_SCREAMING_SNAKE_CASE )) + [1]
return [1] + ([0] * len(_SCREAMING_SNAKE_CASE )) + [1] + ([0] * len(_SCREAMING_SNAKE_CASE )) + [1]
def _lowercase ( self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = None ) -> List[int]:
_snake_case = [self.sep_token_id]
_snake_case = [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 _lowercase ( self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = None ) -> Tuple[str]:
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(_SCREAMING_SNAKE_CASE ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
_snake_case = os.path.join(
_SCREAMING_SNAKE_CASE ,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_SCREAMING_SNAKE_CASE ):
copyfile(self.vocab_file ,_SCREAMING_SNAKE_CASE )
return (out_vocab_file,)
| 142 | 1 |
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
A : List[Any] = np.linspace(start=0, stop=7_5, num=7_5, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any membership function
# (trapmf(), gbellmf(), gaussmf(), etc).
A : Tuple = [0, 2_5, 5_0]
A : Optional[Any] = [2_5, 5_0, 7_5]
A : int = fuzz.membership.trimf(X, abca)
A : Dict = fuzz.membership.trimf(X, abca)
# Compute the different operations using inbuilt functions.
A : Optional[int] = np.ones(7_5)
A : Any = np.zeros((7_5,))
# 1. Union = max(µA(x), µB(x))
A : Any = fuzz.fuzzy_or(X, young, X, middle_aged)[1]
# 2. Intersection = min(µA(x), µB(x))
A : Tuple = fuzz.fuzzy_and(X, young, X, middle_aged)[1]
# 3. Complement (A) = (1- min(µA(x))
A : List[Any] = fuzz.fuzzy_not(young)
# 4. Difference (A/B) = min(µA(x),(1- µB(x)))
A : List[Any] = fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
# 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
A : Optional[int] = young + middle_aged - (young * middle_aged)
# 6. Algebraic Product = (µA(x) * µB(x))
A : str = young * middle_aged
# 7. Bounded Sum = min[1,(µA(x), µB(x))]
A : Optional[Any] = fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
# 8. Bounded difference = min[0,(µA(x), µB(x))]
A : str = fuzz.fuzzy_or(X, zero, X, young - middle_aged)[1]
# max-min composition
# max-product composition
# Plot each set A, set B and each operation result using plot() and subplot().
from matplotlib import pyplot as plt
plt.figure()
plt.subplot(4, 3, 1)
plt.plot(X, young)
plt.title('''Young''')
plt.grid(True)
plt.subplot(4, 3, 2)
plt.plot(X, middle_aged)
plt.title('''Middle aged''')
plt.grid(True)
plt.subplot(4, 3, 3)
plt.plot(X, union)
plt.title('''union''')
plt.grid(True)
plt.subplot(4, 3, 4)
plt.plot(X, intersection)
plt.title('''intersection''')
plt.grid(True)
plt.subplot(4, 3, 5)
plt.plot(X, complement_a)
plt.title('''complement_a''')
plt.grid(True)
plt.subplot(4, 3, 6)
plt.plot(X, difference)
plt.title('''difference a/b''')
plt.grid(True)
plt.subplot(4, 3, 7)
plt.plot(X, alg_sum)
plt.title('''alg_sum''')
plt.grid(True)
plt.subplot(4, 3, 8)
plt.plot(X, alg_product)
plt.title('''alg_product''')
plt.grid(True)
plt.subplot(4, 3, 9)
plt.plot(X, bdd_sum)
plt.title('''bdd_sum''')
plt.grid(True)
plt.subplot(4, 3, 1_0)
plt.plot(X, bdd_difference)
plt.title('''bdd_difference''')
plt.grid(True)
plt.subplots_adjust(hspace=0.5)
plt.show()
| 274 |
import gc
import math
import unittest
import torch
from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
__UpperCAmelCase = logging.get_logger(__name__)
enable_full_determinism()
class UpperCamelCase__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase_ =UNetaDModel
UpperCAmelCase_ ="sample"
@property
def _UpperCamelCase ( self ) -> Optional[int]:
SCREAMING_SNAKE_CASE_ = 4
SCREAMING_SNAKE_CASE_ = 3
SCREAMING_SNAKE_CASE_ = (32, 32)
SCREAMING_SNAKE_CASE_ = floats_tensor((batch_size, num_channels) + sizes ).to(_A )
SCREAMING_SNAKE_CASE_ = torch.tensor([10] ).to(_A )
return {"sample": noise, "timestep": time_step}
@property
def _UpperCamelCase ( self ) -> List[str]:
return (3, 32, 32)
@property
def _UpperCamelCase ( self ) -> Tuple:
return (3, 32, 32)
def _UpperCamelCase ( self ) -> Optional[int]:
SCREAMING_SNAKE_CASE_ = {
'''block_out_channels''': (32, 64),
'''down_block_types''': ('''DownBlock2D''', '''AttnDownBlock2D'''),
'''up_block_types''': ('''AttnUpBlock2D''', '''UpBlock2D'''),
'''attention_head_dim''': 3,
'''out_channels''': 3,
'''in_channels''': 3,
'''layers_per_block''': 2,
'''sample_size''': 32,
}
SCREAMING_SNAKE_CASE_ = self.dummy_input
return init_dict, inputs_dict
class UpperCamelCase__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase_ =UNetaDModel
UpperCAmelCase_ ="sample"
@property
def _UpperCamelCase ( self ) -> Optional[Any]:
SCREAMING_SNAKE_CASE_ = 4
SCREAMING_SNAKE_CASE_ = 4
SCREAMING_SNAKE_CASE_ = (32, 32)
SCREAMING_SNAKE_CASE_ = floats_tensor((batch_size, num_channels) + sizes ).to(_A )
SCREAMING_SNAKE_CASE_ = torch.tensor([10] ).to(_A )
return {"sample": noise, "timestep": time_step}
@property
def _UpperCamelCase ( self ) -> Tuple:
return (4, 32, 32)
@property
def _UpperCamelCase ( self ) -> Tuple:
return (4, 32, 32)
def _UpperCamelCase ( self ) -> Tuple:
SCREAMING_SNAKE_CASE_ = {
'''sample_size''': 32,
'''in_channels''': 4,
'''out_channels''': 4,
'''layers_per_block''': 2,
'''block_out_channels''': (32, 64),
'''attention_head_dim''': 32,
'''down_block_types''': ('''DownBlock2D''', '''DownBlock2D'''),
'''up_block_types''': ('''UpBlock2D''', '''UpBlock2D'''),
}
SCREAMING_SNAKE_CASE_ = self.dummy_input
return init_dict, inputs_dict
def _UpperCamelCase ( self ) -> Tuple:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update''' , output_loading_info=_A )
self.assertIsNotNone(_A )
self.assertEqual(len(loading_info['''missing_keys'''] ) , 0 )
model.to(_A )
SCREAMING_SNAKE_CASE_ = model(**self.dummy_input ).sample
assert image is not None, "Make sure output is not None"
@unittest.skipIf(torch_device != '''cuda''' , '''This test is supposed to run on GPU''' )
def _UpperCamelCase ( self ) -> Dict:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update''' , output_loading_info=_A )
model.to(_A )
SCREAMING_SNAKE_CASE_ = model(**self.dummy_input ).sample
assert image is not None, "Make sure output is not None"
@unittest.skipIf(torch_device != '''cuda''' , '''This test is supposed to run on GPU''' )
def _UpperCamelCase ( self ) -> Dict:
# by defautl model loading will use accelerate as `low_cpu_mem_usage=True`
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update''' , output_loading_info=_A )
model_accelerate.to(_A )
model_accelerate.eval()
SCREAMING_SNAKE_CASE_ = torch.randn(
1 , model_accelerate.config.in_channels , model_accelerate.config.sample_size , model_accelerate.config.sample_size , generator=torch.manual_seed(0 ) , )
SCREAMING_SNAKE_CASE_ = noise.to(_A )
SCREAMING_SNAKE_CASE_ = torch.tensor([10] * noise.shape[0] ).to(_A )
SCREAMING_SNAKE_CASE_ = model_accelerate(_A , _A )['''sample''']
# two models don't need to stay in the device at the same time
del model_accelerate
torch.cuda.empty_cache()
gc.collect()
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = UNetaDModel.from_pretrained(
'''fusing/unet-ldm-dummy-update''' , output_loading_info=_A , low_cpu_mem_usage=_A )
model_normal_load.to(_A )
model_normal_load.eval()
SCREAMING_SNAKE_CASE_ = model_normal_load(_A , _A )['''sample''']
assert torch_all_close(_A , _A , rtol=1E-3 )
def _UpperCamelCase ( self ) -> List[str]:
SCREAMING_SNAKE_CASE_ = UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update''' )
model.eval()
model.to(_A )
SCREAMING_SNAKE_CASE_ = torch.randn(
1 , model.config.in_channels , model.config.sample_size , model.config.sample_size , generator=torch.manual_seed(0 ) , )
SCREAMING_SNAKE_CASE_ = noise.to(_A )
SCREAMING_SNAKE_CASE_ = torch.tensor([10] * noise.shape[0] ).to(_A )
with torch.no_grad():
SCREAMING_SNAKE_CASE_ = model(_A , _A ).sample
SCREAMING_SNAKE_CASE_ = output[0, -1, -3:, -3:].flatten().cpu()
# fmt: off
SCREAMING_SNAKE_CASE_ = torch.tensor([-13.3258, -20.1100, -15.9873, -17.6617, -23.0596, -17.9419, -13.3675, -16.1889, -12.3800] )
# fmt: on
self.assertTrue(torch_all_close(_A , _A , rtol=1E-3 ) )
class UpperCamelCase__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase_ =UNetaDModel
UpperCAmelCase_ ="sample"
@property
def _UpperCamelCase ( self , _A=(32, 32) ) -> int:
SCREAMING_SNAKE_CASE_ = 4
SCREAMING_SNAKE_CASE_ = 3
SCREAMING_SNAKE_CASE_ = floats_tensor((batch_size, num_channels) + sizes ).to(_A )
SCREAMING_SNAKE_CASE_ = torch.tensor(batch_size * [10] ).to(dtype=torch.intaa , device=_A )
return {"sample": noise, "timestep": time_step}
@property
def _UpperCamelCase ( self ) -> List[str]:
return (3, 32, 32)
@property
def _UpperCamelCase ( self ) -> List[Any]:
return (3, 32, 32)
def _UpperCamelCase ( self ) -> Optional[int]:
SCREAMING_SNAKE_CASE_ = {
'''block_out_channels''': [32, 64, 64, 64],
'''in_channels''': 3,
'''layers_per_block''': 1,
'''out_channels''': 3,
'''time_embedding_type''': '''fourier''',
'''norm_eps''': 1E-6,
'''mid_block_scale_factor''': math.sqrt(2.0 ),
'''norm_num_groups''': None,
'''down_block_types''': [
'''SkipDownBlock2D''',
'''AttnSkipDownBlock2D''',
'''SkipDownBlock2D''',
'''SkipDownBlock2D''',
],
'''up_block_types''': [
'''SkipUpBlock2D''',
'''SkipUpBlock2D''',
'''AttnSkipUpBlock2D''',
'''SkipUpBlock2D''',
],
}
SCREAMING_SNAKE_CASE_ = self.dummy_input
return init_dict, inputs_dict
@slow
def _UpperCamelCase ( self ) -> Tuple:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = UNetaDModel.from_pretrained('''google/ncsnpp-celebahq-256''' , output_loading_info=_A )
self.assertIsNotNone(_A )
self.assertEqual(len(loading_info['''missing_keys'''] ) , 0 )
model.to(_A )
SCREAMING_SNAKE_CASE_ = self.dummy_input
SCREAMING_SNAKE_CASE_ = floats_tensor((4, 3) + (256, 256) ).to(_A )
SCREAMING_SNAKE_CASE_ = noise
SCREAMING_SNAKE_CASE_ = model(**_A )
assert image is not None, "Make sure output is not None"
@slow
def _UpperCamelCase ( self ) -> List[Any]:
SCREAMING_SNAKE_CASE_ = UNetaDModel.from_pretrained('''google/ncsnpp-celebahq-256''' )
model.to(_A )
SCREAMING_SNAKE_CASE_ = 4
SCREAMING_SNAKE_CASE_ = 3
SCREAMING_SNAKE_CASE_ = (256, 256)
SCREAMING_SNAKE_CASE_ = torch.ones((batch_size, num_channels) + sizes ).to(_A )
SCREAMING_SNAKE_CASE_ = torch.tensor(batch_size * [1E-4] ).to(_A )
with torch.no_grad():
SCREAMING_SNAKE_CASE_ = model(_A , _A ).sample
SCREAMING_SNAKE_CASE_ = output[0, -3:, -3:, -1].flatten().cpu()
# fmt: off
SCREAMING_SNAKE_CASE_ = torch.tensor([-4842.8691, -6499.6631, -3800.1953, -7978.2686, -1_0980.7129, -2_0028.8535, 8148.2822, 2342.2905, 567.7608] )
# fmt: on
self.assertTrue(torch_all_close(_A , _A , rtol=1E-2 ) )
def _UpperCamelCase ( self ) -> int:
SCREAMING_SNAKE_CASE_ = UNetaDModel.from_pretrained('''fusing/ncsnpp-ffhq-ve-dummy-update''' )
model.to(_A )
SCREAMING_SNAKE_CASE_ = 4
SCREAMING_SNAKE_CASE_ = 3
SCREAMING_SNAKE_CASE_ = (32, 32)
SCREAMING_SNAKE_CASE_ = torch.ones((batch_size, num_channels) + sizes ).to(_A )
SCREAMING_SNAKE_CASE_ = torch.tensor(batch_size * [1E-4] ).to(_A )
with torch.no_grad():
SCREAMING_SNAKE_CASE_ = model(_A , _A ).sample
SCREAMING_SNAKE_CASE_ = output[0, -3:, -3:, -1].flatten().cpu()
# fmt: off
SCREAMING_SNAKE_CASE_ = torch.tensor([-0.0325, -0.0900, -0.0869, -0.0332, -0.0725, -0.0270, -0.0101, 0.0227, 0.0256] )
# fmt: on
self.assertTrue(torch_all_close(_A , _A , rtol=1E-2 ) )
def _UpperCamelCase ( self ) -> Dict:
# not required for this model
pass
| 299 | 0 |
from functools import reduce
lowercase : Union[str, Any] = (
'73167176531330624919225119674426574742355349194934'
'96983520312774506326239578318016984801869478851843'
'85861560789112949495459501737958331952853208805511'
'12540698747158523863050715693290963295227443043557'
'66896648950445244523161731856403098711121722383113'
'62229893423380308135336276614282806444486645238749'
'30358907296290491560440772390713810515859307960866'
'70172427121883998797908792274921901699720888093776'
'65727333001053367881220235421809751254540594752243'
'52584907711670556013604839586446706324415722155397'
'53697817977846174064955149290862569321978468622482'
'83972241375657056057490261407972968652414535100474'
'82166370484403199890008895243450658541227588666881'
'16427171479924442928230863465674813919123162824586'
'17866458359124566529476545682848912883142607690042'
'24219022671055626321111109370544217506941658960408'
'07198403850962455444362981230987879927244284909188'
'84580156166097919133875499200524063689912560717606'
'05886116467109405077541002256983155200055935729725'
'71636269561882670428252483600823257530420752963450'
)
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : str = N) -> int:
'''simple docstring'''
return max(
# mypy cannot properly interpret reduce
int(reduce(lambda _lowerCamelCase , _lowerCamelCase: str(int(_lowerCamelCase) * int(_lowerCamelCase)) , n[i : i + 13]))
for i in range(len(_lowerCamelCase) - 12))
if __name__ == "__main__":
print(f"{solution() = }") | 151 |
from __future__ import annotations
import unittest
from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFDebertaVaForMaskedLM,
TFDebertaVaForQuestionAnswering,
TFDebertaVaForSequenceClassification,
TFDebertaVaForTokenClassification,
TFDebertaVaModel,
)
class lowerCamelCase__ :
'''simple docstring'''
def __init__( self :Optional[Any] , a :Optional[Any] , a :Dict=1_3 , a :Tuple=7 , a :List[Any]=True , a :List[str]=True , a :List[Any]=True , a :Optional[Any]=True , a :Union[str, Any]=9_9 , a :int=3_2 , a :Optional[Any]=2 , a :List[str]=4 , a :Optional[Any]=3_7 , a :Union[str, Any]="gelu" , a :Optional[int]=0.1 , a :Dict=0.1 , a :Tuple=5_1_2 , a :Union[str, Any]=1_6 , a :int=2 , a :Any=0.02 , a :Union[str, Any]=False , a :int=True , a :str="None" , a :Union[str, Any]=3 , a :str=4 , a :List[Any]=None , ) -> Tuple:
__UpperCamelCase : Tuple = parent
__UpperCamelCase : List[str] = batch_size
__UpperCamelCase : Optional[Any] = seq_length
__UpperCamelCase : Any = is_training
__UpperCamelCase : Dict = use_input_mask
__UpperCamelCase : List[str] = use_token_type_ids
__UpperCamelCase : Optional[int] = use_labels
__UpperCamelCase : Optional[Any] = vocab_size
__UpperCamelCase : Optional[Any] = hidden_size
__UpperCamelCase : Dict = num_hidden_layers
__UpperCamelCase : Any = num_attention_heads
__UpperCamelCase : str = intermediate_size
__UpperCamelCase : Union[str, Any] = hidden_act
__UpperCamelCase : Union[str, Any] = hidden_dropout_prob
__UpperCamelCase : Optional[Any] = attention_probs_dropout_prob
__UpperCamelCase : Tuple = max_position_embeddings
__UpperCamelCase : Tuple = type_vocab_size
__UpperCamelCase : Any = type_sequence_label_size
__UpperCamelCase : int = initializer_range
__UpperCamelCase : Dict = num_labels
__UpperCamelCase : Dict = num_choices
__UpperCamelCase : List[str] = relative_attention
__UpperCamelCase : Union[str, Any] = position_biased_input
__UpperCamelCase : Any = pos_att_type
__UpperCamelCase : Optional[Any] = scope
def _lowerCamelCase ( self :List[Any] ) -> List[Any]:
__UpperCamelCase : Dict = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__UpperCamelCase : Tuple = None
if self.use_input_mask:
__UpperCamelCase : Any = random_attention_mask([self.batch_size, self.seq_length] )
__UpperCamelCase : List[Any] = None
if self.use_token_type_ids:
__UpperCamelCase : str = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__UpperCamelCase : Union[str, Any] = None
__UpperCamelCase : Optional[int] = None
__UpperCamelCase : List[Any] = None
if self.use_labels:
__UpperCamelCase : int = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__UpperCamelCase : Dict = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__UpperCamelCase : List[str] = DebertaVaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , relative_attention=self.relative_attention , position_biased_input=self.position_biased_input , initializer_range=self.initializer_range , return_dict=a , )
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def _lowerCamelCase ( self :Optional[int] , a :int , a :List[Any] , a :Optional[int] , a :Union[str, Any] , a :Union[str, Any] , a :str , a :int ) -> Optional[int]:
__UpperCamelCase : List[str] = TFDebertaVaModel(config=a )
__UpperCamelCase : int = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
__UpperCamelCase : Optional[Any] = [input_ids, input_mask]
__UpperCamelCase : Optional[int] = model(a )
__UpperCamelCase : Any = model(a )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _lowerCamelCase ( self :str , a :List[Any] , a :Dict , a :Tuple , a :Union[str, Any] , a :str , a :Optional[int] , a :Optional[int] ) -> Optional[int]:
__UpperCamelCase : List[Any] = TFDebertaVaForMaskedLM(config=a )
__UpperCamelCase : Optional[int] = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
__UpperCamelCase : Tuple = model(a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def _lowerCamelCase ( self :List[Any] , a :Optional[int] , a :Optional[Any] , a :int , a :Optional[int] , a :Any , a :Dict , a :List[Any] ) -> Optional[int]:
__UpperCamelCase : Optional[int] = self.num_labels
__UpperCamelCase : int = TFDebertaVaForSequenceClassification(config=a )
__UpperCamelCase : Any = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
__UpperCamelCase : Optional[int] = model(a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _lowerCamelCase ( self :Optional[Any] , a :int , a :Dict , a :Union[str, Any] , a :Tuple , a :Tuple , a :Union[str, Any] , a :str ) -> int:
__UpperCamelCase : Tuple = self.num_labels
__UpperCamelCase : str = TFDebertaVaForTokenClassification(config=a )
__UpperCamelCase : Optional[int] = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
__UpperCamelCase : Union[str, Any] = model(a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def _lowerCamelCase ( self :List[str] , a :List[Any] , a :Union[str, Any] , a :List[str] , a :Union[str, Any] , a :Optional[Any] , a :Union[str, Any] , a :Tuple ) -> int:
__UpperCamelCase : List[Any] = TFDebertaVaForQuestionAnswering(config=a )
__UpperCamelCase : Dict = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
__UpperCamelCase : Tuple = model(a )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def _lowerCamelCase ( self :List[str] ) -> List[Any]:
__UpperCamelCase : Optional[int] = self.prepare_config_and_inputs()
(
(
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) ,
) : List[Any] = config_and_inputs
__UpperCamelCase : Optional[Any] = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_tf
class lowerCamelCase__ ( __lowercase , __lowercase , unittest.TestCase):
'''simple docstring'''
_A = (
(
TFDebertaVaModel,
TFDebertaVaForMaskedLM,
TFDebertaVaForQuestionAnswering,
TFDebertaVaForSequenceClassification,
TFDebertaVaForTokenClassification,
)
if is_tf_available()
else ()
)
_A = (
{
'feature-extraction': TFDebertaVaModel,
'fill-mask': TFDebertaVaForMaskedLM,
'question-answering': TFDebertaVaForQuestionAnswering,
'text-classification': TFDebertaVaForSequenceClassification,
'token-classification': TFDebertaVaForTokenClassification,
'zero-shot': TFDebertaVaForSequenceClassification,
}
if is_tf_available()
else {}
)
_A = False
_A = False
def _lowerCamelCase ( self :Dict ) -> str:
__UpperCamelCase : Dict = TFDebertaVaModelTester(self )
__UpperCamelCase : int = ConfigTester(self , config_class=a , hidden_size=3_7 )
def _lowerCamelCase ( self :Tuple ) -> Optional[int]:
self.config_tester.run_common_tests()
def _lowerCamelCase ( self :List[Any] ) -> List[str]:
__UpperCamelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*a )
def _lowerCamelCase ( self :Optional[int] ) -> List[Any]:
__UpperCamelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*a )
def _lowerCamelCase ( self :Optional[Any] ) -> str:
__UpperCamelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*a )
def _lowerCamelCase ( self :Optional[Any] ) -> Dict:
__UpperCamelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*a )
def _lowerCamelCase ( self :Any ) -> Optional[Any]:
__UpperCamelCase : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*a )
@slow
def _lowerCamelCase ( self :int ) -> int:
__UpperCamelCase : Tuple = TFDebertaVaModel.from_pretrained("kamalkraj/deberta-v2-xlarge" )
self.assertIsNotNone(a )
@require_tf
class lowerCamelCase__ ( unittest.TestCase):
'''simple docstring'''
@unittest.skip(reason="Model not available yet" )
def _lowerCamelCase ( self :Optional[Any] ) -> Any:
pass
@slow
def _lowerCamelCase ( self :Any ) -> Optional[int]:
__UpperCamelCase : List[Any] = TFDebertaVaModel.from_pretrained("kamalkraj/deberta-v2-xlarge" )
__UpperCamelCase : List[Any] = tf.constant([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]] )
__UpperCamelCase : Optional[Any] = tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
__UpperCamelCase : str = model(a , attention_mask=a )[0]
__UpperCamelCase : Optional[int] = tf.constant(
[[[0.2356, 0.1948, 0.0369], [-0.1063, 0.3586, -0.5152], [-0.6399, -0.0259, -0.2525]]] )
tf.debugging.assert_near(output[:, 1:4, 1:4] , a , atol=1E-4 ) | 151 | 1 |
"""simple docstring"""
def _snake_case ( lowercase__ : list , lowercase__ : int , lowercase__ : int = 0 , lowercase__ : int = 0 ) -> int:
'''simple docstring'''
lowerCAmelCase_ :List[str] = right or len(lowercase__ ) - 1
if left > right:
return -1
elif list_data[left] == key:
return left
elif list_data[right] == key:
return right
else:
return search(lowercase__ , lowercase__ , left + 1 , right - 1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 84 |
"""simple docstring"""
import unittest
from typing import Dict, List, Optional, Union
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 BridgeTowerImageProcessor
class __a (unittest.TestCase):
'''simple docstring'''
def __init__( self , _a , _a = True , _a = None , _a = 32 , _a = True , _a = 1 / 255 , _a = True , _a = True , _a = [0.48_145_466, 0.4_578_275, 0.40_821_073] , _a = [0.26_862_954, 0.26_130_258, 0.27_577_711] , _a = True , _a=7 , _a=30 , _a=400 , _a=3 , ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = parent
SCREAMING_SNAKE_CASE__ : Tuple = do_resize
SCREAMING_SNAKE_CASE__ : Union[str, Any] = size if size is not None else {"""shortest_edge""": 288}
SCREAMING_SNAKE_CASE__ : List[str] = size_divisor
SCREAMING_SNAKE_CASE__ : Tuple = do_rescale
SCREAMING_SNAKE_CASE__ : List[str] = rescale_factor
SCREAMING_SNAKE_CASE__ : Union[str, Any] = do_normalize
SCREAMING_SNAKE_CASE__ : List[str] = do_center_crop
SCREAMING_SNAKE_CASE__ : Union[str, Any] = image_mean
SCREAMING_SNAKE_CASE__ : List[str] = image_std
SCREAMING_SNAKE_CASE__ : List[str] = do_pad
SCREAMING_SNAKE_CASE__ : Union[str, Any] = batch_size
SCREAMING_SNAKE_CASE__ : int = num_channels
SCREAMING_SNAKE_CASE__ : Dict = min_resolution
SCREAMING_SNAKE_CASE__ : str = max_resolution
def _a ( self ) -> List[str]:
"""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,
"size_divisor": self.size_divisor,
}
def _a ( self , _a , _a=False ) -> int:
"""simple docstring"""
if not batched:
SCREAMING_SNAKE_CASE__ : List[Any] = self.size["""shortest_edge"""]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = image_inputs[0]
if isinstance(_a , Image.Image ):
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Tuple = image.size
else:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[Any] = image.shape[1], image.shape[2]
SCREAMING_SNAKE_CASE__ : Tuple = size / min(_a , _a )
if h < w:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[str] = size, scale * w
else:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[str] = scale * h, size
SCREAMING_SNAKE_CASE__ : Union[str, Any] = int((1_333 / 800) * size )
if max(_a , _a ) > max_size:
SCREAMING_SNAKE_CASE__ : List[str] = max_size / max(_a , _a )
SCREAMING_SNAKE_CASE__ : Any = newh * scale
SCREAMING_SNAKE_CASE__ : Any = neww * scale
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[Any] = int(newh + 0.5 ), int(neww + 0.5 )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[int] = (
newh // self.size_divisor * self.size_divisor,
neww // self.size_divisor * self.size_divisor,
)
else:
SCREAMING_SNAKE_CASE__ : Dict = []
for image in image_inputs:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[Any] = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
SCREAMING_SNAKE_CASE__ : Any = max(_a , key=lambda _a : item[0] )[0]
SCREAMING_SNAKE_CASE__ : Any = max(_a , key=lambda _a : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class __a (UpperCamelCase_ , unittest.TestCase):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Optional[int] = BridgeTowerImageProcessor if is_vision_available() else None
def _a ( self ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = BridgeTowerImageProcessingTester(self )
@property
def _a ( self ) -> Optional[int]:
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def _a ( self ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = 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""" ) )
self.assertTrue(hasattr(_a , """size_divisor""" ) )
def _a ( self ) -> List[str]:
"""simple docstring"""
pass
def _a ( self ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
SCREAMING_SNAKE_CASE__ : Optional[int] = prepare_image_inputs(self.image_processor_tester , equal_resolution=_a )
for image in image_inputs:
self.assertIsInstance(_a , Image.Image )
# Test not batched input
SCREAMING_SNAKE_CASE__ : Optional[Any] = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict = self.image_processor_tester.get_expected_values(_a )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
SCREAMING_SNAKE_CASE__ : List[str] = image_processing(_a , return_tensors="""pt""" ).pixel_values
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[str] = self.image_processor_tester.get_expected_values(_a , batched=_a )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def _a ( self ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
SCREAMING_SNAKE_CASE__ : Any = prepare_image_inputs(self.image_processor_tester , equal_resolution=_a , numpify=_a )
for image in image_inputs:
self.assertIsInstance(_a , np.ndarray )
# Test not batched input
SCREAMING_SNAKE_CASE__ : Any = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Tuple = self.image_processor_tester.get_expected_values(_a )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
SCREAMING_SNAKE_CASE__ : Tuple = image_processing(_a , return_tensors="""pt""" ).pixel_values
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Tuple = self.image_processor_tester.get_expected_values(_a , batched=_a )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def _a ( self ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
SCREAMING_SNAKE_CASE__ : int = prepare_image_inputs(self.image_processor_tester , equal_resolution=_a , torchify=_a )
for image in image_inputs:
self.assertIsInstance(_a , torch.Tensor )
# Test not batched input
SCREAMING_SNAKE_CASE__ : Union[str, Any] = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[Any] = self.image_processor_tester.get_expected_values(_a )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
SCREAMING_SNAKE_CASE__ : Dict = image_processing(_a , return_tensors="""pt""" ).pixel_values
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : int = self.image_processor_tester.get_expected_values(_a , batched=_a )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
| 132 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase: str = logging.get_logger(__name__)
_lowercase: Optional[Any] = {
'abeja/gpt-neox-japanese-2.7b': 'https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json',
}
class _lowercase ( UpperCamelCase__ ):
"""simple docstring"""
__A = """gpt_neox_japanese"""
def __init__(self , lowerCamelCase_=32000 , lowerCamelCase_=2560 , lowerCamelCase_=32 , lowerCamelCase_=32 , lowerCamelCase_=4 , lowerCamelCase_="gelu" , lowerCamelCase_=1.00 , lowerCamelCase_=10000 , lowerCamelCase_=2048 , lowerCamelCase_=0.02 , lowerCamelCase_=1E-5 , lowerCamelCase_=True , lowerCamelCase_=31996 , lowerCamelCase_=31999 , lowerCamelCase_=0.1 , lowerCamelCase_=0.0 , **lowerCamelCase_ , ):
"""simple docstring"""
super().__init__(bos_token_id=__a , eos_token_id=__a , **__a )
a = vocab_size
a = max_position_embeddings
a = hidden_size
a = num_hidden_layers
a = num_attention_heads
a = intermediate_multiple_size
a = hidden_act
a = rotary_pct
a = rotary_emb_base
a = initializer_range
a = layer_norm_eps
a = use_cache
a = attention_dropout
a = hidden_dropout
| 367 |
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple
import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaaagf
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs
from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.utils import logging
logging.set_verbosity_info()
_lowercase: Dict = logging.get_logger()
@dataclass
class _lowercase :
"""simple docstring"""
__A = 42
__A = field(default_factory=lowerCAmelCase )
__A = field(default_factory=lowerCAmelCase )
def UpperCamelCase_ (self , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
a = len(list(m.modules() ) ) == 1 or isinstance(lowerCamelCase_ , nn.Convad ) or isinstance(lowerCamelCase_ , nn.BatchNormad )
if has_not_submodules:
self.traced.append(lowerCamelCase_ )
def __call__(self , lowerCamelCase_ ):
"""simple docstring"""
for m in self.module.modules():
self.handles.append(m.register_forward_hook(self._forward_hook ) )
self.module(lowerCamelCase_ )
[x.remove() for x in self.handles]
return self
@property
def UpperCamelCase_ (self ):
"""simple docstring"""
return list(filter(lambda lowerCamelCase_ : len(list(x.state_dict().keys() ) ) > 0 , self.traced ) )
@dataclass
class _lowercase :
"""simple docstring"""
__A = 42
__A = 42
__A = 1
__A = field(default_factory=lowerCAmelCase )
__A = field(default_factory=lowerCAmelCase )
__A = True
def __call__(self , lowerCamelCase_ ):
"""simple docstring"""
a = Tracker(self.dest )(lowerCamelCase_ ).parametrized
a = Tracker(self.src )(lowerCamelCase_ ).parametrized
a = list(filter(lambda lowerCamelCase_ : type(lowerCamelCase_ ) not in self.src_skip , lowerCamelCase_ ) )
a = list(filter(lambda lowerCamelCase_ : type(lowerCamelCase_ ) not in self.dest_skip , lowerCamelCase_ ) )
if len(lowerCamelCase_ ) != len(lowerCamelCase_ ) and self.raise_if_mismatch:
raise Exception(
F'''Numbers of operations are different. Source module has {len(lowerCamelCase_ )} operations while'''
F''' destination module has {len(lowerCamelCase_ )}.''' )
for dest_m, src_m in zip(lowerCamelCase_ , lowerCamelCase_ ):
dest_m.load_state_dict(src_m.state_dict() )
if self.verbose == 1:
print(F'''Transfered from={src_m} to={dest_m}''' )
class _lowercase ( nn.Module ):
"""simple docstring"""
def __init__(self , lowerCamelCase_ ):
"""simple docstring"""
super().__init__()
a = []
# - get the stem
feature_blocks.append(("conv1", model.stem) )
# - get all the feature blocks
for k, v in model.trunk_output.named_children():
assert k.startswith("block" ), F'''Unexpected layer name {k}'''
a = len(lowerCamelCase_ ) + 1
feature_blocks.append((F'''res{block_index}''', v) )
a = nn.ModuleDict(lowerCamelCase_ )
def UpperCamelCase_ (self , lowerCamelCase_ ):
"""simple docstring"""
return get_trunk_forward_outputs(
lowerCamelCase_ , out_feat_keys=lowerCamelCase_ , feature_blocks=self._feature_blocks , )
class _lowercase ( lowerCAmelCase ):
"""simple docstring"""
def UpperCamelCase_ (self , lowerCamelCase_ ):
"""simple docstring"""
a = x.split("-" )
return x_split[0] + x_split[1] + "_" + "".join(x_split[2:] )
def __getitem__(self , lowerCamelCase_ ):
"""simple docstring"""
if x not in self:
a = self.convert_name_to_timm(lowerCamelCase_ )
a = partial(lambda: (timm.create_model(lowerCamelCase_ , pretrained=lowerCamelCase_ ).eval(), None) )
else:
a = super().__getitem__(lowerCamelCase_ )
return val
class _lowercase ( lowerCAmelCase ):
"""simple docstring"""
def __getitem__(self , lowerCamelCase_ ):
"""simple docstring"""
if "seer" in x and "in1k" not in x:
a = RegNetModel
else:
a = RegNetForImageClassification
return val
def a( A : Dict , A : List[Any] , A : List[Tuple[str, str]] ) -> Union[str, Any]:
"""simple docstring"""
for from_key, to_key in keys:
a = from_state_dict[from_key].clone()
print(f'''Copied key={from_key} to={to_key}''' )
return to_state_dict
def a( A : str , A : Callable[[], nn.Module] , A : Callable[[], nn.Module] , A : RegNetConfig , A : Path , A : bool = True , ) -> List[str]:
"""simple docstring"""
print(f'''Converting {name}...''' )
with torch.no_grad():
a , a = from_model_func()
a = our_model_func(A ).eval()
a = ModuleTransfer(src=A , dest=A , raise_if_mismatch=A )
a = torch.randn((1, 3, 224, 224) )
module_transfer(A )
if from_state_dict is not None:
a = []
# for seer - in1k finetuned we have to manually copy the head
if "seer" in name and "in1k" in name:
a = [("0.clf.0.weight", "classifier.1.weight"), ("0.clf.0.bias", "classifier.1.bias")]
a = manually_copy_vissl_head(A , our_model.state_dict() , A )
our_model.load_state_dict(A )
a = our_model(A , output_hidden_states=A )
a = (
our_outputs.logits if isinstance(A , A ) else our_outputs.last_hidden_state
)
a = from_model(A )
a = from_output[-1] if type(A ) is list else from_output
# now since I don't want to use any config files, vissl seer model doesn't actually have an head, so let's just check the last hidden state
if "seer" in name and "in1k" in name:
a = our_outputs.hidden_states[-1]
assert torch.allclose(A , A ), "The model logits don't match the original one."
if push_to_hub:
our_model.push_to_hub(
repo_path_or_name=save_directory / name , commit_message="Add model" , use_temp_dir=A , )
a = 224 if "seer" not in name else 384
# we can use the convnext one
a = AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k" , size=A )
image_processor.push_to_hub(
repo_path_or_name=save_directory / name , commit_message="Add image processor" , use_temp_dir=A , )
print(f'''Pushed {name}''' )
def a( A : Path , A : str = None , A : bool = True ) -> Dict:
"""simple docstring"""
a = "imagenet-1k-id2label.json"
a = 1000
a = (1, num_labels)
a = "huggingface/label-files"
a = num_labels
a = json.load(open(cached_download(hf_hub_url(A , A , repo_type="dataset" ) ) , "r" ) )
a = {int(A ): v for k, v in idalabel.items()}
a = idalabel
a = {v: k for k, v in idalabel.items()}
a = partial(A , num_labels=A , idalabel=A , labelaid=A )
a = {
"regnet-x-002": ImageNetPreTrainedConfig(
depths=[1, 1, 4, 7] , hidden_sizes=[24, 56, 152, 368] , groups_width=8 , layer_type="x" ),
"regnet-x-004": ImageNetPreTrainedConfig(
depths=[1, 2, 7, 12] , hidden_sizes=[32, 64, 160, 384] , groups_width=16 , layer_type="x" ),
"regnet-x-006": ImageNetPreTrainedConfig(
depths=[1, 3, 5, 7] , hidden_sizes=[48, 96, 240, 528] , groups_width=24 , layer_type="x" ),
"regnet-x-008": ImageNetPreTrainedConfig(
depths=[1, 3, 7, 5] , hidden_sizes=[64, 128, 288, 672] , groups_width=16 , layer_type="x" ),
"regnet-x-016": ImageNetPreTrainedConfig(
depths=[2, 4, 10, 2] , hidden_sizes=[72, 168, 408, 912] , groups_width=24 , layer_type="x" ),
"regnet-x-032": ImageNetPreTrainedConfig(
depths=[2, 6, 15, 2] , hidden_sizes=[96, 192, 432, 1008] , groups_width=48 , layer_type="x" ),
"regnet-x-040": ImageNetPreTrainedConfig(
depths=[2, 5, 14, 2] , hidden_sizes=[80, 240, 560, 1360] , groups_width=40 , layer_type="x" ),
"regnet-x-064": ImageNetPreTrainedConfig(
depths=[2, 4, 10, 1] , hidden_sizes=[168, 392, 784, 1624] , groups_width=56 , layer_type="x" ),
"regnet-x-080": ImageNetPreTrainedConfig(
depths=[2, 5, 15, 1] , hidden_sizes=[80, 240, 720, 1920] , groups_width=120 , layer_type="x" ),
"regnet-x-120": ImageNetPreTrainedConfig(
depths=[2, 5, 11, 1] , hidden_sizes=[224, 448, 896, 2240] , groups_width=112 , layer_type="x" ),
"regnet-x-160": ImageNetPreTrainedConfig(
depths=[2, 6, 13, 1] , hidden_sizes=[256, 512, 896, 2048] , groups_width=128 , layer_type="x" ),
"regnet-x-320": ImageNetPreTrainedConfig(
depths=[2, 7, 13, 1] , hidden_sizes=[336, 672, 1344, 2520] , groups_width=168 , layer_type="x" ),
# y variant
"regnet-y-002": ImageNetPreTrainedConfig(depths=[1, 1, 4, 7] , hidden_sizes=[24, 56, 152, 368] , groups_width=8 ),
"regnet-y-004": ImageNetPreTrainedConfig(
depths=[1, 3, 6, 6] , hidden_sizes=[48, 104, 208, 440] , groups_width=8 ),
"regnet-y-006": ImageNetPreTrainedConfig(
depths=[1, 3, 7, 4] , hidden_sizes=[48, 112, 256, 608] , groups_width=16 ),
"regnet-y-008": ImageNetPreTrainedConfig(
depths=[1, 3, 8, 2] , hidden_sizes=[64, 128, 320, 768] , groups_width=16 ),
"regnet-y-016": ImageNetPreTrainedConfig(
depths=[2, 6, 17, 2] , hidden_sizes=[48, 120, 336, 888] , groups_width=24 ),
"regnet-y-032": ImageNetPreTrainedConfig(
depths=[2, 5, 13, 1] , hidden_sizes=[72, 216, 576, 1512] , groups_width=24 ),
"regnet-y-040": ImageNetPreTrainedConfig(
depths=[2, 6, 12, 2] , hidden_sizes=[128, 192, 512, 1088] , groups_width=64 ),
"regnet-y-064": ImageNetPreTrainedConfig(
depths=[2, 7, 14, 2] , hidden_sizes=[144, 288, 576, 1296] , groups_width=72 ),
"regnet-y-080": ImageNetPreTrainedConfig(
depths=[2, 4, 10, 1] , hidden_sizes=[168, 448, 896, 2016] , groups_width=56 ),
"regnet-y-120": ImageNetPreTrainedConfig(
depths=[2, 5, 11, 1] , hidden_sizes=[224, 448, 896, 2240] , groups_width=112 ),
"regnet-y-160": ImageNetPreTrainedConfig(
depths=[2, 4, 11, 1] , hidden_sizes=[224, 448, 1232, 3024] , groups_width=112 ),
"regnet-y-320": ImageNetPreTrainedConfig(
depths=[2, 5, 12, 1] , hidden_sizes=[232, 696, 1392, 3712] , groups_width=232 ),
# models created by SEER -> https://arxiv.org/abs/2202.08360
"regnet-y-320-seer": RegNetConfig(depths=[2, 5, 12, 1] , hidden_sizes=[232, 696, 1392, 3712] , groups_width=232 ),
"regnet-y-640-seer": RegNetConfig(depths=[2, 5, 12, 1] , hidden_sizes=[328, 984, 1968, 4920] , groups_width=328 ),
"regnet-y-1280-seer": RegNetConfig(
depths=[2, 7, 17, 1] , hidden_sizes=[528, 1056, 2904, 7392] , groups_width=264 ),
"regnet-y-2560-seer": RegNetConfig(
depths=[3, 7, 16, 1] , hidden_sizes=[640, 1696, 2544, 5088] , groups_width=640 ),
"regnet-y-10b-seer": ImageNetPreTrainedConfig(
depths=[2, 7, 17, 1] , hidden_sizes=[2020, 4040, 1_1110, 2_8280] , groups_width=1010 ),
# finetuned on imagenet
"regnet-y-320-seer-in1k": ImageNetPreTrainedConfig(
depths=[2, 5, 12, 1] , hidden_sizes=[232, 696, 1392, 3712] , groups_width=232 ),
"regnet-y-640-seer-in1k": ImageNetPreTrainedConfig(
depths=[2, 5, 12, 1] , hidden_sizes=[328, 984, 1968, 4920] , groups_width=328 ),
"regnet-y-1280-seer-in1k": ImageNetPreTrainedConfig(
depths=[2, 7, 17, 1] , hidden_sizes=[528, 1056, 2904, 7392] , groups_width=264 ),
"regnet-y-2560-seer-in1k": ImageNetPreTrainedConfig(
depths=[3, 7, 16, 1] , hidden_sizes=[640, 1696, 2544, 5088] , groups_width=640 ),
"regnet-y-10b-seer-in1k": ImageNetPreTrainedConfig(
depths=[2, 7, 17, 1] , hidden_sizes=[2020, 4040, 1_1110, 2_8280] , groups_width=1010 ),
}
a = NameToOurModelFuncMap()
a = NameToFromModelFuncMap()
# add seer weights logic
def load_using_classy_vision(A : str , A : Callable[[], nn.Module] ) -> Tuple[nn.Module, Dict]:
a = torch.hub.load_state_dict_from_url(A , model_dir=str(A ) , map_location="cpu" )
a = model_func()
# check if we have a head, if yes add it
a = files["classy_state_dict"]["base_model"]["model"]
a = model_state_dict["trunk"]
model.load_state_dict(A )
return model.eval(), model_state_dict["heads"]
# pretrained
a = partial(
A , "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch" , lambda: FakeRegNetVisslWrapper(RegNetYaagf() ) , )
a = partial(
A , "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch" , lambda: FakeRegNetVisslWrapper(RegNetYaagf() ) , )
a = partial(
A , "https://dl.fbaipublicfiles.com/vissl/model_zoo/swav_ig1b_regnet128Gf_cnstant_bs32_node16_sinkhorn10_proto16k_syncBN64_warmup8k/model_final_checkpoint_phase0.torch" , lambda: FakeRegNetVisslWrapper(RegNetYaaagf() ) , )
a = partial(
A , "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch" , lambda: FakeRegNetVisslWrapper(
RegNet(RegNetParams(depth=27 , group_width=1010 , w_a=1744 , w_a=620.83 , w_m=2.52 ) ) ) , )
# IN1K finetuned
a = partial(
A , "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch" , lambda: FakeRegNetVisslWrapper(RegNetYaagf() ) , )
a = partial(
A , "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch" , lambda: FakeRegNetVisslWrapper(RegNetYaagf() ) , )
a = partial(
A , "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch" , lambda: FakeRegNetVisslWrapper(RegNetYaaagf() ) , )
a = partial(
A , "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch" , lambda: FakeRegNetVisslWrapper(
RegNet(RegNetParams(depth=27 , group_width=1010 , w_a=1744 , w_a=620.83 , w_m=2.52 ) ) ) , )
if model_name:
convert_weight_and_push(
A , names_to_from_model_map[model_name] , names_to_ours_model_map[model_name] , names_to_config[model_name] , A , A , )
else:
for model_name, config in names_to_config.items():
convert_weight_and_push(
A , names_to_from_model_map[model_name] , names_to_ours_model_map[model_name] , A , A , A , )
return config, expected_shape
if __name__ == "__main__":
_lowercase: Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default=None,
type=str,
help=(
"The name of the model you wish to convert, it must be one of the supported regnet* architecture,"
" currently: regnetx-*, regnety-*. If `None`, all of them will the converted."
),
)
parser.add_argument(
"--pytorch_dump_folder_path",
default=None,
type=Path,
required=True,
help="Path to the output PyTorch model directory.",
)
parser.add_argument(
"--push_to_hub",
default=True,
type=bool,
required=False,
help="If True, push model and image processor to the hub.",
)
_lowercase: Optional[int] = parser.parse_args()
_lowercase: Path = args.pytorch_dump_folder_path
pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 71 | 0 |
def _A ( SCREAMING_SNAKE_CASE : list ):
"""simple docstring"""
if any(not isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) or x < 0 for x in sequence ):
raise TypeError("Sequence must be list of non-negative integers" )
for _ in range(len(SCREAMING_SNAKE_CASE ) ):
for i, (rod_upper, rod_lower) in enumerate(zip(SCREAMING_SNAKE_CASE , sequence[1:] ) ):
if rod_upper > rod_lower:
sequence[i] -= rod_upper - rod_lower
sequence[i + 1] += rod_upper - rod_lower
return sequence
if __name__ == "__main__":
assert bead_sort([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
assert bead_sort([7, 9, 4, 3, 5]) == [3, 4, 5, 7, 9]
| 95 |
"""simple docstring"""
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class UpperCamelCase_ :
def __init__( self : str ) -> Dict:
UpperCAmelCase_ : List[Any] = ""
UpperCAmelCase_ : int = ""
UpperCAmelCase_ : Dict = []
UpperCAmelCase_ : int = 0
UpperCAmelCase_ : List[Any] = 256
UpperCAmelCase_ : Dict = 0
UpperCAmelCase_ : List[Any] = 0
UpperCAmelCase_ : str = 0
UpperCAmelCase_ : List[str] = 0
def _SCREAMING_SNAKE_CASE ( self : List[str] , lowerCAmelCase_ : Dict ) -> Optional[Any]:
UpperCAmelCase_ : Dict = cva.imread(lowerCAmelCase_ , 0 )
UpperCAmelCase_ : Union[str, Any] = copy.deepcopy(self.img )
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ : List[Any] = plt.hist(self.img.ravel() , 256 , [0, 256] , label="x" )
UpperCAmelCase_ : List[Any] = np.sum(lowerCAmelCase_ )
for i in range(len(lowerCAmelCase_ ) ):
UpperCAmelCase_ : List[Any] = x[i] / self.k
self.sk += prk
UpperCAmelCase_ : Optional[Any] = (self.L - 1) * self.sk
if self.rem != 0:
UpperCAmelCase_ : Any = int(last % last )
UpperCAmelCase_ : List[str] = int(last + 1 if self.rem >= 0.5 else last )
self.last_list.append(lowerCAmelCase_ )
UpperCAmelCase_ : Optional[Any] = int(np.ma.count(self.img ) / self.img[1].size )
UpperCAmelCase_ : Dict = self.img[1].size
for i in range(self.number_of_cols ):
for j in range(self.number_of_rows ):
UpperCAmelCase_ : Any = self.img[j][i]
if num != self.last_list[num]:
UpperCAmelCase_ : Tuple = self.last_list[num]
cva.imwrite("output_data/output.jpg" , self.img )
def _SCREAMING_SNAKE_CASE ( self : List[Any] ) -> Union[str, Any]:
plt.hist(self.img.ravel() , 256 , [0, 256] )
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ) -> str:
cva.imshow("Output-Image" , self.img )
cva.imshow("Input-Image" , self.original_image )
cva.waitKey(5_000 )
cva.destroyAllWindows()
if __name__ == "__main__":
lowerCamelCase_ = os.path.join(os.path.basename(__file__), '''image_data/input.jpg''')
lowerCamelCase_ = ConstantStretch()
stretcher.stretch(file_path)
stretcher.plot_histogram()
stretcher.show_image()
| 268 | 0 |
import inspect
import math
import tempfile
import unittest
import numpy as np
from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import ViTMAEForPreTraining, ViTMAEModel
from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class a_ :
'''simple docstring'''
def __init__( self : Any , lowercase__ : Tuple , lowercase__ : Tuple=13 , lowercase__ : Optional[int]=30 , lowercase__ : List[str]=2 , lowercase__ : Dict=3 , lowercase__ : Optional[Any]=True , lowercase__ : List[str]=True , lowercase__ : str=32 , lowercase__ : Union[str, Any]=5 , lowercase__ : Optional[int]=4 , lowercase__ : Union[str, Any]=37 , lowercase__ : Tuple="gelu" , lowercase__ : List[str]=0.1 , lowercase__ : Optional[int]=0.1 , lowercase__ : str=10 , lowercase__ : int=0.02 , lowercase__ : Union[str, Any]=3 , lowercase__ : int=0.6 , lowercase__ : Optional[Any]=None , ):
'''simple docstring'''
lowerCAmelCase__ = parent
lowerCAmelCase__ = batch_size
lowerCAmelCase__ = image_size
lowerCAmelCase__ = patch_size
lowerCAmelCase__ = num_channels
lowerCAmelCase__ = is_training
lowerCAmelCase__ = use_labels
lowerCAmelCase__ = hidden_size
lowerCAmelCase__ = num_hidden_layers
lowerCAmelCase__ = num_attention_heads
lowerCAmelCase__ = intermediate_size
lowerCAmelCase__ = hidden_act
lowerCAmelCase__ = hidden_dropout_prob
lowerCAmelCase__ = attention_probs_dropout_prob
lowerCAmelCase__ = type_sequence_label_size
lowerCAmelCase__ = initializer_range
lowerCAmelCase__ = mask_ratio
lowerCAmelCase__ = scope
# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
# (we add 1 for the [CLS] token)
lowerCAmelCase__ = (image_size // patch_size) ** 2
lowerCAmelCase__ = int(math.ceil((1 - mask_ratio) * (num_patches + 1)))
def __snake_case ( self : List[Any]):
'''simple docstring'''
lowerCAmelCase__ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
lowerCAmelCase__ = None
if self.use_labels:
lowerCAmelCase__ = ids_tensor([self.batch_size] , self.type_sequence_label_size)
lowerCAmelCase__ = self.get_config()
return config, pixel_values, labels
def __snake_case ( self : str):
'''simple docstring'''
return ViTMAEConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=lowercase__ , initializer_range=self.initializer_range , mask_ratio=self.mask_ratio , )
def __snake_case ( self : List[Any] , lowercase__ : List[str] , lowercase__ : Any , lowercase__ : Union[str, Any]):
'''simple docstring'''
lowerCAmelCase__ = ViTMAEModel(config=lowercase__)
model.to(lowercase__)
model.eval()
lowerCAmelCase__ = model(lowercase__)
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size))
def __snake_case ( self : Tuple , lowercase__ : List[Any] , lowercase__ : Optional[int] , lowercase__ : Any):
'''simple docstring'''
lowerCAmelCase__ = ViTMAEForPreTraining(lowercase__)
model.to(lowercase__)
model.eval()
lowerCAmelCase__ = model(lowercase__)
lowerCAmelCase__ = (self.image_size // self.patch_size) ** 2
lowerCAmelCase__ = self.patch_size**2 * self.num_channels
self.parent.assertEqual(result.logits.shape , (self.batch_size, num_patches, expected_num_channels))
# test greyscale images
lowerCAmelCase__ = 1
lowerCAmelCase__ = ViTMAEForPreTraining(lowercase__)
model.to(lowercase__)
model.eval()
lowerCAmelCase__ = floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
lowerCAmelCase__ = model(lowercase__)
lowerCAmelCase__ = self.patch_size**2
self.parent.assertEqual(result.logits.shape , (self.batch_size, num_patches, expected_num_channels))
def __snake_case ( self : Dict):
'''simple docstring'''
lowerCAmelCase__ = self.prepare_config_and_inputs()
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = config_and_inputs
lowerCAmelCase__ = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class a_ ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
UpperCAmelCase_ = (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
UpperCAmelCase_ = {'feature-extraction': ViTMAEModel} if is_torch_available() else {}
UpperCAmelCase_ = False
UpperCAmelCase_ = False
UpperCAmelCase_ = False
UpperCAmelCase_ = False
def __snake_case ( self : str):
'''simple docstring'''
lowerCAmelCase__ = ViTMAEModelTester(self)
lowerCAmelCase__ = ConfigTester(self , config_class=lowercase__ , has_text_modality=lowercase__ , hidden_size=37)
def __snake_case ( self : Union[str, Any]):
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason='ViTMAE does not use inputs_embeds')
def __snake_case ( self : Optional[Any]):
'''simple docstring'''
pass
def __snake_case ( self : Optional[Any]):
'''simple docstring'''
lowerCAmelCase__ , lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase__ = model_class(lowercase__)
self.assertIsInstance(model.get_input_embeddings() , (nn.Module))
lowerCAmelCase__ = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(lowercase__ , nn.Linear))
def __snake_case ( self : List[Any]):
'''simple docstring'''
lowerCAmelCase__ , lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase__ = model_class(lowercase__)
lowerCAmelCase__ = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowerCAmelCase__ = [*signature.parameters.keys()]
lowerCAmelCase__ = ['pixel_values']
self.assertListEqual(arg_names[:1] , lowercase__)
def __snake_case ( self : Dict):
'''simple docstring'''
lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowercase__)
def __snake_case ( self : List[Any]):
'''simple docstring'''
lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*lowercase__)
def __snake_case ( self : Optional[int] , lowercase__ : Optional[int] , lowercase__ : Union[str, Any] , lowercase__ : Dict):
'''simple docstring'''
np.random.seed(2)
lowerCAmelCase__ = int((pt_model.config.image_size // pt_model.config.patch_size) ** 2)
lowerCAmelCase__ = np.random.uniform(size=(self.model_tester.batch_size, num_patches))
lowerCAmelCase__ = torch.from_numpy(lowercase__)
# Add `noise` argument.
# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
lowerCAmelCase__ = pt_noise
super().check_pt_tf_models(lowercase__ , lowercase__ , lowercase__)
def __snake_case ( self : List[Any]):
'''simple docstring'''
lowerCAmelCase__ , lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase__ = model_class(lowercase__)
model.to(lowercase__)
model.eval()
# make random mask reproducible
torch.manual_seed(2)
with torch.no_grad():
lowerCAmelCase__ = model(**self._prepare_for_class(lowercase__ , lowercase__))
lowerCAmelCase__ = outputs[0].cpu().numpy()
lowerCAmelCase__ = 0
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(lowercase__)
lowerCAmelCase__ = model_class.from_pretrained(lowercase__)
model.to(lowercase__)
# make random mask reproducible
torch.manual_seed(2)
with torch.no_grad():
lowerCAmelCase__ = model(**self._prepare_for_class(lowercase__ , lowercase__))
# Make sure we don't have nans
lowerCAmelCase__ = after_outputs[0].cpu().numpy()
lowerCAmelCase__ = 0
lowerCAmelCase__ = np.amax(np.abs(out_a - out_a))
self.assertLessEqual(lowercase__ , 1e-5)
@unittest.skip(
reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n to get deterministic results.')
def __snake_case ( self : List[Any]):
'''simple docstring'''
pass
@unittest.skip(
reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n to get deterministic results.')
def __snake_case ( self : Tuple):
'''simple docstring'''
pass
@unittest.skip(
reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n to get deterministic results.')
def __snake_case ( self : Union[str, Any]):
'''simple docstring'''
pass
@unittest.skip(reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load')
def __snake_case ( self : int):
'''simple docstring'''
pass
@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.')
def __snake_case ( self : Optional[Any]):
'''simple docstring'''
pass
@slow
def __snake_case ( self : Tuple):
'''simple docstring'''
for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCAmelCase__ = ViTMAEModel.from_pretrained(lowercase__)
self.assertIsNotNone(lowercase__)
def __lowerCamelCase ( ):
lowerCAmelCase__ = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_torch
@require_vision
class a_ ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def __snake_case ( self : int):
'''simple docstring'''
return ViTImageProcessor.from_pretrained('facebook/vit-mae-base') if is_vision_available() else None
@slow
def __snake_case ( self : int):
'''simple docstring'''
np.random.seed(2)
lowerCAmelCase__ = ViTMAEForPreTraining.from_pretrained('facebook/vit-mae-base').to(lowercase__)
lowerCAmelCase__ = self.default_image_processor
lowerCAmelCase__ = prepare_img()
lowerCAmelCase__ = image_processor(images=lowercase__ , return_tensors='pt').to(lowercase__)
# prepare a noise vector that will be also used for testing the TF model
# (this way we can ensure that the PT and TF models operate on the same inputs)
lowerCAmelCase__ = ViTMAEConfig()
lowerCAmelCase__ = int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2)
lowerCAmelCase__ = np.random.uniform(size=(1, num_patches))
# forward pass
with torch.no_grad():
lowerCAmelCase__ = model(**lowercase__ , noise=torch.from_numpy(lowercase__).to(device=lowercase__))
# verify the logits
lowerCAmelCase__ = torch.Size((1, 196, 768))
self.assertEqual(outputs.logits.shape , lowercase__)
lowerCAmelCase__ = torch.tensor(
[[-0.0_548, -1.7_023, -0.9_325], [0.3_721, -0.5_670, -0.2_233], [0.8_235, -1.3_878, -0.3_524]])
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3] , expected_slice.to(lowercase__) , atol=1e-4))
| 355 | from typing import Optional
import pyspark
from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class a_ ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self : str , lowercase__ : pyspark.sql.DataFrame , lowercase__ : Optional[NamedSplit] = None , lowercase__ : Optional[Features] = None , lowercase__ : bool = True , lowercase__ : str = None , lowercase__ : bool = False , lowercase__ : str = None , lowercase__ : bool = True , lowercase__ : str = "arrow" , **lowercase__ : Any , ):
'''simple docstring'''
super().__init__(
split=lowercase__ , features=lowercase__ , cache_dir=lowercase__ , keep_in_memory=lowercase__ , streaming=lowercase__ , **lowercase__ , )
lowerCAmelCase__ = load_from_cache_file
lowerCAmelCase__ = file_format
lowerCAmelCase__ = Spark(
df=lowercase__ , features=lowercase__ , cache_dir=lowercase__ , working_dir=lowercase__ , **lowercase__ , )
def __snake_case ( self : Tuple):
'''simple docstring'''
if self.streaming:
return self.builder.as_streaming_dataset(split=self.split)
lowerCAmelCase__ = None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
self.builder.download_and_prepare(
download_mode=lowercase__ , file_format=self._file_format , )
return self.builder.as_dataset(split=self.split)
| 119 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class lowerCAmelCase_ :
__lowerCamelCase : int
__lowerCamelCase : TreeNode | None = None
__lowerCamelCase : TreeNode | None = None
_SCREAMING_SNAKE_CASE = namedtuple("CoinsDistribResult", "moves excess")
def __a(SCREAMING_SNAKE_CASE_ : TreeNode | None ):
'''simple docstring'''
if root is None:
return 0
# Validation
def count_nodes(SCREAMING_SNAKE_CASE_ : TreeNode | None ) -> int:
if node is None:
return 0
return count_nodes(node.left ) + count_nodes(node.right ) + 1
def count_coins(SCREAMING_SNAKE_CASE_ : TreeNode | None ) -> int:
if node is None:
return 0
return count_coins(node.left ) + count_coins(node.right ) + node.data
if count_nodes(SCREAMING_SNAKE_CASE_ ) != count_coins(SCREAMING_SNAKE_CASE_ ):
raise ValueError("The nodes number should be same as the number of coins" )
# Main calculation
def get_distrib(SCREAMING_SNAKE_CASE_ : TreeNode | None ) -> CoinsDistribResult:
if node is None:
return CoinsDistribResult(0 , 1 )
_lowerCAmelCase , _lowerCAmelCase = get_distrib(node.left )
_lowerCAmelCase , _lowerCAmelCase = get_distrib(node.right )
_lowerCAmelCase = 1 - left_distrib_excess
_lowerCAmelCase = 1 - right_distrib_excess
_lowerCAmelCase = (
left_distrib_moves
+ right_distrib_moves
+ abs(SCREAMING_SNAKE_CASE_ )
+ abs(SCREAMING_SNAKE_CASE_ )
)
_lowerCAmelCase = node.data - coins_to_left - coins_to_right
return CoinsDistribResult(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
return get_distrib(SCREAMING_SNAKE_CASE_ )[0]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 158 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin
@dataclass
class lowerCAmelCase_ ( __magic_name__ ):
__lowerCamelCase : torch.FloatTensor
class lowerCAmelCase_ ( __magic_name__ ,__magic_name__ ):
@register_to_config
def __init__( self , _lowerCAmelCase = 16 , _lowerCAmelCase = 88 , _lowerCAmelCase = None , _lowerCAmelCase = None , _lowerCAmelCase = 1 , _lowerCAmelCase = 0.0 , _lowerCAmelCase = 32 , _lowerCAmelCase = None , _lowerCAmelCase = False , _lowerCAmelCase = None , _lowerCAmelCase = "geglu" , _lowerCAmelCase = True , _lowerCAmelCase = True , ) -> Union[str, Any]:
super().__init__()
_lowerCAmelCase = num_attention_heads
_lowerCAmelCase = attention_head_dim
_lowerCAmelCase = num_attention_heads * attention_head_dim
_lowerCAmelCase = in_channels
_lowerCAmelCase = torch.nn.GroupNorm(num_groups=_lowerCAmelCase , num_channels=_lowerCAmelCase , eps=1E-6 , affine=_lowerCAmelCase )
_lowerCAmelCase = nn.Linear(_lowerCAmelCase , _lowerCAmelCase )
# 3. Define transformers blocks
_lowerCAmelCase = nn.ModuleList(
[
BasicTransformerBlock(
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , dropout=_lowerCAmelCase , cross_attention_dim=_lowerCAmelCase , activation_fn=_lowerCAmelCase , attention_bias=_lowerCAmelCase , double_self_attention=_lowerCAmelCase , norm_elementwise_affine=_lowerCAmelCase , )
for d in range(_lowerCAmelCase )
] )
_lowerCAmelCase = nn.Linear(_lowerCAmelCase , _lowerCAmelCase )
def _snake_case ( self , _lowerCAmelCase , _lowerCAmelCase=None , _lowerCAmelCase=None , _lowerCAmelCase=None , _lowerCAmelCase=1 , _lowerCAmelCase=None , _lowerCAmelCase = True , ) -> Union[str, Any]:
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = hidden_states.shape
_lowerCAmelCase = batch_frames // num_frames
_lowerCAmelCase = hidden_states
_lowerCAmelCase = hidden_states[None, :].reshape(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
_lowerCAmelCase = hidden_states.permute(0 , 2 , 1 , 3 , 4 )
_lowerCAmelCase = self.norm(_lowerCAmelCase )
_lowerCAmelCase = hidden_states.permute(0 , 3 , 4 , 2 , 1 ).reshape(batch_size * height * width , _lowerCAmelCase , _lowerCAmelCase )
_lowerCAmelCase = self.proj_in(_lowerCAmelCase )
# 2. Blocks
for block in self.transformer_blocks:
_lowerCAmelCase = block(
_lowerCAmelCase , encoder_hidden_states=_lowerCAmelCase , timestep=_lowerCAmelCase , cross_attention_kwargs=_lowerCAmelCase , class_labels=_lowerCAmelCase , )
# 3. Output
_lowerCAmelCase = self.proj_out(_lowerCAmelCase )
_lowerCAmelCase = (
hidden_states[None, None, :]
.reshape(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
.permute(0 , 3 , 4 , 1 , 2 )
.contiguous()
)
_lowerCAmelCase = hidden_states.reshape(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
_lowerCAmelCase = hidden_states + residual
if not return_dict:
return (output,)
return TransformerTemporalModelOutput(sample=_lowerCAmelCase )
| 158 | 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__ = {
"facebook/levit-128S": "https://huggingface.co/facebook/levit-128S/resolve/main/config.json",
# See all LeViT models at https://huggingface.co/models?filter=levit
}
class lowercase_ ( lowercase ):
'''simple docstring'''
__snake_case = '''levit'''
def __init__( self : str , __UpperCAmelCase : Dict=224 , __UpperCAmelCase : int=3 , __UpperCAmelCase : Dict=3 , __UpperCAmelCase : Optional[Any]=2 , __UpperCAmelCase : Dict=1 , __UpperCAmelCase : Optional[Any]=16 , __UpperCAmelCase : Optional[int]=[128, 256, 384] , __UpperCAmelCase : Optional[int]=[4, 8, 12] , __UpperCAmelCase : Dict=[4, 4, 4] , __UpperCAmelCase : Dict=[16, 16, 16] , __UpperCAmelCase : Optional[int]=0 , __UpperCAmelCase : Optional[int]=[2, 2, 2] , __UpperCAmelCase : Optional[int]=[2, 2, 2] , __UpperCAmelCase : Dict=0.02 , **__UpperCAmelCase : Dict , ) ->int:
"""simple docstring"""
super().__init__(**__UpperCAmelCase )
a = image_size
a = num_channels
a = kernel_size
a = stride
a = padding
a = hidden_sizes
a = num_attention_heads
a = depths
a = key_dim
a = drop_path_rate
a = patch_size
a = attention_ratio
a = mlp_ratio
a = initializer_range
a = [
['''Subsample''', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
['''Subsample''', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
]
class lowercase_ ( lowercase ):
'''simple docstring'''
__snake_case = version.parse('''1.11''' )
@property
def __lowerCAmelCase ( self : str ) ->Mapping[str, Mapping[int, str]]:
"""simple docstring"""
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def __lowerCAmelCase ( self : int ) ->float:
"""simple docstring"""
return 1e-4
| 370 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import (
AutoProcessor,
BertTokenizerFast,
BlipImageProcessor,
GPTaTokenizer,
InstructBlipProcessor,
PreTrainedTokenizerFast,
)
@require_vision
class lowercase_ ( unittest.TestCase ):
'''simple docstring'''
def __lowerCAmelCase ( self : Optional[int] ) ->Tuple:
"""simple docstring"""
a = tempfile.mkdtemp()
a = BlipImageProcessor()
a = GPTaTokenizer.from_pretrained('''hf-internal-testing/tiny-random-GPT2Model''' )
a = BertTokenizerFast.from_pretrained('''hf-internal-testing/tiny-random-bert''' )
a = InstructBlipProcessor(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
processor.save_pretrained(self.tmpdirname )
def __lowerCAmelCase ( self : Optional[Any] , **__UpperCAmelCase : Tuple ) ->List[str]:
"""simple docstring"""
return AutoProcessor.from_pretrained(self.tmpdirname , **__UpperCAmelCase ).tokenizer
def __lowerCAmelCase ( self : int , **__UpperCAmelCase : str ) ->List[str]:
"""simple docstring"""
return AutoProcessor.from_pretrained(self.tmpdirname , **__UpperCAmelCase ).image_processor
def __lowerCAmelCase ( self : Optional[Any] , **__UpperCAmelCase : Any ) ->Optional[Any]:
"""simple docstring"""
return AutoProcessor.from_pretrained(self.tmpdirname , **__UpperCAmelCase ).qformer_tokenizer
def __lowerCAmelCase ( self : str ) ->Tuple:
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def __lowerCAmelCase ( self : Optional[int] ) ->str:
"""simple docstring"""
a = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
a = [Image.fromarray(np.moveaxis(__UpperCAmelCase , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def __lowerCAmelCase ( self : Optional[Any] ) ->List[str]:
"""simple docstring"""
a = InstructBlipProcessor(
tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() , qformer_tokenizer=self.get_qformer_tokenizer() , )
processor.save_pretrained(self.tmpdirname )
a = self.get_tokenizer(bos_token='''(BOS)''' , eos_token='''(EOS)''' )
a = self.get_image_processor(do_normalize=__UpperCAmelCase , padding_value=1.0 )
a = InstructBlipProcessor.from_pretrained(
self.tmpdirname , bos_token='''(BOS)''' , eos_token='''(EOS)''' , do_normalize=__UpperCAmelCase , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , __UpperCAmelCase )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , __UpperCAmelCase )
self.assertIsInstance(processor.qformer_tokenizer , __UpperCAmelCase )
def __lowerCAmelCase ( self : Optional[Any] ) ->Any:
"""simple docstring"""
a = self.get_image_processor()
a = self.get_tokenizer()
a = self.get_qformer_tokenizer()
a = InstructBlipProcessor(
tokenizer=__UpperCAmelCase , image_processor=__UpperCAmelCase , qformer_tokenizer=__UpperCAmelCase )
a = self.prepare_image_inputs()
a = image_processor(__UpperCAmelCase , return_tensors='''np''' )
a = processor(images=__UpperCAmelCase , return_tensors='''np''' )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1e-2 )
def __lowerCAmelCase ( self : List[str] ) ->List[Any]:
"""simple docstring"""
a = self.get_image_processor()
a = self.get_tokenizer()
a = self.get_qformer_tokenizer()
a = InstructBlipProcessor(
tokenizer=__UpperCAmelCase , image_processor=__UpperCAmelCase , qformer_tokenizer=__UpperCAmelCase )
a = '''lower newer'''
a = processor(text=__UpperCAmelCase )
a = tokenizer(__UpperCAmelCase , return_token_type_ids=__UpperCAmelCase )
a = qformer_tokenizer(__UpperCAmelCase , return_token_type_ids=__UpperCAmelCase )
for key in encoded_tokens.keys():
self.assertListEqual(encoded_tokens[key] , encoded_processor[key] )
for key in encoded_tokens_qformer.keys():
self.assertListEqual(encoded_tokens_qformer[key] , encoded_processor['''qformer_''' + key] )
def __lowerCAmelCase ( self : Dict ) ->Optional[Any]:
"""simple docstring"""
a = self.get_image_processor()
a = self.get_tokenizer()
a = self.get_qformer_tokenizer()
a = InstructBlipProcessor(
tokenizer=__UpperCAmelCase , image_processor=__UpperCAmelCase , qformer_tokenizer=__UpperCAmelCase )
a = '''lower newer'''
a = self.prepare_image_inputs()
a = processor(text=__UpperCAmelCase , images=__UpperCAmelCase )
self.assertListEqual(
list(inputs.keys() ) , ['''input_ids''', '''attention_mask''', '''qformer_input_ids''', '''qformer_attention_mask''', '''pixel_values'''] , )
# test if it raises when no input is passed
with pytest.raises(__UpperCAmelCase ):
processor()
def __lowerCAmelCase ( self : Dict ) ->List[Any]:
"""simple docstring"""
a = self.get_image_processor()
a = self.get_tokenizer()
a = self.get_qformer_tokenizer()
a = InstructBlipProcessor(
tokenizer=__UpperCAmelCase , image_processor=__UpperCAmelCase , qformer_tokenizer=__UpperCAmelCase )
a = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
a = processor.batch_decode(__UpperCAmelCase )
a = tokenizer.batch_decode(__UpperCAmelCase )
self.assertListEqual(__UpperCAmelCase , __UpperCAmelCase )
def __lowerCAmelCase ( self : Union[str, Any] ) ->str:
"""simple docstring"""
a = self.get_image_processor()
a = self.get_tokenizer()
a = self.get_qformer_tokenizer()
a = InstructBlipProcessor(
tokenizer=__UpperCAmelCase , image_processor=__UpperCAmelCase , qformer_tokenizer=__UpperCAmelCase )
a = '''lower newer'''
a = self.prepare_image_inputs()
a = processor(text=__UpperCAmelCase , images=__UpperCAmelCase )
self.assertListEqual(
list(inputs.keys() ) , ['''input_ids''', '''attention_mask''', '''qformer_input_ids''', '''qformer_attention_mask''', '''pixel_values'''] , )
| 26 | 0 |
'''simple docstring'''
import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def __lowerCamelCase ( A__ = 8 ) -> str:
"""simple docstring"""
UpperCamelCase = ascii_letters + digits + punctuation
return "".join(secrets.choice(A__ ) for _ in range(A__ ) )
def __lowerCamelCase ( A__ , A__ ) -> str:
"""simple docstring"""
# Password Generator = full boot with random_number, random_letters, and
# random_character FUNCTIONS
# Put your code here...
i -= len(A__ )
UpperCamelCase = i // 3
UpperCamelCase = i % 3
# chars = chars_incl + random_letters(ascii_letters, i / 3 + remainder) +
# random_number(digits, i / 3) + random_characters(punctuation, i / 3)
UpperCamelCase = (
chars_incl
+ random(A__ , quotient + remainder )
+ random(A__ , A__ )
+ random(A__ , A__ )
)
UpperCamelCase = list(A__ )
shuffle(A__ )
return "".join(A__ )
# random is a generalised function for letters, characters and numbers
def __lowerCamelCase ( A__ , A__ ) -> str:
"""simple docstring"""
return "".join(secrets.choice(A__ ) for _ in range(A__ ) )
def __lowerCamelCase ( A__ , A__ ) -> Dict:
"""simple docstring"""
pass # Put your code here...
def __lowerCamelCase ( A__ , A__ ) -> Any:
"""simple docstring"""
pass # Put your code here...
def __lowerCamelCase ( A__ , A__ ) -> Union[str, Any]:
"""simple docstring"""
pass # Put your code here...
def __lowerCamelCase ( A__ , A__ = 8 ) -> bool:
"""simple docstring"""
if len(A__ ) < min_length:
# Your Password must be at least 8 characters long
return False
UpperCamelCase = any(char in ascii_uppercase for char in password )
UpperCamelCase = any(char in ascii_lowercase for char in password )
UpperCamelCase = any(char in digits for char in password )
UpperCamelCase = any(char in punctuation for char in password )
return upper and lower and num and spec_char
# Passwords should contain UPPERCASE, lowerase
# numbers, and special characters
def __lowerCamelCase ( ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase = int(input('Please indicate the max length of your password: ' ).strip() )
UpperCamelCase = input(
'Please indicate the characters that must be in your password: ' ).strip()
print('Password generated:' , password_generator(A__ ) )
print(
'Alternative Password generated:' , alternative_password_generator(A__ , A__ ) , )
print('[If you are thinking of using this passsword, You better save it.]' )
if __name__ == "__main__":
main()
| 28 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor
_lowerCamelCase : str = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE ( _a ):
"""simple docstring"""
def __init__( self : Dict , *UpperCamelCase__ : List[Any] , **UpperCamelCase__ : List[Any] ):
"""simple docstring"""
warnings.warn(
'The class ChineseCLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
' Please use ChineseCLIPImageProcessor instead.' , UpperCamelCase__ , )
super().__init__(*UpperCamelCase__ , **UpperCamelCase__ )
| 28 | 1 |
import multiprocessing
import os
from typing import BinaryIO, Optional, Union
import fsspec
from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class _snake_case ( A__ ):
def __init__( self , a , a = None , a = None , a = None , a = False , a = False , a = None , a = None , **a , ) -> Any:
super().__init__(
a , split=a , features=a , cache_dir=a , keep_in_memory=a , streaming=a , num_proc=a , **a , )
SCREAMING_SNAKE_CASE = field
SCREAMING_SNAKE_CASE = path_or_paths if isinstance(a , a) else {self.split: path_or_paths}
SCREAMING_SNAKE_CASE = Json(
cache_dir=a , data_files=a , features=a , field=a , **a , )
def SCREAMING_SNAKE_CASE__ ( self) -> Tuple:
# Build iterable dataset
if self.streaming:
SCREAMING_SNAKE_CASE = self.builder.as_streaming_dataset(split=self.split)
# Build regular (map-style) dataset
else:
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = None
self.builder.download_and_prepare(
download_config=a , download_mode=a , verification_mode=a , base_path=a , num_proc=self.num_proc , )
SCREAMING_SNAKE_CASE = self.builder.as_dataset(
split=self.split , verification_mode=a , in_memory=self.keep_in_memory)
return dataset
class _snake_case :
def __init__( self , a , a , a = None , a = None , **a , ) -> int:
if num_proc is not None and num_proc <= 0:
raise ValueError(f'''num_proc {num_proc} must be an integer > 0.''')
SCREAMING_SNAKE_CASE = dataset
SCREAMING_SNAKE_CASE = path_or_buf
SCREAMING_SNAKE_CASE = batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
SCREAMING_SNAKE_CASE = num_proc
SCREAMING_SNAKE_CASE = 'utf-8'
SCREAMING_SNAKE_CASE = to_json_kwargs
def SCREAMING_SNAKE_CASE__ ( self) -> int:
SCREAMING_SNAKE_CASE = self.to_json_kwargs.pop('path_or_buf' , a)
SCREAMING_SNAKE_CASE = self.to_json_kwargs.pop('orient' , 'records')
SCREAMING_SNAKE_CASE = self.to_json_kwargs.pop('lines' , True if orient == 'records' else False)
SCREAMING_SNAKE_CASE = self.to_json_kwargs.pop('index' , False if orient in ['split', 'table'] else True)
SCREAMING_SNAKE_CASE = self.to_json_kwargs.pop('compression' , a)
if compression not in [None, "infer", "gzip", "bz2", "xz"]:
raise NotImplementedError(f'''`datasets` currently does not support {compression} compression''')
if isinstance(self.path_or_buf , (str, bytes, os.PathLike)):
with fsspec.open(self.path_or_buf , 'wb' , compression=a) as buffer:
SCREAMING_SNAKE_CASE = self._write(file_obj=a , orient=a , lines=a , index=a , **self.to_json_kwargs)
else:
if compression:
raise NotImplementedError(
f'''The compression parameter is not supported when writing to a buffer, but compression={compression}'''
' was passed. Please provide a local path instead.')
SCREAMING_SNAKE_CASE = self._write(
file_obj=self.path_or_buf , orient=a , lines=a , index=a , **self.to_json_kwargs)
return written
def SCREAMING_SNAKE_CASE__ ( self , a) -> Optional[int]:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = args
SCREAMING_SNAKE_CASE = query_table(
table=self.dataset.data , key=slice(a , offset + self.batch_size) , indices=self.dataset._indices , )
SCREAMING_SNAKE_CASE = batch.to_pandas().to_json(
path_or_buf=a , orient=a , lines=a , index=a , **a)
if not json_str.endswith('\n'):
json_str += "\n"
return json_str.encode(self.encoding)
def SCREAMING_SNAKE_CASE__ ( self , a , a , a , a , **a , ) -> int:
SCREAMING_SNAKE_CASE = 0
if self.num_proc is None or self.num_proc == 1:
for offset in logging.tqdm(
range(0 , len(self.dataset) , self.batch_size) , unit='ba' , disable=not logging.is_progress_bar_enabled() , desc='Creating json from Arrow format' , ):
SCREAMING_SNAKE_CASE = self._batch_json((offset, orient, lines, index, to_json_kwargs))
written += file_obj.write(a)
else:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = len(self.dataset), self.batch_size
with multiprocessing.Pool(self.num_proc) as pool:
for json_str in logging.tqdm(
pool.imap(
self._batch_json , [(offset, orient, lines, index, to_json_kwargs) for offset in range(0 , a , a)] , ) , total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size , unit='ba' , disable=not logging.is_progress_bar_enabled() , desc='Creating json from Arrow format' , ):
written += file_obj.write(a)
return written
| 352 |
import argparse
import datetime
def lowerCamelCase__ (_UpperCAmelCase):
SCREAMING_SNAKE_CASE = {
'0': 'Sunday',
'1': 'Monday',
'2': 'Tuesday',
'3': 'Wednesday',
'4': 'Thursday',
'5': 'Friday',
'6': 'Saturday',
}
SCREAMING_SNAKE_CASE = {0: 1, 1: 2, 2: 3, 3: 4, 4: 5, 5: 6, 6: 0}
# Validate
if not 0 < len(_UpperCAmelCase) < 11:
raise ValueError('Must be 10 characters long')
# Get month
SCREAMING_SNAKE_CASE = int(date_input[0] + date_input[1])
# Validate
if not 0 < m < 13:
raise ValueError('Month must be between 1 - 12')
SCREAMING_SNAKE_CASE = date_input[2]
# Validate
if sep_a not in ["-", "/"]:
raise ValueError('Date separator must be \'-\' or \'/\'')
# Get day
SCREAMING_SNAKE_CASE = int(date_input[3] + date_input[4])
# Validate
if not 0 < d < 32:
raise ValueError('Date must be between 1 - 31')
# Get second separator
SCREAMING_SNAKE_CASE = date_input[5]
# Validate
if sep_a not in ["-", "/"]:
raise ValueError('Date separator must be \'-\' or \'/\'')
# Get year
SCREAMING_SNAKE_CASE = int(date_input[6] + date_input[7] + date_input[8] + date_input[9])
# Arbitrary year range
if not 45 < y < 8500:
raise ValueError(
'Year out of range. There has to be some sort of limit...right?')
# Get datetime obj for validation
SCREAMING_SNAKE_CASE = datetime.date(int(_UpperCAmelCase) , int(_UpperCAmelCase) , int(_UpperCAmelCase))
# Start math
if m <= 2:
SCREAMING_SNAKE_CASE = y - 1
SCREAMING_SNAKE_CASE = m + 12
# maths var
SCREAMING_SNAKE_CASE = int(str(_UpperCAmelCase)[:2])
SCREAMING_SNAKE_CASE = int(str(_UpperCAmelCase)[2:])
SCREAMING_SNAKE_CASE = int(2.6 * m - 5.39)
SCREAMING_SNAKE_CASE = int(c / 4)
SCREAMING_SNAKE_CASE = int(k / 4)
SCREAMING_SNAKE_CASE = int(d + k)
SCREAMING_SNAKE_CASE = int(t + u + v + x)
SCREAMING_SNAKE_CASE = int(z - (2 * c))
SCREAMING_SNAKE_CASE = round(w % 7)
# End math
# Validate math
if f != convert_datetime_days[dt_ck.weekday()]:
raise AssertionError('The date was evaluated incorrectly. Contact developer.')
# Response
SCREAMING_SNAKE_CASE = F'''Your date {date_input}, is a {days[str(_UpperCAmelCase)]}!'''
return response
if __name__ == "__main__":
import doctest
doctest.testmod()
a_ : Tuple = argparse.ArgumentParser(
description=(
'Find out what day of the week nearly any date is or was. Enter '
'date as a string in the mm-dd-yyyy or mm/dd/yyyy format'
)
)
parser.add_argument(
'date_input', type=str, help='Date as a string (mm-dd-yyyy or mm/dd/yyyy)'
)
a_ : Any = parser.parse_args()
zeller(args.date_input)
| 327 | 0 |
"""simple docstring"""
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 _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def __init__( self , __a , __a=13 , __a=7 , __a=True , __a=True , __a=True , __a=True , __a=99 , __a=32 , __a=5 , __a=4 , __a=37 , __a="gelu" , __a=0.1 , __a=0.1 , __a=5_12 , __a=16 , __a=2 , __a=0.0_2 , __a=4 , ):
__lowerCAmelCase = parent
__lowerCAmelCase = batch_size
__lowerCAmelCase = seq_length
__lowerCAmelCase = is_training
__lowerCAmelCase = use_attention_mask
__lowerCAmelCase = use_token_type_ids
__lowerCAmelCase = use_labels
__lowerCAmelCase = vocab_size
__lowerCAmelCase = hidden_size
__lowerCAmelCase = num_hidden_layers
__lowerCAmelCase = num_attention_heads
__lowerCAmelCase = intermediate_size
__lowerCAmelCase = hidden_act
__lowerCAmelCase = hidden_dropout_prob
__lowerCAmelCase = attention_probs_dropout_prob
__lowerCAmelCase = max_position_embeddings
__lowerCAmelCase = type_vocab_size
__lowerCAmelCase = type_sequence_label_size
__lowerCAmelCase = initializer_range
__lowerCAmelCase = num_choices
def snake_case ( self ):
__lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__lowerCAmelCase = None
if self.use_attention_mask:
__lowerCAmelCase = random_attention_mask([self.batch_size, self.seq_length] )
__lowerCAmelCase = None
if self.use_token_type_ids:
__lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__lowerCAmelCase = 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 snake_case ( self ):
__lowerCAmelCase = self.prepare_config_and_inputs()
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = config_and_inputs
__lowerCAmelCase = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
return config, inputs_dict
@require_flax
class _UpperCamelCase ( lowerCAmelCase__ ,unittest.TestCase ):
'''simple docstring'''
__UpperCAmelCase : Union[str, Any] =True
__UpperCAmelCase : List[Any] =(
(
FlaxRoFormerModel,
FlaxRoFormerForMaskedLM,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
)
if is_flax_available()
else ()
)
def snake_case ( self ):
__lowerCAmelCase = FlaxRoFormerModelTester(self )
@slow
def snake_case ( self ):
for model_class_name in self.all_model_classes:
__lowerCAmelCase = model_class_name.from_pretrained("junnyu/roformer_chinese_small" , from_pt=__a )
__lowerCAmelCase = model(np.ones((1, 1) ) )
self.assertIsNotNone(__a )
@require_flax
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
@slow
def snake_case ( self ):
__lowerCAmelCase = FlaxRoFormerForMaskedLM.from_pretrained("junnyu/roformer_chinese_base" )
__lowerCAmelCase = jnp.array([[0, 1, 2, 3, 4, 5]] )
__lowerCAmelCase = model(__a )[0]
__lowerCAmelCase = 5_00_00
__lowerCAmelCase = (1, 6, vocab_size)
self.assertEqual(output.shape , __a )
__lowerCAmelCase = 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 ) )
| 57 |
"""simple docstring"""
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def __UpperCAmelCase ( __lowerCamelCase ) -> Any:
lowercase__ : Optional[int] = []
embed.append(
(
f"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight""",
f"""stage{idx}.patch_embed.proj.weight""",
) )
embed.append(
(
f"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias""",
f"""stage{idx}.patch_embed.proj.bias""",
) )
embed.append(
(
f"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight""",
f"""stage{idx}.patch_embed.norm.weight""",
) )
embed.append(
(
f"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias""",
f"""stage{idx}.patch_embed.norm.bias""",
) )
return embed
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase ) -> Dict:
lowercase__ : str = []
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight""",
f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight""",
f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias""",
f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean""",
f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var""",
f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked""",
f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight""",
f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight""",
f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias""",
f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean""",
f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var""",
f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked""",
f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight""",
f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight""",
f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias""",
f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean""",
f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var""",
f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked""",
f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight""",
f"""stage{idx}.blocks.{cnt}.attn.proj_q.weight""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias""",
f"""stage{idx}.blocks.{cnt}.attn.proj_q.bias""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight""",
f"""stage{idx}.blocks.{cnt}.attn.proj_k.weight""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias""",
f"""stage{idx}.blocks.{cnt}.attn.proj_k.bias""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight""",
f"""stage{idx}.blocks.{cnt}.attn.proj_v.weight""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias""",
f"""stage{idx}.blocks.{cnt}.attn.proj_v.bias""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight""",
f"""stage{idx}.blocks.{cnt}.attn.proj.weight""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias""",
f"""stage{idx}.blocks.{cnt}.attn.proj.bias""",
) )
attention_weights.append(
(f"""cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight""", f"""stage{idx}.blocks.{cnt}.mlp.fc1.weight""") )
attention_weights.append(
(f"""cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias""", f"""stage{idx}.blocks.{cnt}.mlp.fc1.bias""") )
attention_weights.append(
(f"""cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight""", f"""stage{idx}.blocks.{cnt}.mlp.fc2.weight""") )
attention_weights.append(
(f"""cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias""", f"""stage{idx}.blocks.{cnt}.mlp.fc2.bias""") )
attention_weights.append(
(f"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight""", f"""stage{idx}.blocks.{cnt}.norm1.weight""") )
attention_weights.append(
(f"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias""", f"""stage{idx}.blocks.{cnt}.norm1.bias""") )
attention_weights.append(
(f"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight""", f"""stage{idx}.blocks.{cnt}.norm2.weight""") )
attention_weights.append(
(f"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias""", f"""stage{idx}.blocks.{cnt}.norm2.bias""") )
return attention_weights
def __UpperCAmelCase ( __lowerCamelCase ) -> Tuple:
lowercase__ : List[str] = []
token.append((f"""cvt.encoder.stages.{idx}.cls_token""", '''stage2.cls_token''') )
return token
def __UpperCAmelCase ( ) -> Optional[int]:
lowercase__ : List[str] = []
head.append(('''layernorm.weight''', '''norm.weight''') )
head.append(('''layernorm.bias''', '''norm.bias''') )
head.append(('''classifier.weight''', '''head.weight''') )
head.append(('''classifier.bias''', '''head.bias''') )
return head
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) -> int:
lowercase__ : List[Any] = '''imagenet-1k-id2label.json'''
lowercase__ : Optional[Any] = 10_00
lowercase__ : Optional[Any] = '''huggingface/label-files'''
lowercase__ : Dict = num_labels
lowercase__ : Union[str, Any] = json.load(open(cached_download(hf_hub_url(__lowerCamelCase , __lowerCamelCase , repo_type='''dataset''' ) ) , '''r''' ) )
lowercase__ : int = {int(__lowerCamelCase ): v for k, v in idalabel.items()}
lowercase__ : Optional[Any] = idalabel
lowercase__ : str = {v: k for k, v in idalabel.items()}
lowercase__ : Any = CvtConfig(num_labels=__lowerCamelCase , idalabel=__lowerCamelCase , labelaid=__lowerCamelCase )
# For depth size 13 (13 = 1+2+10)
if cvt_model.rsplit('''/''' , 1 )[-1][4:6] == "13":
lowercase__ : int = [1, 2, 10]
# For depth size 21 (21 = 1+4+16)
elif cvt_model.rsplit('''/''' , 1 )[-1][4:6] == "21":
lowercase__ : int = [1, 4, 16]
# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
else:
lowercase__ : List[Any] = [2, 2, 20]
lowercase__ : Any = [3, 12, 16]
lowercase__ : Tuple = [1_92, 7_68, 10_24]
lowercase__ : List[Any] = CvtForImageClassification(__lowerCamelCase )
lowercase__ : str = AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k''' )
lowercase__ : List[str] = image_size
lowercase__ : Union[str, Any] = torch.load(__lowerCamelCase , map_location=torch.device('''cpu''' ) )
lowercase__ : int = OrderedDict()
lowercase__ : List[Any] = []
for idx in range(len(config.depth ) ):
if config.cls_token[idx]:
lowercase__ : Any = list_of_state_dict + cls_token(__lowerCamelCase )
lowercase__ : Any = list_of_state_dict + embeddings(__lowerCamelCase )
for cnt in range(config.depth[idx] ):
lowercase__ : Tuple = list_of_state_dict + attention(__lowerCamelCase , __lowerCamelCase )
lowercase__ : List[Any] = list_of_state_dict + final()
for gg in list_of_state_dict:
print(__lowerCamelCase )
for i in range(len(__lowerCamelCase ) ):
lowercase__ : Optional[Any] = original_weights[list_of_state_dict[i][1]]
model.load_state_dict(__lowerCamelCase )
model.save_pretrained(__lowerCamelCase )
image_processor.save_pretrained(__lowerCamelCase )
# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al
if __name__ == "__main__":
lowerCAmelCase_ = argparse.ArgumentParser()
parser.add_argument(
'--cvt_model',
default='cvt-w24',
type=str,
help='Name of the cvt model you\'d like to convert.',
)
parser.add_argument(
'--image_size',
default=384,
type=int,
help='Input Image Size',
)
parser.add_argument(
'--cvt_file_name',
default=R'cvtmodels\CvT-w24-384x384-IN-22k.pth',
type=str,
help='Input Image Size',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
)
lowerCAmelCase_ = parser.parse_args()
convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)
| 16 | 0 |
'''simple docstring'''
import argparse
import collections
import os
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
lowerCamelCase_ = '''src/transformers'''
lowerCamelCase_ = '''docs/source/en'''
lowerCamelCase_ = '''.'''
def __lowercase ( __lowercase , __lowercase , __lowercase ) -> Union[str, Any]:
'''simple docstring'''
with open(__lowercase , "r" , encoding="utf-8" , newline="\n" ) as f:
_A = f.readlines()
# Find the start prompt.
_A = 0
while not lines[start_index].startswith(__lowercase ):
start_index += 1
start_index += 1
_A = start_index
while not lines[end_index].startswith(__lowercase ):
end_index += 1
end_index -= 1
while len(lines[start_index] ) <= 1:
start_index += 1
while len(lines[end_index] ) <= 1:
end_index -= 1
end_index += 1
return "".join(lines[start_index:end_index] ), start_index, end_index, lines
# Add here suffixes that are used to identify models, separated by |
lowerCamelCase_ = '''Model|Encoder|Decoder|ForConditionalGeneration'''
# Regexes that match TF/Flax/PT model names.
lowerCamelCase_ = re.compile(r'''TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
lowerCamelCase_ = re.compile(r'''Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
lowerCamelCase_ = re.compile(r'''(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
# This is to make sure the transformers module imported is the one in the repo.
lowerCamelCase_ = direct_transformers_import(TRANSFORMERS_PATH)
def __lowercase ( __lowercase ) -> Optional[Any]:
'''simple docstring'''
_A = re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)" , __lowercase )
return [m.group(0 ) for m in matches]
def __lowercase ( __lowercase , __lowercase ) -> Dict:
'''simple docstring'''
_A = 2 if text == "✅" or text == "❌" else len(__lowercase )
_A = (width - text_length) // 2
_A = width - text_length - left_indent
return " " * left_indent + text + " " * right_indent
def __lowercase ( ) -> str:
'''simple docstring'''
_A = transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
_A = {
name: config_maping_names[code]
for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
if code in config_maping_names
}
_A = {name: config.replace("Config" , "" ) for name, config in model_name_to_config.items()}
# Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
_A = collections.defaultdict(__lowercase )
_A = collections.defaultdict(__lowercase )
_A = collections.defaultdict(__lowercase )
_A = collections.defaultdict(__lowercase )
_A = collections.defaultdict(__lowercase )
# Let's lookup through all transformers object (once).
for attr_name in dir(__lowercase ):
_A = None
if attr_name.endswith("Tokenizer" ):
_A = slow_tokenizers
_A = attr_name[:-9]
elif attr_name.endswith("TokenizerFast" ):
_A = fast_tokenizers
_A = attr_name[:-13]
elif _re_tf_models.match(__lowercase ) is not None:
_A = tf_models
_A = _re_tf_models.match(__lowercase ).groups()[0]
elif _re_flax_models.match(__lowercase ) is not None:
_A = flax_models
_A = _re_flax_models.match(__lowercase ).groups()[0]
elif _re_pt_models.match(__lowercase ) is not None:
_A = pt_models
_A = _re_pt_models.match(__lowercase ).groups()[0]
if lookup_dict is not None:
while len(__lowercase ) > 0:
if attr_name in model_name_to_prefix.values():
_A = True
break
# Try again after removing the last word in the name
_A = "".join(camel_case_split(__lowercase )[:-1] )
# Let's build that table!
_A = list(model_name_to_config.keys() )
model_names.sort(key=str.lower )
_A = ["Model", "Tokenizer slow", "Tokenizer fast", "PyTorch support", "TensorFlow support", "Flax Support"]
# We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
_A = [len(__lowercase ) + 2 for c in columns]
_A = max([len(__lowercase ) for name in model_names] ) + 2
# Build the table per se
_A = "|" + "|".join([_center_text(__lowercase , __lowercase ) for c, w in zip(__lowercase , __lowercase )] ) + "|\n"
# Use ":-----:" format to center-aligned table cell texts
table += "|" + "|".join([":" + "-" * (w - 2) + ":" for w in widths] ) + "|\n"
_A = {True: "✅", False: "❌"}
for name in model_names:
_A = model_name_to_prefix[name]
_A = [
name,
check[slow_tokenizers[prefix]],
check[fast_tokenizers[prefix]],
check[pt_models[prefix]],
check[tf_models[prefix]],
check[flax_models[prefix]],
]
table += "|" + "|".join([_center_text(__lowercase , __lowercase ) for l, w in zip(__lowercase , __lowercase )] ) + "|\n"
return table
def __lowercase ( __lowercase=False ) -> Optional[Any]:
'''simple docstring'''
_A , _A , _A , _A = _find_text_in_file(
filename=os.path.join(__lowercase , "index.md" ) , start_prompt="<!--This table is updated automatically from the auto modules" , end_prompt="<!-- End table-->" , )
_A = get_model_table_from_auto_modules()
if current_table != new_table:
if overwrite:
with open(os.path.join(__lowercase , "index.md" ) , "w" , encoding="utf-8" , newline="\n" ) as f:
f.writelines(lines[:start_index] + [new_table] + lines[end_index:] )
else:
raise ValueError(
"The model table in the `index.md` has not been updated. Run `make fix-copies` to fix this." )
if __name__ == "__main__":
lowerCamelCase_ = argparse.ArgumentParser()
parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
lowerCamelCase_ = parser.parse_args()
check_model_table(args.fix_and_overwrite)
| 365 |
'''simple docstring'''
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,
)
lowerCamelCase_ = {'''configuration_mbart''': ['''MBART_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MBartConfig''', '''MBartOnnxConfig''']}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase_ = ['''MBartTokenizer''']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase_ = ['''MBartTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase_ = [
'''MBART_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''MBartForCausalLM''',
'''MBartForConditionalGeneration''',
'''MBartForQuestionAnswering''',
'''MBartForSequenceClassification''',
'''MBartModel''',
'''MBartPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase_ = [
'''TFMBartForConditionalGeneration''',
'''TFMBartModel''',
'''TFMBartPreTrainedModel''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase_ = [
'''FlaxMBartForConditionalGeneration''',
'''FlaxMBartForQuestionAnswering''',
'''FlaxMBartForSequenceClassification''',
'''FlaxMBartModel''',
'''FlaxMBartPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mbart import MBartTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mbart_fast import MBartTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mbart import (
MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
MBartForCausalLM,
MBartForConditionalGeneration,
MBartForQuestionAnswering,
MBartForSequenceClassification,
MBartModel,
MBartPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_mbart import (
FlaxMBartForConditionalGeneration,
FlaxMBartForQuestionAnswering,
FlaxMBartForSequenceClassification,
FlaxMBartModel,
FlaxMBartPreTrainedModel,
)
else:
import sys
lowerCamelCase_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 174 | 0 |
from ... import PretrainedConfig
UpperCAmelCase_ : Optional[Any] = {
'sijunhe/nezha-cn-base': 'https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json',
}
class SCREAMING_SNAKE_CASE__ ( lowercase__ ):
snake_case__ : str = NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
snake_case__ : Any = '''nezha'''
def __init__( self : str , SCREAMING_SNAKE_CASE__ : Optional[int]=2_1_1_2_8 , SCREAMING_SNAKE_CASE__ : List[str]=7_6_8 , SCREAMING_SNAKE_CASE__ : List[str]=1_2 , SCREAMING_SNAKE_CASE__ : Dict=1_2 , SCREAMING_SNAKE_CASE__ : List[str]=3_0_7_2 , SCREAMING_SNAKE_CASE__ : Any="gelu" , SCREAMING_SNAKE_CASE__ : List[str]=0.1 , SCREAMING_SNAKE_CASE__ : Any=0.1 , SCREAMING_SNAKE_CASE__ : Optional[int]=5_1_2 , SCREAMING_SNAKE_CASE__ : Tuple=6_4 , SCREAMING_SNAKE_CASE__ : Tuple=2 , SCREAMING_SNAKE_CASE__ : Tuple=0.02 , SCREAMING_SNAKE_CASE__ : List[str]=1E-12 , SCREAMING_SNAKE_CASE__ : Dict=0.1 , SCREAMING_SNAKE_CASE__ : Tuple=0 , SCREAMING_SNAKE_CASE__ : List[str]=2 , SCREAMING_SNAKE_CASE__ : Dict=3 , SCREAMING_SNAKE_CASE__ : str=True , **SCREAMING_SNAKE_CASE__ : Union[str, Any] , ) -> int:
super().__init__(pad_token_id=SCREAMING_SNAKE_CASE__ , bos_token_id=SCREAMING_SNAKE_CASE__ , eos_token_id=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
a_ : Union[str, Any] = vocab_size
a_ : Tuple = hidden_size
a_ : List[Any] = num_hidden_layers
a_ : Dict = num_attention_heads
a_ : List[str] = hidden_act
a_ : Tuple = intermediate_size
a_ : int = hidden_dropout_prob
a_ : Dict = attention_probs_dropout_prob
a_ : List[Any] = max_position_embeddings
a_ : Optional[Any] = max_relative_position
a_ : Dict = type_vocab_size
a_ : str = initializer_range
a_ : List[Any] = layer_norm_eps
a_ : Optional[Any] = classifier_dropout
a_ : Optional[Any] = use_cache
| 32 |
'''simple docstring'''
from __future__ import annotations
import math
def a ( lowerCamelCase__ ):
'''simple docstring'''
if num <= 0:
A_ : List[Any] = f'{num}: Invalid input, please enter a positive integer.'
raise ValueError(lowerCamelCase__ )
A_ : Dict = [True] * (num + 1)
A_ : List[Any] = []
A_ : Tuple = 2
A_ : Optional[int] = int(math.sqrt(lowerCamelCase__ ) )
while start <= end:
# If start is a prime
if sieve[start] is True:
prime.append(lowerCamelCase__ )
# Set multiples of start be False
for i in range(start * start , num + 1 , lowerCamelCase__ ):
if sieve[i] is True:
A_ : List[Any] = False
start += 1
for j in range(end + 1 , num + 1 ):
if sieve[j] is True:
prime.append(lowerCamelCase__ )
return prime
if __name__ == "__main__":
print(prime_sieve(int(input('''Enter a positive integer: ''').strip()))) | 206 | 0 |
import argparse
import os
import re
import packaging.version
_lowercase: List[Any] = "examples/"
_lowercase: Union[str, Any] = {
"examples": (re.compile(r"^check_min_version\(\"[^\"]+\"\)\s*$", re.MULTILINE), "check_min_version(\"VERSION\")\n"),
"init": (re.compile(r"^__version__\s+=\s+\"([^\"]+)\"\s*$", re.MULTILINE), "__version__ = \"VERSION\"\n"),
"setup": (re.compile(r"^(\s*)version\s*=\s*\"[^\"]+\",", re.MULTILINE), r"\1version=\"VERSION\","),
"doc": (re.compile(r"^(\s*)release\s*=\s*\"[^\"]+\"$", re.MULTILINE), "release = \"VERSION\"\n"),
}
_lowercase: str = {
"init": "src/transformers/__init__.py",
"setup": "setup.py",
}
_lowercase: Union[str, Any] = "README.md"
def a( A : str , A : Optional[Any] , A : Optional[Any] ) -> List[str]:
"""simple docstring"""
with open(snake_case__ , "r" , encoding="utf-8" , newline="\n" ) as f:
a = f.read()
a , a = REPLACE_PATTERNS[pattern]
a = replace.replace("VERSION" , snake_case__ )
a = re_pattern.sub(snake_case__ , snake_case__ )
with open(snake_case__ , "w" , encoding="utf-8" , newline="\n" ) as f:
f.write(snake_case__ )
def a( A : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
for folder, directories, fnames in os.walk(snake_case__ ):
# Removing some of the folders with non-actively maintained examples from the walk
if "research_projects" in directories:
directories.remove("research_projects" )
if "legacy" in directories:
directories.remove("legacy" )
for fname in fnames:
if fname.endswith(".py" ):
update_version_in_file(os.path.join(snake_case__ , snake_case__ ) , snake_case__ , pattern="examples" )
def a( A : int , A : Union[str, Any]=False ) -> Optional[Any]:
"""simple docstring"""
for pattern, fname in REPLACE_FILES.items():
update_version_in_file(snake_case__ , snake_case__ , snake_case__ )
if not patch:
update_version_in_examples(snake_case__ )
def a( ) -> Optional[int]:
"""simple docstring"""
a = "🤗 Transformers currently provides the following architectures"
a = "1. Want to contribute a new model?"
with open(snake_case__ , "r" , encoding="utf-8" , newline="\n" ) as f:
a = f.readlines()
# Find the start of the list.
a = 0
while not lines[start_index].startswith(_start_prompt ):
start_index += 1
start_index += 1
a = start_index
# Update the lines in the model list.
while not lines[index].startswith(_end_prompt ):
if lines[index].startswith("1." ):
a = lines[index].replace(
"https://huggingface.co/docs/transformers/main/model_doc" , "https://huggingface.co/docs/transformers/model_doc" , )
index += 1
with open(snake_case__ , "w" , encoding="utf-8" , newline="\n" ) as f:
f.writelines(snake_case__ )
def a( ) -> List[str]:
"""simple docstring"""
with open(REPLACE_FILES["init"] , "r" ) as f:
a = f.read()
a = REPLACE_PATTERNS["init"][0].search(snake_case__ ).groups()[0]
return packaging.version.parse(snake_case__ )
def a( A : str=False ) -> Optional[Any]:
"""simple docstring"""
a = get_version()
if patch and default_version.is_devrelease:
raise ValueError("Can't create a patch version from the dev branch, checkout a released version!" )
if default_version.is_devrelease:
a = default_version.base_version
elif patch:
a = f'''{default_version.major}.{default_version.minor}.{default_version.micro + 1}'''
else:
a = f'''{default_version.major}.{default_version.minor + 1}.0'''
# Now let's ask nicely if that's the right one.
a = input(f'''Which version are you releasing? [{default_version}]''' )
if len(snake_case__ ) == 0:
a = default_version
print(f'''Updating version to {version}.''' )
global_version_update(snake_case__ , patch=snake_case__ )
if not patch:
print("Cleaning main README, don't forget to run `make fix-copies`." )
clean_main_ref_in_model_list()
def a( ) -> str:
"""simple docstring"""
a = get_version()
a = f'''{current_version.major}.{current_version.minor + 1}.0.dev0'''
a = current_version.base_version
# Check with the user we got that right.
a = input(f'''Which version are we developing now? [{dev_version}]''' )
if len(snake_case__ ) == 0:
a = dev_version
print(f'''Updating version to {version}.''' )
global_version_update(snake_case__ )
print("Cleaning main README, don't forget to run `make fix-copies`." )
clean_main_ref_in_model_list()
if __name__ == "__main__":
_lowercase: Dict = argparse.ArgumentParser()
parser.add_argument("--post_release", action="store_true", help="Whether this is pre or post release.")
parser.add_argument("--patch", action="store_true", help="Whether or not this is a patch release.")
_lowercase: List[Any] = parser.parse_args()
if not args.post_release:
pre_release_work(patch=args.patch)
elif args.patch:
print("Nothing to do after a patch :-)")
else:
post_release_work()
| 364 |
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
_lowercase: List[Any] = {
"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( A : List[str] ) -> Union[str, Any]:
"""simple docstring"""
a = ["layers", "blocks"]
for k in ignore_keys:
state_dict.pop(A , A )
_lowercase: Optional[int] = {
"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( A : str ) -> Union[str, Any]:
"""simple docstring"""
a = list(s_dict.keys() )
for key in keys:
a = key
for k, v in WHISPER_MAPPING.items():
if k in key:
a = new_key.replace(A , A )
print(f'''{key} -> {new_key}''' )
a = s_dict.pop(A )
return s_dict
def a( A : Union[str, Any] ) -> Tuple:
"""simple docstring"""
a , a = emb.weight.shape
a = nn.Linear(A , A , bias=A )
a = emb.weight.data
return lin_layer
def a( A : str , A : str ) -> bytes:
"""simple docstring"""
os.makedirs(A , exist_ok=A )
a = os.path.basename(A )
a = url.split("/" )[-2]
a = os.path.join(A , A )
if os.path.exists(A ) and not os.path.isfile(A ):
raise RuntimeError(f'''{download_target} exists and is not a regular file''' )
if os.path.isfile(A ):
a = open(A , "rb" ).read()
if hashlib.shaaaa(A ).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(A ) as source, open(A , "wb" ) as output:
with tqdm(
total=int(source.info().get("Content-Length" ) ) , ncols=80 , unit="iB" , unit_scale=A , unit_divisor=1024 ) as loop:
while True:
a = source.read(8192 )
if not buffer:
break
output.write(A )
loop.update(len(A ) )
a = open(A , "rb" ).read()
if hashlib.shaaaa(A ).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( A : List[str] , A : Union[str, Any] ) -> str:
"""simple docstring"""
if ".pt" not in checkpoint_path:
a = _download(_MODELS[checkpoint_path] )
else:
a = torch.load(A , map_location="cpu" )
a = original_checkpoint["dims"]
a = original_checkpoint["model_state_dict"]
a = state_dict["decoder.token_embedding.weight"]
remove_ignore_keys_(A )
rename_keys(A )
a = True
a = state_dict["decoder.layers.0.fc1.weight"].shape[0]
a = WhisperConfig(
vocab_size=dimensions["n_vocab"] , encoder_ffn_dim=A , decoder_ffn_dim=A , 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"] , )
a = WhisperForConditionalGeneration(A )
a , a = model.model.load_state_dict(A , strict=A )
if len(A ) > 0 and not set(A ) <= {
"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:
a = make_linear_from_emb(model.model.decoder.embed_tokens )
else:
a = proj_out_weights
model.save_pretrained(A )
if __name__ == "__main__":
_lowercase: Dict = 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.")
_lowercase: List[str] = parser.parse_args()
convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)
| 71 | 0 |
"""simple docstring"""
def UpperCAmelCase ( UpperCamelCase__ = 200 ):
"""simple docstring"""
A__ = [1, 2, 5, 10, 20, 50, 100, 200]
A__ = [0] * (pence + 1)
A__ = 1 # base case: 1 way to make 0 pence
for coin in coins:
for i in range(snake_case__ , pence + 1 , 1 ):
number_of_ways[i] += number_of_ways[i - coin]
return number_of_ways[pence]
if __name__ == "__main__":
assert solution(2_00) == 7_36_82
| 221 |
"""simple docstring"""
import argparse
import torch
from torch import nn
from transformers import MBartConfig, MBartForConditionalGeneration
def _snake_case ( snake_case__ : Dict ):
A = [
'encoder.version',
'decoder.version',
'model.encoder.version',
'model.decoder.version',
'_float_tensor',
'decoder.output_projection.weight',
]
for k in ignore_keys:
state_dict.pop(snake_case__ , snake_case__ )
def _snake_case ( snake_case__ : int ):
A , A = emb.weight.shape
A = nn.Linear(snake_case__ , snake_case__ , bias=snake_case__ )
A = emb.weight.data
return lin_layer
def _snake_case ( snake_case__ : List[str] , snake_case__ : Any="facebook/mbart-large-en-ro" , snake_case__ : Optional[int]=False , snake_case__ : List[str]=False ):
A = torch.load(snake_case__ , map_location='cpu' )['model']
remove_ignore_keys_(snake_case__ )
A = state_dict['encoder.embed_tokens.weight'].shape[0]
A = MBartConfig.from_pretrained(snake_case__ , vocab_size=snake_case__ )
if mbart_aa and finetuned:
A = 'relu'
A = state_dict['decoder.embed_tokens.weight']
A = MBartForConditionalGeneration(snake_case__ )
model.model.load_state_dict(snake_case__ )
if finetuned:
A = make_linear_from_emb(model.model.shared )
return model
if __name__ == "__main__":
_lowercase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''fairseq_path''', type=str, help='''bart.large, bart.large.cnn or a path to a model.pt on local filesystem.'''
)
parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
parser.add_argument(
'''--hf_config''',
default='''facebook/mbart-large-cc25''',
type=str,
help='''Which huggingface architecture to use: mbart-large''',
)
parser.add_argument('''--mbart_50''', action='''store_true''', help='''whether the model is mMART-50 checkpoint''')
parser.add_argument('''--finetuned''', action='''store_true''', help='''whether the model is a fine-tuned checkpoint''')
_lowercase = parser.parse_args()
_lowercase = convert_fairseq_mbart_checkpoint_from_disk(
args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
)
model.save_pretrained(args.pytorch_dump_folder_path) | 74 | 0 |
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_video_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import VivitImageProcessor
class _lowerCamelCase( unittest.TestCase ):
def __init__( self, lowerCamelCase, lowerCamelCase=7, lowerCamelCase=3, lowerCamelCase=10, lowerCamelCase=18, lowerCamelCase=30, lowerCamelCase=4_00, lowerCamelCase=True, lowerCamelCase=None, lowerCamelCase=True, lowerCamelCase=[0.5, 0.5, 0.5], lowerCamelCase=[0.5, 0.5, 0.5], lowerCamelCase=None, ) -> str:
"""simple docstring"""
_lowercase : Dict = size if size is not None else {'shortest_edge': 18}
_lowercase : List[Any] = crop_size if crop_size is not None else {'height': 18, 'width': 18}
_lowercase : Dict = parent
_lowercase : Optional[Any] = batch_size
_lowercase : List[str] = num_channels
_lowercase : Any = num_frames
_lowercase : Dict = image_size
_lowercase : Optional[Any] = min_resolution
_lowercase : Any = max_resolution
_lowercase : Dict = do_resize
_lowercase : Any = size
_lowercase : Tuple = do_normalize
_lowercase : List[Any] = image_mean
_lowercase : List[Any] = image_std
_lowercase : Tuple = crop_size
def UpperCamelCase ( self) -> List[str]:
"""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,
"crop_size": self.crop_size,
}
@require_torch
@require_vision
class _lowerCamelCase( _a, unittest.TestCase ):
lowercase_ : Any = VivitImageProcessor if is_vision_available() else None
def UpperCamelCase ( self) -> Optional[Any]:
"""simple docstring"""
_lowercase : Tuple = VivitImageProcessingTester(self)
@property
def UpperCamelCase ( self) -> Tuple:
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def UpperCamelCase ( self) -> List[Any]:
"""simple docstring"""
_lowercase : Optional[Any] = self.image_processing_class(**self.image_processor_dict)
self.assertTrue(hasattr(lowerCamelCase, 'image_mean'))
self.assertTrue(hasattr(lowerCamelCase, 'image_std'))
self.assertTrue(hasattr(lowerCamelCase, 'do_normalize'))
self.assertTrue(hasattr(lowerCamelCase, 'do_resize'))
self.assertTrue(hasattr(lowerCamelCase, 'do_center_crop'))
self.assertTrue(hasattr(lowerCamelCase, 'size'))
def UpperCamelCase ( self) -> List[str]:
"""simple docstring"""
_lowercase : Dict = self.image_processing_class.from_dict(self.image_processor_dict)
self.assertEqual(image_processor.size, {'shortest_edge': 18})
self.assertEqual(image_processor.crop_size, {'height': 18, 'width': 18})
_lowercase : Dict = self.image_processing_class.from_dict(self.image_processor_dict, size=42, crop_size=84)
self.assertEqual(image_processor.size, {'shortest_edge': 42})
self.assertEqual(image_processor.crop_size, {'height': 84, 'width': 84})
def UpperCamelCase ( self) -> Any:
"""simple docstring"""
_lowercase : Optional[int] = self.image_processing_class(**self.image_processor_dict)
# create random PIL videos
_lowercase : List[str] = prepare_video_inputs(self.image_processor_tester, equal_resolution=lowerCamelCase)
for video in video_inputs:
self.assertIsInstance(lowerCamelCase, lowerCamelCase)
self.assertIsInstance(video[0], Image.Image)
# Test not batched input
_lowercase : Union[str, Any] = image_processing(video_inputs[0], return_tensors='pt').pixel_values
self.assertEqual(
encoded_videos.shape, (
1,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
), )
# Test batched
_lowercase : Tuple = image_processing(lowerCamelCase, return_tensors='pt').pixel_values
self.assertEqual(
encoded_videos.shape, (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
), )
def UpperCamelCase ( self) -> Optional[Any]:
"""simple docstring"""
_lowercase : Any = self.image_processing_class(**self.image_processor_dict)
# create random numpy tensors
_lowercase : Union[str, Any] = prepare_video_inputs(self.image_processor_tester, equal_resolution=lowerCamelCase, numpify=lowerCamelCase)
for video in video_inputs:
self.assertIsInstance(lowerCamelCase, lowerCamelCase)
self.assertIsInstance(video[0], np.ndarray)
# Test not batched input
_lowercase : Dict = image_processing(video_inputs[0], return_tensors='pt').pixel_values
self.assertEqual(
encoded_videos.shape, (
1,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
), )
# Test batched
_lowercase : Optional[Any] = image_processing(lowerCamelCase, return_tensors='pt').pixel_values
self.assertEqual(
encoded_videos.shape, (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
), )
def UpperCamelCase ( self) -> str:
"""simple docstring"""
_lowercase : int = self.image_processing_class(**self.image_processor_dict)
# create random PyTorch tensors
_lowercase : Dict = prepare_video_inputs(self.image_processor_tester, equal_resolution=lowerCamelCase, torchify=lowerCamelCase)
for video in video_inputs:
self.assertIsInstance(lowerCamelCase, lowerCamelCase)
self.assertIsInstance(video[0], torch.Tensor)
# Test not batched input
_lowercase : Dict = image_processing(video_inputs[0], return_tensors='pt').pixel_values
self.assertEqual(
encoded_videos.shape, (
1,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
), )
# Test batched
_lowercase : Optional[Any] = image_processing(lowerCamelCase, return_tensors='pt').pixel_values
self.assertEqual(
encoded_videos.shape, (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
), )
| 84 |
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
"The `inpainting.py` script is outdated. Please use directly `from diffusers import"
" StableDiffusionInpaintPipeline` instead."
)
| 84 | 1 |
"""simple docstring"""
import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = """▁"""
lowerCamelCase__ = {"""vocab_file""": """prophetnet.tokenizer"""}
lowerCamelCase__ = {
"""vocab_file""": {
"""microsoft/xprophetnet-large-wiki100-cased""": (
"""https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer"""
),
}
}
lowerCamelCase__ = {
"""microsoft/xprophetnet-large-wiki100-cased""": {"""do_lower_case""": False},
}
lowerCamelCase__ = {
"""microsoft/xprophetnet-large-wiki100-cased""": 512,
}
def __lowerCAmelCase (_UpperCamelCase ):
__lowerCAmelCase : List[Any] = collections.OrderedDict()
with open(_UpperCamelCase , 'r' , encoding='utf-8' ) as reader:
__lowerCAmelCase : Any = reader.readlines()
for index, token in enumerate(_UpperCamelCase ):
__lowerCAmelCase : int = token.rstrip('\n' )
__lowerCAmelCase : Optional[int] = index
return vocab
class A__ ( _lowerCamelCase):
A_ : Dict = VOCAB_FILES_NAMES
A_ : List[Any] = PRETRAINED_VOCAB_FILES_MAP
A_ : List[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
A_ : Tuple = ['input_ids', 'attention_mask']
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE="[SEP]" , _SCREAMING_SNAKE_CASE="[SEP]" , _SCREAMING_SNAKE_CASE="[SEP]" , _SCREAMING_SNAKE_CASE="[UNK]" , _SCREAMING_SNAKE_CASE="[PAD]" , _SCREAMING_SNAKE_CASE="[CLS]" , _SCREAMING_SNAKE_CASE="[MASK]" , _SCREAMING_SNAKE_CASE = None , **_SCREAMING_SNAKE_CASE , ):
__lowerCAmelCase : Tuple = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=_SCREAMING_SNAKE_CASE , eos_token=_SCREAMING_SNAKE_CASE , sep_token=_SCREAMING_SNAKE_CASE , unk_token=_SCREAMING_SNAKE_CASE , pad_token=_SCREAMING_SNAKE_CASE , cls_token=_SCREAMING_SNAKE_CASE , mask_token=_SCREAMING_SNAKE_CASE , sp_model_kwargs=self.sp_model_kwargs , **_SCREAMING_SNAKE_CASE , )
try:
import sentencepiece as spm
except ImportError:
logger.warning(
'You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'
' pip install sentencepiece' )
raise
__lowerCAmelCase : List[Any] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(_SCREAMING_SNAKE_CASE ) )
__lowerCAmelCase : str = vocab_file
# Original fairseq vocab and spm vocab must be "aligned":
# Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9
# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
# fairseq | '<s>' | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's' | '▁de' | '-'
# spm | '<unk>' | '<s>' | '</s>' | ',' | '.' | '▁' | 's' | '▁de' | '-' | '▁a'
# put special tokens and [unused] tokens into the vocab
__lowerCAmelCase : List[Any] = {'[PAD]': 0, '[CLS]': 1, '[SEP]': 2, '[UNK]': 3, '[MASK]': 4}
for i in range(10 ):
__lowerCAmelCase : List[Any] = f"[unused{i}]"
__lowerCAmelCase : List[str] = 5 + i
# The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
__lowerCAmelCase : Tuple = 12
__lowerCAmelCase : Any = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
for k in self.fairseq_tokens_to_ids.keys():
self.unique_no_split_tokens.append(_SCREAMING_SNAKE_CASE )
def __getstate__( self ):
__lowerCAmelCase : List[Any] = self.__dict__.copy()
__lowerCAmelCase : Tuple = None
return state
def __setstate__( self , _SCREAMING_SNAKE_CASE ):
__lowerCAmelCase : Any = d
try:
import sentencepiece as spm
except ImportError:
logger.warning(
'You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'
' pip install sentencepiece' )
raise
# for backward compatibility
if not hasattr(self , 'sp_model_kwargs' ):
__lowerCAmelCase : Tuple = {}
__lowerCAmelCase : Tuple = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def __lowerCamelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_SCREAMING_SNAKE_CASE , token_ids_a=_SCREAMING_SNAKE_CASE , already_has_special_tokens=_SCREAMING_SNAKE_CASE )
if token_ids_a is None:
return ([0] * len(_SCREAMING_SNAKE_CASE )) + [1]
return ([0] * len(_SCREAMING_SNAKE_CASE )) + [1] + ([0] * len(_SCREAMING_SNAKE_CASE )) + [1]
def __lowerCamelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None ):
__lowerCAmelCase : Optional[int] = [self.sep_token_id]
if token_ids_a is None:
return len(token_ids_a + sep ) * [0]
return len(token_ids_a + sep + sep + token_ids_a + sep ) * [0]
@property
def __lowerCamelCase ( self ):
return len(self.sp_model ) + self.fairseq_offset
def __lowerCamelCase ( self ):
__lowerCAmelCase : List[Any] = {self.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __lowerCamelCase ( self , _SCREAMING_SNAKE_CASE ):
return self.sp_model.encode(_SCREAMING_SNAKE_CASE , out_type=_SCREAMING_SNAKE_CASE )
def __lowerCamelCase ( self , _SCREAMING_SNAKE_CASE ):
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
__lowerCAmelCase : Dict = self.sp_model.PieceToId(_SCREAMING_SNAKE_CASE )
# Need to return unknown token if the SP model returned 0
return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
def __lowerCamelCase ( self , _SCREAMING_SNAKE_CASE ):
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 , _SCREAMING_SNAKE_CASE ):
__lowerCAmelCase : List[Any] = ''.join(_SCREAMING_SNAKE_CASE ).replace(_SCREAMING_SNAKE_CASE , ' ' ).strip()
return out_string
def __lowerCamelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None ):
if not os.path.isdir(_SCREAMING_SNAKE_CASE ):
logger.error(f"Vocabulary path ({save_directory}) should be a directory" )
return
__lowerCAmelCase : int = os.path.join(
_SCREAMING_SNAKE_CASE , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_SCREAMING_SNAKE_CASE ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , _SCREAMING_SNAKE_CASE )
elif not os.path.isfile(self.vocab_file ):
with open(_SCREAMING_SNAKE_CASE , 'wb' ) as fi:
__lowerCAmelCase : List[str] = self.sp_model.serialized_model_proto()
fi.write(_SCREAMING_SNAKE_CASE )
return (out_vocab_file,)
def __lowerCamelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None ):
if token_ids_a is None:
return token_ids_a + [self.sep_token_id]
__lowerCAmelCase : int = [self.sep_token_id]
return token_ids_a + sep + token_ids_a + sep | 86 |
"""simple docstring"""
from __future__ import annotations
import bisect
def __lowerCAmelCase (_UpperCamelCase , _UpperCamelCase , _UpperCamelCase = 0 , _UpperCamelCase = -1 ):
if hi < 0:
__lowerCAmelCase : Tuple = len(_UpperCamelCase )
while lo < hi:
__lowerCAmelCase : Optional[int] = lo + (hi - lo) // 2
if sorted_collection[mid] < item:
__lowerCAmelCase : int = mid + 1
else:
__lowerCAmelCase : List[str] = mid
return lo
def __lowerCAmelCase (_UpperCamelCase , _UpperCamelCase , _UpperCamelCase = 0 , _UpperCamelCase = -1 ):
if hi < 0:
__lowerCAmelCase : List[Any] = len(_UpperCamelCase )
while lo < hi:
__lowerCAmelCase : Union[str, Any] = lo + (hi - lo) // 2
if sorted_collection[mid] <= item:
__lowerCAmelCase : Dict = mid + 1
else:
__lowerCAmelCase : str = mid
return lo
def __lowerCAmelCase (_UpperCamelCase , _UpperCamelCase , _UpperCamelCase = 0 , _UpperCamelCase = -1 ):
sorted_collection.insert(bisect_left(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) , _UpperCamelCase )
def __lowerCAmelCase (_UpperCamelCase , _UpperCamelCase , _UpperCamelCase = 0 , _UpperCamelCase = -1 ):
sorted_collection.insert(bisect_right(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) , _UpperCamelCase )
def __lowerCAmelCase (_UpperCamelCase , _UpperCamelCase ):
__lowerCAmelCase : List[Any] = 0
__lowerCAmelCase : int = len(_UpperCamelCase ) - 1
while left <= right:
__lowerCAmelCase : List[Any] = left + (right - left) // 2
__lowerCAmelCase : Union[str, Any] = sorted_collection[midpoint]
if current_item == item:
return midpoint
elif item < current_item:
__lowerCAmelCase : Tuple = midpoint - 1
else:
__lowerCAmelCase : str = midpoint + 1
return None
def __lowerCAmelCase (_UpperCamelCase , _UpperCamelCase ):
__lowerCAmelCase : Union[str, Any] = bisect.bisect_left(_UpperCamelCase , _UpperCamelCase )
if index != len(_UpperCamelCase ) and sorted_collection[index] == item:
return index
return None
def __lowerCAmelCase (_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
if right < left:
return None
__lowerCAmelCase : List[str] = left + (right - left) // 2
if sorted_collection[midpoint] == item:
return midpoint
elif sorted_collection[midpoint] > item:
return binary_search_by_recursion(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , midpoint - 1 )
else:
return binary_search_by_recursion(_UpperCamelCase , _UpperCamelCase , midpoint + 1 , _UpperCamelCase )
if __name__ == "__main__":
lowerCamelCase__ = input("""Enter numbers separated by comma:\n""").strip()
lowerCamelCase__ = sorted(int(item) for item in user_input.split(""","""))
lowerCamelCase__ = int(input("""Enter a single number to be found in the list:\n"""))
lowerCamelCase__ = binary_search(collection, target)
if result is None:
print(f'{target} was not found in {collection}.')
else:
print(f'{target} was found at position {result} in {collection}.') | 86 | 1 |
'''simple docstring'''
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
A : Optional[Any] = get_tests_dir() + '''/test_data/fsmt/fsmt_val_data.json'''
with io.open(filename, '''r''', encoding='''utf-8''') as f:
A : str = json.load(f)
@require_torch
class __lowerCamelCase ( unittest.TestCase ):
"""simple docstring"""
def A ( self : str , SCREAMING_SNAKE_CASE : Optional[int]):
return FSMTTokenizer.from_pretrained(SCREAMING_SNAKE_CASE)
def A ( self : Optional[Any] , SCREAMING_SNAKE_CASE : Tuple):
_A : Union[str, Any] = FSMTForConditionalGeneration.from_pretrained(SCREAMING_SNAKE_CASE).to(SCREAMING_SNAKE_CASE)
if torch_device == "cuda":
model.half()
return model
@parameterized.expand(
[
['en-ru', 26.0],
['ru-en', 22.0],
['en-de', 22.0],
['de-en', 29.0],
])
@slow
def A ( self : Dict , SCREAMING_SNAKE_CASE : List[Any] , SCREAMING_SNAKE_CASE : Optional[int]):
# note: this test is not testing the best performance since it only evals a small batch
# but it should be enough to detect a regression in the output quality
_A : int = F'facebook/wmt19-{pair}'
_A : Dict = self.get_tokenizer(SCREAMING_SNAKE_CASE)
_A : List[str] = self.get_model(SCREAMING_SNAKE_CASE)
_A : Optional[int] = bleu_data[pair]['src']
_A : Optional[int] = bleu_data[pair]['tgt']
_A : List[str] = tokenizer(SCREAMING_SNAKE_CASE , return_tensors='pt' , truncation=SCREAMING_SNAKE_CASE , padding='longest').to(SCREAMING_SNAKE_CASE)
_A : Optional[int] = model.generate(
input_ids=batch.input_ids , num_beams=8 , )
_A : List[Any] = tokenizer.batch_decode(
SCREAMING_SNAKE_CASE , skip_special_tokens=SCREAMING_SNAKE_CASE , clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE)
_A : Tuple = calculate_bleu(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE)
print(SCREAMING_SNAKE_CASE)
self.assertGreaterEqual(scores['bleu'] , SCREAMING_SNAKE_CASE)
| 227 |
'''simple docstring'''
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate
@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class __lowerCamelCase ( a_ ):
"""simple docstring"""
a = 42
a = None
def lowerCAmelCase__ ( lowerCamelCase : int ,lowerCamelCase : Tuple=0.999 ,lowerCamelCase : int="cosine" ,):
if alpha_transform_type == "cosine":
def alpha_bar_fn(lowerCamelCase : str ):
return math.cos((t + 0.008) / 1.008 * math.pi / 2 ) ** 2
elif alpha_transform_type == "exp":
def alpha_bar_fn(lowerCamelCase : Dict ):
return math.exp(t * -12.0 )
else:
raise ValueError(F'Unsupported alpha_tranform_type: {alpha_transform_type}' )
_A : Tuple = []
for i in range(lowerCamelCase ):
_A : Optional[Any] = i / num_diffusion_timesteps
_A : Tuple = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar_fn(lowerCamelCase ) / alpha_bar_fn(lowerCamelCase ) ,lowerCamelCase ) )
return torch.tensor(lowerCamelCase ,dtype=torch.floataa )
class __lowerCamelCase ( a_ , a_ ):
"""simple docstring"""
a = 1
@register_to_config
def __init__( self : Tuple , SCREAMING_SNAKE_CASE : int = 1000 , SCREAMING_SNAKE_CASE : float = 0.0001 , SCREAMING_SNAKE_CASE : float = 0.02 , SCREAMING_SNAKE_CASE : str = "linear" , SCREAMING_SNAKE_CASE : Optional[Union[np.ndarray, List[float]]] = None , SCREAMING_SNAKE_CASE : bool = True , SCREAMING_SNAKE_CASE : bool = True , SCREAMING_SNAKE_CASE : int = 0 , SCREAMING_SNAKE_CASE : str = "epsilon" , SCREAMING_SNAKE_CASE : float = 1.0 , **SCREAMING_SNAKE_CASE : List[str] , ):
if kwargs.get('set_alpha_to_one' , SCREAMING_SNAKE_CASE) is not None:
_A : Tuple = (
'The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead.'
)
deprecate('set_alpha_to_one' , '1.0.0' , SCREAMING_SNAKE_CASE , standard_warn=SCREAMING_SNAKE_CASE)
_A : Tuple = kwargs['set_alpha_to_one']
if trained_betas is not None:
_A : Any = torch.tensor(SCREAMING_SNAKE_CASE , dtype=torch.floataa)
elif beta_schedule == "linear":
_A : List[Any] = torch.linspace(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , dtype=torch.floataa)
elif beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
_A : List[str] = (
torch.linspace(beta_start**0.5 , beta_end**0.5 , SCREAMING_SNAKE_CASE , dtype=torch.floataa) ** 2
)
elif beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
_A : List[Any] = betas_for_alpha_bar(SCREAMING_SNAKE_CASE)
else:
raise NotImplementedError(F'{beta_schedule} does is not implemented for {self.__class__}')
_A : Optional[int] = 1.0 - self.betas
_A : Union[str, Any] = torch.cumprod(self.alphas , dim=0)
# At every step in inverted ddim, we are looking into the next alphas_cumprod
# For the final step, there is no next alphas_cumprod, and the index is out of bounds
# `set_alpha_to_zero` decides whether we set this parameter simply to zero
# in this case, self.step() just output the predicted noise
# or whether we use the final alpha of the "non-previous" one.
_A : Optional[int] = torch.tensor(0.0) if set_alpha_to_zero else self.alphas_cumprod[-1]
# standard deviation of the initial noise distribution
_A : Union[str, Any] = 1.0
# setable values
_A : List[str] = None
_A : Dict = torch.from_numpy(np.arange(0 , SCREAMING_SNAKE_CASE).copy().astype(np.intaa))
def A ( self : str , SCREAMING_SNAKE_CASE : torch.FloatTensor , SCREAMING_SNAKE_CASE : Optional[int] = None):
return sample
def A ( self : Dict , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : Union[str, torch.device] = None):
if num_inference_steps > self.config.num_train_timesteps:
raise ValueError(
F'`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'
F' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'
F' maximal {self.config.num_train_timesteps} timesteps.')
_A : Optional[Any] = num_inference_steps
_A : List[Any] = self.config.num_train_timesteps // self.num_inference_steps
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
_A : List[str] = (np.arange(0 , SCREAMING_SNAKE_CASE) * step_ratio).round().copy().astype(np.intaa)
_A : int = torch.from_numpy(SCREAMING_SNAKE_CASE).to(SCREAMING_SNAKE_CASE)
self.timesteps += self.config.steps_offset
def A ( self : List[Any] , SCREAMING_SNAKE_CASE : torch.FloatTensor , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : torch.FloatTensor , SCREAMING_SNAKE_CASE : float = 0.0 , SCREAMING_SNAKE_CASE : bool = False , SCREAMING_SNAKE_CASE : Optional[torch.FloatTensor] = None , SCREAMING_SNAKE_CASE : bool = True , ):
# 1. get previous step value (=t+1)
_A : Union[str, Any] = timestep + self.config.num_train_timesteps // self.num_inference_steps
# 2. compute alphas, betas
# change original implementation to exactly match noise levels for analogous forward process
_A : List[str] = self.alphas_cumprod[timestep]
_A : List[str] = (
self.alphas_cumprod[prev_timestep]
if prev_timestep < self.config.num_train_timesteps
else self.final_alpha_cumprod
)
_A : List[str] = 1 - alpha_prod_t
# 3. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
if self.config.prediction_type == "epsilon":
_A : Any = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
_A : List[Any] = model_output
elif self.config.prediction_type == "sample":
_A : List[Any] = model_output
_A : Dict = (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
elif self.config.prediction_type == "v_prediction":
_A : List[str] = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
_A : Optional[int] = (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
else:
raise ValueError(
F'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or'
' `v_prediction`')
# 4. Clip or threshold "predicted x_0"
if self.config.clip_sample:
_A : str = pred_original_sample.clamp(
-self.config.clip_sample_range , self.config.clip_sample_range)
# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
_A : Any = (1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon
# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
_A : Tuple = alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction
if not return_dict:
return (prev_sample, pred_original_sample)
return DDIMSchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE , pred_original_sample=SCREAMING_SNAKE_CASE)
def __len__( self : List[Any]):
return self.config.num_train_timesteps
| 227 | 1 |
'''simple docstring'''
import io
import json
import fsspec
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def snake_case_ ( _lowerCAmelCase : List[str] , _lowerCAmelCase : Optional[Any] ) -> Optional[int]:
assert isinstance(_lowerCAmelCase , _lowerCAmelCase )
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
@pytest.mark.parametrize('''keep_in_memory''' , [False, True] )
def snake_case_ ( _lowerCAmelCase : int , _lowerCAmelCase : Dict , _lowerCAmelCase : Optional[int] ) -> Dict:
UpperCAmelCase : Dict = tmp_path / '''cache'''
UpperCAmelCase : List[Any] = {'''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 : str = JsonDatasetReader(_lowerCAmelCase , cache_dir=_lowerCAmelCase , keep_in_memory=_lowerCAmelCase ).read()
_check_json_dataset(_lowerCAmelCase , _lowerCAmelCase )
@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 snake_case_ ( _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Dict , _lowerCAmelCase : int ) -> Optional[int]:
UpperCAmelCase : Any = tmp_path / '''cache'''
UpperCAmelCase : Tuple = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
UpperCAmelCase : Any = features.copy() if features else default_expected_features
UpperCAmelCase : List[Any] = (
Features({feature: Value(_lowerCAmelCase ) for feature, dtype in features.items()} ) if features is not None else None
)
UpperCAmelCase : Dict = JsonDatasetReader(_lowerCAmelCase , features=_lowerCAmelCase , cache_dir=_lowerCAmelCase ).read()
_check_json_dataset(_lowerCAmelCase , _lowerCAmelCase )
@pytest.mark.parametrize(
'''features''' , [
None,
{'''col_3''': '''float64''', '''col_1''': '''string''', '''col_2''': '''int64'''},
] , )
def snake_case_ ( _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : List[Any] , _lowerCAmelCase : Tuple ) -> Tuple:
UpperCAmelCase : Optional[Any] = tmp_path / '''cache'''
UpperCAmelCase : Optional[int] = {'''col_3''': '''float64''', '''col_1''': '''string''', '''col_2''': '''int64'''}
UpperCAmelCase : int = features.copy() if features else default_expected_features
UpperCAmelCase : Any = (
Features({feature: Value(_lowerCAmelCase ) for feature, dtype in features.items()} ) if features is not None else None
)
UpperCAmelCase : Tuple = JsonDatasetReader(_lowerCAmelCase , features=_lowerCAmelCase , cache_dir=_lowerCAmelCase ).read()
assert isinstance(_lowerCAmelCase , _lowerCAmelCase )
assert dataset.num_rows == 2
assert dataset.num_columns == 3
assert dataset.column_names == ["col_3", "col_1", "col_2"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
def snake_case_ ( _lowerCAmelCase : List[str] , _lowerCAmelCase : Dict ) -> Union[str, Any]:
# jsonl_312_path features are {"col_3": "float64", "col_1": "string", "col_2": "int64"}
UpperCAmelCase : Tuple = {'''col_2''': '''int64''', '''col_3''': '''float64''', '''col_1''': '''string'''}
UpperCAmelCase : List[str] = features.copy()
UpperCAmelCase : Union[str, Any] = (
Features({feature: Value(_lowerCAmelCase ) for feature, dtype in features.items()} ) if features is not None else None
)
UpperCAmelCase : Tuple = tmp_path / '''cache'''
UpperCAmelCase : List[str] = JsonDatasetReader(_lowerCAmelCase , features=_lowerCAmelCase , cache_dir=_lowerCAmelCase ).read()
assert isinstance(_lowerCAmelCase , _lowerCAmelCase )
assert dataset.num_rows == 2
assert dataset.num_columns == 3
assert dataset.column_names == ["col_2", "col_3", "col_1"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('''split''' , [None, NamedSplit('''train''' ), '''train''', '''test'''] )
def snake_case_ ( _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Tuple , _lowerCAmelCase : List[str] ) -> Optional[Any]:
UpperCAmelCase : Any = tmp_path / '''cache'''
UpperCAmelCase : List[Any] = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
UpperCAmelCase : List[Any] = JsonDatasetReader(_lowerCAmelCase , cache_dir=_lowerCAmelCase , split=_lowerCAmelCase ).read()
_check_json_dataset(_lowerCAmelCase , _lowerCAmelCase )
assert dataset.split == split if split else "train"
@pytest.mark.parametrize('''path_type''' , [str, list] )
def snake_case_ ( _lowerCAmelCase : List[Any] , _lowerCAmelCase : str , _lowerCAmelCase : Any ) -> Dict:
if issubclass(_lowerCAmelCase , _lowerCAmelCase ):
UpperCAmelCase : str = jsonl_path
elif issubclass(_lowerCAmelCase , _lowerCAmelCase ):
UpperCAmelCase : Dict = [jsonl_path]
UpperCAmelCase : int = tmp_path / '''cache'''
UpperCAmelCase : Dict = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
UpperCAmelCase : Optional[int] = JsonDatasetReader(_lowerCAmelCase , cache_dir=_lowerCAmelCase ).read()
_check_json_dataset(_lowerCAmelCase , _lowerCAmelCase )
def snake_case_ ( _lowerCAmelCase : List[str] , _lowerCAmelCase : Dict , _lowerCAmelCase : Dict=("train",) ) -> Union[str, Any]:
assert isinstance(_lowerCAmelCase , _lowerCAmelCase )
for split in splits:
UpperCAmelCase : List[str] = dataset_dict[split]
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
@pytest.mark.parametrize('''keep_in_memory''' , [False, True] )
def snake_case_ ( _lowerCAmelCase : int , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : str ) -> Any:
UpperCAmelCase : Optional[Any] = tmp_path / '''cache'''
UpperCAmelCase : List[str] = {'''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 : Optional[int] = JsonDatasetReader({'''train''': jsonl_path} , cache_dir=_lowerCAmelCase , keep_in_memory=_lowerCAmelCase ).read()
_check_json_datasetdict(_lowerCAmelCase , _lowerCAmelCase )
@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 snake_case_ ( _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Union[str, Any] ) -> int:
UpperCAmelCase : Dict = tmp_path / '''cache'''
UpperCAmelCase : Dict = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
UpperCAmelCase : Optional[int] = features.copy() if features else default_expected_features
UpperCAmelCase : Union[str, Any] = (
Features({feature: Value(_lowerCAmelCase ) for feature, dtype in features.items()} ) if features is not None else None
)
UpperCAmelCase : Tuple = JsonDatasetReader({'''train''': jsonl_path} , features=_lowerCAmelCase , cache_dir=_lowerCAmelCase ).read()
_check_json_datasetdict(_lowerCAmelCase , _lowerCAmelCase )
@pytest.mark.parametrize('''split''' , [None, NamedSplit('''train''' ), '''train''', '''test'''] )
def snake_case_ ( _lowerCAmelCase : Any , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Dict ) -> Union[str, Any]:
if split:
UpperCAmelCase : Optional[int] = {split: jsonl_path}
else:
UpperCAmelCase : Any = '''train'''
UpperCAmelCase : Any = {'''train''': jsonl_path, '''test''': jsonl_path}
UpperCAmelCase : Tuple = tmp_path / '''cache'''
UpperCAmelCase : int = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
UpperCAmelCase : Optional[Any] = JsonDatasetReader(_lowerCAmelCase , cache_dir=_lowerCAmelCase ).read()
_check_json_datasetdict(_lowerCAmelCase , _lowerCAmelCase , splits=list(path.keys() ) )
assert all(dataset[split].split == split for split in path.keys() )
def snake_case_ ( _lowerCAmelCase : List[str] ) -> str:
return json.load(_lowerCAmelCase )
def snake_case_ ( _lowerCAmelCase : Dict ) -> str:
return [json.loads(_lowerCAmelCase ) for line in buffer]
class SCREAMING_SNAKE_CASE:
"""simple docstring"""
@pytest.mark.parametrize('''lines, load_json_function''' , [(True, load_json_lines), (False, load_json)] )
def A ( self : Union[str, Any] , __snake_case : Tuple , __snake_case : Optional[Any] , __snake_case : Optional[int] ) -> Dict:
with io.BytesIO() as buffer:
JsonDatasetWriter(__snake_case , __snake_case , lines=__snake_case ).write()
buffer.seek(0 )
UpperCAmelCase : Union[str, Any] = load_json_function(__snake_case )
assert isinstance(__snake_case , __snake_case )
assert isinstance(exported_content[0] , __snake_case )
assert len(__snake_case ) == 10
@pytest.mark.parametrize(
'''orient, container, keys, len_at''' , [
('''records''', list, {'''tokens''', '''labels''', '''answers''', '''id'''}, None),
('''split''', dict, {'''columns''', '''data'''}, '''data'''),
('''index''', dict, set('''0123456789''' ), None),
('''columns''', dict, {'''tokens''', '''labels''', '''answers''', '''id'''}, '''tokens'''),
('''values''', list, None, None),
('''table''', dict, {'''schema''', '''data'''}, '''data'''),
] , )
def A ( self : Optional[int] , __snake_case : Optional[Any] , __snake_case : str , __snake_case : Optional[Any] , __snake_case : List[str] , __snake_case : Optional[Any] ) -> List[Any]:
with io.BytesIO() as buffer:
JsonDatasetWriter(__snake_case , __snake_case , lines=__snake_case , orient=__snake_case ).write()
buffer.seek(0 )
UpperCAmelCase : Union[str, Any] = load_json(__snake_case )
assert isinstance(__snake_case , __snake_case )
if keys:
if container is dict:
assert exported_content.keys() == keys
else:
assert exported_content[0].keys() == keys
else:
assert not hasattr(__snake_case , '''keys''' ) and not hasattr(exported_content[0] , '''keys''' )
if len_at:
assert len(exported_content[len_at] ) == 10
else:
assert len(__snake_case ) == 10
@pytest.mark.parametrize('''lines, load_json_function''' , [(True, load_json_lines), (False, load_json)] )
def A ( self : str , __snake_case : str , __snake_case : str , __snake_case : int ) -> Any:
with io.BytesIO() as buffer:
JsonDatasetWriter(__snake_case , __snake_case , lines=__snake_case , num_proc=2 ).write()
buffer.seek(0 )
UpperCAmelCase : Any = load_json_function(__snake_case )
assert isinstance(__snake_case , __snake_case )
assert isinstance(exported_content[0] , __snake_case )
assert len(__snake_case ) == 10
@pytest.mark.parametrize(
'''orient, container, keys, len_at''' , [
('''records''', list, {'''tokens''', '''labels''', '''answers''', '''id'''}, None),
('''split''', dict, {'''columns''', '''data'''}, '''data'''),
('''index''', dict, set('''0123456789''' ), None),
('''columns''', dict, {'''tokens''', '''labels''', '''answers''', '''id'''}, '''tokens'''),
('''values''', list, None, None),
('''table''', dict, {'''schema''', '''data'''}, '''data'''),
] , )
def A ( self : Any , __snake_case : int , __snake_case : Union[str, Any] , __snake_case : Optional[int] , __snake_case : Optional[Any] , __snake_case : List[str] ) -> Any:
with io.BytesIO() as buffer:
JsonDatasetWriter(__snake_case , __snake_case , lines=__snake_case , orient=__snake_case , num_proc=2 ).write()
buffer.seek(0 )
UpperCAmelCase : List[str] = load_json(__snake_case )
assert isinstance(__snake_case , __snake_case )
if keys:
if container is dict:
assert exported_content.keys() == keys
else:
assert exported_content[0].keys() == keys
else:
assert not hasattr(__snake_case , '''keys''' ) and not hasattr(exported_content[0] , '''keys''' )
if len_at:
assert len(exported_content[len_at] ) == 10
else:
assert len(__snake_case ) == 10
def A ( self : List[Any] , __snake_case : str ) -> Dict:
with pytest.raises(__snake_case ):
with io.BytesIO() as buffer:
JsonDatasetWriter(__snake_case , __snake_case , num_proc=0 )
@pytest.mark.parametrize('''compression, extension''' , [('''gzip''', '''gz'''), ('''bz2''', '''bz2'''), ('''xz''', '''xz''')] )
def A ( self : Optional[int] , __snake_case : Any , __snake_case : str , __snake_case : Union[str, Any] , __snake_case : Union[str, Any] , __snake_case : Dict ) -> Union[str, Any]:
UpperCAmelCase : List[str] = tmp_path_factory.mktemp('''data''' ) / F"""test.json.{extension}"""
UpperCAmelCase : List[Any] = str(shared_datadir / F"""test_file.json.{extension}""" )
JsonDatasetWriter(__snake_case , __snake_case , compression=__snake_case ).write()
with fsspec.open(__snake_case , '''rb''' , compression='''infer''' ) as f:
UpperCAmelCase : str = f.read()
with fsspec.open(__snake_case , '''rb''' , compression='''infer''' ) as f:
UpperCAmelCase : Optional[int] = f.read()
assert exported_content == original_content
| 23 |
'''simple docstring'''
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__: Tuple = logging.get_logger(__name__)
UpperCamelCase__: Optional[int] = {"vocab_file": "sentencepiece.bpe.model"}
UpperCamelCase__: Optional[int] = {
"vocab_file": {
"moussaKam/mbarthez": "https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model",
"moussaKam/barthez": "https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model",
"moussaKam/barthez-orangesum-title": (
"https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"
),
},
}
UpperCamelCase__: Dict = {
"moussaKam/mbarthez": 1024,
"moussaKam/barthez": 1024,
"moussaKam/barthez-orangesum-title": 1024,
}
UpperCamelCase__: Tuple = "▁"
class SCREAMING_SNAKE_CASE( A__ ):
"""simple docstring"""
lowerCamelCase__ = VOCAB_FILES_NAMES
lowerCamelCase__ = PRETRAINED_VOCAB_FILES_MAP
lowerCamelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCamelCase__ = ["""input_ids""", """attention_mask"""]
def __init__( self : List[Any] , __snake_case : List[Any] , __snake_case : Tuple="<s>" , __snake_case : List[Any]="</s>" , __snake_case : int="</s>" , __snake_case : Any="<s>" , __snake_case : Optional[int]="<unk>" , __snake_case : Union[str, Any]="<pad>" , __snake_case : Union[str, Any]="<mask>" , __snake_case : Optional[Dict[str, Any]] = None , **__snake_case : Dict , ) -> None:
# Mask token behave like a normal word, i.e. include the space before it
UpperCAmelCase : int = AddedToken(__snake_case , lstrip=__snake_case , rstrip=__snake_case ) if isinstance(__snake_case , __snake_case ) else mask_token
UpperCAmelCase : Union[str, Any] = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=__snake_case , eos_token=__snake_case , unk_token=__snake_case , sep_token=__snake_case , cls_token=__snake_case , pad_token=__snake_case , mask_token=__snake_case , sp_model_kwargs=self.sp_model_kwargs , **__snake_case , )
UpperCAmelCase : Optional[int] = vocab_file
UpperCAmelCase : int = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(__snake_case ) )
UpperCAmelCase : int = {'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}
UpperCAmelCase : Optional[Any] = len(self.sp_model ) - 1
UpperCAmelCase : Optional[int] = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
def A ( self : Tuple , __snake_case : List[int] , __snake_case : Optional[List[int]] = None ) -> List[int]:
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
UpperCAmelCase : Union[str, Any] = [self.cls_token_id]
UpperCAmelCase : Optional[Any] = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def A ( self : int , __snake_case : List[int] , __snake_case : Optional[List[int]] = None , __snake_case : bool = False ) -> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=__snake_case , token_ids_a=__snake_case , already_has_special_tokens=__snake_case )
if token_ids_a is None:
return [1] + ([0] * len(__snake_case )) + [1]
return [1] + ([0] * len(__snake_case )) + [1, 1] + ([0] * len(__snake_case )) + [1]
def A ( self : Optional[int] , __snake_case : List[int] , __snake_case : Optional[List[int]] = None ) -> List[int]:
UpperCAmelCase : Tuple = [self.sep_token_id]
UpperCAmelCase : Tuple = [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 A ( self : Dict ) -> Optional[int]:
return len(self.sp_model )
def A ( self : List[str] ) -> Dict:
UpperCAmelCase : Optional[Any] = {self.convert_ids_to_tokens(__snake_case ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def A ( self : Optional[Any] , __snake_case : str ) -> List[str]:
return self.sp_model.encode(__snake_case , out_type=__snake_case )
def A ( self : int , __snake_case : int ) -> int:
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
UpperCAmelCase : Optional[Any] = self.sp_model.PieceToId(__snake_case )
return spm_id if spm_id else self.unk_token_id
def A ( self : int , __snake_case : Any ) -> List[Any]:
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(__snake_case )
def A ( self : List[Any] , __snake_case : Union[str, Any] ) -> List[str]:
UpperCAmelCase : Optional[Any] = []
UpperCAmelCase : int = ''''''
UpperCAmelCase : Union[str, Any] = 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(__snake_case ) + token
UpperCAmelCase : str = True
UpperCAmelCase : List[str] = []
else:
current_sub_tokens.append(__snake_case )
UpperCAmelCase : Optional[int] = False
out_string += self.sp_model.decode(__snake_case )
return out_string.strip()
def __getstate__( self : Union[str, Any] ) -> Union[str, Any]:
UpperCAmelCase : Optional[Any] = self.__dict__.copy()
UpperCAmelCase : Any = None
return state
def __setstate__( self : Optional[int] , __snake_case : Union[str, Any] ) -> List[Any]:
UpperCAmelCase : Dict = d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
UpperCAmelCase : Optional[Any] = {}
UpperCAmelCase : Any = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def A ( self : Optional[int] , __snake_case : str , __snake_case : Optional[str] = None ) -> Tuple[str]:
if not os.path.isdir(__snake_case ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
UpperCAmelCase : Union[str, Any] = os.path.join(
__snake_case , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(__snake_case ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , __snake_case )
elif not os.path.isfile(self.vocab_file ):
with open(__snake_case , '''wb''' ) as fi:
UpperCAmelCase : Any = self.sp_model.serialized_model_proto()
fi.write(__snake_case )
return (out_vocab_file,)
| 23 | 1 |
import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load_dataset, load_metric
from torch.utils.data import DataLoader
from utils_qa import postprocess_qa_predictions
import transformers
from transformers import AutoTokenizer, EvalPrediction, default_data_collator, set_seed
from transformers.trainer_pt_utils import nested_concat, nested_truncate
_A : int = trt.Logger(trt.Logger.WARNING)
_A : List[Any] = absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)
_A : Optional[int] = logging.getLogger(__name__)
_A : List[str] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--onnx_model_path',
default=None,
type=str,
required=True,
help='Path to ONNX model: ',
)
parser.add_argument(
'--output_dir',
default=None,
type=str,
required=True,
help='The output directory where the model checkpoints and predictions will be written.',
)
# Other parameters
parser.add_argument(
'--tokenizer_name',
default='',
type=str,
required=True,
help='Pretrained tokenizer name or path if not the same as model_name',
)
parser.add_argument(
'--version_2_with_negative',
action='store_true',
help='If true, the SQuAD examples contain some that do not have an answer.',
)
parser.add_argument(
'--null_score_diff_threshold',
type=float,
default=0.0,
help='If null_score - best_non_null is greater than the threshold predict null.',
)
parser.add_argument(
'--max_seq_length',
default=3_84,
type=int,
help=(
'The maximum total input sequence length after WordPiece tokenization. Sequences '
'longer than this will be truncated, and sequences shorter than this will be padded.'
),
)
parser.add_argument(
'--doc_stride',
default=1_28,
type=int,
help='When splitting up a long document into chunks, how much stride to take between chunks.',
)
parser.add_argument('--per_device_eval_batch_size', default=8, type=int, help='Batch size per GPU/CPU for evaluation.')
parser.add_argument(
'--n_best_size',
default=20,
type=int,
help='The total number of n-best predictions to generate in the nbest_predictions.json output file.',
)
parser.add_argument(
'--max_answer_length',
default=30,
type=int,
help=(
'The maximum length of an answer that can be generated. This is needed because the start '
'and end predictions are not conditioned on one another.'
),
)
parser.add_argument('--seed', type=int, default=42, help='random seed for initialization')
parser.add_argument(
'--dataset_name',
type=str,
default=None,
required=True,
help='The name of the dataset to use (via the datasets library).',
)
parser.add_argument(
'--dataset_config_name',
type=str,
default=None,
help='The configuration name of the dataset to use (via the datasets library).',
)
parser.add_argument(
'--preprocessing_num_workers', type=int, default=4, help='A csv or a json file containing the training data.'
)
parser.add_argument('--overwrite_cache', action='store_true', help='Overwrite the cached training and evaluation sets')
parser.add_argument(
'--fp16',
action='store_true',
help='Whether to use 16-bit (mixed) precision instead of 32-bit',
)
parser.add_argument(
'--int8',
action='store_true',
help='Whether to use INT8',
)
_A : Optional[int] = parser.parse_args()
if args.tokenizer_name:
_A : List[Any] = AutoTokenizer.from_pretrained(args.tokenizer_name, use_fast=True)
else:
raise ValueError(
'You are instantiating a new tokenizer from scratch. This is not supported by this script.'
'You can do it from another script, save it, and load it from here, using --tokenizer_name.'
)
logger.info('Training/evaluation parameters %s', args)
_A : List[Any] = args.per_device_eval_batch_size
_A : List[Any] = (args.eval_batch_size, args.max_seq_length)
# TRT Engine properties
_A : Optional[int] = True
_A : Optional[Any] = 'temp_engine/bert-fp32.engine'
if args.fpaa:
_A : Dict = 'temp_engine/bert-fp16.engine'
if args.inta:
_A : str = 'temp_engine/bert-int8.engine'
# import ONNX file
if not os.path.exists('temp_engine'):
os.makedirs('temp_engine')
_A : Optional[int] = 1 << (int)(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
with trt.Builder(TRT_LOGGER) as builder, builder.create_network(EXPLICIT_BATCH) as network, trt.OnnxParser(
network, TRT_LOGGER
) as parser:
with open(args.onnx_model_path, 'rb') as model:
if not parser.parse(model.read()):
for error in range(parser.num_errors):
print(parser.get_error(error))
# Query input names and shapes from parsed TensorRT network
_A : Dict = [network.get_input(i) for i in range(network.num_inputs)]
_A : List[Any] = [_input.name for _input in network_inputs] # ex: ["actual_input1"]
with builder.create_builder_config() as config:
_A : List[str] = 1 << 50
if STRICT_TYPES:
config.set_flag(trt.BuilderFlag.STRICT_TYPES)
if args.fpaa:
config.set_flag(trt.BuilderFlag.FPaa)
if args.inta:
config.set_flag(trt.BuilderFlag.INTa)
_A : Optional[int] = builder.create_optimization_profile()
config.add_optimization_profile(profile)
for i in range(len(input_names)):
profile.set_shape(input_names[i], INPUT_SHAPE, INPUT_SHAPE, INPUT_SHAPE)
_A : Dict = builder.build_engine(network, config)
# serialize_engine and store in file (can be directly loaded and deserialized):
with open(engine_name, 'wb') as f:
f.write(engine.serialize())
def _a ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) -> Tuple:
"""simple docstring"""
lowerCamelCase__ : Dict = np.asarray(inputs['''input_ids'''] , dtype=np.intaa )
lowerCamelCase__ : List[Any] = np.asarray(inputs['''attention_mask'''] , dtype=np.intaa )
lowerCamelCase__ : List[Any] = np.asarray(inputs['''token_type_ids'''] , dtype=np.intaa )
# Copy inputs
cuda.memcpy_htod_async(d_inputs[0] , input_ids.ravel() , UpperCAmelCase )
cuda.memcpy_htod_async(d_inputs[1] , attention_mask.ravel() , UpperCAmelCase )
cuda.memcpy_htod_async(d_inputs[2] , token_type_ids.ravel() , UpperCAmelCase )
# start time
lowerCamelCase__ : int = time.time()
# Run inference
context.execute_async(
bindings=[int(UpperCAmelCase ) for d_inp in d_inputs] + [int(UpperCAmelCase ), int(UpperCAmelCase )] , stream_handle=stream.handle )
# Transfer predictions back from GPU
cuda.memcpy_dtoh_async(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
cuda.memcpy_dtoh_async(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
# Synchronize the stream and take time
stream.synchronize()
# end time
lowerCamelCase__ : Union[str, Any] = time.time()
lowerCamelCase__ : List[str] = end_time - start_time
lowerCamelCase__ : List[Any] = (h_outputa, h_outputa)
# print(outputs)
return outputs, infer_time
# Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
_A : Tuple = Accelerator()
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
datefmt='%m/%d/%Y %H:%M:%S',
level=logging.INFO,
)
# Setup logging, we only want one process per machine to log things on the screen.
# accelerator.is_local_main_process is only True for one process per machine.
logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR)
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_info()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed)
# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
#
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
if args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
_A : Union[str, Any] = load_dataset(args.dataset_name, args.dataset_config_name)
else:
raise ValueError('Evaluation requires a dataset name')
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Preprocessing the datasets.
# Preprocessing is slighlty different for training and evaluation.
_A : Optional[Any] = raw_datasets['validation'].column_names
_A : List[str] = 'question' if 'question' in column_names else column_names[0]
_A : int = 'context' if 'context' in column_names else column_names[1]
_A : int = 'answers' if 'answers' in column_names else column_names[2]
# Padding side determines if we do (question|context) or (context|question).
_A : Optional[Any] = tokenizer.padding_side == 'right'
if args.max_seq_length > tokenizer.model_max_length:
logger.warning(
F'''The max_seq_length passed ({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}.'''
)
_A : Optional[int] = min(args.max_seq_length, tokenizer.model_max_length)
def _a ( UpperCAmelCase ) -> Any:
"""simple docstring"""
lowerCamelCase__ : Tuple = [q.lstrip() for q in examples[question_column_name]]
# Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
# in one example possible giving several features when a context is long, each of those features having a
# context that overlaps a bit the context of the previous feature.
lowerCamelCase__ : List[Any] = tokenizer(
examples[question_column_name if pad_on_right else context_column_name] , examples[context_column_name if pad_on_right else question_column_name] , truncation='''only_second''' if pad_on_right else '''only_first''' , max_length=UpperCAmelCase , stride=args.doc_stride , return_overflowing_tokens=UpperCAmelCase , return_offsets_mapping=UpperCAmelCase , padding='''max_length''' , )
# Since one example might give us several features if it has a long context, we need a map from a feature to
# its corresponding example. This key gives us just that.
lowerCamelCase__ : Dict = tokenized_examples.pop('''overflow_to_sample_mapping''' )
# For evaluation, we will need to convert our predictions to substrings of the context, so we keep the
# corresponding example_id and we will store the offset mappings.
lowerCamelCase__ : Union[str, Any] = []
for i in range(len(tokenized_examples['''input_ids'''] ) ):
# Grab the sequence corresponding to that example (to know what is the context and what is the question).
lowerCamelCase__ : Optional[int] = tokenized_examples.sequence_ids(UpperCAmelCase )
lowerCamelCase__ : List[str] = 1 if pad_on_right else 0
# One example can give several spans, this is the index of the example containing this span of text.
lowerCamelCase__ : int = sample_mapping[i]
tokenized_examples["example_id"].append(examples['''id'''][sample_index] )
# Set to None the offset_mapping that are not part of the context so it's easy to determine if a token
# position is part of the context or not.
lowerCamelCase__ : int = [
(o if sequence_ids[k] == context_index else None)
for k, o in enumerate(tokenized_examples['''offset_mapping'''][i] )
]
return tokenized_examples
_A : Optional[Any] = raw_datasets['validation']
# Validation Feature Creation
_A : Any = eval_examples.map(
prepare_validation_features,
batched=True,
num_proc=args.preprocessing_num_workers,
remove_columns=column_names,
load_from_cache_file=not args.overwrite_cache,
desc='Running tokenizer on validation dataset',
)
_A : int = default_data_collator
_A : Optional[Any] = eval_dataset.remove_columns(['example_id', 'offset_mapping'])
_A : Tuple = DataLoader(
eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)
def _a ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase="eval" ) -> List[Any]:
"""simple docstring"""
lowerCamelCase__ : Union[str, Any] = postprocess_qa_predictions(
examples=UpperCAmelCase , features=UpperCAmelCase , predictions=UpperCAmelCase , version_2_with_negative=args.version_2_with_negative , n_best_size=args.n_best_size , max_answer_length=args.max_answer_length , null_score_diff_threshold=args.null_score_diff_threshold , output_dir=args.output_dir , prefix=UpperCAmelCase , )
# Format the result to the format the metric expects.
if args.version_2_with_negative:
lowerCamelCase__ : Tuple = [
{'''id''': k, '''prediction_text''': v, '''no_answer_probability''': 0.0} for k, v in predictions.items()
]
else:
lowerCamelCase__ : List[str] = [{'''id''': k, '''prediction_text''': v} for k, v in predictions.items()]
lowerCamelCase__ : Union[str, Any] = [{'''id''': ex['''id'''], '''answers''': ex[answer_column_name]} for ex in examples]
return EvalPrediction(predictions=UpperCAmelCase , label_ids=UpperCAmelCase )
_A : List[str] = load_metric('squad_v2' if args.version_2_with_negative else 'squad')
# Evaluation!
logger.info('Loading ONNX model %s for evaluation', args.onnx_model_path)
with open(engine_name, 'rb') as f, trt.Runtime(TRT_LOGGER) as runtime, runtime.deserialize_cuda_engine(
f.read()
) as engine, engine.create_execution_context() as context:
# setup for TRT inferrence
for i in range(len(input_names)):
context.set_binding_shape(i, INPUT_SHAPE)
assert context.all_binding_shapes_specified
def _a ( UpperCAmelCase ) -> Optional[int]:
"""simple docstring"""
return trt.volume(engine.get_binding_shape(UpperCAmelCase ) ) * engine.get_binding_dtype(UpperCAmelCase ).itemsize
# Allocate device memory for inputs and outputs.
_A : Tuple = [cuda.mem_alloc(binding_nbytes(binding)) for binding in engine if engine.binding_is_input(binding)]
# Allocate output buffer
_A : List[Any] = cuda.pagelocked_empty(tuple(context.get_binding_shape(3)), dtype=np.floataa)
_A : Any = cuda.pagelocked_empty(tuple(context.get_binding_shape(4)), dtype=np.floataa)
_A : Dict = cuda.mem_alloc(h_outputa.nbytes)
_A : Optional[int] = cuda.mem_alloc(h_outputa.nbytes)
# Create a stream in which to copy inputs/outputs and run inference.
_A : str = cuda.Stream()
# Evaluation
logger.info('***** Running Evaluation *****')
logger.info(F''' Num examples = {len(eval_dataset)}''')
logger.info(F''' Batch size = {args.per_device_eval_batch_size}''')
_A : Optional[Any] = 0.0
_A : Optional[Any] = 0
_A : int = timeit.default_timer()
_A : int = None
for step, batch in enumerate(eval_dataloader):
_A : Optional[Any] = model_infer(batch, context, d_inputs, h_outputa, h_outputa, d_outputa, d_outputa, stream)
total_time += infer_time
niter += 1
_A : List[Any] = outputs
_A : str = torch.tensor(start_logits)
_A : Tuple = torch.tensor(end_logits)
# necessary to pad predictions and labels for being gathered
_A : Optional[Any] = accelerator.pad_across_processes(start_logits, dim=1, pad_index=-1_00)
_A : List[str] = accelerator.pad_across_processes(end_logits, dim=1, pad_index=-1_00)
_A : Optional[Any] = (accelerator.gather(start_logits).cpu().numpy(), accelerator.gather(end_logits).cpu().numpy())
_A : Dict = logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-1_00)
if all_preds is not None:
_A : str = nested_truncate(all_preds, len(eval_dataset))
_A : str = timeit.default_timer() - start_time
logger.info(' Evaluation done in total %f secs (%f sec per example)', evalTime, evalTime / len(eval_dataset))
# Inference time from TRT
logger.info('Average Inference Time = {:.3f} ms'.format(total_time * 10_00 / niter))
logger.info('Total Inference Time = {:.3f} ms'.format(total_time * 10_00))
logger.info('Total Number of Inference = %d', niter)
_A : int = post_processing_function(eval_examples, eval_dataset, all_preds)
_A : Optional[Any] = metric.compute(predictions=prediction.predictions, references=prediction.label_ids)
logger.info(F'''Evaluation metrics: {eval_metric}''')
| 369 |
def _a ( UpperCAmelCase ) -> bool:
"""simple docstring"""
return str(UpperCAmelCase ) == str(UpperCAmelCase )[::-1]
def _a ( UpperCAmelCase ) -> int:
"""simple docstring"""
return int(UpperCAmelCase ) + int(str(UpperCAmelCase )[::-1] )
def _a ( UpperCAmelCase = 10000 ) -> int:
"""simple docstring"""
lowerCamelCase__ : Tuple = []
for num in range(1 , UpperCAmelCase ):
lowerCamelCase__ : List[str] = 0
lowerCamelCase__ : Union[str, Any] = num
while iterations < 50:
lowerCamelCase__ : Dict = sum_reverse(UpperCAmelCase )
iterations += 1
if is_palindrome(UpperCAmelCase ):
break
else:
lychrel_nums.append(UpperCAmelCase )
return len(UpperCAmelCase )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 265 | 0 |
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> Any:
"""simple docstring"""
A__ = [0] * len(lowercase_ )
A__ = []
A__ = [1] * len(lowercase_ )
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(lowercase_ ) ):
if indegree[i] == 0:
queue.append(lowercase_ )
while queue:
A__ = queue.pop(0 )
for x in graph[vertex]:
indegree[x] -= 1
if long_dist[vertex] + 1 > long_dist[x]:
A__ = long_dist[vertex] + 1
if indegree[x] == 0:
queue.append(lowercase_ )
print(max(lowercase_ ) )
# Adjacency list of Graph
_lowerCamelCase : Optional[int] = {0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}
longest_distance(graph)
| 14 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers.models.xglm.modeling_tf_xglm import (
TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXGLMForCausalLM,
TFXGLMModel,
)
@require_tf
class A_ :
'''simple docstring'''
UpperCAmelCase_ : Optional[Any] = XGLMConfig
UpperCAmelCase_ : str = {}
UpperCAmelCase_ : List[str] = """gelu"""
def __init__( self : Tuple , lowercase_ : str , lowercase_ : List[str]=14 , lowercase_ : Optional[int]=7 , lowercase_ : Optional[int]=True , lowercase_ : List[str]=True , lowercase_ : Union[str, Any]=True , lowercase_ : Dict=99 , lowercase_ : Optional[int]=32 , lowercase_ : Any=2 , lowercase_ : Union[str, Any]=4 , lowercase_ : Optional[int]=37 , lowercase_ : List[str]="gelu" , lowercase_ : Tuple=0.1 , lowercase_ : List[Any]=0.1 , lowercase_ : List[Any]=512 , lowercase_ : Union[str, Any]=0.02 , ) -> str:
UpperCAmelCase : Optional[Any] = parent
UpperCAmelCase : Optional[int] = batch_size
UpperCAmelCase : List[str] = seq_length
UpperCAmelCase : List[str] = is_training
UpperCAmelCase : str = use_input_mask
UpperCAmelCase : int = use_labels
UpperCAmelCase : Union[str, Any] = vocab_size
UpperCAmelCase : Optional[int] = d_model
UpperCAmelCase : str = num_hidden_layers
UpperCAmelCase : Optional[int] = num_attention_heads
UpperCAmelCase : List[str] = ffn_dim
UpperCAmelCase : Optional[int] = activation_function
UpperCAmelCase : Optional[Any] = activation_dropout
UpperCAmelCase : Dict = attention_dropout
UpperCAmelCase : List[str] = max_position_embeddings
UpperCAmelCase : List[Any] = initializer_range
UpperCAmelCase : Optional[Any] = None
UpperCAmelCase : str = 0
UpperCAmelCase : List[Any] = 2
UpperCAmelCase : Optional[Any] = 1
def UpperCAmelCase_ ( self : int ) -> Union[str, Any]:
return XGLMConfig.from_pretrained('facebook/xglm-564M' )
def UpperCAmelCase_ ( self : Dict ) -> int:
UpperCAmelCase : Any = tf.clip_by_value(
ids_tensor([self.batch_size, self.seq_length] , self.vocab_size ) , clip_value_min=0 , clip_value_max=3 )
UpperCAmelCase : Optional[Any] = None
if self.use_input_mask:
UpperCAmelCase : Optional[int] = random_attention_mask([self.batch_size, self.seq_length] )
UpperCAmelCase : Any = self.get_config()
UpperCAmelCase : Optional[int] = floats_tensor([self.num_hidden_layers, self.num_attention_heads] , 2 )
return (
config,
input_ids,
input_mask,
head_mask,
)
def UpperCAmelCase_ ( self : Union[str, Any] ) -> Tuple:
return XGLMConfig(
vocab_size=self.vocab_size , d_model=self.hidden_size , num_layers=self.num_hidden_layers , attention_heads=self.num_attention_heads , ffn_dim=self.ffn_dim , activation_function=self.activation_function , activation_dropout=self.activation_dropout , attention_dropout=self.attention_dropout , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , use_cache=lowercase_ , bos_token_id=self.bos_token_id , eos_token_id=self.eos_token_id , pad_token_id=self.pad_token_id , return_dict=lowercase_ , )
def UpperCAmelCase_ ( self : List[str] ) -> Dict:
UpperCAmelCase : Union[str, Any] = self.prepare_config_and_inputs()
(
(
UpperCAmelCase
) , (
UpperCAmelCase
) , (
UpperCAmelCase
) , (
UpperCAmelCase
) ,
) : Union[str, Any] = config_and_inputs
UpperCAmelCase : List[str] = {
'input_ids': input_ids,
'head_mask': head_mask,
}
return config, inputs_dict
@require_tf
class A_ ( _snake_case , _snake_case , unittest.TestCase ):
'''simple docstring'''
UpperCAmelCase_ : int = (TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
UpperCAmelCase_ : List[str] = (TFXGLMForCausalLM,) if is_tf_available() else ()
UpperCAmelCase_ : str = (
{"""feature-extraction""": TFXGLMModel, """text-generation""": TFXGLMForCausalLM} if is_tf_available() else {}
)
UpperCAmelCase_ : List[str] = False
UpperCAmelCase_ : List[str] = False
UpperCAmelCase_ : str = False
def UpperCAmelCase_ ( self : Union[str, Any] ) -> Any:
UpperCAmelCase : Any = TFXGLMModelTester(self )
UpperCAmelCase : int = ConfigTester(self , config_class=lowercase_ , n_embd=37 )
def UpperCAmelCase_ ( self : Any ) -> List[str]:
self.config_tester.run_common_tests()
@slow
def UpperCAmelCase_ ( self : Tuple ) -> Optional[int]:
for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCAmelCase : int = TFXGLMModel.from_pretrained(lowercase_ )
self.assertIsNotNone(lowercase_ )
@unittest.skip(reason='Currently, model embeddings are going to undergo a major refactor.' )
def UpperCAmelCase_ ( self : List[Any] ) -> List[Any]:
super().test_resize_token_embeddings()
@require_tf
class A_ ( unittest.TestCase ):
'''simple docstring'''
@slow
def UpperCAmelCase_ ( self : int , lowercase_ : str=True ) -> Any:
UpperCAmelCase : str = TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M' )
UpperCAmelCase : Any = tf.convert_to_tensor([[2, 268, 9_865]] , dtype=tf.intaa ) # The dog
# </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
# fmt: off
UpperCAmelCase : Union[str, Any] = [2, 268, 9_865, 67, 11, 1_988, 57_252, 9_865, 5, 984, 67, 1_988, 213_838, 1_658, 53, 70_446, 33, 6_657, 278, 1_581]
# fmt: on
UpperCAmelCase : int = model.generate(lowercase_ , do_sample=lowercase_ , num_beams=1 )
if verify_outputs:
self.assertListEqual(output_ids[0].numpy().tolist() , lowercase_ )
@slow
def UpperCAmelCase_ ( self : Optional[int] ) -> int:
UpperCAmelCase : str = XGLMTokenizer.from_pretrained('facebook/xglm-564M' )
UpperCAmelCase : Tuple = TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M' )
tf.random.set_seed(0 )
UpperCAmelCase : Dict = tokenizer('Today is a nice day and' , return_tensors='tf' )
UpperCAmelCase : Tuple = tokenized.input_ids
# forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
with tf.device(':/CPU:0' ):
UpperCAmelCase : int = model.generate(lowercase_ , do_sample=lowercase_ , seed=[7, 0] )
UpperCAmelCase : Dict = tokenizer.decode(output_ids[0] , skip_special_tokens=lowercase_ )
UpperCAmelCase : Dict = (
'Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due'
)
self.assertEqual(lowercase_ , lowercase_ )
@slow
def UpperCAmelCase_ ( self : int ) -> str:
UpperCAmelCase : List[str] = TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M' )
UpperCAmelCase : Any = XGLMTokenizer.from_pretrained('facebook/xglm-564M' )
UpperCAmelCase : str = 'left'
# use different length sentences to test batching
UpperCAmelCase : Tuple = [
'This is an extremelly long sentence that only exists to test the ability of the model to cope with '
'left-padding, such as in batched generation. The output for the sequence below should be the same '
'regardless of whether left padding is applied or not. When',
'Hello, my dog is a little',
]
UpperCAmelCase : Union[str, Any] = tokenizer(lowercase_ , return_tensors='tf' , padding=lowercase_ )
UpperCAmelCase : Any = inputs['input_ids']
UpperCAmelCase : int = model.generate(input_ids=lowercase_ , attention_mask=inputs['attention_mask'] , max_new_tokens=12 )
UpperCAmelCase : Union[str, Any] = tokenizer(sentences[0] , return_tensors='tf' ).input_ids
UpperCAmelCase : Dict = model.generate(input_ids=lowercase_ , max_new_tokens=12 )
UpperCAmelCase : Tuple = tokenizer(sentences[1] , return_tensors='tf' ).input_ids
UpperCAmelCase : List[Any] = model.generate(input_ids=lowercase_ , max_new_tokens=12 )
UpperCAmelCase : List[str] = tokenizer.batch_decode(lowercase_ , skip_special_tokens=lowercase_ )
UpperCAmelCase : List[str] = tokenizer.decode(output_non_padded[0] , skip_special_tokens=lowercase_ )
UpperCAmelCase : Optional[int] = tokenizer.decode(output_padded[0] , skip_special_tokens=lowercase_ )
UpperCAmelCase : str = [
'This is an extremelly long sentence that only exists to test the ability of the model to cope with '
'left-padding, such as in batched generation. The output for the sequence below should be the same '
'regardless of whether left padding is applied or not. When left padding is applied, the sequence will be '
'a single',
'Hello, my dog is a little bit of a shy one, but he is very friendly',
]
self.assertListEqual(lowercase_ , lowercase_ )
self.assertListEqual(lowercase_ , [non_padded_sentence, padded_sentence] )
| 151 | 0 |
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, NewType, Optional, Tuple, Union, get_type_hints
import yaml
_a = NewType('DataClass', Any)
_a = NewType('DataClassType', Any)
def __a ( __lowerCamelCase ):
if isinstance(a__, a__ ):
return v
if v.lower() in ("yes", "true", "t", "y", "1"):
return True
elif v.lower() in ("no", "false", "f", "n", "0"):
return False
else:
raise ArgumentTypeError(
f"""Truthy value expected: got {v} but expected one of yes/no, true/false, t/f, y/n, 1/0 (case insensitive).""" )
def __a ( __lowerCamelCase ):
UpperCAmelCase_ : Tuple = {str(a__ ): choice for choice in choices}
return lambda __lowerCamelCase : str_to_choice.get(a__, a__ )
def __a ( *,
__lowerCamelCase = None, __lowerCamelCase = None, __lowerCamelCase = dataclasses.MISSING, __lowerCamelCase = dataclasses.MISSING, __lowerCamelCase = None, **__lowerCamelCase, ):
if metadata is None:
# Important, don't use as default param in function signature because dict is mutable and shared across function calls
UpperCAmelCase_ : Any = {}
if aliases is not None:
UpperCAmelCase_ : List[Any] = aliases
if help is not None:
UpperCAmelCase_ : Optional[int] = help
return dataclasses.field(metadata=a__, default=a__, default_factory=a__, **a__ )
class A_ (_lowercase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : List[str] = 42
def __init__( self , lowercase_ , **lowercase_ ):
"""simple docstring"""
# To make the default appear when using --help
if "formatter_class" not in kwargs:
UpperCAmelCase_ : List[str] = ArgumentDefaultsHelpFormatter
super().__init__(**__UpperCamelCase )
if dataclasses.is_dataclass(__UpperCamelCase ):
UpperCAmelCase_ : Union[str, Any] = [dataclass_types]
UpperCAmelCase_ : Optional[Any] = list(__UpperCamelCase )
for dtype in self.dataclass_types:
self._add_dataclass_arguments(__UpperCamelCase )
@staticmethod
def UpperCamelCase__ ( lowercase_ , lowercase_ ):
"""simple docstring"""
UpperCAmelCase_ : Optional[Any] = F"""--{field.name}"""
UpperCAmelCase_ : int = field.metadata.copy()
# field.metadata is not used at all by Data Classes,
# it is provided as a third-party extension mechanism.
if isinstance(field.type , __UpperCamelCase ):
raise RuntimeError(
"Unresolved type detected, which should have been done with the help of "
"`typing.get_type_hints` method by default" )
UpperCAmelCase_ : Optional[Any] = kwargs.pop("aliases" , [] )
if isinstance(__UpperCamelCase , __UpperCamelCase ):
UpperCAmelCase_ : str = [aliases]
UpperCAmelCase_ : List[Any] = getattr(field.type , "__origin__" , field.type )
if origin_type is Union or (hasattr(__UpperCamelCase , "UnionType" ) and isinstance(__UpperCamelCase , types.UnionType )):
if str not in field.type.__args__ and (
len(field.type.__args__ ) != 2 or type(__UpperCamelCase ) not in field.type.__args__
):
raise ValueError(
"Only `Union[X, NoneType]` (i.e., `Optional[X]`) is allowed for `Union` because"
" the argument parser only supports one type per argument."
F""" Problem encountered in field \'{field.name}\'.""" )
if type(__UpperCamelCase ) not in field.type.__args__:
# filter `str` in Union
UpperCAmelCase_ : int = field.type.__args__[0] if field.type.__args__[1] == str else field.type.__args__[1]
UpperCAmelCase_ : List[str] = getattr(field.type , "__origin__" , field.type )
elif bool not in field.type.__args__:
# filter `NoneType` in Union (except for `Union[bool, NoneType]`)
UpperCAmelCase_ : Optional[int] = (
field.type.__args__[0] if isinstance(__UpperCamelCase , field.type.__args__[1] ) else field.type.__args__[1]
)
UpperCAmelCase_ : Dict = getattr(field.type , "__origin__" , field.type )
# A variable to store kwargs for a boolean field, if needed
# so that we can init a `no_*` complement argument (see below)
UpperCAmelCase_ : List[Any] = {}
if origin_type is Literal or (isinstance(field.type , __UpperCamelCase ) and issubclass(field.type , __UpperCamelCase )):
if origin_type is Literal:
UpperCAmelCase_ : List[Any] = field.type.__args__
else:
UpperCAmelCase_ : List[Any] = [x.value for x in field.type]
UpperCAmelCase_ : int = make_choice_type_function(kwargs["choices"] )
if field.default is not dataclasses.MISSING:
UpperCAmelCase_ : List[Any] = field.default
else:
UpperCAmelCase_ : Optional[int] = True
elif field.type is bool or field.type == Optional[bool]:
# Copy the currect kwargs to use to instantiate a `no_*` complement argument below.
# We do not initialize it here because the `no_*` alternative must be instantiated after the real argument
UpperCAmelCase_ : Optional[int] = copy(__UpperCamelCase )
# Hack because type=bool in argparse does not behave as we want.
UpperCAmelCase_ : Union[str, Any] = string_to_bool
if field.type is bool or (field.default is not None and field.default is not dataclasses.MISSING):
# Default value is False if we have no default when of type bool.
UpperCAmelCase_ : Dict = False if field.default is dataclasses.MISSING else field.default
# This is the value that will get picked if we don't include --field_name in any way
UpperCAmelCase_ : Union[str, Any] = default
# This tells argparse we accept 0 or 1 value after --field_name
UpperCAmelCase_ : Optional[int] = "?"
# This is the value that will get picked if we do --field_name (without value)
UpperCAmelCase_ : Any = True
elif isclass(__UpperCamelCase ) and issubclass(__UpperCamelCase , __UpperCamelCase ):
UpperCAmelCase_ : Optional[Any] = field.type.__args__[0]
UpperCAmelCase_ : List[Any] = "+"
if field.default_factory is not dataclasses.MISSING:
UpperCAmelCase_ : Optional[int] = field.default_factory()
elif field.default is dataclasses.MISSING:
UpperCAmelCase_ : List[str] = True
else:
UpperCAmelCase_ : Any = field.type
if field.default is not dataclasses.MISSING:
UpperCAmelCase_ : Optional[Any] = field.default
elif field.default_factory is not dataclasses.MISSING:
UpperCAmelCase_ : str = field.default_factory()
else:
UpperCAmelCase_ : Optional[Any] = True
parser.add_argument(__UpperCamelCase , *__UpperCamelCase , **__UpperCamelCase )
# Add a complement `no_*` argument for a boolean field AFTER the initial field has already been added.
# Order is important for arguments with the same destination!
# We use a copy of earlier kwargs because the original kwargs have changed a lot before reaching down
# here and we do not need those changes/additional keys.
if field.default is True and (field.type is bool or field.type == Optional[bool]):
UpperCAmelCase_ : List[str] = False
parser.add_argument(F"""--no_{field.name}""" , action="store_false" , dest=field.name , **__UpperCamelCase )
def UpperCamelCase__ ( self , lowercase_ ):
"""simple docstring"""
if hasattr(__UpperCamelCase , "_argument_group_name" ):
UpperCAmelCase_ : List[Any] = self.add_argument_group(dtype._argument_group_name )
else:
UpperCAmelCase_ : Any = self
try:
UpperCAmelCase_ : Tuple = get_type_hints(__UpperCamelCase )
except NameError:
raise RuntimeError(
F"""Type resolution failed for {dtype}. Try declaring the class in global scope or """
"removing line of `from __future__ import annotations` which opts in Postponed "
"Evaluation of Annotations (PEP 563)" )
except TypeError as ex:
# Remove this block when we drop Python 3.9 support
if sys.version_info[:2] < (3, 10) and "unsupported operand type(s) for |" in str(__UpperCamelCase ):
UpperCAmelCase_ : str = ".".join(map(__UpperCamelCase , sys.version_info[:3] ) )
raise RuntimeError(
F"""Type resolution failed for {dtype} on Python {python_version}. Try removing """
"line of `from __future__ import annotations` which opts in union types as "
"`X | Y` (PEP 604) via Postponed Evaluation of Annotations (PEP 563). To "
"support Python versions that lower than 3.10, you need to use "
"`typing.Union[X, Y]` instead of `X | Y` and `typing.Optional[X]` instead of "
"`X | None`." ) from ex
raise
for field in dataclasses.fields(__UpperCamelCase ):
if not field.init:
continue
UpperCAmelCase_ : List[str] = type_hints[field.name]
self._parse_dataclass_field(__UpperCamelCase , __UpperCamelCase )
def UpperCamelCase__ ( self , lowercase_=None , lowercase_=False , lowercase_=True , lowercase_=None , lowercase_=None , ):
"""simple docstring"""
if args_file_flag or args_filename or (look_for_args_file and len(sys.argv )):
UpperCAmelCase_ : List[Any] = []
if args_filename:
args_files.append(Path(__UpperCamelCase ) )
elif look_for_args_file and len(sys.argv ):
args_files.append(Path(sys.argv[0] ).with_suffix(".args" ) )
# args files specified via command line flag should overwrite default args files so we add them last
if args_file_flag:
# Create special parser just to extract the args_file_flag values
UpperCAmelCase_ : Dict = ArgumentParser()
args_file_parser.add_argument(__UpperCamelCase , type=__UpperCamelCase , action="append" )
# Use only remaining args for further parsing (remove the args_file_flag)
UpperCAmelCase_ , UpperCAmelCase_ : Optional[int] = args_file_parser.parse_known_args(args=__UpperCamelCase )
UpperCAmelCase_ : Tuple = vars(__UpperCamelCase ).get(args_file_flag.lstrip("-" ) , __UpperCamelCase )
if cmd_args_file_paths:
args_files.extend([Path(__UpperCamelCase ) for p in cmd_args_file_paths] )
UpperCAmelCase_ : str = []
for args_file in args_files:
if args_file.exists():
file_args += args_file.read_text().split()
# in case of duplicate arguments the last one has precedence
# args specified via the command line should overwrite args from files, so we add them last
UpperCAmelCase_ : Any = file_args + args if args is not None else file_args + sys.argv[1:]
UpperCAmelCase_ , UpperCAmelCase_ : Union[str, Any] = self.parse_known_args(args=__UpperCamelCase )
UpperCAmelCase_ : List[Any] = []
for dtype in self.dataclass_types:
UpperCAmelCase_ : Any = {f.name for f in dataclasses.fields(__UpperCamelCase ) if f.init}
UpperCAmelCase_ : Union[str, Any] = {k: v for k, v in vars(__UpperCamelCase ).items() if k in keys}
for k in keys:
delattr(__UpperCamelCase , __UpperCamelCase )
UpperCAmelCase_ : List[Any] = dtype(**__UpperCamelCase )
outputs.append(__UpperCamelCase )
if len(namespace.__dict__ ) > 0:
# additional namespace.
outputs.append(__UpperCamelCase )
if return_remaining_strings:
return (*outputs, remaining_args)
else:
if remaining_args:
raise ValueError(F"""Some specified arguments are not used by the HfArgumentParser: {remaining_args}""" )
return (*outputs,)
def UpperCamelCase__ ( self , lowercase_ , lowercase_ = False ):
"""simple docstring"""
UpperCAmelCase_ : str = set(args.keys() )
UpperCAmelCase_ : List[str] = []
for dtype in self.dataclass_types:
UpperCAmelCase_ : List[str] = {f.name for f in dataclasses.fields(__UpperCamelCase ) if f.init}
UpperCAmelCase_ : Any = {k: v for k, v in args.items() if k in keys}
unused_keys.difference_update(inputs.keys() )
UpperCAmelCase_ : Optional[int] = dtype(**__UpperCamelCase )
outputs.append(__UpperCamelCase )
if not allow_extra_keys and unused_keys:
raise ValueError(F"""Some keys are not used by the HfArgumentParser: {sorted(__UpperCamelCase )}""" )
return tuple(__UpperCamelCase )
def UpperCamelCase__ ( self , lowercase_ , lowercase_ = False ):
"""simple docstring"""
with open(Path(__UpperCamelCase ) , encoding="utf-8" ) as open_json_file:
UpperCAmelCase_ : Optional[Any] = json.loads(open_json_file.read() )
UpperCAmelCase_ : Tuple = self.parse_dict(__UpperCamelCase , allow_extra_keys=__UpperCamelCase )
return tuple(__UpperCamelCase )
def UpperCamelCase__ ( self , lowercase_ , lowercase_ = False ):
"""simple docstring"""
UpperCAmelCase_ : int = self.parse_dict(yaml.safe_load(Path(__UpperCamelCase ).read_text() ) , allow_extra_keys=__UpperCamelCase )
return tuple(__UpperCamelCase )
| 359 |
"""simple docstring"""
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class A_ (unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase__ ( self ):
"""simple docstring"""
UpperCAmelCase_ : str = torch.nn.Linear(10 , 10 )
UpperCAmelCase_ : List[str] = torch.optim.SGD(model.parameters() , 0.1 )
UpperCAmelCase_ : Optional[Any] = Accelerator()
UpperCAmelCase_ : Tuple = accelerator.prepare(lowercase_ )
try:
pickle.loads(pickle.dumps(lowercase_ ) )
except Exception as e:
self.fail(F"""Accelerated optimizer pickling failed with {e}""" )
AcceleratorState._reset_state()
| 23 | 0 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
@slow
@require_torch_gpu
class _UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
__snake_case : Tuple = StableDiffusionKDiffusionPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4''' )
__snake_case : List[str] = sd_pipe.to(a_ )
sd_pipe.set_progress_bar_config(disable=a_ )
sd_pipe.set_scheduler('''sample_euler''' )
__snake_case : Tuple = '''A painting of a squirrel eating a burger'''
__snake_case : str = torch.manual_seed(0 )
__snake_case : int = sd_pipe([prompt] , generator=a_ , guidance_scale=9.0 , num_inference_steps=20 , output_type='''np''' )
__snake_case : List[str] = output.images
__snake_case : Tuple = image[0, -3:, -3:, -1]
assert image.shape == (1, 5_12, 5_12, 3)
__snake_case : Dict = np.array([0.0447, 0.0492, 0.0468, 0.0408, 0.0383, 0.0408, 0.0354, 0.0380, 0.0339] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
__snake_case : Optional[Any] = StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base''' )
__snake_case : Union[str, Any] = sd_pipe.to(a_ )
sd_pipe.set_progress_bar_config(disable=a_ )
sd_pipe.set_scheduler('''sample_euler''' )
__snake_case : str = '''A painting of a squirrel eating a burger'''
__snake_case : List[str] = torch.manual_seed(0 )
__snake_case : Dict = sd_pipe([prompt] , generator=a_ , guidance_scale=9.0 , num_inference_steps=20 , output_type='''np''' )
__snake_case : Any = output.images
__snake_case : Tuple = image[0, -3:, -3:, -1]
assert image.shape == (1, 5_12, 5_12, 3)
__snake_case : Tuple = np.array([0.1237, 0.1320, 0.1438, 0.1359, 0.1390, 0.1132, 0.1277, 0.1175, 0.1112] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5E-1
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
__snake_case : Any = StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base''' )
__snake_case : Union[str, Any] = sd_pipe.to(a_ )
sd_pipe.set_progress_bar_config(disable=a_ )
sd_pipe.set_scheduler('''sample_dpmpp_2m''' )
__snake_case : int = '''A painting of a squirrel eating a burger'''
__snake_case : Optional[Any] = torch.manual_seed(0 )
__snake_case : Dict = sd_pipe(
[prompt] , generator=a_ , guidance_scale=7.5 , num_inference_steps=15 , output_type='''np''' , use_karras_sigmas=a_ , )
__snake_case : Optional[int] = output.images
__snake_case : Any = image[0, -3:, -3:, -1]
assert image.shape == (1, 5_12, 5_12, 3)
__snake_case : Any = np.array(
[0.1138_1689, 0.1211_2921, 0.138_9457, 0.1254_9606, 0.124_4964, 0.1083_1517, 0.1156_2866, 0.1086_7816, 0.1049_9048] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
| 102 |
"""simple docstring"""
from __future__ import annotations
from scipy.special import comb # type: ignore
class _UpperCAmelCase :
'''simple docstring'''
def __init__(self , a_ ):
'''simple docstring'''
__snake_case : List[str] = list_of_points
# Degree determines the flexibility of the curve.
# Degree = 1 will produce a straight line.
__snake_case : Optional[Any] = len(a_ ) - 1
def SCREAMING_SNAKE_CASE (self , a_ ):
'''simple docstring'''
assert 0 <= t <= 1, "Time t must be between 0 and 1."
__snake_case : list[float] = []
for i in range(len(self.list_of_points ) ):
# basis function for each i
output_values.append(
comb(self.degree , a_ ) * ((1 - t) ** (self.degree - i)) * (t**i) )
# the basis must sum up to 1 for it to produce a valid Bezier curve.
assert round(sum(a_ ) , 5 ) == 1
return output_values
def SCREAMING_SNAKE_CASE (self , a_ ):
'''simple docstring'''
assert 0 <= t <= 1, "Time t must be between 0 and 1."
__snake_case : List[str] = self.basis_function(a_ )
__snake_case : str = 0.0
__snake_case : Union[str, Any] = 0.0
for i in range(len(self.list_of_points ) ):
# For all points, sum up the product of i-th basis function and i-th point.
x += basis_function[i] * self.list_of_points[i][0]
y += basis_function[i] * self.list_of_points[i][1]
return (x, y)
def SCREAMING_SNAKE_CASE (self , a_ = 0.01 ):
'''simple docstring'''
from matplotlib import pyplot as plt # type: ignore
__snake_case : list[float] = [] # x coordinates of points to plot
__snake_case : list[float] = [] # y coordinates of points to plot
__snake_case : int = 0.0
while t <= 1:
__snake_case : Union[str, Any] = self.bezier_curve_function(a_ )
to_plot_x.append(value[0] )
to_plot_y.append(value[1] )
t += step_size
__snake_case : List[Any] = [i[0] for i in self.list_of_points]
__snake_case : Any = [i[1] for i in self.list_of_points]
plt.plot(
a_ , a_ , color='''blue''' , label='''Curve of Degree ''' + str(self.degree ) , )
plt.scatter(a_ , a_ , color='''red''' , label='''Control Points''' )
plt.legend()
plt.show()
if __name__ == "__main__":
import doctest
doctest.testmod()
BezierCurve([(1, 2), (3, 5)]).plot_curve() # degree 1
BezierCurve([(0, 0), (5, 5), (5, 0)]).plot_curve() # degree 2
BezierCurve([(0, 0), (5, 5), (5, 0), (2.5, -2.5)]).plot_curve() # degree 3
| 102 | 1 |
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
MusicgenForConditionalGeneration,
MusicgenProcessor,
TaEncoderModel,
)
from transformers.models.musicgen.modeling_musicgen import MusicgenForCausalLM
from transformers.utils import logging
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = ["""model.decoder.embed_positions.weights"""]
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> Tuple:
'''simple docstring'''
if "emb" in name:
SCREAMING_SNAKE_CASE = name.replace("""emb""" , """model.decoder.embed_tokens""" )
if "transformer" in name:
SCREAMING_SNAKE_CASE = name.replace("""transformer""" , """model.decoder""" )
if "cross_attention" in name:
SCREAMING_SNAKE_CASE = name.replace("""cross_attention""" , """encoder_attn""" )
if "linear1" in name:
SCREAMING_SNAKE_CASE = name.replace("""linear1""" , """fc1""" )
if "linear2" in name:
SCREAMING_SNAKE_CASE = name.replace("""linear2""" , """fc2""" )
if "norm1" in name:
SCREAMING_SNAKE_CASE = name.replace("""norm1""" , """self_attn_layer_norm""" )
if "norm_cross" in name:
SCREAMING_SNAKE_CASE = name.replace("""norm_cross""" , """encoder_attn_layer_norm""" )
if "norm2" in name:
SCREAMING_SNAKE_CASE = name.replace("""norm2""" , """final_layer_norm""" )
if "out_norm" in name:
SCREAMING_SNAKE_CASE = name.replace("""out_norm""" , """model.decoder.layer_norm""" )
if "linears" in name:
SCREAMING_SNAKE_CASE = name.replace("""linears""" , """lm_heads""" )
if "condition_provider.conditioners.description.output_proj" in name:
SCREAMING_SNAKE_CASE = name.replace("""condition_provider.conditioners.description.output_proj""" , """enc_to_dec_proj""" )
return name
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Tuple[Dict, Dict]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = list(state_dict.keys() )
SCREAMING_SNAKE_CASE = {}
for key in keys:
SCREAMING_SNAKE_CASE = state_dict.pop(_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE = rename_keys(_SCREAMING_SNAKE_CASE )
if "in_proj_weight" in key:
# split fused qkv proj
SCREAMING_SNAKE_CASE = val[:hidden_size, :]
SCREAMING_SNAKE_CASE = val[hidden_size : 2 * hidden_size, :]
SCREAMING_SNAKE_CASE = val[-hidden_size:, :]
elif "enc_to_dec_proj" in key:
SCREAMING_SNAKE_CASE = val
else:
SCREAMING_SNAKE_CASE = val
return state_dict, enc_dec_proj_state_dict
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> MusicgenDecoderConfig:
'''simple docstring'''
if checkpoint == "small":
# default config values
SCREAMING_SNAKE_CASE = 10_24
SCREAMING_SNAKE_CASE = 24
SCREAMING_SNAKE_CASE = 16
elif checkpoint == "medium":
SCREAMING_SNAKE_CASE = 15_36
SCREAMING_SNAKE_CASE = 48
SCREAMING_SNAKE_CASE = 24
elif checkpoint == "large":
SCREAMING_SNAKE_CASE = 20_48
SCREAMING_SNAKE_CASE = 48
SCREAMING_SNAKE_CASE = 32
else:
raise ValueError(F"""Checkpoint should be one of `['small', 'medium', 'large']`, got {checkpoint}.""" )
SCREAMING_SNAKE_CASE = MusicgenDecoderConfig(
hidden_size=_SCREAMING_SNAKE_CASE , ffn_dim=hidden_size * 4 , num_hidden_layers=_SCREAMING_SNAKE_CASE , num_attention_heads=_SCREAMING_SNAKE_CASE , )
return config
@torch.no_grad()
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE="cpu" ) -> List[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = MusicGen.get_pretrained(_SCREAMING_SNAKE_CASE , device=_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE = decoder_config_from_checkpoint(_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE = fairseq_model.lm.state_dict()
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = rename_state_dict(
_SCREAMING_SNAKE_CASE , hidden_size=decoder_config.hidden_size )
SCREAMING_SNAKE_CASE = TaEncoderModel.from_pretrained("""t5-base""" )
SCREAMING_SNAKE_CASE = EncodecModel.from_pretrained("""facebook/encodec_32khz""" )
SCREAMING_SNAKE_CASE = MusicgenForCausalLM(_SCREAMING_SNAKE_CASE ).eval()
# load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = decoder.load_state_dict(_SCREAMING_SNAKE_CASE , strict=_SCREAMING_SNAKE_CASE )
for key in missing_keys.copy():
if key.startswith(("""text_encoder""", """audio_encoder""") ) or key in EXPECTED_MISSING_KEYS:
missing_keys.remove(_SCREAMING_SNAKE_CASE )
if len(_SCREAMING_SNAKE_CASE ) > 0:
raise ValueError(F"""Missing key(s) in state_dict: {missing_keys}""" )
if len(_SCREAMING_SNAKE_CASE ) > 0:
raise ValueError(F"""Unexpected key(s) in state_dict: {unexpected_keys}""" )
# init the composite model
SCREAMING_SNAKE_CASE = MusicgenForConditionalGeneration(text_encoder=_SCREAMING_SNAKE_CASE , audio_encoder=_SCREAMING_SNAKE_CASE , decoder=_SCREAMING_SNAKE_CASE )
# load the pre-trained enc-dec projection (from the decoder state dict)
model.enc_to_dec_proj.load_state_dict(_SCREAMING_SNAKE_CASE )
# check we can do a forward pass
SCREAMING_SNAKE_CASE = torch.arange(0 , 8 , dtype=torch.long ).reshape(2 , -1 )
SCREAMING_SNAKE_CASE = input_ids.reshape(2 * 4 , -1 )
with torch.no_grad():
SCREAMING_SNAKE_CASE = model(input_ids=_SCREAMING_SNAKE_CASE , decoder_input_ids=_SCREAMING_SNAKE_CASE ).logits
if logits.shape != (8, 1, 20_48):
raise ValueError("""Incorrect shape for logits""" )
# now construct the processor
SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained("""t5-base""" )
SCREAMING_SNAKE_CASE = AutoFeatureExtractor.from_pretrained("""facebook/encodec_32khz""" , padding_side="""left""" )
SCREAMING_SNAKE_CASE = MusicgenProcessor(feature_extractor=_SCREAMING_SNAKE_CASE , tokenizer=_SCREAMING_SNAKE_CASE )
# set the appropriate bos/pad token ids
SCREAMING_SNAKE_CASE = 20_48
SCREAMING_SNAKE_CASE = 20_48
# set other default generation config params
SCREAMING_SNAKE_CASE = int(30 * audio_encoder.config.frame_rate )
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = 3.0
if pytorch_dump_folder is not None:
Path(_SCREAMING_SNAKE_CASE ).mkdir(exist_ok=_SCREAMING_SNAKE_CASE )
logger.info(F"""Saving model {checkpoint} to {pytorch_dump_folder}""" )
model.save_pretrained(_SCREAMING_SNAKE_CASE )
processor.save_pretrained(_SCREAMING_SNAKE_CASE )
if repo_id:
logger.info(F"""Pushing model {checkpoint} to {repo_id}""" )
model.push_to_hub(_SCREAMING_SNAKE_CASE )
processor.push_to_hub(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--checkpoint""",
default="""small""",
type=str,
help="""Checkpoint size of the MusicGen model you'd like to convert. Can be one of: `['small', 'medium', 'large']`.""",
)
parser.add_argument(
"""--pytorch_dump_folder""",
required=True,
default=None,
type=str,
help="""Path to the output PyTorch model directory.""",
)
parser.add_argument(
"""--push_to_hub""", default=None, type=str, help="""Where to upload the converted model on the 🤗 hub."""
)
parser.add_argument(
"""--device""", default="""cpu""", type=str, help="""Torch device to run the conversion, either cpu or cuda."""
)
SCREAMING_SNAKE_CASE_ = parser.parse_args()
convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)
| 193 |
import unittest
from dataclasses import dataclass
import pytest
from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict
@dataclass
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
__snake_case : int = ComputeEnvironment.AMAZON_SAGEMAKER
__snake_case : List[Any] = True
__snake_case : Optional[int] = "ml.p3.2xlarge"
__snake_case : List[str] = "accelerate_sagemaker_execution_role"
__snake_case : Tuple = "hf-sm"
__snake_case : Any = "us-east-1"
__snake_case : Union[str, Any] = 1
__snake_case : Dict = "accelerate-sagemaker-1"
__snake_case : Tuple = "1.6"
__snake_case : List[str] = "4.4"
__snake_case : str = "train.py"
__snake_case : List[str] = [
"--model_name_or_path",
"bert",
"--do_train",
"False",
"--epochs",
"3",
"--learning_rate",
"5e-5",
"--max_steps",
"50.5",
]
__snake_case : Optional[int] = [
"--model_name_or_path",
"bert",
"--do_train",
"--do_test",
"False",
"--do_predict",
"--epochs",
"3",
"--learning_rate",
"5e-5",
"--max_steps",
"50.5",
]
class UpperCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = _convert_nargs_to_dict(MockLaunchConfig.success_training_script_args )
assert isinstance(converted_args["""model_name_or_path"""] ,lowerCamelCase__ )
assert isinstance(converted_args["""do_train"""] ,lowerCamelCase__ )
assert isinstance(converted_args["""epochs"""] ,lowerCamelCase__ )
assert isinstance(converted_args["""learning_rate"""] ,lowerCamelCase__ )
assert isinstance(converted_args["""max_steps"""] ,lowerCamelCase__ )
with pytest.raises(lowerCamelCase__ ):
_convert_nargs_to_dict(MockLaunchConfig.fail_training_script_args )
| 193 | 1 |
"""simple docstring"""
import numpy as np
def a__ ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase = 1E-12 , lowerCAmelCase = 1_00 , ) -> tuple[float, np.ndarray]:
assert np.shape(lowerCAmelCase )[0] == np.shape(lowerCAmelCase )[1]
# Ensure proper dimensionality.
assert np.shape(lowerCAmelCase )[0] == np.shape(lowerCAmelCase )[0]
# Ensure inputs are either both complex or both real
assert np.iscomplexobj(lowerCAmelCase ) == np.iscomplexobj(lowerCAmelCase )
UpperCAmelCase__ : Optional[Any] = np.iscomplexobj(lowerCAmelCase )
if is_complex:
# Ensure complex input_matrix is Hermitian
assert np.array_equal(lowerCAmelCase , 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__ : Optional[Any] = False
UpperCAmelCase__ : Any = 0
UpperCAmelCase__ : Tuple = 0
UpperCAmelCase__ : Optional[int] = 1E12
while not convergence:
# Multiple matrix by the vector.
UpperCAmelCase__ : int = np.dot(lowerCAmelCase , lowerCAmelCase )
# Normalize the resulting output vector.
UpperCAmelCase__ : Optional[Any] = w / np.linalg.norm(lowerCAmelCase )
# Find rayleigh quotient
# (faster than usual b/c we know vector is normalized already)
UpperCAmelCase__ : List[Any] = vector.conj().T if is_complex else vector.T
UpperCAmelCase__ : Optional[Any] = np.dot(lowerCAmelCase , np.dot(lowerCAmelCase , lowerCAmelCase ) )
# Check convergence.
UpperCAmelCase__ : Union[str, Any] = np.abs(lambda_ - lambda_previous ) / lambda_
iterations += 1
if error <= error_tol or iterations >= max_iterations:
UpperCAmelCase__ : Optional[int] = True
UpperCAmelCase__ : List[Any] = lambda_
if is_complex:
UpperCAmelCase__ : Any = np.real(lambda_ )
return lambda_, vector
def a__ ( ) -> None:
UpperCAmelCase__ : Tuple = np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]] )
UpperCAmelCase__ : int = np.array([41, 4, 20] )
UpperCAmelCase__ : str = real_input_matrix.astype(np.complexaaa )
UpperCAmelCase__ : Any = np.triu(1j * complex_input_matrix , 1 )
complex_input_matrix += imag_matrix
complex_input_matrix += -1 * imag_matrix.T
UpperCAmelCase__ : Dict = np.array([41, 4, 20] ).astype(np.complexaaa )
for problem_type in ["real", "complex"]:
if problem_type == "real":
UpperCAmelCase__ : List[str] = real_input_matrix
UpperCAmelCase__ : Any = real_vector
elif problem_type == "complex":
UpperCAmelCase__ : List[Any] = complex_input_matrix
UpperCAmelCase__ : int = complex_vector
# Our implementation.
UpperCAmelCase__ , UpperCAmelCase__ : int = power_iteration(lowerCAmelCase , lowerCAmelCase )
# Numpy implementation.
# Get eigenvalues and eigenvectors using built-in numpy
# eigh (eigh used for symmetric or hermetian matrices).
UpperCAmelCase__ , UpperCAmelCase__ : List[str] = np.linalg.eigh(lowerCAmelCase )
# Last eigenvalue is the maximum one.
UpperCAmelCase__ : str = eigen_values[-1]
# Last column in this matrix is eigenvector corresponding to largest eigenvalue.
UpperCAmelCase__ : List[Any] = 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(lowerCAmelCase ) - np.abs(lowerCAmelCase ) ) <= 1E-6
if __name__ == "__main__":
import doctest
doctest.testmod()
test_power_iteration()
| 171 |
"""simple docstring"""
from __future__ import annotations
def a__ ( lowerCAmelCase , lowerCAmelCase = None , lowerCAmelCase = None ) -> None:
if start is None:
UpperCAmelCase__ : Dict = 0
if end is None:
UpperCAmelCase__ : List[str] = len(lowerCAmelCase ) - 1
if start >= end:
return
UpperCAmelCase__ : int = (start + end) // 2
slowsort(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
slowsort(lowerCAmelCase , mid + 1 , lowerCAmelCase )
if sequence[end] < sequence[mid]:
UpperCAmelCase__ , UpperCAmelCase__ : Tuple = sequence[mid], sequence[end]
slowsort(lowerCAmelCase , lowerCAmelCase , end - 1 )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 171 | 1 |
import unittest
import numpy as np
from transformers import DistilBertConfig, 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.distilbert.modeling_flax_distilbert import (
FlaxDistilBertForMaskedLM,
FlaxDistilBertForMultipleChoice,
FlaxDistilBertForQuestionAnswering,
FlaxDistilBertForSequenceClassification,
FlaxDistilBertForTokenClassification,
FlaxDistilBertModel,
)
class lowerCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def __init__( self : str , __a : List[Any] , __a : Union[str, Any]=13 , __a : Optional[int]=7 , __a : int=True , __a : Tuple=True , __a : int=True , __a : str=True , __a : Union[str, Any]=99 , __a : Union[str, Any]=32 , __a : Tuple=5 , __a : Dict=4 , __a : Optional[int]=37 , __a : Tuple="gelu" , __a : Optional[int]=0.1 , __a : Tuple=0.1 , __a : List[str]=512 , __a : Union[str, Any]=16 , __a : Union[str, Any]=2 , __a : Union[str, Any]=0.02 , __a : Dict=4 , ) -> Union[str, Any]:
"""simple docstring"""
__lowercase : str = parent
__lowercase : Tuple = batch_size
__lowercase : int = seq_length
__lowercase : int = is_training
__lowercase : Optional[int] = use_attention_mask
__lowercase : Dict = use_token_type_ids
__lowercase : Any = use_labels
__lowercase : List[Any] = vocab_size
__lowercase : List[str] = hidden_size
__lowercase : Tuple = num_hidden_layers
__lowercase : List[str] = num_attention_heads
__lowercase : Optional[int] = intermediate_size
__lowercase : int = hidden_act
__lowercase : Union[str, Any] = hidden_dropout_prob
__lowercase : List[str] = attention_probs_dropout_prob
__lowercase : str = max_position_embeddings
__lowercase : List[str] = type_vocab_size
__lowercase : Optional[Any] = type_sequence_label_size
__lowercase : str = initializer_range
__lowercase : Tuple = num_choices
def lowerCAmelCase ( self : str ) -> Union[str, Any]:
"""simple docstring"""
__lowercase : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__lowercase : List[Any] = None
if self.use_attention_mask:
__lowercase : Optional[Any] = random_attention_mask([self.batch_size, self.seq_length] )
__lowercase : List[Any] = DistilBertConfig(
vocab_size=self.vocab_size , dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , hidden_dim=self.intermediate_size , hidden_act=self.hidden_act , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , tie_weights_=__a , )
return config, input_ids, attention_mask
def lowerCAmelCase ( self : List[str] ) -> Optional[int]:
"""simple docstring"""
__lowercase : Tuple = self.prepare_config_and_inputs()
__lowercase , __lowercase , __lowercase : str = config_and_inputs
__lowercase : int = {"""input_ids""": input_ids, """attention_mask""": attention_mask}
return config, inputs_dict
@require_flax
class lowerCAmelCase ( __a , unittest.TestCase ):
'''simple docstring'''
_A : List[str] = (
(
FlaxDistilBertModel,
FlaxDistilBertForMaskedLM,
FlaxDistilBertForMultipleChoice,
FlaxDistilBertForQuestionAnswering,
FlaxDistilBertForSequenceClassification,
FlaxDistilBertForTokenClassification,
FlaxDistilBertForQuestionAnswering,
)
if is_flax_available()
else ()
)
def lowerCAmelCase ( self : Union[str, Any] ) -> List[str]:
"""simple docstring"""
__lowercase : List[str] = FlaxDistilBertModelTester(self )
@slow
def lowerCAmelCase ( self : Optional[Any] ) -> str:
"""simple docstring"""
for model_class_name in self.all_model_classes:
__lowercase : Optional[Any] = model_class_name.from_pretrained("""distilbert-base-uncased""" )
__lowercase : Any = model(np.ones((1, 1) ) )
self.assertIsNotNone(__a )
@require_flax
class lowerCAmelCase ( unittest.TestCase ):
'''simple docstring'''
@slow
def lowerCAmelCase ( self : int ) -> Any:
"""simple docstring"""
__lowercase : str = FlaxDistilBertModel.from_pretrained("""distilbert-base-uncased""" )
__lowercase : Union[str, Any] = np.array([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]] )
__lowercase : Optional[Any] = np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
__lowercase : List[Any] = model(__a , attention_mask=__a )[0]
__lowercase : Union[str, Any] = (1, 11, 768)
self.assertEqual(output.shape , __a )
__lowercase : List[str] = np.array([[[-0.1639, 0.3299, 0.1648], [-0.1746, 0.3289, 0.1710], [-0.1884, 0.3357, 0.1810]]] )
self.assertTrue(jnp.allclose(output[:, 1:4, 1:4] , __a , atol=1E-4 ) ) | 306 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase : Union[str, Any] = logging.get_logger(__name__)
lowerCamelCase : str = {
'''facebook/nllb-moe-54B''': '''https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json''',
}
class lowerCAmelCase ( __a ):
'''simple docstring'''
_A : int = '''nllb-moe'''
_A : List[str] = ['''past_key_values''']
_A : Optional[Any] = {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}
def __init__( self : Dict , __a : List[str]=128112 , __a : List[Any]=1024 , __a : List[Any]=12 , __a : Union[str, Any]=4096 , __a : List[str]=16 , __a : int=12 , __a : Optional[int]=4096 , __a : str=16 , __a : List[Any]=0.05 , __a : Any=0.05 , __a : Dict=True , __a : Optional[Any]=True , __a : List[Any]="relu" , __a : Tuple=1024 , __a : Optional[Any]=0.1 , __a : Tuple=0.1 , __a : Any=0.0 , __a : Optional[Any]=0.02 , __a : List[str]=2 , __a : Union[str, Any]=True , __a : List[Any]=False , __a : Tuple="float32" , __a : Optional[int]=False , __a : Optional[int]=128 , __a : str=64 , __a : Dict=4 , __a : str=4 , __a : List[str]=0.001 , __a : List[Any]=0.001 , __a : Optional[Any]="all" , __a : Optional[int]=False , __a : int=False , __a : int=1.0 , __a : Dict=0.2 , __a : Tuple=1 , __a : Optional[Any]=0 , __a : List[Any]=2 , __a : Any=False , **__a : Any , ) -> Any:
"""simple docstring"""
__lowercase : int = vocab_size
__lowercase : List[Any] = max_position_embeddings
__lowercase : Tuple = d_model
__lowercase : str = encoder_ffn_dim
__lowercase : List[str] = encoder_layers
__lowercase : int = encoder_attention_heads
__lowercase : List[Any] = decoder_ffn_dim
__lowercase : int = decoder_layers
__lowercase : Optional[int] = decoder_attention_heads
__lowercase : Union[str, Any] = dropout
__lowercase : str = attention_dropout
__lowercase : Any = activation_dropout
__lowercase : List[Any] = activation_function
__lowercase : List[str] = init_std
__lowercase : Optional[int] = encoder_layerdrop
__lowercase : str = decoder_layerdrop
__lowercase : Dict = use_cache
__lowercase : Optional[Any] = encoder_layers
__lowercase : str = scale_embedding # scale factor will be sqrt(d_model) if True
__lowercase : List[Any] = router_z_loss_coef
__lowercase : Tuple = router_aux_loss_coef
__lowercase : str = decoder_sparse_step
__lowercase : Any = encoder_sparse_step
__lowercase : str = num_experts
__lowercase : List[Any] = expert_capacity
__lowercase : int = router_bias
if router_dtype not in ["float32", "float16", "bfloat16"]:
raise ValueError(F"`router_dtype` must be one of 'float32', 'float16' or 'bfloat16', got {router_dtype}" )
__lowercase : Optional[int] = router_dtype
__lowercase : Any = router_ignore_padding_tokens
__lowercase : Optional[Any] = batch_prioritized_routing
__lowercase : str = second_expert_policy
__lowercase : List[str] = normalize_router_prob_before_dropping
__lowercase : List[Any] = moe_eval_capacity_token_fraction
__lowercase : List[str] = moe_token_dropout
__lowercase : Optional[Any] = output_router_logits
super().__init__(
pad_token_id=__a , bos_token_id=__a , eos_token_id=__a , is_encoder_decoder=__a , decoder_start_token_id=__a , **__a , ) | 306 | 1 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {
'configuration_mctct': ['MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MCTCTConfig'],
'feature_extraction_mctct': ['MCTCTFeatureExtractor'],
'processing_mctct': ['MCTCTProcessor'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
'MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST',
'MCTCTForCTC',
'MCTCTModel',
'MCTCTPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig
from .feature_extraction_mctct import MCTCTFeatureExtractor
from .processing_mctct import MCTCTProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel
else:
import sys
a_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 175 |
'''simple docstring'''
def _UpperCAmelCase ( _lowerCamelCase : list[int] , _lowerCamelCase : str ) -> list[int]:
_lowerCAmelCase : List[Any] = int(_lowerCamelCase )
# Initialize Result
_lowerCAmelCase : Any = []
# Traverse through all denomination
for denomination in reversed(_lowerCamelCase ):
# Find denominations
while int(_lowerCamelCase ) >= int(_lowerCamelCase ):
total_value -= int(_lowerCamelCase )
answer.append(_lowerCamelCase ) # 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, 1_00, 5_00, 20_00]
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=""" """)
| 309 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
a_ = {
"configuration_mobilevit": ["MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MobileViTConfig", "MobileViTOnnxConfig"],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = ["MobileViTFeatureExtractor"]
a_ = ["MobileViTImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
"MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
"MobileViTForImageClassification",
"MobileViTForSemanticSegmentation",
"MobileViTModel",
"MobileViTPreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
"TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFMobileViTForImageClassification",
"TFMobileViTForSemanticSegmentation",
"TFMobileViTModel",
"TFMobileViTPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_mobilevit import MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileViTConfig, MobileViTOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_mobilevit import MobileViTFeatureExtractor
from .image_processing_mobilevit import MobileViTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mobilevit import (
MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
MobileViTForImageClassification,
MobileViTForSemanticSegmentation,
MobileViTModel,
MobileViTPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_mobilevit import (
TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFMobileViTForImageClassification,
TFMobileViTForSemanticSegmentation,
TFMobileViTModel,
TFMobileViTPreTrainedModel,
)
else:
import sys
a_ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) | 163 |
"""simple docstring"""
import argparse
import re
from pathlib import Path
import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor
from transformers import (
EfficientFormerConfig,
EfficientFormerForImageClassificationWithTeacher,
EfficientFormerImageProcessor,
)
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling
def a__ ( __lowercase , __lowercase ) -> Dict:
_A = old_name
if "patch_embed" in old_name:
_A , _A , _A = old_name.split("." )
if layer == "0":
_A = old_name.replace("0" , "convolution1" )
elif layer == "1":
_A = old_name.replace("1" , "batchnorm_before" )
elif layer == "3":
_A = old_name.replace("3" , "convolution2" )
else:
_A = old_name.replace("4" , "batchnorm_after" )
if "network" in old_name and re.search(R"\d\.\d" , __lowercase ):
_A = R"\b\d{2}\b"
if bool(re.search(__lowercase , __lowercase ) ):
_A = re.search(R"\d\.\d\d." , __lowercase ).group()
else:
_A = re.search(R"\d\.\d." , __lowercase ).group()
if int(match[0] ) < 6:
_A = old_name.replace(__lowercase , "" )
_A = trimmed_name.replace("network" , match[0] + ".meta4D_layers.blocks." + match[2:-1] )
_A = "intermediate_stages." + trimmed_name
else:
_A = old_name.replace(__lowercase , "" )
if int(match[2] ) < num_meta4D_last_stage:
_A = trimmed_name.replace("network" , "meta4D_layers.blocks." + match[2] )
else:
_A = str(int(match[2] ) - num_meta4D_last_stage )
_A = trimmed_name.replace("network" , "meta3D_layers.blocks." + layer_index )
if "norm1" in old_name:
_A = trimmed_name.replace("norm1" , "layernorm1" )
elif "norm2" in old_name:
_A = trimmed_name.replace("norm2" , "layernorm2" )
elif "fc1" in old_name:
_A = trimmed_name.replace("fc1" , "linear_in" )
elif "fc2" in old_name:
_A = trimmed_name.replace("fc2" , "linear_out" )
_A = "last_stage." + trimmed_name
elif "network" in old_name and re.search(R".\d." , __lowercase ):
_A = old_name.replace("network" , "intermediate_stages" )
if "fc" in new_name:
_A = new_name.replace("fc" , "convolution" )
elif ("norm1" in new_name) and ("layernorm1" not in new_name):
_A = new_name.replace("norm1" , "batchnorm_before" )
elif ("norm2" in new_name) and ("layernorm2" not in new_name):
_A = new_name.replace("norm2" , "batchnorm_after" )
if "proj" in new_name:
_A = new_name.replace("proj" , "projection" )
if "dist_head" in new_name:
_A = new_name.replace("dist_head" , "distillation_classifier" )
elif "head" in new_name:
_A = new_name.replace("head" , "classifier" )
elif "patch_embed" in new_name:
_A = "efficientformer." + new_name
elif new_name == "norm.weight" or new_name == "norm.bias":
_A = new_name.replace("norm" , "layernorm" )
_A = "efficientformer." + new_name
else:
_A = "efficientformer.encoder." + new_name
return new_name
def a__ ( __lowercase , __lowercase ) -> List[str]:
for key in checkpoint.copy().keys():
_A = checkpoint.pop(__lowercase )
_A = val
return checkpoint
def a__ ( ) -> Dict:
_A = "http://images.cocodataset.org/val2017/000000039769.jpg"
_A = Image.open(requests.get(__lowercase , stream=__lowercase ).raw )
return image
def a__ ( __lowercase , __lowercase , __lowercase , __lowercase ) -> str:
_A = torch.load(__lowercase , map_location="cpu" )["model"]
_A = EfficientFormerConfig.from_json_file(__lowercase )
_A = EfficientFormerForImageClassificationWithTeacher(__lowercase )
_A = "_".join(checkpoint_path.split("/" )[-1].split("." )[0].split("_" )[:-1] )
_A = config.depths[-1] - config.num_metaad_blocks + 1
_A = convert_torch_checkpoint(__lowercase , __lowercase )
model.load_state_dict(__lowercase )
model.eval()
_A = {
"bilinear": PILImageResampling.BILINEAR,
"bicubic": PILImageResampling.BICUBIC,
"nearest": PILImageResampling.NEAREST,
}
# prepare image
_A = prepare_img()
_A = 256
_A = 224
_A = EfficientFormerImageProcessor(
size={"shortest_edge": image_size} , crop_size={"height": crop_size, "width": crop_size} , resample=pillow_resamplings["bicubic"] , )
_A = processor(images=__lowercase , return_tensors="pt" ).pixel_values
# original processing pipeline
_A = Compose(
[
Resize(__lowercase , interpolation=pillow_resamplings["bicubic"] ),
CenterCrop(__lowercase ),
ToTensor(),
Normalize(__lowercase , __lowercase ),
] )
_A = image_transforms(__lowercase ).unsqueeze(0 )
assert torch.allclose(__lowercase , __lowercase )
_A = model(__lowercase )
_A = outputs.logits
_A = (1, 1000)
if "l1" in model_name:
_A = torch.Tensor(
[-0.1_312, 0.4_353, -1.0_499, -0.5_124, 0.4_183, -0.6_793, -1.3_777, -0.0_893, -0.7_358, -2.4_328] )
assert torch.allclose(logits[0, :10] , __lowercase , atol=1E-3 )
assert logits.shape == expected_shape
elif "l3" in model_name:
_A = torch.Tensor(
[-1.3_150, -1.5_456, -1.2_556, -0.8_496, -0.7_127, -0.7_897, -0.9_728, -0.3_052, 0.3_751, -0.3_127] )
assert torch.allclose(logits[0, :10] , __lowercase , atol=1E-3 )
assert logits.shape == expected_shape
elif "l7" in model_name:
_A = torch.Tensor(
[-1.0_283, -1.4_131, -0.5_644, -1.3_115, -0.5_785, -1.2_049, -0.7_528, 0.1_992, -0.3_822, -0.0_878] )
assert logits.shape == expected_shape
else:
raise ValueError(
f"""Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7""" )
# Save Checkpoints
Path(__lowercase ).mkdir(exist_ok=__lowercase )
model.save_pretrained(__lowercase )
print(f"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}""" )
processor.save_pretrained(__lowercase )
print(f"""Processor successfuly saved at {pytorch_dump_path}""" )
if push_to_hub:
print("Pushing model to the hub..." )
model.push_to_hub(
repo_id=f"""Bearnardd/{pytorch_dump_path}""" , commit_message="Add model" , use_temp_dir=__lowercase , )
processor.push_to_hub(
repo_id=f"""Bearnardd/{pytorch_dump_path}""" , commit_message="Add image processor" , use_temp_dir=__lowercase , )
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--pytorch_model_path",
default=None,
type=str,
required=True,
help="Path to EfficientFormer pytorch checkpoint.",
)
parser.add_argument(
"--config_file",
default=None,
type=str,
required=True,
help="The json file for EfficientFormer model config.",
)
parser.add_argument(
"--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
parser.add_argument("--push_to_hub", action="store_true", help="Push model and image processor to the hub")
parser.add_argument(
"--no-push_to_hub",
dest="push_to_hub",
action="store_false",
help="Do not push model and image processor to the hub",
)
parser.set_defaults(push_to_hub=True)
a_ = parser.parse_args()
convert_efficientformer_checkpoint(
checkpoint_path=args.pytorch_model_path,
efficientformer_config_file=args.config_file,
pytorch_dump_path=args.pytorch_dump_path,
push_to_hub=args.push_to_hub,
) | 163 | 1 |
def _a ( SCREAMING_SNAKE_CASE ):
"""simple docstring"""
lowercase__ = len(snake_case__ )
for i in range(length - 1 ):
lowercase__ = i
for k in range(i + 1 , snake_case__ ):
if collection[k] < collection[least]:
lowercase__ = k
if least != i:
lowercase__ , lowercase__ = (collection[i], collection[least])
return collection
if __name__ == "__main__":
lowerCAmelCase = input('Enter numbers separated by a comma:\n').strip()
lowerCAmelCase = [int(item) for item in user_input.split(',')]
print(selection_sort(unsorted))
| 110 |
"""simple docstring"""
from __future__ import annotations
def a__ ( snake_case__ , snake_case__ ) -> bool:
if len(snake_case__ ) == 0:
return False
lowerCamelCase = len(snake_case__ ) // 2
if a_list[midpoint] == item:
return True
if item < a_list[midpoint]:
return binary_search(a_list[:midpoint] , snake_case__ )
else:
return binary_search(a_list[midpoint + 1 :] , snake_case__ )
if __name__ == "__main__":
lowerCAmelCase : List[Any] = input("""Enter numbers separated by comma:\n""").strip()
lowerCAmelCase : Optional[Any] = [int(item.strip()) for item in user_input.split(""",""")]
lowerCAmelCase : Optional[int] = int(input("""Enter the number to be found in the list:\n""").strip())
lowerCAmelCase : Union[str, Any] = """""" if binary_search(sequence, target) else """not """
print(F"""{target} was {not_str}found in {sequence}""")
| 291 | 0 |
"""simple docstring"""
from typing import Any, Callable, Dict, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionPipeline,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
_A = """CompVis/stable-diffusion-v1-1"""
_A = """CompVis/stable-diffusion-v1-2"""
_A = """CompVis/stable-diffusion-v1-3"""
_A = """CompVis/stable-diffusion-v1-4"""
class lowerCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
def __init__(self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase = True , ):
"""simple docstring"""
super()._init_()
UpperCAmelCase__ : int = StableDiffusionPipeline.from_pretrained(_lowerCamelCase )
UpperCAmelCase__ : str = StableDiffusionPipeline.from_pretrained(_lowerCamelCase )
UpperCAmelCase__ : List[Any] = StableDiffusionPipeline.from_pretrained(_lowerCamelCase )
UpperCAmelCase__ : Dict = StableDiffusionPipeline(
vae=_lowerCamelCase , text_encoder=_lowerCamelCase , tokenizer=_lowerCamelCase , unet=_lowerCamelCase , scheduler=_lowerCamelCase , safety_checker=_lowerCamelCase , feature_extractor=_lowerCamelCase , requires_safety_checker=_lowerCamelCase , )
self.register_modules(pipelinea=self.pipea , pipelinea=self.pipea , pipelinea=self.pipea , pipelinea=self.pipea )
@property
def _a (self ):
"""simple docstring"""
return {k: getattr(self , _lowerCamelCase ) for k in self.config.keys() if not k.startswith("""_""" )}
def _a (self , _lowerCamelCase = "auto" ):
"""simple docstring"""
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
UpperCAmelCase__ : Optional[int] = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(_lowerCamelCase )
def _a (self ):
"""simple docstring"""
self.enable_attention_slicing(_lowerCamelCase )
@torch.no_grad()
def _a (self , _lowerCamelCase , _lowerCamelCase = 512 , _lowerCamelCase = 512 , _lowerCamelCase = 50 , _lowerCamelCase = 7.5 , _lowerCamelCase = None , _lowerCamelCase = 1 , _lowerCamelCase = 0.0 , _lowerCamelCase = None , _lowerCamelCase = None , _lowerCamelCase = "pil" , _lowerCamelCase = True , _lowerCamelCase = None , _lowerCamelCase = 1 , **_lowerCamelCase , ):
"""simple docstring"""
return self.pipea(
prompt=_lowerCamelCase , height=_lowerCamelCase , width=_lowerCamelCase , num_inference_steps=_lowerCamelCase , guidance_scale=_lowerCamelCase , negative_prompt=_lowerCamelCase , num_images_per_prompt=_lowerCamelCase , eta=_lowerCamelCase , generator=_lowerCamelCase , latents=_lowerCamelCase , output_type=_lowerCamelCase , return_dict=_lowerCamelCase , callback=_lowerCamelCase , callback_steps=_lowerCamelCase , **_lowerCamelCase , )
@torch.no_grad()
def _a (self , _lowerCamelCase , _lowerCamelCase = 512 , _lowerCamelCase = 512 , _lowerCamelCase = 50 , _lowerCamelCase = 7.5 , _lowerCamelCase = None , _lowerCamelCase = 1 , _lowerCamelCase = 0.0 , _lowerCamelCase = None , _lowerCamelCase = None , _lowerCamelCase = "pil" , _lowerCamelCase = True , _lowerCamelCase = None , _lowerCamelCase = 1 , **_lowerCamelCase , ):
"""simple docstring"""
return self.pipea(
prompt=_lowerCamelCase , height=_lowerCamelCase , width=_lowerCamelCase , num_inference_steps=_lowerCamelCase , guidance_scale=_lowerCamelCase , negative_prompt=_lowerCamelCase , num_images_per_prompt=_lowerCamelCase , eta=_lowerCamelCase , generator=_lowerCamelCase , latents=_lowerCamelCase , output_type=_lowerCamelCase , return_dict=_lowerCamelCase , callback=_lowerCamelCase , callback_steps=_lowerCamelCase , **_lowerCamelCase , )
@torch.no_grad()
def _a (self , _lowerCamelCase , _lowerCamelCase = 512 , _lowerCamelCase = 512 , _lowerCamelCase = 50 , _lowerCamelCase = 7.5 , _lowerCamelCase = None , _lowerCamelCase = 1 , _lowerCamelCase = 0.0 , _lowerCamelCase = None , _lowerCamelCase = None , _lowerCamelCase = "pil" , _lowerCamelCase = True , _lowerCamelCase = None , _lowerCamelCase = 1 , **_lowerCamelCase , ):
"""simple docstring"""
return self.pipea(
prompt=_lowerCamelCase , height=_lowerCamelCase , width=_lowerCamelCase , num_inference_steps=_lowerCamelCase , guidance_scale=_lowerCamelCase , negative_prompt=_lowerCamelCase , num_images_per_prompt=_lowerCamelCase , eta=_lowerCamelCase , generator=_lowerCamelCase , latents=_lowerCamelCase , output_type=_lowerCamelCase , return_dict=_lowerCamelCase , callback=_lowerCamelCase , callback_steps=_lowerCamelCase , **_lowerCamelCase , )
@torch.no_grad()
def _a (self , _lowerCamelCase , _lowerCamelCase = 512 , _lowerCamelCase = 512 , _lowerCamelCase = 50 , _lowerCamelCase = 7.5 , _lowerCamelCase = None , _lowerCamelCase = 1 , _lowerCamelCase = 0.0 , _lowerCamelCase = None , _lowerCamelCase = None , _lowerCamelCase = "pil" , _lowerCamelCase = True , _lowerCamelCase = None , _lowerCamelCase = 1 , **_lowerCamelCase , ):
"""simple docstring"""
return self.pipea(
prompt=_lowerCamelCase , height=_lowerCamelCase , width=_lowerCamelCase , num_inference_steps=_lowerCamelCase , guidance_scale=_lowerCamelCase , negative_prompt=_lowerCamelCase , num_images_per_prompt=_lowerCamelCase , eta=_lowerCamelCase , generator=_lowerCamelCase , latents=_lowerCamelCase , output_type=_lowerCamelCase , return_dict=_lowerCamelCase , callback=_lowerCamelCase , callback_steps=_lowerCamelCase , **_lowerCamelCase , )
@torch.no_grad()
def _a (self , _lowerCamelCase , _lowerCamelCase = 512 , _lowerCamelCase = 512 , _lowerCamelCase = 50 , _lowerCamelCase = 7.5 , _lowerCamelCase = None , _lowerCamelCase = 1 , _lowerCamelCase = 0.0 , _lowerCamelCase = None , _lowerCamelCase = None , _lowerCamelCase = "pil" , _lowerCamelCase = True , _lowerCamelCase = None , _lowerCamelCase = 1 , **_lowerCamelCase , ):
"""simple docstring"""
UpperCAmelCase__ : Any = """cuda""" if torch.cuda.is_available() else """cpu"""
self.to(_lowerCamelCase )
# Checks if the height and width are divisible by 8 or not
if height % 8 != 0 or width % 8 != 0:
raise ValueError(F"""`height` and `width` must be divisible by 8 but are {height} and {width}.""" )
# Get first result from Stable Diffusion Checkpoint v1.1
UpperCAmelCase__ : Optional[int] = self.textaimg_sda_a(
prompt=_lowerCamelCase , height=_lowerCamelCase , width=_lowerCamelCase , num_inference_steps=_lowerCamelCase , guidance_scale=_lowerCamelCase , negative_prompt=_lowerCamelCase , num_images_per_prompt=_lowerCamelCase , eta=_lowerCamelCase , generator=_lowerCamelCase , latents=_lowerCamelCase , output_type=_lowerCamelCase , return_dict=_lowerCamelCase , callback=_lowerCamelCase , callback_steps=_lowerCamelCase , **_lowerCamelCase , )
# Get first result from Stable Diffusion Checkpoint v1.2
UpperCAmelCase__ : List[Any] = self.textaimg_sda_a(
prompt=_lowerCamelCase , height=_lowerCamelCase , width=_lowerCamelCase , num_inference_steps=_lowerCamelCase , guidance_scale=_lowerCamelCase , negative_prompt=_lowerCamelCase , num_images_per_prompt=_lowerCamelCase , eta=_lowerCamelCase , generator=_lowerCamelCase , latents=_lowerCamelCase , output_type=_lowerCamelCase , return_dict=_lowerCamelCase , callback=_lowerCamelCase , callback_steps=_lowerCamelCase , **_lowerCamelCase , )
# Get first result from Stable Diffusion Checkpoint v1.3
UpperCAmelCase__ : Optional[int] = self.textaimg_sda_a(
prompt=_lowerCamelCase , height=_lowerCamelCase , width=_lowerCamelCase , num_inference_steps=_lowerCamelCase , guidance_scale=_lowerCamelCase , negative_prompt=_lowerCamelCase , num_images_per_prompt=_lowerCamelCase , eta=_lowerCamelCase , generator=_lowerCamelCase , latents=_lowerCamelCase , output_type=_lowerCamelCase , return_dict=_lowerCamelCase , callback=_lowerCamelCase , callback_steps=_lowerCamelCase , **_lowerCamelCase , )
# Get first result from Stable Diffusion Checkpoint v1.4
UpperCAmelCase__ : Any = self.textaimg_sda_a(
prompt=_lowerCamelCase , height=_lowerCamelCase , width=_lowerCamelCase , num_inference_steps=_lowerCamelCase , guidance_scale=_lowerCamelCase , negative_prompt=_lowerCamelCase , num_images_per_prompt=_lowerCamelCase , eta=_lowerCamelCase , generator=_lowerCamelCase , latents=_lowerCamelCase , output_type=_lowerCamelCase , return_dict=_lowerCamelCase , callback=_lowerCamelCase , callback_steps=_lowerCamelCase , **_lowerCamelCase , )
# Get all result images into a single list and pass it via StableDiffusionPipelineOutput for final result
return StableDiffusionPipelineOutput([resa[0], resa[0], resa[0], resa[0]] )
| 166 |
"""simple docstring"""
import sys
import turtle
def a__ ( lowerCAmelCase , lowerCAmelCase ) -> tuple[float, float]:
return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2
def a__ ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , ) -> None:
my_pen.up()
my_pen.goto(vertexa[0] , vertexa[1] )
my_pen.down()
my_pen.goto(vertexa[0] , vertexa[1] )
my_pen.goto(vertexa[0] , vertexa[1] )
my_pen.goto(vertexa[0] , vertexa[1] )
if depth == 0:
return
triangle(lowerCAmelCase , get_mid(lowerCAmelCase , lowerCAmelCase ) , get_mid(lowerCAmelCase , lowerCAmelCase ) , depth - 1 )
triangle(lowerCAmelCase , get_mid(lowerCAmelCase , lowerCAmelCase ) , get_mid(lowerCAmelCase , lowerCAmelCase ) , depth - 1 )
triangle(lowerCAmelCase , get_mid(lowerCAmelCase , lowerCAmelCase ) , get_mid(lowerCAmelCase , lowerCAmelCase ) , depth - 1 )
if __name__ == "__main__":
if len(sys.argv) != 2:
raise ValueError(
"""Correct format for using this script: """
"""python fractals.py <int:depth_for_fractal>"""
)
_A = turtle.Turtle()
my_pen.ht()
my_pen.speed(5)
my_pen.pencolor("""red""")
_A = [(-1_75, -1_25), (0, 1_75), (1_75, -1_25)] # vertices of triangle
triangle(vertices[0], vertices[1], vertices[2], int(sys.argv[1]))
| 166 | 1 |
from __future__ import annotations
def _a ( lowerCamelCase, lowerCamelCase, lowerCamelCase ):
if (voltage, current, resistance).count(0 ) != 1:
raise ValueError("""One and only one argument must be 0""" )
if resistance < 0:
raise ValueError("""Resistance cannot be negative""" )
if voltage == 0:
return {"voltage": float(current * resistance )}
elif current == 0:
return {"current": voltage / resistance}
elif resistance == 0:
return {"resistance": voltage / current}
else:
raise ValueError("""Exactly one argument must be 0""" )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 287 |
from copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import DistributedType, is_torch_version, set_seed
def _a ( lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase ):
for param, grad_param in zip(model_a.parameters(), model_b.parameters() ):
if not param.requires_grad:
continue
if not did_step:
# Grads should not be in sync
assert (
torch.allclose(param.grad, grad_param.grad ) is False
), F'''Gradients in sync when they should not be at iteration {iteration}:\nmodel_a grad ({param.grad}) == model_b grad ({grad_param.grad})'''
else:
# Grads should be in sync
assert (
torch.allclose(param.grad, grad_param.grad ) is True
), F'''Gradients not in sync when they should be at iteration {iteration}:\nmodel_a grad ({param.grad}) != model_b grad ({grad_param.grad})'''
def _a ( lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase=True ):
model.train()
lowerCamelCase : Dict = model(lowerCamelCase )
lowerCamelCase : Any = F.mse_loss(lowerCamelCase, target.to(output.device ) )
if not do_backward:
loss /= accelerator.gradient_accumulation_steps
loss.backward()
else:
accelerator.backward(lowerCamelCase )
def _a ( lowerCamelCase, lowerCamelCase=False ):
set_seed(42 )
lowerCamelCase : Tuple = RegressionModel()
lowerCamelCase : Any = deepcopy(lowerCamelCase )
lowerCamelCase : Any = RegressionDataset(length=80 )
lowerCamelCase : Dict = DataLoader(lowerCamelCase, batch_size=16 )
model.to(accelerator.device )
if sched:
lowerCamelCase : int = AdamW(params=model.parameters(), lr=1e-3 )
lowerCamelCase : Optional[Any] = AdamW(params=ddp_model.parameters(), lr=1e-3 )
lowerCamelCase : str = LambdaLR(lowerCamelCase, lr_lambda=lambda lowerCamelCase : epoch**0.6_5 )
lowerCamelCase : Tuple = LambdaLR(lowerCamelCase, lr_lambda=lambda lowerCamelCase : epoch**0.6_5 )
# Make a copy of `model`
if sched:
lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase : Optional[int] = accelerator.prepare(lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase )
else:
lowerCamelCase , lowerCamelCase : List[Any] = accelerator.prepare(lowerCamelCase, lowerCamelCase )
if sched:
return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
return model, ddp_model, dataloader
def _a ( lowerCamelCase ):
# Test when on a single CPU or GPU that the context manager does nothing
lowerCamelCase , lowerCamelCase , lowerCamelCase : List[Any] = get_training_setup(lowerCamelCase )
# Use a single batch
lowerCamelCase , lowerCamelCase : Union[str, Any] = next(iter(lowerCamelCase ) ).values()
for iteration in range(3 ):
# Gather the distributed inputs and targs for the base model
lowerCamelCase , lowerCamelCase : Optional[Any] = accelerator.gather((ddp_input, ddp_target) )
lowerCamelCase , lowerCamelCase : int = input.to(accelerator.device ), target.to(accelerator.device )
# Perform our initial ground truth step in non "DDP"
step_model(lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase )
# Do "gradient accumulation" (noop)
if iteration % 2 == 0:
# Accumulate grads locally
with accelerator.no_sync(lowerCamelCase ):
step_model(lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase )
else:
# Sync grads
step_model(lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase )
# Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
check_model_parameters(lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase )
for param, ddp_param in zip(model.parameters(), ddp_model.parameters() ):
if not param.requires_grad:
continue
assert torch.allclose(
param.grad, ddp_param.grad ), F'''Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'''
# Shuffle ddp_input on each iteration
torch.manual_seed(1337 + iteration )
lowerCamelCase : List[Any] = ddp_input[torch.randperm(len(lowerCamelCase ) )]
def _a ( lowerCamelCase ):
# Test on distributed setup that context manager behaves properly
lowerCamelCase , lowerCamelCase , lowerCamelCase : int = get_training_setup(lowerCamelCase )
# Use a single batch
lowerCamelCase , lowerCamelCase : Union[str, Any] = next(iter(lowerCamelCase ) ).values()
for iteration in range(3 ):
# Gather the distributed inputs and targs for the base model
lowerCamelCase , lowerCamelCase : Any = accelerator.gather((ddp_input, ddp_target) )
lowerCamelCase , lowerCamelCase : str = input.to(accelerator.device ), target.to(accelerator.device )
# Perform our initial ground truth step in non "DDP"
step_model(lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase )
# Do "gradient accumulation" (noop)
if iteration % 2 == 0:
# Accumulate grads locally
with accelerator.no_sync(lowerCamelCase ):
step_model(lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase )
else:
# Sync grads
step_model(lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase )
# DDP model and model should only be in sync when not (iteration % 2 == 0)
for param, ddp_param in zip(model.parameters(), ddp_model.parameters() ):
if not param.requires_grad:
continue
if iteration % 2 == 0:
# Grads should not be in sync
assert (
torch.allclose(param.grad, ddp_param.grad ) is False
), F'''Gradients in sync when they should not be:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})'''
else:
# Grads should be in sync
assert (
torch.allclose(param.grad, ddp_param.grad ) is True
), F'''Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'''
# Shuffle ddp_input on each iteration
torch.manual_seed(1337 + iteration )
lowerCamelCase : Optional[Any] = ddp_input[torch.randperm(len(lowerCamelCase ) )]
def _a ( lowerCamelCase=False, lowerCamelCase=False ):
lowerCamelCase : Any = Accelerator(
split_batches=lowerCamelCase, dispatch_batches=lowerCamelCase, gradient_accumulation_steps=2 )
# Test that context manager behaves properly
lowerCamelCase , lowerCamelCase , lowerCamelCase : Any = get_training_setup(lowerCamelCase )
for iteration, batch in enumerate(lowerCamelCase ):
lowerCamelCase , lowerCamelCase : Union[str, Any] = batch.values()
# Gather the distributed inputs and targs for the base model
lowerCamelCase , lowerCamelCase : str = accelerator.gather((ddp_input, ddp_target) )
lowerCamelCase , lowerCamelCase : Tuple = input.to(accelerator.device ), target.to(accelerator.device )
# Perform our initial ground truth step in non "DDP"
step_model(lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase )
# Do "gradient accumulation" (noop)
with accelerator.accumulate(lowerCamelCase ):
step_model(lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase )
# DDP model and model should only be in sync when not (iteration % 2 == 0)
for param, ddp_param in zip(model.parameters(), ddp_model.parameters() ):
if not param.requires_grad:
continue
if ((iteration + 1) % 2 == 0) or (iteration == len(lowerCamelCase ) - 1):
# Grads should be in sync
assert (
torch.allclose(param.grad, ddp_param.grad ) is True
), F'''Gradients not in sync when they should be at iteration {iteration}:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'''
else:
# Grads should not be in sync
assert (
torch.allclose(param.grad, ddp_param.grad ) is False
), F'''Gradients in sync when they should not be at iteration {iteration}:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})'''
# Shuffle ddp_input on each iteration
torch.manual_seed(1337 + iteration )
lowerCamelCase : Any = ddp_input[torch.randperm(len(lowerCamelCase ) )]
GradientState._reset_state()
def _a ( lowerCamelCase=False, lowerCamelCase=False ):
lowerCamelCase : List[Any] = Accelerator(
split_batches=lowerCamelCase, dispatch_batches=lowerCamelCase, gradient_accumulation_steps=2 )
# Test that context manager behaves properly
lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase : List[Any] = get_training_setup(lowerCamelCase, lowerCamelCase )
for iteration, batch in enumerate(lowerCamelCase ):
lowerCamelCase , lowerCamelCase : Union[str, Any] = batch.values()
# Gather the distributed inputs and targs for the base model
lowerCamelCase , lowerCamelCase : Optional[Any] = accelerator.gather((ddp_input, ddp_target) )
lowerCamelCase , lowerCamelCase : Tuple = input.to(accelerator.device ), target.to(accelerator.device )
# Perform our initial ground truth step in non "DDP"
model.train()
ddp_model.train()
step_model(lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase )
opt.step()
if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(lowerCamelCase )):
if split_batches:
sched.step()
else:
for _ in range(accelerator.num_processes ):
sched.step()
opt.zero_grad()
# Perform gradient accumulation under wrapper
with accelerator.accumulate(lowerCamelCase ):
step_model(lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase )
ddp_opt.step()
ddp_sched.step()
ddp_opt.zero_grad()
# Learning rates should be the same
assert (
opt.param_groups[0]["lr"] == ddp_opt.param_groups[0]["lr"]
), F'''Learning rates found in each optimizer did not align\nopt: {opt.param_groups[0]["lr"]}\nDDP opt: {ddp_opt.param_groups[0]["lr"]}\n'''
lowerCamelCase : Union[str, Any] = (((iteration + 1) % 2) == 0) or ((iteration + 1) == len(lowerCamelCase ))
if accelerator.num_processes > 1:
check_model_parameters(lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase )
# Shuffle ddp_input on each iteration
torch.manual_seed(1337 + iteration )
GradientState._reset_state()
def _a ( ):
lowerCamelCase : int = Accelerator()
lowerCamelCase : Optional[Any] = RegressionDataset(length=80 )
lowerCamelCase : List[str] = DataLoader(lowerCamelCase, batch_size=16 )
lowerCamelCase : int = RegressionDataset(length=96 )
lowerCamelCase : Optional[int] = DataLoader(lowerCamelCase, batch_size=16 )
lowerCamelCase , lowerCamelCase : Optional[Any] = accelerator.prepare(lowerCamelCase, lowerCamelCase )
assert accelerator.gradient_state.active_dataloader is None
for iteration, _ in enumerate(lowerCamelCase ):
assert id(accelerator.gradient_state.active_dataloader ) == id(lowerCamelCase )
if iteration < len(lowerCamelCase ) - 1:
assert not accelerator.gradient_state.end_of_dataloader
if iteration == 1:
for batch_num, _ in enumerate(lowerCamelCase ):
assert id(accelerator.gradient_state.active_dataloader ) == id(lowerCamelCase )
if batch_num < len(lowerCamelCase ) - 1:
assert not accelerator.gradient_state.end_of_dataloader
else:
assert accelerator.gradient_state.end_of_dataloader
else:
assert accelerator.gradient_state.end_of_dataloader
assert accelerator.gradient_state.active_dataloader is None
def _a ( ):
lowerCamelCase : List[Any] = Accelerator()
lowerCamelCase : int = accelerator.state
if state.local_process_index == 0:
print("""**Test `accumulate` gradient accumulation with dataloader break**""" )
test_dataloader_break()
if state.distributed_type == DistributedType.NO:
if state.local_process_index == 0:
print("""**Test NOOP `no_sync` context manager**""" )
test_noop_sync(lowerCamelCase )
if state.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_CPU):
if state.local_process_index == 0:
print("""**Test Distributed `no_sync` context manager**""" )
test_distributed_sync(lowerCamelCase )
if state.distributed_type == DistributedType.MULTI_GPU:
for split_batch in [True, False]:
for dispatch_batches in [True, False]:
if state.local_process_index == 0:
print(
"""**Test `accumulate` gradient accumulation, """, F'''`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**''', )
test_gradient_accumulation(lowerCamelCase, lowerCamelCase )
# Currently will break on torch 2.0 +, need to investigate why
if is_torch_version("""<""", """2.0""" ) or state.distributed_type == DistributedType.NO:
if state.local_process_index == 0:
print(
"""**Test `accumulate` gradient accumulation with optimizer and scheduler, """, """`split_batches=False`, `dispatch_batches=False`**""", )
test_gradient_accumulation_with_opt_and_scheduler()
if state.distributed_type == DistributedType.MULTI_GPU:
for split_batch in [True, False]:
for dispatch_batches in [True, False]:
if not split_batch and not dispatch_batches:
continue
if state.local_process_index == 0:
print(
"""**Test `accumulate` gradient accumulation with optimizer and scheduler, """, F'''`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**''', )
test_gradient_accumulation_with_opt_and_scheduler(lowerCamelCase, lowerCamelCase )
def _a ( lowerCamelCase ):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 287 | 1 |
'''simple docstring'''
_SCREAMING_SNAKE_CASE = {
'''joule''': 1.0,
'''kilojoule''': 1_0_0_0,
'''megajoule''': 1_0_0_0_0_0_0,
'''gigajoule''': 1_0_0_0_0_0_0_0_0_0,
'''wattsecond''': 1.0,
'''watthour''': 3_6_0_0,
'''kilowatthour''': 3_6_0_0_0_0_0,
'''newtonmeter''': 1.0,
'''calorie_nutr''': 4_1_8_6.8,
'''kilocalorie_nutr''': 4_1_8_6_8_0_0.0_0,
'''electronvolt''': 1.6_02_17_66_34e-19,
'''britishthermalunit_it''': 1_0_5_5.0_5_5_8_5,
'''footpound''': 1.3_5_5_8_1_8,
}
def _lowerCAmelCase ( lowerCamelCase_ : str , lowerCamelCase_ : str , lowerCamelCase_ : float ):
if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
__lowercase = (
f"Incorrect \'from_type\' or \'to_type\' value: {from_type!r}, {to_type!r}\n"
f"Valid values are: {', '.join(a__ )}"
)
raise ValueError(a__ )
return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 360 |
'''simple docstring'''
def _lowerCAmelCase ( lowerCamelCase_ : str , lowerCamelCase_ : str ):
__lowercase = len(lowerCamelCase_ ) + 1
__lowercase = len(lowerCamelCase_ ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
# length i of input_string matches with prefix string of length j of
# given pattern.
# "dp" stands for dynamic programming.
__lowercase = [[0 for i in range(lowerCamelCase_ )] for j in range(lowerCamelCase_ )]
# since string of zero length match pattern of zero length
__lowercase = 1
# since pattern of zero length will never match with string of non-zero length
for i in range(1 , lowerCamelCase_ ):
__lowercase = 0
# since string of zero length will match with pattern where there
# is at least one * alternatively
for j in range(1 , lowerCamelCase_ ):
__lowercase = dp[0][j - 2] if pattern[j - 1] == '''*''' else 0
# now using bottom-up approach to find for all remaining lengths
for i in range(1 , lowerCamelCase_ ):
for j in range(1 , lowerCamelCase_ ):
if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
__lowercase = dp[i - 1][j - 1]
elif pattern[j - 1] == "*":
if dp[i][j - 2] == 1:
__lowercase = 1
elif pattern[j - 2] in (input_string[i - 1], "."):
__lowercase = dp[i - 1][j]
else:
__lowercase = 0
else:
__lowercase = 0
return bool(dp[-1][-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
# inputing the strings
# input_string = input("input a string :")
# pattern = input("input a pattern :")
_SCREAMING_SNAKE_CASE = '''aab'''
_SCREAMING_SNAKE_CASE = '''c*a*b'''
# using function to check whether given string matches the given pattern
if match_pattern(input_string, pattern):
print(f'''{input_string} matches the given pattern {pattern}''')
else:
print(f'''{input_string} does not match with the given pattern {pattern}''')
| 217 | 0 |
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
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
a_ = 'platform'
import jax
import jax.numpy as jnp
import numpy as np
from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel
@require_flax
class _lowercase :
lowercase = PegasusConfig
lowercase = {}
lowercase = 'gelu'
def __init__( self : Union[str, Any] , snake_case : Optional[int] , snake_case : Union[str, Any]=1_3 , snake_case : int=7 , snake_case : int=True , snake_case : Union[str, Any]=False , snake_case : Tuple=9_9 , snake_case : List[str]=3_2 , snake_case : Tuple=5 , snake_case : Dict=4 , snake_case : Optional[Any]=3_7 , snake_case : Union[str, Any]=0.1 , snake_case : List[Any]=0.1 , snake_case : Dict=2_0 , snake_case : Optional[Any]=2 , snake_case : List[Any]=1 , snake_case : Any=0 , ) -> Dict:
"""simple docstring"""
UpperCamelCase_ : int = parent
UpperCamelCase_ : Optional[int] = batch_size
UpperCamelCase_ : Optional[Any] = seq_length
UpperCamelCase_ : Tuple = is_training
UpperCamelCase_ : Union[str, Any] = use_labels
UpperCamelCase_ : List[str] = vocab_size
UpperCamelCase_ : Optional[int] = hidden_size
UpperCamelCase_ : Optional[int] = num_hidden_layers
UpperCamelCase_ : str = num_attention_heads
UpperCamelCase_ : List[Any] = intermediate_size
UpperCamelCase_ : List[str] = hidden_dropout_prob
UpperCamelCase_ : int = attention_probs_dropout_prob
UpperCamelCase_ : List[Any] = max_position_embeddings
UpperCamelCase_ : Tuple = eos_token_id
UpperCamelCase_ : Optional[int] = pad_token_id
UpperCamelCase_ : Tuple = bos_token_id
def SCREAMING_SNAKE_CASE__ ( self : str ) -> int:
"""simple docstring"""
UpperCamelCase_ : List[str] = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size ).clip(3 , self.vocab_size )
UpperCamelCase_ : str = np.expand_dims(np.array([self.eos_token_id] * self.batch_size ) , 1 )
UpperCamelCase_ : int = np.concatenate([input_ids, eos_tensor] , axis=1 )
UpperCamelCase_ : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCamelCase_ : Optional[int] = self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , **self.config_updates , )
UpperCamelCase_ : Tuple = prepare_pegasus_inputs_dict(snake_case , snake_case , snake_case )
return config, inputs_dict
def SCREAMING_SNAKE_CASE__ ( self : int , snake_case : Tuple , snake_case : Optional[int] , snake_case : Dict ) -> List[str]:
"""simple docstring"""
UpperCamelCase_ : Union[str, Any] = 2_0
UpperCamelCase_ : List[str] = model_class_name(snake_case )
UpperCamelCase_ : List[Any] = model.encode(inputs_dict['input_ids'] )
UpperCamelCase_, UpperCamelCase_ : Union[str, Any] = (
inputs_dict['decoder_input_ids'],
inputs_dict['decoder_attention_mask'],
)
UpperCamelCase_ : List[str] = model.init_cache(decoder_input_ids.shape[0] , snake_case , snake_case )
UpperCamelCase_ : List[str] = jnp.ones((decoder_input_ids.shape[0], max_decoder_length) , dtype='i4' )
UpperCamelCase_ : Optional[int] = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
UpperCamelCase_ : List[Any] = model.decode(
decoder_input_ids[:, :-1] , snake_case , decoder_attention_mask=snake_case , past_key_values=snake_case , decoder_position_ids=snake_case , )
UpperCamelCase_ : Union[str, Any] = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype='i4' )
UpperCamelCase_ : Union[str, Any] = model.decode(
decoder_input_ids[:, -1:] , snake_case , decoder_attention_mask=snake_case , past_key_values=outputs_cache.past_key_values , decoder_position_ids=snake_case , )
UpperCamelCase_ : Union[str, Any] = model.decode(snake_case , snake_case )
UpperCamelCase_ : str = 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 SCREAMING_SNAKE_CASE__ ( self : Tuple , snake_case : Union[str, Any] , snake_case : str , snake_case : List[str] ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase_ : int = 2_0
UpperCamelCase_ : Optional[Any] = model_class_name(snake_case )
UpperCamelCase_ : Dict = model.encode(inputs_dict['input_ids'] )
UpperCamelCase_, UpperCamelCase_ : Dict = (
inputs_dict['decoder_input_ids'],
inputs_dict['decoder_attention_mask'],
)
UpperCamelCase_ : int = jnp.concatenate(
[
decoder_attention_mask,
jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1]) ),
] , axis=-1 , )
UpperCamelCase_ : Dict = model.init_cache(decoder_input_ids.shape[0] , snake_case , snake_case )
UpperCamelCase_ : List[str] = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
UpperCamelCase_ : List[Any] = model.decode(
decoder_input_ids[:, :-1] , snake_case , decoder_attention_mask=snake_case , past_key_values=snake_case , decoder_position_ids=snake_case , )
UpperCamelCase_ : List[Any] = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype='i4' )
UpperCamelCase_ : Union[str, Any] = model.decode(
decoder_input_ids[:, -1:] , snake_case , past_key_values=outputs_cache.past_key_values , decoder_attention_mask=snake_case , decoder_position_ids=snake_case , )
UpperCamelCase_ : List[Any] = model.decode(snake_case , snake_case , decoder_attention_mask=snake_case )
UpperCamelCase_ : str = 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 __lowercase ( lowerCamelCase : Any , lowerCamelCase : str , lowerCamelCase : Any , lowerCamelCase : int=None , lowerCamelCase : Dict=None , ):
if attention_mask is None:
UpperCamelCase_ : Optional[Any] = np.not_equal(lowerCamelCase , config.pad_token_id ).astype(np.inta )
if decoder_attention_mask is None:
UpperCamelCase_ : Optional[Any] = np.concatenate(
[
np.ones(decoder_input_ids[:, :1].shape , dtype=np.inta ),
np.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ).astype(np.inta ),
] , axis=-1 , )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
}
@require_flax
class _lowercase ( snake_case_ , unittest.TestCase ):
lowercase = (
(
FlaxPegasusForConditionalGeneration,
FlaxPegasusModel,
)
if is_flax_available()
else ()
)
lowercase = (FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
lowercase = True
lowercase = False
lowercase = False
lowercase = False
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ) -> Any:
"""simple docstring"""
UpperCamelCase_ : Tuple = FlaxPegasusModelTester(self )
UpperCamelCase_ : Optional[int] = ConfigTester(self , config_class=snake_case )
def SCREAMING_SNAKE_CASE__ ( self : int ) -> str:
"""simple docstring"""
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE__ ( self : int ) -> str:
"""simple docstring"""
UpperCamelCase_, UpperCamelCase_ : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward(snake_case , snake_case , snake_case )
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
UpperCamelCase_, UpperCamelCase_ : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward_with_attn_mask(snake_case , snake_case , snake_case )
def SCREAMING_SNAKE_CASE__ ( self : Any ) -> List[Any]:
"""simple docstring"""
UpperCamelCase_, UpperCamelCase_ : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
UpperCamelCase_ : Any = self._prepare_for_class(snake_case , snake_case )
UpperCamelCase_ : str = model_class(snake_case )
@jax.jit
def encode_jitted(snake_case : Any , snake_case : Optional[Any]=None , **snake_case : Optional[int] ):
return model.encode(input_ids=snake_case , attention_mask=snake_case )
with self.subTest('JIT Enabled' ):
UpperCamelCase_ : Optional[int] = encode_jitted(**snake_case ).to_tuple()
with self.subTest('JIT Disabled' ):
with jax.disable_jit():
UpperCamelCase_ : List[Any] = encode_jitted(**snake_case ).to_tuple()
self.assertEqual(len(snake_case ) , len(snake_case ) )
for jitted_output, output in zip(snake_case , snake_case ):
self.assertEqual(jitted_output.shape , output.shape )
def SCREAMING_SNAKE_CASE__ ( self : List[str] ) -> Dict:
"""simple docstring"""
UpperCamelCase_, UpperCamelCase_ : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
UpperCamelCase_ : Tuple = model_class(snake_case )
UpperCamelCase_ : Optional[int] = model.encode(inputs_dict['input_ids'] , inputs_dict['attention_mask'] )
UpperCamelCase_ : str = {
'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(snake_case : Optional[Any] , snake_case : int , snake_case : Optional[Any] ):
return model.decode(
decoder_input_ids=snake_case , decoder_attention_mask=snake_case , encoder_outputs=snake_case , )
with self.subTest('JIT Enabled' ):
UpperCamelCase_ : Union[str, Any] = decode_jitted(**snake_case ).to_tuple()
with self.subTest('JIT Disabled' ):
with jax.disable_jit():
UpperCamelCase_ : Optional[Any] = decode_jitted(**snake_case ).to_tuple()
self.assertEqual(len(snake_case ) , len(snake_case ) )
for jitted_output, output in zip(snake_case , snake_case ):
self.assertEqual(jitted_output.shape , output.shape )
@slow
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> List[Any]:
"""simple docstring"""
for model_class_name in self.all_model_classes:
UpperCamelCase_ : Any = model_class_name.from_pretrained('google/pegasus-large' , from_pt=snake_case )
UpperCamelCase_ : int = np.ones((1, 1) )
UpperCamelCase_ : Any = model(snake_case )
self.assertIsNotNone(snake_case )
@slow
def SCREAMING_SNAKE_CASE__ ( self : Any ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase_ : Optional[int] = FlaxPegasusForConditionalGeneration.from_pretrained('google/pegasus-xsum' )
UpperCamelCase_ : Any = PegasusTokenizer.from_pretrained('google/pegasus-xsum' )
UpperCamelCase_ : int = [
' PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.',
' The London trio are up for best UK act and best album, as well as getting two nominations in the best song category."We got told like this morning \'Oh I think you\'re nominated\'", said Dappy."And I was like \'Oh yeah, which one?\' And now we\'ve got nominated for four awards. I mean, wow!"Bandmate Fazer added: "We thought it\'s best of us to come down and mingle with everyone and say hello to the cameras. And now we find we\'ve got four nominations."The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn\'t be too disappointed if they didn\'t win this time around."At the end of the day we\'re grateful to be where we are in our careers."If it don\'t happen then it don\'t happen - live to fight another day and keep on making albums and hits for the fans."Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a re-working of The Killers\' All These Things That I\'ve Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year\'s Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border."We just done Edinburgh the other day," said Dappy."We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!" ',
]
UpperCamelCase_ : Union[str, Any] = [
'California\'s largest electricity provider has turned off power to hundreds of thousands of customers.',
'Pop group N-Dubz have revealed they were surprised to get four nominations for this year\'s Mobo Awards.',
]
UpperCamelCase_ : Any = tokenizer(snake_case , return_tensors='np' , truncation=snake_case , max_length=5_1_2 , padding=snake_case )
UpperCamelCase_ : str = model.generate(**snake_case , num_beams=2 ).sequences
UpperCamelCase_ : List[Any] = tokenizer.batch_decode(snake_case , skip_special_tokens=snake_case )
assert tgt_text == decoded
| 175 | import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union
import numpy as np
import pyarrow as pa
import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config import MAX_SHARD_SIZE
from datasets.filesystems import (
is_remote_filesystem,
rename,
)
from datasets.iterable_dataset import _BaseExamplesIterable
from datasets.utils.py_utils import convert_file_size_to_int
a_ = datasets.utils.logging.get_logger(__name__)
if TYPE_CHECKING:
import pyspark
@dataclass
class _lowercase ( datasets.BuilderConfig ):
lowercase = None
def __lowercase ( lowerCamelCase : "pyspark.sql.DataFrame" , lowerCamelCase : List[int] , ):
import pyspark
def generate_fn():
UpperCamelCase_ : Dict = df.select('*' , pyspark.sql.functions.spark_partition_id().alias('part_id' ) )
for partition_id in partition_order:
UpperCamelCase_ : Tuple = df_with_partition_id.select('*' ).where(F"part_id = {partition_id}" ).drop('part_id' )
UpperCamelCase_ : Union[str, Any] = partition_df.collect()
UpperCamelCase_ : Any = 0
for row in rows:
yield F"{partition_id}_{row_id}", row.asDict()
row_id += 1
return generate_fn
class _lowercase ( _BaseExamplesIterable ):
def __init__( self : Optional[int] , snake_case : "pyspark.sql.DataFrame" , snake_case : Tuple=None , ) -> Tuple:
"""simple docstring"""
UpperCamelCase_ : Dict = df
UpperCamelCase_ : int = partition_order or range(self.df.rdd.getNumPartitions() )
UpperCamelCase_ : Optional[Any] = _generate_iterable_examples(self.df , self.partition_order )
def __iter__( self : Optional[int] ) -> Any:
"""simple docstring"""
yield from self.generate_examples_fn()
def SCREAMING_SNAKE_CASE__ ( self : Any , snake_case : np.random.Generator ) -> "SparkExamplesIterable":
"""simple docstring"""
UpperCamelCase_ : Optional[Any] = list(range(self.df.rdd.getNumPartitions() ) )
generator.shuffle(snake_case )
return SparkExamplesIterable(self.df , partition_order=snake_case )
def SCREAMING_SNAKE_CASE__ ( self : int , snake_case : int , snake_case : int ) -> "SparkExamplesIterable":
"""simple docstring"""
UpperCamelCase_ : Tuple = self.split_shard_indices_by_worker(snake_case , snake_case )
return SparkExamplesIterable(self.df , partition_order=snake_case )
@property
def SCREAMING_SNAKE_CASE__ ( self : int ) -> int:
"""simple docstring"""
return len(self.partition_order )
class _lowercase ( datasets.DatasetBuilder ):
lowercase = SparkConfig
def __init__( self : List[Any] , snake_case : "pyspark.sql.DataFrame" , snake_case : str = None , snake_case : str = None , **snake_case : Optional[Any] , ) -> List[str]:
"""simple docstring"""
import pyspark
UpperCamelCase_ : List[Any] = pyspark.sql.SparkSession.builder.getOrCreate()
UpperCamelCase_ : str = df
UpperCamelCase_ : Tuple = working_dir
super().__init__(
cache_dir=snake_case , config_name=str(self.df.semanticHash() ) , **snake_case , )
def SCREAMING_SNAKE_CASE__ ( self : Any ) -> Dict:
"""simple docstring"""
def create_cache_and_write_probe(snake_case : str ):
# makedirs with exist_ok will recursively create the directory. It will not throw an error if directories
# already exist.
os.makedirs(self._cache_dir , exist_ok=snake_case )
UpperCamelCase_ : Tuple = os.path.join(self._cache_dir , 'fs_test' + uuid.uuida().hex )
# Opening the file in append mode will create a new file unless it already exists, in which case it will not
# change the file contents.
open(snake_case , 'a' )
return [probe_file]
if self._spark.conf.get('spark.master' , '' ).startswith('local' ):
return
# If the cluster is multi-node, make sure that the user provided a cache_dir and that it is on an NFS
# accessible to the driver.
# TODO: Stream batches to the driver using ArrowCollectSerializer instead of throwing an error.
if self._cache_dir:
UpperCamelCase_ : Tuple = (
self._spark.sparkContext.parallelize(range(1 ) , 1 ).mapPartitions(snake_case ).collect()
)
if os.path.isfile(probe[0] ):
return
raise ValueError(
'When using Dataset.from_spark on a multi-node cluster, the driver and all workers should be able to access cache_dir' )
def SCREAMING_SNAKE_CASE__ ( self : int ) -> Union[str, Any]:
"""simple docstring"""
return datasets.DatasetInfo(features=self.config.features )
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] , snake_case : datasets.download.download_manager.DownloadManager ) -> Optional[int]:
"""simple docstring"""
return [datasets.SplitGenerator(name=datasets.Split.TRAIN )]
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] , snake_case : Optional[int] ) -> List[Any]:
"""simple docstring"""
import pyspark
def get_arrow_batch_size(snake_case : Dict ):
for batch in it:
yield pa.RecordBatch.from_pydict({'batch_bytes': [batch.nbytes]} )
UpperCamelCase_ : List[str] = self.df.count()
UpperCamelCase_ : Union[str, Any] = df_num_rows if df_num_rows <= 1_0_0 else 1_0_0
# Approximate the size of each row (in Arrow format) by averaging over a max-100-row sample.
UpperCamelCase_ : str = (
self.df.limit(snake_case )
.repartition(1 )
.mapInArrow(snake_case , 'batch_bytes: long' )
.agg(pyspark.sql.functions.sum('batch_bytes' ).alias('sample_bytes' ) )
.collect()[0]
.sample_bytes
/ sample_num_rows
)
UpperCamelCase_ : Optional[int] = approx_bytes_per_row * df_num_rows
if approx_total_size > max_shard_size:
# Make sure there is at least one row per partition.
UpperCamelCase_ : Optional[Any] = min(snake_case , int(approx_total_size / max_shard_size ) )
UpperCamelCase_ : int = self.df.repartition(snake_case )
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] , snake_case : str , snake_case : str , snake_case : int , ) -> Iterable[Tuple[int, bool, Union[int, tuple]]]:
"""simple docstring"""
import pyspark
UpperCamelCase_ : List[Any] = ParquetWriter if file_format == 'parquet' else ArrowWriter
UpperCamelCase_ : List[str] = os.path.join(self._working_dir , os.path.basename(snake_case ) ) if self._working_dir else fpath
UpperCamelCase_ : Union[str, Any] = file_format == 'parquet'
# Define these so that we don't reference self in write_arrow, which will result in a pickling error due to
# pickling the SparkContext.
UpperCamelCase_ : Union[str, Any] = self.config.features
UpperCamelCase_ : Any = self._writer_batch_size
UpperCamelCase_ : Dict = self._fs.storage_options
def write_arrow(snake_case : List[str] ):
# Within the same SparkContext, no two task attempts will share the same attempt ID.
UpperCamelCase_ : Any = pyspark.TaskContext().taskAttemptId()
UpperCamelCase_ : str = next(snake_case , snake_case )
if first_batch is None:
# Some partitions might not receive any data.
return pa.RecordBatch.from_arrays(
[[task_id], [0], [0]] , names=['task_id', 'num_examples', 'num_bytes'] , )
UpperCamelCase_ : Any = 0
UpperCamelCase_ : Optional[Any] = writer_class(
features=snake_case , path=working_fpath.replace('SSSSS' , f"{shard_id:05d}" ).replace('TTTTT' , f"{task_id:05d}" ) , writer_batch_size=snake_case , storage_options=snake_case , embed_local_files=snake_case , )
UpperCamelCase_ : str = pa.Table.from_batches([first_batch] )
writer.write_table(snake_case )
for batch in it:
if max_shard_size is not None and writer._num_bytes >= max_shard_size:
UpperCamelCase_, UpperCamelCase_ : str = writer.finalize()
writer.close()
yield pa.RecordBatch.from_arrays(
[[task_id], [num_examples], [num_bytes]] , names=['task_id', 'num_examples', 'num_bytes'] , )
shard_id += 1
UpperCamelCase_ : Union[str, Any] = writer_class(
features=writer._features , path=working_fpath.replace('SSSSS' , f"{shard_id:05d}" ).replace('TTTTT' , f"{task_id:05d}" ) , writer_batch_size=snake_case , storage_options=snake_case , embed_local_files=snake_case , )
UpperCamelCase_ : Optional[Any] = pa.Table.from_batches([batch] )
writer.write_table(snake_case )
if writer._num_bytes > 0:
UpperCamelCase_, UpperCamelCase_ : str = writer.finalize()
writer.close()
yield pa.RecordBatch.from_arrays(
[[task_id], [num_examples], [num_bytes]] , names=['task_id', 'num_examples', 'num_bytes'] , )
if working_fpath != fpath:
for file in os.listdir(os.path.dirname(snake_case ) ):
UpperCamelCase_ : Dict = os.path.join(os.path.dirname(snake_case ) , os.path.basename(snake_case ) )
shutil.move(snake_case , snake_case )
UpperCamelCase_ : int = (
self.df.mapInArrow(snake_case , 'task_id: long, num_examples: long, num_bytes: long' )
.groupBy('task_id' )
.agg(
pyspark.sql.functions.sum('num_examples' ).alias('total_num_examples' ) , pyspark.sql.functions.sum('num_bytes' ).alias('total_num_bytes' ) , pyspark.sql.functions.count('num_bytes' ).alias('num_shards' ) , pyspark.sql.functions.collect_list('num_examples' ).alias('shard_lengths' ) , )
.collect()
)
for row in stats:
yield row.task_id, (row.total_num_examples, row.total_num_bytes, row.num_shards, row.shard_lengths)
def SCREAMING_SNAKE_CASE__ ( self : int , snake_case : "datasets.SplitGenerator" , snake_case : str = "arrow" , snake_case : Optional[Union[str, int]] = None , snake_case : Optional[int] = None , **snake_case : Any , ) -> int:
"""simple docstring"""
self._validate_cache_dir()
UpperCamelCase_ : Optional[int] = convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE )
self._repartition_df_if_needed(snake_case )
UpperCamelCase_ : List[str] = not is_remote_filesystem(self._fs )
UpperCamelCase_ : List[Any] = os.path.join if is_local else posixpath.join
UpperCamelCase_ : Optional[int] = '-TTTTT-SSSSS-of-NNNNN'
UpperCamelCase_ : Dict = f"{self.name}-{split_generator.name}{SUFFIX}.{file_format}"
UpperCamelCase_ : int = path_join(self._output_dir , snake_case )
UpperCamelCase_ : int = 0
UpperCamelCase_ : Optional[int] = 0
UpperCamelCase_ : Union[str, Any] = 0
UpperCamelCase_ : Optional[Any] = []
UpperCamelCase_ : Any = []
for task_id, content in self._prepare_split_single(snake_case , snake_case , snake_case ):
(
(
UpperCamelCase_
), (
UpperCamelCase_
), (
UpperCamelCase_
), (
UpperCamelCase_
),
) : Optional[Any] = content
if num_bytes > 0:
total_num_examples += num_examples
total_num_bytes += num_bytes
total_shards += num_shards
task_id_and_num_shards.append((task_id, num_shards) )
all_shard_lengths.extend(snake_case )
UpperCamelCase_ : Optional[Any] = total_num_examples
UpperCamelCase_ : Any = total_num_bytes
# should rename everything at the end
logger.debug(f"Renaming {total_shards} shards." )
if total_shards > 1:
UpperCamelCase_ : List[Any] = all_shard_lengths
# Define fs outside of _rename_shard so that we don't reference self in the function, which will result in a
# pickling error due to pickling the SparkContext.
UpperCamelCase_ : int = self._fs
# use the -SSSSS-of-NNNNN pattern
def _rename_shard(
snake_case : int , snake_case : int , snake_case : int , ):
rename(
snake_case , fpath.replace('SSSSS' , f"{shard_id:05d}" ).replace('TTTTT' , f"{task_id:05d}" ) , fpath.replace('TTTTT-SSSSS' , f"{global_shard_id:05d}" ).replace('NNNNN' , f"{total_shards:05d}" ) , )
UpperCamelCase_ : Any = []
UpperCamelCase_ : Optional[int] = 0
for i in range(len(snake_case ) ):
UpperCamelCase_, UpperCamelCase_ : Union[str, Any] = task_id_and_num_shards[i]
for shard_id in range(snake_case ):
args.append([task_id, shard_id, global_shard_id] )
global_shard_id += 1
self._spark.sparkContext.parallelize(snake_case , len(snake_case ) ).map(lambda snake_case : _rename_shard(*snake_case ) ).collect()
else:
# don't use any pattern
UpperCamelCase_ : Tuple = 0
UpperCamelCase_ : Optional[Any] = task_id_and_num_shards[0][0]
self._rename(
fpath.replace('SSSSS' , f"{shard_id:05d}" ).replace('TTTTT' , f"{task_id:05d}" ) , fpath.replace(snake_case , '' ) , )
def SCREAMING_SNAKE_CASE__ ( self : Tuple , snake_case : "datasets.SplitGenerator" , ) -> SparkExamplesIterable:
"""simple docstring"""
return SparkExamplesIterable(self.df )
| 175 | 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 GLPNImageProcessor
class __A( unittest.TestCase ):
"""simple docstring"""
def __init__(self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=7 , SCREAMING_SNAKE_CASE_=3 , SCREAMING_SNAKE_CASE_=18 , SCREAMING_SNAKE_CASE_=30 , SCREAMING_SNAKE_CASE_=4_00 , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=32 , SCREAMING_SNAKE_CASE_=True , ):
UpperCamelCase__ = parent
UpperCamelCase__ = batch_size
UpperCamelCase__ = num_channels
UpperCamelCase__ = image_size
UpperCamelCase__ = min_resolution
UpperCamelCase__ = max_resolution
UpperCamelCase__ = do_resize
UpperCamelCase__ = size_divisor
UpperCamelCase__ = do_rescale
def UpperCAmelCase_ (self ):
return {
"do_resize": self.do_resize,
"size_divisor": self.size_divisor,
"do_rescale": self.do_rescale,
}
@require_torch
@require_vision
class __A( __lowerCamelCase , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = GLPNImageProcessor if is_vision_available() else None
def UpperCAmelCase_ (self ):
UpperCamelCase__ = GLPNImageProcessingTester(self )
@property
def UpperCAmelCase_ (self ):
return self.image_processor_tester.prepare_image_processor_dict()
def UpperCAmelCase_ (self ):
UpperCamelCase__ = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , """do_resize""" ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , """size_divisor""" ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , """resample""" ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , """do_rescale""" ) )
def UpperCAmelCase_ (self ):
pass
def UpperCAmelCase_ (self ):
# Initialize image_processing
UpperCamelCase__ = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
UpperCamelCase__ = prepare_image_inputs(self.image_processor_tester , equal_resolution=SCREAMING_SNAKE_CASE_ )
for image in image_inputs:
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , Image.Image )
# Test not batched input (GLPNImageProcessor doesn't support batching)
UpperCamelCase__ = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0 )
self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0 )
def UpperCAmelCase_ (self ):
# Initialize image_processing
UpperCamelCase__ = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
UpperCamelCase__ = prepare_image_inputs(self.image_processor_tester , equal_resolution=SCREAMING_SNAKE_CASE_ , numpify=SCREAMING_SNAKE_CASE_ )
for image in image_inputs:
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , np.ndarray )
# Test not batched input (GLPNImageProcessor doesn't support batching)
UpperCamelCase__ = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0 )
self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0 )
def UpperCAmelCase_ (self ):
# Initialize image_processing
UpperCamelCase__ = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
UpperCamelCase__ = prepare_image_inputs(self.image_processor_tester , equal_resolution=SCREAMING_SNAKE_CASE_ , torchify=SCREAMING_SNAKE_CASE_ )
for image in image_inputs:
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , torch.Tensor )
# Test not batched input (GLPNImageProcessor doesn't support batching)
UpperCamelCase__ = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0 )
self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0 )
| 178 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowerCamelCase_ = {
'''configuration_canine''': ['''CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CanineConfig'''],
'''tokenization_canine''': ['''CanineTokenizer'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase_ = [
'''CANINE_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''CanineForMultipleChoice''',
'''CanineForQuestionAnswering''',
'''CanineForSequenceClassification''',
'''CanineForTokenClassification''',
'''CanineLayer''',
'''CanineModel''',
'''CaninePreTrainedModel''',
'''load_tf_weights_in_canine''',
]
if TYPE_CHECKING:
from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
from .tokenization_canine import CanineTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_canine import (
CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
CanineForMultipleChoice,
CanineForQuestionAnswering,
CanineForSequenceClassification,
CanineForTokenClassification,
CanineLayer,
CanineModel,
CaninePreTrainedModel,
load_tf_weights_in_canine,
)
else:
import sys
lowerCamelCase_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 178 | 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,
)
lowerCAmelCase__ = {
'''configuration_albert''': ['''ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''AlbertConfig''', '''AlbertOnnxConfig'''],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = ['''AlbertTokenizer''']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = ['''AlbertTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = [
'''ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''AlbertForMaskedLM''',
'''AlbertForMultipleChoice''',
'''AlbertForPreTraining''',
'''AlbertForQuestionAnswering''',
'''AlbertForSequenceClassification''',
'''AlbertForTokenClassification''',
'''AlbertModel''',
'''AlbertPreTrainedModel''',
'''load_tf_weights_in_albert''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = [
'''TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFAlbertForMaskedLM''',
'''TFAlbertForMultipleChoice''',
'''TFAlbertForPreTraining''',
'''TFAlbertForQuestionAnswering''',
'''TFAlbertForSequenceClassification''',
'''TFAlbertForTokenClassification''',
'''TFAlbertMainLayer''',
'''TFAlbertModel''',
'''TFAlbertPreTrainedModel''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = [
'''FlaxAlbertForMaskedLM''',
'''FlaxAlbertForMultipleChoice''',
'''FlaxAlbertForPreTraining''',
'''FlaxAlbertForQuestionAnswering''',
'''FlaxAlbertForSequenceClassification''',
'''FlaxAlbertForTokenClassification''',
'''FlaxAlbertModel''',
'''FlaxAlbertPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig, AlbertOnnxConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_albert import AlbertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_albert_fast import AlbertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_albert import (
ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
AlbertForMaskedLM,
AlbertForMultipleChoice,
AlbertForPreTraining,
AlbertForQuestionAnswering,
AlbertForSequenceClassification,
AlbertForTokenClassification,
AlbertModel,
AlbertPreTrainedModel,
load_tf_weights_in_albert,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_albert import (
TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFAlbertForMaskedLM,
TFAlbertForMultipleChoice,
TFAlbertForPreTraining,
TFAlbertForQuestionAnswering,
TFAlbertForSequenceClassification,
TFAlbertForTokenClassification,
TFAlbertMainLayer,
TFAlbertModel,
TFAlbertPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_albert import (
FlaxAlbertForMaskedLM,
FlaxAlbertForMultipleChoice,
FlaxAlbertForPreTraining,
FlaxAlbertForQuestionAnswering,
FlaxAlbertForSequenceClassification,
FlaxAlbertForTokenClassification,
FlaxAlbertModel,
FlaxAlbertPreTrainedModel,
)
else:
import sys
lowerCAmelCase__ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 130 |
lowerCAmelCase__ = 0 # The first color of the flag.
lowerCAmelCase__ = 1 # The second color of the flag.
lowerCAmelCase__ = 2 # The third color of the flag.
lowerCAmelCase__ = (red, white, blue)
def __lowerCamelCase ( lowerCamelCase__ ):
"""simple docstring"""
if not sequence:
return []
if len(lowerCamelCase__ ) == 1:
return list(lowerCamelCase__ )
lowercase__ : List[Any] = 0
lowercase__ : Any = len(lowerCamelCase__ ) - 1
lowercase__ : Dict = 0
while mid <= high:
if sequence[mid] == colors[0]:
lowercase__ , lowercase__ : int = sequence[mid], sequence[low]
low += 1
mid += 1
elif sequence[mid] == colors[1]:
mid += 1
elif sequence[mid] == colors[2]:
lowercase__ , lowercase__ : Union[str, Any] = sequence[high], sequence[mid]
high -= 1
else:
lowercase__ : Tuple = F"""The elements inside the sequence must contains only {colors} values"""
raise ValueError(lowerCamelCase__ )
return sequence
if __name__ == "__main__":
import doctest
doctest.testmod()
lowerCAmelCase__ = input('''Enter numbers separated by commas:\n''').strip()
lowerCAmelCase__ = [int(item.strip()) for item in user_input.split(''',''')]
print(f'''{dutch_national_flag_sort(unsorted)}''')
| 130 | 1 |
import argparse
import json
import os
import torch
from torch import nn
from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
def lowerCAmelCase_ ( __lowerCamelCase ):
__snake_case : Union[str, Any] = [
"encoder.version",
"decoder.version",
"model.encoder.version",
"model.decoder.version",
"decoder.output_projection.weight",
"_float_tensor",
"encoder.embed_positions._float_tensor",
"decoder.embed_positions._float_tensor",
]
for k in ignore_keys:
state_dict.pop(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def lowerCAmelCase_ ( __lowerCamelCase ):
__snake_case , __snake_case : Optional[Any] = emb.weight.shape
__snake_case : Dict = nn.Linear(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , bias=SCREAMING_SNAKE_CASE_ )
__snake_case : Dict = emb.weight.data
return lin_layer
def lowerCAmelCase_ ( __lowerCamelCase , __lowerCamelCase=None ):
__snake_case : List[Any] = {}
for old_key in state_dict.keys():
__snake_case : Any = old_key
if "moe_layer.experts." in key:
if expert_idx is not None:
__snake_case : str = key.replace("moe_layer.experts.0" , F'ffn.experts.expert_{expert_idx}' )
else:
__snake_case : Union[str, Any] = key.replace("moe_layer.experts." , "ffn.experts.expert_" )
if "gate" in key:
__snake_case : Any = key.replace(".moe_layer.gate.wg" , ".ffn.router.classifier" )
if "fc2" and "experts" not in key:
__snake_case : Any = key.replace(".fc2." , ".ffn.fc2." )
if "fc1" and "experts" not in key:
__snake_case : Tuple = key.replace(".fc1." , ".ffn.fc1." )
if ".encoder_attn." in key:
__snake_case : Union[str, Any] = key.replace(".encoder_attn." , ".cross_attention." )
if "encoder_attn_layer_norm" in key:
__snake_case : Any = key.replace("encoder_attn_layer_norm" , "cross_attention_layer_norm" )
if "final_layer_norm" in key:
__snake_case : Optional[int] = key.replace("final_layer_norm" , "ff_layer_norm" )
__snake_case : Any = state_dict[old_key]
return new_dict
def lowerCAmelCase_ ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase = WEIGHTS_NAME ):
__snake_case : str = []
__snake_case : int = 0
os.makedirs(SCREAMING_SNAKE_CASE_ , exist_ok=SCREAMING_SNAKE_CASE_ )
for expert in range(SCREAMING_SNAKE_CASE_ ):
__snake_case : Dict = switch_checkpoint_path + F'-rank-{expert}.pt'
if os.path.isfile(SCREAMING_SNAKE_CASE_ ):
__snake_case : List[Any] = torch.load(SCREAMING_SNAKE_CASE_ )["model"]
remove_ignore_keys_(SCREAMING_SNAKE_CASE_ )
__snake_case : int = rename_fairseq_keys(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
__snake_case : str = os.path.join(
SCREAMING_SNAKE_CASE_ , weights_name.replace(".bin" , F'-{len(SCREAMING_SNAKE_CASE_ )+1:05d}-of-???.bin' ) )
torch.save(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
sharded_state_dicts.append(expert_state.keys() )
total_size += sum([value.numel() for key, value in expert_state.items()] ) * dtype_byte_size(
expert_state[list(SCREAMING_SNAKE_CASE_ )[0]].dtype )
# Add the last block
__snake_case : Dict = os.path.join(SCREAMING_SNAKE_CASE_ , weights_name.replace(".bin" , F'-{len(SCREAMING_SNAKE_CASE_ )+1:05d}-of-???.bin' ) )
__snake_case : Tuple = torch.load(switch_checkpoint_path + "-shared.pt" )["model"]
remove_ignore_keys_(SCREAMING_SNAKE_CASE_ )
__snake_case : List[str] = rename_fairseq_keys(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
__snake_case : List[Any] = shared_weights["decoder.embed_tokens.weight"]
sharded_state_dicts.append(shared_weights.keys() )
# If we only have the shared weights (dummy model/experts saved on the same file)
if len(SCREAMING_SNAKE_CASE_ ) == 1:
__snake_case : str = os.path.join(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
torch.save(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
return {weights_name: sharded_state_dicts[0]}, None
else:
torch.save(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# Otherwise, let's build the index
__snake_case : Dict = {}
for idx, shard in enumerate(SCREAMING_SNAKE_CASE_ ):
__snake_case : Any = weights_name.replace(".bin" , F'-{idx+1:05d}-of-{len(SCREAMING_SNAKE_CASE_ ):05d}.bin' )
__snake_case : Optional[int] = os.path.join(SCREAMING_SNAKE_CASE_ , weights_name.replace(".bin" , F'-{idx+1:05d}-of-???.bin' ) )
os.rename(SCREAMING_SNAKE_CASE_ , os.path.join(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
for key in shard:
__snake_case : Optional[Any] = shard_file
# Add the metadata
__snake_case : List[Any] = {"total_size": total_size}
__snake_case : Tuple = {"metadata": metadata, "weight_map": weight_map}
with open(os.path.join(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) , "w" , encoding="utf-8" ) as f:
__snake_case : Dict = json.dumps(SCREAMING_SNAKE_CASE_ , indent=2 , sort_keys=SCREAMING_SNAKE_CASE_ ) + "\n"
f.write(SCREAMING_SNAKE_CASE_ )
return metadata, index
if __name__ == "__main__":
_snake_case : Dict = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--nllb_moe_checkpoint_path",
default="/home/arthur_huggingface_co/fairseq/weights/checkpoints/model_moe_54b/checkpoint_2_300000",
type=str,
required=False,
help="Path to a directory containing a folder per layer. Follows the original Google format.",
)
parser.add_argument("--dtype", default="float32", type=str, required=False, help="dtype of the saved model")
parser.add_argument(
"--pytorch_dump_folder_path",
default="/home/arthur_huggingface_co/fairseq/weights/checkpoints/hf-converted-moe-54b",
type=str,
required=False,
help="Path to the output pytorch model.",
)
_snake_case : Union[str, Any] = parser.parse_args()
_snake_case , _snake_case : List[Any] = shard_on_the_fly(
args.nllb_moe_checkpoint_path,
args.pytorch_dump_folder_path,
128,
args.dtype,
)
_snake_case : int = NllbMoeConfig.from_pretrained(
"facebook/nllb-200-3.3B", encoder_sparse_step=4, decoder_sparse_step=4, num_experts=128
)
config.save_pretrained(args.pytorch_dump_folder_path)
_snake_case : str = NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
print("Done")
model.save_pretrained(args.pytorch_dump_folder_path)
| 364 |
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
_snake_case : int = logging.get_logger(__name__)
_snake_case : int = {
"microsoft/beit-base-patch16-224-pt22k": (
"https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json"
),
# See all BEiT models at https://huggingface.co/models?filter=beit
}
class a (_lowerCAmelCase ):
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = "beit"
def __init__( self : Union[str, Any] , lowerCamelCase : Any=8192 , lowerCamelCase : Dict=768 , lowerCamelCase : int=12 , lowerCamelCase : Optional[Any]=12 , lowerCamelCase : List[str]=3072 , lowerCamelCase : Tuple="gelu" , lowerCamelCase : Union[str, Any]=0.0 , lowerCamelCase : int=0.0 , lowerCamelCase : Dict=0.02 , lowerCamelCase : List[str]=1E-12 , lowerCamelCase : Optional[Any]=224 , lowerCamelCase : Optional[int]=16 , lowerCamelCase : Any=3 , lowerCamelCase : Optional[int]=False , lowerCamelCase : Any=False , lowerCamelCase : Optional[Any]=False , lowerCamelCase : int=False , lowerCamelCase : Any=0.1 , lowerCamelCase : Tuple=0.1 , lowerCamelCase : Optional[int]=True , lowerCamelCase : int=[3, 5, 7, 11] , lowerCamelCase : str=[1, 2, 3, 6] , lowerCamelCase : int=True , lowerCamelCase : List[Any]=0.4 , lowerCamelCase : int=256 , lowerCamelCase : str=1 , lowerCamelCase : List[str]=False , lowerCamelCase : List[str]=255 , **lowerCamelCase : Dict , ) -> int:
super().__init__(**lowerCamelCase )
__snake_case : Any = vocab_size
__snake_case : List[str] = hidden_size
__snake_case : List[Any] = num_hidden_layers
__snake_case : Tuple = num_attention_heads
__snake_case : Dict = intermediate_size
__snake_case : Union[str, Any] = hidden_act
__snake_case : Optional[Any] = hidden_dropout_prob
__snake_case : Optional[int] = attention_probs_dropout_prob
__snake_case : Union[str, Any] = initializer_range
__snake_case : str = layer_norm_eps
__snake_case : Optional[Any] = image_size
__snake_case : List[str] = patch_size
__snake_case : Optional[Any] = num_channels
__snake_case : Any = use_mask_token
__snake_case : List[str] = use_absolute_position_embeddings
__snake_case : List[Any] = use_relative_position_bias
__snake_case : str = use_shared_relative_position_bias
__snake_case : str = layer_scale_init_value
__snake_case : Any = drop_path_rate
__snake_case : int = use_mean_pooling
# decode head attributes (semantic segmentation)
__snake_case : Optional[Any] = out_indices
__snake_case : List[str] = pool_scales
# auxiliary head attributes (semantic segmentation)
__snake_case : int = use_auxiliary_head
__snake_case : int = auxiliary_loss_weight
__snake_case : Optional[int] = auxiliary_channels
__snake_case : int = auxiliary_num_convs
__snake_case : str = auxiliary_concat_input
__snake_case : List[str] = semantic_loss_ignore_index
class a (_lowerCAmelCase ):
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = version.parse("1.11" )
@property
def __snake_case ( self : Dict ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
] )
@property
def __snake_case ( self : str ) -> float:
return 1E-4
| 134 | 0 |
def UpperCAmelCase__ ( _A : int ):
'''simple docstring'''
if n == 1 or not isinstance(_A , _A ):
return 0
elif n == 2:
return 1
else:
a__ =[0, 1]
for i in range(2 , n + 1 ):
sequence.append(sequence[i - 1] + sequence[i - 2] )
return sequence[n]
def UpperCAmelCase__ ( _A : int ):
'''simple docstring'''
a__ =0
a__ =2
while digits < n:
index += 1
a__ =len(str(fibonacci(_A ) ) )
return index
def UpperCAmelCase__ ( _A : int = 10_00 ):
'''simple docstring'''
return fibonacci_digits_index(_A )
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 188 |
import os
def UpperCAmelCase__ ( _A : Any ):
'''simple docstring'''
a__ =len(grid[0] )
a__ =len(_A )
a__ =0
a__ =0
a__ =0
# Check vertically, horizontally, diagonally at the same time (only works
# for nxn grid)
for i in range(_A ):
for j in range(n_rows - 3 ):
a__ =grid[j][i] * grid[j + 1][i] * grid[j + 2][i] * grid[j + 3][i]
a__ =grid[i][j] * grid[i][j + 1] * grid[i][j + 2] * grid[i][j + 3]
# Left-to-right diagonal (\) product
if i < n_columns - 3:
a__ =(
grid[i][j]
* grid[i + 1][j + 1]
* grid[i + 2][j + 2]
* grid[i + 3][j + 3]
)
# Right-to-left diagonal(/) product
if i > 2:
a__ =(
grid[i][j]
* grid[i - 1][j + 1]
* grid[i - 2][j + 2]
* grid[i - 3][j + 3]
)
a__ =max(
_A , _A , _A , _A )
if max_product > largest:
a__ =max_product
return largest
def UpperCAmelCase__ ( ):
'''simple docstring'''
a__ =[]
with open(os.path.dirname(_A ) + '''/grid.txt''' ) as file:
for line in file:
grid.append(line.strip('''\n''' ).split(''' ''' ) )
a__ =[[int(_A ) for i in grid[j]] for j in range(len(_A ) )]
return largest_product(_A )
if __name__ == "__main__":
print(solution())
| 188 | 1 |
__UpperCamelCase : Dict = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
def __A ( ) -> None:
a = input("""Enter message: """ )
a = input("""Enter key [alphanumeric]: """ )
a = input("""Encrypt/Decrypt [e/d]: """ )
if mode.lower().startswith("""e""" ):
a = """encrypt"""
a = encrypt_message(__lowerCamelCase , __lowerCamelCase )
elif mode.lower().startswith("""d""" ):
a = """decrypt"""
a = decrypt_message(__lowerCamelCase , __lowerCamelCase )
print(f'\n{mode.title()}ed message:' )
print(__lowerCamelCase )
def __A ( __lowerCamelCase , __lowerCamelCase ) -> str:
return translate_message(__lowerCamelCase , __lowerCamelCase , """encrypt""" )
def __A ( __lowerCamelCase , __lowerCamelCase ) -> str:
return translate_message(__lowerCamelCase , __lowerCamelCase , """decrypt""" )
def __A ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) -> str:
a = []
a = 0
a = key.upper()
for symbol in message:
a = LETTERS.find(symbol.upper() )
if num != -1:
if mode == "encrypt":
num += LETTERS.find(key[key_index] )
elif mode == "decrypt":
num -= LETTERS.find(key[key_index] )
num %= len(__lowerCamelCase )
if symbol.isupper():
translated.append(LETTERS[num] )
elif symbol.islower():
translated.append(LETTERS[num].lower() )
key_index += 1
if key_index == len(__lowerCamelCase ):
a = 0
else:
translated.append(__lowerCamelCase )
return "".join(__lowerCamelCase )
if __name__ == "__main__":
main() | 362 |
def __A ( __lowerCamelCase ) -> bool:
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 347 | 0 |
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
if TYPE_CHECKING:
from ... import PreTrainedTokenizerBase, TensorType
__lowerCamelCase = logging.get_logger(__name__)
class UpperCAmelCase ( A_ ):
A__ : int = "vision-encoder-decoder"
A__ : Tuple = True
def __init__(self : Optional[Any] , **snake_case__ : Optional[int] ) -> List[Any]:
'''simple docstring'''
super().__init__(**snake_case__ )
if "encoder" not in kwargs or "decoder" not in kwargs:
raise ValueError(
f"""A configuraton of type {self.model_type} cannot be instantiated because """
f"""not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}""" )
snake_case : int = kwargs.pop("encoder" )
snake_case : Tuple = encoder_config.pop("model_type" )
snake_case : int = kwargs.pop("decoder" )
snake_case : Optional[Any] = decoder_config.pop("model_type" )
snake_case : Tuple = AutoConfig.for_model(snake_case__ , **snake_case__ )
snake_case : List[Any] = AutoConfig.for_model(snake_case__ , **snake_case__ )
snake_case : List[Any] = True
@classmethod
def _SCREAMING_SNAKE_CASE (cls : Optional[Any] , snake_case__ : PretrainedConfig , snake_case__ : PretrainedConfig , **snake_case__ : int ) -> PretrainedConfig:
'''simple docstring'''
logger.info("Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config" )
snake_case : List[str] = True
snake_case : Optional[Any] = True
return cls(encoder=encoder_config.to_dict() , decoder=decoder_config.to_dict() , **snake_case__ )
def _SCREAMING_SNAKE_CASE (self : str ) -> Dict:
'''simple docstring'''
snake_case : Any = copy.deepcopy(self.__dict__ )
snake_case : List[Any] = self.encoder.to_dict()
snake_case : Tuple = self.decoder.to_dict()
snake_case : Union[str, Any] = self.__class__.model_type
return output
class UpperCAmelCase ( A_ ):
A__ : str = version.parse("1.11" )
@property
def _SCREAMING_SNAKE_CASE (self : Dict ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
] )
@property
def _SCREAMING_SNAKE_CASE (self : Any ) -> float:
'''simple docstring'''
return 1e-4
@property
def _SCREAMING_SNAKE_CASE (self : Tuple ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
return OrderedDict({"last_hidden_state": {0: "batch", 1: "encoder_sequence"}} )
class UpperCAmelCase ( A_ ):
@property
def _SCREAMING_SNAKE_CASE (self : Any ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
snake_case : Dict = OrderedDict()
snake_case : Optional[Any] = {0: "batch", 1: "past_decoder_sequence + sequence"}
snake_case : Tuple = {0: "batch", 1: "past_decoder_sequence + sequence"}
snake_case : List[Any] = {0: "batch", 1: "encoder_sequence"}
return common_inputs
def _SCREAMING_SNAKE_CASE (self : Optional[Any] , snake_case__ : "PreTrainedTokenizerBase" , snake_case__ : int = -1 , snake_case__ : int = -1 , snake_case__ : bool = False , snake_case__ : Optional["TensorType"] = None , ) -> Mapping[str, Any]:
'''simple docstring'''
import torch
snake_case : str = OrderedDict()
snake_case : Dict = super().generate_dummy_inputs(
snake_case__ , batch_size=snake_case__ , seq_length=snake_case__ , is_pair=snake_case__ , framework=snake_case__ )
snake_case , snake_case : Dict = dummy_input["input_ids"].shape
snake_case : Dict = (batch, encoder_sequence, self._config.encoder_hidden_size)
snake_case : List[str] = dummy_input.pop("input_ids" )
snake_case : Optional[int] = dummy_input.pop("attention_mask" )
snake_case : Union[str, Any] = torch.zeros(snake_case__ )
return common_inputs
class UpperCAmelCase ( A_ ):
@property
def _SCREAMING_SNAKE_CASE (self : int ) -> None:
'''simple docstring'''
pass
def _SCREAMING_SNAKE_CASE (self : str , snake_case__ : PretrainedConfig ) -> OnnxConfig:
'''simple docstring'''
return VisionEncoderDecoderEncoderOnnxConfig(snake_case__ )
def _SCREAMING_SNAKE_CASE (self : Dict , snake_case__ : PretrainedConfig , snake_case__ : PretrainedConfig , snake_case__ : str = "default" ) -> OnnxConfig:
'''simple docstring'''
snake_case : Union[str, Any] = encoder_config.hidden_size
return VisionEncoderDecoderDecoderOnnxConfig(snake_case__ , snake_case__ )
| 59 |
"""simple docstring"""
import pickle
import numpy as np
from matplotlib import pyplot as plt
class snake_case :
def __init__( self : int , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : List[str] , UpperCamelCase__ : List[str] , UpperCamelCase__ : Dict , UpperCamelCase__ : Union[str, Any]=0.2 , UpperCamelCase__ : Any=0.2)-> Optional[int]:
'''simple docstring'''
__lowerCAmelCase: Optional[Any] = bp_numa
__lowerCAmelCase: Optional[int] = bp_numa
__lowerCAmelCase: Tuple = bp_numa
__lowerCAmelCase: Optional[int] = conva_get[:2]
__lowerCAmelCase: int = conva_get[2]
__lowerCAmelCase: List[str] = size_pa
__lowerCAmelCase: Tuple = rate_w
__lowerCAmelCase: Dict = rate_t
__lowerCAmelCase: List[Any] = [
np.mat(-1 * np.random.rand(self.conva[0] , self.conva[0]) + 0.5)
for i in range(self.conva[1])
]
__lowerCAmelCase: Union[str, Any] = np.mat(-1 * np.random.rand(self.num_bpa , self.num_bpa) + 0.5)
__lowerCAmelCase: int = np.mat(-1 * np.random.rand(self.num_bpa , self.num_bpa) + 0.5)
__lowerCAmelCase: Optional[Any] = -2 * np.random.rand(self.conva[1]) + 1
__lowerCAmelCase: int = -2 * np.random.rand(self.num_bpa) + 1
__lowerCAmelCase: str = -2 * np.random.rand(self.num_bpa) + 1
def lowercase_ ( self : Optional[int] , UpperCamelCase__ : int)-> List[str]:
'''simple docstring'''
__lowerCAmelCase: Any = {
"num_bp1": self.num_bpa,
"num_bp2": self.num_bpa,
"num_bp3": self.num_bpa,
"conv1": self.conva,
"step_conv1": self.step_conva,
"size_pooling1": self.size_poolinga,
"rate_weight": self.rate_weight,
"rate_thre": self.rate_thre,
"w_conv1": self.w_conva,
"wkj": self.wkj,
"vji": self.vji,
"thre_conv1": self.thre_conva,
"thre_bp2": self.thre_bpa,
"thre_bp3": self.thre_bpa,
}
with open(UpperCamelCase__ , "wb") as f:
pickle.dump(UpperCamelCase__ , UpperCamelCase__)
print(f"Model saved： {save_path}")
@classmethod
def lowercase_ ( cls : Dict , UpperCamelCase__ : Union[str, Any])-> List[Any]:
'''simple docstring'''
with open(UpperCamelCase__ , "rb") as f:
__lowerCAmelCase: Dict = pickle.load(UpperCamelCase__) # noqa: S301
__lowerCAmelCase: Optional[int] = model_dic.get("conv1")
conv_get.append(model_dic.get("step_conv1"))
__lowerCAmelCase: List[str] = model_dic.get("size_pooling1")
__lowerCAmelCase: Union[str, Any] = model_dic.get("num_bp1")
__lowerCAmelCase: Any = model_dic.get("num_bp2")
__lowerCAmelCase: Union[str, Any] = model_dic.get("num_bp3")
__lowerCAmelCase: Optional[int] = model_dic.get("rate_weight")
__lowerCAmelCase: int = model_dic.get("rate_thre")
# create model instance
__lowerCAmelCase: Tuple = CNN(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__)
# modify model parameter
__lowerCAmelCase: Any = model_dic.get("w_conv1")
__lowerCAmelCase: Optional[Any] = model_dic.get("wkj")
__lowerCAmelCase: Any = model_dic.get("vji")
__lowerCAmelCase: Dict = model_dic.get("thre_conv1")
__lowerCAmelCase: int = model_dic.get("thre_bp2")
__lowerCAmelCase: Optional[int] = model_dic.get("thre_bp3")
return conv_ins
def lowercase_ ( self : Dict , UpperCamelCase__ : List[Any])-> List[Any]:
'''simple docstring'''
return 1 / (1 + np.exp(-1 * x))
def lowercase_ ( self : Dict , UpperCamelCase__ : List[Any])-> Optional[Any]:
'''simple docstring'''
return round(UpperCamelCase__ , 3)
def lowercase_ ( self : Optional[int] , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : Any , UpperCamelCase__ : List[str] , UpperCamelCase__ : Tuple , UpperCamelCase__ : int)-> Dict:
'''simple docstring'''
__lowerCAmelCase: List[Any] = convs[0]
__lowerCAmelCase: int = convs[1]
__lowerCAmelCase: Union[str, Any] = np.shape(UpperCamelCase__)[0]
# get the data slice of original image data, data_focus
__lowerCAmelCase: Optional[Any] = []
for i_focus in range(0 , size_data - size_conv + 1 , UpperCamelCase__):
for j_focus in range(0 , size_data - size_conv + 1 , UpperCamelCase__):
__lowerCAmelCase: Union[str, Any] = data[
i_focus : i_focus + size_conv, j_focus : j_focus + size_conv
]
data_focus.append(UpperCamelCase__)
# calculate the feature map of every single kernel, and saved as list of matrix
__lowerCAmelCase: int = []
__lowerCAmelCase: Optional[int] = int((size_data - size_conv) / conv_step + 1)
for i_map in range(UpperCamelCase__):
__lowerCAmelCase: List[str] = []
for i_focus in range(len(UpperCamelCase__)):
__lowerCAmelCase: Union[str, Any] = (
np.sum(np.multiply(data_focus[i_focus] , w_convs[i_map]))
- thre_convs[i_map]
)
featuremap.append(self.sig(UpperCamelCase__))
__lowerCAmelCase: str = np.asmatrix(UpperCamelCase__).reshape(
UpperCamelCase__ , UpperCamelCase__)
data_featuremap.append(UpperCamelCase__)
# expanding the data slice to One dimenssion
__lowerCAmelCase: Optional[Any] = []
for each_focus in data_focus:
focusa_list.extend(self.Expand_Mat(UpperCamelCase__))
__lowerCAmelCase: List[Any] = np.asarray(UpperCamelCase__)
return focus_list, data_featuremap
def lowercase_ ( self : Any , UpperCamelCase__ : List[str] , UpperCamelCase__ : List[Any] , UpperCamelCase__ : Optional[Any]="average_pool")-> str:
'''simple docstring'''
__lowerCAmelCase: Tuple = len(featuremaps[0])
__lowerCAmelCase: List[Any] = int(size_map / size_pooling)
__lowerCAmelCase: int = []
for i_map in range(len(UpperCamelCase__)):
__lowerCAmelCase: str = featuremaps[i_map]
__lowerCAmelCase: List[Any] = []
for i_focus in range(0 , UpperCamelCase__ , UpperCamelCase__):
for j_focus in range(0 , UpperCamelCase__ , UpperCamelCase__):
__lowerCAmelCase: Any = feature_map[
i_focus : i_focus + size_pooling,
j_focus : j_focus + size_pooling,
]
if pooling_type == "average_pool":
# average pooling
map_pooled.append(np.average(UpperCamelCase__))
elif pooling_type == "max_pooling":
# max pooling
map_pooled.append(np.max(UpperCamelCase__))
__lowerCAmelCase: Optional[int] = np.asmatrix(UpperCamelCase__).reshape(UpperCamelCase__ , UpperCamelCase__)
featuremap_pooled.append(UpperCamelCase__)
return featuremap_pooled
def lowercase_ ( self : Union[str, Any] , UpperCamelCase__ : str)-> int:
'''simple docstring'''
__lowerCAmelCase: List[Any] = []
for i in range(len(UpperCamelCase__)):
__lowerCAmelCase: Union[str, Any] = np.shape(data[i])
__lowerCAmelCase: int = data[i].reshape(1 , shapes[0] * shapes[1])
__lowerCAmelCase: Dict = data_listed.getA().tolist()[0]
data_expanded.extend(UpperCamelCase__)
__lowerCAmelCase: Any = np.asarray(UpperCamelCase__)
return data_expanded
def lowercase_ ( self : Union[str, Any] , UpperCamelCase__ : Union[str, Any])-> Optional[Any]:
'''simple docstring'''
__lowerCAmelCase: Dict = np.asarray(UpperCamelCase__)
__lowerCAmelCase: Optional[int] = np.shape(UpperCamelCase__)
__lowerCAmelCase: Optional[int] = data_mat.reshape(1 , shapes[0] * shapes[1])
return data_expanded
def lowercase_ ( self : Dict , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : Dict , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : List[Any] , UpperCamelCase__ : Dict)-> List[Any]:
'''simple docstring'''
__lowerCAmelCase: Optional[int] = []
__lowerCAmelCase: Any = 0
for i_map in range(UpperCamelCase__):
__lowerCAmelCase: Optional[Any] = np.ones((size_map, size_map))
for i in range(0 , UpperCamelCase__ , UpperCamelCase__):
for j in range(0 , UpperCamelCase__ , UpperCamelCase__):
__lowerCAmelCase: Optional[Any] = pd_pool[
i_pool
]
__lowerCAmelCase: str = i_pool + 1
__lowerCAmelCase: Dict = np.multiply(
UpperCamelCase__ , np.multiply(out_map[i_map] , (1 - out_map[i_map])))
pd_all.append(UpperCamelCase__)
return pd_all
def lowercase_ ( self : str , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : int , UpperCamelCase__ : Dict , UpperCamelCase__ : List[Any] , UpperCamelCase__ : Any , UpperCamelCase__ : str=bool)-> List[str]:
'''simple docstring'''
print("----------------------Start Training-------------------------")
print((" - - Shape: Train_Data ", np.shape(UpperCamelCase__)))
print((" - - Shape: Teach_Data ", np.shape(UpperCamelCase__)))
__lowerCAmelCase: str = 0
__lowerCAmelCase: Optional[int] = []
__lowerCAmelCase: List[Any] = 1_0_0_0_0
while rp < n_repeat and mse >= error_accuracy:
__lowerCAmelCase: Optional[Any] = 0
print(f"-------------Learning Time {rp}--------------")
for p in range(len(UpperCamelCase__)):
# print('------------Learning Image: %d--------------'%p)
__lowerCAmelCase: Dict = np.asmatrix(datas_train[p])
__lowerCAmelCase: Dict = np.asarray(datas_teach[p])
__lowerCAmelCase , __lowerCAmelCase: int = self.convolute(
UpperCamelCase__ , self.conva , self.w_conva , self.thre_conva , conv_step=self.step_conva , )
__lowerCAmelCase: Any = self.pooling(UpperCamelCase__ , self.size_poolinga)
__lowerCAmelCase: Optional[Any] = np.shape(UpperCamelCase__)
__lowerCAmelCase: str = self._expand(UpperCamelCase__)
__lowerCAmelCase: str = data_bp_input
__lowerCAmelCase: int = np.dot(UpperCamelCase__ , self.vji.T) - self.thre_bpa
__lowerCAmelCase: int = self.sig(UpperCamelCase__)
__lowerCAmelCase: Optional[Any] = np.dot(UpperCamelCase__ , self.wkj.T) - self.thre_bpa
__lowerCAmelCase: str = self.sig(UpperCamelCase__)
# --------------Model Leaning ------------------------
# calculate error and gradient---------------
__lowerCAmelCase: Union[str, Any] = np.multiply(
(data_teach - bp_outa) , np.multiply(UpperCamelCase__ , (1 - bp_outa)))
__lowerCAmelCase: Any = np.multiply(
np.dot(UpperCamelCase__ , self.wkj) , np.multiply(UpperCamelCase__ , (1 - bp_outa)))
__lowerCAmelCase: str = np.dot(UpperCamelCase__ , self.vji)
__lowerCAmelCase: Union[str, Any] = pd_i_all / (self.size_poolinga * self.size_poolinga)
__lowerCAmelCase: str = pd_conva_pooled.T.getA().tolist()
__lowerCAmelCase: str = self._calculate_gradient_from_pool(
UpperCamelCase__ , UpperCamelCase__ , shape_featuremapa[0] , shape_featuremapa[1] , self.size_poolinga , )
# weight and threshold learning process---------
# convolution layer
for k_conv in range(self.conva[1]):
__lowerCAmelCase: List[Any] = self._expand_mat(pd_conva_all[k_conv])
__lowerCAmelCase: int = self.rate_weight * np.dot(UpperCamelCase__ , UpperCamelCase__)
__lowerCAmelCase: Tuple = self.w_conva[k_conv] + delta_w.reshape(
(self.conva[0], self.conva[0]))
__lowerCAmelCase: Tuple = (
self.thre_conva[k_conv]
- np.sum(pd_conva_all[k_conv]) * self.rate_thre
)
# all connected layer
__lowerCAmelCase: List[Any] = self.wkj + pd_k_all.T * bp_outa * self.rate_weight
__lowerCAmelCase: Union[str, Any] = self.vji + pd_j_all.T * bp_outa * self.rate_weight
__lowerCAmelCase: Tuple = self.thre_bpa - pd_k_all * self.rate_thre
__lowerCAmelCase: Optional[int] = self.thre_bpa - pd_j_all * self.rate_thre
# calculate the sum error of all single image
__lowerCAmelCase: List[str] = np.sum(abs(data_teach - bp_outa))
error_count += errors
# print(' ----Teach ',data_teach)
# print(' ----BP_output ',bp_out3)
__lowerCAmelCase: Tuple = rp + 1
__lowerCAmelCase: Optional[Any] = error_count / patterns
all_mse.append(UpperCamelCase__)
def draw_error():
__lowerCAmelCase: Dict = [error_accuracy for i in range(int(n_repeat * 1.2))]
plt.plot(UpperCamelCase__ , "+-")
plt.plot(UpperCamelCase__ , "r--")
plt.xlabel("Learning Times")
plt.ylabel("All_mse")
plt.grid(UpperCamelCase__ , alpha=0.5)
plt.show()
print("------------------Training Complished---------------------")
print((" - - Training epoch: ", rp, f" - - Mse: {mse:.6f}"))
if draw_e:
draw_error()
return mse
def lowercase_ ( self : Union[str, Any] , UpperCamelCase__ : Tuple)-> List[str]:
'''simple docstring'''
__lowerCAmelCase: int = []
print("-------------------Start Testing-------------------------")
print((" - - Shape: Test_Data ", np.shape(UpperCamelCase__)))
for p in range(len(UpperCamelCase__)):
__lowerCAmelCase: Dict = np.asmatrix(datas_test[p])
__lowerCAmelCase , __lowerCAmelCase: Optional[int] = self.convolute(
UpperCamelCase__ , self.conva , self.w_conva , self.thre_conva , conv_step=self.step_conva , )
__lowerCAmelCase: Tuple = self.pooling(UpperCamelCase__ , self.size_poolinga)
__lowerCAmelCase: List[str] = self._expand(UpperCamelCase__)
__lowerCAmelCase: int = data_bp_input
__lowerCAmelCase: List[Any] = bp_outa * self.vji.T - self.thre_bpa
__lowerCAmelCase: Any = self.sig(UpperCamelCase__)
__lowerCAmelCase: Union[str, Any] = bp_outa * self.wkj.T - self.thre_bpa
__lowerCAmelCase: List[str] = self.sig(UpperCamelCase__)
produce_out.extend(bp_outa.getA().tolist())
__lowerCAmelCase: Tuple = [list(map(self.do_round , UpperCamelCase__)) for each in produce_out]
return np.asarray(UpperCamelCase__)
def lowercase_ ( self : int , UpperCamelCase__ : Any)-> Any:
'''simple docstring'''
__lowerCAmelCase: Union[str, Any] = np.asmatrix(UpperCamelCase__)
__lowerCAmelCase , __lowerCAmelCase: Optional[Any] = self.convolute(
UpperCamelCase__ , self.conva , self.w_conva , self.thre_conva , conv_step=self.step_conva , )
__lowerCAmelCase: Any = self.pooling(UpperCamelCase__ , self.size_poolinga)
return data_conveda, data_pooleda
if __name__ == "__main__":
pass
| 217 | 0 |
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
lowerCamelCase_ : List[Any] = logging.get_logger(__name__)
lowerCamelCase_ : int = {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}
lowerCamelCase_ : Optional[Any] = {
"""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"""
),
},
}
lowerCamelCase_ : Dict = {
"""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"""
),
},
}
lowerCamelCase_ : str = {
"""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"""
),
},
}
lowerCamelCase_ : Any = {
"""facebook/dpr-ctx_encoder-single-nq-base""": 5_12,
"""facebook/dpr-ctx_encoder-multiset-base""": 5_12,
}
lowerCamelCase_ : str = {
"""facebook/dpr-question_encoder-single-nq-base""": 5_12,
"""facebook/dpr-question_encoder-multiset-base""": 5_12,
}
lowerCamelCase_ : Tuple = {
"""facebook/dpr-reader-single-nq-base""": 5_12,
"""facebook/dpr-reader-multiset-base""": 5_12,
}
lowerCamelCase_ : Optional[Any] = {
"""facebook/dpr-ctx_encoder-single-nq-base""": {"""do_lower_case""": True},
"""facebook/dpr-ctx_encoder-multiset-base""": {"""do_lower_case""": True},
}
lowerCamelCase_ : Tuple = {
"""facebook/dpr-question_encoder-single-nq-base""": {"""do_lower_case""": True},
"""facebook/dpr-question_encoder-multiset-base""": {"""do_lower_case""": True},
}
lowerCamelCase_ : Any = {
"""facebook/dpr-reader-single-nq-base""": {"""do_lower_case""": True},
"""facebook/dpr-reader-multiset-base""": {"""do_lower_case""": True},
}
class _UpperCamelCase ( _A ):
'''simple docstring'''
__UpperCamelCase : int = VOCAB_FILES_NAMES
__UpperCamelCase : str = CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
__UpperCamelCase : str = CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__UpperCamelCase : Any = CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
__UpperCamelCase : Union[str, Any] = DPRContextEncoderTokenizer
class _UpperCamelCase ( _A ):
'''simple docstring'''
__UpperCamelCase : str = VOCAB_FILES_NAMES
__UpperCamelCase : List[str] = QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
__UpperCamelCase : List[str] = QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__UpperCamelCase : Dict = QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
__UpperCamelCase : Tuple = DPRQuestionEncoderTokenizer
lowerCamelCase_ : Optional[Any] = collections.namedtuple(
"""DPRSpanPrediction""", ["""span_score""", """relevance_score""", """doc_id""", """start_index""", """end_index""", """text"""]
)
lowerCamelCase_ : Any = collections.namedtuple("""DPRReaderOutput""", ["""start_logits""", """end_logits""", """relevance_logits"""])
lowerCamelCase_ : Dict = r"""
Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.
It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),
using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`
with the format:
[CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>
Args:
questions (`str` or `List[str]`):
The questions to be encoded. You can specify one question for many passages. In this case, the question
will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in
`titles` or `texts`.
titles (`str` or `List[str]`):
The passages titles to be encoded. This can be a string or a list of strings if there are several passages.
texts (`str` or `List[str]`):
The passages texts to be encoded. This can be a string or a list of strings if there are several passages.
padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
Activates and controls padding. Accepts the following values:
- `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence
if provided).
- `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
acceptable input length for the model if that argument is not provided.
- `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
lengths).
truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):
Activates and controls truncation. Accepts the following values:
- `True` or `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or to
the maximum acceptable input length for the model if that argument is not provided. This will truncate
token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch
of pairs) is provided.
- `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
acceptable input length for the model if that argument is not provided. This will only truncate the first
sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
- `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
acceptable input length for the model if that argument is not provided. This will only truncate the
second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
- `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths
greater than the model maximum admissible input size).
max_length (`int`, *optional*):
Controls the maximum length to use by one of the truncation/padding parameters.
If left unset or set to `None`, this will use the predefined model maximum length if a maximum length
is required by one of the truncation/padding parameters. If the model has no specific maximum input
length (like XLNet) truncation/padding to a maximum length will be deactivated.
return_tensors (`str` or [`~utils.TensorType`], *optional*):
If set, will return tensors instead of list of python integers. Acceptable values are:
- `'tf'`: Return TensorFlow `tf.constant` objects.
- `'pt'`: Return PyTorch `torch.Tensor` objects.
- `'np'`: Return Numpy `np.ndarray` objects.
return_attention_mask (`bool`, *optional*):
Whether or not to return the attention mask. If not set, will return the attention mask according to the
specific tokenizer's default, defined by the `return_outputs` attribute.
[What are attention masks?](../glossary#attention-mask)
Return:
`Dict[str, List[List[int]]]`: A dictionary with the following keys:
- `input_ids`: List of token ids to be fed to a model.
- `attention_mask`: List of indices specifying which tokens should be attended to by the model.
"""
@add_start_docstrings(_A )
class _UpperCamelCase :
'''simple docstring'''
def __call__( self : Optional[Any] , snake_case_ : int , snake_case_ : Optional[str] = None , snake_case_ : Optional[str] = None , snake_case_ : Union[bool, str] = False , snake_case_ : Union[bool, str] = False , snake_case_ : Optional[int] = None , snake_case_ : Optional[Union[str, TensorType]] = None , snake_case_ : Optional[bool] = None , **snake_case_ : int , ):
if titles is None and texts is None:
return super().__call__(
snake_case_ , padding=snake_case_ , truncation=snake_case_ , max_length=snake_case_ , return_tensors=snake_case_ , return_attention_mask=snake_case_ , **snake_case_ , )
elif titles is None or texts is None:
UpperCamelCase_: Dict = titles if texts is None else texts
return super().__call__(
snake_case_ , snake_case_ , padding=snake_case_ , truncation=snake_case_ , max_length=snake_case_ , return_tensors=snake_case_ , return_attention_mask=snake_case_ , **snake_case_ , )
UpperCamelCase_: Union[str, Any] = titles if not isinstance(snake_case_ , snake_case_ ) else [titles]
UpperCamelCase_: List[str] = texts if not isinstance(snake_case_ , snake_case_ ) else [texts]
UpperCamelCase_: List[Any] = len(snake_case_ )
UpperCamelCase_: Optional[int] = questions if not isinstance(snake_case_ , snake_case_ ) else [questions] * n_passages
assert len(snake_case_ ) == len(
snake_case_ ), f'''There should be as many titles than texts but got {len(snake_case_ )} titles and {len(snake_case_ )} texts.'''
UpperCamelCase_: Union[str, Any] = super().__call__(snake_case_ , snake_case_ , padding=snake_case_ , truncation=snake_case_ )["""input_ids"""]
UpperCamelCase_: List[Any] = super().__call__(snake_case_ , add_special_tokens=snake_case_ , padding=snake_case_ , truncation=snake_case_ )["""input_ids"""]
UpperCamelCase_: Tuple = {
"""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(snake_case_ , snake_case_ )
]
}
if return_attention_mask is not False:
UpperCamelCase_: Optional[int] = []
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_: Tuple = attention_mask
return self.pad(snake_case_ , padding=snake_case_ , max_length=snake_case_ , return_tensors=snake_case_ )
def lowerCAmelCase__ ( self : Any , snake_case_ : BatchEncoding , snake_case_ : DPRReaderOutput , snake_case_ : int = 16 , snake_case_ : int = 64 , snake_case_ : int = 4 , ):
UpperCamelCase_: Optional[Any] = reader_input["""input_ids"""]
UpperCamelCase_: Any = reader_output[:3]
UpperCamelCase_: Dict = len(snake_case_ )
UpperCamelCase_: Optional[int] = sorted(range(snake_case_ ) , reverse=snake_case_ , key=relevance_logits.__getitem__ )
UpperCamelCase_: List[DPRReaderOutput] = []
for doc_id in sorted_docs:
UpperCamelCase_: List[str] = list(input_ids[doc_id] )
# assuming question & title information is at the beginning of the sequence
UpperCamelCase_: int = sequence_ids.index(self.sep_token_id , 2 ) + 1 # second sep id
if sequence_ids[-1] == self.pad_token_id:
UpperCamelCase_: int = sequence_ids.index(self.pad_token_id )
else:
UpperCamelCase_: List[str] = len(snake_case_ )
UpperCamelCase_: Tuple = 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=snake_case_ , top_spans=snake_case_ , )
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=snake_case_ , start_index=snake_case_ , end_index=snake_case_ , text=self.decode(sequence_ids[start_index : end_index + 1] ) , ) )
if len(snake_case_ ) >= num_spans:
break
return nbest_spans_predictions[:num_spans]
def lowerCAmelCase__ ( self : Tuple , snake_case_ : List[int] , snake_case_ : List[int] , snake_case_ : int , snake_case_ : int , ):
UpperCamelCase_: Dict = []
for start_index, start_score in enumerate(snake_case_ ):
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_: List[str] = sorted(snake_case_ , key=lambda snake_case_ : x[1] , reverse=snake_case_ )
UpperCamelCase_: Optional[Any] = []
for (start_index, end_index), score in scores:
assert start_index <= end_index, f'''Wrong span indices: [{start_index}:{end_index}]'''
UpperCamelCase_: List[Any] = 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(snake_case_ ) == top_spans:
break
return chosen_span_intervals
@add_end_docstrings(_A )
class _UpperCamelCase ( _A , _A ):
'''simple docstring'''
__UpperCamelCase : Optional[int] = VOCAB_FILES_NAMES
__UpperCamelCase : Tuple = READER_PRETRAINED_VOCAB_FILES_MAP
__UpperCamelCase : Dict = READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__UpperCamelCase : Optional[int] = READER_PRETRAINED_INIT_CONFIGURATION
__UpperCamelCase : str = ["""input_ids""", """attention_mask"""]
__UpperCamelCase : int = DPRReaderTokenizer
| 358 |
import argparse
import torch
from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def A__ ( lowerCamelCase , lowerCamelCase , lowerCamelCase ) -> Union[str, Any]:
# Construct model
if gpta_config_file == "":
UpperCamelCase_: Union[str, Any] = GPTaConfig()
else:
UpperCamelCase_: Optional[Any] = GPTaConfig.from_json_file(lowerCamelCase )
UpperCamelCase_: List[Any] = GPTaModel(lowerCamelCase )
# Load weights from numpy
load_tf_weights_in_gpta(lowerCamelCase , lowerCamelCase , lowerCamelCase )
# Save pytorch-model
UpperCamelCase_: int = pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
UpperCamelCase_: Any = pytorch_dump_folder_path + """/""" + CONFIG_NAME
print(F'''Save PyTorch model to {pytorch_weights_dump_path}''' )
torch.save(model.state_dict() , lowerCamelCase )
print(F'''Save configuration file to {pytorch_config_dump_path}''' )
with open(lowerCamelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(config.to_json_string() )
if __name__ == "__main__":
lowerCamelCase_ : int = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--gpt2_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
)
parser.add_argument(
"""--gpt2_config_file""",
default="""""",
type=str,
help=(
"""An optional config json file corresponding to the pre-trained OpenAI model. \n"""
"""This specifies the model architecture."""
),
)
lowerCamelCase_ : List[str] = parser.parse_args()
convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)
| 223 | 0 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline,
AutoencoderKL,
DDIMScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from ..pipeline_params import TEXT_TO_AUDIO_BATCH_PARAMS, TEXT_TO_AUDIO_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class __a (UpperCamelCase_ , unittest.TestCase):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Union[str, Any] = AudioLDMPipeline
_SCREAMING_SNAKE_CASE :List[Any] = TEXT_TO_AUDIO_PARAMS
_SCREAMING_SNAKE_CASE :Any = TEXT_TO_AUDIO_BATCH_PARAMS
_SCREAMING_SNAKE_CASE :int = frozenset(
[
"""num_inference_steps""",
"""num_waveforms_per_prompt""",
"""generator""",
"""latents""",
"""output_type""",
"""return_dict""",
"""callback""",
"""callback_steps""",
])
def _a ( self ) -> Optional[Any]:
"""simple docstring"""
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ : Optional[Any] = 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, 64) , class_embed_type="""simple_projection""" , projection_class_embeddings_input_dim=32 , class_embeddings_concat=_a , )
SCREAMING_SNAKE_CASE__ : int = DDIMScheduler(
beta_start=0.00_085 , beta_end=0.012 , beta_schedule="""scaled_linear""" , clip_sample=_a , set_alpha_to_one=_a , )
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ : Tuple = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=1 , out_channels=1 , down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] , up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] , latent_channels=4 , )
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = ClapTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_000 , projection_dim=32 , )
SCREAMING_SNAKE_CASE__ : Optional[int] = ClapTextModelWithProjection(_a )
SCREAMING_SNAKE_CASE__ : str = RobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-roberta""" , model_max_length=77 )
SCREAMING_SNAKE_CASE__ : str = SpeechTaHifiGanConfig(
model_in_dim=8 , sampling_rate=16_000 , upsample_initial_channel=16 , upsample_rates=[2, 2] , upsample_kernel_sizes=[4, 4] , resblock_kernel_sizes=[3, 7] , resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5]] , normalize_before=_a , )
SCREAMING_SNAKE_CASE__ : Any = SpeechTaHifiGan(_a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {
"""unet""": unet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""vocoder""": vocoder,
}
return components
def _a ( self , _a , _a=0 ) -> Optional[int]:
"""simple docstring"""
if str(_a ).startswith("""mps""" ):
SCREAMING_SNAKE_CASE__ : Tuple = torch.manual_seed(_a )
else:
SCREAMING_SNAKE_CASE__ : int = torch.Generator(device=_a ).manual_seed(_a )
SCREAMING_SNAKE_CASE__ : int = {
"""prompt""": """A hammer hitting a wooden surface""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 6.0,
}
return inputs
def _a ( self ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = """cpu""" # ensure determinism for the device-dependent torch.Generator
SCREAMING_SNAKE_CASE__ : int = self.get_dummy_components()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = AudioLDMPipeline(**_a )
SCREAMING_SNAKE_CASE__ : Any = audioldm_pipe.to(_a )
audioldm_pipe.set_progress_bar_config(disable=_a )
SCREAMING_SNAKE_CASE__ : Dict = self.get_dummy_inputs(_a )
SCREAMING_SNAKE_CASE__ : Any = audioldm_pipe(**_a )
SCREAMING_SNAKE_CASE__ : int = output.audios[0]
assert audio.ndim == 1
assert len(_a ) == 256
SCREAMING_SNAKE_CASE__ : Dict = audio[:10]
SCREAMING_SNAKE_CASE__ : str = np.array(
[-0.0_050, 0.0_050, -0.0_060, 0.0_033, -0.0_026, 0.0_033, -0.0_027, 0.0_033, -0.0_028, 0.0_033] )
assert np.abs(audio_slice - expected_slice ).max() < 1E-2
def _a ( self ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = self.get_dummy_components()
SCREAMING_SNAKE_CASE__ : int = AudioLDMPipeline(**_a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = audioldm_pipe.to(_a )
SCREAMING_SNAKE_CASE__ : Tuple = audioldm_pipe.to(_a )
audioldm_pipe.set_progress_bar_config(disable=_a )
SCREAMING_SNAKE_CASE__ : List[Any] = self.get_dummy_inputs(_a )
SCREAMING_SNAKE_CASE__ : Tuple = 3 * [inputs["""prompt"""]]
# forward
SCREAMING_SNAKE_CASE__ : Tuple = audioldm_pipe(**_a )
SCREAMING_SNAKE_CASE__ : List[Any] = output.audios[0]
SCREAMING_SNAKE_CASE__ : Optional[int] = self.get_dummy_inputs(_a )
SCREAMING_SNAKE_CASE__ : List[Any] = 3 * [inputs.pop("""prompt""" )]
SCREAMING_SNAKE_CASE__ : Tuple = audioldm_pipe.tokenizer(
_a , padding="""max_length""" , max_length=audioldm_pipe.tokenizer.model_max_length , truncation=_a , return_tensors="""pt""" , )
SCREAMING_SNAKE_CASE__ : List[Any] = text_inputs["""input_ids"""].to(_a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = audioldm_pipe.text_encoder(
_a , )
SCREAMING_SNAKE_CASE__ : str = prompt_embeds.text_embeds
# additional L_2 normalization over each hidden-state
SCREAMING_SNAKE_CASE__ : List[str] = F.normalize(_a , dim=-1 )
SCREAMING_SNAKE_CASE__ : str = prompt_embeds
# forward
SCREAMING_SNAKE_CASE__ : List[str] = audioldm_pipe(**_a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = output.audios[0]
assert np.abs(audio_a - audio_a ).max() < 1E-2
def _a ( self ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_dummy_components()
SCREAMING_SNAKE_CASE__ : List[str] = AudioLDMPipeline(**_a )
SCREAMING_SNAKE_CASE__ : Tuple = audioldm_pipe.to(_a )
SCREAMING_SNAKE_CASE__ : Any = audioldm_pipe.to(_a )
audioldm_pipe.set_progress_bar_config(disable=_a )
SCREAMING_SNAKE_CASE__ : Any = self.get_dummy_inputs(_a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 3 * ["""this is a negative prompt"""]
SCREAMING_SNAKE_CASE__ : Tuple = negative_prompt
SCREAMING_SNAKE_CASE__ : List[Any] = 3 * [inputs["""prompt"""]]
# forward
SCREAMING_SNAKE_CASE__ : List[Any] = audioldm_pipe(**_a )
SCREAMING_SNAKE_CASE__ : Optional[int] = output.audios[0]
SCREAMING_SNAKE_CASE__ : Tuple = self.get_dummy_inputs(_a )
SCREAMING_SNAKE_CASE__ : Optional[int] = 3 * [inputs.pop("""prompt""" )]
SCREAMING_SNAKE_CASE__ : Dict = []
for p in [prompt, negative_prompt]:
SCREAMING_SNAKE_CASE__ : Tuple = audioldm_pipe.tokenizer(
_a , padding="""max_length""" , max_length=audioldm_pipe.tokenizer.model_max_length , truncation=_a , return_tensors="""pt""" , )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = text_inputs["""input_ids"""].to(_a )
SCREAMING_SNAKE_CASE__ : str = audioldm_pipe.text_encoder(
_a , )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = text_embeds.text_embeds
# additional L_2 normalization over each hidden-state
SCREAMING_SNAKE_CASE__ : Dict = F.normalize(_a , dim=-1 )
embeds.append(_a )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[int] = embeds
# forward
SCREAMING_SNAKE_CASE__ : Optional[int] = audioldm_pipe(**_a )
SCREAMING_SNAKE_CASE__ : Any = output.audios[0]
assert np.abs(audio_a - audio_a ).max() < 1E-2
def _a ( self ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = """cpu""" # ensure determinism for the device-dependent torch.Generator
SCREAMING_SNAKE_CASE__ : List[str] = self.get_dummy_components()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = PNDMScheduler(skip_prk_steps=_a )
SCREAMING_SNAKE_CASE__ : str = AudioLDMPipeline(**_a )
SCREAMING_SNAKE_CASE__ : List[str] = audioldm_pipe.to(_a )
audioldm_pipe.set_progress_bar_config(disable=_a )
SCREAMING_SNAKE_CASE__ : Tuple = self.get_dummy_inputs(_a )
SCREAMING_SNAKE_CASE__ : Any = """egg cracking"""
SCREAMING_SNAKE_CASE__ : List[Any] = audioldm_pipe(**_a , negative_prompt=_a )
SCREAMING_SNAKE_CASE__ : Tuple = output.audios[0]
assert audio.ndim == 1
assert len(_a ) == 256
SCREAMING_SNAKE_CASE__ : Tuple = audio[:10]
SCREAMING_SNAKE_CASE__ : Tuple = np.array(
[-0.0_051, 0.0_050, -0.0_060, 0.0_034, -0.0_026, 0.0_033, -0.0_027, 0.0_033, -0.0_028, 0.0_032] )
assert np.abs(audio_slice - expected_slice ).max() < 1E-2
def _a ( self ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = """cpu""" # ensure determinism for the device-dependent torch.Generator
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_dummy_components()
SCREAMING_SNAKE_CASE__ : int = PNDMScheduler(skip_prk_steps=_a )
SCREAMING_SNAKE_CASE__ : Tuple = AudioLDMPipeline(**_a )
SCREAMING_SNAKE_CASE__ : List[Any] = audioldm_pipe.to(_a )
audioldm_pipe.set_progress_bar_config(disable=_a )
SCREAMING_SNAKE_CASE__ : List[Any] = """A hammer hitting a wooden surface"""
# test num_waveforms_per_prompt=1 (default)
SCREAMING_SNAKE_CASE__ : int = audioldm_pipe(_a , num_inference_steps=2 ).audios
assert audios.shape == (1, 256)
# test num_waveforms_per_prompt=1 (default) for batch of prompts
SCREAMING_SNAKE_CASE__ : str = 2
SCREAMING_SNAKE_CASE__ : Union[str, Any] = audioldm_pipe([prompt] * batch_size , num_inference_steps=2 ).audios
assert audios.shape == (batch_size, 256)
# test num_waveforms_per_prompt for single prompt
SCREAMING_SNAKE_CASE__ : Optional[Any] = 2
SCREAMING_SNAKE_CASE__ : Tuple = audioldm_pipe(_a , num_inference_steps=2 , num_waveforms_per_prompt=_a ).audios
assert audios.shape == (num_waveforms_per_prompt, 256)
# test num_waveforms_per_prompt for batch of prompts
SCREAMING_SNAKE_CASE__ : Optional[int] = 2
SCREAMING_SNAKE_CASE__ : List[Any] = audioldm_pipe(
[prompt] * batch_size , num_inference_steps=2 , num_waveforms_per_prompt=_a ).audios
assert audios.shape == (batch_size * num_waveforms_per_prompt, 256)
def _a ( self ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = """cpu""" # ensure determinism for the device-dependent torch.Generator
SCREAMING_SNAKE_CASE__ : Any = self.get_dummy_components()
SCREAMING_SNAKE_CASE__ : Tuple = AudioLDMPipeline(**_a )
SCREAMING_SNAKE_CASE__ : int = audioldm_pipe.to(_a )
audioldm_pipe.set_progress_bar_config(disable=_a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = audioldm_pipe.vocoder.config.sampling_rate
SCREAMING_SNAKE_CASE__ : List[str] = self.get_dummy_inputs(_a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = audioldm_pipe(audio_length_in_s=0.016 , **_a )
SCREAMING_SNAKE_CASE__ : List[Any] = output.audios[0]
assert audio.ndim == 1
assert len(_a ) / vocoder_sampling_rate == 0.016
SCREAMING_SNAKE_CASE__ : Union[str, Any] = audioldm_pipe(audio_length_in_s=0.032 , **_a )
SCREAMING_SNAKE_CASE__ : Optional[int] = output.audios[0]
assert audio.ndim == 1
assert len(_a ) / vocoder_sampling_rate == 0.032
def _a ( self ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = self.get_dummy_components()
SCREAMING_SNAKE_CASE__ : Tuple = AudioLDMPipeline(**_a )
SCREAMING_SNAKE_CASE__ : Any = audioldm_pipe.to(_a )
audioldm_pipe.set_progress_bar_config(disable=_a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = ["""hey"""]
SCREAMING_SNAKE_CASE__ : Optional[Any] = audioldm_pipe(_a , num_inference_steps=1 )
SCREAMING_SNAKE_CASE__ : Dict = output.audios.shape
assert audio_shape == (1, 256)
SCREAMING_SNAKE_CASE__ : Tuple = audioldm_pipe.vocoder.config
config.model_in_dim *= 2
SCREAMING_SNAKE_CASE__ : Any = SpeechTaHifiGan(_a ).to(_a )
SCREAMING_SNAKE_CASE__ : str = audioldm_pipe(_a , num_inference_steps=1 )
SCREAMING_SNAKE_CASE__ : Any = output.audios.shape
# waveform shape is unchanged, we just have 2x the number of mel channels in the spectrogram
assert audio_shape == (1, 256)
def _a ( self ) -> Optional[int]:
"""simple docstring"""
self._test_attention_slicing_forward_pass(test_mean_pixel_difference=_a )
def _a ( self ) -> Optional[Any]:
"""simple docstring"""
self._test_inference_batch_single_identical(test_mean_pixel_difference=_a )
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() , reason="""XFormers attention is only available with CUDA and `xformers` installed""" , )
def _a ( self ) -> str:
"""simple docstring"""
self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=_a )
@slow
class __a (unittest.TestCase):
'''simple docstring'''
def _a ( self ) -> Optional[int]:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _a ( self , _a , _a="cpu" , _a=torch.floataa , _a=0 ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = torch.Generator(device=_a ).manual_seed(_a )
SCREAMING_SNAKE_CASE__ : str = np.random.RandomState(_a ).standard_normal((1, 8, 128, 16) )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.from_numpy(_a ).to(device=_a , dtype=_a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {
"""prompt""": """A hammer hitting a wooden surface""",
"""latents""": latents,
"""generator""": generator,
"""num_inference_steps""": 3,
"""guidance_scale""": 2.5,
}
return inputs
def _a ( self ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = AudioLDMPipeline.from_pretrained("""cvssp/audioldm""" )
SCREAMING_SNAKE_CASE__ : Any = audioldm_pipe.to(_a )
audioldm_pipe.set_progress_bar_config(disable=_a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_inputs(_a )
SCREAMING_SNAKE_CASE__ : List[str] = 25
SCREAMING_SNAKE_CASE__ : Optional[int] = audioldm_pipe(**_a ).audios[0]
assert audio.ndim == 1
assert len(_a ) == 81_920
SCREAMING_SNAKE_CASE__ : Tuple = audio[77_230:77_240]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = np.array(
[-0.4_884, -0.4_607, 0.0_023, 0.5_007, 0.5_896, 0.5_151, 0.3_813, -0.0_208, -0.3_687, -0.4_315] )
SCREAMING_SNAKE_CASE__ : Tuple = np.abs(expected_slice - audio_slice ).max()
assert max_diff < 1E-2
def _a ( self ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = AudioLDMPipeline.from_pretrained("""cvssp/audioldm""" )
SCREAMING_SNAKE_CASE__ : int = LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config )
SCREAMING_SNAKE_CASE__ : Optional[Any] = audioldm_pipe.to(_a )
audioldm_pipe.set_progress_bar_config(disable=_a )
SCREAMING_SNAKE_CASE__ : List[Any] = self.get_inputs(_a )
SCREAMING_SNAKE_CASE__ : Tuple = audioldm_pipe(**_a ).audios[0]
assert audio.ndim == 1
assert len(_a ) == 81_920
SCREAMING_SNAKE_CASE__ : str = audio[27_780:27_790]
SCREAMING_SNAKE_CASE__ : List[str] = np.array([-0.2_131, -0.0_873, -0.0_124, -0.0_189, 0.0_569, 0.1_373, 0.1_883, 0.2_886, 0.3_297, 0.2_212] )
SCREAMING_SNAKE_CASE__ : int = np.abs(expected_slice - audio_slice ).max()
assert max_diff < 3E-2
| 132 |
"""simple docstring"""
from typing import Optional, Tuple
import jax
import jax.numpy as jnp
from flax import linen as nn
from flax.core.frozen_dict import FrozenDict
from transformers import CLIPConfig, FlaxPreTrainedModel
from transformers.models.clip.modeling_flax_clip import FlaxCLIPVisionModule
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=1E-12 ) -> str:
SCREAMING_SNAKE_CASE__ : Optional[int] = jnp.divide(emb_a.T , jnp.clip(jnp.linalg.norm(__lowerCAmelCase , axis=1 ) , a_min=__lowerCAmelCase ) ).T
SCREAMING_SNAKE_CASE__ : str = jnp.divide(emb_a.T , jnp.clip(jnp.linalg.norm(__lowerCAmelCase , axis=1 ) , a_min=__lowerCAmelCase ) ).T
return jnp.matmul(__lowerCAmelCase , norm_emb_a.T )
class __a (nn.Module):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :CLIPConfig
_SCREAMING_SNAKE_CASE :jnp.dtype = jnp.floataa
def _a ( self ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = FlaxCLIPVisionModule(self.config.vision_config )
SCREAMING_SNAKE_CASE__ : Optional[Any] = nn.Dense(self.config.projection_dim , use_bias=_a , dtype=self.dtype )
SCREAMING_SNAKE_CASE__ : Tuple = self.param("""concept_embeds""" , jax.nn.initializers.ones , (17, self.config.projection_dim) )
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.param(
"""special_care_embeds""" , jax.nn.initializers.ones , (3, self.config.projection_dim) )
SCREAMING_SNAKE_CASE__ : Any = self.param("""concept_embeds_weights""" , jax.nn.initializers.ones , (17,) )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.param("""special_care_embeds_weights""" , jax.nn.initializers.ones , (3,) )
def __call__( self , _a ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = self.vision_model(_a )[1]
SCREAMING_SNAKE_CASE__ : str = self.visual_projection(_a )
SCREAMING_SNAKE_CASE__ : List[str] = jax_cosine_distance(_a , self.special_care_embeds )
SCREAMING_SNAKE_CASE__ : Optional[Any] = jax_cosine_distance(_a , self.concept_embeds )
# increase this value to create a stronger `nfsw` filter
# at the cost of increasing the possibility of filtering benign image inputs
SCREAMING_SNAKE_CASE__ : int = 0.0
SCREAMING_SNAKE_CASE__ : Optional[int] = special_cos_dist - self.special_care_embeds_weights[None, :] + adjustment
SCREAMING_SNAKE_CASE__ : Dict = jnp.round(_a , 3 )
SCREAMING_SNAKE_CASE__ : Dict = jnp.any(special_scores > 0 , axis=1 , keepdims=_a )
# Use a lower threshold if an image has any special care concept
SCREAMING_SNAKE_CASE__ : Any = is_special_care * 0.01
SCREAMING_SNAKE_CASE__ : List[Any] = cos_dist - self.concept_embeds_weights[None, :] + special_adjustment
SCREAMING_SNAKE_CASE__ : Union[str, Any] = jnp.round(_a , 3 )
SCREAMING_SNAKE_CASE__ : List[str] = jnp.any(concept_scores > 0 , axis=1 )
return has_nsfw_concepts
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Dict = CLIPConfig
_SCREAMING_SNAKE_CASE :Union[str, Any] = """clip_input"""
_SCREAMING_SNAKE_CASE :Dict = FlaxStableDiffusionSafetyCheckerModule
def __init__( self , _a , _a = None , _a = 0 , _a = jnp.floataa , _a = True , **_a , ) -> Optional[int]:
"""simple docstring"""
if input_shape is None:
SCREAMING_SNAKE_CASE__ : List[Any] = (1, 224, 224, 3)
SCREAMING_SNAKE_CASE__ : Any = self.module_class(config=_a , dtype=_a , **_a )
super().__init__(_a , _a , input_shape=_a , seed=_a , dtype=_a , _do_init=_do_init )
def _a ( self , _a , _a , _a = None ) -> FrozenDict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = jax.random.normal(_a , _a )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Tuple = jax.random.split(_a )
SCREAMING_SNAKE_CASE__ : List[str] = {"""params""": params_rng, """dropout""": dropout_rng}
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.module.init(_a , _a )["""params"""]
return random_params
def __call__( self , _a , _a = None , ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = jnp.transpose(_a , (0, 2, 3, 1) )
return self.module.apply(
{"""params""": params or self.params} , jnp.array(_a , dtype=jnp.floataa ) , rngs={} , )
| 132 | 1 |
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...test_tokenization_common import TokenizerTesterMixin
a =get_tests_dir("""fixtures/test_sentencepiece.model""")
if is_torch_available():
from transformers.models.mbart.modeling_mbart import shift_tokens_right
a =250004
a =250020
@require_sentencepiece
@require_tokenizers
class A_ ( SCREAMING_SNAKE_CASE , unittest.TestCase ):
_UpperCAmelCase : Tuple = MBartTokenizer
_UpperCAmelCase : Optional[int] = MBartTokenizerFast
_UpperCAmelCase : Union[str, Any] = True
_UpperCAmelCase : Union[str, Any] = True
def lowerCAmelCase ( self : Union[str, Any]):
super().setUp()
# We have a SentencePiece fixture for testing
__lowerCamelCase : Optional[int] = MBartTokenizer(SCREAMING_SNAKE_CASE__ ,keep_accents=SCREAMING_SNAKE_CASE__)
tokenizer.save_pretrained(self.tmpdirname)
def lowerCAmelCase ( self : Dict):
__lowerCamelCase : Optional[int] = MBartTokenizer(SCREAMING_SNAKE_CASE__ ,keep_accents=SCREAMING_SNAKE_CASE__)
__lowerCamelCase : Any = tokenizer.tokenize('This is a test')
self.assertListEqual(SCREAMING_SNAKE_CASE__ ,['▁This', '▁is', '▁a', '▁t', 'est'])
self.assertListEqual(
tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__) ,[value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]] ,)
__lowerCamelCase : Optional[Any] = tokenizer.tokenize('I was born in 92000, and this is falsé.')
self.assertListEqual(
SCREAMING_SNAKE_CASE__ ,[
SPIECE_UNDERLINE + 'I',
SPIECE_UNDERLINE + 'was',
SPIECE_UNDERLINE + 'b',
'or',
'n',
SPIECE_UNDERLINE + 'in',
SPIECE_UNDERLINE + '',
'9',
'2',
'0',
'0',
'0',
',',
SPIECE_UNDERLINE + 'and',
SPIECE_UNDERLINE + 'this',
SPIECE_UNDERLINE + 'is',
SPIECE_UNDERLINE + 'f',
'al',
's',
'é',
'.',
] ,)
__lowerCamelCase : Tuple = tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__)
self.assertListEqual(
SCREAMING_SNAKE_CASE__ ,[
value + tokenizer.fairseq_offset
for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
# ^ unk: 2 + 1 = 3 unk: 2 + 1 = 3 ^
] ,)
__lowerCamelCase : Optional[int] = tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__)
self.assertListEqual(
SCREAMING_SNAKE_CASE__ ,[
SPIECE_UNDERLINE + 'I',
SPIECE_UNDERLINE + 'was',
SPIECE_UNDERLINE + 'b',
'or',
'n',
SPIECE_UNDERLINE + 'in',
SPIECE_UNDERLINE + '',
'<unk>',
'2',
'0',
'0',
'0',
',',
SPIECE_UNDERLINE + 'and',
SPIECE_UNDERLINE + 'this',
SPIECE_UNDERLINE + 'is',
SPIECE_UNDERLINE + 'f',
'al',
's',
'<unk>',
'.',
] ,)
def lowerCAmelCase ( self : List[str]):
if not self.test_slow_tokenizer:
# as we don't have a slow version, we can't compare the outputs between slow and fast versions
return
__lowerCamelCase : Optional[Any] = (self.rust_tokenizer_class, 'hf-internal-testing/tiny-random-mbart', {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"):
__lowerCamelCase : str = self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__ ,**SCREAMING_SNAKE_CASE__)
__lowerCamelCase : Dict = self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__ ,**SCREAMING_SNAKE_CASE__)
__lowerCamelCase : List[Any] = tempfile.mkdtemp()
__lowerCamelCase : Any = tokenizer_r.save_pretrained(SCREAMING_SNAKE_CASE__)
__lowerCamelCase : str = tokenizer_p.save_pretrained(SCREAMING_SNAKE_CASE__)
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files))
__lowerCamelCase : str = tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f)
self.assertSequenceEqual(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__)
# Checks everything loads correctly in the same way
__lowerCamelCase : Tuple = tokenizer_r.from_pretrained(SCREAMING_SNAKE_CASE__)
__lowerCamelCase : Dict = tokenizer_p.from_pretrained(SCREAMING_SNAKE_CASE__)
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__))
# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))
shutil.rmtree(SCREAMING_SNAKE_CASE__)
# Save tokenizer rust, legacy_format=True
__lowerCamelCase : Tuple = tempfile.mkdtemp()
__lowerCamelCase : Optional[Any] = tokenizer_r.save_pretrained(SCREAMING_SNAKE_CASE__ ,legacy_format=SCREAMING_SNAKE_CASE__)
__lowerCamelCase : Union[str, Any] = tokenizer_p.save_pretrained(SCREAMING_SNAKE_CASE__)
# Checks it save with the same files
self.assertSequenceEqual(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__)
# Checks everything loads correctly in the same way
__lowerCamelCase : int = tokenizer_r.from_pretrained(SCREAMING_SNAKE_CASE__)
__lowerCamelCase : Optional[int] = tokenizer_p.from_pretrained(SCREAMING_SNAKE_CASE__)
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__))
shutil.rmtree(SCREAMING_SNAKE_CASE__)
# Save tokenizer rust, legacy_format=False
__lowerCamelCase : str = tempfile.mkdtemp()
__lowerCamelCase : int = tokenizer_r.save_pretrained(SCREAMING_SNAKE_CASE__ ,legacy_format=SCREAMING_SNAKE_CASE__)
__lowerCamelCase : Optional[Any] = tokenizer_p.save_pretrained(SCREAMING_SNAKE_CASE__)
# Checks it saved the tokenizer.json file
self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files))
# Checks everything loads correctly in the same way
__lowerCamelCase : Optional[int] = tokenizer_r.from_pretrained(SCREAMING_SNAKE_CASE__)
__lowerCamelCase : Union[str, Any] = tokenizer_p.from_pretrained(SCREAMING_SNAKE_CASE__)
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__))
shutil.rmtree(SCREAMING_SNAKE_CASE__)
@require_torch
@require_sentencepiece
@require_tokenizers
class A_ ( unittest.TestCase ):
_UpperCAmelCase : Any = '''facebook/mbart-large-en-ro'''
_UpperCAmelCase : List[Any] = [
''' UN Chief Says There Is No Military Solution in Syria''',
''' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.''',
]
_UpperCAmelCase : str = [
'''Şeful ONU declară că nu există o soluţie militară în Siria''',
'''Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'''
''' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'''
''' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.''',
]
_UpperCAmelCase : int = [8_274, 127_873, 25_916, 7, 8_622, 2_071, 438, 67_485, 53, 187_895, 23, 51_712, 2, EN_CODE]
@classmethod
def lowerCAmelCase ( cls : Dict):
__lowerCamelCase : MBartTokenizer = MBartTokenizer.from_pretrained(
cls.checkpoint_name ,src_lang='en_XX' ,tgt_lang='ro_RO')
__lowerCamelCase : Any = 1
return cls
def lowerCAmelCase ( self : int):
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ar_AR'] ,2_5_0_0_0_1)
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['en_EN'] ,2_5_0_0_0_4)
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ro_RO'] ,2_5_0_0_2_0)
def lowerCAmelCase ( self : Dict):
__lowerCamelCase : List[str] = self.tokenizer.batch_encode_plus(self.src_text).input_ids[0]
self.assertListEqual(self.expected_src_tokens ,SCREAMING_SNAKE_CASE__)
def lowerCAmelCase ( self : Tuple):
self.assertIn(SCREAMING_SNAKE_CASE__ ,self.tokenizer.all_special_ids)
__lowerCamelCase : Optional[Any] = [RO_CODE, 8_8_4, 9_0_1_9, 9_6, 9, 9_1_6, 8_6_7_9_2, 3_6, 1_8_7_4_3, 1_5_5_9_6, 5, 2]
__lowerCamelCase : str = self.tokenizer.decode(SCREAMING_SNAKE_CASE__ ,skip_special_tokens=SCREAMING_SNAKE_CASE__)
__lowerCamelCase : Union[str, Any] = self.tokenizer.decode(generated_ids[1:] ,skip_special_tokens=SCREAMING_SNAKE_CASE__)
self.assertEqual(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__)
self.assertNotIn(self.tokenizer.eos_token ,SCREAMING_SNAKE_CASE__)
def lowerCAmelCase ( self : List[Any]):
__lowerCamelCase : int = ['this is gunna be a long sentence ' * 2_0]
assert isinstance(src_text[0] ,SCREAMING_SNAKE_CASE__)
__lowerCamelCase : Dict = 1_0
__lowerCamelCase : List[Any] = self.tokenizer(SCREAMING_SNAKE_CASE__ ,max_length=SCREAMING_SNAKE_CASE__ ,truncation=SCREAMING_SNAKE_CASE__).input_ids[0]
self.assertEqual(ids[-2] ,2)
self.assertEqual(ids[-1] ,SCREAMING_SNAKE_CASE__)
self.assertEqual(len(SCREAMING_SNAKE_CASE__) ,SCREAMING_SNAKE_CASE__)
def lowerCAmelCase ( self : Union[str, Any]):
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR']) ,[2_5_0_0_2_6, 2_5_0_0_0_1])
def lowerCAmelCase ( self : Dict):
__lowerCamelCase : str = tempfile.mkdtemp()
__lowerCamelCase : List[Any] = self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(SCREAMING_SNAKE_CASE__)
__lowerCamelCase : Tuple = MBartTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__)
self.assertDictEqual(new_tok.fairseq_tokens_to_ids ,SCREAMING_SNAKE_CASE__)
@require_torch
def lowerCAmelCase ( self : Union[str, Any]):
__lowerCamelCase : Optional[Any] = self.tokenizer(self.src_text ,text_target=self.tgt_text ,padding=SCREAMING_SNAKE_CASE__ ,return_tensors='pt')
__lowerCamelCase : Optional[Any] = shift_tokens_right(batch['labels'] ,self.tokenizer.pad_token_id)
# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
assert batch.input_ids[1][-2:].tolist() == [2, EN_CODE]
assert batch.decoder_input_ids[1][0].tolist() == RO_CODE
assert batch.decoder_input_ids[1][-1] == 2
assert batch.labels[1][-2:].tolist() == [2, RO_CODE]
@require_torch
def lowerCAmelCase ( self : List[str]):
__lowerCamelCase : Union[str, Any] = self.tokenizer(
self.src_text ,text_target=self.tgt_text ,padding=SCREAMING_SNAKE_CASE__ ,truncation=SCREAMING_SNAKE_CASE__ ,max_length=len(self.expected_src_tokens) ,return_tensors='pt' ,)
__lowerCamelCase : List[Any] = shift_tokens_right(batch['labels'] ,self.tokenizer.pad_token_id)
self.assertIsInstance(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__)
self.assertEqual((2, 1_4) ,batch.input_ids.shape)
self.assertEqual((2, 1_4) ,batch.attention_mask.shape)
__lowerCamelCase : Any = batch.input_ids.tolist()[0]
self.assertListEqual(self.expected_src_tokens ,SCREAMING_SNAKE_CASE__)
self.assertEqual(2 ,batch.decoder_input_ids[0, -1]) # EOS
# Test that special tokens are reset
self.assertEqual(self.tokenizer.prefix_tokens ,[])
self.assertEqual(self.tokenizer.suffix_tokens ,[self.tokenizer.eos_token_id, EN_CODE])
def lowerCAmelCase ( self : List[Any]):
__lowerCamelCase : Optional[int] = self.tokenizer(self.src_text ,padding=SCREAMING_SNAKE_CASE__ ,truncation=SCREAMING_SNAKE_CASE__ ,max_length=3 ,return_tensors='pt')
__lowerCamelCase : Any = self.tokenizer(
text_target=self.tgt_text ,padding=SCREAMING_SNAKE_CASE__ ,truncation=SCREAMING_SNAKE_CASE__ ,max_length=1_0 ,return_tensors='pt')
__lowerCamelCase : Tuple = targets['input_ids']
__lowerCamelCase : int = shift_tokens_right(SCREAMING_SNAKE_CASE__ ,self.tokenizer.pad_token_id)
self.assertEqual(batch.input_ids.shape[1] ,3)
self.assertEqual(batch.decoder_input_ids.shape[1] ,1_0)
@require_torch
def lowerCAmelCase ( self : Optional[int]):
__lowerCamelCase : Tuple = self.tokenizer._build_translation_inputs(
'A test' ,return_tensors='pt' ,src_lang='en_XX' ,tgt_lang='ar_AR')
self.assertEqual(
nested_simplify(SCREAMING_SNAKE_CASE__) ,{
# A, test, EOS, en_XX
'input_ids': [[6_2, 3_0_3_4, 2, 2_5_0_0_0_4]],
'attention_mask': [[1, 1, 1, 1]],
# ar_AR
'forced_bos_token_id': 2_5_0_0_0_1,
} ,)
| 113 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , ) -> None:
__lowerCamelCase : int = len(lowerCamelCase__ )
# If row is equal to the size of the board it means there are a queen in each row in
# the current board (possible_board)
if row == n:
# We convert the variable possible_board that looks like this: [1, 3, 0, 2] to
# this: ['. Q . . ', '. . . Q ', 'Q . . . ', '. . Q . ']
boards.append(['. ' * i + 'Q ' + '. ' * (n - 1 - i) for i in possible_board] )
return
# We iterate each column in the row to find all possible results in each row
for col in range(lowerCamelCase__ ):
# We apply that we learned previously. First we check that in the current board
# (possible_board) there are not other same value because if there is it means
# that there are a collision in vertical. Then we apply the two formulas we
# learned before:
#
# 45º: y - x = b or 45: row - col = b
# 135º: y + x = b or row + col = b.
#
# And we verify if the results of this two formulas not exist in their variables
# respectively. (diagonal_right_collisions, diagonal_left_collisions)
#
# If any or these are True it means there is a collision so we continue to the
# next value in the for loop.
if (
col in possible_board
or row - col in diagonal_right_collisions
or row + col in diagonal_left_collisions
):
continue
# If it is False we call dfs function again and we update the inputs
depth_first_search(
[*possible_board, col] , [*diagonal_right_collisions, row - col] , [*diagonal_left_collisions, row + col] , lowerCamelCase__ , lowerCamelCase__ , )
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ ) -> None:
__lowerCamelCase : list[list[str]] = []
depth_first_search([] , [] , [] , lowerCamelCase__ , lowerCamelCase__ )
# Print all the boards
for board in boards:
for column in board:
print(lowerCamelCase__ )
print('' )
print(len(lowerCamelCase__ ) , 'solutions were found.' )
if __name__ == "__main__":
import doctest
doctest.testmod()
n_queens_solution(4)
| 113 | 1 |
'''simple docstring'''
import inspect
import os
import re
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
__a = "src/transformers"
# This is to make sure the transformers module imported is the one in the repo.
__a = direct_transformers_import(PATH_TO_TRANSFORMERS)
__a = transformers.models.auto.configuration_auto.CONFIG_MAPPING
__a = {
# used to compute the property `self.chunk_length`
"EncodecConfig": ["overlap"],
# used as `self.bert_model = BertModel(config, ...)`
"DPRConfig": True,
# not used in modeling files, but it's an important information
"FSMTConfig": ["langs"],
# used internally in the configuration class file
"GPTNeoConfig": ["attention_types"],
# used internally in the configuration class file
"EsmConfig": ["is_folding_model"],
# used during training (despite we don't have training script for these models yet)
"Mask2FormerConfig": ["ignore_value"],
# `ignore_value` used during training (despite we don't have training script for these models yet)
# `norm` used in conversion script (despite not using in the modeling file)
"OneFormerConfig": ["ignore_value", "norm"],
# used during preprocessing and collation, see `collating_graphormer.py`
"GraphormerConfig": ["spatial_pos_max"],
# used internally in the configuration class file
"T5Config": ["feed_forward_proj"],
# used internally in the configuration class file
# `tokenizer_class` get default value `T5Tokenizer` intentionally
"MT5Config": ["feed_forward_proj", "tokenizer_class"],
"UMT5Config": ["feed_forward_proj", "tokenizer_class"],
# used internally in the configuration class file
"LongT5Config": ["feed_forward_proj"],
# used internally in the configuration class file
"SwitchTransformersConfig": ["feed_forward_proj"],
# having default values other than `1e-5` - we can't fix them without breaking
"BioGptConfig": ["layer_norm_eps"],
# having default values other than `1e-5` - we can't fix them without breaking
"GLPNConfig": ["layer_norm_eps"],
# having default values other than `1e-5` - we can't fix them without breaking
"SegformerConfig": ["layer_norm_eps"],
# having default values other than `1e-5` - we can't fix them without breaking
"CvtConfig": ["layer_norm_eps"],
# having default values other than `1e-5` - we can't fix them without breaking
"PerceiverConfig": ["layer_norm_eps"],
# used internally to calculate the feature size
"InformerConfig": ["num_static_real_features", "num_time_features"],
# used internally to calculate the feature size
"TimeSeriesTransformerConfig": ["num_static_real_features", "num_time_features"],
# used internally to calculate the feature size
"AutoformerConfig": ["num_static_real_features", "num_time_features"],
# used internally to calculate `mlp_dim`
"SamVisionConfig": ["mlp_ratio"],
# For (head) training, but so far not implemented
"ClapAudioConfig": ["num_classes"],
# Not used, but providing useful information to users
"SpeechT5HifiGanConfig": ["sampling_rate"],
}
# TODO (ydshieh): Check the failing cases, try to fix them or move some cases to the above block once we are sure
SPECIAL_CASES_TO_ALLOW.update(
{
"CLIPSegConfig": True,
"DeformableDetrConfig": True,
"DetaConfig": True,
"DinatConfig": True,
"DonutSwinConfig": True,
"EfficientFormerConfig": True,
"FSMTConfig": True,
"JukeboxConfig": True,
"LayoutLMv2Config": True,
"MaskFormerSwinConfig": True,
"MT5Config": True,
"NatConfig": True,
"OneFormerConfig": True,
"PerceiverConfig": True,
"RagConfig": True,
"SpeechT5Config": True,
"SwinConfig": True,
"Swin2SRConfig": True,
"Swinv2Config": True,
"SwitchTransformersConfig": True,
"TableTransformerConfig": True,
"TapasConfig": True,
"TransfoXLConfig": True,
"UniSpeechConfig": True,
"UniSpeechSatConfig": True,
"WavLMConfig": True,
"WhisperConfig": True,
# TODO: @Arthur (for `alignment_head` and `alignment_layer`)
"JukeboxPriorConfig": True,
# TODO: @Younes (for `is_decoder`)
"Pix2StructTextConfig": True,
}
)
def __snake_case( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> List[str]:
snake_case__ : List[str] = False
for attribute in attributes:
for modeling_source in source_strings:
# check if we can find `config.xxx`, `getattr(config, "xxx", ...)` or `getattr(self.config, "xxx", ...)`
if (
f"config.{attribute}" in modeling_source
or f"getattr(config, \"{attribute}\"" in modeling_source
or f"getattr(self.config, \"{attribute}\"" in modeling_source
):
snake_case__ : str = True
# Deal with multi-line cases
elif (
re.search(
rf"getattr[ \t\v\n\r\f]*\([ \t\v\n\r\f]*(self\.)?config,[ \t\v\n\r\f]*\"{attribute}\"" , _lowerCAmelCase , )
is not None
):
snake_case__ : Optional[Any] = True
# `SequenceSummary` is called with `SequenceSummary(config)`
elif attribute in [
"summary_type",
"summary_use_proj",
"summary_activation",
"summary_last_dropout",
"summary_proj_to_labels",
"summary_first_dropout",
]:
if "SequenceSummary" in modeling_source:
snake_case__ : Union[str, Any] = True
if attribute_used:
break
if attribute_used:
break
# common and important attributes, even if they do not always appear in the modeling files
snake_case__ : List[Any] = [
"""bos_index""",
"""eos_index""",
"""pad_index""",
"""unk_index""",
"""mask_index""",
"""image_size""",
"""use_cache""",
"""out_features""",
"""out_indices""",
]
snake_case__ : Union[str, Any] = ["""encoder_no_repeat_ngram_size"""]
# Special cases to be allowed
snake_case__ : Union[str, Any] = True
if not attribute_used:
snake_case__ : Any = False
for attribute in attributes:
# Allow if the default value in the configuration class is different from the one in `PretrainedConfig`
if attribute in ["is_encoder_decoder"] and default_value is True:
snake_case__ : Dict = True
elif attribute in ["tie_word_embeddings"] and default_value is False:
snake_case__ : Any = True
# Allow cases without checking the default value in the configuration class
elif attribute in attributes_to_allow + attributes_used_in_generation:
snake_case__ : Union[str, Any] = True
elif attribute.endswith("""_token_id""" ):
snake_case__ : Union[str, Any] = True
# configuration class specific cases
if not case_allowed:
snake_case__ : str = SPECIAL_CASES_TO_ALLOW.get(config_class.__name__ , [] )
snake_case__ : Tuple = allowed_cases is True or attribute in allowed_cases
return attribute_used or case_allowed
def __snake_case( _lowerCAmelCase ) -> str:
snake_case__ : List[str] = dict(inspect.signature(config_class.__init__ ).parameters )
snake_case__ : Tuple = [x for x in list(signature.keys() ) if x not in ["""self""", """kwargs"""]]
snake_case__ : List[str] = [signature[param].default for param in parameter_names]
# If `attribute_map` exists, an attribute can have different names to be used in the modeling files, and as long
# as one variant is used, the test should pass
snake_case__ : str = {}
if len(config_class.attribute_map ) > 0:
snake_case__ : Dict = {v: k for k, v in config_class.attribute_map.items()}
# Get the path to modeling source files
snake_case__ : int = inspect.getsourcefile(_lowerCAmelCase )
snake_case__ : int = os.path.dirname(_lowerCAmelCase )
# Let's check against all frameworks: as long as one framework uses an attribute, we are good.
snake_case__ : List[str] = [os.path.join(_lowerCAmelCase , _lowerCAmelCase ) for fn in os.listdir(_lowerCAmelCase ) if fn.startswith("""modeling_""" )]
# Get the source code strings
snake_case__ : Dict = []
for path in modeling_paths:
if os.path.isfile(_lowerCAmelCase ):
with open(_lowerCAmelCase ) as fp:
modeling_sources.append(fp.read() )
snake_case__ : List[str] = []
for config_param, default_value in zip(_lowerCAmelCase , _lowerCAmelCase ):
# `attributes` here is all the variant names for `config_param`
snake_case__ : Tuple = [config_param]
# some configuration classes have non-empty `attribute_map`, and both names could be used in the
# corresponding modeling files. As long as one of them appears, it is fine.
if config_param in reversed_attribute_map:
attributes.append(reversed_attribute_map[config_param] )
if not check_attribute_being_used(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ):
unused_attributes.append(attributes[0] )
return sorted(_lowerCAmelCase )
def __snake_case( ) -> List[str]:
snake_case__ : str = {}
for _config_class in list(CONFIG_MAPPING.values() ):
# Skip deprecated models
if "models.deprecated" in _config_class.__module__:
continue
# Some config classes are not in `CONFIG_MAPPING` (e.g. `CLIPVisionConfig`, `Blip2VisionConfig`, etc.)
snake_case__ : List[Any] = [
cls
for name, cls in inspect.getmembers(
inspect.getmodule(_config_class ) , lambda _lowerCAmelCase : inspect.isclass(_lowerCAmelCase )
and issubclass(_lowerCAmelCase , _lowerCAmelCase )
and inspect.getmodule(_lowerCAmelCase ) == inspect.getmodule(_config_class ) , )
]
for config_class in config_classes_in_module:
snake_case__ : Union[str, Any] = check_config_attributes_being_used(_lowerCAmelCase )
if len(_lowerCAmelCase ) > 0:
snake_case__ : Union[str, Any] = unused_attributes
if len(_lowerCAmelCase ) > 0:
snake_case__ : str = """The following configuration classes contain unused attributes in the corresponding modeling files:\n"""
for name, attributes in configs_with_unused_attributes.items():
error += f"{name}: {attributes}\n"
raise ValueError(_lowerCAmelCase )
if __name__ == "__main__":
check_config_attributes()
| 35 |
import argparse
import json
from pathlib import Path
import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging
logging.set_verbosity_info()
lowerCamelCase__ = logging.get_logger(__name__)
def lowerCAmelCase__ ( _SCREAMING_SNAKE_CASE : Any ):
"""simple docstring"""
__a = ASTConfig()
if "10-10" in model_name:
pass
elif "speech-commands" in model_name:
__a = 128
elif "12-12" in model_name:
__a = 12
__a = 12
elif "14-14" in model_name:
__a = 14
__a = 14
elif "16-16" in model_name:
__a = 16
__a = 16
else:
raise ValueError("""Model not supported""" )
__a = """huggingface/label-files"""
if "speech-commands" in model_name:
__a = 35
__a = """speech-commands-v2-id2label.json"""
else:
__a = 527
__a = """audioset-id2label.json"""
__a = json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , repo_type="""dataset""" ) , """r""" ) )
__a = {int(_SCREAMING_SNAKE_CASE ): v for k, v in idalabel.items()}
__a = idalabel
__a = {v: k for k, v in idalabel.items()}
return config
def lowerCAmelCase__ ( _SCREAMING_SNAKE_CASE : Dict ):
"""simple docstring"""
if "module.v" in name:
__a = name.replace("""module.v""" , """audio_spectrogram_transformer""" )
if "cls_token" in name:
__a = name.replace("""cls_token""" , """embeddings.cls_token""" )
if "dist_token" in name:
__a = name.replace("""dist_token""" , """embeddings.distillation_token""" )
if "pos_embed" in name:
__a = name.replace("""pos_embed""" , """embeddings.position_embeddings""" )
if "patch_embed.proj" in name:
__a = name.replace("""patch_embed.proj""" , """embeddings.patch_embeddings.projection""" )
# transformer blocks
if "blocks" in name:
__a = name.replace("""blocks""" , """encoder.layer""" )
if "attn.proj" in name:
__a = name.replace("""attn.proj""" , """attention.output.dense""" )
if "attn" in name:
__a = name.replace("""attn""" , """attention.self""" )
if "norm1" in name:
__a = name.replace("""norm1""" , """layernorm_before""" )
if "norm2" in name:
__a = name.replace("""norm2""" , """layernorm_after""" )
if "mlp.fc1" in name:
__a = name.replace("""mlp.fc1""" , """intermediate.dense""" )
if "mlp.fc2" in name:
__a = name.replace("""mlp.fc2""" , """output.dense""" )
# final layernorm
if "audio_spectrogram_transformer.norm" in name:
__a = name.replace("""audio_spectrogram_transformer.norm""" , """audio_spectrogram_transformer.layernorm""" )
# classifier head
if "module.mlp_head.0" in name:
__a = name.replace("""module.mlp_head.0""" , """classifier.layernorm""" )
if "module.mlp_head.1" in name:
__a = name.replace("""module.mlp_head.1""" , """classifier.dense""" )
return name
def lowerCAmelCase__ ( _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : Union[str, Any] ):
"""simple docstring"""
for key in orig_state_dict.copy().keys():
__a = orig_state_dict.pop(_SCREAMING_SNAKE_CASE )
if "qkv" in key:
__a = key.split(""".""" )
__a = int(key_split[3] )
__a = config.hidden_size
if "weight" in key:
__a = val[:dim, :]
__a = val[dim : dim * 2, :]
__a = val[-dim:, :]
else:
__a = val[:dim]
__a = val[dim : dim * 2]
__a = val[-dim:]
else:
__a = val
return orig_state_dict
def lowerCAmelCase__ ( _SCREAMING_SNAKE_CASE : List[str] ):
"""simple docstring"""
__a = [
"""module.v.head.weight""",
"""module.v.head.bias""",
"""module.v.head_dist.weight""",
"""module.v.head_dist.bias""",
]
for k in ignore_keys:
state_dict.pop(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
@torch.no_grad()
def lowerCAmelCase__ ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : List[str] , _SCREAMING_SNAKE_CASE : List[str]=False ):
"""simple docstring"""
__a = get_audio_spectrogram_transformer_config(_SCREAMING_SNAKE_CASE )
__a = {
"""ast-finetuned-audioset-10-10-0.4593""": (
"""https://www.dropbox.com/s/ca0b1v2nlxzyeb4/audioset_10_10_0.4593.pth?dl=1"""
),
"""ast-finetuned-audioset-10-10-0.450""": (
"""https://www.dropbox.com/s/1tv0hovue1bxupk/audioset_10_10_0.4495.pth?dl=1"""
),
"""ast-finetuned-audioset-10-10-0.448""": (
"""https://www.dropbox.com/s/6u5sikl4b9wo4u5/audioset_10_10_0.4483.pth?dl=1"""
),
"""ast-finetuned-audioset-10-10-0.448-v2""": (
"""https://www.dropbox.com/s/kt6i0v9fvfm1mbq/audioset_10_10_0.4475.pth?dl=1"""
),
"""ast-finetuned-audioset-12-12-0.447""": (
"""https://www.dropbox.com/s/snfhx3tizr4nuc8/audioset_12_12_0.4467.pth?dl=1"""
),
"""ast-finetuned-audioset-14-14-0.443""": (
"""https://www.dropbox.com/s/z18s6pemtnxm4k7/audioset_14_14_0.4431.pth?dl=1"""
),
"""ast-finetuned-audioset-16-16-0.442""": (
"""https://www.dropbox.com/s/mdsa4t1xmcimia6/audioset_16_16_0.4422.pth?dl=1"""
),
"""ast-finetuned-speech-commands-v2""": (
"""https://www.dropbox.com/s/q0tbqpwv44pquwy/speechcommands_10_10_0.9812.pth?dl=1"""
),
}
# load original state_dict
__a = model_name_to_url[model_name]
__a = torch.hub.load_state_dict_from_url(_SCREAMING_SNAKE_CASE , map_location="""cpu""" )
# remove some keys
remove_keys(_SCREAMING_SNAKE_CASE )
# rename some keys
__a = convert_state_dict(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# load 🤗 model
__a = ASTForAudioClassification(_SCREAMING_SNAKE_CASE )
model.eval()
model.load_state_dict(_SCREAMING_SNAKE_CASE )
# verify outputs on dummy input
# source: https://github.com/YuanGongND/ast/blob/79e873b8a54d0a3b330dd522584ff2b9926cd581/src/run.py#L62
__a = -4.267_7393 if """speech-commands""" not in model_name else -6.84_5978
__a = 4.568_9974 if """speech-commands""" not in model_name else 5.565_4526
__a = 1024 if """speech-commands""" not in model_name else 128
__a = ASTFeatureExtractor(mean=_SCREAMING_SNAKE_CASE , std=_SCREAMING_SNAKE_CASE , max_length=_SCREAMING_SNAKE_CASE )
if "speech-commands" in model_name:
__a = load_dataset("""speech_commands""" , """v0.02""" , split="""validation""" )
__a = dataset[0]["""audio"""]["""array"""]
else:
__a = hf_hub_download(
repo_id="""nielsr/audio-spectogram-transformer-checkpoint""" , filename="""sample_audio.flac""" , repo_type="""dataset""" , )
__a , __a = torchaudio.load(_SCREAMING_SNAKE_CASE )
__a = waveform.squeeze().numpy()
__a = feature_extractor(_SCREAMING_SNAKE_CASE , sampling_rate=1_6000 , return_tensors="""pt""" )
# forward pass
__a = model(**_SCREAMING_SNAKE_CASE )
__a = outputs.logits
if model_name == "ast-finetuned-audioset-10-10-0.4593":
__a = torch.tensor([-0.8760, -7.0042, -8.6602] )
elif model_name == "ast-finetuned-audioset-10-10-0.450":
__a = torch.tensor([-1.1986, -7.0903, -8.2718] )
elif model_name == "ast-finetuned-audioset-10-10-0.448":
__a = torch.tensor([-2.6128, -8.0080, -9.4344] )
elif model_name == "ast-finetuned-audioset-10-10-0.448-v2":
__a = torch.tensor([-1.5080, -7.4534, -8.8917] )
elif model_name == "ast-finetuned-audioset-12-12-0.447":
__a = torch.tensor([-0.5050, -6.5833, -8.0843] )
elif model_name == "ast-finetuned-audioset-14-14-0.443":
__a = torch.tensor([-0.3826, -7.0336, -8.2413] )
elif model_name == "ast-finetuned-audioset-16-16-0.442":
__a = torch.tensor([-1.2113, -6.9101, -8.3470] )
elif model_name == "ast-finetuned-speech-commands-v2":
__a = torch.tensor([6.1589, -8.0566, -8.7984] )
else:
raise ValueError("""Unknown model name""" )
if not torch.allclose(logits[0, :3] , _SCREAMING_SNAKE_CASE , atol=1e-4 ):
raise ValueError("""Logits don't match""" )
print("""Looks ok!""" )
if pytorch_dump_folder_path is not None:
Path(_SCREAMING_SNAKE_CASE ).mkdir(exist_ok=_SCREAMING_SNAKE_CASE )
print(f"Saving model {model_name} to {pytorch_dump_folder_path}" )
model.save_pretrained(_SCREAMING_SNAKE_CASE )
print(f"Saving feature extractor to {pytorch_dump_folder_path}" )
feature_extractor.save_pretrained(_SCREAMING_SNAKE_CASE )
if push_to_hub:
print("""Pushing model and feature extractor to the hub...""" )
model.push_to_hub(f"MIT/{model_name}" )
feature_extractor.push_to_hub(f"MIT/{model_name}" )
if __name__ == "__main__":
lowerCamelCase__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--model_name""",
default="""ast-finetuned-audioset-10-10-0.4593""",
type=str,
help="""Name of the Audio Spectrogram Transformer model you'd like to convert.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
)
parser.add_argument(
"""--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
)
lowerCamelCase__ = parser.parse_args()
convert_audio_spectrogram_transformer_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 302 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType
__lowercase = logging.get_logger(__name__)
__lowercase = {
"""microsoft/deberta-v2-xlarge""": """https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json""",
"""microsoft/deberta-v2-xxlarge""": """https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json""",
"""microsoft/deberta-v2-xlarge-mnli""": (
"""https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json"""
),
"""microsoft/deberta-v2-xxlarge-mnli""": (
"""https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json"""
),
}
class _A ( _a ):
"""simple docstring"""
UpperCAmelCase : Any = """deberta-v2"""
def __init__( self : Union[str, Any] , __UpperCAmelCase : Optional[int]=128100 , __UpperCAmelCase : Union[str, Any]=1536 , __UpperCAmelCase : Tuple=24 , __UpperCAmelCase : Tuple=24 , __UpperCAmelCase : Dict=6144 , __UpperCAmelCase : Any="gelu" , __UpperCAmelCase : str=0.1 , __UpperCAmelCase : Tuple=0.1 , __UpperCAmelCase : List[Any]=512 , __UpperCAmelCase : List[str]=0 , __UpperCAmelCase : Union[str, Any]=0.02 , __UpperCAmelCase : int=1e-7 , __UpperCAmelCase : str=False , __UpperCAmelCase : int=-1 , __UpperCAmelCase : Optional[Any]=0 , __UpperCAmelCase : Optional[int]=True , __UpperCAmelCase : Dict=None , __UpperCAmelCase : Tuple=0 , __UpperCAmelCase : List[Any]="gelu" , **__UpperCAmelCase : str , ):
super().__init__(**__UpperCAmelCase)
a : List[str] = hidden_size
a : Dict = num_hidden_layers
a : Optional[int] = num_attention_heads
a : Optional[Any] = intermediate_size
a : Any = hidden_act
a : str = hidden_dropout_prob
a : str = attention_probs_dropout_prob
a : str = max_position_embeddings
a : Union[str, Any] = type_vocab_size
a : int = initializer_range
a : List[Any] = relative_attention
a : List[Any] = max_relative_positions
a : List[Any] = pad_token_id
a : List[str] = position_biased_input
# Backwards compatibility
if type(__UpperCAmelCase) == str:
a : Tuple = [x.strip() for x in pos_att_type.lower().split("|")]
a : Tuple = pos_att_type
a : Optional[Any] = vocab_size
a : List[Any] = layer_norm_eps
a : int = kwargs.get("pooler_hidden_size" , __UpperCAmelCase)
a : Union[str, Any] = pooler_dropout
a : Tuple = pooler_hidden_act
class _A ( _a ):
"""simple docstring"""
@property
def __snake_case ( self : Optional[Any]):
if self.task == "multiple-choice":
a : Dict = {0: "batch", 1: "choice", 2: "sequence"}
else:
a : str = {0: "batch", 1: "sequence"}
if self._config.type_vocab_size > 0:
return OrderedDict(
[("input_ids", dynamic_axis), ("attention_mask", dynamic_axis), ("token_type_ids", dynamic_axis)])
else:
return OrderedDict([("input_ids", dynamic_axis), ("attention_mask", dynamic_axis)])
@property
def __snake_case ( self : List[str]):
return 12
def __snake_case ( self : List[Any] , __UpperCAmelCase : Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"] , __UpperCAmelCase : int = -1 , __UpperCAmelCase : int = -1 , __UpperCAmelCase : int = -1 , __UpperCAmelCase : bool = False , __UpperCAmelCase : Optional["TensorType"] = None , __UpperCAmelCase : int = 3 , __UpperCAmelCase : int = 40 , __UpperCAmelCase : int = 40 , __UpperCAmelCase : "PreTrainedTokenizerBase" = None , ):
a : Any = super().generate_dummy_inputs(preprocessor=__UpperCAmelCase , framework=__UpperCAmelCase)
if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
del dummy_inputs["token_type_ids"]
return dummy_inputs
| 354 |
"""simple docstring"""
__lowercase = frozenset(
[
"""prompt""",
"""height""",
"""width""",
"""guidance_scale""",
"""negative_prompt""",
"""prompt_embeds""",
"""negative_prompt_embeds""",
"""cross_attention_kwargs""",
]
)
__lowercase = frozenset(["""prompt""", """negative_prompt"""])
__lowercase = frozenset([])
__lowercase = frozenset(["""image"""])
__lowercase = frozenset(
[
"""image""",
"""height""",
"""width""",
"""guidance_scale""",
]
)
__lowercase = frozenset(["""image"""])
__lowercase = frozenset(
[
"""prompt""",
"""image""",
"""height""",
"""width""",
"""guidance_scale""",
"""negative_prompt""",
"""prompt_embeds""",
"""negative_prompt_embeds""",
]
)
__lowercase = frozenset(["""prompt""", """image""", """negative_prompt"""])
__lowercase = frozenset(
[
# Text guided image variation with an image mask
"""prompt""",
"""image""",
"""mask_image""",
"""height""",
"""width""",
"""guidance_scale""",
"""negative_prompt""",
"""prompt_embeds""",
"""negative_prompt_embeds""",
]
)
__lowercase = frozenset(["""prompt""", """image""", """mask_image""", """negative_prompt"""])
__lowercase = frozenset(
[
# image variation with an image mask
"""image""",
"""mask_image""",
"""height""",
"""width""",
"""guidance_scale""",
]
)
__lowercase = frozenset(["""image""", """mask_image"""])
__lowercase = frozenset(
[
"""example_image""",
"""image""",
"""mask_image""",
"""height""",
"""width""",
"""guidance_scale""",
]
)
__lowercase = frozenset(["""example_image""", """image""", """mask_image"""])
__lowercase = frozenset(["""class_labels"""])
__lowercase = frozenset(["""class_labels"""])
__lowercase = frozenset(["""batch_size"""])
__lowercase = frozenset([])
__lowercase = frozenset(["""batch_size"""])
__lowercase = frozenset([])
__lowercase = frozenset(
[
"""prompt""",
"""audio_length_in_s""",
"""guidance_scale""",
"""negative_prompt""",
"""prompt_embeds""",
"""negative_prompt_embeds""",
"""cross_attention_kwargs""",
]
)
__lowercase = frozenset(["""prompt""", """negative_prompt"""])
__lowercase = frozenset(["""input_tokens"""])
__lowercase = frozenset(["""input_tokens"""])
| 226 | 0 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPENAI_CLIP_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
__snake_case : Dict = logging.get_logger(__name__)
class A__ ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = ['pixel_values']
def __init__( self: Tuple , _SCREAMING_SNAKE_CASE: bool = True , _SCREAMING_SNAKE_CASE: Dict[str, int] = None , _SCREAMING_SNAKE_CASE: PILImageResampling = PILImageResampling.BICUBIC , _SCREAMING_SNAKE_CASE: bool = True , _SCREAMING_SNAKE_CASE: Union[int, float] = 1 / 255 , _SCREAMING_SNAKE_CASE: bool = True , _SCREAMING_SNAKE_CASE: Optional[Union[float, List[float]]] = None , _SCREAMING_SNAKE_CASE: Optional[Union[float, List[float]]] = None , _SCREAMING_SNAKE_CASE: bool = True , **_SCREAMING_SNAKE_CASE: int , ) -> None:
"""simple docstring"""
super().__init__(**_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : List[str] = size if size is not None else {"height": 384, "width": 384}
__lowerCAmelCase : Union[str, Any] = get_size_dict(_SCREAMING_SNAKE_CASE , default_to_square=_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : int = do_resize
__lowerCAmelCase : Optional[Any] = size
__lowerCAmelCase : Any = resample
__lowerCAmelCase : Union[str, Any] = do_rescale
__lowerCAmelCase : Optional[int] = rescale_factor
__lowerCAmelCase : Dict = do_normalize
__lowerCAmelCase : Any = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
__lowerCAmelCase : Tuple = image_std if image_std is not None else OPENAI_CLIP_STD
__lowerCAmelCase : List[Any] = do_convert_rgb
def _SCREAMING_SNAKE_CASE ( self: Tuple , _SCREAMING_SNAKE_CASE: np.ndarray , _SCREAMING_SNAKE_CASE: Dict[str, int] , _SCREAMING_SNAKE_CASE: PILImageResampling = PILImageResampling.BICUBIC , _SCREAMING_SNAKE_CASE: Optional[Union[str, ChannelDimension]] = None , **_SCREAMING_SNAKE_CASE: int , ) -> np.ndarray:
"""simple docstring"""
__lowerCAmelCase : int = get_size_dict(_SCREAMING_SNAKE_CASE , default_to_square=_SCREAMING_SNAKE_CASE)
if "height" not in size or "width" not in size:
raise ValueError(F"""The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}""")
__lowerCAmelCase : Tuple = (size["height"], size["width"])
return resize(_SCREAMING_SNAKE_CASE , size=_SCREAMING_SNAKE_CASE , resample=_SCREAMING_SNAKE_CASE , data_format=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE)
def _SCREAMING_SNAKE_CASE ( self: Dict , _SCREAMING_SNAKE_CASE: np.ndarray , _SCREAMING_SNAKE_CASE: Union[int, float] , _SCREAMING_SNAKE_CASE: Optional[Union[str, ChannelDimension]] = None , **_SCREAMING_SNAKE_CASE: List[str] , ) -> List[Any]:
"""simple docstring"""
return rescale(_SCREAMING_SNAKE_CASE , scale=_SCREAMING_SNAKE_CASE , data_format=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE)
def _SCREAMING_SNAKE_CASE ( self: Any , _SCREAMING_SNAKE_CASE: np.ndarray , _SCREAMING_SNAKE_CASE: Union[float, List[float]] , _SCREAMING_SNAKE_CASE: Union[float, List[float]] , _SCREAMING_SNAKE_CASE: Optional[Union[str, ChannelDimension]] = None , **_SCREAMING_SNAKE_CASE: int , ) -> np.ndarray:
"""simple docstring"""
return normalize(_SCREAMING_SNAKE_CASE , mean=_SCREAMING_SNAKE_CASE , std=_SCREAMING_SNAKE_CASE , data_format=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE)
def _SCREAMING_SNAKE_CASE ( self: List[str] , _SCREAMING_SNAKE_CASE: ImageInput , _SCREAMING_SNAKE_CASE: Optional[bool] = None , _SCREAMING_SNAKE_CASE: Optional[Dict[str, int]] = None , _SCREAMING_SNAKE_CASE: PILImageResampling = None , _SCREAMING_SNAKE_CASE: Optional[bool] = None , _SCREAMING_SNAKE_CASE: Optional[float] = None , _SCREAMING_SNAKE_CASE: Optional[bool] = None , _SCREAMING_SNAKE_CASE: Optional[Union[float, List[float]]] = None , _SCREAMING_SNAKE_CASE: Optional[Union[float, List[float]]] = None , _SCREAMING_SNAKE_CASE: Optional[Union[str, TensorType]] = None , _SCREAMING_SNAKE_CASE: bool = None , _SCREAMING_SNAKE_CASE: ChannelDimension = ChannelDimension.FIRST , **_SCREAMING_SNAKE_CASE: int , ) -> PIL.Image.Image:
"""simple docstring"""
__lowerCAmelCase : Optional[Any] = do_resize if do_resize is not None else self.do_resize
__lowerCAmelCase : Union[str, Any] = resample if resample is not None else self.resample
__lowerCAmelCase : Optional[int] = do_rescale if do_rescale is not None else self.do_rescale
__lowerCAmelCase : Optional[Any] = rescale_factor if rescale_factor is not None else self.rescale_factor
__lowerCAmelCase : str = do_normalize if do_normalize is not None else self.do_normalize
__lowerCAmelCase : Optional[int] = image_mean if image_mean is not None else self.image_mean
__lowerCAmelCase : Optional[Any] = image_std if image_std is not None else self.image_std
__lowerCAmelCase : Optional[int] = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
__lowerCAmelCase : str = size if size is not None else self.size
__lowerCAmelCase : List[str] = get_size_dict(_SCREAMING_SNAKE_CASE , default_to_square=_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : Any = make_list_of_images(_SCREAMING_SNAKE_CASE)
if not valid_images(_SCREAMING_SNAKE_CASE):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray.")
if do_resize and size is None or resample is None:
raise ValueError("Size and resample must be specified if do_resize is True.")
if do_rescale and rescale_factor is None:
raise ValueError("Rescale factor must be specified if do_rescale is True.")
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("Image mean and std must be specified if do_normalize is True.")
# PIL RGBA images are converted to RGB
if do_convert_rgb:
__lowerCAmelCase : List[Any] = [convert_to_rgb(_SCREAMING_SNAKE_CASE) for image in images]
# All transformations expect numpy arrays.
__lowerCAmelCase : Any = [to_numpy_array(_SCREAMING_SNAKE_CASE) for image in images]
if do_resize:
__lowerCAmelCase : str = [self.resize(image=_SCREAMING_SNAKE_CASE , size=_SCREAMING_SNAKE_CASE , resample=_SCREAMING_SNAKE_CASE) for image in images]
if do_rescale:
__lowerCAmelCase : List[Any] = [self.rescale(image=_SCREAMING_SNAKE_CASE , scale=_SCREAMING_SNAKE_CASE) for image in images]
if do_normalize:
__lowerCAmelCase : List[str] = [self.normalize(image=_SCREAMING_SNAKE_CASE , mean=_SCREAMING_SNAKE_CASE , std=_SCREAMING_SNAKE_CASE) for image in images]
__lowerCAmelCase : Any = [to_channel_dimension_format(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE) for image in images]
__lowerCAmelCase : Optional[Any] = BatchFeature(data={"pixel_values": images} , tensor_type=_SCREAMING_SNAKE_CASE)
return encoded_outputs | 269 |
"""simple docstring"""
import os
import unittest
from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class A__ ( __SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = FunnelTokenizer
SCREAMING_SNAKE_CASE = FunnelTokenizerFast
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = True
def _SCREAMING_SNAKE_CASE ( self: Tuple) -> Optional[int]:
"""simple docstring"""
super().setUp()
__lowerCAmelCase : str = [
"<unk>",
"<cls>",
"<sep>",
"want",
"##want",
"##ed",
"wa",
"un",
"runn",
"##ing",
",",
"low",
"lowest",
]
__lowerCAmelCase : int = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"])
with open(self.vocab_file , "w" , encoding="utf-8") as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))
def _SCREAMING_SNAKE_CASE ( self: Optional[Any] , **_SCREAMING_SNAKE_CASE: Union[str, Any]) -> int:
"""simple docstring"""
return FunnelTokenizer.from_pretrained(self.tmpdirname , **_SCREAMING_SNAKE_CASE)
def _SCREAMING_SNAKE_CASE ( self: Any , **_SCREAMING_SNAKE_CASE: Any) -> str:
"""simple docstring"""
return FunnelTokenizerFast.from_pretrained(self.tmpdirname , **_SCREAMING_SNAKE_CASE)
def _SCREAMING_SNAKE_CASE ( self: Tuple , _SCREAMING_SNAKE_CASE: str) -> Any:
"""simple docstring"""
__lowerCAmelCase : Union[str, Any] = "UNwant\u00E9d,running"
__lowerCAmelCase : str = "unwanted, running"
return input_text, output_text
def _SCREAMING_SNAKE_CASE ( self: Optional[int]) -> List[str]:
"""simple docstring"""
__lowerCAmelCase : Any = self.tokenizer_class(self.vocab_file)
__lowerCAmelCase : Any = tokenizer.tokenize("UNwant\u00E9d,running")
self.assertListEqual(_SCREAMING_SNAKE_CASE , ["un", "##want", "##ed", ",", "runn", "##ing"])
self.assertListEqual(tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE) , [7, 4, 5, 10, 8, 9])
def _SCREAMING_SNAKE_CASE ( self: List[str]) -> List[str]:
"""simple docstring"""
__lowerCAmelCase : Optional[Any] = self.get_tokenizers(do_lower_case=_SCREAMING_SNAKE_CASE)
for tokenizer in tokenizers:
__lowerCAmelCase : List[str] = tokenizer("UNwant\u00E9d,running")
__lowerCAmelCase : Optional[int] = len(inputs["input_ids"]) - 1
self.assertListEqual(inputs["token_type_ids"] , [2] + [0] * sentence_len)
__lowerCAmelCase : List[str] = tokenizer("UNwant\u00E9d,running" , "UNwant\u00E9d,running")
self.assertListEqual(inputs["token_type_ids"] , [2] + [0] * sentence_len + [1] * sentence_len) | 269 | 1 |
'''simple docstring'''
from __future__ import annotations
from random import random
from typing import Generic, TypeVar
lowerCAmelCase : int =TypeVar('''KT''')
lowerCAmelCase : Union[str, Any] =TypeVar('''VT''')
class a_ ( Generic[KT, VT] ):
def __init__( self : Optional[Any] , lowercase : KT | str = "root" , lowercase : VT | None = None ):
"""simple docstring"""
lowercase_ :str = key
lowercase_ :List[Any] = value
lowercase_ :list[Node[KT, VT]] = []
def __repr__( self : Optional[int] ):
"""simple docstring"""
return F'Node({self.key}: {self.value})'
@property
def lowercase__ ( self : Dict ):
"""simple docstring"""
return len(self.forward )
class a_ ( Generic[KT, VT] ):
def __init__( self : int , lowercase : float = 0.5 , lowercase : int = 16 ):
"""simple docstring"""
lowercase_ :Node[KT, VT] = Node[KT, VT]()
lowercase_ :str = 0
lowercase_ :str = p
lowercase_ :List[Any] = max_level
def __str__( self : Tuple ):
"""simple docstring"""
lowercase_ :Optional[Any] = list(self )
if len(lowercase ) == 0:
return F'SkipList(level={self.level})'
lowercase_ :List[str] = max((len(str(lowercase ) ) for item in items) , default=4 )
lowercase_ :List[str] = max(lowercase , 4 ) + 4
lowercase_ :Tuple = self.head
lowercase_ :int = []
lowercase_ :Any = node.forward.copy()
lines.append(F'[{node.key}]'.ljust(lowercase , "-" ) + "* " * len(lowercase ) )
lines.append(" " * label_size + "| " * len(lowercase ) )
while len(node.forward ) != 0:
lowercase_ :str = node.forward[0]
lines.append(
F'[{node.key}]'.ljust(lowercase , "-" )
+ " ".join(str(n.key ) if n.key == node.key else "|" for n in forwards ) )
lines.append(" " * label_size + "| " * len(lowercase ) )
lowercase_ :Optional[Any] = node.forward
lines.append("None".ljust(lowercase ) + "* " * len(lowercase ) )
return F'SkipList(level={self.level})\n' + "\n".join(lowercase )
def __iter__( self : int ):
"""simple docstring"""
lowercase_ :str = self.head
while len(node.forward ) != 0:
yield node.forward[0].key
lowercase_ :List[Any] = node.forward[0]
def lowercase__ ( self : Optional[Any] ):
"""simple docstring"""
lowercase_ :Any = 1
while random() < self.p and level < self.max_level:
level += 1
return level
def lowercase__ ( self : Dict , lowercase : Any ):
"""simple docstring"""
lowercase_ :Optional[int] = []
lowercase_ :Dict = self.head
for i in reversed(range(self.level ) ):
# i < node.level - When node level is lesser than `i` decrement `i`.
# node.forward[i].key < key - Jumping to node with key value higher
# or equal to searched key would result
# in skipping searched key.
while i < node.level and node.forward[i].key < key:
lowercase_ :Optional[int] = node.forward[i]
# Each leftmost node (relative to searched node) will potentially have to
# be updated.
update_vector.append(lowercase )
update_vector.reverse() # Note that we were inserting values in reverse order.
# len(node.forward) != 0 - If current node doesn't contain any further
# references then searched key is not present.
# node.forward[0].key == key - Next node key should be equal to search key
# if key is present.
if len(node.forward ) != 0 and node.forward[0].key == key:
return node.forward[0], update_vector
else:
return None, update_vector
def lowercase__ ( self : Tuple , lowercase : KT ):
"""simple docstring"""
lowercase_ , lowercase_ :int = self._locate_node(lowercase )
if node is not None:
for i, update_node in enumerate(lowercase ):
# Remove or replace all references to removed node.
if update_node.level > i and update_node.forward[i].key == key:
if node.level > i:
lowercase_ :Dict = node.forward[i]
else:
lowercase_ :Optional[int] = update_node.forward[:i]
def lowercase__ ( self : int , lowercase : KT , lowercase : VT ):
"""simple docstring"""
lowercase_ , lowercase_ :List[str] = self._locate_node(lowercase )
if node is not None:
lowercase_ :str = value
else:
lowercase_ :Optional[int] = self.random_level()
if level > self.level:
# After level increase we have to add additional nodes to head.
for _ in range(self.level - 1 , lowercase ):
update_vector.append(self.head )
lowercase_ :List[str] = level
lowercase_ :Tuple = Node(lowercase , lowercase )
for i, update_node in enumerate(update_vector[:level] ):
# Change references to pass through new node.
if update_node.level > i:
new_node.forward.append(update_node.forward[i] )
if update_node.level < i + 1:
update_node.forward.append(lowercase )
else:
lowercase_ :List[Any] = new_node
def lowercase__ ( self : str , lowercase : VT ):
"""simple docstring"""
lowercase_ , lowercase_ :Optional[int] = self._locate_node(lowercase )
if node is not None:
return node.value
return None
def UpperCAmelCase_ ( ):
lowercase_ :int = SkipList()
skip_list.insert("Key1" ,3 )
skip_list.insert("Key2" ,12 )
skip_list.insert("Key3" ,41 )
skip_list.insert("Key4" ,-19 )
lowercase_ :List[str] = skip_list.head
lowercase_ :Optional[int] = {}
while node.level != 0:
lowercase_ :Optional[int] = node.forward[0]
lowercase_ :Union[str, Any] = node.value
assert len(__lowerCamelCase ) == 4
assert all_values["Key1"] == 3
assert all_values["Key2"] == 12
assert all_values["Key3"] == 41
assert all_values["Key4"] == -19
def UpperCAmelCase_ ( ):
lowercase_ :List[str] = SkipList()
skip_list.insert("Key1" ,10 )
skip_list.insert("Key1" ,12 )
skip_list.insert("Key5" ,7 )
skip_list.insert("Key7" ,10 )
skip_list.insert("Key10" ,5 )
skip_list.insert("Key7" ,7 )
skip_list.insert("Key5" ,5 )
skip_list.insert("Key10" ,10 )
lowercase_ :Tuple = skip_list.head
lowercase_ :int = {}
while node.level != 0:
lowercase_ :List[str] = node.forward[0]
lowercase_ :str = node.value
if len(__lowerCamelCase ) != 4:
print()
assert len(__lowerCamelCase ) == 4
assert all_values["Key1"] == 12
assert all_values["Key7"] == 7
assert all_values["Key5"] == 5
assert all_values["Key10"] == 10
def UpperCAmelCase_ ( ):
lowercase_ :Union[str, Any] = SkipList()
assert skip_list.find("Some key" ) is None
def UpperCAmelCase_ ( ):
lowercase_ :str = SkipList()
skip_list.insert("Key2" ,20 )
assert skip_list.find("Key2" ) == 20
skip_list.insert("Some Key" ,10 )
skip_list.insert("Key2" ,8 )
skip_list.insert("V" ,13 )
assert skip_list.find("Y" ) is None
assert skip_list.find("Key2" ) == 8
assert skip_list.find("Some Key" ) == 10
assert skip_list.find("V" ) == 13
def UpperCAmelCase_ ( ):
lowercase_ :int = SkipList()
skip_list.delete("Some key" )
assert len(skip_list.head.forward ) == 0
def UpperCAmelCase_ ( ):
lowercase_ :str = SkipList()
skip_list.insert("Key1" ,12 )
skip_list.insert("V" ,13 )
skip_list.insert("X" ,14 )
skip_list.insert("Key2" ,15 )
skip_list.delete("V" )
skip_list.delete("Key2" )
assert skip_list.find("V" ) is None
assert skip_list.find("Key2" ) is None
def UpperCAmelCase_ ( ):
lowercase_ :str = SkipList()
skip_list.insert("Key1" ,12 )
skip_list.insert("V" ,13 )
skip_list.insert("X" ,14 )
skip_list.insert("Key2" ,15 )
skip_list.delete("V" )
assert skip_list.find("V" ) is None
assert skip_list.find("X" ) == 14
assert skip_list.find("Key1" ) == 12
assert skip_list.find("Key2" ) == 15
skip_list.delete("X" )
assert skip_list.find("V" ) is None
assert skip_list.find("X" ) is None
assert skip_list.find("Key1" ) == 12
assert skip_list.find("Key2" ) == 15
skip_list.delete("Key1" )
assert skip_list.find("V" ) is None
assert skip_list.find("X" ) is None
assert skip_list.find("Key1" ) is None
assert skip_list.find("Key2" ) == 15
skip_list.delete("Key2" )
assert skip_list.find("V" ) is None
assert skip_list.find("X" ) is None
assert skip_list.find("Key1" ) is None
assert skip_list.find("Key2" ) is None
def UpperCAmelCase_ ( ):
lowercase_ :Dict = SkipList()
skip_list.insert("Key1" ,12 )
skip_list.insert("V" ,13 )
skip_list.insert("X" ,1_42 )
skip_list.insert("Key2" ,15 )
skip_list.delete("X" )
def traverse_keys(__lowerCamelCase : int ):
yield node.key
for forward_node in node.forward:
yield from traverse_keys(__lowerCamelCase )
assert len(set(traverse_keys(skip_list.head ) ) ) == 4
def UpperCAmelCase_ ( ):
def is_sorted(__lowerCamelCase : List[Any] ):
return all(next_item >= item for item, next_item in zip(__lowerCamelCase ,lst[1:] ) )
lowercase_ :List[Any] = SkipList()
for i in range(10 ):
skip_list.insert(__lowerCamelCase ,__lowerCamelCase )
assert is_sorted(list(__lowerCamelCase ) )
skip_list.delete(5 )
skip_list.delete(8 )
skip_list.delete(2 )
assert is_sorted(list(__lowerCamelCase ) )
skip_list.insert(-12 ,-12 )
skip_list.insert(77 ,77 )
assert is_sorted(list(__lowerCamelCase ) )
def UpperCAmelCase_ ( ):
for _ in range(1_00 ):
# Repeat test 100 times due to the probabilistic nature of skip list
# random values == random bugs
test_insert()
test_insert_overrides_existing_value()
test_searching_empty_list_returns_none()
test_search()
test_deleting_item_from_empty_list_do_nothing()
test_deleted_items_are_not_founded_by_find_method()
test_delete_removes_only_given_key()
test_delete_doesnt_leave_dead_nodes()
test_iter_always_yields_sorted_values()
def UpperCAmelCase_ ( ):
lowercase_ :Union[str, Any] = SkipList()
skip_list.insert(2 ,"2" )
skip_list.insert(4 ,"4" )
skip_list.insert(6 ,"4" )
skip_list.insert(4 ,"5" )
skip_list.insert(8 ,"4" )
skip_list.insert(9 ,"4" )
skip_list.delete(4 )
print(__lowerCamelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 147 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase : Optional[int] ={'''configuration_wavlm''': ['''WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''WavLMConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase : Optional[Any] =[
'''WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''WavLMForAudioFrameClassification''',
'''WavLMForCTC''',
'''WavLMForSequenceClassification''',
'''WavLMForXVector''',
'''WavLMModel''',
'''WavLMPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_wavlm import (
WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
WavLMForAudioFrameClassification,
WavLMForCTC,
WavLMForSequenceClassification,
WavLMForXVector,
WavLMModel,
WavLMPreTrainedModel,
)
else:
import sys
lowerCAmelCase : Union[str, Any] =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 147 | 1 |
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"
# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
__lowerCAmelCase : Tuple = 'facebook/wmt19-en-de'
__lowerCAmelCase : Union[str, Any] = FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
__lowerCAmelCase : Dict = FSMTConfig.from_pretrained(mname)
config.update(
dict(
d_model=4,
encoder_layers=1,
decoder_layers=1,
encoder_ffn_dim=4,
decoder_ffn_dim=4,
encoder_attention_heads=1,
decoder_attention_heads=1,
)
)
__lowerCAmelCase : List[str] = FSMTForConditionalGeneration(config)
print(F'''num of params {tiny_model.num_parameters()}''')
# Test
__lowerCAmelCase : Optional[int] = tokenizer(['Making tiny model'], return_tensors='pt')
__lowerCAmelCase : List[str] = tiny_model(**batch)
print('test output:', len(outputs.logits[0]))
# Save
__lowerCAmelCase : Any = 'tiny-wmt19-en-de'
tiny_model.half() # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)
print(F'''Generated {mname_tiny}''')
# Upload
# transformers-cli upload tiny-wmt19-en-de
| 88 |
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def __lowerCamelCase ( ):
"""simple docstring"""
lowercase__ : Dict = ArgumentParser(
description=(
"PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes"
) )
# Optional arguments for the launch helper
parser.add_argument("--num_cores" , type=lowerCamelCase__ , default=1 , help="Number of TPU cores to use (1 or 8)." )
# positional
parser.add_argument(
"training_script" , type=lowerCamelCase__ , help=(
"The full path to the single TPU training "
"program/script to be launched in parallel, "
"followed by all the arguments for the "
"training script"
) , )
# rest from the training program
parser.add_argument("training_script_args" , nargs=lowerCamelCase__ )
return parser.parse_args()
def __lowerCamelCase ( ):
"""simple docstring"""
lowercase__ : int = parse_args()
# Import training_script as a module.
lowercase__ : str = Path(args.training_script )
sys.path.append(str(script_fpath.parent.resolve() ) )
lowercase__ : Dict = script_fpath.stem
lowercase__ : int = importlib.import_module(lowerCamelCase__ )
# Patch sys.argv
lowercase__ : Dict = [args.training_script] + args.training_script_args + ["--tpu_num_cores", str(args.num_cores )]
xmp.spawn(mod._mp_fn , args=() , nprocs=args.num_cores )
if __name__ == "__main__":
main()
| 130 | 0 |
_A : Union[str, Any] = range(2, 20 + 1)
_A : Tuple = [10**k for k in range(ks[-1] + 1)]
_A : dict[int, dict[int, list[list[int]]]] = {}
def _a ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) -> List[Any]:
"""simple docstring"""
lowerCamelCase__ : str = sum(a_i[j] for j in range(UpperCAmelCase , len(UpperCAmelCase ) ) )
lowerCamelCase__ : Any = sum(a_i[j] * base[j] for j in range(min(len(UpperCAmelCase ) , UpperCAmelCase ) ) )
lowerCamelCase__ , lowerCamelCase__ : str = 0, 0
lowerCamelCase__ : str = n - i
lowerCamelCase__ : Optional[int] = memo.get(UpperCAmelCase )
if sub_memo is not None:
lowerCamelCase__ : Union[str, Any] = sub_memo.get(UpperCAmelCase )
if jumps is not None and len(UpperCAmelCase ) > 0:
# find and make the largest jump without going over
lowerCamelCase__ : Union[str, Any] = -1
for _k in range(len(UpperCAmelCase ) - 1 , -1 , -1 ):
if jumps[_k][2] <= k and jumps[_k][1] <= max_dn:
lowerCamelCase__ : Tuple = _k
break
if max_jump >= 0:
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ : Any = jumps[max_jump]
# since the difference between jumps is cached, add c
lowerCamelCase__ : int = diff + c
for j in range(min(UpperCAmelCase , len(UpperCAmelCase ) ) ):
lowerCamelCase__ , lowerCamelCase__ : Optional[int] = divmod(UpperCAmelCase , 10 )
if new_c > 0:
add(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
else:
lowerCamelCase__ : Union[str, Any] = []
else:
lowerCamelCase__ : List[Any] = {c: []}
lowerCamelCase__ : Union[str, Any] = sub_memo
if dn >= max_dn or c + diff >= base[k]:
return diff, dn
if k > ks[0]:
while True:
# keep doing smaller jumps
lowerCamelCase__ , lowerCamelCase__ : Tuple = next_term(UpperCAmelCase , k - 1 , i + dn , UpperCAmelCase )
diff += _diff
dn += terms_jumped
if dn >= max_dn or c + diff >= base[k]:
break
else:
# would be too small a jump, just compute sequential terms instead
lowerCamelCase__ , lowerCamelCase__ : List[str] = compute(UpperCAmelCase , UpperCAmelCase , i + dn , UpperCAmelCase )
diff += _diff
dn += terms_jumped
lowerCamelCase__ : str = sub_memo[c]
# keep jumps sorted by # of terms skipped
lowerCamelCase__ : Optional[int] = 0
while j < len(UpperCAmelCase ):
if jumps[j][1] > dn:
break
j += 1
# cache the jump for this value digitsum(b) and c
sub_memo[c].insert(UpperCAmelCase , (diff, dn, k) )
return (diff, dn)
def _a ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) -> List[Any]:
"""simple docstring"""
if i >= n:
return 0, i
if k > len(UpperCAmelCase ):
a_i.extend([0 for _ in range(k - len(UpperCAmelCase ) )] )
# note: a_i -> b * 10^k + c
# ds_b -> digitsum(b)
# ds_c -> digitsum(c)
lowerCamelCase__ : str = i
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ : Union[str, Any] = 0, 0, 0
for j in range(len(UpperCAmelCase ) ):
if j >= k:
ds_b += a_i[j]
else:
ds_c += a_i[j]
while i < n:
i += 1
lowerCamelCase__ : List[Any] = ds_c + ds_b
diff += addend
lowerCamelCase__ : str = 0
for j in range(UpperCAmelCase ):
lowerCamelCase__ : Optional[int] = a_i[j] + addend
lowerCamelCase__ , lowerCamelCase__ : Any = divmod(UpperCAmelCase , 10 )
ds_c += a_i[j]
if addend > 0:
break
if addend > 0:
add(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
return diff, i - start_i
def _a ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) -> List[Any]:
"""simple docstring"""
for j in range(UpperCAmelCase , len(UpperCAmelCase ) ):
lowerCamelCase__ : int = digits[j] + addend
if s >= 10:
lowerCamelCase__ , lowerCamelCase__ : Dict = divmod(UpperCAmelCase , 10 )
lowerCamelCase__ : Optional[int] = addend // 10 + quotient
else:
lowerCamelCase__ : str = s
lowerCamelCase__ : Dict = addend // 10
if addend == 0:
break
while addend > 0:
lowerCamelCase__ , lowerCamelCase__ : str = divmod(UpperCAmelCase , 10 )
digits.append(UpperCAmelCase )
def _a ( UpperCAmelCase = 10**15 ) -> int:
"""simple docstring"""
lowerCamelCase__ : Any = [1]
lowerCamelCase__ : Optional[int] = 1
lowerCamelCase__ : int = 0
while True:
lowerCamelCase__ , lowerCamelCase__ : Union[str, Any] = next_term(UpperCAmelCase , 20 , i + dn , UpperCAmelCase )
dn += terms_jumped
if dn == n - i:
break
lowerCamelCase__ : str = 0
for j in range(len(UpperCAmelCase ) ):
a_n += digits[j] * 10**j
return a_n
if __name__ == "__main__":
print(F'''{solution() = }''')
| 265 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
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 DetrImageProcessor
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __init__( self : Optional[int] , A : Union[str, Any] , A : Any=7 , A : Optional[int]=3 , A : Tuple=3_0 , A : List[Any]=4_0_0 , A : str=True , A : Optional[int]=None , A : Tuple=True , A : Union[str, Any]=1 / 2_5_5 , A : Any=True , A : Optional[int]=[0.5, 0.5, 0.5] , A : Optional[int]=[0.5, 0.5, 0.5] , A : str=True , ) ->List[Any]:
# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
lowerCamelCase__ : Union[str, Any] = size if size is not None else {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3}
lowerCamelCase__ : List[str] = parent
lowerCamelCase__ : Union[str, Any] = batch_size
lowerCamelCase__ : int = num_channels
lowerCamelCase__ : Optional[Any] = min_resolution
lowerCamelCase__ : List[Any] = max_resolution
lowerCamelCase__ : int = do_resize
lowerCamelCase__ : List[str] = size
lowerCamelCase__ : Union[str, Any] = do_rescale
lowerCamelCase__ : Optional[int] = rescale_factor
lowerCamelCase__ : List[str] = do_normalize
lowerCamelCase__ : Tuple = image_mean
lowerCamelCase__ : str = image_std
lowerCamelCase__ : List[Any] = do_pad
def __lowerCamelCase ( self : List[str] ) ->Dict:
return {
"do_resize": self.do_resize,
"size": self.size,
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_pad": self.do_pad,
}
def __lowerCamelCase ( self : Tuple , A : int , A : List[str]=False ) ->int:
if not batched:
lowerCamelCase__ : Union[str, Any] = image_inputs[0]
if isinstance(A , Image.Image ):
lowerCamelCase__ , lowerCamelCase__ : Union[str, Any] = image.size
else:
lowerCamelCase__ , lowerCamelCase__ : Optional[int] = image.shape[1], image.shape[2]
if w < h:
lowerCamelCase__ : List[Any] = int(self.size['''shortest_edge'''] * h / w )
lowerCamelCase__ : List[Any] = self.size['''shortest_edge''']
elif w > h:
lowerCamelCase__ : List[Any] = self.size['''shortest_edge''']
lowerCamelCase__ : Dict = int(self.size['''shortest_edge'''] * w / h )
else:
lowerCamelCase__ : List[Any] = self.size['''shortest_edge''']
lowerCamelCase__ : Any = self.size['''shortest_edge''']
else:
lowerCamelCase__ : Optional[Any] = []
for image in image_inputs:
lowerCamelCase__ , lowerCamelCase__ : Any = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
lowerCamelCase__ : Dict = max(A , key=lambda A : item[0] )[0]
lowerCamelCase__ : Dict = max(A , key=lambda A : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class __SCREAMING_SNAKE_CASE ( lowerCAmelCase_ ,unittest.TestCase ):
_UpperCAmelCase : Optional[Any] = DetrImageProcessor if is_vision_available() else None
def __lowerCamelCase ( self : Optional[int] ) ->Dict:
lowerCamelCase__ : Optional[int] = DetrImageProcessingTester(self )
@property
def __lowerCamelCase ( self : Dict ) ->Any:
return self.image_processor_tester.prepare_image_processor_dict()
def __lowerCamelCase ( self : int ) ->Union[str, Any]:
lowerCamelCase__ : Optional[Any] = 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_rescale''' ) )
self.assertTrue(hasattr(A , '''rescale_factor''' ) )
self.assertTrue(hasattr(A , '''do_resize''' ) )
self.assertTrue(hasattr(A , '''size''' ) )
self.assertTrue(hasattr(A , '''do_pad''' ) )
def __lowerCamelCase ( self : int ) ->Union[str, Any]:
lowerCamelCase__ : List[Any] = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3} )
self.assertEqual(image_processor.do_pad , A )
lowerCamelCase__ : str = self.image_processing_class.from_dict(
self.image_processor_dict , size=4_2 , max_size=8_4 , pad_and_return_pixel_mask=A )
self.assertEqual(image_processor.size , {'''shortest_edge''': 4_2, '''longest_edge''': 8_4} )
self.assertEqual(image_processor.do_pad , A )
def __lowerCamelCase ( self : Union[str, Any] ) ->Optional[Any]:
pass
def __lowerCamelCase ( self : str ) ->Optional[Any]:
# Initialize image_processing
lowerCamelCase__ : Any = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
lowerCamelCase__ : str = prepare_image_inputs(self.image_processor_tester , equal_resolution=A )
for image in image_inputs:
self.assertIsInstance(A , Image.Image )
# Test not batched input
lowerCamelCase__ : Optional[Any] = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
lowerCamelCase__ , lowerCamelCase__ : str = self.image_processor_tester.get_expected_values(A )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
lowerCamelCase__ , lowerCamelCase__ : int = self.image_processor_tester.get_expected_values(A , batched=A )
lowerCamelCase__ : Any = image_processing(A , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def __lowerCamelCase ( self : List[str] ) ->List[str]:
# Initialize image_processing
lowerCamelCase__ : Any = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
lowerCamelCase__ : int = prepare_image_inputs(self.image_processor_tester , equal_resolution=A , numpify=A )
for image in image_inputs:
self.assertIsInstance(A , np.ndarray )
# Test not batched input
lowerCamelCase__ : List[str] = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
lowerCamelCase__ , lowerCamelCase__ : str = self.image_processor_tester.get_expected_values(A )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
lowerCamelCase__ : List[Any] = image_processing(A , return_tensors='''pt''' ).pixel_values
lowerCamelCase__ , lowerCamelCase__ : Optional[Any] = self.image_processor_tester.get_expected_values(A , batched=A )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def __lowerCamelCase ( self : Any ) ->Any:
# Initialize image_processing
lowerCamelCase__ : Optional[Any] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
lowerCamelCase__ : List[str] = prepare_image_inputs(self.image_processor_tester , equal_resolution=A , torchify=A )
for image in image_inputs:
self.assertIsInstance(A , torch.Tensor )
# Test not batched input
lowerCamelCase__ : str = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
lowerCamelCase__ , lowerCamelCase__ : str = self.image_processor_tester.get_expected_values(A )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
lowerCamelCase__ : str = image_processing(A , return_tensors='''pt''' ).pixel_values
lowerCamelCase__ , lowerCamelCase__ : str = self.image_processor_tester.get_expected_values(A , batched=A )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
@slow
def __lowerCamelCase ( self : Tuple ) ->List[Any]:
# prepare image and target
lowerCamelCase__ : Union[str, Any] = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt''' , '''r''' ) as f:
lowerCamelCase__ : Union[str, Any] = json.loads(f.read() )
lowerCamelCase__ : List[str] = {'''image_id''': 3_9_7_6_9, '''annotations''': target}
# encode them
lowerCamelCase__ : Optional[int] = DetrImageProcessor.from_pretrained('''facebook/detr-resnet-50''' )
lowerCamelCase__ : List[Any] = image_processing(images=A , annotations=A , return_tensors='''pt''' )
# verify pixel values
lowerCamelCase__ : Optional[int] = torch.Size([1, 3, 8_0_0, 1_0_6_6] )
self.assertEqual(encoding['''pixel_values'''].shape , A )
lowerCamelCase__ : Any = torch.tensor([0.27_96, 0.31_38, 0.34_81] )
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , A , atol=1e-4 ) )
# verify area
lowerCamelCase__ : Dict = torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , A ) )
# verify boxes
lowerCamelCase__ : Any = torch.Size([6, 4] )
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , A )
lowerCamelCase__ : str = torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , A , atol=1e-3 ) )
# verify image_id
lowerCamelCase__ : str = torch.tensor([3_9_7_6_9] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , A ) )
# verify is_crowd
lowerCamelCase__ : List[str] = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , A ) )
# verify class_labels
lowerCamelCase__ : List[Any] = torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , A ) )
# verify orig_size
lowerCamelCase__ : str = torch.tensor([4_8_0, 6_4_0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , A ) )
# verify size
lowerCamelCase__ : str = torch.tensor([8_0_0, 1_0_6_6] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , A ) )
@slow
def __lowerCamelCase ( self : Optional[Any] ) ->List[str]:
# prepare image, target and masks_path
lowerCamelCase__ : Any = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt''' , '''r''' ) as f:
lowerCamelCase__ : Optional[Any] = json.loads(f.read() )
lowerCamelCase__ : Union[str, Any] = {'''file_name''': '''000000039769.png''', '''image_id''': 3_9_7_6_9, '''segments_info''': target}
lowerCamelCase__ : List[Any] = pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic''' )
# encode them
lowerCamelCase__ : Any = DetrImageProcessor.from_pretrained('''facebook/detr-resnet-50-panoptic''' )
lowerCamelCase__ : Tuple = image_processing(images=A , annotations=A , masks_path=A , return_tensors='''pt''' )
# verify pixel values
lowerCamelCase__ : Optional[Any] = torch.Size([1, 3, 8_0_0, 1_0_6_6] )
self.assertEqual(encoding['''pixel_values'''].shape , A )
lowerCamelCase__ : str = torch.tensor([0.27_96, 0.31_38, 0.34_81] )
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , A , atol=1e-4 ) )
# verify area
lowerCamelCase__ : int = torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , A ) )
# verify boxes
lowerCamelCase__ : Optional[Any] = torch.Size([6, 4] )
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , A )
lowerCamelCase__ : Optional[Any] = torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , A , atol=1e-3 ) )
# verify image_id
lowerCamelCase__ : int = torch.tensor([3_9_7_6_9] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , A ) )
# verify is_crowd
lowerCamelCase__ : Dict = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , A ) )
# verify class_labels
lowerCamelCase__ : int = torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , A ) )
# verify masks
lowerCamelCase__ : Union[str, Any] = 8_2_2_8_7_3
self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item() , A )
# verify orig_size
lowerCamelCase__ : List[Any] = torch.tensor([4_8_0, 6_4_0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , A ) )
# verify size
lowerCamelCase__ : Tuple = torch.tensor([8_0_0, 1_0_6_6] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , A ) )
| 265 | 1 |
"""simple docstring"""
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import (
AutoProcessor,
BertTokenizerFast,
BlipImageProcessor,
GPTaTokenizer,
InstructBlipProcessor,
PreTrainedTokenizerFast,
)
@require_vision
class __lowerCamelCase ( unittest.TestCase ):
'''simple docstring'''
def lowerCamelCase ( self : Dict ):
lowerCAmelCase_ : int = tempfile.mkdtemp()
lowerCAmelCase_ : Tuple = BlipImageProcessor()
lowerCAmelCase_ : Dict = GPTaTokenizer.from_pretrained("hf-internal-testing/tiny-random-GPT2Model" )
lowerCAmelCase_ : str = BertTokenizerFast.from_pretrained("hf-internal-testing/tiny-random-bert" )
lowerCAmelCase_ : Optional[int] = InstructBlipProcessor(a_ , a_ , a_ )
processor.save_pretrained(self.tmpdirname )
def lowerCamelCase ( self : Tuple , **a_ : Dict ):
return AutoProcessor.from_pretrained(self.tmpdirname , **a_ ).tokenizer
def lowerCamelCase ( self : Union[str, Any] , **a_ : Any ):
return AutoProcessor.from_pretrained(self.tmpdirname , **a_ ).image_processor
def lowerCamelCase ( self : List[Any] , **a_ : List[str] ):
return AutoProcessor.from_pretrained(self.tmpdirname , **a_ ).qformer_tokenizer
def lowerCamelCase ( self : List[Any] ):
shutil.rmtree(self.tmpdirname )
def lowerCamelCase ( self : Optional[Any] ):
lowerCAmelCase_ : Optional[int] = [np.random.randint(2_55 , size=(3, 30, 4_00) , dtype=np.uinta )]
lowerCAmelCase_ : str = [Image.fromarray(np.moveaxis(a_ , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def lowerCamelCase ( self : Any ):
lowerCAmelCase_ : Union[str, Any] = InstructBlipProcessor(
tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() , qformer_tokenizer=self.get_qformer_tokenizer() , )
processor.save_pretrained(self.tmpdirname )
lowerCAmelCase_ : List[str] = self.get_tokenizer(bos_token="(BOS)" , eos_token="(EOS)" )
lowerCAmelCase_ : str = self.get_image_processor(do_normalize=a_ , padding_value=1.0 )
lowerCAmelCase_ : Union[str, Any] = InstructBlipProcessor.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_ )
self.assertIsInstance(processor.qformer_tokenizer , a_ )
def lowerCamelCase ( self : int ):
lowerCAmelCase_ : Dict = self.get_image_processor()
lowerCAmelCase_ : Optional[Any] = self.get_tokenizer()
lowerCAmelCase_ : int = self.get_qformer_tokenizer()
lowerCAmelCase_ : List[Any] = InstructBlipProcessor(
tokenizer=a_ , image_processor=a_ , qformer_tokenizer=a_ )
lowerCAmelCase_ : Optional[Any] = self.prepare_image_inputs()
lowerCAmelCase_ : int = image_processor(a_ , return_tensors="np" )
lowerCAmelCase_ : str = processor(images=a_ , return_tensors="np" )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1e-2 )
def lowerCamelCase ( self : Optional[int] ):
lowerCAmelCase_ : Dict = self.get_image_processor()
lowerCAmelCase_ : Dict = self.get_tokenizer()
lowerCAmelCase_ : Any = self.get_qformer_tokenizer()
lowerCAmelCase_ : Dict = InstructBlipProcessor(
tokenizer=a_ , image_processor=a_ , qformer_tokenizer=a_ )
lowerCAmelCase_ : Tuple = "lower newer"
lowerCAmelCase_ : int = processor(text=a_ )
lowerCAmelCase_ : Dict = tokenizer(a_ , return_token_type_ids=a_ )
lowerCAmelCase_ : Optional[Any] = qformer_tokenizer(a_ , return_token_type_ids=a_ )
for key in encoded_tokens.keys():
self.assertListEqual(encoded_tokens[key] , encoded_processor[key] )
for key in encoded_tokens_qformer.keys():
self.assertListEqual(encoded_tokens_qformer[key] , encoded_processor["qformer_" + key] )
def lowerCamelCase ( self : int ):
lowerCAmelCase_ : str = self.get_image_processor()
lowerCAmelCase_ : Optional[int] = self.get_tokenizer()
lowerCAmelCase_ : Optional[Any] = self.get_qformer_tokenizer()
lowerCAmelCase_ : Union[str, Any] = InstructBlipProcessor(
tokenizer=a_ , image_processor=a_ , qformer_tokenizer=a_ )
lowerCAmelCase_ : str = "lower newer"
lowerCAmelCase_ : Optional[Any] = self.prepare_image_inputs()
lowerCAmelCase_ : List[str] = processor(text=a_ , images=a_ )
self.assertListEqual(
list(inputs.keys() ) , ["input_ids", "attention_mask", "qformer_input_ids", "qformer_attention_mask", "pixel_values"] , )
# test if it raises when no input is passed
with pytest.raises(a_ ):
processor()
def lowerCamelCase ( self : Dict ):
lowerCAmelCase_ : List[Any] = self.get_image_processor()
lowerCAmelCase_ : Union[str, Any] = self.get_tokenizer()
lowerCAmelCase_ : Tuple = self.get_qformer_tokenizer()
lowerCAmelCase_ : Optional[int] = InstructBlipProcessor(
tokenizer=a_ , image_processor=a_ , qformer_tokenizer=a_ )
lowerCAmelCase_ : List[str] = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
lowerCAmelCase_ : List[Any] = processor.batch_decode(a_ )
lowerCAmelCase_ : Union[str, Any] = tokenizer.batch_decode(a_ )
self.assertListEqual(a_ , a_ )
def lowerCamelCase ( self : Any ):
lowerCAmelCase_ : str = self.get_image_processor()
lowerCAmelCase_ : List[Any] = self.get_tokenizer()
lowerCAmelCase_ : Dict = self.get_qformer_tokenizer()
lowerCAmelCase_ : Optional[Any] = InstructBlipProcessor(
tokenizer=a_ , image_processor=a_ , qformer_tokenizer=a_ )
lowerCAmelCase_ : Tuple = "lower newer"
lowerCAmelCase_ : Dict = self.prepare_image_inputs()
lowerCAmelCase_ : Union[str, Any] = processor(text=a_ , images=a_ )
self.assertListEqual(
list(inputs.keys() ) , ["input_ids", "attention_mask", "qformer_input_ids", "qformer_attention_mask", "pixel_values"] , )
| 241 |
"""simple docstring"""
import inspect
import os
import sys
import unittest
import accelerate
from accelerate.test_utils import execute_subprocess_async, require_tpu
class __lowerCamelCase ( unittest.TestCase ):
'''simple docstring'''
def lowerCamelCase ( self : Optional[int] ):
lowerCAmelCase_ : int = inspect.getfile(accelerate.test_utils )
lowerCAmelCase_ : List[str] = os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ["scripts", "test_script.py"] )
lowerCAmelCase_ : Dict = os.path.sep.join(inspect.getfile(self.__class__ ).split(os.path.sep )[:-1] )
@require_tpu
def lowerCamelCase ( self : Optional[int] ):
lowerCAmelCase_ : List[Any] = f'''
{self.test_dir}/xla_spawn.py
--num_cores 8
{self.test_file_path}
'''.split()
lowerCAmelCase_ : Dict = [sys.executable] + distributed_args
execute_subprocess_async(a_ , env=os.environ.copy() )
| 241 | 1 |
"""simple docstring"""
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()
def _a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> str:
# Initialise PyTorch model.
# If you want to convert a checkpoint that uses absolute position embeddings, make sure to set reset_position_index_per_cell of
# TapasConfig to False.
# initialize configuration from json file
snake_case_ = TapasConfig.from_json_file(_SCREAMING_SNAKE_CASE )
# set absolute/relative position embeddings parameter
snake_case_ = reset_position_index_per_cell
# set remaining parameters of TapasConfig as well as the model based on the task
if task == "SQA":
snake_case_ = TapasForQuestionAnswering(config=_SCREAMING_SNAKE_CASE )
elif task == "WTQ":
# run_task_main.py hparams
snake_case_ = 4
snake_case_ = True
# hparam_utils.py hparams
snake_case_ = 0.66_4694
snake_case_ = 0.20_7951
snake_case_ = 0.12_1194
snake_case_ = True
snake_case_ = True
snake_case_ = False
snake_case_ = 0.035_2513
snake_case_ = TapasForQuestionAnswering(config=_SCREAMING_SNAKE_CASE )
elif task == "WIKISQL_SUPERVISED":
# run_task_main.py hparams
snake_case_ = 4
snake_case_ = False
# hparam_utils.py hparams
snake_case_ = 36.4519
snake_case_ = 0.90_3421
snake_case_ = 222.088
snake_case_ = True
snake_case_ = True
snake_case_ = True
snake_case_ = 0.76_3141
snake_case_ = TapasForQuestionAnswering(config=_SCREAMING_SNAKE_CASE )
elif task == "TABFACT":
snake_case_ = TapasForSequenceClassification(config=_SCREAMING_SNAKE_CASE )
elif task == "MLM":
snake_case_ = TapasForMaskedLM(config=_SCREAMING_SNAKE_CASE )
elif task == "INTERMEDIATE_PRETRAINING":
snake_case_ = TapasModel(config=_SCREAMING_SNAKE_CASE )
else:
raise ValueError(f"""Task {task} not supported.""" )
print(f"""Building PyTorch model from configuration: {config}""" )
# Load weights from tf checkpoint
load_tf_weights_in_tapas(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# Save pytorch-model (weights and configuration)
print(f"""Save PyTorch model to {pytorch_dump_path}""" )
model.save_pretrained(_SCREAMING_SNAKE_CASE )
# Save tokenizer files
print(f"""Save tokenizer files to {pytorch_dump_path}""" )
snake_case_ = TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + """vocab.txt""" , model_max_length=512 )
tokenizer.save_pretrained(_SCREAMING_SNAKE_CASE )
print("""Used relative position embeddings:""" , model.config.reset_position_index_per_cell )
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE : Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--task', default='SQA', type=str, help='Model task for which to convert a checkpoint. Defaults to SQA.'
)
parser.add_argument(
'--reset_position_index_per_cell',
default=False,
action='store_true',
help='Whether to use relative position embeddings or not. Defaults to True.',
)
parser.add_argument(
'--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
)
parser.add_argument(
'--tapas_config_file',
default=None,
type=str,
required=True,
help=(
'The config json file corresponding to the pre-trained TAPAS model. \n'
'This specifies the model architecture.'
),
)
parser.add_argument(
'--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
)
__SCREAMING_SNAKE_CASE : Optional[Any] = parser.parse_args()
convert_tf_checkpoint_to_pytorch(
args.task,
args.reset_position_index_per_cell,
args.tf_checkpoint_path,
args.tapas_config_file,
args.pytorch_dump_path,
)
| 233 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE : Union[str, Any] = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : int = {
'google/canine-s': 'https://huggingface.co/google/canine-s/resolve/main/config.json',
# See all CANINE models at https://huggingface.co/models?filter=canine
}
class __A (snake_case__):
'''simple docstring'''
__lowercase: List[str] = """canine"""
def __init__( self : Union[str, Any] , UpperCAmelCase_ : str=768 , UpperCAmelCase_ : Union[str, Any]=12 , UpperCAmelCase_ : Optional[Any]=12 , UpperCAmelCase_ : Optional[Any]=3_072 , UpperCAmelCase_ : List[Any]="gelu" , UpperCAmelCase_ : int=0.1 , UpperCAmelCase_ : Any=0.1 , UpperCAmelCase_ : List[str]=16_384 , UpperCAmelCase_ : Tuple=16 , UpperCAmelCase_ : int=0.02 , UpperCAmelCase_ : Tuple=1E-12 , UpperCAmelCase_ : str=0 , UpperCAmelCase_ : int=0XE000 , UpperCAmelCase_ : Optional[int]=0XE001 , UpperCAmelCase_ : Dict=4 , UpperCAmelCase_ : List[Any]=4 , UpperCAmelCase_ : List[Any]=8 , UpperCAmelCase_ : Dict=16_384 , UpperCAmelCase_ : Optional[int]=128 , **UpperCAmelCase_ : Any , ) ->int:
"""simple docstring"""
super().__init__(pad_token_id=UpperCAmelCase_ , bos_token_id=UpperCAmelCase_ , eos_token_id=UpperCAmelCase_ , **UpperCAmelCase_ )
snake_case_ = max_position_embeddings
snake_case_ = hidden_size
snake_case_ = num_hidden_layers
snake_case_ = num_attention_heads
snake_case_ = intermediate_size
snake_case_ = hidden_act
snake_case_ = hidden_dropout_prob
snake_case_ = attention_probs_dropout_prob
snake_case_ = initializer_range
snake_case_ = type_vocab_size
snake_case_ = layer_norm_eps
# Character config:
snake_case_ = downsampling_rate
snake_case_ = upsampling_kernel_size
snake_case_ = num_hash_functions
snake_case_ = num_hash_buckets
snake_case_ = local_transformer_stride
| 233 | 1 |
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
lowerCAmelCase : str = ["""small""", """medium""", """large"""]
lowerCAmelCase : Dict = """lm_head.decoder.weight"""
lowerCAmelCase : Optional[Any] = """lm_head.weight"""
def A_ ( _UpperCAmelCase , _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: int = torch.load(_UpperCAmelCase )
SCREAMING_SNAKE_CASE_: List[Any] = d.pop(_UpperCAmelCase )
os.makedirs(_UpperCAmelCase , exist_ok=_UpperCAmelCase )
torch.save(_UpperCAmelCase , os.path.join(_UpperCAmelCase , _UpperCAmelCase ) )
if __name__ == "__main__":
lowerCAmelCase : Optional[int] = argparse.ArgumentParser()
parser.add_argument("""--dialogpt_path""", default=""".""", type=str)
lowerCAmelCase : List[Any] = parser.parse_args()
for MODEL in DIALOGPT_MODELS:
lowerCAmelCase : Tuple = os.path.join(args.dialogpt_path, f'''{MODEL}_ft.pkl''')
lowerCAmelCase : List[Any] = f'''./DialoGPT-{MODEL}'''
convert_dialogpt_checkpoint(
checkpoint_path,
pytorch_dump_folder_path,
)
| 13 |
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
is_tf_available,
is_torch_available,
)
_UpperCAmelCase : Optional[Any] = logging.getLogger(__name__)
@dataclass(frozen=__UpperCamelCase )
class lowerCAmelCase :
UpperCAmelCase__ = 42
UpperCAmelCase__ = 42
UpperCAmelCase__ = None
UpperCAmelCase__ = None
UpperCAmelCase__ = None
@dataclass(frozen=__UpperCamelCase )
class lowerCAmelCase :
UpperCAmelCase__ = 42
UpperCAmelCase__ = None
UpperCAmelCase__ = None
UpperCAmelCase__ = None
UpperCAmelCase__ = None
if is_torch_available():
import torch
from torch.utils.data import Dataset
class lowerCAmelCase ( __UpperCamelCase ):
UpperCAmelCase__ = 42
def __init__( self : int , UpperCAmelCase : str , UpperCAmelCase : PreTrainedTokenizer , UpperCAmelCase : str , UpperCAmelCase : Optional[int] = None , UpperCAmelCase : List[str]=False , UpperCAmelCase : bool = False , ) -> List[str]:
lowerCamelCase__ : int = hans_processors[task]()
lowerCamelCase__ : Optional[Any] = os.path.join(
UpperCAmelCase , 'cached_{}_{}_{}_{}'.format(
'dev' if evaluate else 'train' , tokenizer.__class__.__name__ , str(UpperCAmelCase ) , UpperCAmelCase , ) , )
lowerCamelCase__ : int = processor.get_labels()
if tokenizer.__class__ in (
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
BartTokenizer,
BartTokenizerFast,
):
# HACK(label indices are swapped in RoBERTa pretrained model)
lowerCamelCase__ , lowerCamelCase__ : Union[str, Any] = label_list[2], label_list[1]
lowerCamelCase__ : List[str] = label_list
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
lowerCamelCase__ : str = cached_features_file + '.lock'
with FileLock(UpperCAmelCase ):
if os.path.exists(UpperCAmelCase ) and not overwrite_cache:
logger.info(F"""Loading features from cached file {cached_features_file}""" )
lowerCamelCase__ : int = torch.load(UpperCAmelCase )
else:
logger.info(F"""Creating features from dataset file at {data_dir}""" )
lowerCamelCase__ : str = (
processor.get_dev_examples(UpperCAmelCase ) if evaluate else processor.get_train_examples(UpperCAmelCase )
)
logger.info('Training examples: %s' , len(UpperCAmelCase ) )
lowerCamelCase__ : Dict = hans_convert_examples_to_features(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
logger.info('Saving features into cached file %s' , UpperCAmelCase )
torch.save(self.features , UpperCAmelCase )
def __len__( self : Optional[int] ) -> Optional[Any]:
return len(self.features )
def __getitem__( self : Tuple , UpperCAmelCase : Dict ) -> InputFeatures:
return self.features[i]
def A_ ( self : int ) -> int:
return self.label_list
if is_tf_available():
import tensorflow as tf
class lowerCAmelCase :
UpperCAmelCase__ = 42
def __init__( self : Optional[Any] , UpperCAmelCase : str , UpperCAmelCase : PreTrainedTokenizer , UpperCAmelCase : str , UpperCAmelCase : Optional[int] = 128 , UpperCAmelCase : Any=False , UpperCAmelCase : bool = False , ) -> Union[str, Any]:
lowerCamelCase__ : Any = hans_processors[task]()
lowerCamelCase__ : Optional[Any] = processor.get_labels()
if tokenizer.__class__ in (
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
BartTokenizer,
BartTokenizerFast,
):
# HACK(label indices are swapped in RoBERTa pretrained model)
lowerCamelCase__ , lowerCamelCase__ : str = label_list[2], label_list[1]
lowerCamelCase__ : Optional[int] = label_list
lowerCamelCase__ : int = processor.get_dev_examples(UpperCAmelCase ) if evaluate else processor.get_train_examples(UpperCAmelCase )
lowerCamelCase__ : Union[str, Any] = hans_convert_examples_to_features(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
def gen():
for ex_index, ex in tqdm.tqdm(enumerate(self.features ) , desc='convert examples to features' ):
if ex_index % 10000 == 0:
logger.info('Writing example %d of %d' % (ex_index, len(UpperCAmelCase )) )
yield (
{
"example_id": 0,
"input_ids": ex.input_ids,
"attention_mask": ex.attention_mask,
"token_type_ids": ex.token_type_ids,
},
ex.label,
)
lowerCamelCase__ : Optional[int] = tf.data.Dataset.from_generator(
UpperCAmelCase , (
{
'example_id': tf.intaa,
'input_ids': tf.intaa,
'attention_mask': tf.intaa,
'token_type_ids': tf.intaa,
},
tf.intaa,
) , (
{
'example_id': tf.TensorShape([] ),
'input_ids': tf.TensorShape([None, None] ),
'attention_mask': tf.TensorShape([None, None] ),
'token_type_ids': tf.TensorShape([None, None] ),
},
tf.TensorShape([] ),
) , )
def A_ ( self : Any ) -> Any:
return self.dataset
def __len__( self : Tuple ) -> int:
return len(self.features )
def __getitem__( self : List[str] , UpperCAmelCase : Any ) -> InputFeatures:
return self.features[i]
def A_ ( self : Dict ) -> str:
return self.label_list
class lowerCAmelCase ( __UpperCamelCase ):
def A_ ( self : int , UpperCAmelCase : List[Any] ) -> int:
return self._create_examples(self._read_tsv(os.path.join(UpperCAmelCase , 'heuristics_train_set.txt' ) ) , 'train' )
def A_ ( self : Any , UpperCAmelCase : int ) -> List[Any]:
return self._create_examples(self._read_tsv(os.path.join(UpperCAmelCase , 'heuristics_evaluation_set.txt' ) ) , 'dev' )
def A_ ( self : Any ) -> List[Any]:
return ["contradiction", "entailment", "neutral"]
def A_ ( self : Optional[int] , UpperCAmelCase : str , UpperCAmelCase : List[str] ) -> List[str]:
lowerCamelCase__ : List[str] = []
for i, line in enumerate(UpperCAmelCase ):
if i == 0:
continue
lowerCamelCase__ : Tuple = '%s-%s' % (set_type, line[0])
lowerCamelCase__ : str = line[5]
lowerCamelCase__ : Dict = line[6]
lowerCamelCase__ : int = line[7][2:] if line[7].startswith('ex' ) else line[7]
lowerCamelCase__ : Dict = line[0]
examples.append(InputExample(guid=UpperCAmelCase , text_a=UpperCAmelCase , text_b=UpperCAmelCase , label=UpperCAmelCase , pairID=UpperCAmelCase ) )
return examples
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , ) -> Optional[int]:
lowerCamelCase__ : int = {label: i for i, label in enumerate(_UpperCAmelCase )}
lowerCamelCase__ : List[Any] = []
for ex_index, example in tqdm.tqdm(enumerate(_UpperCAmelCase ) , desc='convert examples to features' ):
if ex_index % 1_0000 == 0:
logger.info('Writing example %d' % (ex_index) )
lowerCamelCase__ : List[Any] = tokenizer(
example.text_a , example.text_b , add_special_tokens=_UpperCAmelCase , max_length=_UpperCAmelCase , padding='max_length' , truncation=_UpperCAmelCase , return_overflowing_tokens=_UpperCAmelCase , )
lowerCamelCase__ : List[str] = label_map[example.label] if example.label in label_map else 0
lowerCamelCase__ : Optional[int] = int(example.pairID )
features.append(InputFeatures(**_UpperCAmelCase , label=_UpperCAmelCase , pairID=_UpperCAmelCase ) )
for i, example in enumerate(examples[:5] ):
logger.info('*** Example ***' )
logger.info(F"""guid: {example}""" )
logger.info(F"""features: {features[i]}""" )
return features
_UpperCAmelCase : str = {
"""hans""": 3,
}
_UpperCAmelCase : List[Any] = {
"""hans""": HansProcessor,
}
| 50 | 0 |
"""simple docstring"""
from .constants import (
MODEL_NAME,
OPTIMIZER_NAME,
RNG_STATE_NAME,
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
SCALER_NAME,
SCHEDULER_NAME,
TORCH_LAUNCH_PARAMS,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
)
from .dataclasses import (
BnbQuantizationConfig,
ComputeEnvironment,
CustomDtype,
DeepSpeedPlugin,
DistributedDataParallelKwargs,
DistributedType,
DynamoBackend,
FPaRecipeKwargs,
FullyShardedDataParallelPlugin,
GradientAccumulationPlugin,
GradScalerKwargs,
InitProcessGroupKwargs,
KwargsHandler,
LoggerType,
MegatronLMPlugin,
PrecisionType,
ProjectConfiguration,
RNGType,
SageMakerDistributedType,
TensorInformation,
TorchDynamoPlugin,
)
from .environment import get_int_from_env, parse_choice_from_env, parse_flag_from_env
from .imports import (
get_ccl_version,
is_abit_bnb_available,
is_abit_bnb_available,
is_aim_available,
is_bfaa_available,
is_bnb_available,
is_botoa_available,
is_ccl_available,
is_comet_ml_available,
is_datasets_available,
is_deepspeed_available,
is_fpa_available,
is_ipex_available,
is_megatron_lm_available,
is_mlflow_available,
is_mps_available,
is_npu_available,
is_rich_available,
is_safetensors_available,
is_sagemaker_available,
is_tensorboard_available,
is_tpu_available,
is_transformers_available,
is_wandb_available,
is_xpu_available,
)
from .modeling import (
check_device_map,
check_tied_parameters_in_config,
check_tied_parameters_on_same_device,
compute_module_sizes,
convert_file_size_to_int,
dtype_byte_size,
find_tied_parameters,
get_balanced_memory,
get_max_layer_size,
get_max_memory,
get_mixed_precision_context_manager,
id_tensor_storage,
infer_auto_device_map,
load_checkpoint_in_model,
load_offloaded_weights,
load_state_dict,
named_module_tensors,
retie_parameters,
set_module_tensor_to_device,
shard_checkpoint,
)
from .offload import (
OffloadedWeightsLoader,
PrefixedDataset,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_weight,
save_offload_index,
)
from .operations import (
broadcast,
broadcast_object_list,
concatenate,
convert_outputs_to_fpaa,
convert_to_fpaa,
find_batch_size,
find_device,
gather,
gather_object,
get_data_structure,
honor_type,
initialize_tensors,
is_namedtuple,
is_tensor_information,
is_torch_tensor,
listify,
pad_across_processes,
recursively_apply,
reduce,
send_to_device,
slice_tensors,
)
from .versions import compare_versions, is_torch_version
if is_deepspeed_available():
from .deepspeed import (
DeepSpeedEngineWrapper,
DeepSpeedOptimizerWrapper,
DeepSpeedSchedulerWrapper,
DummyOptim,
DummyScheduler,
HfDeepSpeedConfig,
)
from .bnb import has_abit_bnb_layers, load_and_quantize_model
from .fsdp_utils import load_fsdp_model, load_fsdp_optimizer, save_fsdp_model, save_fsdp_optimizer
from .launch import (
PrepareForLaunch,
_filter_args,
prepare_deepspeed_cmd_env,
prepare_multi_gpu_env,
prepare_sagemager_args_inputs,
prepare_simple_launcher_cmd_env,
prepare_tpu,
)
from .megatron_lm import (
AbstractTrainStep,
BertTrainStep,
GPTTrainStep,
MegatronEngine,
MegatronLMDummyDataLoader,
MegatronLMDummyScheduler,
MegatronLMOptimizerWrapper,
MegatronLMSchedulerWrapper,
TaTrainStep,
avg_losses_across_data_parallel_group,
gather_across_data_parallel_groups,
)
from .megatron_lm import initialize as megatron_lm_initialize
from .megatron_lm import prepare_data_loader as megatron_lm_prepare_data_loader
from .megatron_lm import prepare_model as megatron_lm_prepare_model
from .megatron_lm import prepare_optimizer as megatron_lm_prepare_optimizer
from .megatron_lm import prepare_scheduler as megatron_lm_prepare_scheduler
from .memory import find_executable_batch_size, release_memory
from .other import (
extract_model_from_parallel,
get_pretty_name,
is_port_in_use,
merge_dicts,
patch_environment,
save,
wait_for_everyone,
write_basic_config,
)
from .random import set_seed, synchronize_rng_state, synchronize_rng_states
from .torch_xla import install_xla
from .tqdm import tqdm
from .transformer_engine import convert_model, has_transformer_engine_layers
| 289 |
"""simple docstring"""
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class UpperCamelCase__ :
"""simple docstring"""
pass
| 289 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class lowercase__ :
lowercase__ = 42
lowercase__ = 42
class lowercase__ :
def __init__( self : List[str] ,lowerCamelCase__ : int ):
'''simple docstring'''
_UpperCamelCase : list[list[Edge]] = [[] for _ in range(lowerCamelCase__ )]
_UpperCamelCase : Dict = size
def __getitem__( self : Tuple ,lowerCamelCase__ : int ):
'''simple docstring'''
return iter(self._graph[vertex] )
@property
def UpperCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
return self._size
def UpperCamelCase_ ( self : Tuple ,lowerCamelCase__ : int ,lowerCamelCase__ : int ,lowerCamelCase__ : int ):
'''simple docstring'''
if weight not in (0, 1):
raise ValueError('Edge weight must be either 0 or 1.' )
if to_vertex < 0 or to_vertex >= self.size:
raise ValueError('Vertex indexes must be in [0; size).' )
self._graph[from_vertex].append(Edge(lowerCamelCase__ ,lowerCamelCase__ ) )
def UpperCamelCase_ ( self : Dict ,lowerCamelCase__ : int ,lowerCamelCase__ : int ):
'''simple docstring'''
_UpperCamelCase : Optional[int] = deque([start_vertex] )
_UpperCamelCase : list[int | None] = [None] * self.size
_UpperCamelCase : Union[str, Any] = 0
while queue:
_UpperCamelCase : str = queue.popleft()
_UpperCamelCase : int = distances[current_vertex]
if current_distance is None:
continue
for edge in self[current_vertex]:
_UpperCamelCase : Optional[int] = current_distance + edge.weight
_UpperCamelCase : Any = distances[edge.destination_vertex]
if (
isinstance(lowerCamelCase__ ,lowerCamelCase__ )
and new_distance >= dest_vertex_distance
):
continue
_UpperCamelCase : Optional[Any] = new_distance
if edge.weight == 0:
queue.appendleft(edge.destination_vertex )
else:
queue.append(edge.destination_vertex )
if distances[finish_vertex] is None:
raise ValueError('No path from start_vertex to finish_vertex.' )
return distances[finish_vertex]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 83 | """simple docstring"""
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__lowerCAmelCase : List[Any] =logging.get_logger(__name__)
__lowerCAmelCase : Union[str, Any] ={
"""google/efficientnet-b7""": """https://huggingface.co/google/efficientnet-b7/resolve/main/config.json""",
}
class _A ( lowerCAmelCase ):
snake_case__ : Optional[int] = 'efficientnet'
def __init__( self , __lowerCAmelCase = 3 , __lowerCAmelCase = 600 , __lowerCAmelCase = 2.0 , __lowerCAmelCase = 3.1 , __lowerCAmelCase = 8 , __lowerCAmelCase = [3, 3, 5, 3, 5, 5, 3] , __lowerCAmelCase = [32, 16, 24, 40, 80, 112, 192] , __lowerCAmelCase = [16, 24, 40, 80, 112, 192, 320] , __lowerCAmelCase = [] , __lowerCAmelCase = [1, 2, 2, 2, 1, 2, 1] , __lowerCAmelCase = [1, 2, 2, 3, 3, 4, 1] , __lowerCAmelCase = [1, 6, 6, 6, 6, 6, 6] , __lowerCAmelCase = 0.2_5 , __lowerCAmelCase = "swish" , __lowerCAmelCase = 2560 , __lowerCAmelCase = "mean" , __lowerCAmelCase = 0.0_2 , __lowerCAmelCase = 0.0_0_1 , __lowerCAmelCase = 0.9_9 , __lowerCAmelCase = 0.5 , __lowerCAmelCase = 0.2 , **__lowerCAmelCase , ):
"""simple docstring"""
super().__init__(**__lowerCAmelCase )
lowercase = num_channels
lowercase = image_size
lowercase = width_coefficient
lowercase = depth_coefficient
lowercase = depth_divisor
lowercase = kernel_sizes
lowercase = in_channels
lowercase = out_channels
lowercase = depthwise_padding
lowercase = strides
lowercase = num_block_repeats
lowercase = expand_ratios
lowercase = squeeze_expansion_ratio
lowercase = hidden_act
lowercase = hidden_dim
lowercase = pooling_type
lowercase = initializer_range
lowercase = batch_norm_eps
lowercase = batch_norm_momentum
lowercase = dropout_rate
lowercase = drop_connect_rate
lowercase = sum(__lowerCAmelCase ) * 4
class _A ( lowerCAmelCase ):
snake_case__ : Dict = version.parse('1.11' )
@property
def A__ ( self ):
"""simple docstring"""
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def A__ ( self ):
"""simple docstring"""
return 1E-5
| 197 | 0 |
"""simple docstring"""
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
__A : Optional[int] = 10
def lowercase ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : list[int] , _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
for i in range(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
if array[i] == target:
return i
return -1
def lowercase ( _SCREAMING_SNAKE_CASE : list[int] , _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
_UpperCAmelCase = 0
_UpperCAmelCase = len(_SCREAMING_SNAKE_CASE )
while left <= right:
if right - left < precision:
return lin_search(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
_UpperCAmelCase = (left + right) // 3 + 1
_UpperCAmelCase = 2 * (left + right) // 3 + 1
if array[one_third] == target:
return one_third
elif array[two_third] == target:
return two_third
elif target < array[one_third]:
_UpperCAmelCase = one_third - 1
elif array[two_third] < target:
_UpperCAmelCase = two_third + 1
else:
_UpperCAmelCase = one_third + 1
_UpperCAmelCase = two_third - 1
else:
return -1
def lowercase ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : list[int] , _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
if left < right:
if right - left < precision:
return lin_search(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
_UpperCAmelCase = (left + right) // 3 + 1
_UpperCAmelCase = 2 * (left + right) // 3 + 1
if array[one_third] == target:
return one_third
elif array[two_third] == target:
return two_third
elif target < array[one_third]:
return rec_ternary_search(_SCREAMING_SNAKE_CASE , one_third - 1 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
elif array[two_third] < target:
return rec_ternary_search(two_third + 1 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
else:
return rec_ternary_search(one_third + 1 , two_third - 1 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
else:
return -1
if __name__ == "__main__":
import doctest
doctest.testmod()
__A : Optional[int] = input("Enter numbers separated by comma:\n").strip()
__A : str = [int(item.strip()) for item in user_input.split(",")]
assert collection == sorted(collection), f"List must be ordered.\n{collection}."
__A : str = int(input("Enter the number to be found in the list:\n").strip())
__A : str = ite_ternary_search(collection, target)
__A : Any = rec_ternary_search(0, len(collection) - 1, collection, target)
if resulta != -1:
print(f'''Iterative search: {target} found at positions: {resulta}''')
print(f'''Recursive search: {target} found at positions: {resulta}''')
else:
print("Not found")
| 326 |
"""simple docstring"""
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate
@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class _a ( lowerCAmelCase):
"""simple docstring"""
UpperCamelCase__ = 42
UpperCamelCase__ = None
def lowercase ( _SCREAMING_SNAKE_CASE : Union[str, Any] , _SCREAMING_SNAKE_CASE : Dict=0.999 , _SCREAMING_SNAKE_CASE : Any="cosine" , ):
'''simple docstring'''
if alpha_transform_type == "cosine":
def alpha_bar_fn(_SCREAMING_SNAKE_CASE : Tuple ):
return math.cos((t + 0.008) / 1.008 * math.pi / 2 ) ** 2
elif alpha_transform_type == "exp":
def alpha_bar_fn(_SCREAMING_SNAKE_CASE : Any ):
return math.exp(t * -12.0 )
else:
raise ValueError(f'Unsupported alpha_tranform_type: {alpha_transform_type}' )
_UpperCAmelCase = []
for i in range(_SCREAMING_SNAKE_CASE ):
_UpperCAmelCase = i / num_diffusion_timesteps
_UpperCAmelCase = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar_fn(_SCREAMING_SNAKE_CASE ) / alpha_bar_fn(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE ) )
return torch.tensor(_SCREAMING_SNAKE_CASE , dtype=torch.floataa )
class _a ( lowerCAmelCase , lowerCAmelCase):
"""simple docstring"""
UpperCamelCase__ = 1
@register_to_config
def __init__( self : List[Any] , __UpperCamelCase : int = 1_0_0_0 , __UpperCamelCase : float = 0.0_0_0_1 , __UpperCamelCase : float = 0.0_2 , __UpperCamelCase : str = "linear" , __UpperCamelCase : Optional[Union[np.ndarray, List[float]]] = None , __UpperCamelCase : bool = True , __UpperCamelCase : bool = True , __UpperCamelCase : int = 0 , __UpperCamelCase : str = "epsilon" , __UpperCamelCase : float = 1.0 , **__UpperCamelCase : Optional[int] , )->Dict:
if kwargs.get('''set_alpha_to_one''' , __UpperCamelCase ) is not None:
_UpperCAmelCase = (
'''The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead.'''
)
deprecate('''set_alpha_to_one''' , '''1.0.0''' , __UpperCamelCase , standard_warn=__UpperCamelCase )
_UpperCAmelCase = kwargs['''set_alpha_to_one''']
if trained_betas is not None:
_UpperCAmelCase = torch.tensor(__UpperCamelCase , dtype=torch.floataa )
elif beta_schedule == "linear":
_UpperCAmelCase = torch.linspace(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , dtype=torch.floataa )
elif beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
_UpperCAmelCase = (
torch.linspace(beta_start**0.5 , beta_end**0.5 , __UpperCamelCase , dtype=torch.floataa ) ** 2
)
elif beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
_UpperCAmelCase = betas_for_alpha_bar(__UpperCamelCase )
else:
raise NotImplementedError(F'{beta_schedule} does is not implemented for {self.__class__}' )
_UpperCAmelCase = 1.0 - self.betas
_UpperCAmelCase = torch.cumprod(self.alphas , dim=0 )
# At every step in inverted ddim, we are looking into the next alphas_cumprod
# For the final step, there is no next alphas_cumprod, and the index is out of bounds
# `set_alpha_to_zero` decides whether we set this parameter simply to zero
# in this case, self.step() just output the predicted noise
# or whether we use the final alpha of the "non-previous" one.
_UpperCAmelCase = torch.tensor(0.0 ) if set_alpha_to_zero else self.alphas_cumprod[-1]
# standard deviation of the initial noise distribution
_UpperCAmelCase = 1.0
# setable values
_UpperCAmelCase = None
_UpperCAmelCase = torch.from_numpy(np.arange(0 , __UpperCamelCase ).copy().astype(np.intaa ) )
def lowercase__ ( self : str , __UpperCamelCase : torch.FloatTensor , __UpperCamelCase : Optional[int] = None )->torch.FloatTensor:
return sample
def lowercase__ ( self : Any , __UpperCamelCase : int , __UpperCamelCase : Union[str, torch.device] = None )->Any:
if num_inference_steps > self.config.num_train_timesteps:
raise ValueError(
F'`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'
F' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'
F' maximal {self.config.num_train_timesteps} timesteps.' )
_UpperCAmelCase = num_inference_steps
_UpperCAmelCase = self.config.num_train_timesteps // self.num_inference_steps
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
_UpperCAmelCase = (np.arange(0 , __UpperCamelCase ) * step_ratio).round().copy().astype(np.intaa )
_UpperCAmelCase = torch.from_numpy(__UpperCamelCase ).to(__UpperCamelCase )
self.timesteps += self.config.steps_offset
def lowercase__ ( self : Any , __UpperCamelCase : torch.FloatTensor , __UpperCamelCase : int , __UpperCamelCase : torch.FloatTensor , __UpperCamelCase : float = 0.0 , __UpperCamelCase : bool = False , __UpperCamelCase : Optional[torch.FloatTensor] = None , __UpperCamelCase : bool = True , )->Union[DDIMSchedulerOutput, Tuple]:
# 1. get previous step value (=t+1)
_UpperCAmelCase = timestep + self.config.num_train_timesteps // self.num_inference_steps
# 2. compute alphas, betas
# change original implementation to exactly match noise levels for analogous forward process
_UpperCAmelCase = self.alphas_cumprod[timestep]
_UpperCAmelCase = (
self.alphas_cumprod[prev_timestep]
if prev_timestep < self.config.num_train_timesteps
else self.final_alpha_cumprod
)
_UpperCAmelCase = 1 - alpha_prod_t
# 3. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
if self.config.prediction_type == "epsilon":
_UpperCAmelCase = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
_UpperCAmelCase = model_output
elif self.config.prediction_type == "sample":
_UpperCAmelCase = model_output
_UpperCAmelCase = (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
elif self.config.prediction_type == "v_prediction":
_UpperCAmelCase = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
_UpperCAmelCase = (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
else:
raise ValueError(
F'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or'
''' `v_prediction`''' )
# 4. Clip or threshold "predicted x_0"
if self.config.clip_sample:
_UpperCAmelCase = pred_original_sample.clamp(
-self.config.clip_sample_range , self.config.clip_sample_range )
# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
_UpperCAmelCase = (1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon
# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
_UpperCAmelCase = alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction
if not return_dict:
return (prev_sample, pred_original_sample)
return DDIMSchedulerOutput(prev_sample=__UpperCamelCase , pred_original_sample=__UpperCamelCase )
def __len__( self : Any )->str:
return self.config.num_train_timesteps
| 326 | 1 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel
if is_vision_available():
from transformers import MaskaFormerImageProcessor
if is_vision_available():
from PIL import Image
class __lowerCAmelCase :
def __init__( self , _snake_case , _snake_case=2 , _snake_case=True , _snake_case=False , _snake_case=10 , _snake_case=3 , _snake_case=32 * 8 , _snake_case=32 * 8 , _snake_case=4 , _snake_case=64 , ):
"""simple docstring"""
_lowerCAmelCase = parent
_lowerCAmelCase = batch_size
_lowerCAmelCase = is_training
_lowerCAmelCase = use_auxiliary_loss
_lowerCAmelCase = num_queries
_lowerCAmelCase = num_channels
_lowerCAmelCase = min_size
_lowerCAmelCase = max_size
_lowerCAmelCase = num_labels
_lowerCAmelCase = hidden_dim
_lowerCAmelCase = hidden_dim
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
_snake_case )
_lowerCAmelCase = torch.ones([self.batch_size, self.min_size, self.max_size] , device=_snake_case )
_lowerCAmelCase = (
torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] , device=_snake_case ) > 0.5
).float()
_lowerCAmelCase = (torch.rand((self.batch_size, self.num_labels) , device=_snake_case ) > 0.5).long()
_lowerCAmelCase = self.get_config()
return config, pixel_values, pixel_mask, mask_labels, class_labels
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = MaskaFormerConfig(
hidden_size=self.hidden_dim , )
_lowerCAmelCase = self.num_queries
_lowerCAmelCase = self.num_labels
_lowerCAmelCase = [1, 1, 1, 1]
_lowerCAmelCase = self.num_channels
_lowerCAmelCase = 64
_lowerCAmelCase = 128
_lowerCAmelCase = self.hidden_dim
_lowerCAmelCase = self.hidden_dim
_lowerCAmelCase = self.hidden_dim
return config
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = self.prepare_config_and_inputs()
_lowerCAmelCase = {"""pixel_values""": pixel_values, """pixel_mask""": pixel_mask}
return config, inputs_dict
def snake_case ( self , _snake_case , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = output.encoder_hidden_states
_lowerCAmelCase = output.pixel_decoder_hidden_states
_lowerCAmelCase = output.transformer_decoder_hidden_states
self.parent.assertTrue(len(_snake_case ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(_snake_case ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(_snake_case ) , config.decoder_layers )
def snake_case ( self , _snake_case , _snake_case , _snake_case , _snake_case=False ):
"""simple docstring"""
with torch.no_grad():
_lowerCAmelCase = MaskaFormerModel(config=_snake_case )
model.to(_snake_case )
model.eval()
_lowerCAmelCase = model(pixel_values=_snake_case , pixel_mask=_snake_case )
_lowerCAmelCase = model(_snake_case , output_hidden_states=_snake_case )
self.parent.assertEqual(
output.transformer_decoder_last_hidden_state.shape , (self.batch_size, self.num_queries, self.hidden_dim) , )
# let's ensure the other two hidden state exists
self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(output.encoder_last_hidden_state is not None )
if output_hidden_states:
self.check_output_hidden_state(_snake_case , _snake_case )
def snake_case ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = MaskaFormerForUniversalSegmentation(config=_snake_case )
model.to(_snake_case )
model.eval()
def comm_check_on_output(_snake_case ):
# let's still check that all the required stuff is there
self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.encoder_last_hidden_state is not None )
# okay, now we need to check the logits shape
# due to the encoder compression, masks have a //4 spatial size
self.parent.assertEqual(
result.masks_queries_logits.shape , (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) , )
# + 1 for null class
self.parent.assertEqual(
result.class_queries_logits.shape , (self.batch_size, self.num_queries, self.num_labels + 1) )
with torch.no_grad():
_lowerCAmelCase = model(pixel_values=_snake_case , pixel_mask=_snake_case )
_lowerCAmelCase = model(_snake_case )
comm_check_on_output(_snake_case )
_lowerCAmelCase = model(
pixel_values=_snake_case , pixel_mask=_snake_case , mask_labels=_snake_case , class_labels=_snake_case )
comm_check_on_output(_snake_case )
self.parent.assertTrue(result.loss is not None )
self.parent.assertEqual(result.loss.shape , torch.Size([1] ) )
@require_torch
class __lowerCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , unittest.TestCase ):
__lowerCamelCase = (MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
__lowerCamelCase = {'''feature-extraction''': MaskaFormerModel} if is_torch_available() else {}
__lowerCamelCase = False
__lowerCamelCase = False
__lowerCamelCase = False
__lowerCamelCase = False
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = MaskaFormerModelTester(self )
_lowerCAmelCase = ConfigTester(self , config_class=_snake_case , has_text_modality=_snake_case )
def snake_case ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskaformer_model(_snake_case , **_snake_case , output_hidden_states=_snake_case )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*_snake_case )
@unittest.skip(reason="""Mask2Former does not use inputs_embeds""" )
def snake_case ( self ):
"""simple docstring"""
pass
@unittest.skip(reason="""Mask2Former does not have a get_input_embeddings method""" )
def snake_case ( self ):
"""simple docstring"""
pass
@unittest.skip(reason="""Mask2Former is not a generative model""" )
def snake_case ( self ):
"""simple docstring"""
pass
@unittest.skip(reason="""Mask2Former does not use token embeddings""" )
def snake_case ( self ):
"""simple docstring"""
pass
@require_torch_multi_gpu
@unittest.skip(
reason="""Mask2Former has some layers using `add_module` which doesn't work well with `nn.DataParallel`""" )
def snake_case ( self ):
"""simple docstring"""
pass
@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" )
def snake_case ( self ):
"""simple docstring"""
pass
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_lowerCAmelCase = model_class(_snake_case )
_lowerCAmelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_lowerCAmelCase = [*signature.parameters.keys()]
_lowerCAmelCase = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , _snake_case )
@slow
def snake_case ( self ):
"""simple docstring"""
for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
_lowerCAmelCase = MaskaFormerModel.from_pretrained(_snake_case )
self.assertIsNotNone(_snake_case )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = (self.model_tester.min_size,) * 2
_lowerCAmelCase = {
"""pixel_values""": torch.randn((2, 3, *size) , device=_snake_case ),
"""mask_labels""": torch.randn((2, 10, *size) , device=_snake_case ),
"""class_labels""": torch.zeros(2 , 10 , device=_snake_case ).long(),
}
_lowerCAmelCase = self.model_tester.get_config()
_lowerCAmelCase = MaskaFormerForUniversalSegmentation(_snake_case ).to(_snake_case )
_lowerCAmelCase = model(**_snake_case )
self.assertTrue(outputs.loss is not None )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskaformer_model(_snake_case , **_snake_case , output_hidden_states=_snake_case )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_lowerCAmelCase = model_class(_snake_case ).to(_snake_case )
_lowerCAmelCase = model(**_snake_case , output_attentions=_snake_case )
self.assertTrue(outputs.attentions is not None )
def snake_case ( self ):
"""simple docstring"""
if not self.model_tester.is_training:
return
_lowerCAmelCase = self.all_model_classes[1]
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
_lowerCAmelCase = model_class(_snake_case )
model.to(_snake_case )
model.train()
_lowerCAmelCase = model(_snake_case , mask_labels=_snake_case , class_labels=_snake_case ).loss
loss.backward()
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.all_model_classes[1]
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
_lowerCAmelCase = True
_lowerCAmelCase = True
_lowerCAmelCase = model_class(_snake_case ).to(_snake_case )
model.train()
_lowerCAmelCase = model(_snake_case , mask_labels=_snake_case , class_labels=_snake_case )
_lowerCAmelCase = outputs.encoder_hidden_states[0]
encoder_hidden_states.retain_grad()
_lowerCAmelCase = outputs.pixel_decoder_hidden_states[0]
pixel_decoder_hidden_states.retain_grad()
_lowerCAmelCase = outputs.transformer_decoder_hidden_states[0]
transformer_decoder_hidden_states.retain_grad()
_lowerCAmelCase = outputs.attentions[0]
attentions.retain_grad()
outputs.loss.backward(retain_graph=_snake_case )
self.assertIsNotNone(encoder_hidden_states.grad )
self.assertIsNotNone(pixel_decoder_hidden_states.grad )
self.assertIsNotNone(transformer_decoder_hidden_states.grad )
self.assertIsNotNone(attentions.grad )
A__ = 1e-4
def _UpperCAmelCase ( ):
"""simple docstring"""
_lowerCAmelCase = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_vision
@slow
class __lowerCAmelCase ( unittest.TestCase ):
@cached_property
def snake_case ( self ):
"""simple docstring"""
return "facebook/mask2former-swin-small-coco-instance"
@cached_property
def snake_case ( self ):
"""simple docstring"""
return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints ) if is_vision_available() else None
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = MaskaFormerModel.from_pretrained(self.model_checkpoints ).to(_snake_case )
_lowerCAmelCase = self.default_image_processor
_lowerCAmelCase = prepare_img()
_lowerCAmelCase = image_processor(_snake_case , return_tensors="""pt""" ).to(_snake_case )
_lowerCAmelCase = inputs["""pixel_values"""].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(_snake_case , (1, 3, 384, 384) )
with torch.no_grad():
_lowerCAmelCase = model(**_snake_case )
_lowerCAmelCase = torch.tensor(
[[-0.2790, -1.0717, -1.1668], [-0.5128, -0.3128, -0.4987], [-0.5832, 0.1971, -0.0197]] ).to(_snake_case )
self.assertTrue(
torch.allclose(
outputs.encoder_last_hidden_state[0, 0, :3, :3] , _snake_case , atol=_snake_case ) )
_lowerCAmelCase = torch.tensor(
[[0.8973, 1.1847, 1.1776], [1.1934, 1.5040, 1.5128], [1.1153, 1.4486, 1.4951]] ).to(_snake_case )
self.assertTrue(
torch.allclose(
outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] , _snake_case , atol=_snake_case ) )
_lowerCAmelCase = torch.tensor(
[[2.1152, 1.7000, -0.8603], [1.5808, 1.8004, -0.9353], [1.6043, 1.7495, -0.5999]] ).to(_snake_case )
self.assertTrue(
torch.allclose(
outputs.transformer_decoder_last_hidden_state[0, :3, :3] , _snake_case , atol=_snake_case ) )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints ).to(_snake_case ).eval()
_lowerCAmelCase = self.default_image_processor
_lowerCAmelCase = prepare_img()
_lowerCAmelCase = image_processor(_snake_case , return_tensors="""pt""" ).to(_snake_case )
_lowerCAmelCase = inputs["""pixel_values"""].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(_snake_case , (1, 3, 384, 384) )
with torch.no_grad():
_lowerCAmelCase = model(**_snake_case )
# masks_queries_logits
_lowerCAmelCase = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) )
_lowerCAmelCase = [
[-8.7839, -9.0056, -8.8121],
[-7.4104, -7.0313, -6.5401],
[-6.6105, -6.3427, -6.4675],
]
_lowerCAmelCase = torch.tensor(_snake_case ).to(_snake_case )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , _snake_case , atol=_snake_case ) )
# class_queries_logits
_lowerCAmelCase = outputs.class_queries_logits
self.assertEqual(class_queries_logits.shape , (1, model.config.num_queries, model.config.num_labels + 1) )
_lowerCAmelCase = torch.tensor(
[
[1.8324, -8.0835, -4.1922],
[0.8450, -9.0050, -3.6053],
[0.3045, -7.7293, -3.0275],
] ).to(_snake_case )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , _snake_case , atol=_snake_case ) )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints ).to(_snake_case ).eval()
_lowerCAmelCase = self.default_image_processor
_lowerCAmelCase = image_processor(
[np.zeros((3, 800, 1333) ), np.zeros((3, 800, 1333) )] , segmentation_maps=[np.zeros((384, 384) ).astype(np.floataa ), np.zeros((384, 384) ).astype(np.floataa )] , return_tensors="""pt""" , )
_lowerCAmelCase = inputs["""pixel_values"""].to(_snake_case )
_lowerCAmelCase = [el.to(_snake_case ) for el in inputs["""mask_labels"""]]
_lowerCAmelCase = [el.to(_snake_case ) for el in inputs["""class_labels"""]]
with torch.no_grad():
_lowerCAmelCase = model(**_snake_case )
self.assertTrue(outputs.loss is not None )
| 82 | def UpperCamelCase ( __lowercase : list[list[int]] ,__lowercase : int ,__lowercase : int ,__lowercase : list[int] ):
'''simple docstring'''
if graph[path[curr_ind - 1]][next_ver] == 0:
return False
# 2. Validate that next vertex is not already in path
return not any(vertex == next_ver for vertex in path )
def UpperCamelCase ( __lowercase : list[list[int]] ,__lowercase : list[int] ,__lowercase : int ):
'''simple docstring'''
if curr_ind == len(__lowercase ):
# return whether path exists between current and starting vertices
return graph[path[curr_ind - 1]][path[0]] == 1
# Recursive Step
for next_ver in range(0 ,len(__lowercase ) ):
if valid_connection(__lowercase ,__lowercase ,__lowercase ,__lowercase ):
# Insert current vertex into path as next transition
A_ : Tuple = next_ver
# Validate created path
if util_hamilton_cycle(__lowercase ,__lowercase ,curr_ind + 1 ):
return True
# Backtrack
A_ : Tuple = -1
return False
def UpperCamelCase ( __lowercase : list[list[int]] ,__lowercase : int = 0 ):
'''simple docstring'''
A_ : Any = [-1] * (len(__lowercase ) + 1)
# initialize start and end of path with starting index
A_ : Dict = start_index
# evaluate and if we find answer return path either return empty array
return path if util_hamilton_cycle(__lowercase ,__lowercase ,1 ) else []
| 140 | 0 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
__lowercase = logging.get_logger(__name__)
__lowercase = {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}
__lowercase = {
'''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'''
),
},
}
__lowercase = {
'''squeezebert/squeezebert-uncased''': 512,
'''squeezebert/squeezebert-mnli''': 512,
'''squeezebert/squeezebert-mnli-headless''': 512,
}
__lowercase = {
'''squeezebert/squeezebert-uncased''': {'''do_lower_case''': True},
'''squeezebert/squeezebert-mnli''': {'''do_lower_case''': True},
'''squeezebert/squeezebert-mnli-headless''': {'''do_lower_case''': True},
}
class _lowercase ( __a ):
"""simple docstring"""
lowercase__ = VOCAB_FILES_NAMES
lowercase__ = PRETRAINED_VOCAB_FILES_MAP
lowercase__ = PRETRAINED_INIT_CONFIGURATION
lowercase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase__ = SqueezeBertTokenizer
def __init__( self : Union[str, Any] , UpperCamelCase__ : List[str]=None , UpperCamelCase__ : int=None , UpperCamelCase__ : str=True , UpperCamelCase__ : Any="[UNK]" , UpperCamelCase__ : Union[str, Any]="[SEP]" , UpperCamelCase__ : str="[PAD]" , UpperCamelCase__ : str="[CLS]" , UpperCamelCase__ : List[Any]="[MASK]" , UpperCamelCase__ : str=True , UpperCamelCase__ : str=None , **UpperCamelCase__ : List[str] , ) -> Tuple:
'''simple docstring'''
super().__init__(
UpperCamelCase__ , tokenizer_file=UpperCamelCase__ , do_lower_case=UpperCamelCase__ , unk_token=UpperCamelCase__ , sep_token=UpperCamelCase__ , pad_token=UpperCamelCase__ , cls_token=UpperCamelCase__ , mask_token=UpperCamelCase__ , tokenize_chinese_chars=UpperCamelCase__ , strip_accents=UpperCamelCase__ , **UpperCamelCase__ , )
__UpperCamelCase =json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('''lowercase''' , UpperCamelCase__ ) != do_lower_case
or normalizer_state.get('''strip_accents''' , UpperCamelCase__ ) != strip_accents
or normalizer_state.get('''handle_chinese_chars''' , UpperCamelCase__ ) != tokenize_chinese_chars
):
__UpperCamelCase =getattr(UpperCamelCase__ , normalizer_state.pop('''type''' ) )
__UpperCamelCase =do_lower_case
__UpperCamelCase =strip_accents
__UpperCamelCase =tokenize_chinese_chars
__UpperCamelCase =normalizer_class(**UpperCamelCase__ )
__UpperCamelCase =do_lower_case
def UpperCAmelCase_ ( self : List[Any] , UpperCamelCase__ : Tuple , UpperCamelCase__ : Optional[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 UpperCAmelCase_ ( self : Any , UpperCamelCase__ : List[int] , UpperCamelCase__ : 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 UpperCAmelCase_ ( self : Union[str, Any] , UpperCamelCase__ : str , UpperCamelCase__ : Optional[str] = None ) -> Tuple[str]:
'''simple docstring'''
__UpperCamelCase =self._tokenizer.model.save(UpperCamelCase__ , name=UpperCamelCase__ )
return tuple(UpperCamelCase__ )
| 85 | """simple docstring"""
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class _lowercase :
"""simple docstring"""
def __init__( self : int , UpperCamelCase__ : Any ) -> Optional[Any]:
'''simple docstring'''
__UpperCamelCase =data
__UpperCamelCase =None
class _lowercase :
"""simple docstring"""
def __init__( self : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
__UpperCamelCase =None
__UpperCamelCase =None
def __iter__( self : int ) -> Iterator[Any]:
'''simple docstring'''
__UpperCamelCase =self.head
while self.head:
yield node.data
__UpperCamelCase =node.next
if node == self.head:
break
def __len__( self : Union[str, Any] ) -> int:
'''simple docstring'''
return sum(1 for _ in self )
def __repr__( self : str ) -> Union[str, Any]:
'''simple docstring'''
return "->".join(str(UpperCamelCase__ ) for item in iter(self ) )
def UpperCAmelCase_ ( self : List[str] , UpperCamelCase__ : Any ) -> None:
'''simple docstring'''
self.insert_nth(len(self ) , UpperCamelCase__ )
def UpperCAmelCase_ ( self : Optional[int] , UpperCamelCase__ : Any ) -> None:
'''simple docstring'''
self.insert_nth(0 , UpperCamelCase__ )
def UpperCAmelCase_ ( self : List[str] , UpperCamelCase__ : int , UpperCamelCase__ : Any ) -> None:
'''simple docstring'''
if index < 0 or index > len(self ):
raise IndexError('''list index out of range.''' )
__UpperCamelCase =Node(UpperCamelCase__ )
if self.head is None:
__UpperCamelCase =new_node # first node points itself
__UpperCamelCase =__UpperCamelCase =new_node
elif index == 0: # insert at head
__UpperCamelCase =self.head
__UpperCamelCase =__UpperCamelCase =new_node
else:
__UpperCamelCase =self.head
for _ in range(index - 1 ):
__UpperCamelCase =temp.next
__UpperCamelCase =temp.next
__UpperCamelCase =new_node
if index == len(self ) - 1: # insert at tail
__UpperCamelCase =new_node
def UpperCAmelCase_ ( self : Any ) -> Any:
'''simple docstring'''
return self.delete_nth(0 )
def UpperCAmelCase_ ( self : Optional[int] ) -> Any:
'''simple docstring'''
return self.delete_nth(len(self ) - 1 )
def UpperCAmelCase_ ( self : int , UpperCamelCase__ : int = 0 ) -> Any:
'''simple docstring'''
if not 0 <= index < len(self ):
raise IndexError('''list index out of range.''' )
__UpperCamelCase =self.head
if self.head == self.tail: # just one node
__UpperCamelCase =__UpperCamelCase =None
elif index == 0: # delete head node
__UpperCamelCase =self.tail.next.next
__UpperCamelCase =self.head.next
else:
__UpperCamelCase =self.head
for _ in range(index - 1 ):
__UpperCamelCase =temp.next
__UpperCamelCase =temp.next
__UpperCamelCase =temp.next.next
if index == len(self ) - 1: # delete at tail
__UpperCamelCase =temp
return delete_node.data
def UpperCAmelCase_ ( self : str ) -> bool:
'''simple docstring'''
return len(self ) == 0
def lowerCAmelCase ():
"""simple docstring"""
__UpperCamelCase =CircularLinkedList()
assert len(__UpperCamelCase ) == 0
assert circular_linked_list.is_empty() is True
assert str(__UpperCamelCase ) == ""
try:
circular_linked_list.delete_front()
raise AssertionError # This should not happen
except IndexError:
assert True # This should happen
try:
circular_linked_list.delete_tail()
raise AssertionError # This should not happen
except IndexError:
assert True # This should happen
try:
circular_linked_list.delete_nth(-1 )
raise AssertionError
except IndexError:
assert True
try:
circular_linked_list.delete_nth(0 )
raise AssertionError
except IndexError:
assert True
assert circular_linked_list.is_empty() is True
for i in range(5 ):
assert len(__UpperCamelCase ) == i
circular_linked_list.insert_nth(__UpperCamelCase , i + 1 )
assert str(__UpperCamelCase ) == "->".join(str(__UpperCamelCase ) for i in range(1 , 6 ) )
circular_linked_list.insert_tail(6 )
assert str(__UpperCamelCase ) == "->".join(str(__UpperCamelCase ) for i in range(1 , 7 ) )
circular_linked_list.insert_head(0 )
assert str(__UpperCamelCase ) == "->".join(str(__UpperCamelCase ) for i in range(0 , 7 ) )
assert circular_linked_list.delete_front() == 0
assert circular_linked_list.delete_tail() == 6
assert str(__UpperCamelCase ) == "->".join(str(__UpperCamelCase ) for i in range(1 , 6 ) )
assert circular_linked_list.delete_nth(2 ) == 3
circular_linked_list.insert_nth(2 , 3 )
assert str(__UpperCamelCase ) == "->".join(str(__UpperCamelCase ) for i in range(1 , 6 ) )
assert circular_linked_list.is_empty() is False
if __name__ == "__main__":
import doctest
doctest.testmod()
| 85 | 1 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer
A =logging.get_logger(__name__)
A ={'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}
A =[
'small',
'small-base',
'medium',
'medium-base',
'intermediate',
'intermediate-base',
'large',
'large-base',
'xlarge',
'xlarge-base',
]
A ={
'vocab_file': {
'funnel-transformer/small': 'https://huggingface.co/funnel-transformer/small/resolve/main/vocab.txt',
'funnel-transformer/small-base': 'https://huggingface.co/funnel-transformer/small-base/resolve/main/vocab.txt',
'funnel-transformer/medium': 'https://huggingface.co/funnel-transformer/medium/resolve/main/vocab.txt',
'funnel-transformer/medium-base': (
'https://huggingface.co/funnel-transformer/medium-base/resolve/main/vocab.txt'
),
'funnel-transformer/intermediate': (
'https://huggingface.co/funnel-transformer/intermediate/resolve/main/vocab.txt'
),
'funnel-transformer/intermediate-base': (
'https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/vocab.txt'
),
'funnel-transformer/large': 'https://huggingface.co/funnel-transformer/large/resolve/main/vocab.txt',
'funnel-transformer/large-base': 'https://huggingface.co/funnel-transformer/large-base/resolve/main/vocab.txt',
'funnel-transformer/xlarge': 'https://huggingface.co/funnel-transformer/xlarge/resolve/main/vocab.txt',
'funnel-transformer/xlarge-base': (
'https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/vocab.txt'
),
},
'tokenizer_file': {
'funnel-transformer/small': 'https://huggingface.co/funnel-transformer/small/resolve/main/tokenizer.json',
'funnel-transformer/small-base': (
'https://huggingface.co/funnel-transformer/small-base/resolve/main/tokenizer.json'
),
'funnel-transformer/medium': 'https://huggingface.co/funnel-transformer/medium/resolve/main/tokenizer.json',
'funnel-transformer/medium-base': (
'https://huggingface.co/funnel-transformer/medium-base/resolve/main/tokenizer.json'
),
'funnel-transformer/intermediate': (
'https://huggingface.co/funnel-transformer/intermediate/resolve/main/tokenizer.json'
),
'funnel-transformer/intermediate-base': (
'https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/tokenizer.json'
),
'funnel-transformer/large': 'https://huggingface.co/funnel-transformer/large/resolve/main/tokenizer.json',
'funnel-transformer/large-base': (
'https://huggingface.co/funnel-transformer/large-base/resolve/main/tokenizer.json'
),
'funnel-transformer/xlarge': 'https://huggingface.co/funnel-transformer/xlarge/resolve/main/tokenizer.json',
'funnel-transformer/xlarge-base': (
'https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/tokenizer.json'
),
},
}
A ={f"""funnel-transformer/{name}""": 5_12 for name in _model_names}
A ={f"""funnel-transformer/{name}""": {'do_lower_case': True} for name in _model_names}
class _a ( snake_case__ ):
__a : Optional[Any] = VOCAB_FILES_NAMES
__a : Tuple = PRETRAINED_VOCAB_FILES_MAP
__a : Dict = PRETRAINED_INIT_CONFIGURATION
__a : List[Any] = FunnelTokenizer
__a : Any = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__a : int = 2
def __init__( self : Any , lowercase : str=None , lowercase : Dict=None , lowercase : Union[str, Any]=True , lowercase : Tuple="<unk>" , lowercase : str="<sep>" , lowercase : List[str]="<pad>" , lowercase : Tuple="<cls>" , lowercase : str="<mask>" , lowercase : Any="<s>" , lowercase : Any="</s>" , lowercase : Dict=True , lowercase : str=True , lowercase : List[Any]=None , lowercase : int="##" , **lowercase : Union[str, Any] , ):
'''simple docstring'''
super().__init__(
snake_case__ , tokenizer_file=snake_case__ , do_lower_case=snake_case__ , unk_token=snake_case__ , sep_token=snake_case__ , pad_token=snake_case__ , cls_token=snake_case__ , mask_token=snake_case__ , bos_token=snake_case__ , eos_token=snake_case__ , clean_text=snake_case__ , tokenize_chinese_chars=snake_case__ , strip_accents=snake_case__ , wordpieces_prefix=snake_case__ , **snake_case__ , )
UpperCAmelCase = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('''lowercase''' , snake_case__ ) != do_lower_case
or normalizer_state.get('''strip_accents''' , snake_case__ ) != strip_accents
or normalizer_state.get('''handle_chinese_chars''' , snake_case__ ) != tokenize_chinese_chars
):
UpperCAmelCase = getattr(snake_case__ , normalizer_state.pop('''type''' ) )
UpperCAmelCase = do_lower_case
UpperCAmelCase = strip_accents
UpperCAmelCase = tokenize_chinese_chars
UpperCAmelCase = normalizer_class(**snake_case__ )
UpperCAmelCase = do_lower_case
def A ( self : int , lowercase : Tuple , lowercase : int=None ):
'''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 A ( self : Optional[Any] , lowercase : List[int] , lowercase : Optional[List[int]] = None ):
'''simple docstring'''
UpperCAmelCase = [self.sep_token_id]
UpperCAmelCase = [self.cls_token_id]
if token_ids_a is None:
return len(cls ) * [self.cls_token_type_id] + len(token_ids_a + sep ) * [0]
return len(cls ) * [self.cls_token_type_id] + len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def A ( self : Tuple , lowercase : str , lowercase : Optional[str] = None ):
'''simple docstring'''
UpperCAmelCase = self._tokenizer.model.save(snake_case__ , name=snake_case__ )
return tuple(snake_case__ )
| 34 |
"""simple docstring"""
import argparse
import logging
import pickle
import random
import time
import numpy as np
from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)
A__ : Optional[Any] = logging.getLogger(__name__)
def _snake_case ( ) -> int:
lowerCamelCase_ : Tuple =argparse.ArgumentParser(
description="Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids)." )
parser.add_argument("--file_path" , type=lowerCamelCase__ , default="data/dump.txt" , help="The path to the data." )
parser.add_argument("--tokenizer_type" , type=lowerCamelCase__ , default="bert" , choices=["bert", "roberta", "gpt2"] )
parser.add_argument("--tokenizer_name" , type=lowerCamelCase__ , default="bert-base-uncased" , help="The tokenizer to use." )
parser.add_argument("--dump_file" , type=lowerCamelCase__ , default="data/dump" , help="The dump file prefix." )
lowerCamelCase_ : Tuple =parser.parse_args()
logger.info(F"""Loading Tokenizer ({args.tokenizer_name})""" )
if args.tokenizer_type == "bert":
lowerCamelCase_ : Tuple =BertTokenizer.from_pretrained(args.tokenizer_name )
lowerCamelCase_ : Optional[Any] =tokenizer.special_tokens_map["cls_token"] # `[CLS]`
lowerCamelCase_ : Any =tokenizer.special_tokens_map["sep_token"] # `[SEP]`
elif args.tokenizer_type == "roberta":
lowerCamelCase_ : str =RobertaTokenizer.from_pretrained(args.tokenizer_name )
lowerCamelCase_ : List[Any] =tokenizer.special_tokens_map["cls_token"] # `<s>`
lowerCamelCase_ : Any =tokenizer.special_tokens_map["sep_token"] # `</s>`
elif args.tokenizer_type == "gpt2":
lowerCamelCase_ : Tuple =GPTaTokenizer.from_pretrained(args.tokenizer_name )
lowerCamelCase_ : Dict =tokenizer.special_tokens_map["bos_token"] # `<|endoftext|>`
lowerCamelCase_ : Any =tokenizer.special_tokens_map["eos_token"] # `<|endoftext|>`
logger.info(F"""Loading text from {args.file_path}""" )
with open(args.file_path , "r" , encoding="utf8" ) as fp:
lowerCamelCase_ : Optional[int] =fp.readlines()
logger.info("Start encoding" )
logger.info(F"""{len(lowerCamelCase__ )} examples to process.""" )
lowerCamelCase_ : str =[]
lowerCamelCase_ : Union[str, Any] =0
lowerCamelCase_ : List[str] =10_000
lowerCamelCase_ : int =time.time()
for text in data:
lowerCamelCase_ : List[str] =F"""{bos} {text.strip()} {sep}"""
lowerCamelCase_ : str =tokenizer.encode(lowerCamelCase__ , add_special_tokens=lowerCamelCase__ )
rslt.append(lowerCamelCase__ )
iter += 1
if iter % interval == 0:
lowerCamelCase_ : List[Any] =time.time()
logger.info(F"""{iter} examples processed. - {(end-start):.2f}s/{interval}expl""" )
lowerCamelCase_ : Tuple =time.time()
logger.info("Finished binarization" )
logger.info(F"""{len(lowerCamelCase__ )} examples processed.""" )
lowerCamelCase_ : Optional[Any] =F"""{args.dump_file}.{args.tokenizer_name}.pickle"""
lowerCamelCase_ : Optional[int] =tokenizer.vocab_size
if vocab_size < (1 << 16):
lowerCamelCase_ : int =[np.uintaa(lowerCamelCase__ ) for d in rslt]
else:
lowerCamelCase_ : Tuple =[np.intaa(lowerCamelCase__ ) for d in rslt]
random.shuffle(rslt_ )
logger.info(F"""Dump to {dp_file}""" )
with open(lowerCamelCase__ , "wb" ) as handle:
pickle.dump(rslt_ , lowerCamelCase__ , protocol=pickle.HIGHEST_PROTOCOL )
if __name__ == "__main__":
main()
| 144 | 0 |
def __A ( __lowerCamelCase ) -> bool:
a = (1 + 24 * n) ** 0.5
return ((1 + root) / 6) % 1 == 0
def __A ( __lowerCamelCase = 5000 ) -> int:
a = [(i * (3 * i - 1)) // 2 for i in range(1 , __lowerCamelCase )]
for i, pentagonal_i in enumerate(__lowerCamelCase ):
for j in range(__lowerCamelCase , len(__lowerCamelCase ) ):
a = pentagonal_nums[j]
a = pentagonal_i + pentagonal_j
a = pentagonal_j - pentagonal_i
if is_pentagonal(__lowerCamelCase ) and is_pentagonal(__lowerCamelCase ):
return b
return -1
if __name__ == "__main__":
print(F'{solution() = }')
| 347 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__UpperCamelCase : Optional[Any] = logging.get_logger(__name__)
__UpperCamelCase : int = {
"shi-labs/nat-mini-in1k-224": "https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json",
# See all Nat models at https://huggingface.co/models?filter=nat
}
class __lowerCAmelCase ( __magic_name__ , __magic_name__ ):
UpperCamelCase__ = '''nat'''
UpperCamelCase__ = {
'''num_attention_heads''': '''num_heads''',
'''num_hidden_layers''': '''num_layers''',
}
def __init__( self :Any , __magic_name__ :int=4 , __magic_name__ :Dict=3 , __magic_name__ :List[str]=64 , __magic_name__ :Optional[int]=[3, 4, 6, 5] , __magic_name__ :int=[2, 4, 8, 16] , __magic_name__ :str=7 , __magic_name__ :Tuple=3.0 , __magic_name__ :Dict=True , __magic_name__ :List[Any]=0.0 , __magic_name__ :List[Any]=0.0 , __magic_name__ :List[Any]=0.1 , __magic_name__ :Optional[Any]="gelu" , __magic_name__ :Optional[Any]=0.02 , __magic_name__ :Tuple=1E-5 , __magic_name__ :Union[str, Any]=0.0 , __magic_name__ :int=None , __magic_name__ :Any=None , **__magic_name__ :Dict , ):
'''simple docstring'''
super().__init__(**__magic_name__ )
a = patch_size
a = num_channels
a = embed_dim
a = depths
a = len(__magic_name__ )
a = num_heads
a = kernel_size
a = mlp_ratio
a = qkv_bias
a = hidden_dropout_prob
a = attention_probs_dropout_prob
a = drop_path_rate
a = hidden_act
a = layer_norm_eps
a = initializer_range
# we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
a = int(embed_dim * 2 ** (len(__magic_name__ ) - 1) )
a = layer_scale_init_value
a = ["""stem"""] + [F'stage{idx}' for idx in range(1 , len(__magic_name__ ) + 1 )]
a , a = get_aligned_output_features_output_indices(
out_features=__magic_name__ , out_indices=__magic_name__ , stage_names=self.stage_names )
| 347 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowerCAmelCase = {
'configuration_mvp': ['MVP_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MvpConfig', 'MvpOnnxConfig'],
'tokenization_mvp': ['MvpTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase = ['MvpTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase = [
'MVP_PRETRAINED_MODEL_ARCHIVE_LIST',
'MvpForCausalLM',
'MvpForConditionalGeneration',
'MvpForQuestionAnswering',
'MvpForSequenceClassification',
'MvpModel',
'MvpPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
from .tokenization_mvp import MvpTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mvp_fast import MvpTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mvp import (
MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
MvpForCausalLM,
MvpForConditionalGeneration,
MvpForQuestionAnswering,
MvpForSequenceClassification,
MvpModel,
MvpPreTrainedModel,
)
else:
import sys
lowerCAmelCase = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 110 |
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block
@dataclass
class _a ( UpperCamelCase__ ):
_lowercase : torch.FloatTensor
class _a ( nn.Module ):
def __init__( self: int , UpperCamelCase_: int=3 , UpperCamelCase_: Optional[Any]=3 , UpperCamelCase_: Union[str, Any]=("DownEncoderBlock2D",) , UpperCamelCase_: Optional[int]=(64,) , UpperCamelCase_: Tuple=2 , UpperCamelCase_: Dict=32 , UpperCamelCase_: Any="silu" , UpperCamelCase_: Optional[int]=True , ) -> Union[str, Any]:
"""simple docstring"""
super().__init__()
lowercase__ = layers_per_block
lowercase__ = torch.nn.Convad(
UpperCamelCase_ , block_out_channels[0] , kernel_size=3 , stride=1 , padding=1 , )
lowercase__ = None
lowercase__ = nn.ModuleList([] )
# down
lowercase__ = block_out_channels[0]
for i, down_block_type in enumerate(UpperCamelCase_ ):
lowercase__ = output_channel
lowercase__ = block_out_channels[i]
lowercase__ = i == len(UpperCamelCase_ ) - 1
lowercase__ = get_down_block(
UpperCamelCase_ , num_layers=self.layers_per_block , in_channels=UpperCamelCase_ , out_channels=UpperCamelCase_ , add_downsample=not is_final_block , resnet_eps=1E-6 , downsample_padding=0 , resnet_act_fn=UpperCamelCase_ , resnet_groups=UpperCamelCase_ , attention_head_dim=UpperCamelCase_ , temb_channels=UpperCamelCase_ , )
self.down_blocks.append(UpperCamelCase_ )
# mid
lowercase__ = UNetMidBlockaD(
in_channels=block_out_channels[-1] , resnet_eps=1E-6 , resnet_act_fn=UpperCamelCase_ , output_scale_factor=1 , resnet_time_scale_shift='''default''' , attention_head_dim=block_out_channels[-1] , resnet_groups=UpperCamelCase_ , temb_channels=UpperCamelCase_ , )
# out
lowercase__ = nn.GroupNorm(num_channels=block_out_channels[-1] , num_groups=UpperCamelCase_ , eps=1E-6 )
lowercase__ = nn.SiLU()
lowercase__ = 2 * out_channels if double_z else out_channels
lowercase__ = nn.Convad(block_out_channels[-1] , UpperCamelCase_ , 3 , padding=1 )
lowercase__ = False
def lowerCamelCase_ ( self: Optional[Any] , UpperCamelCase_: str ) -> str:
"""simple docstring"""
lowercase__ = x
lowercase__ = self.conv_in(UpperCamelCase_ )
if self.training and self.gradient_checkpointing:
def create_custom_forward(UpperCamelCase_: Dict ):
def custom_forward(*UpperCamelCase_: List[str] ):
return module(*UpperCamelCase_ )
return custom_forward
# down
if is_torch_version('''>=''' , '''1.11.0''' ):
for down_block in self.down_blocks:
lowercase__ = torch.utils.checkpoint.checkpoint(
create_custom_forward(UpperCamelCase_ ) , UpperCamelCase_ , use_reentrant=UpperCamelCase_ )
# middle
lowercase__ = torch.utils.checkpoint.checkpoint(
create_custom_forward(self.mid_block ) , UpperCamelCase_ , use_reentrant=UpperCamelCase_ )
else:
for down_block in self.down_blocks:
lowercase__ = torch.utils.checkpoint.checkpoint(create_custom_forward(UpperCamelCase_ ) , UpperCamelCase_ )
# middle
lowercase__ = torch.utils.checkpoint.checkpoint(create_custom_forward(self.mid_block ) , UpperCamelCase_ )
else:
# down
for down_block in self.down_blocks:
lowercase__ = down_block(UpperCamelCase_ )
# middle
lowercase__ = self.mid_block(UpperCamelCase_ )
# post-process
lowercase__ = self.conv_norm_out(UpperCamelCase_ )
lowercase__ = self.conv_act(UpperCamelCase_ )
lowercase__ = self.conv_out(UpperCamelCase_ )
return sample
class _a ( nn.Module ):
def __init__( self: Dict , UpperCamelCase_: int=3 , UpperCamelCase_: Optional[Any]=3 , UpperCamelCase_: int=("UpDecoderBlock2D",) , UpperCamelCase_: Any=(64,) , UpperCamelCase_: Optional[Any]=2 , UpperCamelCase_: List[Any]=32 , UpperCamelCase_: Optional[Any]="silu" , UpperCamelCase_: Dict="group" , ) -> Any:
"""simple docstring"""
super().__init__()
lowercase__ = layers_per_block
lowercase__ = nn.Convad(
UpperCamelCase_ , block_out_channels[-1] , kernel_size=3 , stride=1 , padding=1 , )
lowercase__ = None
lowercase__ = nn.ModuleList([] )
lowercase__ = in_channels if norm_type == '''spatial''' else None
# mid
lowercase__ = UNetMidBlockaD(
in_channels=block_out_channels[-1] , resnet_eps=1E-6 , resnet_act_fn=UpperCamelCase_ , output_scale_factor=1 , resnet_time_scale_shift='''default''' if norm_type == '''group''' else norm_type , attention_head_dim=block_out_channels[-1] , resnet_groups=UpperCamelCase_ , temb_channels=UpperCamelCase_ , )
# up
lowercase__ = list(reversed(UpperCamelCase_ ) )
lowercase__ = reversed_block_out_channels[0]
for i, up_block_type in enumerate(UpperCamelCase_ ):
lowercase__ = output_channel
lowercase__ = reversed_block_out_channels[i]
lowercase__ = i == len(UpperCamelCase_ ) - 1
lowercase__ = get_up_block(
UpperCamelCase_ , num_layers=self.layers_per_block + 1 , in_channels=UpperCamelCase_ , out_channels=UpperCamelCase_ , prev_output_channel=UpperCamelCase_ , add_upsample=not is_final_block , resnet_eps=1E-6 , resnet_act_fn=UpperCamelCase_ , resnet_groups=UpperCamelCase_ , attention_head_dim=UpperCamelCase_ , temb_channels=UpperCamelCase_ , resnet_time_scale_shift=UpperCamelCase_ , )
self.up_blocks.append(UpperCamelCase_ )
lowercase__ = output_channel
# out
if norm_type == "spatial":
lowercase__ = SpatialNorm(block_out_channels[0] , UpperCamelCase_ )
else:
lowercase__ = nn.GroupNorm(num_channels=block_out_channels[0] , num_groups=UpperCamelCase_ , eps=1E-6 )
lowercase__ = nn.SiLU()
lowercase__ = nn.Convad(block_out_channels[0] , UpperCamelCase_ , 3 , padding=1 )
lowercase__ = False
def lowerCamelCase_ ( self: Optional[Any] , UpperCamelCase_: Union[str, Any] , UpperCamelCase_: List[str]=None ) -> int:
"""simple docstring"""
lowercase__ = z
lowercase__ = self.conv_in(UpperCamelCase_ )
lowercase__ = next(iter(self.up_blocks.parameters() ) ).dtype
if self.training and self.gradient_checkpointing:
def create_custom_forward(UpperCamelCase_: List[str] ):
def custom_forward(*UpperCamelCase_: int ):
return module(*UpperCamelCase_ )
return custom_forward
if is_torch_version('''>=''' , '''1.11.0''' ):
# middle
lowercase__ = torch.utils.checkpoint.checkpoint(
create_custom_forward(self.mid_block ) , UpperCamelCase_ , UpperCamelCase_ , use_reentrant=UpperCamelCase_ )
lowercase__ = sample.to(UpperCamelCase_ )
# up
for up_block in self.up_blocks:
lowercase__ = torch.utils.checkpoint.checkpoint(
create_custom_forward(UpperCamelCase_ ) , UpperCamelCase_ , UpperCamelCase_ , use_reentrant=UpperCamelCase_ )
else:
# middle
lowercase__ = torch.utils.checkpoint.checkpoint(
create_custom_forward(self.mid_block ) , UpperCamelCase_ , UpperCamelCase_ )
lowercase__ = sample.to(UpperCamelCase_ )
# up
for up_block in self.up_blocks:
lowercase__ = torch.utils.checkpoint.checkpoint(create_custom_forward(UpperCamelCase_ ) , UpperCamelCase_ , UpperCamelCase_ )
else:
# middle
lowercase__ = self.mid_block(UpperCamelCase_ , UpperCamelCase_ )
lowercase__ = sample.to(UpperCamelCase_ )
# up
for up_block in self.up_blocks:
lowercase__ = up_block(UpperCamelCase_ , UpperCamelCase_ )
# post-process
if latent_embeds is None:
lowercase__ = self.conv_norm_out(UpperCamelCase_ )
else:
lowercase__ = self.conv_norm_out(UpperCamelCase_ , UpperCamelCase_ )
lowercase__ = self.conv_act(UpperCamelCase_ )
lowercase__ = self.conv_out(UpperCamelCase_ )
return sample
class _a ( nn.Module ):
def __init__( self: Tuple , UpperCamelCase_: List[str] , UpperCamelCase_: Optional[Any] , UpperCamelCase_: Optional[Any] , UpperCamelCase_: Optional[int]=None , UpperCamelCase_: List[str]="random" , UpperCamelCase_: Union[str, Any]=False , UpperCamelCase_: Dict=True ) -> List[Any]:
"""simple docstring"""
super().__init__()
lowercase__ = n_e
lowercase__ = vq_embed_dim
lowercase__ = beta
lowercase__ = legacy
lowercase__ = nn.Embedding(self.n_e , self.vq_embed_dim )
self.embedding.weight.data.uniform_(-1.0 / self.n_e , 1.0 / self.n_e )
lowercase__ = remap
if self.remap is not None:
self.register_buffer('''used''' , torch.tensor(np.load(self.remap ) ) )
lowercase__ = self.used.shape[0]
lowercase__ = unknown_index # "random" or "extra" or integer
if self.unknown_index == "extra":
lowercase__ = self.re_embed
lowercase__ = self.re_embed + 1
print(
f'Remapping {self.n_e} indices to {self.re_embed} indices. '
f'Using {self.unknown_index} for unknown indices.' )
else:
lowercase__ = n_e
lowercase__ = sane_index_shape
def lowerCamelCase_ ( self: Optional[int] , UpperCamelCase_: Any ) -> Dict:
"""simple docstring"""
lowercase__ = inds.shape
assert len(UpperCamelCase_ ) > 1
lowercase__ = inds.reshape(ishape[0] , -1 )
lowercase__ = self.used.to(UpperCamelCase_ )
lowercase__ = (inds[:, :, None] == used[None, None, ...]).long()
lowercase__ = match.argmax(-1 )
lowercase__ = match.sum(2 ) < 1
if self.unknown_index == "random":
lowercase__ = torch.randint(0 , self.re_embed , size=new[unknown].shape ).to(device=new.device )
else:
lowercase__ = self.unknown_index
return new.reshape(UpperCamelCase_ )
def lowerCamelCase_ ( self: Union[str, Any] , UpperCamelCase_: Tuple ) -> str:
"""simple docstring"""
lowercase__ = inds.shape
assert len(UpperCamelCase_ ) > 1
lowercase__ = inds.reshape(ishape[0] , -1 )
lowercase__ = self.used.to(UpperCamelCase_ )
if self.re_embed > self.used.shape[0]: # extra token
lowercase__ = 0 # simply set to zero
lowercase__ = torch.gather(used[None, :][inds.shape[0] * [0], :] , 1 , UpperCamelCase_ )
return back.reshape(UpperCamelCase_ )
def lowerCamelCase_ ( self: List[Any] , UpperCamelCase_: Any ) -> Tuple:
"""simple docstring"""
lowercase__ = z.permute(0 , 2 , 3 , 1 ).contiguous()
lowercase__ = z.view(-1 , self.vq_embed_dim )
# distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
lowercase__ = torch.argmin(torch.cdist(UpperCamelCase_ , self.embedding.weight ) , dim=1 )
lowercase__ = self.embedding(UpperCamelCase_ ).view(z.shape )
lowercase__ = None
lowercase__ = None
# compute loss for embedding
if not self.legacy:
lowercase__ = self.beta * torch.mean((z_q.detach() - z) ** 2 ) + torch.mean((z_q - z.detach()) ** 2 )
else:
lowercase__ = torch.mean((z_q.detach() - z) ** 2 ) + self.beta * torch.mean((z_q - z.detach()) ** 2 )
# preserve gradients
lowercase__ = z + (z_q - z).detach()
# reshape back to match original input shape
lowercase__ = z_q.permute(0 , 3 , 1 , 2 ).contiguous()
if self.remap is not None:
lowercase__ = min_encoding_indices.reshape(z.shape[0] , -1 ) # add batch axis
lowercase__ = self.remap_to_used(UpperCamelCase_ )
lowercase__ = min_encoding_indices.reshape(-1 , 1 ) # flatten
if self.sane_index_shape:
lowercase__ = min_encoding_indices.reshape(z_q.shape[0] , z_q.shape[2] , z_q.shape[3] )
return z_q, loss, (perplexity, min_encodings, min_encoding_indices)
def lowerCamelCase_ ( self: List[Any] , UpperCamelCase_: Tuple , UpperCamelCase_: List[Any] ) -> List[str]:
"""simple docstring"""
if self.remap is not None:
lowercase__ = indices.reshape(shape[0] , -1 ) # add batch axis
lowercase__ = self.unmap_to_all(UpperCamelCase_ )
lowercase__ = indices.reshape(-1 ) # flatten again
# get quantized latent vectors
lowercase__ = self.embedding(UpperCamelCase_ )
if shape is not None:
lowercase__ = z_q.view(UpperCamelCase_ )
# reshape back to match original input shape
lowercase__ = z_q.permute(0 , 3 , 1 , 2 ).contiguous()
return z_q
class _a ( UpperCamelCase__ ):
def __init__( self: Union[str, Any] , UpperCamelCase_: Any , UpperCamelCase_: List[Any]=False ) -> str:
"""simple docstring"""
lowercase__ = parameters
lowercase__ , lowercase__ = torch.chunk(UpperCamelCase_ , 2 , dim=1 )
lowercase__ = torch.clamp(self.logvar , -30.0 , 20.0 )
lowercase__ = deterministic
lowercase__ = torch.exp(0.5 * self.logvar )
lowercase__ = torch.exp(self.logvar )
if self.deterministic:
lowercase__ = lowercase__ = torch.zeros_like(
self.mean , device=self.parameters.device , dtype=self.parameters.dtype )
def lowerCamelCase_ ( self: Any , UpperCamelCase_: Optional[torch.Generator] = None ) -> torch.FloatTensor:
"""simple docstring"""
lowercase__ = randn_tensor(
self.mean.shape , generator=UpperCamelCase_ , device=self.parameters.device , dtype=self.parameters.dtype )
lowercase__ = self.mean + self.std * sample
return x
def lowerCamelCase_ ( self: List[str] , UpperCamelCase_: str=None ) -> Tuple:
"""simple docstring"""
if self.deterministic:
return torch.Tensor([0.0] )
else:
if other is None:
return 0.5 * torch.sum(torch.pow(self.mean , 2 ) + self.var - 1.0 - self.logvar , dim=[1, 2, 3] )
else:
return 0.5 * torch.sum(
torch.pow(self.mean - other.mean , 2 ) / other.var
+ self.var / other.var
- 1.0
- self.logvar
+ other.logvar , dim=[1, 2, 3] , )
def lowerCamelCase_ ( self: int , UpperCamelCase_: Any , UpperCamelCase_: Any=[1, 2, 3] ) -> Tuple:
"""simple docstring"""
if self.deterministic:
return torch.Tensor([0.0] )
lowercase__ = np.log(2.0 * np.pi )
return 0.5 * torch.sum(logtwopi + self.logvar + torch.pow(sample - self.mean , 2 ) / self.var , dim=UpperCamelCase_ )
def lowerCamelCase_ ( self: str ) -> Any:
"""simple docstring"""
return self.mean
| 110 | 1 |
from math import ceil
def lowerCAmelCase_ ( snake_case_,snake_case_ ):
_A : List[str] = list(range(0,snake_case_ ) )
_A : int = [item for sublist in list(device_map.values() ) for item in sublist]
# Duplicate check
_A : Tuple = []
for i in device_map_blocks:
if device_map_blocks.count(snake_case_ ) > 1 and i not in duplicate_blocks:
duplicate_blocks.append(snake_case_ )
# Missing blocks
_A : Tuple = [i for i in blocks if i not in device_map_blocks]
_A : Union[str, Any] = [i for i in device_map_blocks if i not in blocks]
if len(snake_case_ ) != 0:
raise ValueError(
"""Duplicate attention blocks specified in device_map. Attention blocks must be specified to one device."""
""" These attention blocks were specified more than once: """ + str(snake_case_ ) )
if len(snake_case_ ) != 0:
raise ValueError(
"""There are attention blocks for this model that are not specified in the device_map. Add these attention """
"""blocks to a device on the device_map: """ + str(snake_case_ ) )
if len(snake_case_ ) != 0:
raise ValueError(
"""The device_map contains more attention blocks than this model has. Remove these from the device_map:"""
+ str(snake_case_ ) )
def lowerCAmelCase_ ( snake_case_,snake_case_ ):
_A : Optional[Any] = list(range(snake_case_ ) )
_A : List[str] = int(ceil(n_layers / len(snake_case_ ) ) )
_A : Tuple = [layers[i : i + n_blocks] for i in range(0,snake_case_,snake_case_ )]
return dict(zip(snake_case_,snake_case_ ) )
| 343 |
import inspect
import unittest
from transformers import ViTMSNConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import ViTMSNForImageClassification, ViTMSNModel
from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class lowercase :
def __init__( self , _a , _a=13 , _a=30 , _a=2 , _a=3 , _a=True , _a=True , _a=32 , _a=5 , _a=4 , _a=37 , _a="gelu" , _a=0.1 , _a=0.1 , _a=10 , _a=0.02 , _a=None , ) -> Union[str, Any]:
_A : Optional[int] = parent
_A : Dict = batch_size
_A : Any = image_size
_A : Optional[int] = patch_size
_A : Optional[int] = num_channels
_A : List[Any] = is_training
_A : Optional[Any] = use_labels
_A : Any = hidden_size
_A : Any = num_hidden_layers
_A : List[Any] = num_attention_heads
_A : int = intermediate_size
_A : Dict = hidden_act
_A : Optional[int] = hidden_dropout_prob
_A : str = attention_probs_dropout_prob
_A : Any = type_sequence_label_size
_A : str = initializer_range
_A : Tuple = scope
# in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
_A : List[Any] = (image_size // patch_size) ** 2
_A : str = num_patches + 1
def a__ ( self ) -> Dict:
_A : List[Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_A : List[str] = None
if self.use_labels:
_A : Optional[int] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_A : List[Any] = self.get_config()
return config, pixel_values, labels
def a__ ( self ) -> Union[str, Any]:
return ViTMSNConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , initializer_range=self.initializer_range , )
def a__ ( self , _a , _a , _a ) -> Dict:
_A : List[str] = ViTMSNModel(config=_a )
model.to(_a )
model.eval()
_A : List[str] = model(_a )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def a__ ( self , _a , _a , _a ) -> List[str]:
_A : Union[str, Any] = self.type_sequence_label_size
_A : Tuple = ViTMSNForImageClassification(_a )
model.to(_a )
model.eval()
_A : Optional[int] = model(_a , labels=_a )
print("""Pixel and labels shape: {pixel_values.shape}, {labels.shape}""" )
print("""Labels: {labels}""" )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
_A : Dict = 1
_A : str = ViTMSNForImageClassification(_a )
model.to(_a )
model.eval()
_A : int = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
_A : int = model(_a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def a__ ( self ) -> Any:
_A : Optional[int] = self.prepare_config_and_inputs()
_A , _A , _A : Dict = config_and_inputs
_A : List[Any] = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class lowercase ( UpperCamelCase__,UpperCamelCase__,unittest.TestCase ):
_a = (ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
_a = (
{"feature-extraction": ViTMSNModel, "image-classification": ViTMSNForImageClassification}
if is_torch_available()
else {}
)
_a = False
_a = False
_a = False
_a = False
def a__ ( self ) -> Tuple:
_A : Tuple = ViTMSNModelTester(self )
_A : List[Any] = ConfigTester(self , config_class=_a , has_text_modality=_a , hidden_size=37 )
def a__ ( self ) -> Optional[int]:
self.config_tester.run_common_tests()
@unittest.skip(reason="""ViTMSN does not use inputs_embeds""" )
def a__ ( self ) -> int:
pass
def a__ ( self ) -> Any:
_A , _A : int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_A : Tuple = model_class(_a )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
_A : str = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(_a , nn.Linear ) )
def a__ ( self ) -> str:
_A , _A : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_A : int = model_class(_a )
_A : Optional[Any] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_A : str = [*signature.parameters.keys()]
_A : Optional[int] = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , _a )
def a__ ( self ) -> List[Any]:
_A : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_a )
def a__ ( self ) -> Any:
_A : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_a )
@slow
def a__ ( self ) -> int:
for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_A : int = ViTMSNModel.from_pretrained(_a )
self.assertIsNotNone(_a )
def lowerCAmelCase_ ( ):
_A : Dict = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_torch
@require_vision
class lowercase ( unittest.TestCase ):
@cached_property
def a__ ( self ) -> int:
return ViTImageProcessor.from_pretrained("""facebook/vit-msn-small""" ) if is_vision_available() else None
@slow
def a__ ( self ) -> Optional[int]:
torch.manual_seed(2 )
_A : Tuple = ViTMSNForImageClassification.from_pretrained("""facebook/vit-msn-small""" ).to(_a )
_A : Tuple = self.default_image_processor
_A : Dict = prepare_img()
_A : Optional[Any] = image_processor(images=_a , return_tensors="""pt""" ).to(_a )
# forward pass
with torch.no_grad():
_A : int = model(**_a )
# verify the logits
_A : Union[str, Any] = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , _a )
_A : Optional[int] = torch.tensor([-0.0803, -0.4454, -0.2375] ).to(_a )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , _a , atol=1e-4 ) )
| 343 | 1 |
"""simple docstring"""
from math import factorial
_UpperCAmelCase = {str(d): factorial(d) for d in range(1_0)}
def __magic_name__ ( lowercase ):
return sum(DIGIT_FACTORIAL[d] for d in str(lowerCamelCase_ ) )
def __magic_name__ ( ):
SCREAMING_SNAKE_CASE_: str =7 * factorial(9 ) + 1
return sum(i for i in range(3 , lowerCamelCase_ ) if sum_of_digit_factorial(lowerCamelCase_ ) == i )
if __name__ == "__main__":
print(f"""{solution() = }""")
| 173 |
from __future__ import annotations
def UpperCamelCase_( lowerCamelCase_ ) -> bool:
if len(lowerCamelCase_ ) < 2:
raise ValueError('Monogons and Digons are not polygons in the Euclidean space' )
if any(i <= 0 for i in nums ):
raise ValueError('All values must be greater than 0' )
_lowercase : Tuple = nums.copy()
copy_nums.sort()
return copy_nums[-1] < sum(copy_nums[:-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 21 | 0 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a__ = logging.get_logger(__name__)
a__ = {
'''hustvl/yolos-small''': '''https://huggingface.co/hustvl/yolos-small/resolve/main/config.json''',
# See all YOLOS models at https://huggingface.co/models?filter=yolos
}
class UpperCAmelCase_ ( __lowercase ):
"""simple docstring"""
UpperCAmelCase__ : Optional[int] = "yolos"
def __init__( self , _a=7_6_8 , _a=1_2 , _a=1_2 , _a=3_0_7_2 , _a="gelu" , _a=0.0 , _a=0.0 , _a=0.02 , _a=1e-1_2 , _a=[5_1_2, 8_6_4] , _a=1_6 , _a=3 , _a=True , _a=1_0_0 , _a=True , _a=False , _a=1 , _a=5 , _a=2 , _a=5 , _a=2 , _a=0.1 , **_a , ) -> Optional[Any]:
super().__init__(**_a )
_a : Dict = hidden_size
_a : int = num_hidden_layers
_a : Optional[Any] = num_attention_heads
_a : Any = intermediate_size
_a : Dict = hidden_act
_a : List[str] = hidden_dropout_prob
_a : Tuple = attention_probs_dropout_prob
_a : Optional[int] = initializer_range
_a : Dict = layer_norm_eps
_a : List[Any] = image_size
_a : Dict = patch_size
_a : Any = num_channels
_a : Tuple = qkv_bias
_a : Optional[int] = num_detection_tokens
_a : int = use_mid_position_embeddings
_a : Dict = auxiliary_loss
# Hungarian matcher
_a : List[str] = class_cost
_a : Optional[int] = bbox_cost
_a : List[Any] = giou_cost
# Loss coefficients
_a : Dict = bbox_loss_coefficient
_a : List[str] = giou_loss_coefficient
_a : List[str] = eos_coefficient
class UpperCAmelCase_ ( __lowercase ):
"""simple docstring"""
UpperCAmelCase__ : Union[str, Any] = version.parse("1.11" )
@property
def __lowercase ( self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def __lowercase ( self ) -> float:
return 1e-4
@property
def __lowercase ( self ) -> int:
return 1_2
| 15 |
def __UpperCAmelCase ( __a : int ,__a : int ,__a : int ) -> int:
"""simple docstring"""
if exponent == 1:
return base
if exponent % 2 == 0:
_a : List[Any] = _modexpt(__a ,exponent // 2 ,__a ) % modulo_value
return (x * x) % modulo_value
else:
return (base * _modexpt(__a ,exponent - 1 ,__a )) % modulo_value
def __UpperCAmelCase ( __a : int = 1_777 ,__a : int = 1_855 ,__a : int = 8 ) -> int:
"""simple docstring"""
_a : List[Any] = base
for _ in range(1 ,__a ):
_a : Any = _modexpt(__a ,__a ,10**digits )
return result
if __name__ == "__main__":
print(f'''{solution() = }''')
| 15 | 1 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer
SCREAMING_SNAKE_CASE_: Any =logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_: Union[str, Any] ={'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}
# See all BART models at https://huggingface.co/models?filter=bart
SCREAMING_SNAKE_CASE_: str ={
'vocab_file': {
'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/vocab.json',
'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/vocab.json',
'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json',
'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json',
'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json',
'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json',
},
'merges_file': {
'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/merges.txt',
'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/merges.txt',
'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt',
'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt',
'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt',
'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt',
},
'tokenizer_file': {
'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json',
'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json',
'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json',
'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json',
'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json',
'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json',
},
}
SCREAMING_SNAKE_CASE_: int ={
'facebook/bart-base': 10_24,
'facebook/bart-large': 10_24,
'facebook/bart-large-mnli': 10_24,
'facebook/bart-large-cnn': 10_24,
'facebook/bart-large-xsum': 10_24,
'yjernite/bart_eli5': 10_24,
}
class __A ( UpperCamelCase__ ):
a__ : Dict = VOCAB_FILES_NAMES
a__ : Any = PRETRAINED_VOCAB_FILES_MAP
a__ : Union[str, Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
a__ : int = ["""input_ids""", """attention_mask"""]
a__ : str = BartTokenizer
def __init__(self : List[str] , __a : List[Any]=None , __a : str=None , __a : List[str]=None , __a : Union[str, Any]="replace" , __a : List[str]="<s>" , __a : List[Any]="</s>" , __a : Dict="</s>" , __a : Optional[int]="<s>" , __a : Any="<unk>" , __a : List[Any]="<pad>" , __a : List[str]="<mask>" , __a : Tuple=False , __a : Optional[Any]=True , **__a : Optional[Any] , ):
super().__init__(
__a , __a , tokenizer_file=__a , errors=__a , bos_token=__a , eos_token=__a , sep_token=__a , cls_token=__a , unk_token=__a , pad_token=__a , mask_token=__a , add_prefix_space=__a , trim_offsets=__a , **__a , )
UpperCAmelCase_ = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get("add_prefix_space" , __a ) != add_prefix_space:
UpperCAmelCase_ = getattr(__a , pre_tok_state.pop("type" ) )
UpperCAmelCase_ = add_prefix_space
UpperCAmelCase_ = pre_tok_class(**__a )
UpperCAmelCase_ = add_prefix_space
# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
UpperCAmelCase_ = "post_processor"
UpperCAmelCase_ = getattr(self.backend_tokenizer , __a , __a )
if tokenizer_component_instance:
UpperCAmelCase_ = json.loads(tokenizer_component_instance.__getstate__() )
# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
if "sep" in state:
UpperCAmelCase_ = tuple(state["sep"] )
if "cls" in state:
UpperCAmelCase_ = tuple(state["cls"] )
UpperCAmelCase_ = False
if state.get("add_prefix_space" , __a ) != add_prefix_space:
UpperCAmelCase_ = add_prefix_space
UpperCAmelCase_ = True
if state.get("trim_offsets" , __a ) != trim_offsets:
UpperCAmelCase_ = trim_offsets
UpperCAmelCase_ = True
if changes_to_apply:
UpperCAmelCase_ = getattr(__a , state.pop("type" ) )
UpperCAmelCase_ = component_class(**__a )
setattr(self.backend_tokenizer , __a , __a )
@property
def _lowercase (self : Dict ):
if self._mask_token is None:
if self.verbose:
logger.error("Using mask_token, but it is not set yet." )
return None
return str(self._mask_token )
@mask_token.setter
def _lowercase (self : Optional[int] , __a : List[Any] ):
UpperCAmelCase_ = AddedToken(__a , lstrip=__a , rstrip=__a ) if isinstance(__a , __a ) else value
UpperCAmelCase_ = value
def _lowercase (self : Any , *__a : List[Any] , **__a : Any ):
UpperCAmelCase_ = kwargs.get("is_split_into_words" , __a )
if is_split_into_words and not self.add_prefix_space:
raise ValueError(
f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
"to use it with pretokenized inputs." )
return super()._batch_encode_plus(*__a , **__a )
def _lowercase (self : List[str] , *__a : Optional[Any] , **__a : Dict ):
UpperCAmelCase_ = kwargs.get("is_split_into_words" , __a )
if is_split_into_words and not self.add_prefix_space:
raise ValueError(
f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
"to use it with pretokenized inputs." )
return super()._encode_plus(*__a , **__a )
def _lowercase (self : str , __a : str , __a : Optional[str] = None ):
UpperCAmelCase_ = self._tokenizer.model.save(__a , name=__a )
return tuple(__a )
def _lowercase (self : Any , __a : Optional[int] , __a : str=None ):
UpperCAmelCase_ = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def _lowercase (self : Union[str, Any] , __a : List[int] , __a : Optional[List[int]] = None ):
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]
| 1 |
'''simple docstring'''
from math import pow
def a ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , ):
'''simple docstring'''
if current_sum == needed_sum:
# If the sum of the powers is equal to needed_sum, then we have a solution.
solutions_count += 1
return current_sum, solutions_count
A_ : Optional[int] = int(pow(lowerCamelCase__ , lowerCamelCase__ ) )
if current_sum + i_to_n <= needed_sum:
# If the sum of the powers is less than needed_sum, then continue adding powers.
current_sum += i_to_n
A_, A_ : int = backtrack(
lowerCamelCase__ , lowerCamelCase__ , current_number + 1 , lowerCamelCase__ , lowerCamelCase__ )
current_sum -= i_to_n
if i_to_n < needed_sum:
# If the power of i is less than needed_sum, then try with the next power.
A_, A_ : int = backtrack(
lowerCamelCase__ , lowerCamelCase__ , current_number + 1 , lowerCamelCase__ , lowerCamelCase__ )
return current_sum, solutions_count
def a ( lowerCamelCase__ , lowerCamelCase__ ):
'''simple docstring'''
if not (1 <= needed_sum <= 10_00 and 2 <= power <= 10):
raise ValueError(
"""Invalid input\n"""
"""needed_sum must be between 1 and 1000, power between 2 and 10.""" )
return backtrack(lowerCamelCase__ , lowerCamelCase__ , 1 , 0 , 0 )[1] # Return the solutions_count
if __name__ == "__main__":
import doctest
doctest.testmod() | 206 | 0 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class UpperCAmelCase_ ( metaclass=SCREAMING_SNAKE_CASE__ ):
__SCREAMING_SNAKE_CASE : List[str] = ['sentencepiece']
def __init__( self : str , *A : Any , **A : Union[str, Any] ):
requires_backends(self , ["sentencepiece"] )
class UpperCAmelCase_ ( metaclass=SCREAMING_SNAKE_CASE__ ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = ['sentencepiece']
def __init__( self : int , *A : int , **A : Optional[int] ):
requires_backends(self , ["sentencepiece"] )
class UpperCAmelCase_ ( metaclass=SCREAMING_SNAKE_CASE__ ):
__SCREAMING_SNAKE_CASE : List[str] = ['sentencepiece']
def __init__( self : Union[str, Any] , *A : Tuple , **A : int ):
requires_backends(self , ["sentencepiece"] )
class UpperCAmelCase_ ( metaclass=SCREAMING_SNAKE_CASE__ ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = ['sentencepiece']
def __init__( self : str , *A : int , **A : int ):
requires_backends(self , ["sentencepiece"] )
class UpperCAmelCase_ ( metaclass=SCREAMING_SNAKE_CASE__ ):
__SCREAMING_SNAKE_CASE : Optional[int] = ['sentencepiece']
def __init__( self : str , *A : Tuple , **A : Optional[Any] ):
requires_backends(self , ["sentencepiece"] )
class UpperCAmelCase_ ( metaclass=SCREAMING_SNAKE_CASE__ ):
__SCREAMING_SNAKE_CASE : List[Any] = ['sentencepiece']
def __init__( self : str , *A : Union[str, Any] , **A : List[Any] ):
requires_backends(self , ["sentencepiece"] )
class UpperCAmelCase_ ( metaclass=SCREAMING_SNAKE_CASE__ ):
__SCREAMING_SNAKE_CASE : Optional[int] = ['sentencepiece']
def __init__( self : Optional[int] , *A : Optional[int] , **A : Tuple ):
requires_backends(self , ["sentencepiece"] )
class UpperCAmelCase_ ( metaclass=SCREAMING_SNAKE_CASE__ ):
__SCREAMING_SNAKE_CASE : List[str] = ['sentencepiece']
def __init__( self : Tuple , *A : Union[str, Any] , **A : Optional[Any] ):
requires_backends(self , ["sentencepiece"] )
class UpperCAmelCase_ ( metaclass=SCREAMING_SNAKE_CASE__ ):
__SCREAMING_SNAKE_CASE : Any = ['sentencepiece']
def __init__( self : Any , *A : Union[str, Any] , **A : Tuple ):
requires_backends(self , ["sentencepiece"] )
class UpperCAmelCase_ ( metaclass=SCREAMING_SNAKE_CASE__ ):
__SCREAMING_SNAKE_CASE : List[str] = ['sentencepiece']
def __init__( self : Any , *A : List[Any] , **A : Optional[Any] ):
requires_backends(self , ["sentencepiece"] )
class UpperCAmelCase_ ( metaclass=SCREAMING_SNAKE_CASE__ ):
__SCREAMING_SNAKE_CASE : int = ['sentencepiece']
def __init__( self : Union[str, Any] , *A : Optional[Any] , **A : str ):
requires_backends(self , ["sentencepiece"] )
class UpperCAmelCase_ ( metaclass=SCREAMING_SNAKE_CASE__ ):
__SCREAMING_SNAKE_CASE : int = ['sentencepiece']
def __init__( self : Dict , *A : List[str] , **A : Tuple ):
requires_backends(self , ["sentencepiece"] )
class UpperCAmelCase_ ( metaclass=SCREAMING_SNAKE_CASE__ ):
__SCREAMING_SNAKE_CASE : Optional[Any] = ['sentencepiece']
def __init__( self : Any , *A : List[str] , **A : Dict ):
requires_backends(self , ["sentencepiece"] )
class UpperCAmelCase_ ( metaclass=SCREAMING_SNAKE_CASE__ ):
__SCREAMING_SNAKE_CASE : Optional[Any] = ['sentencepiece']
def __init__( self : int , *A : List[str] , **A : Optional[Any] ):
requires_backends(self , ["sentencepiece"] )
class UpperCAmelCase_ ( metaclass=SCREAMING_SNAKE_CASE__ ):
__SCREAMING_SNAKE_CASE : List[str] = ['sentencepiece']
def __init__( self : Union[str, Any] , *A : List[str] , **A : Optional[Any] ):
requires_backends(self , ["sentencepiece"] )
class UpperCAmelCase_ ( metaclass=SCREAMING_SNAKE_CASE__ ):
__SCREAMING_SNAKE_CASE : List[str] = ['sentencepiece']
def __init__( self : Union[str, Any] , *A : List[str] , **A : List[str] ):
requires_backends(self , ["sentencepiece"] )
class UpperCAmelCase_ ( metaclass=SCREAMING_SNAKE_CASE__ ):
__SCREAMING_SNAKE_CASE : str = ['sentencepiece']
def __init__( self : Optional[int] , *A : List[Any] , **A : List[Any] ):
requires_backends(self , ["sentencepiece"] )
class UpperCAmelCase_ ( metaclass=SCREAMING_SNAKE_CASE__ ):
__SCREAMING_SNAKE_CASE : str = ['sentencepiece']
def __init__( self : Optional[int] , *A : Optional[int] , **A : Optional[Any] ):
requires_backends(self , ["sentencepiece"] )
class UpperCAmelCase_ ( metaclass=SCREAMING_SNAKE_CASE__ ):
__SCREAMING_SNAKE_CASE : int = ['sentencepiece']
def __init__( self : Dict , *A : Any , **A : Optional[Any] ):
requires_backends(self , ["sentencepiece"] )
class UpperCAmelCase_ ( metaclass=SCREAMING_SNAKE_CASE__ ):
__SCREAMING_SNAKE_CASE : Tuple = ['sentencepiece']
def __init__( self : str , *A : Tuple , **A : Optional[Any] ):
requires_backends(self , ["sentencepiece"] )
class UpperCAmelCase_ ( metaclass=SCREAMING_SNAKE_CASE__ ):
__SCREAMING_SNAKE_CASE : List[Any] = ['sentencepiece']
def __init__( self : List[Any] , *A : List[Any] , **A : int ):
requires_backends(self , ["sentencepiece"] )
class UpperCAmelCase_ ( metaclass=SCREAMING_SNAKE_CASE__ ):
__SCREAMING_SNAKE_CASE : int = ['sentencepiece']
def __init__( self : Dict , *A : str , **A : Dict ):
requires_backends(self , ["sentencepiece"] )
class UpperCAmelCase_ ( metaclass=SCREAMING_SNAKE_CASE__ ):
__SCREAMING_SNAKE_CASE : Dict = ['sentencepiece']
def __init__( self : Union[str, Any] , *A : Tuple , **A : List[str] ):
requires_backends(self , ["sentencepiece"] )
class UpperCAmelCase_ ( metaclass=SCREAMING_SNAKE_CASE__ ):
__SCREAMING_SNAKE_CASE : Any = ['sentencepiece']
def __init__( self : Dict , *A : List[str] , **A : Any ):
requires_backends(self , ["sentencepiece"] )
class UpperCAmelCase_ ( metaclass=SCREAMING_SNAKE_CASE__ ):
__SCREAMING_SNAKE_CASE : Any = ['sentencepiece']
def __init__( self : Union[str, Any] , *A : Optional[int] , **A : Optional[Any] ):
requires_backends(self , ["sentencepiece"] )
class UpperCAmelCase_ ( metaclass=SCREAMING_SNAKE_CASE__ ):
__SCREAMING_SNAKE_CASE : Dict = ['sentencepiece']
def __init__( self : str , *A : int , **A : Optional[Any] ):
requires_backends(self , ["sentencepiece"] )
class UpperCAmelCase_ ( metaclass=SCREAMING_SNAKE_CASE__ ):
__SCREAMING_SNAKE_CASE : int = ['sentencepiece']
def __init__( self : str , *A : Tuple , **A : List[str] ):
requires_backends(self , ["sentencepiece"] )
class UpperCAmelCase_ ( metaclass=SCREAMING_SNAKE_CASE__ ):
__SCREAMING_SNAKE_CASE : str = ['sentencepiece']
def __init__( self : str , *A : int , **A : List[str] ):
requires_backends(self , ["sentencepiece"] )
class UpperCAmelCase_ ( metaclass=SCREAMING_SNAKE_CASE__ ):
__SCREAMING_SNAKE_CASE : Dict = ['sentencepiece']
def __init__( self : List[Any] , *A : List[Any] , **A : str ):
requires_backends(self , ["sentencepiece"] )
class UpperCAmelCase_ ( metaclass=SCREAMING_SNAKE_CASE__ ):
__SCREAMING_SNAKE_CASE : str = ['sentencepiece']
def __init__( self : Optional[int] , *A : Optional[Any] , **A : Dict ):
requires_backends(self , ["sentencepiece"] )
class UpperCAmelCase_ ( metaclass=SCREAMING_SNAKE_CASE__ ):
__SCREAMING_SNAKE_CASE : Optional[int] = ['sentencepiece']
def __init__( self : Any , *A : Optional[int] , **A : Any ):
requires_backends(self , ["sentencepiece"] )
| 359 |
"""simple docstring"""
import inspect
import unittest
from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import MODEL_MAPPING, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel
from transformers.models.dpt.modeling_dpt import DPT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import DPTImageProcessor
class UpperCAmelCase_ :
def __init__( self : Tuple , A : str , A : Dict=2 , A : List[Any]=3_2 , A : Optional[Any]=1_6 , A : Tuple=3 , A : Optional[Any]=True , A : List[Any]=True , A : Optional[int]=3_2 , A : Optional[int]=4 , A : Tuple=[0, 1, 2, 3] , A : Optional[int]=4 , A : Tuple=3_7 , A : List[Any]="gelu" , A : List[Any]=0.1 , A : List[str]=0.1 , A : Union[str, Any]=0.02 , A : Optional[int]=3 , A : Optional[Any]=[1, 3_8_4, 2_4, 2_4] , A : Union[str, Any]=True , A : Any=None , ):
_UpperCAmelCase : str = parent
_UpperCAmelCase : int = batch_size
_UpperCAmelCase : List[str] = image_size
_UpperCAmelCase : Tuple = patch_size
_UpperCAmelCase : Any = num_channels
_UpperCAmelCase : List[str] = is_training
_UpperCAmelCase : Optional[Any] = use_labels
_UpperCAmelCase : str = hidden_size
_UpperCAmelCase : Dict = num_hidden_layers
_UpperCAmelCase : List[Any] = backbone_out_indices
_UpperCAmelCase : Optional[Any] = num_attention_heads
_UpperCAmelCase : Optional[int] = intermediate_size
_UpperCAmelCase : str = hidden_act
_UpperCAmelCase : int = hidden_dropout_prob
_UpperCAmelCase : Any = attention_probs_dropout_prob
_UpperCAmelCase : Optional[Any] = initializer_range
_UpperCAmelCase : Optional[Any] = num_labels
_UpperCAmelCase : Tuple = backbone_featmap_shape
_UpperCAmelCase : Optional[Any] = scope
_UpperCAmelCase : Union[str, Any] = is_hybrid
# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
_UpperCAmelCase : Optional[int] = (image_size // patch_size) ** 2
_UpperCAmelCase : int = num_patches + 1
def snake_case_ ( self : List[str] ):
_UpperCAmelCase : Tuple = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_UpperCAmelCase : Dict = None
if self.use_labels:
_UpperCAmelCase : List[str] = ids_tensor([self.batch_size, self.image_size, self.image_size] , self.num_labels )
_UpperCAmelCase : str = self.get_config()
return config, pixel_values, labels
def snake_case_ ( self : str ):
_UpperCAmelCase : Tuple = {
"global_padding": "same",
"layer_type": "bottleneck",
"depths": [3, 4, 9],
"out_features": ["stage1", "stage2", "stage3"],
"embedding_dynamic_padding": True,
"hidden_sizes": [9_6, 1_9_2, 3_8_4, 7_6_8],
"num_groups": 2,
}
return DPTConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , backbone_out_indices=self.backbone_out_indices , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=A , initializer_range=self.initializer_range , is_hybrid=self.is_hybrid , backbone_config=A , backbone_featmap_shape=self.backbone_featmap_shape , )
def snake_case_ ( self : Any , A : Optional[Any] , A : str , A : Dict ):
_UpperCAmelCase : List[str] = DPTModel(config=A )
model.to(A )
model.eval()
_UpperCAmelCase : List[str] = model(A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def snake_case_ ( self : List[str] , A : str , A : Any , A : List[Any] ):
_UpperCAmelCase : str = self.num_labels
_UpperCAmelCase : Any = DPTForDepthEstimation(A )
model.to(A )
model.eval()
_UpperCAmelCase : Optional[int] = model(A )
self.parent.assertEqual(result.predicted_depth.shape , (self.batch_size, self.image_size, self.image_size) )
def snake_case_ ( self : List[Any] , A : Any , A : Optional[int] , A : Union[str, Any] ):
_UpperCAmelCase : List[Any] = self.num_labels
_UpperCAmelCase : Union[str, Any] = DPTForSemanticSegmentation(A )
model.to(A )
model.eval()
_UpperCAmelCase : int = model(A , labels=A )
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_labels, self.image_size, self.image_size) )
def snake_case_ ( self : Union[str, Any] ):
_UpperCAmelCase : Union[str, Any] = self.prepare_config_and_inputs()
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase : Dict = config_and_inputs
_UpperCAmelCase : int = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class UpperCAmelCase_ ( _UpperCamelCase , _UpperCamelCase , unittest.TestCase ):
__SCREAMING_SNAKE_CASE : List[str] = (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
__SCREAMING_SNAKE_CASE : Any = (
{
'depth-estimation': DPTForDepthEstimation,
'feature-extraction': DPTModel,
'image-segmentation': DPTForSemanticSegmentation,
}
if is_torch_available()
else {}
)
__SCREAMING_SNAKE_CASE : Dict = False
__SCREAMING_SNAKE_CASE : Optional[int] = False
__SCREAMING_SNAKE_CASE : Optional[Any] = False
def snake_case_ ( self : int ):
_UpperCAmelCase : List[str] = DPTModelTester(self )
_UpperCAmelCase : Union[str, Any] = ConfigTester(self , config_class=A , has_text_modality=A , hidden_size=3_7 )
def snake_case_ ( self : int ):
self.config_tester.run_common_tests()
@unittest.skip(reason="DPT does not use inputs_embeds" )
def snake_case_ ( self : Union[str, Any] ):
pass
def snake_case_ ( self : Tuple ):
_UpperCAmelCase , _UpperCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCAmelCase : str = model_class(A )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
_UpperCAmelCase : Dict = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(A , nn.Linear ) )
def snake_case_ ( self : Optional[Any] ):
_UpperCAmelCase , _UpperCAmelCase : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCAmelCase : int = model_class(A )
_UpperCAmelCase : int = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_UpperCAmelCase : str = [*signature.parameters.keys()]
_UpperCAmelCase : Any = ["pixel_values"]
self.assertListEqual(arg_names[:1] , A )
def snake_case_ ( self : List[str] ):
_UpperCAmelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A )
def snake_case_ ( self : Dict ):
_UpperCAmelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_depth_estimation(*A )
def snake_case_ ( self : Dict ):
_UpperCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*A )
def snake_case_ ( self : Optional[int] ):
for model_class in self.all_model_classes:
if model_class.__name__ == "DPTForDepthEstimation":
continue
_UpperCAmelCase , _UpperCAmelCase : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
_UpperCAmelCase : Optional[int] = True
if model_class in get_values(A ):
continue
_UpperCAmelCase : int = model_class(A )
model.to(A )
model.train()
_UpperCAmelCase : Optional[Any] = self._prepare_for_class(A , A , return_labels=A )
_UpperCAmelCase : Optional[Any] = model(**A ).loss
loss.backward()
def snake_case_ ( self : Dict ):
for model_class in self.all_model_classes:
if model_class.__name__ == "DPTForDepthEstimation":
continue
_UpperCAmelCase , _UpperCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
_UpperCAmelCase : Tuple = False
_UpperCAmelCase : Tuple = True
if model_class in get_values(A ) or not model_class.supports_gradient_checkpointing:
continue
_UpperCAmelCase : int = model_class(A )
model.to(A )
model.gradient_checkpointing_enable()
model.train()
_UpperCAmelCase : str = self._prepare_for_class(A , A , return_labels=A )
_UpperCAmelCase : List[str] = model(**A ).loss
loss.backward()
def snake_case_ ( self : Union[str, Any] ):
_UpperCAmelCase , _UpperCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
_UpperCAmelCase : int = _config_zero_init(A )
for model_class in self.all_model_classes:
_UpperCAmelCase : List[Any] = model_class(config=A )
# Skip the check for the backbone
_UpperCAmelCase : Dict = []
for name, module in model.named_modules():
if module.__class__.__name__ == "DPTViTHybridEmbeddings":
_UpperCAmelCase : List[str] = [f'{name}.{key}' for key in module.state_dict().keys()]
break
for name, param in model.named_parameters():
if param.requires_grad:
if name in backbone_params:
continue
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item() , [0.0, 1.0] , msg=f'Parameter {name} of model {model_class} seems not properly initialized' , )
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
def snake_case_ ( self : int ):
pass
@slow
def snake_case_ ( self : Dict ):
for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
_UpperCAmelCase : Any = DPTModel.from_pretrained(A )
self.assertIsNotNone(A )
def snake_case_ ( self : Tuple ):
# We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
_UpperCAmelCase , _UpperCAmelCase : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
_UpperCAmelCase : Optional[Any] = "add"
with self.assertRaises(A ):
_UpperCAmelCase : List[Any] = DPTForDepthEstimation(A )
def __snake_case ( ) -> Optional[int]:
'''simple docstring'''
_UpperCAmelCase : int = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
@require_vision
@slow
class UpperCAmelCase_ ( unittest.TestCase ):
def snake_case_ ( self : List[str] ):
_UpperCAmelCase : Optional[int] = DPTImageProcessor.from_pretrained("Intel/dpt-hybrid-midas" )
_UpperCAmelCase : Any = DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas" ).to(A )
_UpperCAmelCase : str = prepare_img()
_UpperCAmelCase : Tuple = image_processor(images=A , return_tensors="pt" ).to(A )
# forward pass
with torch.no_grad():
_UpperCAmelCase : int = model(**A )
_UpperCAmelCase : List[str] = outputs.predicted_depth
# verify the predicted depth
_UpperCAmelCase : int = torch.Size((1, 3_8_4, 3_8_4) )
self.assertEqual(predicted_depth.shape , A )
_UpperCAmelCase : int = torch.tensor(
[[[5.6_437, 5.6_146, 5.6_511], [5.4_371, 5.5_649, 5.5_958], [5.5_215, 5.5_184, 5.5_293]]] ).to(A )
self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 1_0_0 , A , atol=1e-4 ) )
| 202 | 0 |
a_ = [
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
def _a ( UpperCamelCase_ : int , UpperCamelCase_ : Union[str, Any] , UpperCamelCase_ : Optional[int] , UpperCamelCase_ : Dict ) -> Optional[Any]:
"""simple docstring"""
lowerCAmelCase__ = [False] * len(UpperCamelCase_ )
lowerCAmelCase__ = [s]
lowerCAmelCase__ = True
while queue:
lowerCAmelCase__ = queue.pop(0 )
for ind in range(len(graph[u] ) ):
if visited[ind] is False and graph[u][ind] > 0:
queue.append(UpperCamelCase_ )
lowerCAmelCase__ = True
lowerCAmelCase__ = u
return visited[t]
def _a ( UpperCamelCase_ : Any , UpperCamelCase_ : List[str] , UpperCamelCase_ : Union[str, Any] ) -> Any:
"""simple docstring"""
lowerCAmelCase__ = [-1] * (len(UpperCamelCase_ ))
lowerCAmelCase__ = 0
lowerCAmelCase__ = []
lowerCAmelCase__ = [i[:] for i in graph] # Record original cut, copy.
while bfs(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ):
lowerCAmelCase__ = float("Inf" )
lowerCAmelCase__ = sink
while s != source:
# Find the minimum value in select path
lowerCAmelCase__ = min(UpperCamelCase_ , graph[parent[s]][s] )
lowerCAmelCase__ = parent[s]
max_flow += path_flow
lowerCAmelCase__ = sink
while v != source:
lowerCAmelCase__ = parent[v]
graph[u][v] -= path_flow
graph[v][u] += path_flow
lowerCAmelCase__ = parent[v]
for i in range(len(UpperCamelCase_ ) ):
for j in range(len(graph[0] ) ):
if graph[i][j] == 0 and temp[i][j] > 0:
res.append((i, j) )
return res
if __name__ == "__main__":
print(mincut(test_graph, source=0, sink=5))
| 340 |
from __future__ import annotations
from cmath import sqrt
def _a ( UpperCamelCase_ : int , UpperCamelCase_ : int , UpperCamelCase_ : int ) -> tuple[complex, complex]:
"""simple docstring"""
if a == 0:
raise ValueError("Coefficient 'a' must not be zero." )
lowerCAmelCase__ = b * b - 4 * a * c
lowerCAmelCase__ = (-b + sqrt(UpperCamelCase_ )) / (2 * a)
lowerCAmelCase__ = (-b - sqrt(UpperCamelCase_ )) / (2 * a)
return (
root_a.real if not root_a.imag else root_a,
root_a.real if not root_a.imag else root_a,
)
def _a ( ) -> Optional[Any]:
"""simple docstring"""
lowerCAmelCase__ , lowerCAmelCase__ = quadratic_roots(a=5 , b=6 , c=1 )
print(F"The solutions are: {solutiona} and {solutiona}" )
if __name__ == "__main__":
main()
| 340 | 1 |
import math
import time
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class lowerCamelCase (_snake_case ):
def __init__( self , *_UpperCamelCase , _UpperCamelCase=None , _UpperCamelCase=None , **_UpperCamelCase ) -> Dict:
super().__init__(*_UpperCamelCase , **_UpperCamelCase )
UpperCAmelCase_ : Optional[int] = eval_examples
UpperCAmelCase_ : Optional[int] = post_process_function
def __UpperCAmelCase ( self , _UpperCamelCase=None , _UpperCamelCase=None , _UpperCamelCase=None , _UpperCamelCase = "eval" ) -> List[str]:
UpperCAmelCase_ : Tuple = self.eval_dataset if eval_dataset is None else eval_dataset
UpperCAmelCase_ : Optional[int] = self.get_eval_dataloader(_UpperCamelCase )
UpperCAmelCase_ : Any = self.eval_examples if eval_examples is None else eval_examples
# Temporarily disable metric computation, we will do it in the loop here.
UpperCAmelCase_ : int = self.compute_metrics
UpperCAmelCase_ : Union[str, Any] = None
UpperCAmelCase_ : Dict = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
UpperCAmelCase_ : Optional[int] = time.time()
try:
UpperCAmelCase_ : str = eval_loop(
_UpperCamelCase , description='Evaluation' , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=_UpperCamelCase , metric_key_prefix=_UpperCamelCase , )
finally:
UpperCAmelCase_ : List[str] = compute_metrics
UpperCAmelCase_ : Tuple = self.args.eval_batch_size * self.args.world_size
if f"{metric_key_prefix}_jit_compilation_time" in output.metrics:
start_time += output.metrics[f"{metric_key_prefix}_jit_compilation_time"]
output.metrics.update(
speed_metrics(
_UpperCamelCase , _UpperCamelCase , num_samples=output.num_samples , num_steps=math.ceil(output.num_samples / total_batch_size ) , ) )
if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
# Only the main node write the results by default
UpperCAmelCase_ : List[Any] = self.post_process_function(_UpperCamelCase , _UpperCamelCase , output.predictions )
UpperCAmelCase_ : int = self.compute_metrics(_UpperCamelCase )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(f"{metric_key_prefix}_" ):
UpperCAmelCase_ : List[str] = metrics.pop(_UpperCamelCase )
metrics.update(output.metrics )
else:
UpperCAmelCase_ : List[Any] = output.metrics
if self.args.should_log:
# Only the main node log the results by default
self.log(_UpperCamelCase )
if self.args.tpu_metrics_debug or self.args.debug:
# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
xm.master_print(met.metrics_report() )
UpperCAmelCase_ : Optional[Any] = self.callback_handler.on_evaluate(self.args , self.state , self.control , _UpperCamelCase )
return metrics
def __UpperCAmelCase ( self , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase=None , _UpperCamelCase = "test" ) -> List[str]:
UpperCAmelCase_ : Dict = self.get_test_dataloader(_UpperCamelCase )
# Temporarily disable metric computation, we will do it in the loop here.
UpperCAmelCase_ : Dict = self.compute_metrics
UpperCAmelCase_ : List[Any] = None
UpperCAmelCase_ : Tuple = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
UpperCAmelCase_ : Dict = time.time()
try:
UpperCAmelCase_ : int = eval_loop(
_UpperCamelCase , description='Prediction' , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=_UpperCamelCase , metric_key_prefix=_UpperCamelCase , )
finally:
UpperCAmelCase_ : List[str] = compute_metrics
UpperCAmelCase_ : Union[str, Any] = self.args.eval_batch_size * self.args.world_size
if f"{metric_key_prefix}_jit_compilation_time" in output.metrics:
start_time += output.metrics[f"{metric_key_prefix}_jit_compilation_time"]
output.metrics.update(
speed_metrics(
_UpperCamelCase , _UpperCamelCase , num_samples=output.num_samples , num_steps=math.ceil(output.num_samples / total_batch_size ) , ) )
if self.post_process_function is None or self.compute_metrics is None:
return output
UpperCAmelCase_ : Dict = self.post_process_function(_UpperCamelCase , _UpperCamelCase , output.predictions , 'predict' )
UpperCAmelCase_ : str = self.compute_metrics(_UpperCamelCase )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(f"{metric_key_prefix}_" ):
UpperCAmelCase_ : List[str] = metrics.pop(_UpperCamelCase )
metrics.update(output.metrics )
return PredictionOutput(predictions=predictions.predictions , label_ids=predictions.label_ids , metrics=_UpperCamelCase )
| 354 |
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
__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_copies # noqa: E402
# This is the reference code that will be used in the tests.
# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.
__UpperCAmelCase = ' \"""\n Output class for the scheduler\'s step function output.\n\n Args:\n prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the\n denoising loop.\n pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n The predicted denoised sample (x_{0}) based on the model output from the current timestep.\n `pred_original_sample` can be used to preview progress or for guidance.\n \"""\n\n prev_sample: torch.FloatTensor\n pred_original_sample: Optional[torch.FloatTensor] = None\n'
class lowerCamelCase (unittest.TestCase ):
'''simple docstring'''
def __UpperCAmelCase ( self ) -> Tuple:
UpperCAmelCase_ : str = tempfile.mkdtemp()
os.makedirs(os.path.join(self.diffusers_dir , 'schedulers/' ) )
UpperCAmelCase_ : int = self.diffusers_dir
shutil.copy(
os.path.join(_UpperCamelCase , 'src/diffusers/schedulers/scheduling_ddpm.py' ) , os.path.join(self.diffusers_dir , 'schedulers/scheduling_ddpm.py' ) , )
def __UpperCAmelCase ( self ) -> Optional[int]:
UpperCAmelCase_ : str = 'src/diffusers'
shutil.rmtree(self.diffusers_dir )
def __UpperCAmelCase ( self , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase=None ) -> Optional[Any]:
UpperCAmelCase_ : Dict = comment + f"\nclass {class_name}(nn.Module):\n" + class_code
if overwrite_result is not None:
UpperCAmelCase_ : Optional[Any] = comment + f"\nclass {class_name}(nn.Module):\n" + overwrite_result
UpperCAmelCase_ : Any = black.Mode(target_versions={black.TargetVersion.PYaa} , line_length=1_1_9 )
UpperCAmelCase_ : Optional[Any] = black.format_str(_UpperCamelCase , mode=_UpperCamelCase )
UpperCAmelCase_ : Any = os.path.join(self.diffusers_dir , 'new_code.py' )
with open(_UpperCamelCase , 'w' , newline='\n' ) as f:
f.write(_UpperCamelCase )
if overwrite_result is None:
self.assertTrue(len(check_copies.is_copy_consistent(_UpperCamelCase ) ) == 0 )
else:
check_copies.is_copy_consistent(f.name , overwrite=_UpperCamelCase )
with open(_UpperCamelCase , 'r' ) as f:
self.assertTrue(f.read() , _UpperCamelCase )
def __UpperCAmelCase ( self ) -> List[str]:
UpperCAmelCase_ : List[Any] = check_copies.find_code_in_diffusers('schedulers.scheduling_ddpm.DDPMSchedulerOutput' )
self.assertEqual(_UpperCamelCase , _UpperCamelCase )
def __UpperCAmelCase ( self ) -> str:
# Base copy consistency
self.check_copy_consistency(
'# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput' , 'DDPMSchedulerOutput' , REFERENCE_CODE + '\n' , )
# With no empty line at the end
self.check_copy_consistency(
'# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput' , 'DDPMSchedulerOutput' , _UpperCamelCase , )
# Copy consistency with rename
self.check_copy_consistency(
'# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test' , 'TestSchedulerOutput' , re.sub('DDPM' , 'Test' , _UpperCamelCase ) , )
# Copy consistency with a really long name
UpperCAmelCase_ : Optional[int] = 'TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason'
self.check_copy_consistency(
f"# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}" , f"{long_class_name}SchedulerOutput" , re.sub('Bert' , _UpperCamelCase , _UpperCamelCase ) , )
# Copy consistency with overwrite
self.check_copy_consistency(
'# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test' , 'TestSchedulerOutput' , _UpperCamelCase , overwrite_result=re.sub('DDPM' , 'Test' , _UpperCamelCase ) , )
| 145 | 0 |
"""simple docstring"""
from collections.abc import Callable
class a__ :
def __init__( self , _a = None ):
# Stores actual heap items.
lowercase : list = []
# Stores indexes of each item for supporting updates and deletion.
lowercase : dict = {}
# Stores current size of heap.
lowercase : Optional[int] = 0
# Stores function used to evaluate the score of an item on which basis ordering
# will be done.
lowercase : str = key or (lambda _a : x)
def __magic_name__ ( self , _a ):
return int((i - 1) / 2 ) if i > 0 else None
def __magic_name__ ( self , _a ):
lowercase : str = int(2 * i + 1 )
return left if 0 < left < self.size else None
def __magic_name__ ( self , _a ):
lowercase : Optional[Any] = int(2 * i + 2 )
return right if 0 < right < self.size else None
def __magic_name__ ( self , _a , _a ):
lowercase , lowercase : Optional[int] = (
self.pos_map[self.arr[j][0]],
self.pos_map[self.arr[i][0]],
)
# Then swap the items in the list.
lowercase , lowercase : int = self.arr[j], self.arr[i]
def __magic_name__ ( self , _a , _a ):
return self.arr[i][1] < self.arr[j][1]
def __magic_name__ ( self , _a ):
lowercase : Optional[Any] = self._left(_a )
lowercase : Any = self._right(_a )
lowercase : str = i
if left is not None and not self._cmp(_a , _a ):
lowercase : Optional[Any] = left
if right is not None and not self._cmp(_a , _a ):
lowercase : Optional[Any] = right
return valid_parent
def __magic_name__ ( self , _a ):
lowercase : Optional[Any] = self._parent(_a )
while parent is not None and not self._cmp(_a , _a ):
self._swap(_a , _a )
lowercase , lowercase : Optional[int] = parent, self._parent(_a )
def __magic_name__ ( self , _a ):
lowercase : Dict = self._get_valid_parent(_a )
while valid_parent != index:
self._swap(_a , _a )
lowercase , lowercase : List[str] = valid_parent, self._get_valid_parent(_a )
def __magic_name__ ( self , _a , _a ):
if item not in self.pos_map:
return
lowercase : Dict = self.pos_map[item]
lowercase : Optional[int] = [item, self.key(_a )]
# Make sure heap is right in both up and down direction.
# Ideally only one of them will make any change.
self._heapify_up(_a )
self._heapify_down(_a )
def __magic_name__ ( self , _a ):
if item not in self.pos_map:
return
lowercase : Optional[Any] = self.pos_map[item]
del self.pos_map[item]
lowercase : Union[str, Any] = self.arr[self.size - 1]
lowercase : List[str] = index
self.size -= 1
# Make sure heap is right in both up and down direction. Ideally only one
# of them will make any change- so no performance loss in calling both.
if self.size > index:
self._heapify_up(_a )
self._heapify_down(_a )
def __magic_name__ ( self , _a , _a ):
lowercase : List[str] = len(self.arr )
if arr_len == self.size:
self.arr.append([item, self.key(_a )] )
else:
lowercase : str = [item, self.key(_a )]
lowercase : str = self.size
self.size += 1
self._heapify_up(self.size - 1 )
def __magic_name__ ( self ):
return self.arr[0] if self.size else None
def __magic_name__ ( self ):
lowercase : Optional[Any] = self.get_top()
if top_item_tuple:
self.delete_item(top_item_tuple[0] )
return top_item_tuple
def __magic_name__ ( ) -> None:
pass
if __name__ == "__main__":
import doctest
doctest.testmod()
| 202 |
"""simple docstring"""
from __future__ import annotations
from functools import lru_cache
from math import ceil
_A : Optional[Any] = 1_00
_A : Optional[int] = set(range(3, NUM_PRIMES, 2))
primes.add(2)
_A : int
for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
if prime not in primes:
continue
primes.difference_update(set(range(prime * prime, NUM_PRIMES, prime)))
@lru_cache(maxsize=100 )
def __magic_name__ ( __snake_case : int ) -> set[int]:
if number_to_partition < 0:
return set()
elif number_to_partition == 0:
return {1}
lowercase : set[int] = set()
lowercase : int
lowercase : int
for prime in primes:
if prime > number_to_partition:
continue
for sub in partition(number_to_partition - prime ):
ret.add(sub * prime )
return ret
def __magic_name__ ( __snake_case : int = 5000 ) -> int | None:
for number_to_partition in range(1 , __snake_case ):
if len(partition(__snake_case ) ) > number_unique_partitions:
return number_to_partition
return None
if __name__ == "__main__":
print(F"{solution() = }")
| 202 | 1 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ : Union[str, Any] = logging.get_logger(__name__)
UpperCamelCase__ : List[str] = {
"""microsoft/unispeech-sat-base-100h-libri-ft""": (
"""https://huggingface.co/microsoft/unispeech-sat-base-100h-libri-ft/resolve/main/config.json"""
),
# See all UniSpeechSat models at https://huggingface.co/models?filter=unispeech_sat
}
class lowerCamelCase_ ( a_ ):
SCREAMING_SNAKE_CASE_ = 'unispeech-sat'
def __init__( self : Tuple ,__lowerCamelCase : str=32 ,__lowerCamelCase : Any=7_68 ,__lowerCamelCase : Union[str, Any]=12 ,__lowerCamelCase : Any=12 ,__lowerCamelCase : List[str]=30_72 ,__lowerCamelCase : List[str]="gelu" ,__lowerCamelCase : Optional[int]=0.1 ,__lowerCamelCase : Optional[int]=0.1 ,__lowerCamelCase : List[Any]=0.1 ,__lowerCamelCase : Union[str, Any]=0.0 ,__lowerCamelCase : List[str]=0.0 ,__lowerCamelCase : Dict=0.1 ,__lowerCamelCase : Tuple=0.1 ,__lowerCamelCase : Optional[int]=0.02 ,__lowerCamelCase : List[str]=1e-5 ,__lowerCamelCase : Optional[int]="group" ,__lowerCamelCase : Dict="gelu" ,__lowerCamelCase : int=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12) ,__lowerCamelCase : str=(5, 2, 2, 2, 2, 2, 2) ,__lowerCamelCase : Optional[int]=(10, 3, 3, 3, 3, 2, 2) ,__lowerCamelCase : Optional[Any]=False ,__lowerCamelCase : List[Any]=1_28 ,__lowerCamelCase : Any=16 ,__lowerCamelCase : List[Any]=False ,__lowerCamelCase : str=True ,__lowerCamelCase : str=0.05 ,__lowerCamelCase : str=10 ,__lowerCamelCase : Optional[int]=2 ,__lowerCamelCase : Any=0.0 ,__lowerCamelCase : Optional[Any]=10 ,__lowerCamelCase : str=0 ,__lowerCamelCase : Optional[int]=3_20 ,__lowerCamelCase : List[Any]=2 ,__lowerCamelCase : List[Any]=0.1 ,__lowerCamelCase : Union[str, Any]=1_00 ,__lowerCamelCase : Optional[Any]=2_56 ,__lowerCamelCase : Tuple=2_56 ,__lowerCamelCase : List[Any]=0.1 ,__lowerCamelCase : int="mean" ,__lowerCamelCase : Optional[int]=False ,__lowerCamelCase : int=False ,__lowerCamelCase : List[Any]=2_56 ,__lowerCamelCase : List[str]=(5_12, 5_12, 5_12, 5_12, 15_00) ,__lowerCamelCase : Optional[int]=(5, 3, 3, 1, 1) ,__lowerCamelCase : List[Any]=(1, 2, 3, 1, 1) ,__lowerCamelCase : str=5_12 ,__lowerCamelCase : Union[str, Any]=0 ,__lowerCamelCase : int=1 ,__lowerCamelCase : str=2 ,__lowerCamelCase : List[str]=5_04 ,**__lowerCamelCase : List[str] ,):
'''simple docstring'''
super().__init__(**__lowerCamelCase ,pad_token_id=__lowerCamelCase ,bos_token_id=__lowerCamelCase ,eos_token_id=__lowerCamelCase )
a = hidden_size
a = feat_extract_norm
a = feat_extract_activation
a = list(__lowerCamelCase )
a = list(__lowerCamelCase )
a = list(__lowerCamelCase )
a = conv_bias
a = num_conv_pos_embeddings
a = num_conv_pos_embedding_groups
a = len(self.conv_dim )
a = num_hidden_layers
a = intermediate_size
a = hidden_act
a = num_attention_heads
a = hidden_dropout
a = attention_dropout
a = activation_dropout
a = feat_proj_dropout
a = final_dropout
a = layerdrop
a = layer_norm_eps
a = initializer_range
a = vocab_size
a = num_clusters
a = do_stable_layer_norm
a = use_weighted_layer_sum
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
'''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
F""" {len(self.conv_dim )}`, `len(config.conv_stride) = {len(self.conv_stride )}`,"""
F""" `len(config.conv_kernel) = {len(self.conv_kernel )}`.""" )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
a = apply_spec_augment
a = mask_time_prob
a = mask_time_length
a = mask_time_min_masks
a = mask_feature_prob
a = mask_feature_length
a = mask_feature_min_masks
# parameters for pretraining with codevector quantized representations
a = num_codevectors_per_group
a = num_codevector_groups
a = contrastive_logits_temperature
a = feat_quantizer_dropout
a = num_negatives
a = codevector_dim
a = proj_codevector_dim
a = diversity_loss_weight
# ctc loss
a = ctc_loss_reduction
a = ctc_zero_infinity
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
a = classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
a = list(__lowerCamelCase )
a = list(__lowerCamelCase )
a = list(__lowerCamelCase )
a = xvector_output_dim
@property
def SCREAMING_SNAKE_CASE_ ( self : int ):
'''simple docstring'''
return functools.reduce(operator.mul ,self.conv_stride ,1 )
| 330 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ : str = logging.get_logger(__name__)
UpperCamelCase__ : Optional[int] = {
"""studio-ousia/luke-base""": """https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json""",
"""studio-ousia/luke-large""": """https://huggingface.co/studio-ousia/luke-large/resolve/main/config.json""",
}
class lowerCamelCase_ ( a_ ):
SCREAMING_SNAKE_CASE_ = 'luke'
def __init__( self : Dict ,__lowerCamelCase : Optional[Any]=5_02_67 ,__lowerCamelCase : str=50_00_00 ,__lowerCamelCase : Any=7_68 ,__lowerCamelCase : int=2_56 ,__lowerCamelCase : Optional[int]=12 ,__lowerCamelCase : Tuple=12 ,__lowerCamelCase : Any=30_72 ,__lowerCamelCase : Any="gelu" ,__lowerCamelCase : Any=0.1 ,__lowerCamelCase : Tuple=0.1 ,__lowerCamelCase : Tuple=5_12 ,__lowerCamelCase : int=2 ,__lowerCamelCase : Optional[int]=0.02 ,__lowerCamelCase : List[Any]=1e-12 ,__lowerCamelCase : Dict=True ,__lowerCamelCase : Tuple=None ,__lowerCamelCase : Any=1 ,__lowerCamelCase : Dict=0 ,__lowerCamelCase : Any=2 ,**__lowerCamelCase : str ,):
'''simple docstring'''
super().__init__(pad_token_id=__lowerCamelCase ,bos_token_id=__lowerCamelCase ,eos_token_id=__lowerCamelCase ,**__lowerCamelCase )
a = vocab_size
a = entity_vocab_size
a = hidden_size
a = entity_emb_size
a = num_hidden_layers
a = num_attention_heads
a = hidden_act
a = intermediate_size
a = hidden_dropout_prob
a = attention_probs_dropout_prob
a = max_position_embeddings
a = type_vocab_size
a = initializer_range
a = layer_norm_eps
a = use_entity_aware_attention
a = classifier_dropout
| 330 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
import numpy as np
from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers.models.layoutlm.modeling_tf_layoutlm import (
TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFLayoutLMForMaskedLM,
TFLayoutLMForQuestionAnswering,
TFLayoutLMForSequenceClassification,
TFLayoutLMForTokenClassification,
TFLayoutLMModel,
)
class UpperCAmelCase_ :
"""simple docstring"""
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=13 , SCREAMING_SNAKE_CASE_=7 , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=99 , SCREAMING_SNAKE_CASE_=32 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_=37 , SCREAMING_SNAKE_CASE_="gelu" , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=5_12 , SCREAMING_SNAKE_CASE_=16 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=0.0_2 , SCREAMING_SNAKE_CASE_=3 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=10_00 , ) -> Tuple:
__lowerCamelCase : Tuple = parent
__lowerCamelCase : Union[str, Any] = batch_size
__lowerCamelCase : Dict = seq_length
__lowerCamelCase : int = is_training
__lowerCamelCase : List[Any] = use_input_mask
__lowerCamelCase : List[Any] = use_token_type_ids
__lowerCamelCase : str = use_labels
__lowerCamelCase : Dict = vocab_size
__lowerCamelCase : Tuple = hidden_size
__lowerCamelCase : List[Any] = num_hidden_layers
__lowerCamelCase : List[str] = num_attention_heads
__lowerCamelCase : int = intermediate_size
__lowerCamelCase : str = hidden_act
__lowerCamelCase : str = hidden_dropout_prob
__lowerCamelCase : Dict = attention_probs_dropout_prob
__lowerCamelCase : Tuple = max_position_embeddings
__lowerCamelCase : int = type_vocab_size
__lowerCamelCase : Any = type_sequence_label_size
__lowerCamelCase : int = initializer_range
__lowerCamelCase : Tuple = num_labels
__lowerCamelCase : Optional[Any] = num_choices
__lowerCamelCase : Optional[int] = scope
__lowerCamelCase : List[Any] = range_bbox
def lowercase_ ( self ) -> Dict:
__lowerCamelCase : Dict = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
# convert bbox to numpy since TF does not support item assignment
__lowerCamelCase : Optional[int] = ids_tensor([self.batch_size, self.seq_length, 4] , self.range_bbox ).numpy()
# Ensure that bbox is legal
for i in range(bbox.shape[0] ):
for j in range(bbox.shape[1] ):
if bbox[i, j, 3] < bbox[i, j, 1]:
__lowerCamelCase : Optional[Any] = bbox[i, j, 3]
__lowerCamelCase : Dict = bbox[i, j, 1]
__lowerCamelCase : List[str] = t
if bbox[i, j, 2] < bbox[i, j, 0]:
__lowerCamelCase : Dict = bbox[i, j, 2]
__lowerCamelCase : Union[str, Any] = bbox[i, j, 0]
__lowerCamelCase : Any = t
__lowerCamelCase : Dict = tf.convert_to_tensor(SCREAMING_SNAKE_CASE_ )
__lowerCamelCase : Tuple = None
if self.use_input_mask:
__lowerCamelCase : Tuple = random_attention_mask([self.batch_size, self.seq_length] )
__lowerCamelCase : Tuple = None
if self.use_token_type_ids:
__lowerCamelCase : Dict = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__lowerCamelCase : Optional[Any] = None
__lowerCamelCase : Dict = None
__lowerCamelCase : Tuple = None
if self.use_labels:
__lowerCamelCase : str = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__lowerCamelCase : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__lowerCamelCase : Optional[Any] = ids_tensor([self.batch_size] , self.num_choices )
__lowerCamelCase : Union[str, Any] = LayoutLMConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , )
return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def lowercase_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> List[Any]:
__lowerCamelCase : Any = TFLayoutLMModel(config=SCREAMING_SNAKE_CASE_ )
__lowerCamelCase : List[Any] = model(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , token_type_ids=SCREAMING_SNAKE_CASE_ )
__lowerCamelCase : Dict = model(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , token_type_ids=SCREAMING_SNAKE_CASE_ )
__lowerCamelCase : List[str] = model(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
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 lowercase_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> List[str]:
__lowerCamelCase : Union[str, Any] = TFLayoutLMForMaskedLM(config=SCREAMING_SNAKE_CASE_ )
__lowerCamelCase : List[str] = model(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , token_type_ids=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def lowercase_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Dict:
__lowerCamelCase : str = self.num_labels
__lowerCamelCase : Dict = TFLayoutLMForSequenceClassification(config=SCREAMING_SNAKE_CASE_ )
__lowerCamelCase : List[str] = model(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , token_type_ids=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowercase_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Any:
__lowerCamelCase : Tuple = self.num_labels
__lowerCamelCase : Tuple = TFLayoutLMForTokenClassification(config=SCREAMING_SNAKE_CASE_ )
__lowerCamelCase : List[str] = model(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , token_type_ids=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def lowercase_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Any:
__lowerCamelCase : int = TFLayoutLMForQuestionAnswering(config=SCREAMING_SNAKE_CASE_ )
__lowerCamelCase : List[Any] = model(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , token_type_ids=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def lowercase_ ( self ) -> Optional[int]:
__lowerCamelCase : str = self.prepare_config_and_inputs()
(
(
__lowerCamelCase
) , (
__lowerCamelCase
) , (
__lowerCamelCase
) , (
__lowerCamelCase
) , (
__lowerCamelCase
) , (
__lowerCamelCase
) , (
__lowerCamelCase
) , (
__lowerCamelCase
) ,
) : Tuple = config_and_inputs
__lowerCamelCase : str = {
'input_ids': input_ids,
'bbox': bbox,
'token_type_ids': token_type_ids,
'attention_mask': input_mask,
}
return config, inputs_dict
@require_tf
class UpperCAmelCase_ (_UpperCAmelCase , _UpperCAmelCase , unittest.TestCase ):
"""simple docstring"""
lowerCamelCase : Optional[int] = (
(
TFLayoutLMModel,
TFLayoutLMForMaskedLM,
TFLayoutLMForTokenClassification,
TFLayoutLMForSequenceClassification,
TFLayoutLMForQuestionAnswering,
)
if is_tf_available()
else ()
)
lowerCamelCase : Any = (
{
'feature-extraction': TFLayoutLMModel,
'fill-mask': TFLayoutLMForMaskedLM,
'text-classification': TFLayoutLMForSequenceClassification,
'token-classification': TFLayoutLMForTokenClassification,
'zero-shot': TFLayoutLMForSequenceClassification,
}
if is_tf_available()
else {}
)
lowerCamelCase : int = False
lowerCamelCase : Optional[int] = True
lowerCamelCase : int = 1_0
def lowercase_ ( self ) -> Any:
__lowerCamelCase : Optional[int] = TFLayoutLMModelTester(self )
__lowerCamelCase : Union[str, Any] = ConfigTester(self , config_class=SCREAMING_SNAKE_CASE_ , hidden_size=37 )
def lowercase_ ( self ) -> Tuple:
self.config_tester.run_common_tests()
def lowercase_ ( self ) -> List[Any]:
__lowerCamelCase : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_ )
def lowercase_ ( self ) -> str:
__lowerCamelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*SCREAMING_SNAKE_CASE_ )
def lowercase_ ( self ) -> Optional[Any]:
__lowerCamelCase : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*SCREAMING_SNAKE_CASE_ )
def lowercase_ ( self ) -> Optional[Any]:
__lowerCamelCase : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*SCREAMING_SNAKE_CASE_ )
def lowercase_ ( self ) -> Optional[int]:
__lowerCamelCase : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*SCREAMING_SNAKE_CASE_ )
@slow
def lowercase_ ( self ) -> Union[str, Any]:
for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowerCamelCase : List[str] = TFLayoutLMModel.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
@unittest.skip('Onnx compliancy broke with TF 2.10' )
def lowercase_ ( self ) -> Dict:
pass
def UpperCAmelCase__ ( ) -> Any:
# Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
# fmt: off
__lowerCamelCase : Optional[int] = tf.convert_to_tensor([[1_01,10_19,10_14,10_16,10_37,1_28_49,47_47,10_04,1_42_46,22_78,54_39,45_24,50_02,29_30,21_93,29_30,43_41,32_08,10_05,10_55,21_71,28_48,1_13_00,35_31,1_02],[1_01,40_70,40_34,70_20,10_24,30_58,10_15,10_13,28_61,10_13,60_70,1_92_74,27_72,62_05,2_78_14,1_61_47,1_61_47,43_43,20_47,1_02_83,1_09_69,1_43_89,10_12,23_38,1_02]] ) # noqa: E231
__lowerCamelCase : Union[str, Any] = tf.convert_to_tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],] ) # noqa: E231
__lowerCamelCase : Any = tf.convert_to_tensor([[[0,0,0,0],[4_23,2_37,4_40,2_51],[4_27,2_72,4_41,2_87],[4_19,1_15,4_37,1_29],[9_61,8_85,9_92,9_12],[2_56,38,3_30,58],[2_56,38,3_30,58],[3_36,42,3_53,57],[3_60,39,4_01,56],[3_60,39,4_01,56],[4_11,39,4_71,59],[4_79,41,5_28,59],[5_33,39,6_30,60],[67,1_13,1_34,1_31],[1_41,1_15,2_09,1_32],[68,1_49,1_33,1_66],[1_41,1_49,1_87,1_64],[1_95,1_48,2_87,1_65],[1_95,1_48,2_87,1_65],[1_95,1_48,2_87,1_65],[2_95,1_48,3_49,1_65],[4_41,1_49,4_92,1_66],[4_97,1_49,5_46,1_64],[64,2_01,1_25,2_18],[10_00,10_00,10_00,10_00]],[[0,0,0,0],[6_62,1_50,7_54,1_66],[6_65,1_99,7_42,2_11],[5_19,2_13,5_54,2_28],[5_19,2_13,5_54,2_28],[1_34,4_33,1_87,4_54],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[3_14,4_69,3_76,4_82],[5_04,6_84,5_82,7_06],[9_41,8_25,9_73,9_00],[9_41,8_25,9_73,9_00],[9_41,8_25,9_73,9_00],[9_41,8_25,9_73,9_00],[6_10,7_49,6_52,7_65],[1_30,6_59,1_68,6_72],[1_76,6_57,2_37,6_72],[2_38,6_57,3_12,6_72],[4_43,6_53,6_28,6_72],[4_43,6_53,6_28,6_72],[7_16,3_01,8_25,3_17],[10_00,10_00,10_00,10_00]]] ) # noqa: E231
__lowerCamelCase : Union[str, Any] = tf.convert_to_tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]] ) # noqa: E231
# these are sequence labels (i.e. at the token level)
__lowerCamelCase : Any = tf.convert_to_tensor([[-1_00,10,10,10,9,1,-1_00,7,7,-1_00,7,7,4,2,5,2,8,8,-1_00,-1_00,5,0,3,2,-1_00],[-1_00,12,12,12,-1_00,12,10,-1_00,-1_00,-1_00,-1_00,10,12,9,-1_00,-1_00,-1_00,10,10,10,9,12,-1_00,10,-1_00]] ) # noqa: E231
# fmt: on
return input_ids, attention_mask, bbox, token_type_ids, labels
@require_tf
class UpperCAmelCase_ (unittest.TestCase ):
"""simple docstring"""
@slow
def lowercase_ ( self ) -> List[str]:
__lowerCamelCase : str = TFLayoutLMModel.from_pretrained('microsoft/layoutlm-base-uncased' )
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase : str = prepare_layoutlm_batch_inputs()
# forward pass
__lowerCamelCase : str = model(input_ids=SCREAMING_SNAKE_CASE_ , bbox=SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , token_type_ids=SCREAMING_SNAKE_CASE_ )
# test the sequence output on [0, :3, :3]
__lowerCamelCase : Tuple = tf.convert_to_tensor(
[[0.1_7_8_5, -0.1_9_4_7, -0.0_4_2_5], [-0.3_2_5_4, -0.2_8_0_7, 0.2_5_5_3], [-0.5_3_9_1, -0.3_3_2_2, 0.3_3_6_4]] , )
self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3] , SCREAMING_SNAKE_CASE_ , atol=1E-3 ) )
# test the pooled output on [1, :3]
__lowerCamelCase : List[Any] = tf.convert_to_tensor([-0.6_5_8_0, -0.0_2_1_4, 0.8_5_5_2] )
self.assertTrue(np.allclose(outputs.pooler_output[1, :3] , SCREAMING_SNAKE_CASE_ , atol=1E-3 ) )
@slow
def lowercase_ ( self ) -> Optional[int]:
# initialize model with randomly initialized sequence classification head
__lowerCamelCase : str = TFLayoutLMForSequenceClassification.from_pretrained('microsoft/layoutlm-base-uncased' , num_labels=2 )
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase : int = prepare_layoutlm_batch_inputs()
# forward pass
__lowerCamelCase : Union[str, Any] = model(
input_ids=SCREAMING_SNAKE_CASE_ , bbox=SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , token_type_ids=SCREAMING_SNAKE_CASE_ , labels=tf.convert_to_tensor([1, 1] ) , )
# test whether we get a loss as a scalar
__lowerCamelCase : int = outputs.loss
__lowerCamelCase : List[str] = (2,)
self.assertEqual(loss.shape , SCREAMING_SNAKE_CASE_ )
# test the shape of the logits
__lowerCamelCase : Any = outputs.logits
__lowerCamelCase : Any = (2, 2)
self.assertEqual(logits.shape , SCREAMING_SNAKE_CASE_ )
@slow
def lowercase_ ( self ) -> Tuple:
# initialize model with randomly initialized token classification head
__lowerCamelCase : Optional[Any] = TFLayoutLMForTokenClassification.from_pretrained('microsoft/layoutlm-base-uncased' , num_labels=13 )
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase : Dict = prepare_layoutlm_batch_inputs()
# forward pass
__lowerCamelCase : Optional[Any] = model(
input_ids=SCREAMING_SNAKE_CASE_ , bbox=SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , token_type_ids=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
# test the shape of the logits
__lowerCamelCase : Optional[int] = outputs.logits
__lowerCamelCase : Union[str, Any] = tf.convert_to_tensor((2, 25, 13) )
self.assertEqual(logits.shape , SCREAMING_SNAKE_CASE_ )
@slow
def lowercase_ ( self ) -> List[Any]:
# initialize model with randomly initialized token classification head
__lowerCamelCase : Optional[int] = TFLayoutLMForQuestionAnswering.from_pretrained('microsoft/layoutlm-base-uncased' )
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase : Union[str, Any] = prepare_layoutlm_batch_inputs()
# forward pass
__lowerCamelCase : int = model(input_ids=SCREAMING_SNAKE_CASE_ , bbox=SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , token_type_ids=SCREAMING_SNAKE_CASE_ )
# test the shape of the logits
__lowerCamelCase : Optional[int] = tf.convert_to_tensor((2, 25) )
self.assertEqual(outputs.start_logits.shape , SCREAMING_SNAKE_CASE_ )
self.assertEqual(outputs.end_logits.shape , SCREAMING_SNAKE_CASE_ )
| 185 |
'''simple docstring'''
import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
A__ : str = [
# tf -> hf
("""/""", """."""),
("""layer_""", """layers."""),
("""kernel""", """weight"""),
("""beta""", """bias"""),
("""gamma""", """weight"""),
("""pegasus""", """model"""),
]
A__ : Dict = [
(""".output.dense""", """.fc2"""),
("""intermediate.LayerNorm""", """final_layer_norm"""),
("""intermediate.dense""", """fc1"""),
]
A__ : int = (
INIT_COMMON
+ [
("""attention.self.LayerNorm""", """self_attn_layer_norm"""),
("""attention.output.dense""", """self_attn.out_proj"""),
("""attention.self""", """self_attn"""),
("""attention.encdec.LayerNorm""", """encoder_attn_layer_norm"""),
("""attention.encdec_output.dense""", """encoder_attn.out_proj"""),
("""attention.encdec""", """encoder_attn"""),
("""key""", """k_proj"""),
("""value""", """v_proj"""),
("""query""", """q_proj"""),
("""decoder.LayerNorm""", """decoder.layernorm_embedding"""),
]
+ END_COMMON
)
A__ : Tuple = (
INIT_COMMON
+ [
("""embeddings.word_embeddings""", """shared.weight"""),
("""embeddings.position_embeddings""", """embed_positions.weight"""),
("""attention.self.LayerNorm""", """self_attn_layer_norm"""),
("""attention.output.dense""", """self_attn.output"""),
("""attention.self""", """self_attn.self"""),
("""encoder.LayerNorm""", """encoder.layernorm_embedding"""),
]
+ END_COMMON
)
A__ : Tuple = [
"""encdec/key/bias""",
"""encdec/query/bias""",
"""encdec/value/bias""",
"""self/key/bias""",
"""self/query/bias""",
"""self/value/bias""",
"""encdec_output/dense/bias""",
"""attention/output/dense/bias""",
]
def UpperCAmelCase__ ( UpperCAmelCase_ : Union[str, Any] , UpperCAmelCase_ : Any ) -> List[str]:
for tf_name, hf_name in patterns:
__lowerCamelCase : Optional[int] = k.replace(UpperCAmelCase_ , UpperCAmelCase_ )
return k
def UpperCAmelCase__ ( UpperCAmelCase_ : dict , UpperCAmelCase_ : dict ) -> BigBirdPegasusForConditionalGeneration:
__lowerCamelCase : int = BigBirdPegasusConfig(**UpperCAmelCase_ )
__lowerCamelCase : Any = BigBirdPegasusForConditionalGeneration(UpperCAmelCase_ )
__lowerCamelCase : Union[str, Any] = torch_model.state_dict()
__lowerCamelCase : Tuple = {}
# separating decoder weights
__lowerCamelCase : Dict = {k: tf_weights[k] for k in tf_weights if k.startswith('pegasus/decoder' )}
__lowerCamelCase : str = {k: tf_weights[k] for k in tf_weights if not k.startswith('pegasus/decoder' )}
for k, v in tqdm(decoder_weights.items() , 'tf -> hf conversion' ):
__lowerCamelCase : Tuple = [k.endswith(UpperCAmelCase_ ) for ending in KEYS_TO_IGNORE]
if any(UpperCAmelCase_ ):
continue
__lowerCamelCase : Tuple = DECODER_PATTERNS
__lowerCamelCase : Optional[int] = rename_state_dict_key(UpperCAmelCase_ , UpperCAmelCase_ )
if new_k not in state_dict:
raise ValueError(F'could not find new key {new_k} in state dict. (converted from {k})' )
if any(True if i in k else False for i in ['dense', 'query', 'key', 'value'] ):
__lowerCamelCase : Union[str, Any] = v.T
__lowerCamelCase : str = torch.from_numpy(UpperCAmelCase_ )
assert v.shape == state_dict[new_k].shape, F'{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}'
for k, v in tqdm(remaining_weights.items() , 'tf -> hf conversion' ):
__lowerCamelCase : Optional[Any] = [k.endswith(UpperCAmelCase_ ) for ending in KEYS_TO_IGNORE]
if any(UpperCAmelCase_ ):
continue
__lowerCamelCase : Dict = REMAINING_PATTERNS
__lowerCamelCase : List[str] = rename_state_dict_key(UpperCAmelCase_ , UpperCAmelCase_ )
if new_k not in state_dict and k != "pegasus/embeddings/position_embeddings":
raise ValueError(F'could not find new key {new_k} in state dict. (converted from {k})' )
if any(True if i in k else False for i in ['dense', 'query', 'key', 'value'] ):
__lowerCamelCase : List[str] = v.T
__lowerCamelCase : List[str] = torch.from_numpy(UpperCAmelCase_ )
if k != "pegasus/embeddings/position_embeddings":
assert v.shape == state_dict[new_k].shape, F'{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}'
__lowerCamelCase : Any = mapping['model.embed_positions.weight']
__lowerCamelCase : Union[str, Any] = mapping.pop('model.embed_positions.weight' )
__lowerCamelCase , __lowerCamelCase : List[str] = torch_model.load_state_dict(UpperCAmelCase_ , strict=UpperCAmelCase_ )
__lowerCamelCase : Any = [
k
for k in missing
if k
not in [
'final_logits_bias',
'model.encoder.embed_tokens.weight',
'model.decoder.embed_tokens.weight',
'lm_head.weight',
]
]
assert unexpected_missing == [], F'no matches found for the following torch keys {unexpected_missing}'
assert extra == [], F'no matches found for the following tf keys {extra}'
return torch_model
def UpperCAmelCase__ ( UpperCAmelCase_ : Union[str, Any] ) -> Dict:
__lowerCamelCase : int = tf.train.list_variables(UpperCAmelCase_ )
__lowerCamelCase : List[str] = {}
__lowerCamelCase : List[Any] = ['global_step']
for name, shape in tqdm(UpperCAmelCase_ , desc='converting tf checkpoint to dict' ):
__lowerCamelCase : Any = any(pat in name for pat in ignore_name )
if skip_key:
continue
__lowerCamelCase : Dict = tf.train.load_variable(UpperCAmelCase_ , UpperCAmelCase_ )
__lowerCamelCase : List[Any] = array
return tf_weights
def UpperCAmelCase__ ( UpperCAmelCase_ : str , UpperCAmelCase_ : str , UpperCAmelCase_ : dict ) -> Union[str, Any]:
__lowerCamelCase : Optional[int] = get_tf_weights_as_numpy(UpperCAmelCase_ )
__lowerCamelCase : List[str] = convert_bigbird_pegasus(UpperCAmelCase_ , UpperCAmelCase_ )
torch_model.save_pretrained(UpperCAmelCase_ )
if __name__ == "__main__":
A__ : Any = argparse.ArgumentParser()
parser.add_argument("""--tf_ckpt_path""", type=str, help="""passed to tf.train.list_variables""")
parser.add_argument("""--save_dir""", default=None, type=str, help="""Path to the output PyTorch model.""")
A__ : List[str] = parser.parse_args()
A__ : Optional[int] = {}
convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)
| 185 | 1 |
def A__ ( lowerCamelCase ) -> list:
if n_term == "":
return []
UpperCamelCase_: list = []
for temp in range(int(lowerCamelCase ) ):
series.append(F'''1/{temp + 1}''' if series else """1""" )
return series
if __name__ == "__main__":
lowerCamelCase_ : Union[str, Any] = input("""Enter the last number (nth term) of the Harmonic Series""")
print("""Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n""")
print(harmonic_series(nth_term))
| 363 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
lowerCamelCase_ : Dict = {"""tokenization_byt5""": ["""ByT5Tokenizer"""]}
if TYPE_CHECKING:
from .tokenization_byta import ByTaTokenizer
else:
import sys
lowerCamelCase_ : Any = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 223 | 0 |
from importlib import import_module
from .logging import get_logger
UpperCAmelCase__ = get_logger(__name__)
class lowercase_ :
'''simple docstring'''
def __init__( self : int , __UpperCAmelCase : Optional[int] , __UpperCAmelCase : str=None ) ->int:
"""simple docstring"""
a = attrs or []
if module is not None:
for key in module.__dict__:
if key in attrs or not key.startswith('''__''' ):
setattr(self , __UpperCAmelCase , getattr(__UpperCAmelCase , __UpperCAmelCase ) )
a = module._original_module if isinstance(__UpperCAmelCase , _PatchedModuleObj ) else module
class lowercase_ :
'''simple docstring'''
__snake_case = []
def __init__( self : List[str] , __UpperCAmelCase : List[Any] , __UpperCAmelCase : str , __UpperCAmelCase : Optional[Any] , __UpperCAmelCase : Dict=None ) ->Union[str, Any]:
"""simple docstring"""
a = obj
a = target
a = new
a = target.split('''.''' )[0]
a = {}
a = attrs or []
def __enter__( self : Tuple ) ->Union[str, Any]:
"""simple docstring"""
*a , a = self.target.split('''.''' )
# Patch modules:
# it's used to patch attributes of submodules like "os.path.join";
# in this case we need to patch "os" and "os.path"
for i in range(len(__UpperCAmelCase ) ):
try:
a = import_module('''.'''.join(submodules[: i + 1] ) )
except ModuleNotFoundError:
continue
# We iterate over all the globals in self.obj in case we find "os" or "os.path"
for attr in self.obj.__dir__():
a = getattr(self.obj , __UpperCAmelCase )
# We don't check for the name of the global, but rather if its value *is* "os" or "os.path".
# This allows to patch renamed modules like "from os import path as ospath".
if obj_attr is submodule or (
(isinstance(__UpperCAmelCase , _PatchedModuleObj ) and obj_attr._original_module is submodule)
):
a = obj_attr
# patch at top level
setattr(self.obj , __UpperCAmelCase , _PatchedModuleObj(__UpperCAmelCase , attrs=self.attrs ) )
a = getattr(self.obj , __UpperCAmelCase )
# construct lower levels patches
for key in submodules[i + 1 :]:
setattr(__UpperCAmelCase , __UpperCAmelCase , _PatchedModuleObj(getattr(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) , attrs=self.attrs ) )
a = getattr(__UpperCAmelCase , __UpperCAmelCase )
# finally set the target attribute
setattr(__UpperCAmelCase , __UpperCAmelCase , self.new )
# Patch attribute itself:
# it's used for builtins like "open",
# and also to patch "os.path.join" we may also need to patch "join"
# itself if it was imported as "from os.path import join".
if submodules: # if it's an attribute of a submodule like "os.path.join"
try:
a = getattr(import_module('''.'''.join(__UpperCAmelCase ) ) , __UpperCAmelCase )
except (AttributeError, ModuleNotFoundError):
return
# We iterate over all the globals in self.obj in case we find "os.path.join"
for attr in self.obj.__dir__():
# We don't check for the name of the global, but rather if its value *is* "os.path.join".
# This allows to patch renamed attributes like "from os.path import join as pjoin".
if getattr(self.obj , __UpperCAmelCase ) is attr_value:
a = getattr(self.obj , __UpperCAmelCase )
setattr(self.obj , __UpperCAmelCase , self.new )
elif target_attr in globals()["__builtins__"]: # if it'a s builtin like "open"
a = globals()['''__builtins__'''][target_attr]
setattr(self.obj , __UpperCAmelCase , self.new )
else:
raise RuntimeError(F"""Tried to patch attribute {target_attr} instead of a submodule.""" )
def __exit__( self : List[str] , *__UpperCAmelCase : Optional[int] ) ->Tuple:
"""simple docstring"""
for attr in list(self.original ):
setattr(self.obj , __UpperCAmelCase , self.original.pop(__UpperCAmelCase ) )
def __lowerCAmelCase ( self : Any ) ->List[Any]:
"""simple docstring"""
self.__enter__()
self._active_patches.append(self )
def __lowerCAmelCase ( self : Any ) ->Union[str, Any]:
"""simple docstring"""
try:
self._active_patches.remove(self )
except ValueError:
# If the patch hasn't been started this will fail
return None
return self.__exit__()
| 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_electra import ElectraTokenizer
UpperCAmelCase__ = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
UpperCAmelCase__ = {
"vocab_file": {
"google/electra-small-generator": (
"https://huggingface.co/google/electra-small-generator/resolve/main/vocab.txt"
),
"google/electra-base-generator": "https://huggingface.co/google/electra-base-generator/resolve/main/vocab.txt",
"google/electra-large-generator": (
"https://huggingface.co/google/electra-large-generator/resolve/main/vocab.txt"
),
"google/electra-small-discriminator": (
"https://huggingface.co/google/electra-small-discriminator/resolve/main/vocab.txt"
),
"google/electra-base-discriminator": (
"https://huggingface.co/google/electra-base-discriminator/resolve/main/vocab.txt"
),
"google/electra-large-discriminator": (
"https://huggingface.co/google/electra-large-discriminator/resolve/main/vocab.txt"
),
},
"tokenizer_file": {
"google/electra-small-generator": (
"https://huggingface.co/google/electra-small-generator/resolve/main/tokenizer.json"
),
"google/electra-base-generator": (
"https://huggingface.co/google/electra-base-generator/resolve/main/tokenizer.json"
),
"google/electra-large-generator": (
"https://huggingface.co/google/electra-large-generator/resolve/main/tokenizer.json"
),
"google/electra-small-discriminator": (
"https://huggingface.co/google/electra-small-discriminator/resolve/main/tokenizer.json"
),
"google/electra-base-discriminator": (
"https://huggingface.co/google/electra-base-discriminator/resolve/main/tokenizer.json"
),
"google/electra-large-discriminator": (
"https://huggingface.co/google/electra-large-discriminator/resolve/main/tokenizer.json"
),
},
}
UpperCAmelCase__ = {
"google/electra-small-generator": 512,
"google/electra-base-generator": 512,
"google/electra-large-generator": 512,
"google/electra-small-discriminator": 512,
"google/electra-base-discriminator": 512,
"google/electra-large-discriminator": 512,
}
UpperCAmelCase__ = {
"google/electra-small-generator": {"do_lower_case": True},
"google/electra-base-generator": {"do_lower_case": True},
"google/electra-large-generator": {"do_lower_case": True},
"google/electra-small-discriminator": {"do_lower_case": True},
"google/electra-base-discriminator": {"do_lower_case": True},
"google/electra-large-discriminator": {"do_lower_case": True},
}
class lowercase_ ( lowercase ):
'''simple docstring'''
__snake_case = VOCAB_FILES_NAMES
__snake_case = PRETRAINED_VOCAB_FILES_MAP
__snake_case = PRETRAINED_INIT_CONFIGURATION
__snake_case = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__snake_case = ElectraTokenizer
def __init__( self : Dict , __UpperCAmelCase : int=None , __UpperCAmelCase : str=None , __UpperCAmelCase : Optional[int]=True , __UpperCAmelCase : str="[UNK]" , __UpperCAmelCase : Any="[SEP]" , __UpperCAmelCase : str="[PAD]" , __UpperCAmelCase : Optional[Any]="[CLS]" , __UpperCAmelCase : Union[str, Any]="[MASK]" , __UpperCAmelCase : List[str]=True , __UpperCAmelCase : Tuple=None , **__UpperCAmelCase : Optional[int] , ) ->str:
"""simple docstring"""
super().__init__(
__UpperCAmelCase , tokenizer_file=__UpperCAmelCase , do_lower_case=__UpperCAmelCase , unk_token=__UpperCAmelCase , sep_token=__UpperCAmelCase , pad_token=__UpperCAmelCase , cls_token=__UpperCAmelCase , mask_token=__UpperCAmelCase , tokenize_chinese_chars=__UpperCAmelCase , strip_accents=__UpperCAmelCase , **__UpperCAmelCase , )
a = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('''lowercase''' , __UpperCAmelCase ) != do_lower_case
or normalizer_state.get('''strip_accents''' , __UpperCAmelCase ) != strip_accents
or normalizer_state.get('''handle_chinese_chars''' , __UpperCAmelCase ) != tokenize_chinese_chars
):
a = getattr(__UpperCAmelCase , normalizer_state.pop('''type''' ) )
a = do_lower_case
a = strip_accents
a = tokenize_chinese_chars
a = normalizer_class(**__UpperCAmelCase )
a = do_lower_case
def __lowerCAmelCase ( self : List[Any] , __UpperCAmelCase : Optional[int] , __UpperCAmelCase : Tuple=None ) ->str:
"""simple docstring"""
a = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def __lowerCAmelCase ( self : Optional[Any] , __UpperCAmelCase : List[int] , __UpperCAmelCase : Optional[List[int]] = None ) ->List[int]:
"""simple docstring"""
a = [self.sep_token_id]
a = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def __lowerCAmelCase ( self : List[str] , __UpperCAmelCase : str , __UpperCAmelCase : Optional[str] = None ) ->Tuple[str]:
"""simple docstring"""
a = self._tokenizer.model.save(__UpperCAmelCase , name=__UpperCAmelCase )
return tuple(__UpperCAmelCase )
| 0 | 1 |
"""simple docstring"""
from __future__ import annotations
from decimal import Decimal
from numpy import array
def lowercase_ ( _lowerCamelCase: list[list[float]] ) -> list[list[float]]:
'''simple docstring'''
__lowerCamelCase : Dict = Decimal
# Check if the provided matrix has 2 rows and 2 columns
# since this implementation only works for 2x2 matrices
if len(_lowerCamelCase ) == 2 and len(matrix[0] ) == 2 and len(matrix[1] ) == 2:
# Calculate the determinant of the matrix
__lowerCamelCase : Any = float(
d(matrix[0][0] ) * d(matrix[1][1] ) - d(matrix[1][0] ) * d(matrix[0][1] ) )
if determinant == 0:
raise ValueError("This matrix has no inverse." )
# Creates a copy of the matrix with swapped positions of the elements
__lowerCamelCase : Optional[int] = [[0.0, 0.0], [0.0, 0.0]]
__lowerCamelCase , __lowerCamelCase : Any = matrix[1][1], matrix[0][0]
__lowerCamelCase , __lowerCamelCase : Tuple = -matrix[1][0], -matrix[0][1]
# Calculate the inverse of the matrix
return [
[(float(d(_lowerCamelCase ) ) / determinant) or 0.0 for n in row] for row in swapped_matrix
]
elif (
len(_lowerCamelCase ) == 3
and len(matrix[0] ) == 3
and len(matrix[1] ) == 3
and len(matrix[2] ) == 3
):
# Calculate the determinant of the matrix using Sarrus rule
__lowerCamelCase : Tuple = float(
(
(d(matrix[0][0] ) * d(matrix[1][1] ) * d(matrix[2][2] ))
+ (d(matrix[0][1] ) * d(matrix[1][2] ) * d(matrix[2][0] ))
+ (d(matrix[0][2] ) * d(matrix[1][0] ) * d(matrix[2][1] ))
)
- (
(d(matrix[0][2] ) * d(matrix[1][1] ) * d(matrix[2][0] ))
+ (d(matrix[0][1] ) * d(matrix[1][0] ) * d(matrix[2][2] ))
+ (d(matrix[0][0] ) * d(matrix[1][2] ) * d(matrix[2][1] ))
) )
if determinant == 0:
raise ValueError("This matrix has no inverse." )
# Creating cofactor matrix
__lowerCamelCase : Tuple = [
[d(0.0 ), d(0.0 ), d(0.0 )],
[d(0.0 ), d(0.0 ), d(0.0 )],
[d(0.0 ), d(0.0 ), d(0.0 )],
]
__lowerCamelCase : List[Any] = (d(matrix[1][1] ) * d(matrix[2][2] )) - (
d(matrix[1][2] ) * d(matrix[2][1] )
)
__lowerCamelCase : Union[str, Any] = -(
(d(matrix[1][0] ) * d(matrix[2][2] )) - (d(matrix[1][2] ) * d(matrix[2][0] ))
)
__lowerCamelCase : Tuple = (d(matrix[1][0] ) * d(matrix[2][1] )) - (
d(matrix[1][1] ) * d(matrix[2][0] )
)
__lowerCamelCase : Any = -(
(d(matrix[0][1] ) * d(matrix[2][2] )) - (d(matrix[0][2] ) * d(matrix[2][1] ))
)
__lowerCamelCase : Dict = (d(matrix[0][0] ) * d(matrix[2][2] )) - (
d(matrix[0][2] ) * d(matrix[2][0] )
)
__lowerCamelCase : List[Any] = -(
(d(matrix[0][0] ) * d(matrix[2][1] )) - (d(matrix[0][1] ) * d(matrix[2][0] ))
)
__lowerCamelCase : Optional[Any] = (d(matrix[0][1] ) * d(matrix[1][2] )) - (
d(matrix[0][2] ) * d(matrix[1][1] )
)
__lowerCamelCase : str = -(
(d(matrix[0][0] ) * d(matrix[1][2] )) - (d(matrix[0][2] ) * d(matrix[1][0] ))
)
__lowerCamelCase : str = (d(matrix[0][0] ) * d(matrix[1][1] )) - (
d(matrix[0][1] ) * d(matrix[1][0] )
)
# Transpose the cofactor matrix (Adjoint matrix)
__lowerCamelCase : Dict = array(_lowerCamelCase )
for i in range(3 ):
for j in range(3 ):
__lowerCamelCase : Tuple = cofactor_matrix[j][i]
# Inverse of the matrix using the formula (1/determinant) * adjoint matrix
__lowerCamelCase : Any = array(_lowerCamelCase )
for i in range(3 ):
for j in range(3 ):
inverse_matrix[i][j] /= d(_lowerCamelCase )
# Calculate the inverse of the matrix
return [[float(d(_lowerCamelCase ) ) or 0.0 for n in row] for row in inverse_matrix]
raise ValueError("Please provide a matrix of size 2x2 or 3x3." ) | 363 | """simple docstring"""
def lowercase_ ( _lowerCamelCase: int = 100 ) -> int:
'''simple docstring'''
__lowerCamelCase : Optional[Any] = set()
__lowerCamelCase : Union[str, Any] = 0
__lowerCamelCase : Optional[Any] = n + 1 # maximum limit
for a in range(2 , _lowerCamelCase ):
for b in range(2 , _lowerCamelCase ):
__lowerCamelCase : Union[str, Any] = a**b # calculates the current power
collect_powers.add(_lowerCamelCase ) # adds the result to the set
return len(_lowerCamelCase )
if __name__ == "__main__":
print('''Number of terms ''', solution(int(str(input()).strip()))) | 64 | 0 |
from __future__ import annotations
def lowercase__ ( __snake_case : str ):
'''simple docstring'''
return [ord(__snake_case ) - 96 for elem in plain]
def lowercase__ ( __snake_case : list[int] ):
'''simple docstring'''
return "".join(chr(elem + 96 ) for elem in encoded )
def lowercase__ ( ):
'''simple docstring'''
UpperCAmelCase_ : List[Any] = encode(input('-> ' ).strip().lower() )
print('Encoded: ' , __snake_case )
print('Decoded:' , decode(__snake_case ) )
if __name__ == "__main__":
main()
| 29 |
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 | 0 |
from __future__ import annotations
from collections import namedtuple
def __lowerCamelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
lowercase__ : Optional[int] = namedtuple("result" , "name value" )
if (voltage, current, power).count(0 ) != 1:
raise ValueError("Only one argument must be 0" )
elif power < 0:
raise ValueError(
"Power cannot be negative in any electrical/electronics system" )
elif voltage == 0:
return result("voltage" , power / current )
elif current == 0:
return result("current" , power / voltage )
elif power == 0:
return result("power" , float(round(abs(voltage * current ) , 2 ) ) )
else:
raise ValueError("Exactly one argument must be 0" )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 121 |
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
TimesformerForVideoClassification,
TimesformerModel,
)
from transformers.models.timesformer.modeling_timesformer import TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from transformers import VideoMAEImageProcessor
class snake_case__:
"""simple docstring"""
def __init__( self : Any , SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : List[str]=13 , SCREAMING_SNAKE_CASE : Union[str, Any]=10 , SCREAMING_SNAKE_CASE : Union[str, Any]=3 , SCREAMING_SNAKE_CASE : int=2 , SCREAMING_SNAKE_CASE : int=2 , SCREAMING_SNAKE_CASE : Optional[int]=True , SCREAMING_SNAKE_CASE : str=True , SCREAMING_SNAKE_CASE : int=32 , SCREAMING_SNAKE_CASE : Dict=5 , SCREAMING_SNAKE_CASE : str=4 , SCREAMING_SNAKE_CASE : Optional[int]=37 , SCREAMING_SNAKE_CASE : Any="gelu" , SCREAMING_SNAKE_CASE : List[Any]=0.1 , SCREAMING_SNAKE_CASE : Optional[Any]=0.1 , SCREAMING_SNAKE_CASE : Dict=10 , SCREAMING_SNAKE_CASE : Optional[int]=0.02 , SCREAMING_SNAKE_CASE : str="divided_space_time" , SCREAMING_SNAKE_CASE : Tuple=None , ):
lowercase__ : List[str] = parent
lowercase__ : Optional[int] = batch_size
lowercase__ : List[Any] = image_size
lowercase__ : Optional[Any] = num_channels
lowercase__ : List[str] = patch_size
lowercase__ : str = num_frames
lowercase__ : List[str] = is_training
lowercase__ : List[str] = use_labels
lowercase__ : int = hidden_size
lowercase__ : List[Any] = num_hidden_layers
lowercase__ : Optional[Any] = num_attention_heads
lowercase__ : Dict = intermediate_size
lowercase__ : Union[str, Any] = hidden_act
lowercase__ : str = hidden_dropout_prob
lowercase__ : Tuple = attention_probs_dropout_prob
lowercase__ : Tuple = attention_type
lowercase__ : Union[str, Any] = initializer_range
lowercase__ : Any = scope
lowercase__ : Optional[int] = num_labels
# in TimeSformer, the number of spatial tokens equals num_frames * num_patches per frame + 1 CLS token
lowercase__ : Union[str, Any] = (image_size // patch_size) ** 2
lowercase__ : Union[str, Any] = (num_frames) * self.num_patches_per_frame + 1
def snake_case ( self : Optional[int] ):
lowercase__ : Any = floats_tensor(
[self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size] )
lowercase__ : Optional[Any] = None
if self.use_labels:
lowercase__ : str = ids_tensor([self.batch_size] , self.num_labels )
lowercase__ : List[Any] = self.get_config()
return config, pixel_values, labels
def snake_case ( self : Any ):
lowercase__ : Optional[int] = TimesformerConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , num_frames=self.num_frames , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , initializer_range=self.initializer_range , attention_type=self.attention_type , )
lowercase__ : List[Any] = self.num_labels
return config
def snake_case ( self : Optional[int] , SCREAMING_SNAKE_CASE : Optional[int] , SCREAMING_SNAKE_CASE : List[Any] , SCREAMING_SNAKE_CASE : Tuple ):
lowercase__ : Optional[Any] = TimesformerModel(config=SCREAMING_SNAKE_CASE )
model.to(SCREAMING_SNAKE_CASE )
model.eval()
lowercase__ : Union[str, Any] = model(SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def snake_case ( self : Dict , SCREAMING_SNAKE_CASE : Optional[Any] , SCREAMING_SNAKE_CASE : Optional[int] , SCREAMING_SNAKE_CASE : Any ):
lowercase__ : List[Any] = TimesformerForVideoClassification(SCREAMING_SNAKE_CASE )
model.to(SCREAMING_SNAKE_CASE )
model.eval()
lowercase__ : List[str] = model(SCREAMING_SNAKE_CASE )
# verify the logits shape
lowercase__ : List[str] = torch.Size((self.batch_size, self.num_labels) )
self.parent.assertEqual(result.logits.shape , SCREAMING_SNAKE_CASE )
def snake_case ( self : Optional[Any] ):
lowercase__ : List[str] = self.prepare_config_and_inputs()
lowercase__ , lowercase__ , lowercase__ : int = config_and_inputs
lowercase__ : Optional[int] = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class snake_case__(_UpperCamelCase , _UpperCamelCase , unittest.TestCase ):
"""simple docstring"""
lowercase_ = (TimesformerModel, TimesformerForVideoClassification) if is_torch_available() else ()
lowercase_ = (
{"""feature-extraction""": TimesformerModel, """video-classification""": TimesformerForVideoClassification}
if is_torch_available()
else {}
)
lowercase_ = False
lowercase_ = False
lowercase_ = False
lowercase_ = False
def snake_case ( self : Dict ):
lowercase__ : Tuple = TimesformerModelTester(self )
lowercase__ : Any = ConfigTester(
self , config_class=SCREAMING_SNAKE_CASE , has_text_modality=SCREAMING_SNAKE_CASE , hidden_size=37 )
def snake_case ( self : Dict , SCREAMING_SNAKE_CASE : List[str] , SCREAMING_SNAKE_CASE : Any , SCREAMING_SNAKE_CASE : Tuple=False ):
lowercase__ : Union[str, Any] = copy.deepcopy(SCREAMING_SNAKE_CASE )
if return_labels:
if model_class in get_values(SCREAMING_SNAKE_CASE ):
lowercase__ : int = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=SCREAMING_SNAKE_CASE )
return inputs_dict
def snake_case ( self : Optional[Any] ):
self.config_tester.run_common_tests()
@unittest.skip(reason="TimeSformer does not use inputs_embeds" )
def snake_case ( self : Any ):
pass
def snake_case ( self : Tuple ):
lowercase__ , lowercase__ : str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ : Union[str, Any] = model_class(SCREAMING_SNAKE_CASE )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
lowercase__ : Dict = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE , nn.Linear ) )
def snake_case ( self : Union[str, Any] ):
lowercase__ , lowercase__ : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ : str = model_class(SCREAMING_SNAKE_CASE )
lowercase__ : Any = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase__ : Any = [*signature.parameters.keys()]
lowercase__ : Dict = ["pixel_values"]
self.assertListEqual(arg_names[:1] , SCREAMING_SNAKE_CASE )
def snake_case ( self : Tuple ):
lowercase__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE )
def snake_case ( self : str ):
lowercase__ : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_video_classification(*SCREAMING_SNAKE_CASE )
@slow
def snake_case ( self : Optional[int] ):
for model_name in TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase__ : Optional[int] = TimesformerModel.from_pretrained(SCREAMING_SNAKE_CASE )
self.assertIsNotNone(SCREAMING_SNAKE_CASE )
def snake_case ( self : Any ):
if not self.has_attentions:
pass
else:
lowercase__ , lowercase__ : Any = self.model_tester.prepare_config_and_inputs_for_common()
lowercase__ : Union[str, Any] = True
for model_class in self.all_model_classes:
lowercase__ : List[str] = self.model_tester.seq_length
lowercase__ : Any = self.model_tester.num_frames
lowercase__ : Optional[int] = True
lowercase__ : List[str] = False
lowercase__ : List[Any] = True
lowercase__ : Tuple = model_class(SCREAMING_SNAKE_CASE )
model.to(SCREAMING_SNAKE_CASE )
model.eval()
with torch.no_grad():
lowercase__ : Any = model(**self._prepare_for_class(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) )
lowercase__ : List[Any] = outputs.attentions
self.assertEqual(len(SCREAMING_SNAKE_CASE ) , self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
lowercase__ : Dict = True
lowercase__ : int = model_class(SCREAMING_SNAKE_CASE )
model.to(SCREAMING_SNAKE_CASE )
model.eval()
with torch.no_grad():
lowercase__ : Optional[Any] = model(**self._prepare_for_class(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) )
lowercase__ : Any = outputs.attentions
self.assertEqual(len(SCREAMING_SNAKE_CASE ) , self.model_tester.num_hidden_layers )
# attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1] , )
lowercase__ : Any = len(SCREAMING_SNAKE_CASE )
# Check attention is always last and order is fine
lowercase__ : Tuple = True
lowercase__ : Tuple = True
lowercase__ : Union[str, Any] = model_class(SCREAMING_SNAKE_CASE )
model.to(SCREAMING_SNAKE_CASE )
model.eval()
with torch.no_grad():
lowercase__ : Union[str, Any] = model(**self._prepare_for_class(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) )
self.assertEqual(out_len + 1 , len(SCREAMING_SNAKE_CASE ) )
lowercase__ : Optional[Any] = outputs.attentions
self.assertEqual(len(SCREAMING_SNAKE_CASE ) , self.model_tester.num_hidden_layers )
# attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1] , )
def snake_case ( self : List[Any] ):
def check_hidden_states_output(SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : Union[str, Any] ):
lowercase__ : Optional[int] = model_class(SCREAMING_SNAKE_CASE )
model.to(SCREAMING_SNAKE_CASE )
model.eval()
with torch.no_grad():
lowercase__ : Tuple = model(**self._prepare_for_class(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) )
lowercase__ : Optional[Any] = outputs.hidden_states
lowercase__ : List[str] = self.model_tester.num_hidden_layers + 1
self.assertEqual(len(SCREAMING_SNAKE_CASE ) , SCREAMING_SNAKE_CASE )
lowercase__ : int = self.model_tester.seq_length
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [seq_length, self.model_tester.hidden_size] , )
lowercase__ , lowercase__ : str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ : Dict = True
check_hidden_states_output(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowercase__ : Tuple = True
check_hidden_states_output(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
def __lowerCamelCase ( ):
"""simple docstring"""
lowercase__ : Optional[Any] = hf_hub_download(
repo_id="hf-internal-testing/spaghetti-video" , filename="eating_spaghetti.npy" , repo_type="dataset" )
lowercase__ : Optional[Any] = np.load(lowerCamelCase__ )
return list(lowerCamelCase__ )
@require_torch
@require_vision
class snake_case__(unittest.TestCase ):
"""simple docstring"""
@cached_property
def snake_case ( self : Dict ):
# logits were tested with a different mean and std, so we use the same here
return (
VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5] , image_std=[0.5, 0.5, 0.5] )
if is_vision_available()
else None
)
@slow
def snake_case ( self : List[Any] ):
lowercase__ : List[Any] = TimesformerForVideoClassification.from_pretrained("facebook/timesformer-base-finetuned-k400" ).to(
SCREAMING_SNAKE_CASE )
lowercase__ : int = self.default_image_processor
lowercase__ : List[str] = prepare_video()
lowercase__ : str = image_processor(video[:8] , return_tensors="pt" ).to(SCREAMING_SNAKE_CASE )
# forward pass
with torch.no_grad():
lowercase__ : str = model(**SCREAMING_SNAKE_CASE )
# verify the logits
lowercase__ : Union[str, Any] = torch.Size((1, 400) )
self.assertEqual(outputs.logits.shape , SCREAMING_SNAKE_CASE )
lowercase__ : Optional[Any] = torch.tensor([-0.3_016, -0.7_713, -0.4_205] ).to(SCREAMING_SNAKE_CASE )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , SCREAMING_SNAKE_CASE , atol=1E-4 ) )
| 121 | 1 |
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
def A__ ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase ):
if isinstance(snake_case__, snake_case__ ):
SCREAMING_SNAKE_CASE_ = np.full((len(snake_case__ ), sequence_length, 2), snake_case__ )
else:
SCREAMING_SNAKE_CASE_ = np.full((len(snake_case__ ), sequence_length), snake_case__ )
for i, tensor in enumerate(snake_case__ ):
if padding_side == "right":
if isinstance(snake_case__, snake_case__ ):
SCREAMING_SNAKE_CASE_ = tensor[:sequence_length]
else:
SCREAMING_SNAKE_CASE_ = tensor[:sequence_length]
else:
if isinstance(snake_case__, snake_case__ ):
SCREAMING_SNAKE_CASE_ = tensor[:sequence_length]
else:
SCREAMING_SNAKE_CASE_ = tensor[:sequence_length]
return out_tensor.tolist()
def A__ ( __lowerCamelCase ):
SCREAMING_SNAKE_CASE_ = ord(snake_case__ )
if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 1_23 and cp <= 1_26):
return True
SCREAMING_SNAKE_CASE_ = unicodedata.category(snake_case__ )
if cat.startswith('''P''' ):
return True
return False
@dataclass
class UpperCamelCase__ ( _UpperCAmelCase ):
"""simple docstring"""
UpperCAmelCase_ =42
UpperCAmelCase_ =True
UpperCAmelCase_ =None
UpperCAmelCase_ =None
UpperCAmelCase_ =-100
UpperCAmelCase_ ="pt"
def _UpperCamelCase ( self , _A ) -> int:
import torch
SCREAMING_SNAKE_CASE_ = '''label''' if '''label''' in features[0].keys() else '''labels'''
SCREAMING_SNAKE_CASE_ = [feature[label_name] for feature in features] if label_name in features[0].keys() else None
SCREAMING_SNAKE_CASE_ = self.tokenizer.pad(
UpperCAmelCase_ , padding=self.padding , max_length=self.max_length , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors='''pt''' if labels is None else None , )
if labels is None:
return batch
SCREAMING_SNAKE_CASE_ = torch.tensor(batch['''entity_ids'''] ).shape[1]
SCREAMING_SNAKE_CASE_ = self.tokenizer.padding_side
if padding_side == "right":
SCREAMING_SNAKE_CASE_ = [
list(UpperCAmelCase_ ) + [self.label_pad_token_id] * (sequence_length - len(UpperCAmelCase_ )) for label in labels
]
else:
SCREAMING_SNAKE_CASE_ = [
[self.label_pad_token_id] * (sequence_length - len(UpperCAmelCase_ )) + list(UpperCAmelCase_ ) for label in labels
]
SCREAMING_SNAKE_CASE_ = [feature['''ner_tags'''] for feature in features]
SCREAMING_SNAKE_CASE_ = padding_tensor(UpperCAmelCase_ , -1 , UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE_ = [feature['''original_entity_spans'''] for feature in features]
SCREAMING_SNAKE_CASE_ = padding_tensor(UpperCAmelCase_ , (-1, -1) , UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE_ = {k: torch.tensor(UpperCAmelCase_ , dtype=torch.intaa ) for k, v in batch.items()}
return batch
| 299 |
import argparse
import hashlib # hashlib is only used inside the Test class
import struct
class __UpperCAmelCase :
def __init__( self: List[str] , UpperCAmelCase_: Dict ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = data
_SCREAMING_SNAKE_CASE = [0x67_452_301, 0xef_cda_b89, 0x98_bad_cfe, 0x10_325_476, 0xc3_d2e_1f0]
@staticmethod
def UpperCamelCase ( UpperCAmelCase_: int , UpperCAmelCase_: List[str] ):
'''simple docstring'''
return ((n << b) | (n >> (32 - b))) & 0xff_fff_fff
def UpperCamelCase ( self: Any ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = B"""\x80""" + B"""\x00""" * (63 - (len(self.data ) + 8) % 64)
_SCREAMING_SNAKE_CASE = self.data + padding + struct.pack(""">Q""" , 8 * len(self.data ) )
return padded_data
def UpperCamelCase ( self: List[Any] ):
'''simple docstring'''
return [
self.padded_data[i : i + 64] for i in range(0 , len(self.padded_data ) , 64 )
]
def UpperCamelCase ( self: Optional[Any] , UpperCAmelCase_: Union[str, Any] ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = list(struct.unpack(""">16L""" , UpperCAmelCase_ ) ) + [0] * 64
for i in range(16 , 80 ):
_SCREAMING_SNAKE_CASE = self.rotate((w[i - 3] ^ w[i - 8] ^ w[i - 14] ^ w[i - 16]) , 1 )
return w
def UpperCamelCase ( self: List[str] ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = self.padding()
_SCREAMING_SNAKE_CASE = self.split_blocks()
for block in self.blocks:
_SCREAMING_SNAKE_CASE = self.expand_block(UpperCAmelCase_ )
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = self.h
for i in range(0 , 80 ):
if 0 <= i < 20:
_SCREAMING_SNAKE_CASE = (b & c) | ((~b) & d)
_SCREAMING_SNAKE_CASE = 0x5a_827_999
elif 20 <= i < 40:
_SCREAMING_SNAKE_CASE = b ^ c ^ d
_SCREAMING_SNAKE_CASE = 0x6e_d9e_ba1
elif 40 <= i < 60:
_SCREAMING_SNAKE_CASE = (b & c) | (b & d) | (c & d)
_SCREAMING_SNAKE_CASE = 0x8f_1bb_cdc
elif 60 <= i < 80:
_SCREAMING_SNAKE_CASE = b ^ c ^ d
_SCREAMING_SNAKE_CASE = 0xca_62c_1d6
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = (
self.rotate(UpperCAmelCase_ , 5 ) + f + e + k + expanded_block[i] & 0xff_fff_fff,
a,
self.rotate(UpperCAmelCase_ , 30 ),
c,
d,
)
_SCREAMING_SNAKE_CASE = (
self.h[0] + a & 0xff_fff_fff,
self.h[1] + b & 0xff_fff_fff,
self.h[2] + c & 0xff_fff_fff,
self.h[3] + d & 0xff_fff_fff,
self.h[4] + e & 0xff_fff_fff,
)
return ("{:08x}" * 5).format(*self.h )
def __lowerCamelCase ( ) -> Optional[int]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = b"""Test String"""
assert SHAaHash(snake_case__ ).final_hash() == hashlib.shaa(snake_case__ ).hexdigest() # noqa: S324
def __lowerCamelCase ( ) -> Union[str, Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = argparse.ArgumentParser(description="""Process some strings or files""" )
parser.add_argument(
"""--string""" ,dest="""input_string""" ,default="""Hello World!! Welcome to Cryptography""" ,help="""Hash the string""" ,)
parser.add_argument("""--file""" ,dest="""input_file""" ,help="""Hash contents of a file""" )
_SCREAMING_SNAKE_CASE = parser.parse_args()
_SCREAMING_SNAKE_CASE = args.input_string
# In any case hash input should be a bytestring
if args.input_file:
with open(args.input_file ,"""rb""" ) as f:
_SCREAMING_SNAKE_CASE = f.read()
else:
_SCREAMING_SNAKE_CASE = bytes(snake_case__ ,"""utf-8""" )
print(SHAaHash(snake_case__ ).final_hash() )
if __name__ == "__main__":
main()
import doctest
doctest.testmod()
| 306 | 0 |
'''simple docstring'''
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_outputs import (
BaseModelOutputWithNoAttention,
BaseModelOutputWithPoolingAndNoAttention,
ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import logging
from .configuration_regnet import RegNetConfig
__lowerCAmelCase = logging.get_logger(__name__)
# General docstring
__lowerCAmelCase = 'RegNetConfig'
# Base docstring
__lowerCAmelCase = 'facebook/regnet-y-040'
__lowerCAmelCase = [1, 1_088, 7, 7]
# Image classification docstring
__lowerCAmelCase = 'facebook/regnet-y-040'
__lowerCAmelCase = 'tabby, tabby cat'
__lowerCAmelCase = [
'facebook/regnet-y-040',
# See all regnet models at https://huggingface.co/models?filter=regnet
]
class _lowerCAmelCase ( nn.Module ):
'''simple docstring'''
def __init__(self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = 3 , UpperCAmelCase = 1 , UpperCAmelCase = 1 , UpperCAmelCase = "relu" , ) -> int:
super().__init__()
_snake_case = nn.Convad(
UpperCAmelCase , UpperCAmelCase , kernel_size=UpperCAmelCase , stride=UpperCAmelCase , padding=kernel_size // 2 , groups=UpperCAmelCase , bias=UpperCAmelCase , )
_snake_case = nn.BatchNormad(UpperCAmelCase )
_snake_case = ACTaFN[activation] if activation is not None else nn.Identity()
def lowercase (self , UpperCAmelCase ) -> Optional[int]:
_snake_case = self.convolution(UpperCAmelCase )
_snake_case = self.normalization(UpperCAmelCase )
_snake_case = self.activation(UpperCAmelCase )
return hidden_state
class _lowerCAmelCase ( nn.Module ):
'''simple docstring'''
def __init__(self , UpperCAmelCase ) -> Any:
super().__init__()
_snake_case = RegNetConvLayer(
config.num_channels , config.embedding_size , kernel_size=3 , stride=2 , activation=config.hidden_act )
_snake_case = config.num_channels
def lowercase (self , UpperCAmelCase ) -> Any:
_snake_case = pixel_values.shape[1]
if num_channels != self.num_channels:
raise ValueError(
"""Make sure that the channel dimension of the pixel values match with the one set in the configuration.""" )
_snake_case = self.embedder(UpperCAmelCase )
return hidden_state
class _lowerCAmelCase ( nn.Module ):
'''simple docstring'''
def __init__(self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = 2 ) -> int:
super().__init__()
_snake_case = nn.Convad(UpperCAmelCase , UpperCAmelCase , kernel_size=1 , stride=UpperCAmelCase , bias=UpperCAmelCase )
_snake_case = nn.BatchNormad(UpperCAmelCase )
def lowercase (self , UpperCAmelCase ) -> Tensor:
_snake_case = self.convolution(UpperCAmelCase )
_snake_case = self.normalization(UpperCAmelCase )
return hidden_state
class _lowerCAmelCase ( nn.Module ):
'''simple docstring'''
def __init__(self , UpperCAmelCase , UpperCAmelCase ) -> Tuple:
super().__init__()
_snake_case = nn.AdaptiveAvgPoolad((1, 1) )
_snake_case = nn.Sequential(
nn.Convad(UpperCAmelCase , UpperCAmelCase , kernel_size=1 ) , nn.ReLU() , nn.Convad(UpperCAmelCase , UpperCAmelCase , kernel_size=1 ) , nn.Sigmoid() , )
def lowercase (self , UpperCAmelCase ) -> int:
# b c h w -> b c 1 1
_snake_case = self.pooler(UpperCAmelCase )
_snake_case = self.attention(UpperCAmelCase )
_snake_case = hidden_state * attention
return hidden_state
class _lowerCAmelCase ( nn.Module ):
'''simple docstring'''
def __init__(self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = 1 ) -> Optional[Any]:
super().__init__()
_snake_case = in_channels != out_channels or stride != 1
_snake_case = max(1 , out_channels // config.groups_width )
_snake_case = (
RegNetShortCut(UpperCAmelCase , UpperCAmelCase , stride=UpperCAmelCase ) if should_apply_shortcut else nn.Identity()
)
_snake_case = nn.Sequential(
RegNetConvLayer(UpperCAmelCase , UpperCAmelCase , kernel_size=1 , activation=config.hidden_act ) , RegNetConvLayer(UpperCAmelCase , UpperCAmelCase , stride=UpperCAmelCase , groups=UpperCAmelCase , activation=config.hidden_act ) , RegNetConvLayer(UpperCAmelCase , UpperCAmelCase , kernel_size=1 , activation=UpperCAmelCase ) , )
_snake_case = ACTaFN[config.hidden_act]
def lowercase (self , UpperCAmelCase ) -> Tuple:
_snake_case = hidden_state
_snake_case = self.layer(UpperCAmelCase )
_snake_case = self.shortcut(UpperCAmelCase )
hidden_state += residual
_snake_case = self.activation(UpperCAmelCase )
return hidden_state
class _lowerCAmelCase ( nn.Module ):
'''simple docstring'''
def __init__(self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = 1 ) -> str:
super().__init__()
_snake_case = in_channels != out_channels or stride != 1
_snake_case = max(1 , out_channels // config.groups_width )
_snake_case = (
RegNetShortCut(UpperCAmelCase , UpperCAmelCase , stride=UpperCAmelCase ) if should_apply_shortcut else nn.Identity()
)
_snake_case = nn.Sequential(
RegNetConvLayer(UpperCAmelCase , UpperCAmelCase , kernel_size=1 , activation=config.hidden_act ) , RegNetConvLayer(UpperCAmelCase , UpperCAmelCase , stride=UpperCAmelCase , groups=UpperCAmelCase , activation=config.hidden_act ) , RegNetSELayer(UpperCAmelCase , reduced_channels=int(round(in_channels / 4 ) ) ) , RegNetConvLayer(UpperCAmelCase , UpperCAmelCase , kernel_size=1 , activation=UpperCAmelCase ) , )
_snake_case = ACTaFN[config.hidden_act]
def lowercase (self , UpperCAmelCase ) -> List[str]:
_snake_case = hidden_state
_snake_case = self.layer(UpperCAmelCase )
_snake_case = self.shortcut(UpperCAmelCase )
hidden_state += residual
_snake_case = self.activation(UpperCAmelCase )
return hidden_state
class _lowerCAmelCase ( nn.Module ):
'''simple docstring'''
def __init__(self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = 2 , UpperCAmelCase = 2 , ) -> int:
super().__init__()
_snake_case = RegNetXLayer if config.layer_type == """x""" else RegNetYLayer
_snake_case = nn.Sequential(
# downsampling is done in the first layer with stride of 2
layer(
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , stride=UpperCAmelCase , ) , *[layer(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) for _ in range(depth - 1 )] , )
def lowercase (self , UpperCAmelCase ) -> Union[str, Any]:
_snake_case = self.layers(UpperCAmelCase )
return hidden_state
class _lowerCAmelCase ( nn.Module ):
'''simple docstring'''
def __init__(self , UpperCAmelCase ) -> Union[str, Any]:
super().__init__()
_snake_case = nn.ModuleList([] )
# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
self.stages.append(
RegNetStage(
UpperCAmelCase , config.embedding_size , config.hidden_sizes[0] , stride=2 if config.downsample_in_first_stage else 1 , depth=config.depths[0] , ) )
_snake_case = zip(config.hidden_sizes , config.hidden_sizes[1:] )
for (in_channels, out_channels), depth in zip(UpperCAmelCase , config.depths[1:] ):
self.stages.append(RegNetStage(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , depth=UpperCAmelCase ) )
def lowercase (self , UpperCAmelCase , UpperCAmelCase = False , UpperCAmelCase = True ) -> BaseModelOutputWithNoAttention:
_snake_case = () if output_hidden_states else None
for stage_module in self.stages:
if output_hidden_states:
_snake_case = hidden_states + (hidden_state,)
_snake_case = stage_module(UpperCAmelCase )
if output_hidden_states:
_snake_case = hidden_states + (hidden_state,)
if not return_dict:
return tuple(v for v in [hidden_state, hidden_states] if v is not None )
return BaseModelOutputWithNoAttention(last_hidden_state=UpperCAmelCase , hidden_states=UpperCAmelCase )
class _lowerCAmelCase ( __snake_case ):
'''simple docstring'''
lowerCAmelCase_ = RegNetConfig
lowerCAmelCase_ = "regnet"
lowerCAmelCase_ = "pixel_values"
lowerCAmelCase_ = True
def lowercase (self , UpperCAmelCase ) -> Any:
if isinstance(UpperCAmelCase , nn.Convad ):
nn.init.kaiming_normal_(module.weight , mode="""fan_out""" , nonlinearity="""relu""" )
elif isinstance(UpperCAmelCase , (nn.BatchNormad, nn.GroupNorm) ):
nn.init.constant_(module.weight , 1 )
nn.init.constant_(module.bias , 0 )
def lowercase (self , UpperCAmelCase , UpperCAmelCase=False ) -> int:
if isinstance(UpperCAmelCase , UpperCAmelCase ):
_snake_case = value
__lowerCAmelCase = r'\n This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it\n as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n behavior.\n\n Parameters:\n config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.\n Initializing with a config file does not load the weights associated with the model, only the\n configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n'
__lowerCAmelCase = r'\n Args:\n pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n [`ConvNextImageProcessor.__call__`] for details.\n\n output_hidden_states (`bool`, *optional*):\n Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n more detail.\n return_dict (`bool`, *optional*):\n Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.\n'
@add_start_docstrings(
"The bare RegNet model outputting raw features without any specific head on top." , __snake_case , )
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class _lowerCAmelCase ( __snake_case ):
'''simple docstring'''
def __init__(self , UpperCAmelCase ) -> Optional[int]:
super().__init__(UpperCAmelCase )
_snake_case = config
_snake_case = RegNetEmbeddings(UpperCAmelCase )
_snake_case = RegNetEncoder(UpperCAmelCase )
_snake_case = nn.AdaptiveAvgPoolad((1, 1) )
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(UpperCAmelCase )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC , output_type=UpperCAmelCase , config_class=_CONFIG_FOR_DOC , modality="""vision""" , expected_output=_EXPECTED_OUTPUT_SHAPE , )
def lowercase (self , UpperCAmelCase , UpperCAmelCase = None , UpperCAmelCase = None ) -> BaseModelOutputWithPoolingAndNoAttention:
_snake_case = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
_snake_case = return_dict if return_dict is not None else self.config.use_return_dict
_snake_case = self.embedder(UpperCAmelCase )
_snake_case = self.encoder(
UpperCAmelCase , output_hidden_states=UpperCAmelCase , return_dict=UpperCAmelCase )
_snake_case = encoder_outputs[0]
_snake_case = self.pooler(UpperCAmelCase )
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=UpperCAmelCase , pooler_output=UpperCAmelCase , hidden_states=encoder_outputs.hidden_states , )
@add_start_docstrings(
"\n RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n ImageNet.\n " , __snake_case , )
# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
class _lowerCAmelCase ( __snake_case ):
'''simple docstring'''
def __init__(self , UpperCAmelCase ) -> List[str]:
super().__init__(UpperCAmelCase )
_snake_case = config.num_labels
_snake_case = RegNetModel(UpperCAmelCase )
# classification head
_snake_case = nn.Sequential(
nn.Flatten() , nn.Linear(config.hidden_sizes[-1] , config.num_labels ) if config.num_labels > 0 else nn.Identity() , )
# initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(UpperCAmelCase )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=UpperCAmelCase , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , )
def lowercase (self , UpperCAmelCase = None , UpperCAmelCase = None , UpperCAmelCase = None , UpperCAmelCase = None , ) -> ImageClassifierOutputWithNoAttention:
_snake_case = return_dict if return_dict is not None else self.config.use_return_dict
_snake_case = self.regnet(UpperCAmelCase , output_hidden_states=UpperCAmelCase , return_dict=UpperCAmelCase )
_snake_case = outputs.pooler_output if return_dict else outputs[1]
_snake_case = self.classifier(UpperCAmelCase )
_snake_case = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
_snake_case = """regression"""
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
_snake_case = """single_label_classification"""
else:
_snake_case = """multi_label_classification"""
if self.config.problem_type == "regression":
_snake_case = MSELoss()
if self.num_labels == 1:
_snake_case = loss_fct(logits.squeeze() , labels.squeeze() )
else:
_snake_case = loss_fct(UpperCAmelCase , UpperCAmelCase )
elif self.config.problem_type == "single_label_classification":
_snake_case = CrossEntropyLoss()
_snake_case = loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
elif self.config.problem_type == "multi_label_classification":
_snake_case = BCEWithLogitsLoss()
_snake_case = loss_fct(UpperCAmelCase , UpperCAmelCase )
if not return_dict:
_snake_case = (logits,) + outputs[2:]
return (loss,) + output if loss is not None else output
return ImageClassifierOutputWithNoAttention(loss=UpperCAmelCase , logits=UpperCAmelCase , hidden_states=outputs.hidden_states ) | 366 |
'''simple docstring'''
import unittest
from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
DebertaForMaskedLM,
DebertaForQuestionAnswering,
DebertaForSequenceClassification,
DebertaForTokenClassification,
DebertaModel,
)
from transformers.models.deberta.modeling_deberta import DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST
class _lowerCAmelCase ( __snake_case ):
'''simple docstring'''
def __init__(self , UpperCAmelCase , UpperCAmelCase=13 , UpperCAmelCase=7 , UpperCAmelCase=True , UpperCAmelCase=True , UpperCAmelCase=True , UpperCAmelCase=True , UpperCAmelCase=99 , UpperCAmelCase=32 , UpperCAmelCase=5 , UpperCAmelCase=4 , UpperCAmelCase=37 , UpperCAmelCase="gelu" , UpperCAmelCase=0.1 , UpperCAmelCase=0.1 , UpperCAmelCase=512 , UpperCAmelCase=16 , UpperCAmelCase=2 , UpperCAmelCase=0.02 , UpperCAmelCase=False , UpperCAmelCase=True , UpperCAmelCase="None" , UpperCAmelCase=3 , UpperCAmelCase=4 , UpperCAmelCase=None , ) -> Optional[Any]:
_snake_case = parent
_snake_case = batch_size
_snake_case = seq_length
_snake_case = is_training
_snake_case = use_input_mask
_snake_case = use_token_type_ids
_snake_case = use_labels
_snake_case = vocab_size
_snake_case = hidden_size
_snake_case = num_hidden_layers
_snake_case = num_attention_heads
_snake_case = intermediate_size
_snake_case = hidden_act
_snake_case = hidden_dropout_prob
_snake_case = attention_probs_dropout_prob
_snake_case = max_position_embeddings
_snake_case = type_vocab_size
_snake_case = type_sequence_label_size
_snake_case = initializer_range
_snake_case = num_labels
_snake_case = num_choices
_snake_case = relative_attention
_snake_case = position_biased_input
_snake_case = pos_att_type
_snake_case = scope
def lowercase (self ) -> List[Any]:
_snake_case = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_snake_case = None
if self.use_input_mask:
_snake_case = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
_snake_case = None
if self.use_token_type_ids:
_snake_case = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_snake_case = None
_snake_case = None
_snake_case = None
if self.use_labels:
_snake_case = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_snake_case = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_snake_case = ids_tensor([self.batch_size] , self.num_choices )
_snake_case = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def lowercase (self ) -> int:
return DebertaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , relative_attention=self.relative_attention , position_biased_input=self.position_biased_input , pos_att_type=self.pos_att_type , )
def lowercase (self ) -> int:
_snake_case = self.get_config()
_snake_case = 300
return config
def lowercase (self , UpperCAmelCase ) -> Dict:
self.parent.assertListEqual(list(result.loss.size() ) , [] )
def lowercase (self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) -> Optional[Any]:
_snake_case = DebertaModel(config=UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
_snake_case = model(UpperCAmelCase , attention_mask=UpperCAmelCase , token_type_ids=UpperCAmelCase )[0]
_snake_case = model(UpperCAmelCase , token_type_ids=UpperCAmelCase )[0]
_snake_case = model(UpperCAmelCase )[0]
self.parent.assertListEqual(list(sequence_output.size() ) , [self.batch_size, self.seq_length, self.hidden_size] )
def lowercase (self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) -> str:
_snake_case = DebertaForMaskedLM(config=UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
_snake_case = model(UpperCAmelCase , attention_mask=UpperCAmelCase , token_type_ids=UpperCAmelCase , labels=UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def lowercase (self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) -> List[str]:
_snake_case = self.num_labels
_snake_case = DebertaForSequenceClassification(UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
_snake_case = model(UpperCAmelCase , attention_mask=UpperCAmelCase , token_type_ids=UpperCAmelCase , labels=UpperCAmelCase )
self.parent.assertListEqual(list(result.logits.size() ) , [self.batch_size, self.num_labels] )
self.check_loss_output(UpperCAmelCase )
def lowercase (self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) -> Any:
_snake_case = self.num_labels
_snake_case = DebertaForTokenClassification(config=UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
_snake_case = model(UpperCAmelCase , attention_mask=UpperCAmelCase , token_type_ids=UpperCAmelCase , labels=UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def lowercase (self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) -> Any:
_snake_case = DebertaForQuestionAnswering(config=UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
_snake_case = model(
UpperCAmelCase , attention_mask=UpperCAmelCase , token_type_ids=UpperCAmelCase , start_positions=UpperCAmelCase , end_positions=UpperCAmelCase , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def lowercase (self ) -> Tuple:
_snake_case = self.prepare_config_and_inputs()
(
(
_snake_case
), (
_snake_case
), (
_snake_case
), (
_snake_case
), (
_snake_case
), (
_snake_case
), (
_snake_case
),
) = config_and_inputs
_snake_case = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class _lowerCAmelCase ( __snake_case , __snake_case , unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase_ = (
(
DebertaModel,
DebertaForMaskedLM,
DebertaForSequenceClassification,
DebertaForTokenClassification,
DebertaForQuestionAnswering,
)
if is_torch_available()
else ()
)
lowerCAmelCase_ = (
{
"feature-extraction": DebertaModel,
"fill-mask": DebertaForMaskedLM,
"question-answering": DebertaForQuestionAnswering,
"text-classification": DebertaForSequenceClassification,
"token-classification": DebertaForTokenClassification,
"zero-shot": DebertaForSequenceClassification,
}
if is_torch_available()
else {}
)
lowerCAmelCase_ = True
lowerCAmelCase_ = False
lowerCAmelCase_ = False
lowerCAmelCase_ = False
lowerCAmelCase_ = False
def lowercase (self ) -> Any:
_snake_case = DebertaModelTester(self )
_snake_case = ConfigTester(self , config_class=UpperCAmelCase , hidden_size=37 )
def lowercase (self ) -> List[str]:
self.config_tester.run_common_tests()
def lowercase (self ) -> Tuple:
_snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_model(*UpperCAmelCase )
def lowercase (self ) -> str:
_snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_sequence_classification(*UpperCAmelCase )
def lowercase (self ) -> int:
_snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_masked_lm(*UpperCAmelCase )
def lowercase (self ) -> Any:
_snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_question_answering(*UpperCAmelCase )
def lowercase (self ) -> str:
_snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_token_classification(*UpperCAmelCase )
@slow
def lowercase (self ) -> Tuple:
for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_snake_case = DebertaModel.from_pretrained(UpperCAmelCase )
self.assertIsNotNone(UpperCAmelCase )
@require_torch
@require_sentencepiece
@require_tokenizers
class _lowerCAmelCase ( unittest.TestCase ):
'''simple docstring'''
@unittest.skip(reason="""Model not available yet""" )
def lowercase (self ) -> Any:
pass
@slow
def lowercase (self ) -> Dict:
_snake_case = DebertaModel.from_pretrained("""microsoft/deberta-base""" )
_snake_case = torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]] )
_snake_case = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
_snake_case = model(UpperCAmelCase , attention_mask=UpperCAmelCase )[0]
# compare the actual values for a slice.
_snake_case = torch.tensor(
[[[-0.5986, -0.8055, -0.8462], [1.4484, -0.9348, -0.8059], [0.3123, 0.0032, -1.4131]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , UpperCAmelCase , atol=1e-4 ) , f"""{output[:, 1:4, 1:4]}""" ) | 270 | 0 |
"""simple docstring"""
import unittest
from transformers import AutoTokenizer, FalconConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
FalconForCausalLM,
FalconForQuestionAnswering,
FalconForSequenceClassification,
FalconForTokenClassification,
FalconModel,
)
class A__ :
'''simple docstring'''
def __init__( self: Optional[Any] , _SCREAMING_SNAKE_CASE: List[Any] , _SCREAMING_SNAKE_CASE: List[str]=3 , _SCREAMING_SNAKE_CASE: int=7 , _SCREAMING_SNAKE_CASE: Optional[Any]=True , _SCREAMING_SNAKE_CASE: List[Any]=True , _SCREAMING_SNAKE_CASE: Tuple=False , _SCREAMING_SNAKE_CASE: str=True , _SCREAMING_SNAKE_CASE: str=99 , _SCREAMING_SNAKE_CASE: int=32 , _SCREAMING_SNAKE_CASE: List[Any]=5 , _SCREAMING_SNAKE_CASE: Union[str, Any]=4 , _SCREAMING_SNAKE_CASE: Optional[Any]=37 , _SCREAMING_SNAKE_CASE: Optional[Any]="gelu" , _SCREAMING_SNAKE_CASE: Tuple=0.1 , _SCREAMING_SNAKE_CASE: int=0.1 , _SCREAMING_SNAKE_CASE: List[Any]=512 , _SCREAMING_SNAKE_CASE: Any=16 , _SCREAMING_SNAKE_CASE: Any=2 , _SCREAMING_SNAKE_CASE: Optional[Any]=0.02 , _SCREAMING_SNAKE_CASE: int=3 , _SCREAMING_SNAKE_CASE: Optional[int]=4 , _SCREAMING_SNAKE_CASE: Any=None , ) -> int:
"""simple docstring"""
__lowerCAmelCase : Optional[Any] = parent
__lowerCAmelCase : Dict = batch_size
__lowerCAmelCase : Tuple = seq_length
__lowerCAmelCase : Tuple = is_training
__lowerCAmelCase : Union[str, Any] = use_input_mask
__lowerCAmelCase : Dict = use_token_type_ids
__lowerCAmelCase : Optional[Any] = use_labels
__lowerCAmelCase : Tuple = vocab_size
__lowerCAmelCase : Union[str, Any] = hidden_size
__lowerCAmelCase : int = num_hidden_layers
__lowerCAmelCase : str = num_attention_heads
__lowerCAmelCase : Optional[Any] = intermediate_size
__lowerCAmelCase : Dict = hidden_act
__lowerCAmelCase : List[Any] = hidden_dropout_prob
__lowerCAmelCase : Optional[int] = attention_probs_dropout_prob
__lowerCAmelCase : str = max_position_embeddings
__lowerCAmelCase : Optional[int] = type_vocab_size
__lowerCAmelCase : List[str] = type_sequence_label_size
__lowerCAmelCase : Any = initializer_range
__lowerCAmelCase : Dict = num_labels
__lowerCAmelCase : Tuple = num_choices
__lowerCAmelCase : Dict = scope
def _SCREAMING_SNAKE_CASE ( self: Any) -> Union[str, Any]:
"""simple docstring"""
__lowerCAmelCase : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
__lowerCAmelCase : List[str] = None
if self.use_input_mask:
__lowerCAmelCase : str = random_attention_mask([self.batch_size, self.seq_length])
__lowerCAmelCase : int = None
__lowerCAmelCase : List[Any] = None
__lowerCAmelCase : Any = None
__lowerCAmelCase : Optional[Any] = None
if self.use_labels:
__lowerCAmelCase : Union[str, Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size)
__lowerCAmelCase : str = ids_tensor([self.batch_size, self.seq_length] , self.num_labels)
__lowerCAmelCase : Optional[int] = ids_tensor([self.batch_size] , self.num_choices)
__lowerCAmelCase : List[str] = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def _SCREAMING_SNAKE_CASE ( self: Any) -> Any:
"""simple docstring"""
return FalconConfig(
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=_SCREAMING_SNAKE_CASE , initializer_range=self.initializer_range , pad_token_id=1 , new_decoder_architecture=_SCREAMING_SNAKE_CASE , )
def _SCREAMING_SNAKE_CASE ( self: int , _SCREAMING_SNAKE_CASE: Any , _SCREAMING_SNAKE_CASE: Any , _SCREAMING_SNAKE_CASE: List[str] , _SCREAMING_SNAKE_CASE: Optional[int] , _SCREAMING_SNAKE_CASE: List[str] , _SCREAMING_SNAKE_CASE: List[str] , _SCREAMING_SNAKE_CASE: Optional[Any]) -> List[Any]:
"""simple docstring"""
__lowerCAmelCase : str = FalconModel(config=_SCREAMING_SNAKE_CASE)
model.to(_SCREAMING_SNAKE_CASE)
model.eval()
__lowerCAmelCase : Dict = model(_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : List[str] = model(_SCREAMING_SNAKE_CASE)
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size))
def _SCREAMING_SNAKE_CASE ( self: List[Any] , _SCREAMING_SNAKE_CASE: int , _SCREAMING_SNAKE_CASE: Optional[int] , _SCREAMING_SNAKE_CASE: Dict , _SCREAMING_SNAKE_CASE: List[Any] , _SCREAMING_SNAKE_CASE: Dict , _SCREAMING_SNAKE_CASE: Optional[int] , _SCREAMING_SNAKE_CASE: Union[str, Any] , _SCREAMING_SNAKE_CASE: List[Any] , _SCREAMING_SNAKE_CASE: List[Any] , ) -> Tuple:
"""simple docstring"""
__lowerCAmelCase : Dict = True
__lowerCAmelCase : List[str] = FalconModel(_SCREAMING_SNAKE_CASE)
model.to(_SCREAMING_SNAKE_CASE)
model.eval()
__lowerCAmelCase : Dict = model(
_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE , encoder_hidden_states=_SCREAMING_SNAKE_CASE , encoder_attention_mask=_SCREAMING_SNAKE_CASE , )
__lowerCAmelCase : List[Any] = model(
_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE , encoder_hidden_states=_SCREAMING_SNAKE_CASE , )
__lowerCAmelCase : int = model(_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE)
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size))
def _SCREAMING_SNAKE_CASE ( self: str , _SCREAMING_SNAKE_CASE: Tuple , _SCREAMING_SNAKE_CASE: Tuple , _SCREAMING_SNAKE_CASE: str , _SCREAMING_SNAKE_CASE: Union[str, Any] , _SCREAMING_SNAKE_CASE: Optional[int] , _SCREAMING_SNAKE_CASE: str , _SCREAMING_SNAKE_CASE: List[Any] , _SCREAMING_SNAKE_CASE: Optional[int] , _SCREAMING_SNAKE_CASE: Dict , ) -> Any:
"""simple docstring"""
__lowerCAmelCase : Optional[int] = FalconForCausalLM(config=_SCREAMING_SNAKE_CASE)
model.to(_SCREAMING_SNAKE_CASE)
model.eval()
__lowerCAmelCase : Any = model(_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE , labels=_SCREAMING_SNAKE_CASE)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size))
def _SCREAMING_SNAKE_CASE ( self: List[str] , _SCREAMING_SNAKE_CASE: Tuple , _SCREAMING_SNAKE_CASE: int , _SCREAMING_SNAKE_CASE: List[Any] , _SCREAMING_SNAKE_CASE: List[Any] , _SCREAMING_SNAKE_CASE: Optional[int] , _SCREAMING_SNAKE_CASE: Any , _SCREAMING_SNAKE_CASE: str , _SCREAMING_SNAKE_CASE: str , _SCREAMING_SNAKE_CASE: Any , ) -> Optional[Any]:
"""simple docstring"""
__lowerCAmelCase : Dict = True
__lowerCAmelCase : Tuple = True
__lowerCAmelCase : Any = FalconForCausalLM(config=_SCREAMING_SNAKE_CASE)
model.to(_SCREAMING_SNAKE_CASE)
model.eval()
# first forward pass
__lowerCAmelCase : Any = model(
_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE , encoder_hidden_states=_SCREAMING_SNAKE_CASE , encoder_attention_mask=_SCREAMING_SNAKE_CASE , use_cache=_SCREAMING_SNAKE_CASE , )
__lowerCAmelCase : Any = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
__lowerCAmelCase : List[Any] = ids_tensor((self.batch_size, 3) , config.vocab_size)
__lowerCAmelCase : Optional[int] = ids_tensor((self.batch_size, 3) , vocab_size=2)
# append to next input_ids and
__lowerCAmelCase : Dict = torch.cat([input_ids, next_tokens] , dim=-1)
__lowerCAmelCase : Optional[int] = torch.cat([input_mask, next_mask] , dim=-1)
__lowerCAmelCase : Any = model(
_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE , encoder_hidden_states=_SCREAMING_SNAKE_CASE , encoder_attention_mask=_SCREAMING_SNAKE_CASE , output_hidden_states=_SCREAMING_SNAKE_CASE , )["hidden_states"][0]
__lowerCAmelCase : Tuple = model(
_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE , encoder_hidden_states=_SCREAMING_SNAKE_CASE , encoder_attention_mask=_SCREAMING_SNAKE_CASE , past_key_values=_SCREAMING_SNAKE_CASE , output_hidden_states=_SCREAMING_SNAKE_CASE , )["hidden_states"][0]
# select random slice
__lowerCAmelCase : List[str] = ids_tensor((1,) , output_from_past.shape[-1]).item()
__lowerCAmelCase : Optional[int] = output_from_no_past[:, -3:, random_slice_idx].detach()
__lowerCAmelCase : Union[str, Any] = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , atol=1e-3))
def _SCREAMING_SNAKE_CASE ( self: List[str]) -> List[Any]:
"""simple docstring"""
__lowerCAmelCase : str = self.prepare_config_and_inputs()
(
(
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) ,
) : List[Any] = config_and_inputs
__lowerCAmelCase : Any = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class A__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = (
(
FalconModel,
FalconForCausalLM,
FalconForSequenceClassification,
FalconForTokenClassification,
FalconForQuestionAnswering,
)
if is_torch_available()
else ()
)
SCREAMING_SNAKE_CASE = (FalconForCausalLM,) if is_torch_available() else ()
SCREAMING_SNAKE_CASE = (
{
'feature-extraction': FalconModel,
'text-classification': FalconForSequenceClassification,
'text-generation': FalconForCausalLM,
'question-answering': FalconForQuestionAnswering,
'token-classification': FalconForTokenClassification,
'zero-shot': FalconForSequenceClassification,
}
if is_torch_available()
else {}
)
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
def _SCREAMING_SNAKE_CASE ( self: Optional[Any]) -> Dict:
"""simple docstring"""
__lowerCAmelCase : Tuple = FalconModelTester(self)
__lowerCAmelCase : Tuple = ConfigTester(self , config_class=_SCREAMING_SNAKE_CASE , hidden_size=37)
def _SCREAMING_SNAKE_CASE ( self: List[str]) -> Dict:
"""simple docstring"""
self.config_tester.run_common_tests()
def _SCREAMING_SNAKE_CASE ( self: str) -> Dict:
"""simple docstring"""
__lowerCAmelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE)
def _SCREAMING_SNAKE_CASE ( self: Optional[int]) -> Tuple:
"""simple docstring"""
__lowerCAmelCase , *__lowerCAmelCase : str = self.model_tester.prepare_config_and_inputs()
for alibi in [True, False]:
__lowerCAmelCase : str = alibi
self.model_tester.create_and_check_model(_SCREAMING_SNAKE_CASE , *_SCREAMING_SNAKE_CASE)
def _SCREAMING_SNAKE_CASE ( self: int) -> Optional[int]:
"""simple docstring"""
__lowerCAmelCase , __lowerCAmelCase : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
__lowerCAmelCase : Optional[Any] = 3
__lowerCAmelCase : List[str] = input_dict["input_ids"]
__lowerCAmelCase : Optional[Any] = input_ids.ne(1).to(_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : List[Any] = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size)
__lowerCAmelCase : int = FalconForSequenceClassification(_SCREAMING_SNAKE_CASE)
model.to(_SCREAMING_SNAKE_CASE)
model.eval()
__lowerCAmelCase : List[str] = model(_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE , labels=_SCREAMING_SNAKE_CASE)
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels))
def _SCREAMING_SNAKE_CASE ( self: Tuple) -> Any:
"""simple docstring"""
__lowerCAmelCase , __lowerCAmelCase : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
__lowerCAmelCase : Tuple = 3
__lowerCAmelCase : Optional[int] = "single_label_classification"
__lowerCAmelCase : Tuple = input_dict["input_ids"]
__lowerCAmelCase : Optional[Any] = input_ids.ne(1).to(_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : Dict = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size)
__lowerCAmelCase : Tuple = FalconForSequenceClassification(_SCREAMING_SNAKE_CASE)
model.to(_SCREAMING_SNAKE_CASE)
model.eval()
__lowerCAmelCase : Union[str, Any] = model(_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE , labels=_SCREAMING_SNAKE_CASE)
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels))
def _SCREAMING_SNAKE_CASE ( self: int) -> Optional[int]:
"""simple docstring"""
__lowerCAmelCase , __lowerCAmelCase : str = self.model_tester.prepare_config_and_inputs_for_common()
__lowerCAmelCase : str = input_dict["input_ids"]
__lowerCAmelCase : Dict = FalconForCausalLM(_SCREAMING_SNAKE_CASE)
model.to(_SCREAMING_SNAKE_CASE)
model.eval()
__lowerCAmelCase : List[str] = model(_SCREAMING_SNAKE_CASE , use_cache=_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : Optional[Any] = input_ids.shape[0]
__lowerCAmelCase : int = model._convert_to_rw_cache(result.past_key_values)
__lowerCAmelCase : Union[str, Any] = model._convert_cache_to_standard_format(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE)
for layer in range(len(_SCREAMING_SNAKE_CASE)):
for tensor_idx in range(2):
self.assertTrue(rw_cache[layer][tensor_idx].ndim == 3)
self.assertTrue(result.past_key_values[layer][tensor_idx].ndim == 4)
self.assertTrue(
torch.all(result.past_key_values[layer][tensor_idx] == standard_cache[layer][tensor_idx]))
def _SCREAMING_SNAKE_CASE ( self: List[Any]) -> Dict:
"""simple docstring"""
__lowerCAmelCase , __lowerCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
__lowerCAmelCase : Any = 3
__lowerCAmelCase : Optional[Any] = "multi_label_classification"
__lowerCAmelCase : Dict = input_dict["input_ids"]
__lowerCAmelCase : Optional[int] = input_ids.ne(1).to(_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : str = ids_tensor(
[self.model_tester.batch_size, config.num_labels] , self.model_tester.type_sequence_label_size).to(torch.float)
__lowerCAmelCase : int = FalconForSequenceClassification(_SCREAMING_SNAKE_CASE)
model.to(_SCREAMING_SNAKE_CASE)
model.eval()
__lowerCAmelCase : int = model(_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE , labels=_SCREAMING_SNAKE_CASE)
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels))
def _SCREAMING_SNAKE_CASE ( self: int) -> Union[str, Any]:
"""simple docstring"""
for model_class in self.all_generative_model_classes:
__lowerCAmelCase , __lowerCAmelCase : Any = self.model_tester.prepare_config_and_inputs_for_common()
# If it doesn't support cache, pass the test
if not hasattr(_SCREAMING_SNAKE_CASE , "use_cache"):
return
__lowerCAmelCase : Dict = model_class(_SCREAMING_SNAKE_CASE).to(_SCREAMING_SNAKE_CASE)
if "use_cache" not in inputs:
__lowerCAmelCase : str = True
__lowerCAmelCase : Tuple = model(**_SCREAMING_SNAKE_CASE)
# If "past_key_values" is not returned, pass the test (e.g. RWKV uses a different cache name and format)
if "past_key_values" not in outputs:
return
__lowerCAmelCase : int = (
getattr(_SCREAMING_SNAKE_CASE , "decoder_layers" , _SCREAMING_SNAKE_CASE)
or getattr(_SCREAMING_SNAKE_CASE , "num_decoder_layers" , _SCREAMING_SNAKE_CASE)
or config.num_hidden_layers
)
__lowerCAmelCase : List[Any] = getattr(_SCREAMING_SNAKE_CASE , "num_kv_heads" , config.num_attention_heads)
__lowerCAmelCase : List[Any] = getattr(_SCREAMING_SNAKE_CASE , "d_model" , config.hidden_size)
__lowerCAmelCase : Optional[int] = embed_dim // num_attention_heads
__lowerCAmelCase : List[Any] = outputs["past_key_values"]
self.assertEqual(len(_SCREAMING_SNAKE_CASE) , _SCREAMING_SNAKE_CASE)
__lowerCAmelCase , __lowerCAmelCase : int = inputs["input_ids"].shape
for i in range(_SCREAMING_SNAKE_CASE):
if config.new_decoder_architecture:
__lowerCAmelCase : List[str] = config.num_attention_heads
elif config.multi_query:
__lowerCAmelCase : Optional[int] = 1
self.assertEqual(len(past_kv[0]) , 2) # K V for the decoder = 2
self.assertEqual(
past_kv[i][0].shape , (batch_size, num_attention_heads, seq_length, per_head_embed_dim))
self.assertEqual(
past_kv[i][1].shape , (batch_size, num_attention_heads, seq_length, per_head_embed_dim))
@require_torch
class A__ ( unittest.TestCase ):
'''simple docstring'''
@slow
def _SCREAMING_SNAKE_CASE ( self: List[Any]) -> Union[str, Any]:
"""simple docstring"""
__lowerCAmelCase : Tuple = AutoTokenizer.from_pretrained("Rocketknight1/falcon-rw-1b")
__lowerCAmelCase : int = FalconForCausalLM.from_pretrained("Rocketknight1/falcon-rw-1b")
model.eval()
model.to(_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : Any = tokenizer("My favorite food is" , return_tensors="pt").to(_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : Tuple = (
"My favorite food is pizza. I love it so much that I have a pizza party every year for my birthday."
)
__lowerCAmelCase : int = model.generate(**_SCREAMING_SNAKE_CASE , do_sample=_SCREAMING_SNAKE_CASE , max_new_tokens=19)
__lowerCAmelCase : Dict = tokenizer.batch_decode(_SCREAMING_SNAKE_CASE)[0]
self.assertEqual(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE)
@slow
def _SCREAMING_SNAKE_CASE ( self: Tuple) -> Any:
"""simple docstring"""
for repo in ["Rocketknight1/tiny-random-falcon-7b", "Rocketknight1/tiny-random-falcon-40b"]:
__lowerCAmelCase : int = AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : int = FalconForCausalLM.from_pretrained(_SCREAMING_SNAKE_CASE)
model.eval()
model.to(_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : Tuple = tokenizer("My favorite food is" , return_tensors="pt").to(_SCREAMING_SNAKE_CASE)
# We just test that these run without errors - the models are randomly initialized
# and so the actual text outputs will be garbage
model.generate(**_SCREAMING_SNAKE_CASE , do_sample=_SCREAMING_SNAKE_CASE , max_new_tokens=4)
model.generate(**_SCREAMING_SNAKE_CASE , do_sample=_SCREAMING_SNAKE_CASE , max_new_tokens=4)
model.generate(**_SCREAMING_SNAKE_CASE , num_beams=2 , max_new_tokens=4)
@slow
def _SCREAMING_SNAKE_CASE ( self: Union[str, Any]) -> Optional[Any]:
"""simple docstring"""
with torch.no_grad():
for repo in [
"Rocketknight1/falcon-rw-1b",
"Rocketknight1/tiny-random-falcon-7b",
"Rocketknight1/tiny-random-falcon-40b",
]:
__lowerCAmelCase : Optional[Any] = AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : Any = FalconForCausalLM.from_pretrained(_SCREAMING_SNAKE_CASE)
model.eval()
model.to(device=_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : List[Any] = tokenizer("My favorite food is" , return_tensors="pt").to(_SCREAMING_SNAKE_CASE)
# Test results are the same with and without cache
__lowerCAmelCase : int = model.generate(**_SCREAMING_SNAKE_CASE , do_sample=_SCREAMING_SNAKE_CASE , max_new_tokens=20 , use_cache=_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : str = model.generate(**_SCREAMING_SNAKE_CASE , do_sample=_SCREAMING_SNAKE_CASE , max_new_tokens=20 , use_cache=_SCREAMING_SNAKE_CASE)
self.assertTrue((outputs_cache - outputs_no_cache).sum().item() == 0) | 269 |
"""simple docstring"""
import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
from transformers.models.ta.modeling_flax_ta import shift_tokens_right
@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class A__ ( unittest.TestCase ):
'''simple docstring'''
@slow
def _SCREAMING_SNAKE_CASE ( self: Union[str, Any]) -> str:
"""simple docstring"""
__lowerCAmelCase : Optional[Any] = FlaxMTaForConditionalGeneration.from_pretrained("google/mt5-small")
__lowerCAmelCase : Optional[Any] = AutoTokenizer.from_pretrained("google/mt5-small")
__lowerCAmelCase : Tuple = tokenizer("Hello there" , return_tensors="np").input_ids
__lowerCAmelCase : Dict = tokenizer("Hi I am" , return_tensors="np").input_ids
__lowerCAmelCase : str = shift_tokens_right(_SCREAMING_SNAKE_CASE , model.config.pad_token_id , model.config.decoder_start_token_id)
__lowerCAmelCase : Optional[int] = model(_SCREAMING_SNAKE_CASE , decoder_input_ids=_SCREAMING_SNAKE_CASE).logits
__lowerCAmelCase : int = optax.softmax_cross_entropy(_SCREAMING_SNAKE_CASE , onehot(_SCREAMING_SNAKE_CASE , logits.shape[-1])).mean()
__lowerCAmelCase : List[str] = -(labels.shape[-1] * loss.item())
__lowerCAmelCase : str = -84.9127
self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 1e-4) | 269 | 1 |
'''simple docstring'''
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default_hp_space_optuna,
default_hp_space_ray,
default_hp_space_sigopt,
default_hp_space_wandb,
)
from .utils import logging
_UpperCAmelCase : Union[str, Any] = logging.get_logger(__name__)
class a__ :
"""simple docstring"""
__UpperCamelCase : Optional[int] = 42
__UpperCamelCase : List[Any] = None
@staticmethod
def _snake_case ():
raise NotImplementedError
def _snake_case (self , __lowercase , __lowercase , __lowercase , **__lowercase ):
raise NotImplementedError
def _snake_case (self , __lowercase ):
raise NotImplementedError
def _snake_case (self ):
if not self.is_available():
raise RuntimeError(
F"""You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}.""" )
@classmethod
def _snake_case (cls ):
return F"""`pip install {cls.pip_package or cls.name}`"""
class a__ ( __A ):
"""simple docstring"""
__UpperCamelCase : str = 'optuna'
@staticmethod
def _snake_case ():
return is_optuna_available()
def _snake_case (self , __lowercase , __lowercase , __lowercase , **__lowercase ):
return run_hp_search_optuna(_a , _a , _a , **_a )
def _snake_case (self , __lowercase ):
return default_hp_space_optuna(_a )
class a__ ( __A ):
"""simple docstring"""
__UpperCamelCase : Dict = 'ray'
__UpperCamelCase : str = '\'ray[tune]\''
@staticmethod
def _snake_case ():
return is_ray_available()
def _snake_case (self , __lowercase , __lowercase , __lowercase , **__lowercase ):
return run_hp_search_ray(_a , _a , _a , **_a )
def _snake_case (self , __lowercase ):
return default_hp_space_ray(_a )
class a__ ( __A ):
"""simple docstring"""
__UpperCamelCase : Tuple = 'sigopt'
@staticmethod
def _snake_case ():
return is_sigopt_available()
def _snake_case (self , __lowercase , __lowercase , __lowercase , **__lowercase ):
return run_hp_search_sigopt(_a , _a , _a , **_a )
def _snake_case (self , __lowercase ):
return default_hp_space_sigopt(_a )
class a__ ( __A ):
"""simple docstring"""
__UpperCamelCase : Union[str, Any] = 'wandb'
@staticmethod
def _snake_case ():
return is_wandb_available()
def _snake_case (self , __lowercase , __lowercase , __lowercase , **__lowercase ):
return run_hp_search_wandb(_a , _a , _a , **_a )
def _snake_case (self , __lowercase ):
return default_hp_space_wandb(_a )
_UpperCAmelCase : int = {
HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}
def __magic_name__( ):
__lowerCAmelCase = [backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
if len(_snake_case) > 0:
__lowerCAmelCase = available_backends[0].name
if len(_snake_case) > 1:
logger.info(
F"""{len(_snake_case)} hyperparameter search backends available. Using {name} as the default.""")
return name
raise RuntimeError(
'''No hyperparameter search backend available.\n'''
+ '''\n'''.join(
F""" - To install {backend.name} run {backend.pip_install()}"""
for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values()))
| 368 |
'''simple docstring'''
# Imports
import numpy as np
class a__ :
"""simple docstring"""
def __init__(self , __lowercase=None , __lowercase=None , __lowercase=None , __lowercase=None , __lowercase=None ):
self.set_matricies(red=__lowercase , green=__lowercase , blue=__lowercase , red_edge=__lowercase , nir=__lowercase )
def _snake_case (self , __lowercase=None , __lowercase=None , __lowercase=None , __lowercase=None , __lowercase=None ):
if red is not None:
__lowerCAmelCase = red
if green is not None:
__lowerCAmelCase = green
if blue is not None:
__lowerCAmelCase = blue
if red_edge is not None:
__lowerCAmelCase = red_edge
if nir is not None:
__lowerCAmelCase = nir
return True
def _snake_case (self , __lowercase="" , __lowercase=None , __lowercase=None , __lowercase=None , __lowercase=None , __lowercase=None ):
self.set_matricies(red=__lowercase , green=__lowercase , blue=__lowercase , red_edge=__lowercase , nir=__lowercase )
__lowerCAmelCase = {
'''ARVI2''': self.arvaa,
'''CCCI''': self.ccci,
'''CVI''': self.cvi,
'''GLI''': self.gli,
'''NDVI''': self.ndvi,
'''BNDVI''': self.bndvi,
'''redEdgeNDVI''': self.red_edge_ndvi,
'''GNDVI''': self.gndvi,
'''GBNDVI''': self.gbndvi,
'''GRNDVI''': self.grndvi,
'''RBNDVI''': self.rbndvi,
'''PNDVI''': self.pndvi,
'''ATSAVI''': self.atsavi,
'''BWDRVI''': self.bwdrvi,
'''CIgreen''': self.ci_green,
'''CIrededge''': self.ci_rededge,
'''CI''': self.ci,
'''CTVI''': self.ctvi,
'''GDVI''': self.gdvi,
'''EVI''': self.evi,
'''GEMI''': self.gemi,
'''GOSAVI''': self.gosavi,
'''GSAVI''': self.gsavi,
'''Hue''': self.hue,
'''IVI''': self.ivi,
'''IPVI''': self.ipvi,
'''I''': self.i,
'''RVI''': self.rvi,
'''MRVI''': self.mrvi,
'''MSAVI''': self.m_savi,
'''NormG''': self.norm_g,
'''NormNIR''': self.norm_nir,
'''NormR''': self.norm_r,
'''NGRDI''': self.ngrdi,
'''RI''': self.ri,
'''S''': self.s,
'''IF''': self._if,
'''DVI''': self.dvi,
'''TVI''': self.tvi,
'''NDRE''': self.ndre,
}
try:
return funcs[index]()
except KeyError:
print('''Index not in the list!''' )
return False
def _snake_case (self ):
return -0.1_8 + (1.1_7 * ((self.nir - self.red) / (self.nir + self.red)))
def _snake_case (self ):
return ((self.nir - self.redEdge) / (self.nir + self.redEdge)) / (
(self.nir - self.red) / (self.nir + self.red)
)
def _snake_case (self ):
return self.nir * (self.red / (self.green**2))
def _snake_case (self ):
return (2 * self.green - self.red - self.blue) / (
2 * self.green + self.red + self.blue
)
def _snake_case (self ):
return (self.nir - self.red) / (self.nir + self.red)
def _snake_case (self ):
return (self.nir - self.blue) / (self.nir + self.blue)
def _snake_case (self ):
return (self.redEdge - self.red) / (self.redEdge + self.red)
def _snake_case (self ):
return (self.nir - self.green) / (self.nir + self.green)
def _snake_case (self ):
return (self.nir - (self.green + self.blue)) / (
self.nir + (self.green + self.blue)
)
def _snake_case (self ):
return (self.nir - (self.green + self.red)) / (
self.nir + (self.green + self.red)
)
def _snake_case (self ):
return (self.nir - (self.blue + self.red)) / (self.nir + (self.blue + self.red))
def _snake_case (self ):
return (self.nir - (self.green + self.red + self.blue)) / (
self.nir + (self.green + self.red + self.blue)
)
def _snake_case (self , __lowercase=0.0_8 , __lowercase=1.2_2 , __lowercase=0.0_3 ):
return a * (
(self.nir - a * self.red - b)
/ (a * self.nir + self.red - a * b + x * (1 + a**2))
)
def _snake_case (self ):
return (0.1 * self.nir - self.blue) / (0.1 * self.nir + self.blue)
def _snake_case (self ):
return (self.nir / self.green) - 1
def _snake_case (self ):
return (self.nir / self.redEdge) - 1
def _snake_case (self ):
return (self.red - self.blue) / self.red
def _snake_case (self ):
__lowerCAmelCase = self.ndvi()
return ((ndvi + 0.5) / (abs(ndvi + 0.5 ))) * (abs(ndvi + 0.5 ) ** (1 / 2))
def _snake_case (self ):
return self.nir - self.green
def _snake_case (self ):
return 2.5 * (
(self.nir - self.red) / (self.nir + 6 * self.red - 7.5 * self.blue + 1)
)
def _snake_case (self ):
__lowerCAmelCase = (2 * (self.nir**2 - self.red**2) + 1.5 * self.nir + 0.5 * self.red) / (
self.nir + self.red + 0.5
)
return n * (1 - 0.2_5 * n) - (self.red - 0.1_2_5) / (1 - self.red)
def _snake_case (self , __lowercase=0.1_6 ):
return (self.nir - self.green) / (self.nir + self.green + y)
def _snake_case (self , __lowercase=0.5 ):
return ((self.nir - self.green) / (self.nir + self.green + n)) * (1 + n)
def _snake_case (self ):
return np.arctan(
((2 * self.red - self.green - self.blue) / 3_0.5) * (self.green - self.blue) )
def _snake_case (self , __lowercase=None , __lowercase=None ):
return (self.nir - b) / (a * self.red)
def _snake_case (self ):
return (self.nir / ((self.nir + self.red) / 2)) * (self.ndvi() + 1)
def _snake_case (self ):
return (self.red + self.green + self.blue) / 3_0.5
def _snake_case (self ):
return self.nir / self.red
def _snake_case (self ):
return (self.rvi() - 1) / (self.rvi() + 1)
def _snake_case (self ):
return (
(2 * self.nir + 1)
- ((2 * self.nir + 1) ** 2 - 8 * (self.nir - self.red)) ** (1 / 2)
) / 2
def _snake_case (self ):
return self.green / (self.nir + self.red + self.green)
def _snake_case (self ):
return self.nir / (self.nir + self.red + self.green)
def _snake_case (self ):
return self.red / (self.nir + self.red + self.green)
def _snake_case (self ):
return (self.green - self.red) / (self.green + self.red)
def _snake_case (self ):
return (self.red - self.green) / (self.red + self.green)
def _snake_case (self ):
__lowerCAmelCase = np.max([np.max(self.red ), np.max(self.green ), np.max(self.blue )] )
__lowerCAmelCase = np.min([np.min(self.red ), np.min(self.green ), np.min(self.blue )] )
return (max_value - min_value) / max_value
def _snake_case (self ):
return (2 * self.red - self.green - self.blue) / (self.green - self.blue)
def _snake_case (self ):
return self.nir / self.red
def _snake_case (self ):
return (self.ndvi() + 0.5) ** (1 / 2)
def _snake_case (self ):
return (self.nir - self.redEdge) / (self.nir + self.redEdge)
| 9 | 0 |
"""simple docstring"""
from __future__ import annotations
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
if nth_term == "":
return [""]
_a : Optional[int] = int(UpperCamelCase__ )
_a : Union[str, Any] = int(UpperCamelCase__ )
_a : list[str] = []
for temp in range(int(UpperCamelCase__ ) ):
series.append(F"""1 / {pow(temp + 1 , int(UpperCamelCase__ ) )}""" if series else """1""" )
return series
if __name__ == "__main__":
import doctest
doctest.testmod()
_snake_case = int(input('Enter the last number (nth term) of the P-Series'))
_snake_case = int(input('Enter the power for P-Series'))
print('Formula of P-Series => 1+1/2^p+1/3^p ..... 1/n^p')
print(p_series(nth_term, power))
| 294 |
"""simple docstring"""
import unittest
import numpy as np
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = None , ):
'''simple docstring'''
_a : List[Any] = np.shape(UpperCamelCase__ )
_a : Any = np.shape(UpperCamelCase__ )
_a : Union[str, Any] = np.shape(UpperCamelCase__ )
if shape_a[0] != shape_b[0]:
_a : int = (
"""Expected the same number of rows for A and B. """
F"""Instead found A of size {shape_a} and B of size {shape_b}"""
)
raise ValueError(UpperCamelCase__ )
if shape_b[1] != shape_c[1]:
_a : Tuple = (
"""Expected the same number of columns for B and C. """
F"""Instead found B of size {shape_b} and C of size {shape_c}"""
)
raise ValueError(UpperCamelCase__ )
_a : int = pseudo_inv
if a_inv is None:
try:
_a : Optional[int] = np.linalg.inv(UpperCamelCase__ )
except np.linalg.LinAlgError:
raise ValueError(
"""Input matrix A is not invertible. Cannot compute Schur complement.""" )
return mat_c - mat_b.T @ a_inv @ mat_b
class UpperCamelCase ( unittest.TestCase ):
def _lowercase ( self : int ) -> None:
_a : str = np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]] )
_a : Tuple = np.array([[0, 3], [3, 0], [2, 3]] )
_a : Optional[int] = np.array([[2, 1], [6, 3]] )
_a : Optional[Any] = schur_complement(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
_a : Union[str, Any] = np.block([[a, b], [b.T, c]] )
_a : int = np.linalg.det(UpperCAmelCase__ )
_a : Union[str, Any] = np.linalg.det(UpperCAmelCase__ )
_a : List[Any] = np.linalg.det(UpperCAmelCase__ )
self.assertAlmostEqual(UpperCAmelCase__ , det_a * det_s )
def _lowercase ( self : int ) -> None:
_a : Optional[int] = np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]] )
_a : Optional[int] = np.array([[0, 3], [3, 0], [2, 3]] )
_a : Union[str, Any] = np.array([[2, 1], [6, 3]] )
with self.assertRaises(UpperCAmelCase__ ):
schur_complement(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
def _lowercase ( self : List[Any] ) -> None:
_a : Any = np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]] )
_a : Dict = np.array([[0, 3], [3, 0], [2, 3]] )
_a : List[Any] = np.array([[2, 1, 3], [6, 3, 5]] )
with self.assertRaises(UpperCAmelCase__ ):
schur_complement(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
if __name__ == "__main__":
import doctest
doctest.testmod()
unittest.main()
| 294 | 1 |
"""simple docstring"""
from __future__ import annotations
import random
import unittest
from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
TFTransfoXLForSequenceClassification,
TFTransfoXLLMHeadModel,
TFTransfoXLModel,
)
class _UpperCAmelCase :
'''simple docstring'''
def __init__( self , A , ) -> str:
_UpperCAmelCase : Dict = parent
_UpperCAmelCase : Any = 1_3
_UpperCAmelCase : List[Any] = 7
_UpperCAmelCase : List[Any] = 3_0
_UpperCAmelCase : Optional[int] = self.seq_length + self.mem_len
_UpperCAmelCase : List[str] = 1_5
_UpperCAmelCase : Union[str, Any] = True
_UpperCAmelCase : List[str] = True
_UpperCAmelCase : str = 9_9
_UpperCAmelCase : Any = [1_0, 5_0, 8_0]
_UpperCAmelCase : Tuple = 3_2
_UpperCAmelCase : Optional[Any] = 3_2
_UpperCAmelCase : List[Any] = 4
_UpperCAmelCase : int = 8
_UpperCAmelCase : List[str] = 1_2_8
_UpperCAmelCase : str = 2
_UpperCAmelCase : Dict = 2
_UpperCAmelCase : Union[str, Any] = None
_UpperCAmelCase : Tuple = 1
_UpperCAmelCase : Tuple = 0
_UpperCAmelCase : List[str] = 3
_UpperCAmelCase : List[Any] = self.vocab_size - 1
_UpperCAmelCase : int = 0.01
def __lowerCAmelCase ( self ) -> Dict:
_UpperCAmelCase : str = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_UpperCAmelCase : List[str] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_UpperCAmelCase : str = None
if self.use_labels:
_UpperCAmelCase : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_UpperCAmelCase : Tuple = TransfoXLConfig(
vocab_size=self.vocab_size , mem_len=self.mem_len , clamp_len=self.clamp_len , cutoffs=self.cutoffs , d_model=self.hidden_size , d_embed=self.d_embed , n_head=self.num_attention_heads , d_head=self.d_head , d_inner=self.d_inner , div_val=self.div_val , n_layer=self.num_hidden_layers , eos_token_id=self.eos_token_id , pad_token_id=self.vocab_size - 1 , init_range=self.init_range , num_labels=self.num_labels , )
return (config, input_ids_a, input_ids_a, lm_labels)
def __lowerCAmelCase ( self ) -> Dict:
random.seed(self.seed )
tf.random.set_seed(self.seed )
def __lowerCAmelCase ( self , A , A , A , A ) -> Tuple:
_UpperCAmelCase : int = TFTransfoXLModel(A )
_UpperCAmelCase , _UpperCAmelCase : Any = model(A ).to_tuple()
_UpperCAmelCase : Union[str, Any] = {'''input_ids''': input_ids_a, '''mems''': mems_a}
_UpperCAmelCase , _UpperCAmelCase : Union[str, Any] = model(A ).to_tuple()
self.parent.assertEqual(hidden_states_a.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(hidden_states_a.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertListEqual(
[mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , )
self.parent.assertListEqual(
[mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , )
def __lowerCAmelCase ( self , A , A , A , A ) -> Tuple:
_UpperCAmelCase : Optional[int] = TFTransfoXLLMHeadModel(A )
_UpperCAmelCase , _UpperCAmelCase : Union[str, Any] = model(A ).to_tuple()
_UpperCAmelCase : Union[str, Any] = {'''input_ids''': input_ids_a, '''labels''': lm_labels}
_UpperCAmelCase , _UpperCAmelCase : Any = model(A ).to_tuple()
_UpperCAmelCase , _UpperCAmelCase : Any = model([input_ids_a, mems_a] ).to_tuple()
_UpperCAmelCase : str = {'''input_ids''': input_ids_a, '''mems''': mems_a, '''labels''': lm_labels}
_UpperCAmelCase , _UpperCAmelCase : Tuple = model(A ).to_tuple()
self.parent.assertEqual(lm_logits_a.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertListEqual(
[mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , )
self.parent.assertEqual(lm_logits_a.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertListEqual(
[mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , )
def __lowerCAmelCase ( self , A , A , A , A ) -> List[Any]:
_UpperCAmelCase : Union[str, Any] = TFTransfoXLForSequenceClassification(A )
_UpperCAmelCase : Optional[Any] = model(A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __lowerCAmelCase ( self ) -> Optional[Any]:
_UpperCAmelCase : Optional[int] = self.prepare_config_and_inputs()
((_UpperCAmelCase) , (_UpperCAmelCase) , (_UpperCAmelCase) , (_UpperCAmelCase)) : Dict = config_and_inputs
_UpperCAmelCase : str = {'''input_ids''': input_ids_a}
return config, inputs_dict
@require_tf
class _UpperCAmelCase ( a ,a ,unittest.TestCase ):
'''simple docstring'''
a__ =(
(TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
)
a__ =() if is_tf_available() else ()
a__ =(
{
'''feature-extraction''': TFTransfoXLModel,
'''text-classification''': TFTransfoXLForSequenceClassification,
'''text-generation''': TFTransfoXLLMHeadModel,
'''zero-shot''': TFTransfoXLForSequenceClassification,
}
if is_tf_available()
else {}
)
# TODO: add this test when TFTransfoXLLMHead has a linear output layer implemented
a__ =False
a__ =False
a__ =False
a__ =False
def __lowerCAmelCase ( self , A , A , A , A , A ) -> List[str]:
if pipeline_test_casse_name == "TextGenerationPipelineTests":
# Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
# `TransfoXLConfig` was never used in pipeline tests: cannot create a simple
# tokenizer.
return True
return False
def __lowerCAmelCase ( self ) -> Tuple:
_UpperCAmelCase : Dict = TFTransfoXLModelTester(self )
_UpperCAmelCase : Optional[int] = ConfigTester(self , config_class=A , d_embed=3_7 )
def __lowerCAmelCase ( self ) -> int:
self.config_tester.run_common_tests()
def __lowerCAmelCase ( self ) -> Optional[int]:
self.model_tester.set_seed()
_UpperCAmelCase : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_transfo_xl_model(*A )
def __lowerCAmelCase ( self ) -> List[Any]:
self.model_tester.set_seed()
_UpperCAmelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_transfo_xl_lm_head(*A )
def __lowerCAmelCase ( self ) -> Optional[int]:
_UpperCAmelCase : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*A )
def __lowerCAmelCase ( self ) -> List[Any]:
_UpperCAmelCase , _UpperCAmelCase : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
_UpperCAmelCase : List[str] = [TFTransfoXLForSequenceClassification]
for model_class in self.all_model_classes:
_UpperCAmelCase : Union[str, Any] = model_class(A )
assert isinstance(model.get_input_embeddings() , tf.keras.layers.Layer )
if model_class in list_other_models_with_output_ebd:
_UpperCAmelCase : Dict = model.get_output_embeddings()
assert isinstance(A , tf.keras.layers.Layer )
_UpperCAmelCase : Tuple = model.get_bias()
assert name is None
else:
_UpperCAmelCase : str = model.get_output_embeddings()
assert x is None
_UpperCAmelCase : Optional[int] = model.get_bias()
assert name is None
def __lowerCAmelCase ( self ) -> Optional[Any]:
# TODO JP: Make TransfoXL XLA compliant
pass
@slow
def __lowerCAmelCase ( self ) -> Optional[int]:
for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCAmelCase : int = TFTransfoXLModel.from_pretrained(A )
self.assertIsNotNone(A )
@unittest.skip(reason='''This model doesn\'t play well with fit() due to not returning a single loss.''' )
def __lowerCAmelCase ( self ) -> Optional[Any]:
pass
@require_tf
class _UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
@unittest.skip('''Skip test until #12651 is resolved.''' )
@slow
def __lowerCAmelCase ( self ) -> Dict:
_UpperCAmelCase : str = TFTransfoXLLMHeadModel.from_pretrained('''transfo-xl-wt103''' )
# fmt: off
_UpperCAmelCase : Any = tf.convert_to_tensor([[3_3,1_2_9_7,2,1,1_0_0_9,4,1_1_0_9,1_1_7_3_9,4_7_6_2,3_5_8,5,2_5,2_4_5,2_2,1_7_0_6,1_7,2_0_0_9_8,5,3_2_1_5,2_1,3_7,1_1_1_0,3,1_3,1_0_4_1,4,2_4,6_0_3,4_9_0,2,7_1_4_7_7,2_0_0_9_8,1_0_4_4_4_7,2,2_0_9_6_1,1,2_6_0_4,4,1,3_2_9,3,6_2_2_4,8_3_1,1_6_0_0_2,2,8,6_0_3,7_8_9_6_7,2_9_5_4_6,2_3,8_0_3,2_0,2_5,4_1_6,5,8,2_3_2,4,2_7_7,6,1_8_5_5,4_6_0_1,3,2_9_5_4_6,5_4,8,3_6_0_9,5,5_7_2_1_1,4_9,4,1,2_7_7,1_8,8,1_7_5_5,1_5_6_9_1,3,3_4_1,2_5,4_1_6,6_9_3,4_2_5_7_3,7_1,1_7,4_0_1,9_4,3_1,1_7_9_1_9,2,2_9_5_4_6,7_8_7_3,1_8,1,4_3_5,2_3,1_1_0_1_1,7_5_5,5,5_1_6_7,3,7_9_8_3,9_8,8_4,2,2_9_5_4_6,3_2_6_7,8,3_6_0_9,4,1,4_8_6_5,1_0_7_5,2,6_0_8_7,7_1,6,3_4_6,8,5_8_5_4,3,2_9_5_4_6,8_2_4,1_4_0_0,1_8_6_8,2,1_9,1_6_0,2,3_1_1,8,5_4_9_6,2,2_0_9_2_0,1_7,2_5,1_5_0_9_7,3,2_4,2_4,0]] , dtype=tf.intaa ) # noqa: E231
# fmt: on
# In 1991 , the remains of Russian Tsar Nicholas II and his family
# ( except for Alexei and Maria ) are discovered .
# The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the
# remainder of the story . 1883 Western Siberia ,
# a young Grigori Rasputin is asked by his father and a group of men to perform magic .
# Rasputin has a vision and denounces one of the men as a horse thief . Although his
# father initially slaps him for making such an accusation , Rasputin watches as the
# man is chased outside and beaten . Twenty years later , Rasputin sees a vision of
# the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous ,
# with people , even a bishop , begging for his blessing . <eod> </s> <eos>
# fmt: off
_UpperCAmelCase : int = [3_3,1_2_9_7,2,1,1_0_0_9,4,1_1_0_9,1_1_7_3_9,4_7_6_2,3_5_8,5,2_5,2_4_5,2_2,1_7_0_6,1_7,2_0_0_9_8,5,3_2_1_5,2_1,3_7,1_1_1_0,3,1_3,1_0_4_1,4,2_4,6_0_3,4_9_0,2,7_1_4_7_7,2_0_0_9_8,1_0_4_4_4_7,2,2_0_9_6_1,1,2_6_0_4,4,1,3_2_9,3,6_2_2_4,8_3_1,1_6_0_0_2,2,8,6_0_3,7_8_9_6_7,2_9_5_4_6,2_3,8_0_3,2_0,2_5,4_1_6,5,8,2_3_2,4,2_7_7,6,1_8_5_5,4_6_0_1,3,2_9_5_4_6,5_4,8,3_6_0_9,5,5_7_2_1_1,4_9,4,1,2_7_7,1_8,8,1_7_5_5,1_5_6_9_1,3,3_4_1,2_5,4_1_6,6_9_3,4_2_5_7_3,7_1,1_7,4_0_1,9_4,3_1,1_7_9_1_9,2,2_9_5_4_6,7_8_7_3,1_8,1,4_3_5,2_3,1_1_0_1_1,7_5_5,5,5_1_6_7,3,7_9_8_3,9_8,8_4,2,2_9_5_4_6,3_2_6_7,8,3_6_0_9,4,1,4_8_6_5,1_0_7_5,2,6_0_8_7,7_1,6,3_4_6,8,5_8_5_4,3,2_9_5_4_6,8_2_4,1_4_0_0,1_8_6_8,2,1_9,1_6_0,2,3_1_1,8,5_4_9_6,2,2_0_9_2_0,1_7,2_5,1_5_0_9_7,3,2_4,2_4,0,3_3,1,1_8_5_7,2,1,1_0_0_9,4,1_1_0_9,1_1_7_3_9,4_7_6_2,3_5_8,5,2_5,2_4_5,2_8,1_1_1_0,3,1_3,1_0_4_1,4,2_4,6_0_3,4_9_0,2,7_1_4_7_7,2_0_0_9_8,1_0_4_4_4_7,2,2_0_9_6_1,1,2_6_0_4,4,1,3_2_9,3,0] # noqa: E231
# fmt: on
# In 1991, the remains of Russian Tsar Nicholas II and his family (
# except for Alexei and Maria ) are discovered. The voice of young son,
# Tsarevich Alexei Nikolaevich, narrates the remainder of the story.
# 1883 Western Siberia, a young Grigori Rasputin is asked by his father
# and a group of men to perform magic. Rasputin has a vision and
# denounces one of the men as a horse thief. Although his father initially
# slaps him for making such an accusation, Rasputin watches as the man
# is chased outside and beaten. Twenty years later, Rasputin sees a vision
# of the Virgin Mary, prompting him to become a priest.
# Rasputin quickly becomes famous, with people, even a bishop, begging for
# his blessing. <unk> <unk> <eos> In the 1990s, the remains of Russian Tsar
# Nicholas II and his family were discovered. The voice of <unk> young son,
# Tsarevich Alexei Nikolaevich, narrates the remainder of the story.<eos>
_UpperCAmelCase : Any = model.generate(A , max_length=2_0_0 , do_sample=A )
self.assertListEqual(output_ids[0].numpy().tolist() , A )
| 68 |
"""simple docstring"""
import qiskit
def lowerCamelCase_ (UpperCamelCase__ : int , UpperCamelCase__ : int ):
_UpperCAmelCase : List[str] = qiskit.Aer.get_backend('''aer_simulator''' )
# Create a Quantum Circuit acting on the q register
_UpperCAmelCase : Union[str, Any] = qiskit.QuantumCircuit(UpperCamelCase__ , UpperCamelCase__ )
# Apply X (NOT) Gate to Qubits 0 & 1
circuit.x(0 )
circuit.x(1 )
# Map the quantum measurement to the classical bits
circuit.measure([0, 1] , [0, 1] )
# Execute the circuit on the qasm simulator
_UpperCAmelCase : Tuple = qiskit.execute(UpperCamelCase__ , UpperCamelCase__ , shots=1000 )
# Return the histogram data of the results of the experiment.
return job.result().get_counts(UpperCamelCase__ )
if __name__ == "__main__":
_lowerCAmelCase :str = single_qubit_measure(2, 2)
print(f"Total count for various states are: {counts}")
| 68 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase = logging.get_logger(__name__)
lowerCAmelCase = {
"""facebook/data2vec-text-base""": """https://huggingface.co/data2vec/resolve/main/config.json""",
}
class _a ( UpperCAmelCase__ ):
_lowercase : List[str] = '''data2vec-text'''
def __init__( self: Union[str, Any] , UpperCamelCase_: List[Any]=30_522 , UpperCamelCase_: Any=768 , UpperCamelCase_: List[str]=12 , UpperCamelCase_: Optional[Any]=12 , UpperCamelCase_: Any=3_072 , UpperCamelCase_: Dict="gelu" , UpperCamelCase_: Any=0.1 , UpperCamelCase_: List[str]=0.1 , UpperCamelCase_: Dict=512 , UpperCamelCase_: List[str]=2 , UpperCamelCase_: List[Any]=0.02 , UpperCamelCase_: List[Any]=1E-1_2 , UpperCamelCase_: List[Any]=1 , UpperCamelCase_: Optional[int]=0 , UpperCamelCase_: Dict=2 , UpperCamelCase_: Optional[int]="absolute" , UpperCamelCase_: Tuple=True , UpperCamelCase_: Any=None , **UpperCamelCase_: List[str] , ) -> Optional[int]:
"""simple docstring"""
super().__init__(pad_token_id=_a , bos_token_id=_a , eos_token_id=_a , **_a )
lowercase__ = vocab_size
lowercase__ = hidden_size
lowercase__ = num_hidden_layers
lowercase__ = num_attention_heads
lowercase__ = hidden_act
lowercase__ = intermediate_size
lowercase__ = hidden_dropout_prob
lowercase__ = attention_probs_dropout_prob
lowercase__ = max_position_embeddings
lowercase__ = type_vocab_size
lowercase__ = initializer_range
lowercase__ = layer_norm_eps
lowercase__ = position_embedding_type
lowercase__ = use_cache
lowercase__ = classifier_dropout
class _a ( UpperCAmelCase__ ):
@property
def lowerCamelCase_ ( self: Tuple ) -> Dict:
"""simple docstring"""
if self.task == "multiple-choice":
lowercase__ = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
lowercase__ = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
] )
| 110 |
"""simple docstring"""
from sklearn.metrics import recall_score
import datasets
lowerCAmelCase : Any = """
Recall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:
Recall = TP / (TP + FN)
Where TP is the true positives and FN is the false negatives.
"""
lowerCAmelCase : Any = """
Args:
- **predictions** (`list` of `int`): The predicted labels.
- **references** (`list` of `int`): The ground truth labels.
- **labels** (`list` of `int`): The set of labels to include when `average` is not set to `binary`, and their order when average is `None`. Labels present in the data can be excluded in this input, for example to calculate a multiclass average ignoring a majority negative class, while labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in y_true and y_pred are used in sorted order. Defaults to None.
- **pos_label** (`int`): The class label to use as the 'positive class' when calculating the recall. Defaults to `1`.
- **average** (`string`): This parameter is required for multiclass/multilabel targets. If None, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `'binary'`.
- `'binary'`: Only report results for the class specified by `pos_label`. This is applicable only if the target labels and predictions are binary.
- `'micro'`: Calculate metrics globally by counting the total true positives, false negatives, and false positives.
- `'macro'`: Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.
- `'weighted'`: Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `'macro'` to account for label imbalance. Note that it can result in an F-score that is not between precision and recall.
- `'samples'`: Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).
- **sample_weight** (`list` of `float`): Sample weights Defaults to `None`.
- **zero_division** (): Sets the value to return when there is a zero division. Defaults to .
- `'warn'`: If there is a zero division, the return value is `0`, but warnings are also raised.
- `0`: If there is a zero division, the return value is `0`.
- `1`: If there is a zero division, the return value is `1`.
Returns:
- **recall** (`float`, or `array` of `float`): Either the general recall score, or the recall scores for individual classes, depending on the values input to `labels` and `average`. Minimum possible value is 0. Maximum possible value is 1. A higher recall means that more of the positive examples have been labeled correctly. Therefore, a higher recall is generally considered better.
Examples:
Example 1-A simple example with some errors
>>> recall_metric = datasets.load_metric('recall')
>>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1])
>>> print(results)
{'recall': 0.6666666666666666}
Example 2-The same example as Example 1, but with `pos_label=0` instead of the default `pos_label=1`.
>>> recall_metric = datasets.load_metric('recall')
>>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], pos_label=0)
>>> print(results)
{'recall': 0.5}
Example 3-The same example as Example 1, but with `sample_weight` included.
>>> recall_metric = datasets.load_metric('recall')
>>> sample_weight = [0.9, 0.2, 0.9, 0.3, 0.8]
>>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], sample_weight=sample_weight)
>>> print(results)
{'recall': 0.55}
Example 4-A multiclass example, using different averages.
>>> recall_metric = datasets.load_metric('recall')
>>> predictions = [0, 2, 1, 0, 0, 1]
>>> references = [0, 1, 2, 0, 1, 2]
>>> results = recall_metric.compute(predictions=predictions, references=references, average='macro')
>>> print(results)
{'recall': 0.3333333333333333}
>>> results = recall_metric.compute(predictions=predictions, references=references, average='micro')
>>> print(results)
{'recall': 0.3333333333333333}
>>> results = recall_metric.compute(predictions=predictions, references=references, average='weighted')
>>> print(results)
{'recall': 0.3333333333333333}
>>> results = recall_metric.compute(predictions=predictions, references=references, average=None)
>>> print(results)
{'recall': array([1., 0., 0.])}
"""
lowerCAmelCase : Any = """
@article{scikit-learn, title={Scikit-learn: Machine Learning in {P}ython}, author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V. and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P. and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.}, journal={Journal of Machine Learning Research}, volume={12}, pages={2825--2830}, year={2011}
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __magic_name__ ( datasets.Metric ):
'''simple docstring'''
def _lowerCAmelCase ( self ):
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Sequence(datasets.Value("""int32""" ) ),
"""references""": datasets.Sequence(datasets.Value("""int32""" ) ),
}
if self.config_name == """multilabel"""
else {
"""predictions""": datasets.Value("""int32""" ),
"""references""": datasets.Value("""int32""" ),
} ) , reference_urls=["""https://scikit-learn.org/stable/modules/generated/sklearn.metrics.recall_score.html"""] , )
def _lowerCAmelCase ( self , _a , _a , _a=None , _a=1 , _a="binary" , _a=None , _a="warn" , ):
"""simple docstring"""
lowerCamelCase = recall_score(
_a , _a , labels=_a , pos_label=_a , average=_a , sample_weight=_a , zero_division=_a , )
return {"recall": float(_a ) if score.size == 1 else score}
| 291 | 0 |
'''simple docstring'''
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
__UpperCAmelCase =logging.get_logger(__name__)
__UpperCAmelCase ={"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}
__UpperCAmelCase ={
"vocab_file": {
"gpt2": "https://huggingface.co/gpt2/resolve/main/vocab.json",
"gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/vocab.json",
"gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/vocab.json",
"gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/vocab.json",
"distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/vocab.json",
},
"merges_file": {
"gpt2": "https://huggingface.co/gpt2/resolve/main/merges.txt",
"gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/merges.txt",
"gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/merges.txt",
"gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/merges.txt",
"distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/merges.txt",
},
"tokenizer_file": {
"gpt2": "https://huggingface.co/gpt2/resolve/main/tokenizer.json",
"gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json",
"gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/tokenizer.json",
"gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json",
"distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/tokenizer.json",
},
}
__UpperCAmelCase ={
"gpt2": 1_0_2_4,
"gpt2-medium": 1_0_2_4,
"gpt2-large": 1_0_2_4,
"gpt2-xl": 1_0_2_4,
"distilgpt2": 1_0_2_4,
}
class a__ ( UpperCAmelCase__ ):
lowerCamelCase : Any =VOCAB_FILES_NAMES
lowerCamelCase : Optional[Any] =PRETRAINED_VOCAB_FILES_MAP
lowerCamelCase : int =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCamelCase : Union[str, Any] =["input_ids", "attention_mask"]
lowerCamelCase : List[Any] =GPTaTokenizer
def __init__( self : List[str] , a : Dict=None , a : Optional[Any]=None , a : int=None , a : Optional[int]="<|endoftext|>" , a : List[str]="<|endoftext|>" , a : Union[str, Any]="<|endoftext|>" , a : Tuple=False , **a : Any , ):
"""simple docstring"""
super().__init__(
a , a , tokenizer_file=a , unk_token=a , bos_token=a , eos_token=a , add_prefix_space=a , **a , )
__lowerCamelCase = kwargs.pop('''add_bos_token''' , a )
__lowerCamelCase = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get('''add_prefix_space''' , a ) != add_prefix_space:
__lowerCamelCase = getattr(a , pre_tok_state.pop('''type''' ) )
__lowerCamelCase = add_prefix_space
__lowerCamelCase = pre_tok_class(**a )
__lowerCamelCase = add_prefix_space
def SCREAMING_SNAKE_CASE__ ( self : str , *a : Optional[Any] , **a : str ):
"""simple docstring"""
__lowerCamelCase = kwargs.get('''is_split_into_words''' , a )
assert self.add_prefix_space or not is_split_into_words, (
f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
"to use it with pretokenized inputs."
)
return super()._batch_encode_plus(*a , **a )
def SCREAMING_SNAKE_CASE__ ( self : int , *a : List[str] , **a : int ):
"""simple docstring"""
__lowerCamelCase = kwargs.get('''is_split_into_words''' , a )
assert self.add_prefix_space or not is_split_into_words, (
f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
"to use it with pretokenized inputs."
)
return super()._encode_plus(*a , **a )
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] , a : str , a : Optional[str] = None ):
"""simple docstring"""
__lowerCamelCase = self._tokenizer.model.save(a , name=a )
return tuple(a )
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] , a : "Conversation" ):
"""simple docstring"""
__lowerCamelCase = []
for is_user, text in conversation.iter_texts():
input_ids.extend(self.encode(a , add_special_tokens=a ) + [self.eos_token_id] )
if len(a ) > self.model_max_length:
__lowerCamelCase = input_ids[-self.model_max_length :]
return input_ids
| 237 | '''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase =logging.get_logger(__name__)
__UpperCAmelCase ={
"microsoft/unispeech-sat-base-100h-libri-ft": (
"https://huggingface.co/microsoft/unispeech-sat-base-100h-libri-ft/resolve/main/config.json"
),
# See all UniSpeechSat models at https://huggingface.co/models?filter=unispeech_sat
}
class a__ ( UpperCAmelCase__ ):
lowerCamelCase : List[Any] ="unispeech-sat"
def __init__( self : Dict , a : str=32 , a : Any=7_68 , a : Optional[Any]=12 , a : Optional[int]=12 , a : int=30_72 , a : int="gelu" , a : Dict=0.1 , a : Dict=0.1 , a : List[Any]=0.1 , a : Tuple=0.0 , a : Optional[Any]=0.0 , a : Tuple=0.1 , a : List[Any]=0.1 , a : str=0.02 , a : List[Any]=1e-5 , a : int="group" , a : Union[str, Any]="gelu" , a : Optional[int]=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12) , a : List[Any]=(5, 2, 2, 2, 2, 2, 2) , a : int=(10, 3, 3, 3, 3, 2, 2) , a : Optional[Any]=False , a : Any=1_28 , a : Tuple=16 , a : str=False , a : Optional[Any]=True , a : Dict=0.05 , a : List[Any]=10 , a : Any=2 , a : Optional[Any]=0.0 , a : Optional[Any]=10 , a : Any=0 , a : Any=3_20 , a : str=2 , a : List[str]=0.1 , a : List[str]=1_00 , a : List[str]=2_56 , a : str=2_56 , a : Dict=0.1 , a : Optional[Any]="mean" , a : str=False , a : Tuple=False , a : Optional[Any]=2_56 , a : int=(5_12, 5_12, 5_12, 5_12, 15_00) , a : int=(5, 3, 3, 1, 1) , a : Any=(1, 2, 3, 1, 1) , a : Union[str, Any]=5_12 , a : Optional[int]=0 , a : Optional[int]=1 , a : Optional[int]=2 , a : int=5_04 , **a : Dict , ):
"""simple docstring"""
super().__init__(**a , pad_token_id=a , bos_token_id=a , eos_token_id=a )
__lowerCamelCase = hidden_size
__lowerCamelCase = feat_extract_norm
__lowerCamelCase = feat_extract_activation
__lowerCamelCase = list(a )
__lowerCamelCase = list(a )
__lowerCamelCase = list(a )
__lowerCamelCase = conv_bias
__lowerCamelCase = num_conv_pos_embeddings
__lowerCamelCase = num_conv_pos_embedding_groups
__lowerCamelCase = len(self.conv_dim )
__lowerCamelCase = num_hidden_layers
__lowerCamelCase = intermediate_size
__lowerCamelCase = hidden_act
__lowerCamelCase = num_attention_heads
__lowerCamelCase = hidden_dropout
__lowerCamelCase = attention_dropout
__lowerCamelCase = activation_dropout
__lowerCamelCase = feat_proj_dropout
__lowerCamelCase = final_dropout
__lowerCamelCase = layerdrop
__lowerCamelCase = layer_norm_eps
__lowerCamelCase = initializer_range
__lowerCamelCase = vocab_size
__lowerCamelCase = num_clusters
__lowerCamelCase = do_stable_layer_norm
__lowerCamelCase = use_weighted_layer_sum
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
'''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
f""" {len(self.conv_dim )}`, `len(config.conv_stride) = {len(self.conv_stride )}`,"""
f""" `len(config.conv_kernel) = {len(self.conv_kernel )}`.""" )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
__lowerCamelCase = apply_spec_augment
__lowerCamelCase = mask_time_prob
__lowerCamelCase = mask_time_length
__lowerCamelCase = mask_time_min_masks
__lowerCamelCase = mask_feature_prob
__lowerCamelCase = mask_feature_length
__lowerCamelCase = mask_feature_min_masks
# parameters for pretraining with codevector quantized representations
__lowerCamelCase = num_codevectors_per_group
__lowerCamelCase = num_codevector_groups
__lowerCamelCase = contrastive_logits_temperature
__lowerCamelCase = feat_quantizer_dropout
__lowerCamelCase = num_negatives
__lowerCamelCase = codevector_dim
__lowerCamelCase = proj_codevector_dim
__lowerCamelCase = diversity_loss_weight
# ctc loss
__lowerCamelCase = ctc_loss_reduction
__lowerCamelCase = ctc_zero_infinity
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
__lowerCamelCase = classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
__lowerCamelCase = list(a )
__lowerCamelCase = list(a )
__lowerCamelCase = list(a )
__lowerCamelCase = xvector_output_dim
@property
def SCREAMING_SNAKE_CASE__ ( self : Any ):
"""simple docstring"""
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 237 | 1 |
"""simple docstring"""
from math import sqrt
def _UpperCAmelCase ( __lowerCamelCase : int = 1_00_00_00 ) -> int:
_snake_case = 0
_snake_case = 0
_snake_case = 42
while num_cuboids <= limit:
max_cuboid_size += 1
for sum_shortest_sides in range(2 , 2 * max_cuboid_size + 1 ):
if sqrt(sum_shortest_sides**2 + max_cuboid_size**2 ).is_integer():
num_cuboids += (
min(__lowerCamelCase , sum_shortest_sides // 2 )
- max(1 , sum_shortest_sides - max_cuboid_size )
+ 1
)
return max_cuboid_size
if __name__ == "__main__":
print(F"{solution() = }")
| 288 |
"""simple docstring"""
import logging
from transformers.configuration_utils import PretrainedConfig
UpperCAmelCase__ = logging.getLogger(__name__)
class lowerCAmelCase__ ( A_ ):
__a = """masked_bert"""
def __init__( self : Union[str, Any] , _lowerCamelCase : Any=30522 , _lowerCamelCase : Union[str, Any]=768 , _lowerCamelCase : Tuple=12 , _lowerCamelCase : Any=12 , _lowerCamelCase : str=3072 , _lowerCamelCase : str="gelu" , _lowerCamelCase : int=0.1 , _lowerCamelCase : Optional[int]=0.1 , _lowerCamelCase : Dict=512 , _lowerCamelCase : List[Any]=2 , _lowerCamelCase : int=0.0_2 , _lowerCamelCase : Union[str, Any]=1e-12 , _lowerCamelCase : Union[str, Any]=0 , _lowerCamelCase : List[str]="topK" , _lowerCamelCase : Optional[Any]="constant" , _lowerCamelCase : Optional[Any]=0.0 , **_lowerCamelCase : str , ):
super().__init__(pad_token_id=_lowerCamelCase , **_lowerCamelCase )
_snake_case = vocab_size
_snake_case = hidden_size
_snake_case = num_hidden_layers
_snake_case = num_attention_heads
_snake_case = hidden_act
_snake_case = intermediate_size
_snake_case = hidden_dropout_prob
_snake_case = attention_probs_dropout_prob
_snake_case = max_position_embeddings
_snake_case = type_vocab_size
_snake_case = initializer_range
_snake_case = layer_norm_eps
_snake_case = pruning_method
_snake_case = mask_init
_snake_case = mask_scale
| 288 | 1 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class _a ( unittest.TestCase ):
@property
def snake_case ( self : str ) -> Any:
'''simple docstring'''
torch.manual_seed(0 )
_UpperCamelCase : Dict = UNetaDModel(
block_out_channels=(3_2, 6_4), layers_per_block=2, sample_size=3_2, in_channels=3, out_channels=3, down_block_types=('''DownBlock2D''', '''AttnDownBlock2D'''), up_block_types=('''AttnUpBlock2D''', '''UpBlock2D'''), )
return model
def snake_case ( self : Optional[Any] ) -> Dict:
'''simple docstring'''
_UpperCamelCase : int = self.dummy_uncond_unet
_UpperCamelCase : str = KarrasVeScheduler()
_UpperCamelCase : Union[str, Any] = KarrasVePipeline(unet=__lowerCAmelCase, scheduler=__lowerCAmelCase )
pipe.to(__lowerCAmelCase )
pipe.set_progress_bar_config(disable=__lowerCAmelCase )
_UpperCamelCase : Optional[Any] = torch.manual_seed(0 )
_UpperCamelCase : Tuple = pipe(num_inference_steps=2, generator=__lowerCAmelCase, output_type='''numpy''' ).images
_UpperCamelCase : str = torch.manual_seed(0 )
_UpperCamelCase : Optional[int] = pipe(num_inference_steps=2, generator=__lowerCAmelCase, output_type='''numpy''', return_dict=__lowerCAmelCase )[0]
_UpperCamelCase : Optional[Any] = image[0, -3:, -3:, -1]
_UpperCamelCase : List[Any] = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 3_2, 3_2, 3)
_UpperCamelCase : Union[str, Any] = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
@slow
@require_torch
class _a ( unittest.TestCase ):
def snake_case ( self : Any ) -> Optional[Any]:
'''simple docstring'''
_UpperCamelCase : Dict = '''google/ncsnpp-celebahq-256'''
_UpperCamelCase : Tuple = UNetaDModel.from_pretrained(__lowerCAmelCase )
_UpperCamelCase : str = KarrasVeScheduler()
_UpperCamelCase : Any = KarrasVePipeline(unet=__lowerCAmelCase, scheduler=__lowerCAmelCase )
pipe.to(__lowerCAmelCase )
pipe.set_progress_bar_config(disable=__lowerCAmelCase )
_UpperCamelCase : Optional[int] = torch.manual_seed(0 )
_UpperCamelCase : Union[str, Any] = pipe(num_inference_steps=2_0, generator=__lowerCAmelCase, output_type='''numpy''' ).images
_UpperCamelCase : List[str] = image[0, -3:, -3:, -1]
assert image.shape == (1, 2_5_6, 2_5_6, 3)
_UpperCamelCase : Optional[Any] = np.array([0.578, 0.5_811, 0.5_924, 0.5_809, 0.587, 0.5_886, 0.5_861, 0.5_802, 0.586] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
| 358 |
"""simple docstring"""
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class _a ( _lowerCAmelCase ):
UpperCamelCase = ['''image_processor''', '''tokenizer''']
UpperCamelCase = '''BlipImageProcessor'''
UpperCamelCase = '''AutoTokenizer'''
def __init__( self : List[str], lowerCAmelCase__ : Optional[int], lowerCAmelCase__ : Optional[int] ) -> int:
'''simple docstring'''
_UpperCamelCase : Any = False
super().__init__(lowerCAmelCase__, lowerCAmelCase__ )
_UpperCamelCase : Tuple = self.image_processor
def __call__( self : str, lowerCAmelCase__ : ImageInput = None, lowerCAmelCase__ : Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None, lowerCAmelCase__ : bool = True, lowerCAmelCase__ : Union[bool, str, PaddingStrategy] = False, lowerCAmelCase__ : Union[bool, str, TruncationStrategy] = None, lowerCAmelCase__ : Optional[int] = None, lowerCAmelCase__ : int = 0, lowerCAmelCase__ : Optional[int] = None, lowerCAmelCase__ : Optional[bool] = None, lowerCAmelCase__ : bool = False, lowerCAmelCase__ : bool = False, lowerCAmelCase__ : bool = False, lowerCAmelCase__ : bool = False, lowerCAmelCase__ : bool = False, lowerCAmelCase__ : bool = True, lowerCAmelCase__ : Optional[Union[str, TensorType]] = None, **lowerCAmelCase__ : Optional[Any], ) -> BatchEncoding:
'''simple docstring'''
if images is None and text is None:
raise ValueError('''You have to specify either images or text.''' )
# Get only text
if images is None:
_UpperCamelCase : int = self.tokenizer
_UpperCamelCase : List[str] = self.tokenizer(
text=lowerCAmelCase__, add_special_tokens=lowerCAmelCase__, padding=lowerCAmelCase__, truncation=lowerCAmelCase__, max_length=lowerCAmelCase__, stride=lowerCAmelCase__, pad_to_multiple_of=lowerCAmelCase__, return_attention_mask=lowerCAmelCase__, return_overflowing_tokens=lowerCAmelCase__, return_special_tokens_mask=lowerCAmelCase__, return_offsets_mapping=lowerCAmelCase__, return_token_type_ids=lowerCAmelCase__, return_length=lowerCAmelCase__, verbose=lowerCAmelCase__, return_tensors=lowerCAmelCase__, **lowerCAmelCase__, )
return text_encoding
# add pixel_values
_UpperCamelCase : List[str] = self.image_processor(lowerCAmelCase__, return_tensors=lowerCAmelCase__ )
if text is not None:
_UpperCamelCase : Any = self.tokenizer(
text=lowerCAmelCase__, add_special_tokens=lowerCAmelCase__, padding=lowerCAmelCase__, truncation=lowerCAmelCase__, max_length=lowerCAmelCase__, stride=lowerCAmelCase__, pad_to_multiple_of=lowerCAmelCase__, return_attention_mask=lowerCAmelCase__, return_overflowing_tokens=lowerCAmelCase__, return_special_tokens_mask=lowerCAmelCase__, return_offsets_mapping=lowerCAmelCase__, return_token_type_ids=lowerCAmelCase__, return_length=lowerCAmelCase__, verbose=lowerCAmelCase__, return_tensors=lowerCAmelCase__, **lowerCAmelCase__, )
else:
_UpperCamelCase : List[Any] = None
if text_encoding is not None:
encoding_image_processor.update(lowerCAmelCase__ )
return encoding_image_processor
def snake_case ( self : List[Any], *lowerCAmelCase__ : List[str], **lowerCAmelCase__ : str ) -> Optional[int]:
'''simple docstring'''
return self.tokenizer.batch_decode(*lowerCAmelCase__, **lowerCAmelCase__ )
def snake_case ( self : List[Any], *lowerCAmelCase__ : Dict, **lowerCAmelCase__ : Any ) -> List[str]:
'''simple docstring'''
return self.tokenizer.decode(*lowerCAmelCase__, **lowerCAmelCase__ )
@property
# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
def snake_case ( self : Any ) -> str:
'''simple docstring'''
_UpperCamelCase : List[str] = self.tokenizer.model_input_names
_UpperCamelCase : Union[str, Any] = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
| 128 | 0 |
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_OBJECT_DETECTION_MAPPING,
AutoFeatureExtractor,
AutoModelForObjectDetection,
ObjectDetectionPipeline,
is_vision_available,
pipeline,
)
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_pytesseract,
require_tf,
require_timm,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
else:
class _snake_case :
@staticmethod
def lowerCAmelCase__ ( *a__ , **a__ ) -> List[str]:
'''simple docstring'''
pass
@is_pipeline_test
@require_vision
@require_timm
@require_torch
class _snake_case ( unittest.TestCase ):
lowerCAmelCase_ : str = MODEL_FOR_OBJECT_DETECTION_MAPPING
def lowerCAmelCase__ ( self , a__ , a__ , a__ ) -> Tuple:
'''simple docstring'''
snake_case_ = ObjectDetectionPipeline(model=a__ , image_processor=a__ )
return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]
def lowerCAmelCase__ ( self , a__ , a__ ) -> List[Any]:
'''simple docstring'''
snake_case_ = object_detector("./tests/fixtures/tests_samples/COCO/000000039769.png" , threshold=0.0 )
self.assertGreater(len(a__ ) , 0 )
for detected_object in outputs:
self.assertEqual(
a__ , {
"score": ANY(a__ ),
"label": ANY(a__ ),
"box": {"xmin": ANY(a__ ), "ymin": ANY(a__ ), "xmax": ANY(a__ ), "ymax": ANY(a__ )},
} , )
import datasets
snake_case_ = datasets.load_dataset("hf-internal-testing/fixtures_image_utils" , "image" , split="test" )
snake_case_ = [
Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" ),
"http://images.cocodataset.org/val2017/000000039769.jpg",
# RGBA
dataset[0]["file"],
# LA
dataset[1]["file"],
# L
dataset[2]["file"],
]
snake_case_ = object_detector(a__ , threshold=0.0 )
self.assertEqual(len(a__ ) , len(a__ ) )
for outputs in batch_outputs:
self.assertGreater(len(a__ ) , 0 )
for detected_object in outputs:
self.assertEqual(
a__ , {
"score": ANY(a__ ),
"label": ANY(a__ ),
"box": {"xmin": ANY(a__ ), "ymin": ANY(a__ ), "xmax": ANY(a__ ), "ymax": ANY(a__ )},
} , )
@require_tf
@unittest.skip("Object detection not implemented in TF" )
def lowerCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
pass
@require_torch
def lowerCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
snake_case_ = "hf-internal-testing/tiny-detr-mobilenetsv3"
snake_case_ = AutoModelForObjectDetection.from_pretrained(a__ )
snake_case_ = AutoFeatureExtractor.from_pretrained(a__ )
snake_case_ = ObjectDetectionPipeline(model=a__ , feature_extractor=a__ )
snake_case_ = object_detector("http://images.cocodataset.org/val2017/000000039769.jpg" , threshold=0.0 )
self.assertEqual(
nested_simplify(a__ , decimals=4 ) , [
{"score": 0.3_3_7_6, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
{"score": 0.3_3_7_6, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
] , )
snake_case_ = object_detector(
[
"http://images.cocodataset.org/val2017/000000039769.jpg",
"http://images.cocodataset.org/val2017/000000039769.jpg",
] , threshold=0.0 , )
self.assertEqual(
nested_simplify(a__ , decimals=4 ) , [
[
{"score": 0.3_3_7_6, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
{"score": 0.3_3_7_6, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
],
[
{"score": 0.3_3_7_6, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
{"score": 0.3_3_7_6, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
],
] , )
@require_torch
@slow
def lowerCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
snake_case_ = "facebook/detr-resnet-50"
snake_case_ = AutoModelForObjectDetection.from_pretrained(a__ )
snake_case_ = AutoFeatureExtractor.from_pretrained(a__ )
snake_case_ = ObjectDetectionPipeline(model=a__ , feature_extractor=a__ )
snake_case_ = object_detector("http://images.cocodataset.org/val2017/000000039769.jpg" )
self.assertEqual(
nested_simplify(a__ , decimals=4 ) , [
{"score": 0.9_9_8_2, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
{"score": 0.9_9_6_0, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
{"score": 0.9_9_5_5, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
{"score": 0.9_9_8_8, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
{"score": 0.9_9_8_7, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
] , )
snake_case_ = object_detector(
[
"http://images.cocodataset.org/val2017/000000039769.jpg",
"http://images.cocodataset.org/val2017/000000039769.jpg",
] )
self.assertEqual(
nested_simplify(a__ , decimals=4 ) , [
[
{"score": 0.9_9_8_2, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
{"score": 0.9_9_6_0, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
{"score": 0.9_9_5_5, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
{"score": 0.9_9_8_8, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
{"score": 0.9_9_8_7, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
],
[
{"score": 0.9_9_8_2, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
{"score": 0.9_9_6_0, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
{"score": 0.9_9_5_5, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
{"score": 0.9_9_8_8, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
{"score": 0.9_9_8_7, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
],
] , )
@require_torch
@slow
def lowerCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ = "facebook/detr-resnet-50"
snake_case_ = pipeline("object-detection" , model=a__ )
snake_case_ = object_detector("http://images.cocodataset.org/val2017/000000039769.jpg" )
self.assertEqual(
nested_simplify(a__ , decimals=4 ) , [
{"score": 0.9_9_8_2, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
{"score": 0.9_9_6_0, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
{"score": 0.9_9_5_5, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
{"score": 0.9_9_8_8, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
{"score": 0.9_9_8_7, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
] , )
snake_case_ = object_detector(
[
"http://images.cocodataset.org/val2017/000000039769.jpg",
"http://images.cocodataset.org/val2017/000000039769.jpg",
] )
self.assertEqual(
nested_simplify(a__ , decimals=4 ) , [
[
{"score": 0.9_9_8_2, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
{"score": 0.9_9_6_0, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
{"score": 0.9_9_5_5, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
{"score": 0.9_9_8_8, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
{"score": 0.9_9_8_7, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
],
[
{"score": 0.9_9_8_2, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
{"score": 0.9_9_6_0, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
{"score": 0.9_9_5_5, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
{"score": 0.9_9_8_8, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
{"score": 0.9_9_8_7, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
],
] , )
@require_torch
@slow
def lowerCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ = 0.9_9_8_5
snake_case_ = "facebook/detr-resnet-50"
snake_case_ = pipeline("object-detection" , model=a__ )
snake_case_ = object_detector("http://images.cocodataset.org/val2017/000000039769.jpg" , threshold=a__ )
self.assertEqual(
nested_simplify(a__ , decimals=4 ) , [
{"score": 0.9_9_8_8, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
{"score": 0.9_9_8_7, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
] , )
@require_torch
@require_pytesseract
@slow
def lowerCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
snake_case_ = "Narsil/layoutlmv3-finetuned-funsd"
snake_case_ = 0.9_9_9_3
snake_case_ = pipeline("object-detection" , model=a__ , threshold=a__ )
snake_case_ = object_detector(
"https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png" )
self.assertEqual(
nested_simplify(a__ , decimals=4 ) , [
{"score": 0.9_9_9_3, "label": "I-ANSWER", "box": {"xmin": 294, "ymin": 254, "xmax": 343, "ymax": 264}},
{"score": 0.9_9_9_3, "label": "I-ANSWER", "box": {"xmin": 294, "ymin": 254, "xmax": 343, "ymax": 264}},
] , )
| 85 |
a__: Dict = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
a__: str = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def UpperCamelCase__( UpperCamelCase__ : dict[int, list[int]] , UpperCamelCase__ : int , UpperCamelCase__ : list[bool] )->list[int]:
A__ = True
A__ = []
for neighbour in graph[vert]:
if not visited[neighbour]:
order += topology_sort(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
order.append(UpperCamelCase__ )
return order
def UpperCamelCase__( UpperCamelCase__ : dict[int, list[int]] , UpperCamelCase__ : int , UpperCamelCase__ : list[bool] )->list[int]:
A__ = True
A__ = [vert]
for neighbour in reversed_graph[vert]:
if not visited[neighbour]:
component += find_components(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
return component
def UpperCamelCase__( UpperCamelCase__ : dict[int, list[int]] )->list[list[int]]:
A__ = len(UpperCamelCase__ ) * [False]
A__ = {vert: [] for vert in range(len(UpperCamelCase__ ) )}
for vert, neighbours in graph.items():
for neighbour in neighbours:
reversed_graph[neighbour].append(UpperCamelCase__ )
A__ = []
for i, was_visited in enumerate(UpperCamelCase__ ):
if not was_visited:
order += topology_sort(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
A__ = []
A__ = len(UpperCamelCase__ ) * [False]
for i in range(len(UpperCamelCase__ ) ):
A__ = order[len(UpperCamelCase__ ) - i - 1]
if not visited[vert]:
A__ = find_components(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
components_list.append(UpperCamelCase__ )
return components_list
| 193 | 0 |
"""simple docstring"""
import json
import os
from typing import Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {'vocab_file': 'vocab.json'}
lowerCAmelCase_ = {
'vocab_file': {
'mgp-str': 'https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json',
}
}
lowerCAmelCase_ = {'mgp-str': 27}
class __A ( A_ ):
'''simple docstring'''
lowerCAmelCase : Optional[Any] = VOCAB_FILES_NAMES
lowerCAmelCase : Dict = PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase : Optional[int] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__( self : List[str] ,_snake_case : Union[str, Any] ,_snake_case : int="[GO]" ,_snake_case : Any="[GO]" ,_snake_case : Tuple="[s]" ,_snake_case : Optional[Any]="[GO]" ,**_snake_case : Tuple ) -> int:
"""simple docstring"""
super().__init__(
unk_token=_snake_case ,bos_token=_snake_case ,eos_token=_snake_case ,pad_token=_snake_case ,**_snake_case ,)
with open(_snake_case ,encoding='''utf-8''' ) as vocab_handle:
lowercase__ : Dict = json.load(_snake_case )
lowercase__ : Any = {v: k for k, v in self.vocab.items()}
@property
def UpperCAmelCase ( self : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
return len(self.vocab )
def UpperCAmelCase ( self : List[Any] ) -> Dict:
"""simple docstring"""
return dict(self.vocab ,**self.added_tokens_encoder )
def UpperCAmelCase ( self : int ,_snake_case : List[Any] ) -> Optional[Any]:
"""simple docstring"""
lowercase__ : List[Any] = []
for s in text:
char_tokens.extend(_snake_case )
return char_tokens
def UpperCAmelCase ( self : Union[str, Any] ,_snake_case : Tuple ) -> Union[str, Any]:
"""simple docstring"""
return self.vocab.get(_snake_case ,self.vocab.get(self.unk_token ) )
def UpperCAmelCase ( self : str ,_snake_case : int ) -> Any:
"""simple docstring"""
return self.decoder.get(_snake_case )
def UpperCAmelCase ( self : Any ,_snake_case : str ,_snake_case : Optional[str] = None ) -> Tuple[str]:
"""simple docstring"""
if not os.path.isdir(_snake_case ):
logger.error('''Vocabulary path ({}) should be a directory'''.format(_snake_case ) )
return
lowercase__ : List[str] = os.path.join(
_snake_case ,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
with open(_snake_case ,'''w''' ,encoding='''utf-8''' ) as f:
f.write(json.dumps(self.vocab ,indent=2 ,sort_keys=_snake_case ,ensure_ascii=_snake_case ) + '''\n''' )
return (vocab_file,)
| 302 |
"""simple docstring"""
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_outputs import (
BaseModelOutputWithNoAttention,
BaseModelOutputWithPoolingAndNoAttention,
ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import logging
from .configuration_regnet import RegNetConfig
lowerCAmelCase_ = logging.get_logger(__name__)
# General docstring
lowerCAmelCase_ = 'RegNetConfig'
# Base docstring
lowerCAmelCase_ = 'facebook/regnet-y-040'
lowerCAmelCase_ = [1, 1_088, 7, 7]
# Image classification docstring
lowerCAmelCase_ = 'facebook/regnet-y-040'
lowerCAmelCase_ = 'tabby, tabby cat'
lowerCAmelCase_ = [
'facebook/regnet-y-040',
# See all regnet models at https://huggingface.co/models?filter=regnet
]
class __A ( nn.Module ):
'''simple docstring'''
def __init__( self : int ,_snake_case : int ,_snake_case : int ,_snake_case : int = 3 ,_snake_case : int = 1 ,_snake_case : int = 1 ,_snake_case : Optional[str] = "relu" ,) -> Union[str, Any]:
"""simple docstring"""
super().__init__()
lowercase__ : Tuple = nn.Convad(
_snake_case ,_snake_case ,kernel_size=_snake_case ,stride=_snake_case ,padding=kernel_size // 2 ,groups=_snake_case ,bias=_snake_case ,)
lowercase__ : List[Any] = nn.BatchNormad(_snake_case )
lowercase__ : Optional[int] = ACTaFN[activation] if activation is not None else nn.Identity()
def UpperCAmelCase ( self : List[str] ,_snake_case : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
lowercase__ : Optional[Any] = self.convolution(_snake_case )
lowercase__ : Tuple = self.normalization(_snake_case )
lowercase__ : Tuple = self.activation(_snake_case )
return hidden_state
class __A ( nn.Module ):
'''simple docstring'''
def __init__( self : Optional[int] ,_snake_case : RegNetConfig ) -> Optional[Any]:
"""simple docstring"""
super().__init__()
lowercase__ : List[Any] = RegNetConvLayer(
config.num_channels ,config.embedding_size ,kernel_size=3 ,stride=2 ,activation=config.hidden_act )
lowercase__ : str = config.num_channels
def UpperCAmelCase ( self : int ,_snake_case : Dict ) -> str:
"""simple docstring"""
lowercase__ : Union[str, Any] = pixel_values.shape[1]
if num_channels != self.num_channels:
raise ValueError(
'''Make sure that the channel dimension of the pixel values match with the one set in the configuration.''' )
lowercase__ : Optional[int] = self.embedder(_snake_case )
return hidden_state
class __A ( nn.Module ):
'''simple docstring'''
def __init__( self : str ,_snake_case : int ,_snake_case : int ,_snake_case : int = 2 ) -> Any:
"""simple docstring"""
super().__init__()
lowercase__ : List[str] = nn.Convad(_snake_case ,_snake_case ,kernel_size=1 ,stride=_snake_case ,bias=_snake_case )
lowercase__ : Any = nn.BatchNormad(_snake_case )
def UpperCAmelCase ( self : List[str] ,_snake_case : Tensor ) -> Tensor:
"""simple docstring"""
lowercase__ : Union[str, Any] = self.convolution(_snake_case )
lowercase__ : Optional[int] = self.normalization(_snake_case )
return hidden_state
class __A ( nn.Module ):
'''simple docstring'''
def __init__( self : Tuple ,_snake_case : int ,_snake_case : int ) -> Dict:
"""simple docstring"""
super().__init__()
lowercase__ : Any = nn.AdaptiveAvgPoolad((1, 1) )
lowercase__ : Dict = nn.Sequential(
nn.Convad(_snake_case ,_snake_case ,kernel_size=1 ) ,nn.ReLU() ,nn.Convad(_snake_case ,_snake_case ,kernel_size=1 ) ,nn.Sigmoid() ,)
def UpperCAmelCase ( self : int ,_snake_case : List[Any] ) -> Optional[Any]:
"""simple docstring"""
lowercase__ : List[str] = self.pooler(_snake_case )
lowercase__ : Union[str, Any] = self.attention(_snake_case )
lowercase__ : List[str] = hidden_state * attention
return hidden_state
class __A ( nn.Module ):
'''simple docstring'''
def __init__( self : List[str] ,_snake_case : RegNetConfig ,_snake_case : int ,_snake_case : int ,_snake_case : int = 1 ) -> List[str]:
"""simple docstring"""
super().__init__()
lowercase__ : Tuple = in_channels != out_channels or stride != 1
lowercase__ : Optional[int] = max(1 ,out_channels // config.groups_width )
lowercase__ : str = (
RegNetShortCut(_snake_case ,_snake_case ,stride=_snake_case ) if should_apply_shortcut else nn.Identity()
)
lowercase__ : Optional[int] = nn.Sequential(
RegNetConvLayer(_snake_case ,_snake_case ,kernel_size=1 ,activation=config.hidden_act ) ,RegNetConvLayer(_snake_case ,_snake_case ,stride=_snake_case ,groups=_snake_case ,activation=config.hidden_act ) ,RegNetConvLayer(_snake_case ,_snake_case ,kernel_size=1 ,activation=_snake_case ) ,)
lowercase__ : str = ACTaFN[config.hidden_act]
def UpperCAmelCase ( self : Optional[Any] ,_snake_case : List[Any] ) -> List[str]:
"""simple docstring"""
lowercase__ : Tuple = hidden_state
lowercase__ : Union[str, Any] = self.layer(_snake_case )
lowercase__ : List[Any] = self.shortcut(_snake_case )
hidden_state += residual
lowercase__ : Optional[int] = self.activation(_snake_case )
return hidden_state
class __A ( nn.Module ):
'''simple docstring'''
def __init__( self : Tuple ,_snake_case : RegNetConfig ,_snake_case : int ,_snake_case : int ,_snake_case : int = 1 ) -> Optional[int]:
"""simple docstring"""
super().__init__()
lowercase__ : List[Any] = in_channels != out_channels or stride != 1
lowercase__ : List[str] = max(1 ,out_channels // config.groups_width )
lowercase__ : Tuple = (
RegNetShortCut(_snake_case ,_snake_case ,stride=_snake_case ) if should_apply_shortcut else nn.Identity()
)
lowercase__ : str = nn.Sequential(
RegNetConvLayer(_snake_case ,_snake_case ,kernel_size=1 ,activation=config.hidden_act ) ,RegNetConvLayer(_snake_case ,_snake_case ,stride=_snake_case ,groups=_snake_case ,activation=config.hidden_act ) ,RegNetSELayer(_snake_case ,reduced_channels=int(round(in_channels / 4 ) ) ) ,RegNetConvLayer(_snake_case ,_snake_case ,kernel_size=1 ,activation=_snake_case ) ,)
lowercase__ : Optional[Any] = ACTaFN[config.hidden_act]
def UpperCAmelCase ( self : Union[str, Any] ,_snake_case : Optional[int] ) -> Tuple:
"""simple docstring"""
lowercase__ : str = hidden_state
lowercase__ : Optional[Any] = self.layer(_snake_case )
lowercase__ : int = self.shortcut(_snake_case )
hidden_state += residual
lowercase__ : str = self.activation(_snake_case )
return hidden_state
class __A ( nn.Module ):
'''simple docstring'''
def __init__( self : Union[str, Any] ,_snake_case : RegNetConfig ,_snake_case : int ,_snake_case : int ,_snake_case : int = 2 ,_snake_case : int = 2 ,) -> Dict:
"""simple docstring"""
super().__init__()
lowercase__ : Optional[Any] = RegNetXLayer if config.layer_type == '''x''' else RegNetYLayer
lowercase__ : Optional[Any] = nn.Sequential(
# downsampling is done in the first layer with stride of 2
layer(
_snake_case ,_snake_case ,_snake_case ,stride=_snake_case ,) ,*[layer(_snake_case ,_snake_case ,_snake_case ) for _ in range(depth - 1 )] ,)
def UpperCAmelCase ( self : Tuple ,_snake_case : int ) -> List[Any]:
"""simple docstring"""
lowercase__ : List[str] = self.layers(_snake_case )
return hidden_state
class __A ( nn.Module ):
'''simple docstring'''
def __init__( self : Dict ,_snake_case : RegNetConfig ) -> List[Any]:
"""simple docstring"""
super().__init__()
lowercase__ : str = nn.ModuleList([] )
# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
self.stages.append(
RegNetStage(
_snake_case ,config.embedding_size ,config.hidden_sizes[0] ,stride=2 if config.downsample_in_first_stage else 1 ,depth=config.depths[0] ,) )
lowercase__ : str = zip(config.hidden_sizes ,config.hidden_sizes[1:] )
for (in_channels, out_channels), depth in zip(_snake_case ,config.depths[1:] ):
self.stages.append(RegNetStage(_snake_case ,_snake_case ,_snake_case ,depth=_snake_case ) )
def UpperCAmelCase ( self : List[str] ,_snake_case : Tensor ,_snake_case : bool = False ,_snake_case : bool = True ) -> BaseModelOutputWithNoAttention:
"""simple docstring"""
lowercase__ : Dict = () if output_hidden_states else None
for stage_module in self.stages:
if output_hidden_states:
lowercase__ : int = hidden_states + (hidden_state,)
lowercase__ : Any = stage_module(_snake_case )
if output_hidden_states:
lowercase__ : Optional[int] = hidden_states + (hidden_state,)
if not return_dict:
return tuple(v for v in [hidden_state, hidden_states] if v is not None )
return BaseModelOutputWithNoAttention(last_hidden_state=_snake_case ,hidden_states=_snake_case )
class __A ( A_ ):
'''simple docstring'''
lowerCAmelCase : int = RegNetConfig
lowerCAmelCase : List[Any] = "regnet"
lowerCAmelCase : Optional[int] = "pixel_values"
lowerCAmelCase : Union[str, Any] = True
def UpperCAmelCase ( self : Any ,_snake_case : Tuple ) -> List[Any]:
"""simple docstring"""
if isinstance(_snake_case ,nn.Convad ):
nn.init.kaiming_normal_(module.weight ,mode='''fan_out''' ,nonlinearity='''relu''' )
elif isinstance(_snake_case ,(nn.BatchNormad, nn.GroupNorm) ):
nn.init.constant_(module.weight ,1 )
nn.init.constant_(module.bias ,0 )
def UpperCAmelCase ( self : Optional[Any] ,_snake_case : Dict ,_snake_case : Any=False ) -> Optional[int]:
"""simple docstring"""
if isinstance(_snake_case ,_snake_case ):
lowercase__ : str = value
lowerCAmelCase_ = R'\n This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it\n as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n behavior.\n\n Parameters:\n config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.\n Initializing with a config file does not load the weights associated with the model, only the\n configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n'
lowerCAmelCase_ = R'\n Args:\n pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n [`ConvNextImageProcessor.__call__`] for details.\n\n output_hidden_states (`bool`, *optional*):\n Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n more detail.\n return_dict (`bool`, *optional*):\n Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.\n'
@add_start_docstrings(
"The bare RegNet model outputting raw features without any specific head on top." ,A_ ,)
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class __A ( A_ ):
'''simple docstring'''
def __init__( self : Optional[Any] ,_snake_case : Any ) -> Tuple:
"""simple docstring"""
super().__init__(_snake_case )
lowercase__ : Any = config
lowercase__ : List[str] = RegNetEmbeddings(_snake_case )
lowercase__ : Any = RegNetEncoder(_snake_case )
lowercase__ : Dict = nn.AdaptiveAvgPoolad((1, 1) )
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(_snake_case )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC ,output_type=_snake_case ,config_class=_CONFIG_FOR_DOC ,modality='''vision''' ,expected_output=_EXPECTED_OUTPUT_SHAPE ,)
def UpperCAmelCase ( self : Dict ,_snake_case : Tensor ,_snake_case : Optional[bool] = None ,_snake_case : Optional[bool] = None ) -> BaseModelOutputWithPoolingAndNoAttention:
"""simple docstring"""
lowercase__ : List[Any] = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
lowercase__ : Dict = return_dict if return_dict is not None else self.config.use_return_dict
lowercase__ : Union[str, Any] = self.embedder(_snake_case )
lowercase__ : List[Any] = self.encoder(
_snake_case ,output_hidden_states=_snake_case ,return_dict=_snake_case )
lowercase__ : str = encoder_outputs[0]
lowercase__ : Optional[int] = self.pooler(_snake_case )
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=_snake_case ,pooler_output=_snake_case ,hidden_states=encoder_outputs.hidden_states ,)
@add_start_docstrings(
"\n RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n ImageNet.\n " ,A_ ,)
# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
class __A ( A_ ):
'''simple docstring'''
def __init__( self : int ,_snake_case : Tuple ) -> Any:
"""simple docstring"""
super().__init__(_snake_case )
lowercase__ : Optional[Any] = config.num_labels
lowercase__ : int = RegNetModel(_snake_case )
# classification head
lowercase__ : str = nn.Sequential(
nn.Flatten() ,nn.Linear(config.hidden_sizes[-1] ,config.num_labels ) if config.num_labels > 0 else nn.Identity() ,)
# initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(_snake_case )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT ,output_type=_snake_case ,config_class=_CONFIG_FOR_DOC ,expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT ,)
def UpperCAmelCase ( self : List[Any] ,_snake_case : Optional[torch.FloatTensor] = None ,_snake_case : Optional[torch.LongTensor] = None ,_snake_case : Optional[bool] = None ,_snake_case : Optional[bool] = None ,) -> ImageClassifierOutputWithNoAttention:
"""simple docstring"""
lowercase__ : Any = return_dict if return_dict is not None else self.config.use_return_dict
lowercase__ : List[Any] = self.regnet(_snake_case ,output_hidden_states=_snake_case ,return_dict=_snake_case )
lowercase__ : List[str] = outputs.pooler_output if return_dict else outputs[1]
lowercase__ : Union[str, Any] = self.classifier(_snake_case )
lowercase__ : Optional[int] = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
lowercase__ : List[Any] = '''regression'''
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
lowercase__ : Dict = '''single_label_classification'''
else:
lowercase__ : Optional[int] = '''multi_label_classification'''
if self.config.problem_type == "regression":
lowercase__ : Union[str, Any] = MSELoss()
if self.num_labels == 1:
lowercase__ : List[Any] = loss_fct(logits.squeeze() ,labels.squeeze() )
else:
lowercase__ : Tuple = loss_fct(_snake_case ,_snake_case )
elif self.config.problem_type == "single_label_classification":
lowercase__ : Tuple = CrossEntropyLoss()
lowercase__ : str = loss_fct(logits.view(-1 ,self.num_labels ) ,labels.view(-1 ) )
elif self.config.problem_type == "multi_label_classification":
lowercase__ : Any = BCEWithLogitsLoss()
lowercase__ : Union[str, Any] = loss_fct(_snake_case ,_snake_case )
if not return_dict:
lowercase__ : Tuple = (logits,) + outputs[2:]
return (loss,) + output if loss is not None else output
return ImageClassifierOutputWithNoAttention(loss=_snake_case ,logits=_snake_case ,hidden_states=outputs.hidden_states )
| 302 | 1 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
UniSpeechConfig,
UniSpeechForCTC,
UniSpeechForPreTraining,
WavaVecaFeatureExtractor,
WavaVecaPhonemeCTCTokenizer,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''post_extract_proj''': '''feature_projection.projection''',
'''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
'''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
'''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
'''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
'''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
'''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
'''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
'''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
'''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
'''encoder.layer_norm''': '''encoder.layer_norm''',
'''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
'''quantizer.weight_proj''': '''quantizer.weight_proj''',
'''quantizer.vars''': '''quantizer.codevectors''',
'''project_q''': '''project_q''',
'''final_proj''': '''project_hid''',
'''w2v_encoder.proj''': '''ctc_proj''',
'''mask_emb''': '''masked_spec_embed''',
}
lowerCamelCase_ = [
'''ctc_proj''',
'''quantizer.weight_proj''',
'''quantizer.codevectors''',
'''project_q''',
'''project_hid''',
]
def __lowercase ( __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase ) -> Dict:
'''simple docstring'''
for attribute in key.split("." ):
if is_finetuned:
if attribute in ["quantizer", "project_q", "project_hid"]:
# those layers are only relevant for pretraining and should be dropped
return
if attribute == "ctc_proj":
# we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
_A = 'lm_head'
_A = getattr(lowerCamelCase__ , lowerCamelCase__ )
if weight_type is not None:
_A = getattr(lowerCamelCase__ , lowerCamelCase__ ).shape
else:
_A = 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":
_A = value
elif weight_type == "weight_g":
_A = value
elif weight_type == "weight_v":
_A = value
elif weight_type == "bias":
_A = value
else:
_A = value
logger.info(F'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.''' )
def __lowercase ( __lowercase , __lowercase , __lowercase ) -> Optional[int]:
'''simple docstring'''
_A = []
_A = fairseq_model.state_dict()
_A = hf_model.unispeech.feature_extractor
for name, value in fairseq_dict.items():
_A = False
if "conv_layers" in name:
load_conv_layer(
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , hf_model.config.feat_extract_norm == "group" , )
_A = True
else:
for key, mapped_key in MAPPING.items():
_A = 'unispeech.' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split("w2v_model." )[-1] == name.split("." )[0]:
_A = True
if "*" in mapped_key:
_A = name.split(lowerCamelCase__ )[0].split("." )[-2]
_A = mapped_key.replace("*" , lowerCamelCase__ )
if "weight_g" in name:
_A = 'weight_g'
elif "weight_v" in name:
_A = 'weight_v'
elif "bias" in name:
_A = 'bias'
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
_A = 'weight'
else:
_A = None
set_recursively(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
continue
if not is_used:
unused_weights.append(lowerCamelCase__ )
logger.warning(F'''Unused weights: {unused_weights}''' )
def __lowercase ( __lowercase , __lowercase , __lowercase , __lowercase , __lowercase ) -> Optional[int]:
'''simple docstring'''
_A = full_name.split("conv_layers." )[-1]
_A = name.split("." )
_A = int(items[0] )
_A = int(items[1] )
if type_id == 0:
if "bias" in name:
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.'''
)
_A = 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.'''
)
_A = value
logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
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."
)
_A = 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.'''
)
_A = value
logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
else:
unused_weights.append(lowerCamelCase__ )
@torch.no_grad()
def __lowercase ( __lowercase , __lowercase , __lowercase=None , __lowercase=None , __lowercase=True ) -> int:
'''simple docstring'''
if config_path is not None:
_A = UniSpeechConfig.from_pretrained(lowerCamelCase__ )
else:
_A = UniSpeechConfig()
if is_finetuned:
if dict_path:
_A = Dictionary.load_from_json(lowerCamelCase__ )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
_A = target_dict.pad_index
_A = target_dict.bos_index
_A = target_dict.eos_index
_A = len(target_dict.symbols )
_A = os.path.join(lowerCamelCase__ , "vocab.json" )
if not os.path.isdir(lowerCamelCase__ ):
logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(lowerCamelCase__ ) )
return
os.makedirs(lowerCamelCase__ , exist_ok=lowerCamelCase__ )
_A = target_dict.indices
# fairseq has the <pad> and <s> switched
_A = 42
_A = 43
with open(lowerCamelCase__ , "w" , encoding="utf-8" ) as vocab_handle:
json.dump(lowerCamelCase__ , lowerCamelCase__ )
_A = WavaVecaPhonemeCTCTokenizer(
lowerCamelCase__ , 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=lowerCamelCase__ , )
_A = True if config.feat_extract_norm == 'layer' else False
_A = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=1_6000 , padding_value=0 , do_normalize=lowerCamelCase__ , return_attention_mask=lowerCamelCase__ , )
_A = WavaVecaProcessor(feature_extractor=lowerCamelCase__ , tokenizer=lowerCamelCase__ )
processor.save_pretrained(lowerCamelCase__ )
_A = UniSpeechForCTC(lowerCamelCase__ )
else:
_A = UniSpeechForPreTraining(lowerCamelCase__ )
if is_finetuned:
_A = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={"data": "/".join(dict_path.split("/" )[:-1] ), "w2v_path": checkpoint_path} )
else:
_A = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] )
_A = model[0].eval()
recursively_load_weights(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
hf_unispeech.save_pretrained(lowerCamelCase__ )
if __name__ == "__main__":
lowerCamelCase_ = argparse.ArgumentParser()
parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
parser.add_argument(
'''--not_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
)
lowerCamelCase_ = parser.parse_args()
convert_unispeech_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
)
| 79 |
# 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
a =open # noqa: we just need to have a builtin inside this module to test it properly
| 73 | 0 |
import argparse
import math
import os
from copy import deepcopy
import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
__UpperCAmelCase = {
"""gwf-440k""": {
"""url""": """https://model-server.zqevans2.workers.dev/gwf-440k.ckpt""",
"""sample_rate""": 48_000,
"""sample_size""": 65_536,
},
"""jmann-small-190k""": {
"""url""": """https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt""",
"""sample_rate""": 48_000,
"""sample_size""": 65_536,
},
"""jmann-large-580k""": {
"""url""": """https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt""",
"""sample_rate""": 48_000,
"""sample_size""": 131_072,
},
"""maestro-uncond-150k""": {
"""url""": """https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt""",
"""sample_rate""": 16_000,
"""sample_size""": 65_536,
},
"""unlocked-uncond-250k""": {
"""url""": """https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt""",
"""sample_rate""": 16_000,
"""sample_size""": 65_536,
},
"""honk-140k""": {
"""url""": """https://model-server.zqevans2.workers.dev/honk-140k.ckpt""",
"""sample_rate""": 16_000,
"""sample_size""": 65_536,
},
}
def snake_case_ (__A : Union[str, Any] , __A : Dict ) -> List[str]:
return torch.atana(__A , __A ) / math.pi * 2
def snake_case_ (__A : int ) -> Any:
__lowerCAmelCase : Optional[int] = torch.sin(t * math.pi / 2 ) ** 2
__lowerCAmelCase : Optional[Any] = (1 - sigma**2) ** 0.5
return alpha_sigma_to_t(__A , __A )
class SCREAMING_SNAKE_CASE ( a_ ):
"""simple docstring"""
pass
class SCREAMING_SNAKE_CASE ( nn.Module ):
"""simple docstring"""
def __init__( self : Optional[int] , lowerCAmelCase : List[Any] ) -> List[Any]:
"""simple docstring"""
super().__init__()
__lowerCAmelCase : List[Any] = DiffusionAttnUnetaD(lowerCAmelCase , n_attn_layers=4 )
__lowerCAmelCase : Optional[Any] = deepcopy(self.diffusion )
__lowerCAmelCase : Tuple = torch.quasirandom.SobolEngine(1 , scramble=lowerCAmelCase )
def snake_case_ (__A : Dict ) -> Any:
__lowerCAmelCase : str = MODELS_MAP[model_name]["""url"""]
os.system(f'''wget {url} ./''' )
return f'''./{model_name}.ckpt'''
__UpperCAmelCase = {
"""1""": """resnets.0""",
"""2""": """attentions.0""",
"""3""": """resnets.1""",
"""4""": """attentions.1""",
"""5""": """resnets.2""",
"""6""": """attentions.2""",
}
__UpperCAmelCase = {
"""8""": """resnets.0""",
"""9""": """attentions.0""",
"""10""": """resnets.1""",
"""11""": """attentions.1""",
"""12""": """resnets.2""",
"""13""": """attentions.2""",
}
__UpperCAmelCase = {
"""1""": """resnets.0""",
"""2""": """attentions.0""",
"""3""": """resnets.1""",
"""4""": """attentions.1""",
"""5""": """resnets.2""",
"""6""": """attentions.2""",
"""8""": """resnets.3""",
"""9""": """attentions.3""",
"""10""": """resnets.4""",
"""11""": """attentions.4""",
"""12""": """resnets.5""",
"""13""": """attentions.5""",
}
__UpperCAmelCase = {
"""0""": """resnets.0""",
"""1""": """resnets.1""",
"""2""": """resnets.2""",
"""4""": """resnets.0""",
"""5""": """resnets.1""",
"""6""": """resnets.2""",
}
__UpperCAmelCase = {
"""skip""": """conv_skip""",
"""main.0""": """conv_1""",
"""main.1""": """group_norm_1""",
"""main.3""": """conv_2""",
"""main.4""": """group_norm_2""",
}
__UpperCAmelCase = {
"""norm""": """group_norm""",
"""qkv_proj""": ["""query""", """key""", """value"""],
"""out_proj""": ["""proj_attn"""],
}
def snake_case_ (__A : int ) -> Optional[int]:
if name.startswith("""skip""" ):
return name.replace("""skip""" , RES_CONV_MAP["""skip"""] )
# name has to be of format main.{digit}
if not name.startswith("""main.""" ):
raise ValueError(f'''ResConvBlock error with {name}''' )
return name.replace(name[:6] , RES_CONV_MAP[name[:6]] )
def snake_case_ (__A : int ) -> int:
for key, value in ATTN_MAP.items():
if name.startswith(__A ) and not isinstance(__A , __A ):
return name.replace(__A , __A )
elif name.startswith(__A ):
return [name.replace(__A , __A ) for v in value]
raise ValueError(f'''Attn error with {name}''' )
def snake_case_ (__A : Any , __A : Optional[Any]=1_3 ) -> Union[str, Any]:
__lowerCAmelCase : List[str] = input_string
if string.split(""".""" )[0] == "timestep_embed":
return string.replace("""timestep_embed""" , """time_proj""" )
__lowerCAmelCase : Union[str, Any] = 0
if string.startswith("""net.3.""" ):
depth += 1
__lowerCAmelCase : Optional[Any] = string[6:]
elif string.startswith("""net.""" ):
__lowerCAmelCase : Dict = string[4:]
while string.startswith("""main.7.""" ):
depth += 1
__lowerCAmelCase : str = string[7:]
if string.startswith("""main.""" ):
__lowerCAmelCase : Dict = string[5:]
# mid block
if string[:2].isdigit():
__lowerCAmelCase : Tuple = string[:2]
__lowerCAmelCase : Union[str, Any] = string[2:]
else:
__lowerCAmelCase : Optional[int] = string[0]
__lowerCAmelCase : Tuple = string[1:]
if depth == max_depth:
__lowerCAmelCase : List[str] = MID_NUM_TO_LAYER[layer_num]
__lowerCAmelCase : Optional[Any] = """mid_block"""
elif depth > 0 and int(__A ) < 7:
__lowerCAmelCase : Union[str, Any] = DOWN_NUM_TO_LAYER[layer_num]
__lowerCAmelCase : Optional[int] = f'''down_blocks.{depth}'''
elif depth > 0 and int(__A ) > 7:
__lowerCAmelCase : Optional[int] = UP_NUM_TO_LAYER[layer_num]
__lowerCAmelCase : Union[str, Any] = f'''up_blocks.{max_depth - depth - 1}'''
elif depth == 0:
__lowerCAmelCase : Optional[int] = DEPTH_0_TO_LAYER[layer_num]
__lowerCAmelCase : Optional[Any] = f'''up_blocks.{max_depth - 1}''' if int(__A ) > 3 else """down_blocks.0"""
if not string_left.startswith(""".""" ):
raise ValueError(f'''Naming error with {input_string} and string_left: {string_left}.''' )
__lowerCAmelCase : Optional[int] = string_left[1:]
if "resnets" in new_layer:
__lowerCAmelCase : Any = convert_resconv_naming(__A )
elif "attentions" in new_layer:
__lowerCAmelCase : Optional[Any] = convert_attn_naming(__A )
__lowerCAmelCase : Tuple = new_string_left
if not isinstance(__A , __A ):
__lowerCAmelCase : Optional[Any] = prefix + """.""" + new_layer + """.""" + string_left
else:
__lowerCAmelCase : Optional[Any] = [prefix + """.""" + new_layer + """.""" + s for s in string_left]
return new_string
def snake_case_ (__A : List[str] ) -> Union[str, Any]:
__lowerCAmelCase : List[str] = {}
for k, v in state_dict.items():
if k.endswith("""kernel""" ):
# up- and downsample layers, don't have trainable weights
continue
__lowerCAmelCase : Union[str, Any] = rename(__A )
# check if we need to transform from Conv => Linear for attention
if isinstance(__A , __A ):
__lowerCAmelCase : Tuple = transform_conv_attns(__A , __A , __A )
else:
__lowerCAmelCase : Union[str, Any] = v
return new_state_dict
def snake_case_ (__A : Dict , __A : Tuple , __A : Tuple ) -> str:
if len(__A ) == 1:
if len(v.shape ) == 3:
# weight
__lowerCAmelCase : Union[str, Any] = v[:, :, 0]
else:
# bias
__lowerCAmelCase : Dict = v
else:
# qkv matrices
__lowerCAmelCase : Any = v.shape[0]
__lowerCAmelCase : List[str] = trippled_shape // 3
for i in range(3 ):
if len(v.shape ) == 3:
__lowerCAmelCase : str = v[i * single_shape : (i + 1) * single_shape, :, 0]
else:
__lowerCAmelCase : Optional[int] = v[i * single_shape : (i + 1) * single_shape]
return new_state_dict
def snake_case_ (__A : Tuple ) -> Union[str, Any]:
__lowerCAmelCase : Any = torch.device("""cuda""" if torch.cuda.is_available() else """cpu""" )
__lowerCAmelCase : Dict = args.model_path.split("""/""" )[-1].split(""".""" )[0]
if not os.path.isfile(args.model_path ):
assert (
model_name == args.model_path
), f'''Make sure to provide one of the official model names {MODELS_MAP.keys()}'''
__lowerCAmelCase : Tuple = download(__A )
__lowerCAmelCase : Optional[int] = MODELS_MAP[model_name]["""sample_rate"""]
__lowerCAmelCase : Tuple = MODELS_MAP[model_name]["""sample_size"""]
__lowerCAmelCase : Dict = Object()
__lowerCAmelCase : List[Any] = sample_size
__lowerCAmelCase : Optional[int] = sample_rate
__lowerCAmelCase : Tuple = 0
__lowerCAmelCase : Any = UNetaDModel(sample_size=__A , sample_rate=__A )
__lowerCAmelCase : Dict = diffusers_model.state_dict()
__lowerCAmelCase : Any = DiffusionUncond(__A )
orig_model.load_state_dict(torch.load(args.model_path , map_location=__A )["""state_dict"""] )
__lowerCAmelCase : str = orig_model.diffusion_ema.eval()
__lowerCAmelCase : Tuple = orig_model.state_dict()
__lowerCAmelCase : str = rename_orig_weights(__A )
__lowerCAmelCase : Optional[int] = set(renamed_state_dict.keys() ) - set(diffusers_state_dict.keys() )
__lowerCAmelCase : Optional[int] = set(diffusers_state_dict.keys() ) - set(renamed_state_dict.keys() )
assert len(__A ) == 0, f'''Problem with {renamed_minus_diffusers}'''
assert all(k.endswith("""kernel""" ) for k in list(__A ) ), f'''Problem with {diffusers_minus_renamed}'''
for key, value in renamed_state_dict.items():
assert (
diffusers_state_dict[key].squeeze().shape == value.squeeze().shape
), f'''Shape for {key} doesn\'t match. Diffusers: {diffusers_state_dict[key].shape} vs. {value.shape}'''
if key == "time_proj.weight":
__lowerCAmelCase : Union[str, Any] = value.squeeze()
__lowerCAmelCase : Optional[int] = value
diffusers_model.load_state_dict(__A )
__lowerCAmelCase : Optional[Any] = 1_0_0
__lowerCAmelCase : Optional[int] = 3_3
__lowerCAmelCase : Dict = IPNDMScheduler(num_train_timesteps=__A )
__lowerCAmelCase : Union[str, Any] = torch.manual_seed(__A )
__lowerCAmelCase : Dict = torch.randn([1, 2, config.sample_size] , generator=__A ).to(__A )
__lowerCAmelCase : List[Any] = torch.linspace(1 , 0 , steps + 1 , device=__A )[:-1]
__lowerCAmelCase : Union[str, Any] = get_crash_schedule(__A )
__lowerCAmelCase : Optional[Any] = DanceDiffusionPipeline(unet=__A , scheduler=__A )
__lowerCAmelCase : Tuple = torch.manual_seed(3_3 )
__lowerCAmelCase : str = pipe(num_inference_steps=__A , generator=__A ).audios
__lowerCAmelCase : Optional[Any] = sampling.iplms_sample(__A , __A , __A , {} )
__lowerCAmelCase : Union[str, Any] = generated.clamp(-1 , 1 )
__lowerCAmelCase : Any = (generated - audio).abs().sum()
__lowerCAmelCase : str = (generated - audio).abs().max()
if args.save:
pipe.save_pretrained(args.checkpoint_path )
print("""Diff sum""" , __A )
print("""Diff max""" , __A )
assert diff_max < 1e-3, f'''Diff max: {diff_max} is too much :-/'''
print(f'''Conversion for {model_name} successful!''' )
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument("""--model_path""", default=None, type=str, required=True, help="""Path to the model to convert.""")
parser.add_argument(
"""--save""", default=True, type=bool, required=False, help="""Whether to save the converted model or not."""
)
parser.add_argument("""--checkpoint_path""", default=None, type=str, required=True, help="""Path to the output model.""")
__UpperCAmelCase = parser.parse_args()
main(args)
| 139 |
import json
import os
import unittest
from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class SCREAMING_SNAKE_CASE ( a_ , unittest.TestCase ):
"""simple docstring"""
lowerCamelCase : str =DebertaTokenizer
lowerCamelCase : Dict =True
lowerCamelCase : List[str] =DebertaTokenizerFast
def SCREAMING_SNAKE_CASE ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
__lowerCAmelCase : Optional[int] = [
"""l""",
"""o""",
"""w""",
"""e""",
"""r""",
"""s""",
"""t""",
"""i""",
"""d""",
"""n""",
"""\u0120""",
"""\u0120l""",
"""\u0120n""",
"""\u0120lo""",
"""\u0120low""",
"""er""",
"""\u0120lowest""",
"""\u0120newer""",
"""\u0120wider""",
"""[UNK]""",
]
__lowerCAmelCase : Optional[int] = dict(zip(lowerCAmelCase , range(len(lowerCAmelCase ) ) ) )
__lowerCAmelCase : List[str] = ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
__lowerCAmelCase : Any = {"""unk_token""": """[UNK]"""}
__lowerCAmelCase : Tuple = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
__lowerCAmelCase : Optional[int] = 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(lowerCAmelCase ) + """\n""" )
with open(self.merges_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write("""\n""".join(lowerCAmelCase ) )
def SCREAMING_SNAKE_CASE ( self : Optional[int] , **lowerCAmelCase : Tuple ) -> Dict:
"""simple docstring"""
kwargs.update(self.special_tokens_map )
return self.tokenizer_class.from_pretrained(self.tmpdirname , **lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self : List[str] , lowerCAmelCase : int ) -> Optional[Any]:
"""simple docstring"""
__lowerCAmelCase : Dict = """lower newer"""
__lowerCAmelCase : Union[str, Any] = """lower newer"""
return input_text, output_text
def SCREAMING_SNAKE_CASE ( self : List[str] ) -> Optional[Any]:
"""simple docstring"""
__lowerCAmelCase : List[Any] = self.get_tokenizer()
__lowerCAmelCase : int = """lower newer"""
__lowerCAmelCase : Any = ["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
__lowerCAmelCase : Union[str, Any] = tokenizer.tokenize(lowerCAmelCase )
self.assertListEqual(lowerCAmelCase , lowerCAmelCase )
__lowerCAmelCase : List[Any] = tokens + [tokenizer.unk_token]
__lowerCAmelCase : Union[str, Any] = [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase ) , lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
__lowerCAmelCase : int = self.get_tokenizer()
__lowerCAmelCase : Any = tokenizer("""Hello""" , """World""" )
__lowerCAmelCase : Tuple = [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
self.assertListEqual(tokd["""token_type_ids"""] , lowerCAmelCase )
@slow
def SCREAMING_SNAKE_CASE ( self : Dict ) -> str:
"""simple docstring"""
__lowerCAmelCase : Optional[int] = self.tokenizer_class.from_pretrained("""microsoft/deberta-base""" )
__lowerCAmelCase : Optional[Any] = tokenizer.encode("""sequence builders""" , add_special_tokens=lowerCAmelCase )
__lowerCAmelCase : List[Any] = tokenizer.encode("""multi-sequence build""" , add_special_tokens=lowerCAmelCase )
__lowerCAmelCase : Dict = tokenizer.encode(
"""sequence builders""" , add_special_tokens=lowerCAmelCase , add_prefix_space=lowerCAmelCase )
__lowerCAmelCase : Dict = tokenizer.encode(
"""sequence builders""" , """multi-sequence build""" , add_special_tokens=lowerCAmelCase , add_prefix_space=lowerCAmelCase )
__lowerCAmelCase : List[str] = tokenizer.build_inputs_with_special_tokens(lowerCAmelCase )
__lowerCAmelCase : Union[str, Any] = tokenizer.build_inputs_with_special_tokens(lowerCAmelCase , lowerCAmelCase )
assert encoded_sentence == encoded_text_from_decode
assert encoded_pair == encoded_pair_from_decode
@slow
def SCREAMING_SNAKE_CASE ( self : List[str] ) -> Optional[Any]:
"""simple docstring"""
__lowerCAmelCase : List[str] = [self.tokenizer_class]
if self.test_rust_tokenizer:
tokenizer_classes.append(self.rust_tokenizer_class )
for tokenizer_class in tokenizer_classes:
__lowerCAmelCase : int = tokenizer_class.from_pretrained("""microsoft/deberta-base""" )
__lowerCAmelCase : Optional[Any] = [
"""ALBERT: A Lite BERT for Self-supervised Learning of Language Representations""",
"""ALBERT incorporates two parameter reduction techniques""",
"""The first one is a factorized embedding parameterization. By decomposing the large vocabulary"""
""" embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"""
""" vocabulary embedding.""",
]
__lowerCAmelCase : List[str] = tokenizer(lowerCAmelCase , padding=lowerCAmelCase )
__lowerCAmelCase : List[str] = [tokenizer.decode(lowerCAmelCase , skip_special_tokens=lowerCAmelCase ) for seq in encoding["""input_ids"""]]
# fmt: off
__lowerCAmelCase : Any = {
"""input_ids""": [
[1, 21_18, 1_11_26, 5_65, 35, 83, 2_51_91, 1_63, 1_88_54, 13, 1_21_56, 12, 1_61_01, 2_53_76, 1_38_07, 9, 2_22_05, 2_78_93, 16_35, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 21_18, 1_11_26, 5_65, 2_45_36, 80, 4_37_97, 48_78, 73_73, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 1_33, 78, 65, 16, 10, 37_24, 15_38, 3_31_83, 1_13_03, 4_37_97, 19_38, 4, 8_70, 2_41_65, 2_91_05, 5, 7_39, 3_26_44, 3_31_83, 1_13_03, 3_61_73, 88, 80, 6_50, 78_21, 4_59_40, 6, 52, 25_59, 5, 18_36, 9, 5, 73_97, 1_31_71, 31, 5, 18_36, 9, 3_26_44, 3_31_83, 1_13_03, 4, 2]
],
"""token_type_ids""": [
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
],
"""attention_mask""": [
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
]
}
# fmt: on
__lowerCAmelCase : int = [
"""ALBERT: A Lite BERT for Self-supervised Learning of Language Representations""",
"""ALBERT incorporates two parameter reduction techniques""",
"""The first one is a factorized embedding parameterization. By decomposing the large vocabulary"""
""" embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"""
""" vocabulary embedding.""",
]
self.assertDictEqual(encoding.data , lowerCAmelCase )
for expected, decoded in zip(lowerCAmelCase , lowerCAmelCase ):
self.assertEqual(lowerCAmelCase , lowerCAmelCase )
| 139 | 1 |
"""simple docstring"""
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
# Check if the input is valid
if not len(__lowerCamelCase ) == len(__lowerCamelCase ) == 3:
raise ValueError("""Please enter a valid equation.""" )
if equationa[0] == equationa[1] == equationa[0] == equationa[1] == 0:
raise ValueError("""Both a & b of two equations can't be zero.""" )
# Extract the coefficients
_a : str = equationa
_a : Tuple = equationa
# Calculate the determinants of the matrices
_a : Any = aa * ba - aa * ba
_a : Optional[int] = ca * ba - ca * ba
_a : int = aa * ca - aa * ca
# Check if the system of linear equations has a solution (using Cramer's rule)
if determinant == 0:
if determinant_x == determinant_y == 0:
raise ValueError("""Infinite solutions. (Consistent system)""" )
else:
raise ValueError("""No solution. (Inconsistent system)""" )
else:
if determinant_x == determinant_y == 0:
# Trivial solution (Inconsistent system)
return (0.0, 0.0)
else:
_a : List[str] = determinant_x / determinant
_a : Optional[int] = determinant_y / determinant
# Non-Trivial Solution (Consistent system)
return (x, y)
| 294 |
"""simple docstring"""
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class A_ :
'''simple docstring'''
pass
| 61 | 0 |
'''simple docstring'''
import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer
lowerCAmelCase :List[str] = logging.get_logger(__name__)
class _lowerCamelCase ( lowercase__ ):
'''simple docstring'''
A_ : int = """AutoTokenizer"""
A_ : str = ["""tokenizer"""]
A_ : str = {
"""semantic_prompt""": 1,
"""coarse_prompt""": 2,
"""fine_prompt""": 2,
}
def __init__( self : List[str] , _A : int , _A : List[Any]=None ) -> Optional[Any]:
super().__init__(_A )
__magic_name__ : Any = speaker_embeddings
@classmethod
def __lowerCAmelCase ( cls : List[Any] , _A : Union[str, Any] , _A : Optional[int]="speaker_embeddings_path.json" , **_A : Union[str, Any] ) -> Any:
if speaker_embeddings_dict_path is not None:
__magic_name__ : Dict = get_file_from_repo(
_A , _A , subfolder=kwargs.pop('subfolder' , _A ) , cache_dir=kwargs.pop('cache_dir' , _A ) , force_download=kwargs.pop('force_download' , _A ) , proxies=kwargs.pop('proxies' , _A ) , resume_download=kwargs.pop('resume_download' , _A ) , local_files_only=kwargs.pop('local_files_only' , _A ) , use_auth_token=kwargs.pop('use_auth_token' , _A ) , revision=kwargs.pop('revision' , _A ) , )
if speaker_embeddings_path is None:
logger.warning(
F'`{os.path.join(_A , _A )}` does not exists\n , no preloaded speaker embeddings will be used - Make sure to provide a correct path to the json\n dictionnary if wanted, otherwise set `speaker_embeddings_dict_path=None`.' )
__magic_name__ : Union[str, Any] = None
else:
with open(_A ) as speaker_embeddings_json:
__magic_name__ : str = json.load(_A )
else:
__magic_name__ : List[str] = None
__magic_name__ : str = AutoTokenizer.from_pretrained(_A , **_A )
return cls(tokenizer=_A , speaker_embeddings=_A )
def __lowerCAmelCase ( self : Optional[int] , _A : Union[str, Any] , _A : Any="speaker_embeddings_path.json" , _A : List[Any]="speaker_embeddings" , _A : bool = False , **_A : List[Any] , ) -> str:
if self.speaker_embeddings is not None:
os.makedirs(os.path.join(_A , _A , 'v2' ) , exist_ok=_A )
__magic_name__ : Tuple = {}
__magic_name__ : Dict = save_directory
for prompt_key in self.speaker_embeddings:
if prompt_key != "repo_or_path":
__magic_name__ : List[str] = self._load_voice_preset(_A )
__magic_name__ : List[Any] = {}
for key in self.speaker_embeddings[prompt_key]:
np.save(
os.path.join(
embeddings_dict['repo_or_path'] , _A , F'{prompt_key}_{key}' ) , voice_preset[key] , allow_pickle=_A , )
__magic_name__ : str = os.path.join(_A , F'{prompt_key}_{key}.npy' )
__magic_name__ : List[Any] = tmp_dict
with open(os.path.join(_A , _A ) , 'w' ) as fp:
json.dump(_A , _A )
super().save_pretrained(_A , _A , **_A )
def __lowerCAmelCase ( self : int , _A : str = None , **_A : Any ) -> Optional[int]:
__magic_name__ : Optional[int] = self.speaker_embeddings[voice_preset]
__magic_name__ : Optional[int] = {}
for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
if key not in voice_preset_paths:
raise ValueError(
F'Voice preset unrecognized, missing {key} as a key in self.speaker_embeddings[{voice_preset}].' )
__magic_name__ : Optional[Any] = get_file_from_repo(
self.speaker_embeddings.get('repo_or_path' , '/' ) , voice_preset_paths[key] , subfolder=kwargs.pop('subfolder' , _A ) , cache_dir=kwargs.pop('cache_dir' , _A ) , force_download=kwargs.pop('force_download' , _A ) , proxies=kwargs.pop('proxies' , _A ) , resume_download=kwargs.pop('resume_download' , _A ) , local_files_only=kwargs.pop('local_files_only' , _A ) , use_auth_token=kwargs.pop('use_auth_token' , _A ) , revision=kwargs.pop('revision' , _A ) , )
if path is None:
raise ValueError(
F'`{os.path.join(self.speaker_embeddings.get("repo_or_path" , "/" ) , voice_preset_paths[key] )}` does not exists\n , no preloaded voice preset will be used - Make sure to provide correct paths to the {voice_preset}\n embeddings.' )
__magic_name__ : Union[str, Any] = np.load(_A )
return voice_preset_dict
def __lowerCAmelCase ( self : Union[str, Any] , _A : Optional[dict] = None ) -> Optional[int]:
for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
if key not in voice_preset:
raise ValueError(F'Voice preset unrecognized, missing {key} as a key.' )
if not isinstance(voice_preset[key] , np.ndarray ):
raise ValueError(F'{key} voice preset must be a {str(self.preset_shape[key] )}D ndarray.' )
if len(voice_preset[key].shape ) != self.preset_shape[key]:
raise ValueError(F'{key} voice preset must be a {str(self.preset_shape[key] )}D ndarray.' )
def __call__( self : int , _A : Any=None , _A : List[str]=None , _A : List[str]="pt" , _A : Dict=256 , _A : List[str]=False , _A : List[str]=True , _A : str=False , **_A : Dict , ) -> Optional[int]:
if voice_preset is not None and not isinstance(_A , _A ):
if (
isinstance(_A , _A )
and self.speaker_embeddings is not None
and voice_preset in self.speaker_embeddings
):
__magic_name__ : List[str] = self._load_voice_preset(_A )
else:
if isinstance(_A , _A ) and not voice_preset.endswith('.npz' ):
__magic_name__ : str = voice_preset + '.npz'
__magic_name__ : Optional[Any] = np.load(_A )
if voice_preset is not None:
self._validate_voice_preset_dict(_A , **_A )
__magic_name__ : int = BatchFeature(data=_A , tensor_type=_A )
__magic_name__ : Any = self.tokenizer(
_A , return_tensors=_A , padding='max_length' , max_length=_A , return_attention_mask=_A , return_token_type_ids=_A , add_special_tokens=_A , **_A , )
if voice_preset is not None:
__magic_name__ : List[str] = voice_preset
return encoded_text | 275 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
WavaVecaProcessor,
logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification
logging.set_verbosity_info()
lowerCAmelCase :str = logging.get_logger(__name__)
lowerCAmelCase :str = {
'''post_extract_proj''': '''feature_projection.projection''',
'''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
'''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
'''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
'''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
'''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
'''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
'''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
'''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
'''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
'''encoder.layer_norm''': '''encoder.layer_norm''',
'''adapter_layer''': '''encoder.layers.*.adapter_layer''',
'''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
'''quantizer.weight_proj''': '''quantizer.weight_proj''',
'''quantizer.vars''': '''quantizer.codevectors''',
'''project_q''': '''project_q''',
'''final_proj''': '''project_hid''',
'''w2v_encoder.proj''': '''lm_head''',
'''mask_emb''': '''masked_spec_embed''',
'''pooling_layer.linear''': '''projector''',
'''pooling_layer.projection''': '''classifier''',
}
lowerCAmelCase :List[str] = [
'''lm_head''',
'''quantizer.weight_proj''',
'''quantizer.codevectors''',
'''project_q''',
'''project_hid''',
'''projector''',
'''classifier''',
]
def lowerCamelCase ( lowerCAmelCase : Tuple ):
"""simple docstring"""
__magic_name__ : Any = {}
with open(lowerCAmelCase , 'r' ) as file:
for line_number, line in enumerate(lowerCAmelCase ):
__magic_name__ : Optional[Any] = line.strip()
if line:
__magic_name__ : Optional[int] = line.split()
__magic_name__ : Any = line_number
__magic_name__ : Union[str, Any] = words[0]
__magic_name__ : Dict = value
return result
def lowerCamelCase ( lowerCAmelCase : Optional[int] , lowerCAmelCase : List[Any] , lowerCAmelCase : Optional[int] , lowerCAmelCase : Optional[Any] , lowerCAmelCase : Tuple ):
"""simple docstring"""
for attribute in key.split('.' ):
__magic_name__ : Optional[Any] = getattr(lowerCAmelCase , lowerCAmelCase )
__magic_name__ : Tuple = None
for param_key in PARAM_MAPPING.keys():
if full_name.endswith(lowerCAmelCase ):
__magic_name__ : Optional[Any] = PARAM_MAPPING[full_name.split('.' )[-1]]
__magic_name__ : List[Any] = 'param'
if weight_type is not None and weight_type != "param":
__magic_name__ : List[str] = getattr(lowerCAmelCase , lowerCAmelCase ).shape
elif weight_type is not None and weight_type == "param":
__magic_name__ : Tuple = hf_pointer
for attribute in hf_param_name.split('.' ):
__magic_name__ : str = getattr(lowerCAmelCase , lowerCAmelCase )
__magic_name__ : Union[str, Any] = shape_pointer.shape
# let's reduce dimension
__magic_name__ : int = value[0]
else:
__magic_name__ : Optional[Any] = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
f'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
f' {value.shape} for {full_name}' )
if weight_type == "weight":
__magic_name__ : Optional[Any] = value
elif weight_type == "weight_g":
__magic_name__ : List[str] = value
elif weight_type == "weight_v":
__magic_name__ : Optional[int] = value
elif weight_type == "bias":
__magic_name__ : Optional[Any] = value
elif weight_type == "param":
for attribute in hf_param_name.split('.' ):
__magic_name__ : Optional[int] = getattr(lowerCAmelCase , lowerCAmelCase )
__magic_name__ : List[str] = value
else:
__magic_name__ : int = value
logger.info(f'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.' )
def lowerCamelCase ( lowerCAmelCase : List[Any] , lowerCAmelCase : str , lowerCAmelCase : List[str] , lowerCAmelCase : str , lowerCAmelCase : str ):
"""simple docstring"""
__magic_name__ : Optional[int] = None
for param_key in PARAM_MAPPING.keys():
if full_name.endswith(lowerCAmelCase ):
__magic_name__ : List[Any] = PARAM_MAPPING[full_name.split('.' )[-1]]
__magic_name__ : Dict = 'param'
if weight_type is not None and weight_type != "param":
__magic_name__ : Union[str, Any] = '.'.join([key, weight_type] )
elif weight_type is not None and weight_type == "param":
__magic_name__ : Optional[Any] = '.'.join([key, hf_param_name] )
else:
__magic_name__ : int = key
__magic_name__ : int = value if 'lm_head' in full_key else value[0]
lowerCAmelCase :int = {
'''W_a''': '''linear_1.weight''',
'''W_b''': '''linear_2.weight''',
'''b_a''': '''linear_1.bias''',
'''b_b''': '''linear_2.bias''',
'''ln_W''': '''norm.weight''',
'''ln_b''': '''norm.bias''',
}
def lowerCamelCase ( lowerCAmelCase : Optional[Any] , lowerCAmelCase : List[Any] , lowerCAmelCase : Tuple=None , lowerCAmelCase : Tuple=None ):
"""simple docstring"""
__magic_name__ : Dict = False
for key, mapped_key in MAPPING.items():
__magic_name__ : int = 'wav2vec2.' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split('w2v_model.' )[-1] == name.split('.' )[0]:
__magic_name__ : Union[str, Any] = True
if "*" in mapped_key:
__magic_name__ : List[Any] = name.split(lowerCAmelCase )[0].split('.' )[-2]
__magic_name__ : List[str] = mapped_key.replace('*' , lowerCAmelCase )
if "weight_g" in name:
__magic_name__ : str = 'weight_g'
elif "weight_v" in name:
__magic_name__ : Optional[int] = 'weight_v'
elif "bias" in name:
__magic_name__ : int = 'bias'
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
__magic_name__ : List[str] = 'weight'
else:
__magic_name__ : Any = None
if hf_dict is not None:
rename_dict(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
else:
set_recursively(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
return is_used
return is_used
def lowerCamelCase ( lowerCAmelCase : Union[str, Any] , lowerCAmelCase : Optional[int] , lowerCAmelCase : Dict ):
"""simple docstring"""
__magic_name__ : Union[str, Any] = []
__magic_name__ : Any = fairseq_model.state_dict()
__magic_name__ : Optional[int] = hf_model.wavaveca.feature_extractor
for name, value in fairseq_dict.items():
__magic_name__ : Optional[Any] = False
if "conv_layers" in name:
load_conv_layer(
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , hf_model.config.feat_extract_norm == 'group' , )
__magic_name__ : Optional[int] = True
else:
__magic_name__ : str = load_wavaveca_layer(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
if not is_used:
unused_weights.append(lowerCAmelCase )
logger.warning(f'Unused weights: {unused_weights}' )
def lowerCamelCase ( lowerCAmelCase : List[str] , lowerCAmelCase : Dict , lowerCAmelCase : Any , lowerCAmelCase : Any , lowerCAmelCase : int ):
"""simple docstring"""
__magic_name__ : Any = full_name.split('conv_layers.' )[-1]
__magic_name__ : int = name.split('.' )
__magic_name__ : Any = int(items[0] )
__magic_name__ : Any = int(items[1] )
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
f'{full_name} has size {value.shape}, but'
f' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.' )
__magic_name__ : Union[str, Any] = value
logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.' )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
f'{full_name} has size {value.shape}, but'
f' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.' )
__magic_name__ : str = value
logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.' )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
f'{full_name} has size {value.shape}, but'
f' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.' )
__magic_name__ : Optional[int] = value
logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.' )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
f'{full_name} has size {value.shape}, but'
f' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.' )
__magic_name__ : Dict = value
logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.' )
else:
unused_weights.append(lowerCAmelCase )
@torch.no_grad()
def lowerCamelCase ( lowerCAmelCase : Tuple , lowerCAmelCase : Optional[int] , lowerCAmelCase : Dict=None , lowerCAmelCase : Any=None , lowerCAmelCase : Optional[Any]=True , lowerCAmelCase : Any=False ):
"""simple docstring"""
if config_path is not None:
__magic_name__ : int = WavaVecaConfig.from_pretrained(lowerCAmelCase )
else:
__magic_name__ : List[str] = WavaVecaConfig()
if is_seq_class:
__magic_name__ : Any = read_txt_into_dict(lowerCAmelCase )
__magic_name__ : Optional[Any] = idalabel
__magic_name__ : Union[str, Any] = WavaVecaForSequenceClassification(lowerCAmelCase )
__magic_name__ : Optional[int] = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=1_6000 , padding_value=0 , do_normalize=lowerCAmelCase , return_attention_mask=lowerCAmelCase , )
feature_extractor.save_pretrained(lowerCAmelCase )
elif is_finetuned:
if dict_path:
__magic_name__ : str = Dictionary.load(lowerCAmelCase )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
__magic_name__ : Dict = target_dict.pad_index
__magic_name__ : Union[str, Any] = target_dict.bos_index
__magic_name__ : Union[str, Any] = target_dict.eos_index
__magic_name__ : Union[str, Any] = len(target_dict.symbols )
__magic_name__ : Dict = os.path.join(lowerCAmelCase , 'vocab.json' )
if not os.path.isdir(lowerCAmelCase ):
logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(lowerCAmelCase ) )
return
os.makedirs(lowerCAmelCase , exist_ok=lowerCAmelCase )
__magic_name__ : List[Any] = target_dict.indices
# fairseq has the <pad> and <s> switched
__magic_name__ : Any = 0
__magic_name__ : Optional[int] = 1
with open(lowerCAmelCase , 'w' , encoding='utf-8' ) as vocab_handle:
json.dump(lowerCAmelCase , lowerCAmelCase )
__magic_name__ : List[str] = WavaVecaCTCTokenizer(
lowerCAmelCase , 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=lowerCAmelCase , )
__magic_name__ : Tuple = True if config.feat_extract_norm == 'layer' else False
__magic_name__ : Optional[Any] = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=1_6000 , padding_value=0 , do_normalize=lowerCAmelCase , return_attention_mask=lowerCAmelCase , )
__magic_name__ : List[Any] = WavaVecaProcessor(feature_extractor=lowerCAmelCase , tokenizer=lowerCAmelCase )
processor.save_pretrained(lowerCAmelCase )
__magic_name__ : Dict = WavaVecaForCTC(lowerCAmelCase )
else:
__magic_name__ : Tuple = WavaVecaForPreTraining(lowerCAmelCase )
if is_finetuned or is_seq_class:
__magic_name__ , __magic_name__ , __magic_name__ : str = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={'data': '/'.join(dict_path.split('/' )[:-1] )} )
else:
__magic_name__ : Optional[Any] = argparse.Namespace(task='audio_pretraining' )
__magic_name__ : Dict = fairseq.tasks.setup_task(lowerCAmelCase )
__magic_name__ , __magic_name__ , __magic_name__ : Optional[Any] = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] , task=lowerCAmelCase )
__magic_name__ : Any = model[0].eval()
recursively_load_weights(lowerCAmelCase , lowerCAmelCase , not is_finetuned )
hf_wavavec.save_pretrained(lowerCAmelCase )
if __name__ == "__main__":
lowerCAmelCase :str = argparse.ArgumentParser()
parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
parser.add_argument(
'''--not_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
)
parser.add_argument(
'''--is_seq_class''',
action='''store_true''',
help='''Whether the model to convert is a fine-tuned sequence classification model or not''',
)
lowerCAmelCase :Dict = parser.parse_args()
lowerCAmelCase :Tuple = not args.not_finetuned and not args.is_seq_class
convert_wavaveca_checkpoint(
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.config_path,
args.dict_path,
is_finetuned,
args.is_seq_class,
) | 275 | 1 |
'''simple docstring'''
import datasets
from .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py
a_ : Tuple = "\\n@INPROCEEDINGS{Papineni02bleu:a,\n author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},\n title = {BLEU: a Method for Automatic Evaluation of Machine Translation},\n booktitle = {},\n year = {2002},\n pages = {311--318}\n}\n@inproceedings{lin-och-2004-orange,\n title = \"{ORANGE}: a Method for Evaluating Automatic Evaluation Metrics for Machine Translation\",\n author = \"Lin, Chin-Yew and\n Och, Franz Josef\",\n booktitle = \"{COLING} 2004: Proceedings of the 20th International Conference on Computational Linguistics\",\n month = \"aug 23{--}aug 27\",\n year = \"2004\",\n address = \"Geneva, Switzerland\",\n publisher = \"COLING\",\n url = \"https://www.aclweb.org/anthology/C04-1072\",\n pages = \"501--507\",\n}\n"
a_ : Optional[int] = "\\nBLEU (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another.\nQuality is considered to be the correspondence between a machine\'s output and that of a human: \"the closer a machine translation is to a professional human translation,\nthe better it is\" – this is the central idea behind BLEU. BLEU was one of the first metrics to claim a high correlation with human judgements of quality, and\nremains one of the most popular automated and inexpensive metrics.\n\nScores are calculated for individual translated segments—generally sentences—by comparing them with a set of good quality reference translations.\nThose scores are then averaged over the whole corpus to reach an estimate of the translation\'s overall quality. Intelligibility or grammatical correctness\nare not taken into account[citation needed].\n\nBLEU\'s output is always a number between 0 and 1. This value indicates how similar the candidate text is to the reference texts, with values closer to 1\nrepresenting more similar texts. Few human translations will attain a score of 1, since this would indicate that the candidate is identical to one of the\nreference translations. For this reason, it is not necessary to attain a score of 1. Because there are more opportunities to match, adding additional\nreference translations will increase the BLEU score.\n"
a_ : Optional[int] = "\nComputes BLEU score of translated segments against one or more references.\nArgs:\n predictions: list of translations to score.\n Each translation should be tokenized into a list of tokens.\n references: list of lists of references for each translation.\n Each reference should be tokenized into a list of tokens.\n max_order: Maximum n-gram order to use when computing BLEU score.\n smooth: Whether or not to apply Lin et al. 2004 smoothing.\nReturns:\n \'bleu\': bleu score,\n \'precisions\': geometric mean of n-gram precisions,\n \'brevity_penalty\': brevity penalty,\n \'length_ratio\': ratio of lengths,\n \'translation_length\': translation_length,\n \'reference_length\': reference_length\nExamples:\n\n >>> predictions = [\n ... [\"hello\", \"there\", \"general\", \"kenobi\"], # tokenized prediction of the first sample\n ... [\"foo\", \"bar\", \"foobar\"] # tokenized prediction of the second sample\n ... ]\n >>> references = [\n ... [[\"hello\", \"there\", \"general\", \"kenobi\"], [\"hello\", \"there\", \"!\"]], # tokenized references for the first sample (2 references)\n ... [[\"foo\", \"bar\", \"foobar\"]] # tokenized references for the second sample (1 reference)\n ... ]\n >>> bleu = datasets.load_metric(\"bleu\")\n >>> results = bleu.compute(predictions=predictions, references=references)\n >>> print(results[\"bleu\"])\n 1.0\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class a ( datasets.Metric ):
def __UpperCAmelCase ( self ) -> Dict:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'predictions': datasets.Sequence(datasets.Value('string' , id='token' ) , id='sequence' ),
'references': datasets.Sequence(
datasets.Sequence(datasets.Value('string' , id='token' ) , id='sequence' ) , id='references' ),
} ) , codebase_urls=['https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py'] , reference_urls=[
'https://en.wikipedia.org/wiki/BLEU',
'https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213',
] , )
def __UpperCAmelCase ( self , __magic_name__ , __magic_name__ , __magic_name__=4 , __magic_name__=False ) -> Optional[Any]:
_a = compute_bleu(
reference_corpus=__magic_name__ , translation_corpus=__magic_name__ , max_order=__magic_name__ , smooth=__magic_name__ )
(_a) = score
return {
"bleu": bleu,
"precisions": precisions,
"brevity_penalty": bp,
"length_ratio": ratio,
"translation_length": translation_length,
"reference_length": reference_length,
}
| 168 |
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase ) -> str:
"""simple docstring"""
return "\n".join(
f'''{number} * {i} = {number * i}''' for i in range(1 , number_of_terms + 1 ) )
if __name__ == "__main__":
print(multiplication_table(number=5, number_of_terms=1_0))
| 279 | 0 |
"""simple docstring"""
from itertools import product
def lowercase__(A , A ) ->str:
"""simple docstring"""
lowercase__ : int= sides_number
lowercase__ : Optional[int]= max_face_number * dice_number
lowercase__ : int= [0] * (max_total + 1)
lowercase__ : int= 1
lowercase__ : List[str]= range(__a , max_face_number + 1 )
for dice_numbers in product(__a , repeat=__a ):
lowercase__ : int= sum(__a )
totals_frequencies[total] += 1
return totals_frequencies
def lowercase__() ->Any:
"""simple docstring"""
lowercase__ : Union[str, Any]= total_frequency_distribution(
sides_number=4 , dice_number=9 )
lowercase__ : int= total_frequency_distribution(
sides_number=6 , dice_number=6 )
lowercase__ : Dict= 0
lowercase__ : Dict= 9
lowercase__ : Tuple= 4 * 9
lowercase__ : List[Any]= 6
for peter_total in range(__a , max_peter_total + 1 ):
peter_wins_count += peter_totals_frequencies[peter_total] * sum(
colin_totals_frequencies[min_colin_total:peter_total] )
lowercase__ : Tuple= (4**9) * (6**6)
lowercase__ : List[str]= peter_wins_count / total_games_number
lowercase__ : Union[str, Any]= round(__a , ndigits=7 )
return rounded_peter_win_probability
if __name__ == "__main__":
print(F"""{solution() = }""")
| 350 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class __UpperCAmelCase:
"""simple docstring"""
def __init__( self , snake_case__ ):
'''simple docstring'''
lowercase__ : Any= data
lowercase__ : Node | None= None
class __UpperCAmelCase:
"""simple docstring"""
def __init__( self ):
'''simple docstring'''
lowercase__ : List[str]= None
lowercase__ : int= None
def __iter__( self ):
'''simple docstring'''
lowercase__ : Any= self.head
while self.head:
yield node.data
lowercase__ : str= node.next
if node == self.head:
break
def __len__( self ):
'''simple docstring'''
return sum(1 for _ in self )
def __repr__( self ):
'''simple docstring'''
return "->".join(str(snake_case__ ) for item in iter(self ) )
def UpperCAmelCase_ ( self , snake_case__ ):
'''simple docstring'''
self.insert_nth(len(self ) , snake_case__ )
def UpperCAmelCase_ ( self , snake_case__ ):
'''simple docstring'''
self.insert_nth(0 , snake_case__ )
def UpperCAmelCase_ ( self , snake_case__ , snake_case__ ):
'''simple docstring'''
if index < 0 or index > len(self ):
raise IndexError("list index out of range." )
lowercase__ : Optional[Any]= Node(snake_case__ )
if self.head is None:
lowercase__ : Tuple= new_node # first node points itself
lowercase__ : str= new_node
elif index == 0: # insert at head
lowercase__ : Dict= self.head
lowercase__ : int= new_node
else:
lowercase__ : Dict= self.head
for _ in range(index - 1 ):
lowercase__ : Any= temp.next
lowercase__ : str= temp.next
lowercase__ : Dict= new_node
if index == len(self ) - 1: # insert at tail
lowercase__ : int= new_node
def UpperCAmelCase_ ( self ):
'''simple docstring'''
return self.delete_nth(0 )
def UpperCAmelCase_ ( self ):
'''simple docstring'''
return self.delete_nth(len(self ) - 1 )
def UpperCAmelCase_ ( self , snake_case__ = 0 ):
'''simple docstring'''
if not 0 <= index < len(self ):
raise IndexError("list index out of range." )
lowercase__ : int= self.head
if self.head == self.tail: # just one node
lowercase__ : Union[str, Any]= None
elif index == 0: # delete head node
lowercase__ : Tuple= self.tail.next.next
lowercase__ : Union[str, Any]= self.head.next
else:
lowercase__ : Union[str, Any]= self.head
for _ in range(index - 1 ):
lowercase__ : Dict= temp.next
lowercase__ : List[str]= temp.next
lowercase__ : Optional[Any]= temp.next.next
if index == len(self ) - 1: # delete at tail
lowercase__ : Optional[Any]= temp
return delete_node.data
def UpperCAmelCase_ ( self ):
'''simple docstring'''
return len(self ) == 0
def lowercase__() ->None:
"""simple docstring"""
lowercase__ : Dict= CircularLinkedList()
assert len(A ) == 0
assert circular_linked_list.is_empty() is True
assert str(A ) == ""
try:
circular_linked_list.delete_front()
raise AssertionError # This should not happen
except IndexError:
assert True # This should happen
try:
circular_linked_list.delete_tail()
raise AssertionError # This should not happen
except IndexError:
assert True # This should happen
try:
circular_linked_list.delete_nth(-1 )
raise AssertionError
except IndexError:
assert True
try:
circular_linked_list.delete_nth(0 )
raise AssertionError
except IndexError:
assert True
assert circular_linked_list.is_empty() is True
for i in range(5 ):
assert len(A ) == i
circular_linked_list.insert_nth(A , i + 1 )
assert str(A ) == "->".join(str(A ) for i in range(1 , 6 ) )
circular_linked_list.insert_tail(6 )
assert str(A ) == "->".join(str(A ) for i in range(1 , 7 ) )
circular_linked_list.insert_head(0 )
assert str(A ) == "->".join(str(A ) for i in range(0 , 7 ) )
assert circular_linked_list.delete_front() == 0
assert circular_linked_list.delete_tail() == 6
assert str(A ) == "->".join(str(A ) for i in range(1 , 6 ) )
assert circular_linked_list.delete_nth(2 ) == 3
circular_linked_list.insert_nth(2 , 3 )
assert str(A ) == "->".join(str(A ) for i in range(1 , 6 ) )
assert circular_linked_list.is_empty() is False
if __name__ == "__main__":
import doctest
doctest.testmod()
| 150 | 0 |
"""simple docstring"""
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def a__ ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , ) -> list[float]:
lowerCamelCase , lowerCamelCase = coefficient_matrix.shape
lowerCamelCase , lowerCamelCase = constant_matrix.shape
if rowsa != colsa:
lowerCamelCase = F'Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}'
raise ValueError(snake_case__ )
if colsa != 1:
lowerCamelCase = F'Constant matrix must be nx1 but received {rowsa}x{colsa}'
raise ValueError(snake_case__ )
if rowsa != rowsa:
lowerCamelCase = (
"""Coefficient and constant matrices dimensions must be nxn and nx1 but """
F'received {rowsa}x{colsa} and {rowsa}x{colsa}'
)
raise ValueError(snake_case__ )
if len(snake_case__ ) != rowsa:
lowerCamelCase = (
"""Number of initial values must be equal to number of rows in coefficient """
F'matrix but received {len(snake_case__ )} and {rowsa}'
)
raise ValueError(snake_case__ )
if iterations <= 0:
raise ValueError("""Iterations must be at least 1""" )
lowerCamelCase = np.concatenate(
(coefficient_matrix, constant_matrix) , axis=1 )
lowerCamelCase , lowerCamelCase = table.shape
strictly_diagonally_dominant(snake_case__ )
# Iterates the whole matrix for given number of times
for _ in range(snake_case__ ):
lowerCamelCase = []
for row in range(snake_case__ ):
lowerCamelCase = 0
for col in range(snake_case__ ):
if col == row:
lowerCamelCase = table[row][col]
elif col == cols - 1:
lowerCamelCase = table[row][col]
else:
temp += (-1) * table[row][col] * init_val[col]
lowerCamelCase = (temp + val) / denom
new_val.append(snake_case__ )
lowerCamelCase = new_val
return [float(snake_case__ ) for i in new_val]
def a__ ( snake_case__ ) -> bool:
lowerCamelCase , lowerCamelCase = table.shape
lowerCamelCase = True
for i in range(0 , snake_case__ ):
lowerCamelCase = 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()
| 291 |
"""simple docstring"""
from collections.abc import Generator
def a__ ( ) -> Generator[int, None, None]:
lowerCamelCase , lowerCamelCase = 0, 1
while True:
lowerCamelCase , lowerCamelCase = b, a + b
yield b
def a__ ( snake_case__ = 10_00 ) -> int:
lowerCamelCase = 1
lowerCamelCase = fibonacci_generator()
while len(str(next(snake_case__ ) ) ) < n:
answer += 1
return answer + 1
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 291 | 1 |
'''simple docstring'''
import torch
from transformers import CamembertForMaskedLM, CamembertTokenizer
def UpperCamelCase_ ( A__ : str , A__ : Union[str, Any] , A__ : Union[str, Any] , A__ : Any=5 ):
'''simple docstring'''
assert masked_input.count("""<mask>""" ) == 1
lowerCAmelCase_ : Tuple = torch.tensor(tokenizer.encode(A__ , add_special_tokens=A__ ) ).unsqueeze(0 ) # Batch size 1
lowerCAmelCase_ : str = model(A__ )[0] # The last hidden-state is the first element of the output tuple
lowerCAmelCase_ : int = (input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
lowerCAmelCase_ : Optional[Any] = logits[0, masked_index, :]
lowerCAmelCase_ : List[Any] = logits.softmax(dim=0 )
lowerCAmelCase_ : List[str] = prob.topk(k=A__ , dim=0 )
lowerCAmelCase_ : Union[str, Any] = """ """.join(
[tokenizer.convert_ids_to_tokens(indices[i].item() ) for i in range(len(A__ ) )] )
lowerCAmelCase_ : Tuple = tokenizer.mask_token
lowerCAmelCase_ : Optional[int] = []
for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(""" """ ) ):
lowerCAmelCase_ : Optional[int] = predicted_token_bpe.replace("""\u2581""" , """ """ )
if " {0}".format(A__ ) in masked_input:
topk_filled_outputs.append(
(
masked_input.replace(""" {0}""".format(A__ ) , A__ ),
values[index].item(),
predicted_token,
) )
else:
topk_filled_outputs.append(
(
masked_input.replace(A__ , A__ ),
values[index].item(),
predicted_token,
) )
return topk_filled_outputs
__A : Union[str, Any] = CamembertTokenizer.from_pretrained("camembert-base")
__A : List[str] = CamembertForMaskedLM.from_pretrained("camembert-base")
model.eval()
__A : Dict = "Le camembert est <mask> :)"
print(fill_mask(masked_input, model, tokenizer, topk=3))
| 363 |
'''simple docstring'''
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel
if is_vision_available():
from transformers import MaskFormerImageProcessor
if is_vision_available():
from PIL import Image
class __snake_case :
"""simple docstring"""
def __init__( self : Union[str, Any] , lowerCamelCase : List[Any] , lowerCamelCase : List[Any]=2 , lowerCamelCase : int=True , lowerCamelCase : str=False , lowerCamelCase : List[str]=10 , lowerCamelCase : Dict=3 , lowerCamelCase : str=32 * 4 , lowerCamelCase : Tuple=32 * 6 , lowerCamelCase : int=4 , lowerCamelCase : Optional[int]=32 , ) -> List[Any]:
lowerCAmelCase_ : Tuple = parent
lowerCAmelCase_ : int = batch_size
lowerCAmelCase_ : Tuple = is_training
lowerCAmelCase_ : str = use_auxiliary_loss
lowerCAmelCase_ : Optional[Any] = num_queries
lowerCAmelCase_ : List[str] = num_channels
lowerCAmelCase_ : Optional[Any] = min_size
lowerCAmelCase_ : Dict = max_size
lowerCAmelCase_ : List[Any] = num_labels
lowerCAmelCase_ : List[Any] = mask_feature_size
def __lowercase ( self : str ) -> List[Any]:
lowerCAmelCase_ : Union[str, Any] = floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
lowerCamelCase )
lowerCAmelCase_ : int = torch.ones([self.batch_size, self.min_size, self.max_size] , device=lowerCamelCase )
lowerCAmelCase_ : List[Any] = (
torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] , device=lowerCamelCase ) > 0.5
).float()
lowerCAmelCase_ : Union[str, Any] = (torch.rand((self.batch_size, self.num_labels) , device=lowerCamelCase ) > 0.5).long()
lowerCAmelCase_ : List[Any] = self.get_config()
return config, pixel_values, pixel_mask, mask_labels, class_labels
def __lowercase ( self : Optional[int] ) -> Optional[int]:
return MaskFormerConfig.from_backbone_and_decoder_configs(
backbone_config=SwinConfig(
depths=[1, 1, 1, 1] , ) , decoder_config=DetrConfig(
decoder_ffn_dim=1_28 , num_queries=self.num_queries , decoder_attention_heads=2 , d_model=self.mask_feature_size , ) , mask_feature_size=self.mask_feature_size , fpn_feature_size=self.mask_feature_size , num_channels=self.num_channels , num_labels=self.num_labels , )
def __lowercase ( self : Tuple ) -> Optional[Any]:
lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ : List[Any] = self.prepare_config_and_inputs()
lowerCAmelCase_ : Union[str, Any] = {"""pixel_values""": pixel_values, """pixel_mask""": pixel_mask}
return config, inputs_dict
def __lowercase ( self : Tuple , lowerCamelCase : Tuple , lowerCamelCase : Optional[Any] ) -> Union[str, Any]:
lowerCAmelCase_ : Any = output.encoder_hidden_states
lowerCAmelCase_ : Dict = output.pixel_decoder_hidden_states
lowerCAmelCase_ : str = output.transformer_decoder_hidden_states
self.parent.assertTrue(len(lowerCamelCase ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(lowerCamelCase ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(lowerCamelCase ) , config.decoder_config.decoder_layers )
def __lowercase ( self : List[Any] , lowerCamelCase : Any , lowerCamelCase : int , lowerCamelCase : Optional[int] , lowerCamelCase : Tuple=False ) -> List[Any]:
with torch.no_grad():
lowerCAmelCase_ : int = MaskFormerModel(config=lowerCamelCase )
model.to(lowerCamelCase )
model.eval()
lowerCAmelCase_ : List[Any] = model(pixel_values=lowerCamelCase , pixel_mask=lowerCamelCase )
lowerCAmelCase_ : Union[str, Any] = model(lowerCamelCase , output_hidden_states=lowerCamelCase )
# the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
# encoder and pixel decoder
self.parent.assertEqual(
output.transformer_decoder_last_hidden_state.shape , (self.batch_size, self.num_queries, self.mask_feature_size) , )
# let's ensure the other two hidden state exists
self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(output.encoder_last_hidden_state is not None )
if output_hidden_states:
self.check_output_hidden_state(lowerCamelCase , lowerCamelCase )
def __lowercase ( self : Any , lowerCamelCase : Dict , lowerCamelCase : Any , lowerCamelCase : Optional[int] , lowerCamelCase : Tuple , lowerCamelCase : Optional[Any] ) -> Dict:
lowerCAmelCase_ : Tuple = MaskFormerForInstanceSegmentation(config=lowerCamelCase )
model.to(lowerCamelCase )
model.eval()
def comm_check_on_output(lowerCamelCase : Optional[Any] ):
# let's still check that all the required stuff is there
self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.encoder_last_hidden_state is not None )
# okay, now we need to check the logits shape
# due to the encoder compression, masks have a //4 spatial size
self.parent.assertEqual(
result.masks_queries_logits.shape , (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) , )
# + 1 for null class
self.parent.assertEqual(
result.class_queries_logits.shape , (self.batch_size, self.num_queries, self.num_labels + 1) )
with torch.no_grad():
lowerCAmelCase_ : Dict = model(pixel_values=lowerCamelCase , pixel_mask=lowerCamelCase )
lowerCAmelCase_ : List[str] = model(lowerCamelCase )
comm_check_on_output(lowerCamelCase )
lowerCAmelCase_ : Union[str, Any] = model(
pixel_values=lowerCamelCase , pixel_mask=lowerCamelCase , mask_labels=lowerCamelCase , class_labels=lowerCamelCase )
comm_check_on_output(lowerCamelCase )
self.parent.assertTrue(result.loss is not None )
self.parent.assertEqual(result.loss.shape , torch.Size([1] ) )
@require_torch
class __snake_case ( _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,unittest.TestCase):
"""simple docstring"""
lowercase = (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
lowercase = (
{'feature-extraction': MaskFormerModel, 'image-segmentation': MaskFormerForInstanceSegmentation}
if is_torch_available()
else {}
)
lowercase = False
lowercase = False
lowercase = False
lowercase = False
def __lowercase ( self : Dict ) -> Optional[Any]:
lowerCAmelCase_ : Tuple = MaskFormerModelTester(self )
lowerCAmelCase_ : str = ConfigTester(self , config_class=lowerCamelCase , has_text_modality=lowerCamelCase )
def __lowercase ( self : List[Any] ) -> Optional[Any]:
self.config_tester.run_common_tests()
def __lowercase ( self : Tuple ) -> List[Any]:
lowerCAmelCase_, lowerCAmelCase_ : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(lowerCamelCase , **lowerCamelCase , output_hidden_states=lowerCamelCase )
def __lowercase ( self : Union[str, Any] ) -> str:
lowerCAmelCase_ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*lowerCamelCase )
@unittest.skip(reason="""MaskFormer does not use inputs_embeds""" )
def __lowercase ( self : Any ) -> Optional[Any]:
pass
@unittest.skip(reason="""MaskFormer does not have a get_input_embeddings method""" )
def __lowercase ( self : Union[str, Any] ) -> Optional[Any]:
pass
@unittest.skip(reason="""MaskFormer is not a generative model""" )
def __lowercase ( self : Any ) -> str:
pass
@unittest.skip(reason="""MaskFormer does not use token embeddings""" )
def __lowercase ( self : List[Any] ) -> Any:
pass
@require_torch_multi_gpu
@unittest.skip(
reason="""MaskFormer has some layers using `add_module` which doesn't work well with `nn.DataParallel`""" )
def __lowercase ( self : List[str] ) -> Optional[int]:
pass
@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" )
def __lowercase ( self : str ) -> Optional[int]:
pass
def __lowercase ( self : Tuple ) -> List[str]:
lowerCAmelCase_, lowerCAmelCase_ : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase_ : Optional[Any] = model_class(lowerCamelCase )
lowerCAmelCase_ : int = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowerCAmelCase_ : List[str] = [*signature.parameters.keys()]
lowerCAmelCase_ : int = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , lowerCamelCase )
@slow
def __lowercase ( self : Optional[int] ) -> List[str]:
for model_name in ["facebook/maskformer-swin-small-coco"]:
lowerCAmelCase_ : List[str] = MaskFormerModel.from_pretrained(lowerCamelCase )
self.assertIsNotNone(lowerCamelCase )
def __lowercase ( self : Dict ) -> int:
lowerCAmelCase_ : Any = (self.model_tester.min_size,) * 2
lowerCAmelCase_ : Dict = {
"""pixel_values""": torch.randn((2, 3, *size) , device=lowerCamelCase ),
"""mask_labels""": torch.randn((2, 10, *size) , device=lowerCamelCase ),
"""class_labels""": torch.zeros(2 , 10 , device=lowerCamelCase ).long(),
}
lowerCAmelCase_ : Dict = MaskFormerForInstanceSegmentation(MaskFormerConfig() ).to(lowerCamelCase )
lowerCAmelCase_ : str = model(**lowerCamelCase )
self.assertTrue(outputs.loss is not None )
def __lowercase ( self : int ) -> int:
lowerCAmelCase_, lowerCAmelCase_ : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(lowerCamelCase , **lowerCamelCase , output_hidden_states=lowerCamelCase )
def __lowercase ( self : Dict ) -> List[str]:
lowerCAmelCase_, lowerCAmelCase_ : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase_ : List[str] = model_class(lowerCamelCase ).to(lowerCamelCase )
lowerCAmelCase_ : Tuple = model(**lowerCamelCase , output_attentions=lowerCamelCase )
self.assertTrue(outputs.attentions is not None )
def __lowercase ( self : int ) -> Union[str, Any]:
if not self.model_tester.is_training:
return
# only MaskFormerForInstanceSegmentation has the loss
lowerCAmelCase_ : Optional[int] = self.all_model_classes[1]
lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ : str = self.model_tester.prepare_config_and_inputs()
lowerCAmelCase_ : Union[str, Any] = model_class(lowerCamelCase )
model.to(lowerCamelCase )
model.train()
lowerCAmelCase_ : str = model(lowerCamelCase , mask_labels=lowerCamelCase , class_labels=lowerCamelCase ).loss
loss.backward()
def __lowercase ( self : Any ) -> Union[str, Any]:
# only MaskFormerForInstanceSegmentation has the loss
lowerCAmelCase_ : List[str] = self.all_model_classes[1]
lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ : Any = self.model_tester.prepare_config_and_inputs()
lowerCAmelCase_ : List[str] = True
lowerCAmelCase_ : List[str] = True
lowerCAmelCase_ : Dict = model_class(lowerCamelCase )
model.to(lowerCamelCase )
model.train()
lowerCAmelCase_ : Union[str, Any] = model(lowerCamelCase , mask_labels=lowerCamelCase , class_labels=lowerCamelCase )
lowerCAmelCase_ : Optional[Any] = outputs.encoder_hidden_states[0]
encoder_hidden_states.retain_grad()
lowerCAmelCase_ : List[str] = outputs.pixel_decoder_hidden_states[0]
pixel_decoder_hidden_states.retain_grad()
# we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
lowerCAmelCase_ : Optional[int] = outputs.transformer_decoder_hidden_states[0]
transformer_decoder_hidden_states.retain_grad()
lowerCAmelCase_ : Tuple = outputs.attentions[0]
attentions.retain_grad()
outputs.loss.backward(retain_graph=lowerCamelCase )
self.assertIsNotNone(encoder_hidden_states.grad )
self.assertIsNotNone(pixel_decoder_hidden_states.grad )
self.assertIsNotNone(transformer_decoder_hidden_states.grad )
self.assertIsNotNone(attentions.grad )
__A : List[Any] = 1E-4
def UpperCamelCase_ ( ):
'''simple docstring'''
lowerCAmelCase_ : int = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_vision
@slow
class __snake_case ( unittest.TestCase):
"""simple docstring"""
@cached_property
def __lowercase ( self : Union[str, Any] ) -> List[Any]:
return (
MaskFormerImageProcessor.from_pretrained("""facebook/maskformer-swin-small-coco""" )
if is_vision_available()
else None
)
def __lowercase ( self : List[Any] ) -> Union[str, Any]:
lowerCAmelCase_ : Optional[int] = MaskFormerModel.from_pretrained("""facebook/maskformer-swin-small-coco""" ).to(lowerCamelCase )
lowerCAmelCase_ : Dict = self.default_image_processor
lowerCAmelCase_ : Optional[int] = prepare_img()
lowerCAmelCase_ : Dict = image_processor(lowerCamelCase , return_tensors="""pt""" ).to(lowerCamelCase )
lowerCAmelCase_ : Union[str, Any] = inputs["""pixel_values"""].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(lowerCamelCase , (1, 3, 8_00, 10_88) )
with torch.no_grad():
lowerCAmelCase_ : List[str] = model(**lowerCamelCase )
lowerCAmelCase_ : Any = torch.tensor(
[[-0.0_482, 0.9_228, 0.4_951], [-0.2_547, 0.8_017, 0.8_527], [-0.0_069, 0.3_385, -0.0_089]] ).to(lowerCamelCase )
self.assertTrue(
torch.allclose(
outputs.encoder_last_hidden_state[0, 0, :3, :3] , lowerCamelCase , atol=lowerCamelCase ) )
lowerCAmelCase_ : Union[str, Any] = torch.tensor(
[[-0.8_422, -0.8_434, -0.9_718], [-1.0_144, -0.5_565, -0.4_195], [-1.0_038, -0.4_484, -0.1_961]] ).to(lowerCamelCase )
self.assertTrue(
torch.allclose(
outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] , lowerCamelCase , atol=lowerCamelCase ) )
lowerCAmelCase_ : Optional[Any] = torch.tensor(
[[0.2_852, -0.0_159, 0.9_735], [0.6_254, 0.1_858, 0.8_529], [-0.0_680, -0.4_116, 1.8_413]] ).to(lowerCamelCase )
self.assertTrue(
torch.allclose(
outputs.transformer_decoder_last_hidden_state[0, :3, :3] , lowerCamelCase , atol=lowerCamelCase ) )
def __lowercase ( self : Optional[int] ) -> Optional[int]:
lowerCAmelCase_ : Union[str, Any] = (
MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-swin-small-coco""" )
.to(lowerCamelCase )
.eval()
)
lowerCAmelCase_ : Union[str, Any] = self.default_image_processor
lowerCAmelCase_ : Optional[Any] = prepare_img()
lowerCAmelCase_ : Optional[int] = image_processor(lowerCamelCase , return_tensors="""pt""" ).to(lowerCamelCase )
lowerCAmelCase_ : Optional[int] = inputs["""pixel_values"""].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(lowerCamelCase , (1, 3, 8_00, 10_88) )
with torch.no_grad():
lowerCAmelCase_ : List[str] = model(**lowerCamelCase )
# masks_queries_logits
lowerCAmelCase_ : Dict = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
lowerCAmelCase_ : Any = [
[-1.3_737_124, -1.7_724_937, -1.9_364_233],
[-1.5_977_281, -1.9_867_939, -2.1_523_695],
[-1.5_795_398, -1.9_269_832, -2.093_942],
]
lowerCAmelCase_ : Optional[int] = torch.tensor(lowerCamelCase ).to(lowerCamelCase )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , lowerCamelCase , atol=lowerCamelCase ) )
# class_queries_logits
lowerCAmelCase_ : Optional[Any] = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
lowerCAmelCase_ : Tuple = torch.tensor(
[
[1.6_512E00, -5.2_572E00, -3.3_519E00],
[3.6_169E-02, -5.9_025E00, -2.9_313E00],
[1.0_766E-04, -7.7_630E00, -5.1_263E00],
] ).to(lowerCamelCase )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , lowerCamelCase , atol=lowerCamelCase ) )
def __lowercase ( self : Optional[int] ) -> List[str]:
lowerCAmelCase_ : List[str] = (
MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-resnet101-coco-stuff""" )
.to(lowerCamelCase )
.eval()
)
lowerCAmelCase_ : Tuple = self.default_image_processor
lowerCAmelCase_ : int = prepare_img()
lowerCAmelCase_ : Any = image_processor(lowerCamelCase , return_tensors="""pt""" ).to(lowerCamelCase )
lowerCAmelCase_ : List[str] = inputs["""pixel_values"""].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(lowerCamelCase , (1, 3, 8_00, 10_88) )
with torch.no_grad():
lowerCAmelCase_ : Optional[Any] = model(**lowerCamelCase )
# masks_queries_logits
lowerCAmelCase_ : List[Any] = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
lowerCAmelCase_ : Optional[Any] = [[-0.9_046, -2.6_366, -4.6_062], [-3.4_179, -5.7_890, -8.8_057], [-4.9_179, -7.6_560, -10.7_711]]
lowerCAmelCase_ : Union[str, Any] = torch.tensor(lowerCamelCase ).to(lowerCamelCase )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , lowerCamelCase , atol=lowerCamelCase ) )
# class_queries_logits
lowerCAmelCase_ : Dict = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
lowerCAmelCase_ : int = torch.tensor(
[[4.7_188, -3.2_585, -2.8_857], [6.6_871, -2.9_181, -1.2_487], [7.2_449, -2.2_764, -2.1_874]] ).to(lowerCamelCase )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , lowerCamelCase , atol=lowerCamelCase ) )
def __lowercase ( self : Union[str, Any] ) -> List[str]:
lowerCAmelCase_ : Dict = (
MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-swin-small-coco""" )
.to(lowerCamelCase )
.eval()
)
lowerCAmelCase_ : List[str] = self.default_image_processor
lowerCAmelCase_ : int = image_processor(
[np.zeros((3, 8_00, 13_33) ), np.zeros((3, 8_00, 13_33) )] , segmentation_maps=[np.zeros((3_84, 3_84) ).astype(np.floataa ), np.zeros((3_84, 3_84) ).astype(np.floataa )] , return_tensors="""pt""" , )
lowerCAmelCase_ : List[str] = inputs["""pixel_values"""].to(lowerCamelCase )
lowerCAmelCase_ : Tuple = [el.to(lowerCamelCase ) for el in inputs["""mask_labels"""]]
lowerCAmelCase_ : Union[str, Any] = [el.to(lowerCamelCase ) for el in inputs["""class_labels"""]]
with torch.no_grad():
lowerCAmelCase_ : Any = model(**lowerCamelCase )
self.assertTrue(outputs.loss is not None )
| 89 | 0 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
_A = abspath(join(dirname(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 lowerCamelCase__ ( __lowerCAmelCase : int ):
"""simple docstring"""
from diffusers.utils.testing_utils import pytest_addoption_shared
pytest_addoption_shared(__lowerCAmelCase )
def lowerCamelCase__ ( __lowerCAmelCase : Tuple ):
"""simple docstring"""
from diffusers.utils.testing_utils import pytest_terminal_summary_main
lowerCAmelCase_ = terminalreporter.config.getoption("--make-reports" )
if make_reports:
pytest_terminal_summary_main(__lowerCAmelCase , id=__lowerCAmelCase )
| 231 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_A = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_A = ["NllbTokenizer"]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_A = ["NllbTokenizerFast"]
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_nllb import NllbTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_nllb_fast import NllbTokenizerFast
else:
import sys
_A = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 231 | 1 |
def lowerCamelCase_ ( UpperCamelCase__ : int = 1000 ):
'''simple docstring'''
UpperCamelCase__ = 2**power
UpperCamelCase__ = 0
while n:
UpperCamelCase__ , UpperCamelCase__ = r + n % 10, n // 10
return r
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 35 | from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFAutoModelForSeqaSeqLM, TFMBartForConditionalGeneration, TFMBartModel
@require_tf
class __lowercase :
'''simple docstring'''
_A : int = MBartConfig
_A : str = {}
_A : str = '''gelu'''
def __init__( self : Tuple , _a : Dict , _a : Optional[Any]=13 , _a : List[Any]=7 , _a : Any=True , _a : List[Any]=False , _a : List[Any]=99 , _a : int=32 , _a : Optional[Any]=2 , _a : Optional[Any]=4 , _a : Any=37 , _a : Any=0.1 , _a : Any=0.1 , _a : Dict=20 , _a : Optional[Any]=2 , _a : List[str]=1 , _a : List[str]=0 , ):
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_dropout_prob
UpperCamelCase__ = attention_probs_dropout_prob
UpperCamelCase__ = max_position_embeddings
UpperCamelCase__ = eos_token_id
UpperCamelCase__ = pad_token_id
UpperCamelCase__ = bos_token_id
def A_ ( self : Any ):
UpperCamelCase__ = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
UpperCamelCase__ = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
UpperCamelCase__ = tf.concat([input_ids, eos_tensor] , axis=1 )
UpperCamelCase__ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCamelCase__ = self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , **self.config_updates , )
UpperCamelCase__ = prepare_mbart_inputs_dict(_a , _a , _a )
return config, inputs_dict
def A_ ( self : Union[str, Any] , _a : Tuple , _a : Dict ):
UpperCamelCase__ = TFMBartModel(config=_a ).get_decoder()
UpperCamelCase__ = inputs_dict['''input_ids''']
UpperCamelCase__ = input_ids[:1, :]
UpperCamelCase__ = inputs_dict['''attention_mask'''][:1, :]
UpperCamelCase__ = inputs_dict['''head_mask''']
UpperCamelCase__ = 1
# first forward pass
UpperCamelCase__ = model(_a , attention_mask=_a , head_mask=_a , use_cache=_a )
UpperCamelCase__ , UpperCamelCase__ = outputs.to_tuple()
UpperCamelCase__ = past_key_values[1]
def lowerCamelCase_ ( UpperCamelCase__ : Dict, UpperCamelCase__ : Optional[int], UpperCamelCase__ : Optional[int], UpperCamelCase__ : Optional[Any]=None, UpperCamelCase__ : Tuple=None, UpperCamelCase__ : Dict=None, UpperCamelCase__ : Tuple=None, UpperCamelCase__ : Tuple=None, ):
'''simple docstring'''
if attention_mask is None:
UpperCamelCase__ = tf.cast(tf.math.not_equal(UpperCamelCase__, config.pad_token_id ), tf.inta )
if decoder_attention_mask is None:
UpperCamelCase__ = tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape, dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:], config.pad_token_id ), tf.inta ),
], axis=-1, )
if head_mask is None:
UpperCamelCase__ = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
UpperCamelCase__ = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
UpperCamelCase__ = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
@require_tf
class __lowercase ( A, A, unittest.TestCase ):
'''simple docstring'''
_A : List[str] = (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
_A : List[str] = (TFMBartForConditionalGeneration,) if is_tf_available() else ()
_A : List[Any] = (
{
'''conversational''': TFMBartForConditionalGeneration,
'''feature-extraction''': TFMBartModel,
'''summarization''': TFMBartForConditionalGeneration,
'''text2text-generation''': TFMBartForConditionalGeneration,
'''translation''': TFMBartForConditionalGeneration,
}
if is_tf_available()
else {}
)
_A : List[Any] = True
_A : Any = False
_A : List[Any] = False
def A_ ( self : Any , _a : Tuple , _a : List[Any] , _a : Tuple , _a : List[str] , _a : List[Any] ):
if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
# Exception encountered when calling layer '...'
return True
return False
def A_ ( self : List[Any] ):
UpperCamelCase__ = TFMBartModelTester(self )
UpperCamelCase__ = ConfigTester(self , config_class=_a )
def A_ ( self : Tuple ):
self.config_tester.run_common_tests()
def A_ ( self : Optional[Any] ):
UpperCamelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*_a )
@require_sentencepiece
@require_tokenizers
@require_tf
class __lowercase ( unittest.TestCase ):
'''simple docstring'''
_A : Union[str, Any] = [
''' UN Chief Says There Is No Military Solution in Syria''',
]
_A : Dict = [
'''Şeful ONU declară că nu există o soluţie militară în Siria''',
]
_A : Dict = '''facebook/mbart-large-en-ro'''
@cached_property
def A_ ( self : Any ):
return AutoTokenizer.from_pretrained(self.model_name )
@cached_property
def A_ ( self : str ):
UpperCamelCase__ = TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
return model
def A_ ( self : Optional[int] , **_a : Optional[int] ):
UpperCamelCase__ = self.translate_src_text(**_a )
self.assertListEqual(self.expected_text , _a )
def A_ ( self : List[str] , **_a : Dict ):
UpperCamelCase__ = self.tokenizer(self.src_text , **_a , return_tensors='''tf''' )
UpperCamelCase__ = self.model.generate(
model_inputs.input_ids , attention_mask=model_inputs.attention_mask , num_beams=2 )
UpperCamelCase__ = self.tokenizer.batch_decode(_a , skip_special_tokens=_a )
return generated_words
@slow
def A_ ( self : Optional[Any] ):
self._assert_generated_batch_equal_expected()
| 35 | 1 |
import os
import tempfile
import unittest
from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
FlaubertForMultipleChoice,
FlaubertForQuestionAnswering,
FlaubertForQuestionAnsweringSimple,
FlaubertForSequenceClassification,
FlaubertForTokenClassification,
FlaubertModel,
FlaubertWithLMHeadModel,
)
from transformers.models.flaubert.modeling_flaubert import FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST
class _lowercase ( snake_case_ ):
def __init__( self : int , snake_case : Optional[int] , snake_case : Tuple=1_3 , snake_case : Tuple=7 , snake_case : Any=True , snake_case : Tuple=True , snake_case : Tuple=True , snake_case : Any=True , snake_case : Optional[int]=True , snake_case : int=False , snake_case : Any=False , snake_case : Dict=False , snake_case : Optional[int]=2 , snake_case : str=9_9 , snake_case : Tuple=0 , snake_case : Any=3_2 , snake_case : List[Any]=5 , snake_case : Any=4 , snake_case : Optional[int]=0.1 , snake_case : str=0.1 , snake_case : int=5_1_2 , snake_case : int=1_2 , snake_case : Tuple=2 , snake_case : Tuple=0.02 , snake_case : Any=3 , snake_case : Dict=4 , snake_case : int="last" , snake_case : Dict=None , snake_case : Any=None , ) -> Tuple:
"""simple docstring"""
UpperCamelCase_ : Optional[Any] = parent
UpperCamelCase_ : Union[str, Any] = batch_size
UpperCamelCase_ : Optional[Any] = seq_length
UpperCamelCase_ : List[str] = is_training
UpperCamelCase_ : str = use_input_lengths
UpperCamelCase_ : Optional[int] = use_token_type_ids
UpperCamelCase_ : Optional[Any] = use_labels
UpperCamelCase_ : int = gelu_activation
UpperCamelCase_ : str = sinusoidal_embeddings
UpperCamelCase_ : Any = causal
UpperCamelCase_ : Dict = asm
UpperCamelCase_ : Union[str, Any] = n_langs
UpperCamelCase_ : Optional[int] = vocab_size
UpperCamelCase_ : Dict = n_special
UpperCamelCase_ : str = hidden_size
UpperCamelCase_ : List[str] = num_hidden_layers
UpperCamelCase_ : List[str] = num_attention_heads
UpperCamelCase_ : int = hidden_dropout_prob
UpperCamelCase_ : int = attention_probs_dropout_prob
UpperCamelCase_ : Any = max_position_embeddings
UpperCamelCase_ : Dict = type_vocab_size
UpperCamelCase_ : List[Any] = type_sequence_label_size
UpperCamelCase_ : int = initializer_range
UpperCamelCase_ : Optional[int] = num_labels
UpperCamelCase_ : Optional[int] = num_choices
UpperCamelCase_ : List[Any] = summary_type
UpperCamelCase_ : List[str] = use_proj
UpperCamelCase_ : str = scope
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ) -> str:
"""simple docstring"""
UpperCamelCase_ : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCamelCase_ : str = random_attention_mask([self.batch_size, self.seq_length] )
UpperCamelCase_ : List[Any] = None
if self.use_input_lengths:
UpperCamelCase_ : Optional[int] = (
ids_tensor([self.batch_size] , vocab_size=2 ) + self.seq_length - 2
) # small variation of seq_length
UpperCamelCase_ : Dict = None
if self.use_token_type_ids:
UpperCamelCase_ : Dict = ids_tensor([self.batch_size, self.seq_length] , self.n_langs )
UpperCamelCase_ : Any = None
UpperCamelCase_ : Optional[Any] = None
UpperCamelCase_ : Optional[int] = None
if self.use_labels:
UpperCamelCase_ : Any = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCamelCase_ : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
UpperCamelCase_ : List[str] = ids_tensor([self.batch_size] , 2 ).float()
UpperCamelCase_ : Tuple = ids_tensor([self.batch_size] , self.num_choices )
UpperCamelCase_ : Optional[int] = self.get_config()
return (
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
)
def SCREAMING_SNAKE_CASE__ ( self : List[str] ) -> int:
"""simple docstring"""
return FlaubertConfig(
vocab_size=self.vocab_size , n_special=self.n_special , emb_dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , gelu_activation=self.gelu_activation , sinusoidal_embeddings=self.sinusoidal_embeddings , asm=self.asm , causal=self.causal , n_langs=self.n_langs , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , summary_type=self.summary_type , use_proj=self.use_proj , )
def SCREAMING_SNAKE_CASE__ ( self : Any , snake_case : Any , snake_case : Union[str, Any] , snake_case : List[str] , snake_case : Optional[Any] , snake_case : str , snake_case : Dict , snake_case : List[str] , snake_case : List[Any] , snake_case : Tuple , ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase_ : str = FlaubertModel(config=snake_case )
model.to(snake_case )
model.eval()
UpperCamelCase_ : Any = model(snake_case , lengths=snake_case , langs=snake_case )
UpperCamelCase_ : List[Any] = model(snake_case , langs=snake_case )
UpperCamelCase_ : Union[str, Any] = model(snake_case )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] , snake_case : Any , snake_case : str , snake_case : Any , snake_case : Union[str, Any] , snake_case : int , snake_case : List[str] , snake_case : List[Any] , snake_case : Optional[Any] , snake_case : Optional[int] , ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase_ : Any = FlaubertWithLMHeadModel(snake_case )
model.to(snake_case )
model.eval()
UpperCamelCase_ : str = model(snake_case , token_type_ids=snake_case , labels=snake_case )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def SCREAMING_SNAKE_CASE__ ( self : List[Any] , snake_case : Optional[Any] , snake_case : Dict , snake_case : List[Any] , snake_case : Tuple , snake_case : Dict , snake_case : Optional[Any] , snake_case : str , snake_case : Any , snake_case : Tuple , ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase_ : List[Any] = FlaubertForQuestionAnsweringSimple(snake_case )
model.to(snake_case )
model.eval()
UpperCamelCase_ : List[str] = model(snake_case )
UpperCamelCase_ : Union[str, Any] = model(snake_case , start_positions=snake_case , end_positions=snake_case )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def SCREAMING_SNAKE_CASE__ ( self : str , snake_case : str , snake_case : List[str] , snake_case : Optional[Any] , snake_case : Tuple , snake_case : str , snake_case : List[Any] , snake_case : Union[str, Any] , snake_case : Dict , snake_case : List[Any] , ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase_ : str = FlaubertForQuestionAnswering(snake_case )
model.to(snake_case )
model.eval()
UpperCamelCase_ : str = model(snake_case )
UpperCamelCase_ : str = model(
snake_case , start_positions=snake_case , end_positions=snake_case , cls_index=snake_case , is_impossible=snake_case , p_mask=snake_case , )
UpperCamelCase_ : Optional[int] = model(
snake_case , start_positions=snake_case , end_positions=snake_case , cls_index=snake_case , is_impossible=snake_case , )
((UpperCamelCase_), ) : Optional[int] = result_with_labels.to_tuple()
UpperCamelCase_ : Union[str, Any] = model(snake_case , start_positions=snake_case , end_positions=snake_case )
((UpperCamelCase_), ) : List[str] = result_with_labels.to_tuple()
self.parent.assertEqual(result_with_labels.loss.shape , () )
self.parent.assertEqual(result.start_top_log_probs.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(result.start_top_index.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(
result.end_top_log_probs.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(
result.end_top_index.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(result.cls_logits.shape , (self.batch_size,) )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] , snake_case : Optional[int] , snake_case : int , snake_case : Any , snake_case : Dict , snake_case : int , snake_case : str , snake_case : Optional[int] , snake_case : str , snake_case : Dict , ) -> str:
"""simple docstring"""
UpperCamelCase_ : Optional[int] = FlaubertForSequenceClassification(snake_case )
model.to(snake_case )
model.eval()
UpperCamelCase_ : List[str] = model(snake_case )
UpperCamelCase_ : Any = model(snake_case , labels=snake_case )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def SCREAMING_SNAKE_CASE__ ( self : Any , snake_case : Any , snake_case : Optional[int] , snake_case : int , snake_case : Any , snake_case : Union[str, Any] , snake_case : Dict , snake_case : Union[str, Any] , snake_case : List[Any] , snake_case : Optional[int] , ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase_ : Dict = self.num_labels
UpperCamelCase_ : int = FlaubertForTokenClassification(snake_case )
model.to(snake_case )
model.eval()
UpperCamelCase_ : Optional[int] = model(snake_case , attention_mask=snake_case , labels=snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] , snake_case : Tuple , snake_case : Optional[int] , snake_case : Dict , snake_case : Any , snake_case : Tuple , snake_case : Union[str, Any] , snake_case : List[Any] , snake_case : Optional[int] , snake_case : str , ) -> Any:
"""simple docstring"""
UpperCamelCase_ : str = self.num_choices
UpperCamelCase_ : List[Any] = FlaubertForMultipleChoice(config=snake_case )
model.to(snake_case )
model.eval()
UpperCamelCase_ : Dict = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCamelCase_ : Optional[int] = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCamelCase_ : int = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCamelCase_ : Tuple = model(
snake_case , attention_mask=snake_case , token_type_ids=snake_case , labels=snake_case , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase_ : Any = self.prepare_config_and_inputs()
(
(
UpperCamelCase_
), (
UpperCamelCase_
), (
UpperCamelCase_
), (
UpperCamelCase_
), (
UpperCamelCase_
), (
UpperCamelCase_
), (
UpperCamelCase_
), (
UpperCamelCase_
), (
UpperCamelCase_
),
) : List[str] = config_and_inputs
UpperCamelCase_ : Union[str, Any] = {
'input_ids': input_ids,
'token_type_ids': token_type_ids,
'lengths': input_lengths,
'attention_mask': input_mask,
}
return config, inputs_dict
@require_torch
class _lowercase ( snake_case_ , snake_case_ , unittest.TestCase ):
lowercase = (
(
FlaubertModel,
FlaubertWithLMHeadModel,
FlaubertForQuestionAnswering,
FlaubertForQuestionAnsweringSimple,
FlaubertForSequenceClassification,
FlaubertForTokenClassification,
FlaubertForMultipleChoice,
)
if is_torch_available()
else ()
)
lowercase = (
{
'feature-extraction': FlaubertModel,
'fill-mask': FlaubertWithLMHeadModel,
'question-answering': FlaubertForQuestionAnsweringSimple,
'text-classification': FlaubertForSequenceClassification,
'token-classification': FlaubertForTokenClassification,
'zero-shot': FlaubertForSequenceClassification,
}
if is_torch_available()
else {}
)
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] , snake_case : Dict , snake_case : str , snake_case : Any , snake_case : Any , snake_case : List[str] ) -> List[str]:
"""simple docstring"""
if (
pipeline_test_casse_name == "QAPipelineTests"
and tokenizer_name is not None
and not tokenizer_name.endswith('Fast' )
):
# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
return True
return False
def SCREAMING_SNAKE_CASE__ ( self : str , snake_case : List[Any] , snake_case : Union[str, Any] , snake_case : Optional[Any]=False ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase_ : List[Any] = super()._prepare_for_class(snake_case , snake_case , return_labels=snake_case )
if return_labels:
if model_class.__name__ == "FlaubertForQuestionAnswering":
UpperCamelCase_ : Tuple = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=snake_case )
UpperCamelCase_ : Tuple = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=snake_case )
return inputs_dict
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase_ : Dict = FlaubertModelTester(self )
UpperCamelCase_ : Dict = ConfigTester(self , config_class=snake_case , emb_dim=3_7 )
def SCREAMING_SNAKE_CASE__ ( self : Any ) -> Union[str, Any]:
"""simple docstring"""
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE__ ( self : Any ) -> str:
"""simple docstring"""
UpperCamelCase_ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_model(*snake_case )
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> Tuple:
"""simple docstring"""
UpperCamelCase_ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_lm_head(*snake_case )
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
UpperCamelCase_ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_simple_qa(*snake_case )
def SCREAMING_SNAKE_CASE__ ( self : Dict ) -> Dict:
"""simple docstring"""
UpperCamelCase_ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_qa(*snake_case )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase_ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_sequence_classif(*snake_case )
def SCREAMING_SNAKE_CASE__ ( self : int ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase_ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_token_classif(*snake_case )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase_ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_multiple_choice(*snake_case )
@slow
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> Any:
"""simple docstring"""
for model_name in FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCamelCase_ : List[str] = FlaubertModel.from_pretrained(snake_case )
self.assertIsNotNone(snake_case )
@slow
@require_torch_gpu
def SCREAMING_SNAKE_CASE__ ( self : List[str] ) -> Any:
"""simple docstring"""
UpperCamelCase_, UpperCamelCase_ : Dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# FlauBertForMultipleChoice behaves incorrectly in JIT environments.
if model_class == FlaubertForMultipleChoice:
return
UpperCamelCase_ : List[str] = True
UpperCamelCase_ : Any = model_class(config=snake_case )
UpperCamelCase_ : str = self._prepare_for_class(snake_case , snake_case )
UpperCamelCase_ : List[str] = torch.jit.trace(
snake_case , (inputs_dict['input_ids'].to('cpu' ), inputs_dict['attention_mask'].to('cpu' )) )
with tempfile.TemporaryDirectory() as tmp:
torch.jit.save(snake_case , os.path.join(snake_case , 'traced_model.pt' ) )
UpperCamelCase_ : Union[str, Any] = torch.jit.load(os.path.join(snake_case , 'traced_model.pt' ) , map_location=snake_case )
loaded(inputs_dict['input_ids'].to(snake_case ) , inputs_dict['attention_mask'].to(snake_case ) )
@require_torch
class _lowercase ( unittest.TestCase ):
@slow
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> Any:
"""simple docstring"""
UpperCamelCase_ : Union[str, Any] = FlaubertModel.from_pretrained('flaubert/flaubert_base_cased' )
UpperCamelCase_ : int = torch.tensor([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]] )
with torch.no_grad():
UpperCamelCase_ : Optional[Any] = model(snake_case )[0]
UpperCamelCase_ : Dict = torch.Size((1, 1_1, 7_6_8) )
self.assertEqual(output.shape , snake_case )
UpperCamelCase_ : Any = torch.tensor(
[[[-2.6251, -1.4298, -0.0227], [-2.8510, -1.6387, 0.2258], [-2.8114, -1.1832, -0.3066]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , snake_case , atol=1e-4 ) )
| 175 | import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union
import numpy as np
import pyarrow as pa
import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config import MAX_SHARD_SIZE
from datasets.filesystems import (
is_remote_filesystem,
rename,
)
from datasets.iterable_dataset import _BaseExamplesIterable
from datasets.utils.py_utils import convert_file_size_to_int
a_ = datasets.utils.logging.get_logger(__name__)
if TYPE_CHECKING:
import pyspark
@dataclass
class _lowercase ( datasets.BuilderConfig ):
lowercase = None
def __lowercase ( lowerCamelCase : "pyspark.sql.DataFrame" , lowerCamelCase : List[int] , ):
import pyspark
def generate_fn():
UpperCamelCase_ : Dict = df.select('*' , pyspark.sql.functions.spark_partition_id().alias('part_id' ) )
for partition_id in partition_order:
UpperCamelCase_ : Tuple = df_with_partition_id.select('*' ).where(F"part_id = {partition_id}" ).drop('part_id' )
UpperCamelCase_ : Union[str, Any] = partition_df.collect()
UpperCamelCase_ : Any = 0
for row in rows:
yield F"{partition_id}_{row_id}", row.asDict()
row_id += 1
return generate_fn
class _lowercase ( _BaseExamplesIterable ):
def __init__( self : Optional[int] , snake_case : "pyspark.sql.DataFrame" , snake_case : Tuple=None , ) -> Tuple:
"""simple docstring"""
UpperCamelCase_ : Dict = df
UpperCamelCase_ : int = partition_order or range(self.df.rdd.getNumPartitions() )
UpperCamelCase_ : Optional[Any] = _generate_iterable_examples(self.df , self.partition_order )
def __iter__( self : Optional[int] ) -> Any:
"""simple docstring"""
yield from self.generate_examples_fn()
def SCREAMING_SNAKE_CASE__ ( self : Any , snake_case : np.random.Generator ) -> "SparkExamplesIterable":
"""simple docstring"""
UpperCamelCase_ : Optional[Any] = list(range(self.df.rdd.getNumPartitions() ) )
generator.shuffle(snake_case )
return SparkExamplesIterable(self.df , partition_order=snake_case )
def SCREAMING_SNAKE_CASE__ ( self : int , snake_case : int , snake_case : int ) -> "SparkExamplesIterable":
"""simple docstring"""
UpperCamelCase_ : Tuple = self.split_shard_indices_by_worker(snake_case , snake_case )
return SparkExamplesIterable(self.df , partition_order=snake_case )
@property
def SCREAMING_SNAKE_CASE__ ( self : int ) -> int:
"""simple docstring"""
return len(self.partition_order )
class _lowercase ( datasets.DatasetBuilder ):
lowercase = SparkConfig
def __init__( self : List[Any] , snake_case : "pyspark.sql.DataFrame" , snake_case : str = None , snake_case : str = None , **snake_case : Optional[Any] , ) -> List[str]:
"""simple docstring"""
import pyspark
UpperCamelCase_ : List[Any] = pyspark.sql.SparkSession.builder.getOrCreate()
UpperCamelCase_ : str = df
UpperCamelCase_ : Tuple = working_dir
super().__init__(
cache_dir=snake_case , config_name=str(self.df.semanticHash() ) , **snake_case , )
def SCREAMING_SNAKE_CASE__ ( self : Any ) -> Dict:
"""simple docstring"""
def create_cache_and_write_probe(snake_case : str ):
# makedirs with exist_ok will recursively create the directory. It will not throw an error if directories
# already exist.
os.makedirs(self._cache_dir , exist_ok=snake_case )
UpperCamelCase_ : Tuple = os.path.join(self._cache_dir , 'fs_test' + uuid.uuida().hex )
# Opening the file in append mode will create a new file unless it already exists, in which case it will not
# change the file contents.
open(snake_case , 'a' )
return [probe_file]
if self._spark.conf.get('spark.master' , '' ).startswith('local' ):
return
# If the cluster is multi-node, make sure that the user provided a cache_dir and that it is on an NFS
# accessible to the driver.
# TODO: Stream batches to the driver using ArrowCollectSerializer instead of throwing an error.
if self._cache_dir:
UpperCamelCase_ : Tuple = (
self._spark.sparkContext.parallelize(range(1 ) , 1 ).mapPartitions(snake_case ).collect()
)
if os.path.isfile(probe[0] ):
return
raise ValueError(
'When using Dataset.from_spark on a multi-node cluster, the driver and all workers should be able to access cache_dir' )
def SCREAMING_SNAKE_CASE__ ( self : int ) -> Union[str, Any]:
"""simple docstring"""
return datasets.DatasetInfo(features=self.config.features )
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] , snake_case : datasets.download.download_manager.DownloadManager ) -> Optional[int]:
"""simple docstring"""
return [datasets.SplitGenerator(name=datasets.Split.TRAIN )]
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] , snake_case : Optional[int] ) -> List[Any]:
"""simple docstring"""
import pyspark
def get_arrow_batch_size(snake_case : Dict ):
for batch in it:
yield pa.RecordBatch.from_pydict({'batch_bytes': [batch.nbytes]} )
UpperCamelCase_ : List[str] = self.df.count()
UpperCamelCase_ : Union[str, Any] = df_num_rows if df_num_rows <= 1_0_0 else 1_0_0
# Approximate the size of each row (in Arrow format) by averaging over a max-100-row sample.
UpperCamelCase_ : str = (
self.df.limit(snake_case )
.repartition(1 )
.mapInArrow(snake_case , 'batch_bytes: long' )
.agg(pyspark.sql.functions.sum('batch_bytes' ).alias('sample_bytes' ) )
.collect()[0]
.sample_bytes
/ sample_num_rows
)
UpperCamelCase_ : Optional[int] = approx_bytes_per_row * df_num_rows
if approx_total_size > max_shard_size:
# Make sure there is at least one row per partition.
UpperCamelCase_ : Optional[Any] = min(snake_case , int(approx_total_size / max_shard_size ) )
UpperCamelCase_ : int = self.df.repartition(snake_case )
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] , snake_case : str , snake_case : str , snake_case : int , ) -> Iterable[Tuple[int, bool, Union[int, tuple]]]:
"""simple docstring"""
import pyspark
UpperCamelCase_ : List[Any] = ParquetWriter if file_format == 'parquet' else ArrowWriter
UpperCamelCase_ : List[str] = os.path.join(self._working_dir , os.path.basename(snake_case ) ) if self._working_dir else fpath
UpperCamelCase_ : Union[str, Any] = file_format == 'parquet'
# Define these so that we don't reference self in write_arrow, which will result in a pickling error due to
# pickling the SparkContext.
UpperCamelCase_ : Union[str, Any] = self.config.features
UpperCamelCase_ : Any = self._writer_batch_size
UpperCamelCase_ : Dict = self._fs.storage_options
def write_arrow(snake_case : List[str] ):
# Within the same SparkContext, no two task attempts will share the same attempt ID.
UpperCamelCase_ : Any = pyspark.TaskContext().taskAttemptId()
UpperCamelCase_ : str = next(snake_case , snake_case )
if first_batch is None:
# Some partitions might not receive any data.
return pa.RecordBatch.from_arrays(
[[task_id], [0], [0]] , names=['task_id', 'num_examples', 'num_bytes'] , )
UpperCamelCase_ : Any = 0
UpperCamelCase_ : Optional[Any] = writer_class(
features=snake_case , path=working_fpath.replace('SSSSS' , f"{shard_id:05d}" ).replace('TTTTT' , f"{task_id:05d}" ) , writer_batch_size=snake_case , storage_options=snake_case , embed_local_files=snake_case , )
UpperCamelCase_ : str = pa.Table.from_batches([first_batch] )
writer.write_table(snake_case )
for batch in it:
if max_shard_size is not None and writer._num_bytes >= max_shard_size:
UpperCamelCase_, UpperCamelCase_ : str = writer.finalize()
writer.close()
yield pa.RecordBatch.from_arrays(
[[task_id], [num_examples], [num_bytes]] , names=['task_id', 'num_examples', 'num_bytes'] , )
shard_id += 1
UpperCamelCase_ : Union[str, Any] = writer_class(
features=writer._features , path=working_fpath.replace('SSSSS' , f"{shard_id:05d}" ).replace('TTTTT' , f"{task_id:05d}" ) , writer_batch_size=snake_case , storage_options=snake_case , embed_local_files=snake_case , )
UpperCamelCase_ : Optional[Any] = pa.Table.from_batches([batch] )
writer.write_table(snake_case )
if writer._num_bytes > 0:
UpperCamelCase_, UpperCamelCase_ : str = writer.finalize()
writer.close()
yield pa.RecordBatch.from_arrays(
[[task_id], [num_examples], [num_bytes]] , names=['task_id', 'num_examples', 'num_bytes'] , )
if working_fpath != fpath:
for file in os.listdir(os.path.dirname(snake_case ) ):
UpperCamelCase_ : Dict = os.path.join(os.path.dirname(snake_case ) , os.path.basename(snake_case ) )
shutil.move(snake_case , snake_case )
UpperCamelCase_ : int = (
self.df.mapInArrow(snake_case , 'task_id: long, num_examples: long, num_bytes: long' )
.groupBy('task_id' )
.agg(
pyspark.sql.functions.sum('num_examples' ).alias('total_num_examples' ) , pyspark.sql.functions.sum('num_bytes' ).alias('total_num_bytes' ) , pyspark.sql.functions.count('num_bytes' ).alias('num_shards' ) , pyspark.sql.functions.collect_list('num_examples' ).alias('shard_lengths' ) , )
.collect()
)
for row in stats:
yield row.task_id, (row.total_num_examples, row.total_num_bytes, row.num_shards, row.shard_lengths)
def SCREAMING_SNAKE_CASE__ ( self : int , snake_case : "datasets.SplitGenerator" , snake_case : str = "arrow" , snake_case : Optional[Union[str, int]] = None , snake_case : Optional[int] = None , **snake_case : Any , ) -> int:
"""simple docstring"""
self._validate_cache_dir()
UpperCamelCase_ : Optional[int] = convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE )
self._repartition_df_if_needed(snake_case )
UpperCamelCase_ : List[str] = not is_remote_filesystem(self._fs )
UpperCamelCase_ : List[Any] = os.path.join if is_local else posixpath.join
UpperCamelCase_ : Optional[int] = '-TTTTT-SSSSS-of-NNNNN'
UpperCamelCase_ : Dict = f"{self.name}-{split_generator.name}{SUFFIX}.{file_format}"
UpperCamelCase_ : int = path_join(self._output_dir , snake_case )
UpperCamelCase_ : int = 0
UpperCamelCase_ : Optional[int] = 0
UpperCamelCase_ : Union[str, Any] = 0
UpperCamelCase_ : Optional[Any] = []
UpperCamelCase_ : Any = []
for task_id, content in self._prepare_split_single(snake_case , snake_case , snake_case ):
(
(
UpperCamelCase_
), (
UpperCamelCase_
), (
UpperCamelCase_
), (
UpperCamelCase_
),
) : Optional[Any] = content
if num_bytes > 0:
total_num_examples += num_examples
total_num_bytes += num_bytes
total_shards += num_shards
task_id_and_num_shards.append((task_id, num_shards) )
all_shard_lengths.extend(snake_case )
UpperCamelCase_ : Optional[Any] = total_num_examples
UpperCamelCase_ : Any = total_num_bytes
# should rename everything at the end
logger.debug(f"Renaming {total_shards} shards." )
if total_shards > 1:
UpperCamelCase_ : List[Any] = all_shard_lengths
# Define fs outside of _rename_shard so that we don't reference self in the function, which will result in a
# pickling error due to pickling the SparkContext.
UpperCamelCase_ : int = self._fs
# use the -SSSSS-of-NNNNN pattern
def _rename_shard(
snake_case : int , snake_case : int , snake_case : int , ):
rename(
snake_case , fpath.replace('SSSSS' , f"{shard_id:05d}" ).replace('TTTTT' , f"{task_id:05d}" ) , fpath.replace('TTTTT-SSSSS' , f"{global_shard_id:05d}" ).replace('NNNNN' , f"{total_shards:05d}" ) , )
UpperCamelCase_ : Any = []
UpperCamelCase_ : Optional[int] = 0
for i in range(len(snake_case ) ):
UpperCamelCase_, UpperCamelCase_ : Union[str, Any] = task_id_and_num_shards[i]
for shard_id in range(snake_case ):
args.append([task_id, shard_id, global_shard_id] )
global_shard_id += 1
self._spark.sparkContext.parallelize(snake_case , len(snake_case ) ).map(lambda snake_case : _rename_shard(*snake_case ) ).collect()
else:
# don't use any pattern
UpperCamelCase_ : Tuple = 0
UpperCamelCase_ : Optional[Any] = task_id_and_num_shards[0][0]
self._rename(
fpath.replace('SSSSS' , f"{shard_id:05d}" ).replace('TTTTT' , f"{task_id:05d}" ) , fpath.replace(snake_case , '' ) , )
def SCREAMING_SNAKE_CASE__ ( self : Tuple , snake_case : "datasets.SplitGenerator" , ) -> SparkExamplesIterable:
"""simple docstring"""
return SparkExamplesIterable(self.df )
| 175 | 1 |
import math
from typing import Callable, List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale import StableDiffusionUpscalePipeline
from diffusers.schedulers import DDIMScheduler, DDPMScheduler, LMSDiscreteScheduler, PNDMScheduler
def __snake_case ( _lowerCAmelCase : str , _lowerCAmelCase : Any , _lowerCAmelCase : str=[] ) -> Optional[int]:
A_ : Tuple = size[0] - overlap_pixels * 2
A_ : Optional[Any] = size[1] - overlap_pixels * 2
for letter in ["l", "r"]:
if letter in remove_borders:
size_x += overlap_pixels
for letter in ["t", "b"]:
if letter in remove_borders:
size_y += overlap_pixels
A_ : List[str] = np.ones((size_y, size_x) , dtype=np.uinta ) * 255
A_ : Tuple = np.pad(_lowerCAmelCase , mode="linear_ramp" , pad_width=_lowerCAmelCase , end_values=0 )
if "l" in remove_borders:
A_ : Optional[Any] = mask[:, overlap_pixels : mask.shape[1]]
if "r" in remove_borders:
A_ : List[Any] = mask[:, 0 : mask.shape[1] - overlap_pixels]
if "t" in remove_borders:
A_ : Optional[int] = mask[overlap_pixels : mask.shape[0], :]
if "b" in remove_borders:
A_ : Dict = mask[0 : mask.shape[0] - overlap_pixels, :]
return mask
def __snake_case ( _lowerCAmelCase : Any , _lowerCAmelCase : Any , _lowerCAmelCase : List[Any] ) -> Union[str, Any]:
return max(_lowerCAmelCase , min(_lowerCAmelCase , _lowerCAmelCase ) )
def __snake_case ( _lowerCAmelCase : [int] , _lowerCAmelCase : [int] , _lowerCAmelCase : [int] ) -> Dict:
return (
clamp(rect[0] , min[0] , max[0] ),
clamp(rect[1] , min[1] , max[1] ),
clamp(rect[2] , min[0] , max[0] ),
clamp(rect[3] , min[1] , max[1] ),
)
def __snake_case ( _lowerCAmelCase : [int] , _lowerCAmelCase : int , _lowerCAmelCase : [int] ) -> List[str]:
A_ : Dict = list(_lowerCAmelCase )
rect[0] -= overlap
rect[1] -= overlap
rect[2] += overlap
rect[3] += overlap
A_ : Optional[int] = clamp_rect(_lowerCAmelCase , [0, 0] , [image_size[0], image_size[1]] )
return rect
def __snake_case ( _lowerCAmelCase : str , _lowerCAmelCase : str , _lowerCAmelCase : str , _lowerCAmelCase : Dict ) -> Tuple:
A_ : Optional[int] = Image.new("RGB" , (tile.size[0] + original_slice, tile.size[1]) )
result.paste(
original_image.resize((tile.size[0], tile.size[1]) , Image.BICUBIC ).crop(
(slice_x, 0, slice_x + original_slice, tile.size[1]) ) , (0, 0) , )
result.paste(_lowerCAmelCase , (original_slice, 0) )
return result
def __snake_case ( _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Optional[Any] ) -> List[Any]:
A_ : Union[str, Any] = (original_image_slice * 4, 0, tile.size[0], tile.size[1])
A_ : List[Any] = tile.crop(_lowerCAmelCase )
return tile
def __snake_case ( _lowerCAmelCase : Any , _lowerCAmelCase : List[str] ) -> str:
A_ : int = n % d
return n - divisor
class __magic_name__ ( lowerCamelCase__ ):
"""simple docstring"""
def __init__( self :Tuple , snake_case :AutoencoderKL , snake_case :CLIPTextModel , snake_case :CLIPTokenizer , snake_case :UNetaDConditionModel , snake_case :DDPMScheduler , snake_case :Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler] , snake_case :int = 350 , ):
'''simple docstring'''
super().__init__(
vae=snake_case , text_encoder=snake_case , tokenizer=snake_case , unet=snake_case , low_res_scheduler=snake_case , scheduler=snake_case , max_noise_level=snake_case , )
def SCREAMING_SNAKE_CASE ( self :str , snake_case :Union[str, Any] , snake_case :Dict , snake_case :List[str] , snake_case :Optional[Any] , snake_case :List[str] , snake_case :str , snake_case :List[Any] , **snake_case :Optional[Any] ):
'''simple docstring'''
torch.manual_seed(0 )
A_ : List[Any] = (
min(image.size[0] - (tile_size + original_image_slice) , x * tile_size ),
min(image.size[1] - (tile_size + original_image_slice) , y * tile_size ),
min(image.size[0] , (x + 1) * tile_size ),
min(image.size[1] , (y + 1) * tile_size ),
)
A_ : Optional[Any] = add_overlap_rect(snake_case , snake_case , image.size )
A_ : List[str] = image.crop(snake_case )
A_ : int = ((crop_rect[0] + ((crop_rect[2] - crop_rect[0]) / 2)) / image.size[0]) * tile.size[0]
A_ : str = translated_slice_x - (original_image_slice / 2)
A_ : int = max(0 , snake_case )
A_ : int = squeeze_tile(snake_case , snake_case , snake_case , snake_case )
A_ : List[Any] = to_input.size
A_ : str = to_input.resize((tile_size, tile_size) , Image.BICUBIC )
A_ : str = super(snake_case , self ).__call__(image=snake_case , **snake_case ).images[0]
A_ : List[str] = upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4) , Image.BICUBIC )
A_ : int = unsqueeze_tile(snake_case , snake_case )
A_ : int = upscaled_tile.resize((tile.size[0] * 4, tile.size[1] * 4) , Image.BICUBIC )
A_ : List[str] = []
if x == 0:
remove_borders.append("l" )
elif crop_rect[2] == image.size[0]:
remove_borders.append("r" )
if y == 0:
remove_borders.append("t" )
elif crop_rect[3] == image.size[1]:
remove_borders.append("b" )
A_ : Union[str, Any] = Image.fromarray(
make_transparency_mask(
(upscaled_tile.size[0], upscaled_tile.size[1]) , tile_border * 4 , remove_borders=snake_case ) , mode="L" , )
final_image.paste(
snake_case , (crop_rect_with_overlap[0] * 4, crop_rect_with_overlap[1] * 4) , snake_case )
@torch.no_grad()
def __call__( self :int , snake_case :Union[str, List[str]] , snake_case :Union[PIL.Image.Image, List[PIL.Image.Image]] , snake_case :int = 75 , snake_case :float = 9.0 , snake_case :int = 50 , snake_case :Optional[Union[str, List[str]]] = None , snake_case :Optional[int] = 1 , snake_case :float = 0.0 , snake_case :Optional[torch.Generator] = None , snake_case :Optional[torch.FloatTensor] = None , snake_case :Optional[Callable[[int, int, torch.FloatTensor], None]] = None , snake_case :int = 1 , snake_case :int = 128 , snake_case :int = 32 , snake_case :int = 32 , ):
'''simple docstring'''
A_ : List[Any] = Image.new("RGB" , (image.size[0] * 4, image.size[1] * 4) )
A_ : List[Any] = math.ceil(image.size[0] / tile_size )
A_ : Union[str, Any] = math.ceil(image.size[1] / tile_size )
A_ : Any = tcx * tcy
A_ : str = 0
for y in range(snake_case ):
for x in range(snake_case ):
self._process_tile(
snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , prompt=snake_case , num_inference_steps=snake_case , guidance_scale=snake_case , noise_level=snake_case , negative_prompt=snake_case , num_images_per_prompt=snake_case , eta=snake_case , generator=snake_case , latents=snake_case , )
current_count += 1
if callback is not None:
callback({"progress": current_count / total_tile_count, "image": final_image} )
return final_image
def __snake_case ( ) -> int:
# Run a demo
A_ : List[Any] = "stabilityai/stable-diffusion-x4-upscaler"
A_ : Dict = StableDiffusionTiledUpscalePipeline.from_pretrained(_lowerCAmelCase , revision="fp16" , torch_dtype=torch.floataa )
A_ : Union[str, Any] = pipe.to("cuda" )
A_ : List[str] = Image.open("../../docs/source/imgs/diffusers_library.jpg" )
def callback(_lowerCAmelCase : Optional[int] ):
print(f"progress: {obj['progress']:.4f}" )
obj["image"].save("diffusers_library_progress.jpg" )
A_ : Optional[int] = pipe(image=_lowerCAmelCase , prompt="Black font, white background, vector" , noise_level=40 , callback=_lowerCAmelCase )
final_image.save("diffusers_library.jpg" )
if __name__ == "__main__":
main()
| 70 |
from random import randint
from tempfile import TemporaryFile
import numpy as np
def __snake_case ( _lowerCAmelCase : Optional[int] , _lowerCAmelCase : str , _lowerCAmelCase : Optional[int] ) -> Dict:
A_ : Optional[Any] = 0
if start < end:
A_ : Tuple = randint(_lowerCAmelCase , _lowerCAmelCase )
A_ : str = a[end]
A_ : Optional[Any] = a[pivot]
A_ : List[str] = temp
A_ , A_ : int = _in_place_partition(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
count += _in_place_quick_sort(_lowerCAmelCase , _lowerCAmelCase , p - 1 )
count += _in_place_quick_sort(_lowerCAmelCase , p + 1 , _lowerCAmelCase )
return count
def __snake_case ( _lowerCAmelCase : Tuple , _lowerCAmelCase : List[str] , _lowerCAmelCase : Optional[Any] ) -> str:
A_ : Union[str, Any] = 0
A_ : List[str] = randint(_lowerCAmelCase , _lowerCAmelCase )
A_ : str = a[end]
A_ : str = a[pivot]
A_ : Any = temp
A_ : int = start - 1
for index in range(_lowerCAmelCase , _lowerCAmelCase ):
count += 1
if a[index] < a[end]: # check if current val is less than pivot value
A_ : Union[str, Any] = new_pivot_index + 1
A_ : Union[str, Any] = a[new_pivot_index]
A_ : Union[str, Any] = a[index]
A_ : Union[str, Any] = temp
A_ : Tuple = a[new_pivot_index + 1]
A_ : Optional[int] = a[end]
A_ : Dict = temp
return new_pivot_index + 1, count
_lowerCAmelCase : List[str] = TemporaryFile()
_lowerCAmelCase : int = 100 # 1000 elements are to be sorted
_lowerCAmelCase , _lowerCAmelCase : Optional[Any] = 0, 1 # mean and standard deviation
_lowerCAmelCase : int = np.random.normal(mu, sigma, p)
np.save(outfile, X)
print('''The array is''')
print(X)
outfile.seek(0) # using the same array
_lowerCAmelCase : Optional[Any] = np.load(outfile)
_lowerCAmelCase : Optional[int] = len(M) - 1
_lowerCAmelCase : Union[str, Any] = _in_place_quick_sort(M, 0, r)
print(
'''No of Comparisons for 100 elements selected from a standard normal distribution'''
'''is :'''
)
print(z)
| 70 | 1 |
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